Behavioral Determinants and Motivation
We know that entrepreneurs are the sum of their life experience, that there is no single entrepreneurial personality, and that entrepreneurial behaviors are dynamic in that they evolve over time. This section explores what we currently understand about the characteristics and cognitions of entrepreneurs: how they behave, what motivates them, and why.
The first thing we know for certain is that entrepreneurs are not born, despite some popular press writings to the contrary. The Global Entrepreneurship Monitor began in 1999, and its 2013 Global Report included an estimated 70 percent of the world's population across 70 economies. If entrepreneurs were born rather than made by their cultural and personal backgrounds, logic dictates that the rate of entrepreneurship would be roughly constant across all countries in the world. It is not. Rates of entrepreneurial activity are higher in countries where entrepreneurship is driven by necessity than in countries where it is driven by opportunity.
The second thing we know for certain is that there is no single entrepreneurial personality in the sense that if you have one you'll be a successful entrepreneur, but if you don't have one you should not even try to start a business on your own. Rather, there are dimensions on which all humans can be measured. Some of these are implicated in entrepreneurial success, others less so. But no single dimension is the one without which entrepreneurial success is impossible.
We also know that entrepreneurial behaviors are dynamic in that they evolve over time; considerable time often passes before entrepreneurs' actions culminate in the establishment of a new firm (Reynolds 2007). Finally, entrepreneurs' behaviors are heterogeneous in that the processes (e.g., opportunity identification, opportunity exploration, opportunity exploitation) that lead to the establishment of a new firm vary from one individual to the next, and also between different start-up efforts by the same individual.
In general, work on the behavioral determinants and motivations of entrepreneurs can be divided into characteristics and cognitions. Characteristics include demographics (e.g., gender, race, education, experiences) as well as competencies (e.g., organizational skills, technical skills, industry skills) and traits (e.g., tenacity, passion, vision, self-efficacy, personality, regulatory focus, need for achievement, locus of control, implicit theories). Cognitions include pattern recognition (Baron 2006), decision-making styles, perception, and heuristics (for a review see Grégoire, Corbett, and McMullen 2011).
Of these characteristics, traits, and cognitions, the most commonly explored constructs in entrepreneurial motivation research include: 1) need for achievement, 2) risk taking, 3) tolerance for ambiguity, 4) locus of control, 5) self-efficacy, and 6) goal setting (Shane, Locke and Collins 2003). Additional dimensions that have individually shown promise are conscientiousness, expectancy, and a variety of cognitive processes (such as entrepreneurial intentions).
We seek to know more about the ways in which these various factors might combine in a single individual and how their influence might vary depending on the particular entrepreneurial activity involved (i.e., organizing the new venture vs. carrying it through a growth stage to profitability). These and other questions point toward promising avenues of further research.
Throughout the world, business schools are often organized into departments that mirror the functional areas of corporate business, such as accounting, finance, legal affairs, management and human relations, marketing and sales, production, and operations. Frequently these functional areas have not only their own bodies of knowledge, but also their own established methods for obtaining new information. At the simplest level, for example, there is only one right way to do double-entry bookkeeping. By contrast, entrepreneurship is a phenomenon, not a function. Consequently, there is no single right way do it. Should an entrepreneur have a business plan? Perhaps, perhaps not. Is an entrepreneurial business always formally organized as a legal entity before accepting money from customers? No.
When a business topic is phenomenon-driven, rather than function-driven, theories that might apply to the topic are rarely home grown, rather, they are imported in pieces from other disciplines. In the case of entrepreneurship, explanatory principles have been borrowed from economics, psychology, sociology, or in some instances even from science and engineering. Given that the present topic deals with the behavior of individual entrepreneurs, most of the borrowing has been from psychology, more specifically social psychology and cognitive psychology.
Before turning to the psychological characteristics that affect behavior of entrepreneurs, it is worth spending a moment on the myth of the entrepreneurial personality. Not surprisingly, there are psychological explanations for the power of this myth.
