To start or not to start: Outcome and ability expectations in the decision to start a new venture

Journal of Business Venturing 25 (2010) 192–202

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Journal of Business Venturing

To start or not to start: Outcome and ability expectations in the decision to start a new venture ☆ David M. Townsend a,⁎, Lowell W. Busenitz a, Jonathan D. Arthurs b,1 a b

University of Oklahoma, Michael F. Price College of Business, Division of Management, 307 W. Brooks, Room 206, Norman, OK 73019-0450, United States Department of Management and Operations, College of Business, Washington State University, P.O. Box 644736, Pullman, WA 99164-4736, United States

a r t i c l e

i n f o

Keywords: Entrepreneur decision-making Ability expectations Outcome expectations New venture creation

a b s t r a c t This study seeks to better understand why some individuals decide to start new businesses and others do not, particularly in light of high base rates of failure. In addressing the question of “Why do some individuals choose to start new ventures?” a common perspective is that potential entrepreneurs with high levels of confidence in potential outcomes are likely to start new ventures. Alternatively, it also may be that firm creation decisions are based largely on individual expectations of one's ability. Hypotheses examining these perspectives are tested using a sample of 316 nascent entrepreneurs with the start-up decision tracked longitudinally. The results indicate that confidence in one's ability to perform tasks relevant to entrepreneurship is a robust predictor of start-up while outcome expectancies appear to play a marginal role. Theoretical and practical implications stemming from these results are discussed. © 2008 Elsevier Inc. All rights reserved.

1. Executive summary There is little doubt that the decision to create a new entrepreneurial venture is fraught with uncertainty. What is interesting about this process to entrepreneurship researchers is that many individuals initiate operations while recognizing that the odds of creating a successful firm are often stacked against them. This naturally raises the question as to why so many individuals still decide to start new ventures. Prior research suggests that perhaps some individuals create firms because they have unrealistic expectations of success (e.g., inflated outcome expectancies). Such predictions would obviously violate typical models of rational decision-making (e.g., logic of consequences). However, are such motives for starting a new venture systematic across a sample of entrepreneurs? Alternatively, prior research suggests expectancies regarding one's ability to be an entrepreneur might also explain why some individuals but not others start new ventures. Furthermore, both outcome and ability expectancies may exert a joint effect on the likelihood an individual will start a firm. To examine the role ability and outcome expectancies play in influencing the decision to start a new venture, we analyzed the start-up decisions of 316 nascent entrepreneurs over a 5-year period. Initially, our statistical analysis indicated that outcome expectancies do play a marginally significant role in encouraging start-up decisions. However, when we included ability expectancies, outcome expectancies were no longer a significant influence on the start-up decision. Furthermore, we also examined

☆ An earlier version of this paper was presented at the Lally–Darden–Humboldt Young Entrepreneurship Scholars Retreat in October 2006 in Berlin, Germany. The authors wish to thank Christian Schade, the participants at the 2006 Lally–Darden–Humboldt Young Entrepreneurship Scholars Retreat (especially Philipp Koellinger and Maria Minitti), Mike Buckley, Fran McKee-Ryan, Rob Mitchell, Mark Bolino, Parthiban David, Mark Sharfman, Susan Houghton, Chuck Murnieks, and two anonymous reviewers for helpful comments on earlier drafts of this paper. ⁎ Corresponding author. Tel.: +1 405 325 5737; fax: +1 405 325 7688. E-mail addresses: [email protected] (D.M. Townsend), [email protected] (L.W. Busenitz), [email protected] (J.D. Arthurs). 1 Tel.: +1 509 335 5628; fax: +1 509 335 7736. 0883-9026/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusvent.2008.05.003