In one of the first general treatments of the topic, Fiske and Taylor (1984) defined social cognition as thinking about people. Though our thoughts about others are often right on the mark, they can in some circumstances be quite mistaken. For example, there are the cognitive heuristics (or methods) initially identified by Kahneman and Tversky (1973) that explain why people are poor intuitive statisticians. One of these heuristics is availability, the assumption that the first instance that comes to mind is typical of a class of social objects. Ask a group of people to name an entrepreneur and it is a safe bet that the number of names will be substantially smaller than the number of people in the group. There will be some who say Bill Gates, some Richard Branson, some Mark Zuckerberg, and mentions of a few other high profile entrepreneurs. The limited set of characteristics possessed by this small group will then become the average person's stereotype of an entrepreneur.
This problem is compounded if the perceivers are, for example, venture capitalists. A study by Zacharakis and Shepherd (2001) invited venture capitalists to make two judgments: estimated success of a described venture and their personal confidence in that likelihood judgment. Some of the cases described had been constructed from real situations whose outcome was known. What is interesting for present purposes is that the venture capitalists were just as confident in their judgments when those success estimates were wrong as when they were correct. This sort of overconfidence bias has been shown in a variety of other arenas and, coupled with the availability heuristic, could easily be the basis of a view that all entrepreneurs share a common set of personality characteristics.
The Big Five (or Six)
If the entrepreneurial personality is a myth, what do we know about the personal dimensions that are implicated in entrepreneurial success? Let's begin with those aspects of being human that are most trait-like. Several years ago research in personality adopted the view that there are five broad behavioral predispositions (the "Big Five") that can characterize people generally (Costa and McCrae 1985). These dimensional predispositions are Conscientiousness, Agreeableness, Neuroticism, Openness to experience, and Extraversion (arranged in this order as a mnemonic device: CANOE).
At least one study tested for differences in entrepreneurial performance based on the Big Five (Ciavarella, Buchholtz, Riordan, Gatewood, and Stokes 2004). Not surprisingly, these investigators found that conscientiousness was positively related to venture survival. There was, however, an unanticipated negative relationship between openness to experience and long-term venture survival. Extraversion, emotional stability (the positive end of neuroticism), and agreeableness were unrelated to venture success.
Before concluding that an extraverted, emotionally stable, and agreeable person would make a terrible entrepreneur, it is worth noting—as have Rauch and Frese (2006)—that the Big Five are distal predispositions, not specific spurs to action. More recently, analyses of the lexical structure of personality descriptors, especially in languages other than English, suggest that there should be a sixth broad predisposition: honesty/humility (Ashton and Lee 2007). When all six dimensions are involved, the acronym used for them is HEXACO (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, and Openness to experience). Although extant work has thoroughly examined the Big Five, research is very limited with regards to honesty/humility (Brandstätter 2011); however, there is emerging work suggesting that more narrow personality traits have stronger relations with entrepreneurial outcomes than the Big Five. For example, data show that vision, proactivity, creativity, and opportunism are related to entrepreneurial ability (Leutner, Ahmetoglu, Akhtar, and Chamorro-Premuzic 2014).
One long-standing specific characteristic is achievement motivation, originally conceived by McClelland and colleagues as a need that leads to goal striving (not merely to entrepreneurial goals) (McClelland, Atkinson, Clark, and Lowell 1953). Very briefly, the tendency to approach any goal is a joint function of the desire for success, the fear of failure, and the probability of success. Achievement motivation has through the years been related to entrepreneurial success, often distinguishing entrepreneurs from other people, or more successful entrepreneurs from less successful ones (see, e.g., Collins, Hanges and Locke 2004; Rauch and Frese 2007).
Another psychological standard that has been adopted by entrepreneurship researchers is self-efficacy (Bandura 1977) (Bandura 1985). In many ways this is a particularly interesting personal characteristic. Self-efficacy refers to the individual's belief in his or her own competence to accomplish a particular task. People differ in their levels of self-efficacy, but despite these individual differences, experienced success can shift the distribution, making self-efficacy one of the few personal characteristics susceptible to change through experience.
Self-efficacy became popular in entrepreneurship research with publication of scales to measure entrepreneurial self-efficacy (Chen, Greene, and Crick 1998) (DeNoble, Jung, and Ehrlich 1999). Although researchers have sought to establish direct links between entrepreneurial self-efficacy and venture performance (Baum and Locke 2004), the current balance of evidence suggests that self-efficacy more likely plays a moderating role in the development of entrepreneurial behavior (Zhao, Seibert, and Hills) (Mauer, Neergaard, and Linstad 2009).
Although cognitive constructs, such as self-efficacy, are increasingly seen as predictors of entrepreneurial behaviors, research has also started to explore the origins of this type of entrepreneurial thinking, and this is where personality differences and entrepreneurial identity research may experience a comeback. Also, reflecting the developments in management and organizational behavior research more widely, research on the softer side of entrepreneurial motivation (such as affect and emotions) has started to develop (Baron 2008).
Recognizing that the creation of a new business venture is a process that requires time and planning, there has been substantial interest in the idea of entrepreneurial intentions (see a recent review by Fayolle and Liñán 2014). These have most frequently been couched in terms of the theory of planned behavior (TPB), originally developed in the attitude realm by Ajzen (1985) out of the prior theory of reasoned action (Fishbein and Ajzen 1975). The TPB has been imported most persuasively into entrepreneurship by Krueger and his colleagues (Krueger 2009) (Krueger and Carsrud 1993) (Krueger, Reilly, and Carsrud 2000).
Basically, the TPB holds that a person's attitude, the surrounding social norms, and perceived behavioral control (quite similar to self-efficacy) combine to produce specific behavioral intentions. The model has received support, particularly for perceived behavioral control (Autio, Keeley, Klofsten, Parker, and Hay 2001), but its inclusion of a social norms component has also raised the importance of cross-country differences in, for example, the social stigma associated with business failure (Elfving, Brännback, and Carsrud 2009).
In the social cognition literature, analysis of intentions leads to a search for the reasons behind such intentions (Shaver 1985). In entrepreneurship there is a long tradition of examining the reasons that people might decide to start new ventures, beginning with a cross-national study by Scheinberg and Macmillan (1988). Over the years, these career reasons have been refined (Birley and Westhead 1994) (Shane, Kolvereid, and Westhead 1991), and eventually found their way into the Panel Studies of Entrepreneurial Dynamics (Carter, Gartner, and Shaver 2004).
In the PSED I (Panel Study of Entrepreneurial Dynamics), there are 18 reasons behind entrepreneurial intent, such as a desire for financial success, freedom to adapt one's own approach to work, and to follow a family tradition. Although there have been different factor structures for the reasons one starts a business, depending primarily on the particular sample used (Davis and Shaver 2009), there is good evidence that at least some of the reasons distinguish entrepreneurs from non-entrepreneurs (Carter, Gartner, Shaver, and Gatewood 2003). Interestingly, however, it is not financial success, but recognition (i.e., achieve something and get recognition for it) and job flexibility that make a difference (Cassar 2007). Related to this line of research, researchers have also explored whether entrepreneurs are pushed (necessity entrepreneurship) or pulled (opportunity entrepreneurship) into starting their businesses, and the economic impact of these types of enterprises (McMullen, Bagby, and Palich 2008) (Wong and Autio 2005).
Turning to the ways in which entrepreneurs might think differently, the process begins with the recognition of an opportunity. There can be legitimate debate about whether opportunities are out there in the world for anyone to see, whether they exist only in the potential entrepreneur's head, or some combination of the two.