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whether ability and outcome expectancies interact to create a joint effect on the probability of start-up. Our results indicate this relationship is also not significant. These results suggest that ability expectancies are the critical driver of the start-up decision. We also examine the role that time lags play in influencing the start-up decision. Specifically, our results indicate that the longer it takes for individuals to act on the intention to become an entrepreneur, the less likely they are to decide to create a new venture. Regarding this finding, our statistical analysis also indicated that the marginally positive influence outcome expectancies play in encouraging start-up decisions are substantially weakened the longer individuals wait to act on their intentions. For entrepreneurs who may be acting on inflated expectations of success, these results suggest that perhaps waiting before acting is not necessarily a bad thing. Conversely, our results also indicate that time lags between intentions and the decision to create a new venture do not affect the relationship between ability expectancies and new venture creation decisions. Therefore, it appears that among the entrepreneurs sampled here, these ability expectancies are quite robust. Our results indicate several important findings for both entrepreneurship research and practice. First, the centrality of ability expectancies in the start-up decision suggests these nascent entrepreneurs have developed certain perceptions regarding the role of an entrepreneur in order to gauge their entrepreneurial abilities. A follow-on question for future research then becomes — how are these role expectations formed? Second, the interaction between outcome expectancies and times lags suggests that if future research can indeed identify a specific link between inflated outcome expectancies and firm failure, encouraging entrepreneurs to exercise patience when considering the possibility of creating a new firm may prevent some high risk firms from being created. Overall, our results indicate that individuals decide to start new ventures because they are confident in their abilities to act entrepreneurially, even when the probability of failure is high. Given the desire of many researchers to help entrepreneurs improve start-up decision-making, a deeper understanding of how these ability expectancies form would be an important extension of this research. 2. Introduction “But as I've learned over the years, a fundamental belief in one's ability to earn hefty amounts – even if it means starting over again a couple times – is at the heart of almost every great entrepreneur's success story” (Anders, 2007) The successful creation of innovative new ventures is a challenging endeavor. Recent studies indicate failure rates for new ventures might be as high as 30% over the first 2 years of operations (Headd, 2003). Among the numerous factors that shape mortality rates in newly created firms include a lack of legitimacy (Stinchcombe, 1965; Singh et al., 1986), a lack of resources (Holtz-Eakin et al., 1994), entrepreneur human capital (Brüderl et al., 1992; Gimeno et al., 1997), and external factors such as environment/industry characteristics (Audretsch, 1991). While explanations of venture failure are certainly numerous, one emerging perspective suggests that specific cognitive factors such as optimistic overconfidence may also play a significant role (Koellinger et al., 2007; Hayward et al., 2006). Specifically, hubris theory suggests that inflated expectations of success at the point of firm creation may contribute to subsequent failure in nascent firms as overconfident entrepreneurs start firms with insufficient capital, or over-allocate acquired capital to high risk projects with little intrinsic chance of success (Hayward et al., 2006). Simply put, firms created by overconfident entrepreneurs might inflate new venture mortality rates. While inflated expectations of future success may play a role in the creation of some ventures, it is uncertain whether these expectations are indeed systematic motivators for entrepreneurs initiating the new venture creation process. Our study examines the question as to whether higher expectations of success really influence the decision to start a new venture. According to hubris theory, overconfidence emerges in entrepreneurs when “… they overestimate the likelihood that their ventures will succeed and that they can ensure such success” (Hayward et al., 2006: 161). One issue, however, with this definition is that it conflates two related, but independent cognitive variables which prior theory suggests would exert distinct effects on an individual's behavior (e.g., ability and outcome expectancies; Bandura, 1977, 1986; Maddux et al., 1982). Specifically, a social cognitive framework would argue that overestimating the likelihood of success would-be representative of outcome expectancies while overestimating one's ability to ensure such success would exemplify ability expectancies. While related, the differences between these variables, in turn, possibly suggest three alternative explanations for why individuals start new businesses. First, some entrepreneurs may create new firms encouraged by inflated expectations of future success (i.e., a belief that ‘this venture will be successful’; Hayward et al., 2006). Second, it may be that individuals with high levels of confidence in their own abilities are more likely to start new ventures (i.e., ‘I can do this;' ’; Koellinger et al., 2007). Third, it may also be that entrepreneurs with high ability and outcome expectancies would be more likely to start a new business (i.e., I can do this and therefore this will be successful). Our question is, if we deconstruct “optimistic overconfidence” into its constitutive parts (e.g., outcome and ability expectancies), what are the respective roles of the different types of expectancies in predicting start-up? To examine these issues, this paper is organized in the following fashion: First, we discuss extant theory on decision-making, linking core concepts from social cognitive theory to the literature on overconfidence in entrepreneur decision-making; Second, we define and outline the respective roles outcome and ability expectancies may play in the start-up decision-making process.; Third, we discuss the sample and methodology used for testing our hypotheses; Fourth, we report the results of our statistical analyses; Fifth, we discuss our findings and lay groundwork for future research. Overall, we believe our study offers important theoretical and practical insights for the literature on entrepreneur decision-making and overconfidence in the new venture creation process.

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3. Theoretical background Rational decision models have four distinct dimensions: alternatives (“What actions are possible?”), expectations (“What future consequences might follow from each alternative? How likely is each possible consequence, assuming that alternative is chosen?”), preferences (“How valuable to the decision decision-maker are the consequences associated with each of the alternatives?”), and the decision rule (“How is a choice to be made among the alternatives in terms of the values of their consequences?”) (March, 1994: 2– 3). Although differences along certain dimensions can systematically influence decision-making, March (1994) argues that rational decision models have a common theme in that all hold to a logic of consequences. Specifically, Standard contemporary discourse, particularly in the traditions of decision theory, tends to equate reason with a logic of consequence. The idea is that reasoning decision maker will consider alternatives in terms of their consequences for preferences… Deviations from a logic of consequence are treated as deviations from reason (March, 1994: 100–1). Therefore, as March intimates, the guiding calculus of rational choice in decision theory is the pursuit of specific ends framed by an individual's assessment of the probability that treasured goals and objectives can be achieved (e.g., outcome expectancies). And while the specific nature of these goals and objectives likely differs among a population of entrepreneurs, the standard assumption, however, is to impute a teleological basis to the entrepreneur's decision-making processes. Therefore, when decision-makers rely on inflated, overconfident expectations of potential decision outcomes, (e.g., creating a successful firm) they are typically characterized as being irrational because they are violating a logic of consequences. For example, a question that has been used to assess inflated expectations in prior research is — how likely is it that this venture will be successful? (e.g., Cooper et al., 1988). Earlier research suggests that optimistic predictions of success as reflected in the “typical” response by entrepreneurs to this question (e.g., mean response of 81% in Cooper et al., 1988) reflect irrational outcome expectancies. Furthermore, to the extent these inflated outcome expectancies play a systematic role in encouraging new venture start-up, the cognitive basis of much of the entrepreneurial actions we see is constructed upon irrational expectations.2 These irrational expectations in turn, as suggested by hubris theory, are a central cause of the high failure rates among new entrepreneurial ventures (Hayward et al., 2006). Given the breadth of recent studies which point to the ubiquitous presence of overconfidence among other biases and heuristics in entrepreneurial decision-making (i.e., see — Busenitz and Barney, 1997; Camerer and Lovallo, 1999; Simon et al., 2000; Keh et al., 2002; Simon and Houghton, 2002; Simon et al., 2003; Forbes, 2005; Lowe and Ziedonis, 2006; Koellinger et al., 2007), a compelling case can be made that inflated outcome expectancies may indeed drive start-up decision-making processes. Towards this end, as Hayward et al. (2006) suggest, scholars can assist entrepreneurs in creating ventures with higher intrinsic potential for success by “rationalizing” entrepreneur decision-making during the start-up phase (i.e., bringing expectancies in line with base rates McDougall et al., 1992; Cooper et al., 1988). In contrast, however, the standard defense to the “heuristics as sub-optimal reasoning” argument is that although there are theoretically limits to the efficacy of heuristics in entrepreneur decision-making, individuals who lack sufficiently high levels of confidence in the likelihood that desired outcomes can be attained may never possess the courage to start a new venture (Busenitz and Barney, 1997). To this specific point, the central contribution of hubris theory takes form as Hayward and colleagues argue that although overconfident predictions of success might indeed encourage overconfident entrepreneurs to start new ventures, these same ventures may also be more likely to fail (Hayward et al., 2006; see also Camerer and Lovallo, 1999; Bazerman, 2006). The core assumption, though, is still anchored in the notion that the entrepreneur's assessment of potential outcomes (e.g., a logic of consequences) drives the start-up process (e.g., Cooper et al., 1988), albeit expectations that might exceed norms of rationality. If we look closer at the evidence for entrepreneur overconfidence, however, the issue is not so clearly defined. Although broadly cited as a classic piece of evidence demonstrating the irrational overconfidence of entrepreneurs, the empirical story presented in Cooper and colleagues' study reveals wide variance in the entrepreneurs' responses to two distinct but related questions. Specifically, the mean response for the question ‘What are the odds of your business succeeding?’ is considerably higher than the mean response for the question ‘What are the odds of any business like yours succeeding?’ (8.1 versus 5.9 out of 10; Cooper et al., 1988). The key difference between the two questions appears to be the entrepreneurs' assessment of the importance of their own abilities in creating a successful venture. According to core concepts in social cognitive theory both the perceived ability to perform certain actions (e.g., ability expectancies) and the expectation that these actions will produce desired outcomes (e.g., outcome expectancies) can jointly serve as the impetus to human action (Bandura, 1986). Specifically, Bandura argues that “people act on their judgments of what they can do, as well as their beliefs about the likely effects of various actions. There are many activities, which, if done well, guarantee cherished outcomes, but they are not pursued by persons who doubt they can do what is needed to succeed” (1986: 231). Consequently, a singular logic of consequences explained solely by outcome expectancies is likely an insufficient description of entrepreneurial action. Additionally, Searle (2001) points out that an individual's sense of their own abilities forms the “background” of human action for if individuals do not perceive that they have the abilities to engage in a particular action, they would not likely attempt the action. This would in turn suggest that entrepreneurs with the goal of starting a new business would be 2 This appears to be the legacy of Cooper et al. (1988) as the citation patterns of their study show that it is predominately cited as an example of overconfidence in decision-making (i.e., see: Daniel et al., 1998; Moskowitz and Vissing-Jorgensen, 2002).

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more likely to engage in start-up activities if they believed in their abilities to “act entrepreneurially” versus those who thought the ultimate success of the venture was a ‘sure thing!’ These differences become increasingly more important as cognition scholars extend the conversation towards the long-run outcomes associated with entrepreneur decision-making. Indeed, if scholars can pinpoint a link between overconfidence and subsequent firm failure, it would seem prudent that we develop a more comprehensive understanding of the cognitive pieces which constitute the whole of “optimistic overconfidence” (Hayward et al., 2006). Furthermore, if we are to design specific interventions to “improve” entrepreneur decision-making during the start-up process, it would seem prudent to understand the relative role of various cognitive variables in shaping the start-up process. 4. Hypotheses 4.1. Outcome expectancies and firm start-up As noted above, recent work in the entrepreneurial cognition domain tends to subscribe to the notion that inflated expectations of outcome success lead to excess entry by entrepreneurs ill-prepared and perhaps ill-suited to effectively manage a new venture (Camerer and Lovallo, 1999; Hayward et al., 2006). Rationalist variations of decision theory (generally appearing in the economics literature) tend to present exactly such a perspective. One of the more prominent examples is the Kihlstrom and Laffont (1979) theory of entrepreneurial action. According to this perspective, the decision between creating a new venture and remaining a wage laborer hinges largely on the probabilistic assessment of the likelihood of success and an individual's risk tolerance (Kanbur, 1982). Earlier research tends to assume these probabilistic assessments of the likelihood of success might evolve into high outcome expectancies. These expectancies are positively related to firm start-up based on the notion that only those confident of successfully achieving some desired outcome would actually start a new venture (e.g., Busenitz and Barney, 1997; Cialdini, 1998; Hayward et al., 2006). Despite the appealing logic of the proposed relationship between high outcome expectancies and firm startup, empirical verification of such a relationship is lacking in extant research (a notable recent exception is experimental work by Camerer and Lovallo (1999) investigating outcome expectations in a lab setting). Furthermore, Bandura (1989: 1180) argues that according to social cognitive theory, outcome expectancies will only exert a measurable effect apart from ability expectancies when outcomes are not solely determined by the “quality of performance. Therefore, it is unclear whether outcome expectancies really exert an effect as postulated by hubris theory. Following the logic of Hayward and colleagues, however, we will test whether outcome expectancies really do exert an independent effect on the likelihood of start-up. Hypothesis 1. There is a positive relationship between outcome expectancies and the decision to start a new venture. Specifically, would-be entrepreneurs with higher outcome expectancies are more likely to start new ventures. 4.2. Ability expectancies and firm start-up An additional explanation, however, is that entrepreneurs might not rely solely on outcome expectancies when creating a new firm. Rather ability expectancies may play a significant role in influencing start-up decisions. Recent work by Gatewood (2004) confirms the significant role expectancy variables play in shaping start-up decisions. Also, self-efficacy perceptions, as a type of ability expectancy (Ambrose and Kulik, 1999), have been linked with both intentions and actual start-up activities in recent research (Boyd and Vozikis, 1994; Scherer et al., 1989; Baum et al., 2001; Zhao et al., 2005). More recently, Koellinger et al. (2007) found that the decision to start new ventures across countries was correlated with individual confidence in one's entrepreneurial skills. Interestingly, they also found that founders who had been active for some time in the entrepreneurial process were less confident in their own skills than nascent entrepreneurs indicating an erosion of belief in one's abilities over time. This, as we discuss later in the paper, may provide a cognitive explanation for firm failure among entrepreneurial ventures. The question here is whether ability expectancies complement or compete with outcome expectancies as a predictor of start-up activities. Previously we linked outcome expectancies with the logic of consequences, but noted that ability expectancies produce a distinct effect on human behavior. Specifically, as Searle (2001) argues, ability expectancies assess the efficacy of individual skills, abilities, experiences, self-concepts, etc. in specific behavioral contexts and appear to be the sine qua non of human action (March, 1994; March, 1996; Bandura, 1986; Bandura, 2006). Therefore, Hypothesis 2. There is a positive relationship between ability expectancies and the decision to start a new venture. Specifically, would-be entrepreneurs with higher ability expectancies are more likely to start new ventures. 4.3. Time lags and firm start-up While social cognitive theory notes the importance of outcome and ability expectancies to human action, certain critical temporal boundaries affect the extent to which these cognitive variables produce action. Specifically, although forethought is a critical determinant of action, planning must inevitably give way to action (Bandura, 2001). However, a common criticism of entrepreneurs is that many are likely to act before engaging in adequate forethought and that this lack of planning may induce overconfidence and subsequent firm failure (Shane and Venkataraman, 2000; Hayward et al., 2006).