In an early conception of alertness, Kirzner (1979) asserted that the mental representations of entrepreneurs allowed discovery of opportunities without search, a view that has not received substantial empirical support. Rather, as Gaglio and Katz (2009) note, a full view of alertness needs to consider the chronic schemata possessed by the entrepreneur. Similar to the difference between novices in a field and experts (the latter have more finely differentiated cognitive categories, enabling them to see connections that are not available to novices), alert individuals are more sensitive to disequilibria that in the classic sense provide entrepreneurial opportunities. The alertness notion of opportunity recognition has recently been summarized by Gaglio and Winter (2009).
Execution of Entrepreneurial Intent
Once an opportunity is identified, the next issue is, "Do I want to do something with it?" In large part, this question involves the person's self-assessment of expectancy (Gatewood 2004) (Renko, Kroeck, and Bullough 2011) (Vroom 1964). Expectancy theory posits that goal motivation is a joint function of the valence of an anticipated outcome, the instrumentality of an action taken (the degree to which successful completion of the action will lead to the outcome), and the person's expectancy of success in the planned performance of the action. In the first Panel Study of Entrepreneurial Dynamics (PSED I), nascent entrepreneurs have been shown clearly to have expectancies for business success that exceeded those of people not organizing new businesses (Gatewood 2004), and to have undertaken organizing efforts consistent with their expectancies (Renko et al. 2011).
Several other cognitive processes are involved in the execution of entrepreneurial intent. For example, detailed consideration of the cognitive scripts used by entrepreneurs can be found in Mitchell, Mitchell, and Mitchell (2009). These authors have developed scales that can distinguish experienced entrepreneurs from novice entrepreneurs. Even among novice entrepreneurs, there is good reason to believe that, compared to non-entrepreneurs, entrepreneurs perceive risk as less threatening (Palich and Bagby 1995) (Stewart and Roth 2004), and that metacognitive adaptability plays a role in entrepreneurial decision making (Haynie, Shepherd, and Patzelt 2012).
Finally, even those entrepreneurs who practice what Sarasvathy (2001) called effectuation (in contrast to the causal planning that characterizes corporate life) must use their cognitive processes to categorize what the market is telling them and determine what their best response should be. In other words, effectual entrepreneurs' behaviors are oriented towards controlling future uncertainties, and in the process of building companies, they continuously change their goals and assumptions.
- In most of the prior research on the traits or personal characteristics implicated in entrepreneurial behavior, characteristics have been examined individually. What is needed, however, is for multiple potential determinants to be studied in concert with one another. It is logically impossible to create a multidimensional picture of the entrepreneur when only one dimension is investigated at a time. Furthermore, future work is advised to explore both intrapersonal (e.g., the typical trait- or characteristic-based focus) as well as interpersonal (e.g., traits and characteristics of business partners, spouses, networking group members, and other social ties) aspects that impact new venture emergence and success.
- To truly understand entrepreneurial motivation and the behavioral antecedents of entrepreneurship, researchers will have to continue to investigate entrepreneurship as a process that occurs at multiple levels and over time. As such, models that integrate individual-level and contextual factors (multi-level models) are needed. At the individual level, researchers need to continue to study how both cognition (e.g., feasibility assessments, heuristics) and emotions together lead to entrepreneurial behaviors.
- One of the empirical challenges in this line of research has been the lack of follow-up. Until recently, most empirical studies have used some kind of behavioral intention as the ultimate dependent variable in their models, instead of exploring whether, how, and when these intentions actually lead to behaviors. This shortcoming is increasingly addressed by research using panel data sets of nascent entrepreneurs (such as the PSED-types of data sets increasingly available in various countries) as well as researchers' own longitudinal, proprietary data sets. Future research should further solidify our understanding of how motivations and intentions lead to actual entrepreneurial behaviors over time.