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Related to this point, Bandura argues that “having adopted an intention and an action plan, one cannot simply sit back and wait for the appropriate performances to appear… agency thus involves… the ability to give shape to appropriate courses of action and to motivate and regulate their execution” (Bandura, 2001: 8). Additionally, the more temporally proximate a particular goal/objective is, the more likely the goal will result in the focal action (Bandura, 2001). Overall, especially in the context of entrepreneurship, action is critical as the windows of opportunity may indeed be short-lived (Busenitz and Barney, 1997). Prolonged waiting, therefore, indicates a lack of self-reactiveness by the entrepreneur resulting in a failure to launch a new venture. Hypothesis 3. There is a negative relationship between time lags and the decision to start a new venture. Specifically, the longer would-be entrepreneurs wait before acting on entrepreneurial intentions, the less likely they are to start a new venture. 4.4. Moderating effects of time lags Along with exerting a direct effect on the decision to create a new venture, we also propose that time lags will moderate the relationship between ability/outcome expectancies and the decision to start a new venture. Specifically, while some amount of planning prior to starting a venture may reduce subsequent risk of failure (e.g., Delmar and Shane, 2003), the simple fact remains that long time lags between the formation of entrepreneurial intentions and the decision to actually launch a venture likely reduces the probability the firm will ever be started. This situation likely occurs because of a failure by would-be entrepreneurs to surmount the decision gap between the formation of intentions and the actual decision to launch the venture (cf. Bandura, 2006; Searle, 2001). Specifically then, a longer time lag between intentions and action would indicate that despite the relative ability/ outcome expectancy values, the would-be entrepreneur cannot muster sufficient motivation to initiate operations. Therefore, we would expect that long time lags would counteract the positive effects of ability/outcome expectancies on the decision to launch a new venture. Hypothesis 4. Time lags will negatively moderate the relationship between outcome expectancies and the decision to start an entrepreneurial venture such that longer time lags will weaken the main effect and reduce the probability the entrepreneur will ever start a new venture. Hypothesis 5. Time lags will negatively moderate the relationship between ability expectancies and the decision to start an entrepreneurial venture such that longer time lags will weaken the main effect and reduce the probability the entrepreneur will ever start a new venture. 4.5. The joint effect of outcome and ability expectancies Our final hypothesis examines the extent to which the joint effect of outcome and ability expectancies increases the likelihood potential entrepreneurs will actually initiate operations. As we noted above, the notion that outcome and ability expectancies jointly affect the likelihood of start-up is at the core of the predictions offered by hubris theory. Specifically, entrepreneurs typically overestimate the likelihood their ventures will be successful and that they have the ability to ensure such success (Hayward et al., 2006). Such “optimistic overconfidence” is constructed from both outcome and ability expectancies. As such, we investigate whether the presence of both outcome and ability expectancies exert joint effects above the individual effects of each type of expectancy on the likelihood of start-up. Hypothesis 6. There is a positive relationship between the joint effect of outcome and ability expectancies and the decision to start an entrepreneurial venture such that the joint effect of the interaction of outcome and ability expectancies is greater than the additive effect. 5. Methodology To test the above hypotheses, we needed data from entrepreneurs before they started their ventures as well as longitudinal data that tracked the development of the start-ups being contemplated. Data from the Panel Study on Entrepreneurial Dynamics (PSED) met these requirements allowing us to examine the effects of certain variables over time. While some of the PSED data are gathered through self-report measures, the inclusion of longitudinal data allows us to test our research questions while making a stronger case for a causal relationship between certain variables. 5.1. Sample The initial PSED data were collected from a representative sample of the U.S. population using random digit dialing telephone survey interviews in 1998 and 1999. The phone survey was followed by a mail survey questionnaire. At the conclusion of the telephone interview, respondents were offered a $25 payment for filling out an additional mail questionnaire. The random digit dialing survey resulted in contact with 31,261 individuals. Several questions were asked of these individuals to determine if they were nascent entrepreneurs. If they were found to be currently trying to start a new business or had been involved in any new venture formation activity within the last year, they were considered to be nascent entrepreneurs. These procedures resulted in a large sample of unique respondents that form the PSED database. Additionally, each entrepreneur was contacted every 12 months

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following the initial data collection for 3 years to track the progress of the venture (e.g., see Reynolds, 1999 for greater detail). Out of a total sample of 1261 (including a random sample of general population), 782 nascent entrepreneurs were identified. To be consistent with our research question, we utilized the following selection criteria to ensure the appropriateness of our sample. First, we only examined those individuals who reported full autonomy in managing their venture. This selection procedure reduced our sample to 687, but allows us to investigate the effects of the core individual-level variables where the sampled individuals have the freedom to make relevant decisions. Second, we only included those individuals which we knew either that they had abandoned/started their venture, or those which the study tracked through the last wave of surveys. While this step reduced our overall sample to 473 respondents, temporal data are essential for accurately estimating the hazard/survival functions as a part of our survival analysis. Next, we elected to only include those cases where the respondents provided the necessary information to measure the independent variables in our study. While this further narrowed the sample size to 316, the eliminated cases tended to be missing most if not all of the data we were utilizing to measure our independent variables. Consequently, these observations were excluded from further analysis.3 5.2. Measures 5.2.1. Dependent variable One of the distinct advantages of using the PSED sample is its longitudinal design. This feature of the dataset effectively allows us to make a stronger case for causation (e.g., Cohen et al., 2003). For our dependent variable (e.g., new venture start-up) we utilized a time-lagged measure from the PSED sample which asks respondents to categorize the status of their potential venture at Time1–3 with four choices: 1 — operating business, 2 — active start-up (still actively pursuing a potential start-up), 3 — non-active start-up, 4 — abandoned idea/business. These results were cross-checked with a related question where the respondents reported the actual year and month the venture began operations. To measure actual start-up activities, all of the responses indicating the firm was operational and that we could cross-check with a reported month and year of start-up were coded as a “1.” Additionally, all of the responses where the respondents reported that they had abandoned their intentions to start a new venture were coded with a “0.” Those respondents indicating that they were still actively/inactively pursuing a potential start-up but had not yet done so in the last round of surveys were treated as censored observations and following standard procedure in survival analysis, were coded as “0.” 5.2.2. Independent variables The study's independent variables include outcome expectancies, ability expectancies, and time lags. To measure outcome expectancies, the respondents were asked to rate the probability the venture would be a successfully operating business in 5 years regardless of ownership (this question is a close derivative of the “business like yours” question asked by Cooper et al., 1988 and cited by Hayward et al., 2006 as evidence of overconfidence in entrepreneurs). Although this measure differs slightly from the question asked by Cooper et al. (1988), the “entrepreneur-free” tenor implicit in the question yields important insights for the core questions we posed regarding the role of these expectancy variables in start-up. Specifically, suppose the nascent entrepreneurs were asked to rate the likelihood they would be running a successfully operating business in 5 years. Although with this question we would still likely expect a similar positive relationship between outcome expectancy and start-up, there would also be a substantial overlap with survey items we utilized to measure ability expectancies (e.g., since the outcome would still be directly linked to the entrepreneur's sense of his or her own agency/abilities). To this point, the only likely recognizable difference between outcome and ability expectancies then would be the difference in the temporal dimension explicit in the questions (e.g., 5 years for outcome expectancies). Furthermore, the “entrepreneur-free” focus of our outcome expectancy question allows us to measure the extent to which the entrepreneur believes the venture would be successful if he or she were not involved thereby allowing us to access the entrepreneur's perception regarding the quality of the business opportunity and other related situational characteristics. Overall, in the discussion section, the results of our empirical analysis stemming from our use of this question to measure outcome expectancies hold important implications for extant theory on entrepreneurial decision-making by emphasizing the critical role of entrepreneurs' perceptions of their abilities. To measure ability expectancies, respondents were asked to respond to three different questions about their ability to start an entrepreneurial firm on a five-point Likert scale (“1” — Completely Disagree to “5” Completely Agree). These questions are: 1) Overall, my skills and abilities will help me start a business; 2) My past experience will be very valuable in starting a business; 3) I am confident I can put in the effort to start a business. Cronbach's alpha, measuring the reliability of the scale, is α = .785, thereby indicating a sufficient level of internal consistency (Nunnally and Bernstein, 1994). From these responses, following standard procedures in regression-based analysis, we used the average score for each respondent across the three questions as the individual score for ability expectancy. For our measure of time lags, the respondents were asked during the initial screening interview to report the month and year they identified and began thinking about their business idea. Using this survey item, we then calculated the number of months between the time the respondents started thinking about their business idea and when they actually decided to start/abandon their venture, or, if no decision had been made, the date of the last survey was utilized as the end date to remain consistent with 3 We did, however, run a test model using mean substitution to generate complete responses for the 157 respondents we excluded. Not surprisingly, since fully 1/3 of our extended sample utilized the mean scores for all the independent variables thereby reducing the random variance of the general sample, none of the hypothesized relationships were significant.

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standard procedures for measuring censored observations (Afifi et al., 2004). Overall, this calculation provides us with a continuous measurement of the gap in time between the formation of entrepreneurial intentions stemming from the identification of a business idea and the actual decision to start/abandon a new venture. This study also included several control variables for alternative explanations. Age was measured based on the actual reported value provided by the respondent (e.g., Forbes, 2005; Levesque and Minniti, 2006). Education was re-coded from the dummy variables reported in the original PSED screening survey to the following ordinal scheme: 10 — Less than High School; 12 — High School Graduate; 14 — Some College; 16 — College Graduate; 18 — Graduate Degree. Gender was coded as a dichotomous variable with “1” representing male and “0” representing female respondents. Also, the amount of personal funds invested by the entrepreneur prior to start-up decisions was recorded to at least partially control for an escalation of commitment bias stemming from any prior decision to invest money in a potential start-up (e.g., Schoorman and Holahan, 1996). Additionally, we also initially ran a test model with just dummy codes entered for the SIC codes reported by the respondents to predict start-up. Of these, only SIC 4049 (transportation) was significant, and was, therefore, the only control variable utilized in the survival model to control for industry effects on the decision to start a new venture. Lastly, we also utilized a dummy code (“1” — much thought and “0” for little thought) to control for the extent to which the respondents spent time thinking about starting their venture. Given the inclusion of the time lags variable measuring the number of months between the idea and the start-up/abandon decision, this control variable allows us to partial out the differences between those would-be entrepreneurs who casually thought about their venture for a certain period of time versus those who engaged in a more systematic planning process. 6. Results Tables 1, 2, and 3 summarize the results of the statistical analysis of the research questions. Table 1 presents the correlation matrix with the means and standard deviations of the variables in use. Table 2 presents the results from the hierarchical Cox regression analysis conducted to test the predicted relationships in the research model. Regarding the research model, we elected to use hierarchical Cox regression to test the effects of ability expectancy, outcome expectancy, time lags, and the interaction terms on the decision to start a new venture. Cox regression is justified for two specific reasons. First, although there are four initial responses to the DV, over time the assumption is that all respondents will either choose to start or abandon their intentions towards start-up. In other words, given enough time, all respondents would eventually become a one (e.g., start-up) or a four (e.g., abandonment). Therefore, the persistence of some individuals to actively/inactively pursue a new venture at the close of the study indicates these cases are censored. Thus, in the presence of censored observations, using either logistic regression or multinomial logit would likely yield inaccurate parameter estimates (Afifi et al., 2004). Second, an alternative strategy would have been to keep each case in its final start-up category at the time of the last wave of surveys. Then, we could potentially use multinomial logit to estimate the likelihood different individuals would belong to the four categories composing the DV. There are several challenges, however, with this approach for analyzing our research model. First, as we mentioned above, we do not expect membership in the four categories to remain stable. Specifically, given enough time, all respondents will migrate into two categories (e.g., start-up or abandonment). Second, consistent with our previous point, there is some dependency among the four categories as evidenced by the eventual aggregation of respondents into two categories. These underlying relationships would violate certain core assumptions regarding the nominal independence of the dependent variable essential to multinomial logit analysis (Long, 1997). Therefore, in light of these issues, Cox regression appears to be the most appropriate analytical method for estimating our model parameters. In the first step, we ran the model with the control variables as predictors of the decision to start a new venture. As Table 2 indicates, this baseline model yielded a significant chi-square of 59.462 with SIC 4049 and self-invest significantly predicting startup. Building from this baseline model, to test Hypothesis 1, we then ran step two where outcome expectancy yielded a marginally Table 1 Variable means, standard deviations, and correlations. a Variable