- Additionally, future research should make a more concerted effort to ensure that when theoretical ideas are imported from other disciplines, the ones used are the most up-to-date versions. This has not always been the case (Shaver 1985). Specifically, for example, none of the chapters in either of the edited volumes on the psychology of entrepreneurship (Baum, Frese, and Baron 2006) (Carsrud and Brännback 2009) makes reference to recent meta-analyses in the psychological literature of such important concepts as mood and creativity (Baas, De Dreu, and Nijstad 2008), the relationship between intentions and behavior (Webb and Sheeran 2006), or the effects of choice on intrinsic motivation (Patall, Cooper, and Robinson 2008). A serious challenge when one is importing theory from other disciplines to help explain a phenomenon is to keep up with literature about the phenomenon itself, much less to remain current with developments in the parent discipline.
- Finally, we could use a larger set of methodological tools. Early research on entrepreneurs had to settle for what it could get. Often that was in-person interviews with willing entrepreneurs (at any stage of their ventures). Such people were typically identified by their presence on one list or another (tax rolls, membership lists of Chambers of Commerce, lists of MBA graduates, or even phonebook listings). From the standpoint of modern research methods in social psychology, there is very little here to like. Entrepreneurs reached at some later stage in their careers cannot help but be influenced by their own prior success (or failure). If they have been selected from a published list, there is little chance that whatever they say will generalize beyond the boundaries of the class specified in the list. They remain excellent candidates for experimentation, a method that fortunately is gaining ground in entrepreneurship research (Shaver 1985).
For example, conjoint analysis (often a form of incomplete factorial design) has been used with great success to identify factors that influence the decisions entrepreneurs make, because the experimental conditions are arranged so that each person serves as his or her own control group (see, for example, Haynie et al. 2012). On the other hand, if the researcher's ultimate goal is external validity rather than internal validity, then the research choice should involve a database (such as the PSED studies) that contains individuals who, through appropriate weighting, can be made to be representative of the entire population of a country. Though the original PSED was conducted in the U.S., there are comparable research projects in several other countries, with most of the non-U.S. publications using the PSED equivalents in either Sweden or Australia (an updated list of the more than 100 PSED publications is available on the University of Michigan's PSED site and on the Social Science Research Network).
In short, to solidify conclusions about the determinants of entrepreneurial behavior, we will need to apply the most recent versions of imported theory in research projects that are explicitly directed either toward testing theory (internal validity) or producing generalizable results (external validity), with that objective determining the choice of method and respondents.
- The Small Business Administration has an extensive listing of small business data resources. This listing includes 35 data resources collected by the U.S. government and 24 additional databases collected by nongovernmental agencies. Of these 59, only 7 government-sponsored data resources and 4 nongovernmental resources contain basic demographic information about the small business's founders/owners/managers. Most of these 11 provide medium-level data, frequently not suitable for research purposes. Only two datasets, the Kauffman Firm Survey (KFS) and the Panel Studies of Entrepreneurial Dynamics (PSED) permit access to microdata.
- Downloading the Kauffman Firm Survey (KFS) provides a variety of resources, including annotated questionnaires, a data Wiki, and other useful links. The KFS is more limited in terms of data on individual entrepreneurs' motivations than PSED; however, it contains data on the behaviors that early-stage entrepreneurs engage in when building their ventures. Full data files available for approved researchers (who join the University of Chicago NORC Data Enclave) free of charge. Limited data files downloadable by anyone free of charge.
- By contrast, the PSED datasets are publicly available for download from the University of Michigan's Institute for Social Research. Panel Study of Entrepreneurial Dynamics (I and II). The U.S. versions of these studies of nascent entrepreneurs and their start-up processes are free – the data as well as the related codebooks. The studies include a wide range of questions on start-up motivations and reasons. PSED types of protocols have been implemented in a number of other countries as well, but those data sets are typically only available to researchers involved in the projects. PSED I lasted for an overall total of four years, PSED II for a total of six years. The PSED has been described in detail in two books (Gartner, Shaver, Carter, and Reynolds 2004) (Curtin and Reynolds 2009), and procedures for cleaning and combining the datasets have been described (including the relevant code) by Shaver, Davis, Kindy, and Blair (2012).
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