Mean

St Dev 1

2

3

4

5

6

7

8

9

10

11

1. Firm start-up b 2. Age 3. Education 4. Gender 5. SIC 4049 6. Self-invest 7. Planning 8. Start-up time 9. Ability expect 10. Ability expect A 11. Ability expect B 12. Ability expect C 13. Outcome expect 14. Time lags

.38 40.52 14.94 .49 .02 12,783 .20 23.21 4.32 4.33 4.21 4.42 81.50 69.12

.485 11.40 2.23 .501 .137 42,149 .400 19.21 .706 .793 .953 .775 23.57 66.94

1.00 .171⁎⁎ .023 .116⁎ .042 − .057 .067 .100⁎ .076† .062 .118⁎ − .009 .255⁎⁎⁎

1.00 − .115⁎ − .114⁎ − .028 − .004 .131⁎ − .006 .043 − .037 − .015 −170⁎⁎ .028

1.00 .049 .054 .033 .019 .022 .028 .004 .027 −.032 .087†

1.00 − .001 − .069 −.081† .035 .029 .043 .014 .077† −.094⁎

1.00 −.080† − 099⁎ .091† .095⁎ .039 .102⁎ .080† .026

1.00 − .024 −.145⁎⁎ − .129⁎ − .068 −.181⁎⁎⁎ − .064 − .128⁎

1.00 −.086† −.082† −081† − .052 − .039 .369⁎⁎⁎

1.00 .856⁎⁎⁎ .861⁎⁎⁎ .798⁎⁎⁎ .166⁎⁎ − .067

1.00 .613⁎⁎⁎ .560⁎⁎⁎ .130⁎ −.092†

1.00 .494⁎⁎⁎ 1.00 .100⁎ .199⁎⁎⁎ 1.00 −.033 −.048 −.017 1.00

1.00 − .017 − .059 − .031 .083† .510⁎⁎⁎ − .045 −.352⁎⁎⁎ .150⁎⁎ .182⁎⁎⁎ .097⁎ .101⁎ .130⁎ −.185⁎⁎⁎

N = 316. ⁎⁎⁎p b .001, ⁎⁎p b .01, ⁎p b .05, †p b .10. a Non-centered means and standard deviations are reported for centered items to simplify interpretation. b Spearman rank correlations are reported since ordinal data are used for certain variables.

12

13

14

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Table 2 Results of the hierarchical Cox regression estimating direct and interaction effects on new venture start-up decision. Dependent variable: new venture start-up

Model 1 controls

Controls Age Gender Education Self-invest SIC 4049 Planning Direct effects Outcome Expectancy (OE) Ability Expectancy (AE) Time Lags (TL) Interaction terms OE ⁎ TL AE ⁎ TL OE ⁎ AT Model statistics − 2 Log likelihood Chi-square Δ Chi-square Degrees of freedom

− .005 .199 − .058 .000⁎⁎⁎ 1.036⁎ − .042

Model 2 direct effects − .006 .195 − .051 .000⁎⁎⁎ .958† − .059 .176†

Model 3 direct effects

Model 4 direct effects

Model 5 interaction

1209.871 62.880⁎⁎⁎ 2.999† 7

Model 7 interaction

− .009 .225 − .050 .000⁎⁎⁎ .962† − .109

.005 .204 − .065 .000⁎⁎⁎ .403 .057

.007 .131 − .057 .000⁎⁎⁎ .279 .043

.007 .130 − .057 .000⁎⁎⁎ .278 .044

.007 .122 − .055 .000⁎⁎⁎ .272 .040

.157 .261⁎

.122 .290⁎ − .860⁎⁎⁎

.116 .275⁎ − .887⁎⁎⁎

.116 .276⁎ − .889⁎⁎⁎

.125 .273⁎ − .898⁎⁎⁎

− .354⁎ .006

− .377⁎ .074 .158

− .353⁎

1212.870 59.462⁎⁎⁎ 28.722⁎⁎⁎ 6

Model 6 interaction

1203.765 67.013⁎⁎⁎ 6.106⁎ 8

1174.184 88.407⁎⁎⁎ 29.581⁎⁎⁎ 9

1169.586 91.596⁎⁎⁎ 4.599⁎ 10

1169.585 95.486⁎⁎⁎ .001 11

1167.568 99.361⁎⁎⁎ 2.017 12

N = 316. ⁎⁎⁎p b .001, ⁎⁎p b .01, ⁎p b .05, †p b .10. Parameter estimates are unstandardized. Independent variables and interaction terms are converted z-scores.

significant parameter estimate in the hypothesized direction while predicting start-up. Additionally, step two yielded a significant Δ chi-square thereby indicating improved model fit. As such, Hypothesis 1 is marginally supported. In step three, ability expectancy yielded a positive, significant parameter estimate and a significant Δ chi-square thereby indicating support for Hypothesis 2. Next, we included time lag in the model and it yielded a negative, significant parameter estimate and a significant Δ chi-square confirming Hypothesis 3. Then, we converted time lag and outcome expectancy to their respective z-scores and investigated an interaction term with time lag moderating the relationship between outcome expectancy and new venture creation. According to the results, the interaction term yielded a negative, significant parameter estimate thereby indicating support for Hypothesis 4. Fourth, we included an interaction term with time lag moderating the relationship between ability expectancy and new venture creation. In this case, the parameter estimate is not significant thereby indicating a lack of support for Hypothesis 5. Lastly, we investigated the possibility that the interaction of outcome and ability expectancies exert a joint effect on the likelihood of start-up. As Table 2 indicates, this relationship is non-significant thereby disconfirming Hypothesis 6. Table 3 provides a concise summary of results. As indicated, three out of the six hypotheses were supported with an additional hypothesis receiving marginal support. For Hypothesis 1, the marginally significant relationship between outcome expectancy and the decision to start a new venture suggests that perceived outcomes play a small role in predicting start-up. However, when ability expectancy is included in the analysis, outcome expectancy ceases to predict start-up. This result is quite interesting as it confirms the core notion of social cognitive theory that regardless of the value placed on certain outcomes (e.g., creating a successful firm), in the sample of entrepreneurs studied here, belief in one's ability appears to drive the process. Furthermore, the results also suggest the widely-assumed relationship between outcome expectancies and start-up may simply be a residual of the relationship between ability expectancies and start-up decisions. An entrepreneur's belief in their abilities seems to be of central importance. For Hypothesis 3, the results, once again confirm core tenets of social cognitive theory, and indicate that the longer would-be entrepreneurs wait to act on their intentions, the less likely a firm will ever be started. Simply put, our results suggest that while planning may indeed reduce long-term failure rates, at some point, entrepreneurship requires action. Specifically, among the entrepreneurs surveyed here, early action, in particular, is strongly associated with start-up. For Hypothesis 4, time lags appear to exert a strong negative effect on the initial positive relationship between outcome expectancies and start-up. Given the implied focus of the outcome expectancy variable on situational characteristics exogenous to the entrepreneur, the negative relationship here suggests long delays in the decision to start a new venture may result in the closure of meaningful windows of opportunity (e.g., Busenitz and Barney, 1997).

Table 3 Summary of results. Summary of results

One: Outcome Expect

Two: Ability Expect

Three: Time Lags (TL)

Four: Outcome⁎ (TL)

Five: Ability⁎ (TL)

Six: Ability⁎ Outcome

Predicted sign Actual sign Supported?

Positive Positive Marginal

Positive Positive Yes

Negative Negative Yes

Negative Negative Yes

Negative Negative No

Positive Positive No

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Lastly, for Hypothesis 5, the non-significant relationship between time lags and ability expectancy suggests an interesting possibility. Specifically, the ability expectations of the entrepreneur appear to be quite strong because these expectations, as a determinant of start-up, are not weakened by the amount of time it takes to translate an idea into an observable action. Therefore, given the relationship between systematic planning and increased survival rates/performance among new ventures (e.g., Delmar and Shane, 2003), those entrepreneurs with strong ability expectancies who take a little extra time during the start-up phase for planning may reduce long-term failure rates without decreasing the likelihood the venture will ever be started. 7. Discussion and research implications 7.1. Theoretical implications This study examined the effects of various cognitive-level variables on the choice to start a new venture. Our results indicate that ability expectancies are a robust predictor of new venture start-up decisions while outcome expectancies play a marginal role. These results are important for future research for both theoretical and practical reasons. From a theoretical perspective, extant theory (particularly in economic-based theories of entrepreneurship) appears to rely largely on a logic (or illogic) of consequences to describe entrepreneurial action. While our results initially support this relationship, the central importance of ability expectations suggests a broader set of concerns for entrepreneurs considering the call to entrepreneurship. Specifically, a strong belief in one's ability to act entrepreneurially appears to largely drive the firm creation process which, furthermore, suggests role set expectations and personal identity concerns are important to nascent entrepreneurs. To be clear, however, our results do not examine whether these nascent entrepreneurs hold inflated expectations of their entrepreneurial abilities although the mean ability expectancy scores are quite high (see Whyte et al., 1997 and Kruger and Dunning, 1999 for a discussion on some of the dangers posed by inflated ability expectations/self-efficacy on decision-making). Broadly then, what is particularly interesting about our results is that it really may be hubris (e.g., extreme self-pride) rather than overconfident predictions of potential outcomes which explains entrepreneurial entry. Related to this point, our results suggest an interesting counter-hypothesis to a core perspective in extant economic theories of entrepreneurial entry. Specifically, Jovanovic (1982) posited that individuals initially enter entrepreneurship with considerable uncertainty regarding their entrepreneurial abilities. Over time, however, as their ventures develop, these entrepreneurs gain valuable feedback regarding their abilities. Jovanovic (1982) suggests that those entrepreneurs who discover their inability to fulfill the role of being an entrepreneur will close their ventures quite early in the lifecycle (Strotmann, 2007). The results of this study along with those reported by Koellinger et al. (2007) indicate that entrepreneurs with high ability expectations are more likely to start new ventures. Specifically, contrary to Jovanovic (1982), entrepreneurs in our sample with high levels of uncertainty regarding their ability to fulfill the role of being an entrepreneur are less likely to start a new venture. Koellinger et al. (2007) demonstrate, however, that confidence in one's abilities erodes over time and that high initial ability expectancies are negatively associated with subsequent firm survival. These results mark a potential contribution of social cognitive theory in providing a cognitive explanation for firm failure that such an erosion of belief in one's ability would indeed predict subsequent exit from an entrepreneurial venture (cf. Bandura, 1986). The overall process, however, differs considerably based on the relative strength of the entrepreneur's perceived self-efficacy. In other words, once we control for the possibility more experienced entrepreneurs would readily perceive early warning signs of probable firm failure, entrepreneurs with stronger self-efficacy beliefs may persevere longer in struggling ventures given their strong belief in their abilities. Furthermore, this possibility naturally gives rise to the question as to whether this perseverance eventually pays off as the venture crosses vital survival thresholds (e.g., Zimmerman and Zeitz, 2002) or if this perseverance simply reflects a biased escalation of commitment (Gimeno et al., 1997). The central question here is the rate at which post start-up experiences erode an entrepreneur's belief in his or her ability to successfully operate the current venture and the subsequent decision to close a failing venture. This, however, differs considerably from Jovanovic's (1982) notion that entrepreneurs start ventures with considerable uncertainty regarding their entrepreneurial abilities. Rather, the erosion of these strong beliefs over time may be the central mechanism leading to the decision to abandon a venture. 7.2. Practical implications Our results are also important for practical reasons. Specifically, linking the specific types of expectancies to explain the firm start-up decision suggests specific sets/types of interventions to help improve the quality of entrepreneurial entry. The good news is that ability expectancies as approximations of self-efficacy (cf. Ambrose and Kulik, 1999) appear to be amenable to training and education (Zhao et al., 2005). To this point, by providing students with thoughtful training and educational experiences, educators may be able to provide potential entrepreneurs with a realistic assessment of their skills and abilities and subsequently improve the quality of associated entrepreneurial entry. On a related point, as outcome expectancies appear to play a marginal but positive role in encouraging firm start-up, our results indicate that time spent in venture planning, in addition to the additional market/industry knowledge obtained, may help prevent excess entry by overly optimistic entrepreneurs. Specifically, for entrepreneurs with inflated outcome expectancies, exercising patience may in fact allow important information to diffuse which may provide a more accurate picture of the nature of the specific identified opportunity. To this point, we see the exercising of patience coupled with planning to co-opt inflated outcome expectancies

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as being a promising intervention. Given that overconfident outcome expectancies arise where relevant base rate information is ignored (Bazerman, 2006), these time lags may allow important information to diffuse to nascent entrepreneurs considering the possibility of starting a new venture. 7.3. Limitations Using PSED data limited our research design in several ways. First, due to the large amount of questions included in the survey, survey designers elected to shorten many of the scales used to measure certain cognitive variables (in some cases) to single responses (Shaver, 2004). We are confident that the measures we use are consistent with the core theory utilized here; however, we do recommend that future research seek to re-confirm our results with more complete scales to further strengthen the evidence we present in this paper. Also, because of the sheer number of questions (over 5000 total questions), we naturally had to contend with some missing data from important variables. To limit the potential of biasing our results, we did not elect to use mean substitution for our missing independent or dependent variables given the tendency for individual respondents to skip entire scales/survey items. However, we recognize that by doing this, we did reduce the size of our sub-sample. Overall, though, we thought reducing our sample was the best course of action given the necessity of generating entire sets of responses for individuals who neglected to respond to focal survey items. Also, we do recognize that there is a long-running debate in the entrepreneurship literature regarding the differences between small business owners and innovating entrepreneurs (Carland et al., 1984; Shane and Venkataraman, 2000; Mahoney and Michael, 2005). Certainly, the majority of respondents in the PSED sample would not traditionally be classified as innovating entrepreneurs. However, in all circumstances, the respondents in the sample were in the process of starting a new venture and since we are directly examining the relevance of certain variables in the start-up process, we believe the broad range of respondents across different industries and geographic locations in the PSED sample enhances the generalizability of our findings. We are, however, intrigued by the possibility of investigating our research model with a sample of technology-based entrepreneurs. Specifically, would the results here of the relationship between outcome/ability expectancies and firm start-up change with a sample of high-tech entrepreneurs? In the PSED sample, there are undoubtedly individuals considering self-employment as a labor market decision.4 In these cases, it may be that identity/role set expectations take on increased importance, thus explaining the central importance of ability expectancies. Alternatively, it may be that technology-based entrepreneurs would be more concerned with creating successful ventures. Therefore, with a more focused sample such as technology entrepreneurs, different results may emerge (e.g., outcome expectancies might become relatively more important). Also, in regards to the implications of our findings to emerging theory on entrepreneurial decision-making, Hayward et al. (2006) note very clearly that the boundaries of their model limit their hypotheses to relationships between VC-funded entrepreneurial ventures and the entrepreneurs running them. As such, although our results have interesting implications for the specific hypotheses made by their article, it should be noted that the broad range of our sample differs from the more focused sample they identify. With that in mind, we believe an important area of future research would be to re-examine the issues we discuss here with a stronger innovation/technology focus. In particular, the distinction between outcome and ability expectancies might play out differently in a sample of entrepreneurs attempting to introduce new-to-the-world inventions. Thus, measuring the effects of innovation on the start-up decision reflects an interesting agenda for future research. 8. Conclusion In summary, the results of our study indicate that within the sub-sample of PSED respondents examined here, confidence in one's abilities to perform tasks relevant to entrepreneurship is a robust predictor of the start-up decision while outcome expectancies appear to play a small, marginal role. These results suggest that although the desired outcomes from starting a venture might differ considerably across a broad sample of entrepreneurs, a common motivating factor encouraging start-up activities is belief in one's abilities. As the very act of creating new ventures under the pressure of considerable uncertainty seems to defy rationality, we believe the logic of appropriateness in entrepreneur decision-making provides an alternative model of rational decision-making more appropriate to the entrepreneurial context. Overall, while theories of entrepreneurship rightly construct models with scientific and practical implications geared towards enhancing prospects for firm survival and success, the phenomenology of entrepreneurship we discuss here suggests that for many entrepreneurs, the expected destination is perhaps not as important as the journey. References Afifi, A., Clark, V., May, S., 2004. Computer-aided Multivariate Analysis. Chapman & Hall/CRC., Boca Raton, FL. Ambrose, M.L., Kulik, C.T., 1999. Old friends, new faces: motivation research in the 1990s. Journal of Management 25 (3), 231–292. Anders, G., 2007. 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