Cognitive biases, organization, and entrepreneurial firm survival

European Management Journal (2013) 31, 278– 294

Adam Smith Business School journal homepage: www.elsevier.com/locate/emj

Cognitive biases, organization, and entrepreneurial firm survival Sveinn Vidar Gudmundsson *, Christian Lechner Toulouse Business School, ESC Toulouse, 20 Boulevard Lascrosses, 31068 Toulouse Cedex, France

KEYWORDS Cognition bias; Entrepreneurship; Optimism bias; Distrust; Overconfidence; Organization; Firm survival; Firm performance

Summary EntrepreneurÕs cognitive biases have emerged as one of the central themes in understanding the performance of entrepreneurial firms. Research has shown that entrepreneurÕs overconfidence and optimism bias help firm creation, but also contribute to firm failure. Prior studies using cognitive biases to explain entrepreneurial outcomes are lacking. First, they usually focus on a single cognitive bias. Second, as yet no studies have identified a cognitive bias that, unlike overconfidence and optimism, acts positively both on firm creation and survival. In research on failure avoidance in high consequence industries, distrust is emerging as an important cognitive bias explaining non-failure in non-routine situations, but entrepreneurship research has paid little attention to distrust in entrepreneurs. Third, research on cognitive biases is generally affected by survival bias: most studies have focused on cognitive biases among surviving firms alone, but we still know little about diverse multilevel impacts on both survivors and non-survivors. To address this gap, we built a multilevel model explaining the interplay of cognitive biases, the different cognitive make-ups of entrepreneurs, and their influence on organization and survival. Our results show that overconfidence is the chief negative influence on survival. Optimism bias and distrust are conflicting cognitive biases influencing overconfidence, but showing a directly opposite influence on firm survival respectively. Further, entrepreneurÕs cognitive types show diverse influence on organization such as the propensity to delegate and financial orientation, but congruent positive influence on opportunity orientation. The study concludes by suggesting that entrepreneurs should balance their organizations, for instance through hiring policies, to prevent extreme overconfidence, optimism or distrust becoming a predominant organizational culture. ª 2013 Elsevier Ltd. All rights reserved.

Introduction * Corresponding author. Tel.: +33 561 29 49 23; fax: +33 561 29 49 94. E-mail address: [email protected] (S.V. Gudmundsson).

Entrepreneurs are considered overconfident (Cooper, Woo, & Dunkelberg, 1988) and overconfidence is associated with self-esteem, ambition and success (Johnson & Fowler, 2011). However, overconfidence, a cognitive bias, is also

0263-2373/$ - see front matter ª 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.emj.2013.01.001

Cognitive biases, organization, and entrepreneurial firm survival associated with failure (Camerer & Lovallo, 1999). In general, from 30 to 40% of entrepreneurial firms are complete failures and many are bought out or never bring expected return on investment, meaning that the real failure rate can be 70 to 80% (Nobel, 2011). Entrepreneurship seems strongly linked to biased perceptions rather than measures of objective reality (Simon et al., 2000), so researchers are increasingly turning to entrepreneurÕs cognitive biases to explain not only entrepreneurial firm creation but also failure (Simon et al., 2000). Cognitive biases are mental simplifications helping to connect information, to identify opportunities, and to deal with hurdles when starting and growing a firm (Mitchell, Busenitz, & Lant, 2002). However, biases interfere with the ability to be impartial, unprejudiced or objective when interpreting reality (Shaver & Scott, 1991). Although entrepreneurship scholars agree cognitive biases can play a positive role in firm creation, how biases influence survival is largely unstudied. We address this gap by presenting a multilevel theoretical model framed in social cognitive theory (Bandura, 1986; Wood & Bandura, 1989) that helps explain the interplay of cognitive biases, i.e. the cognitive make-up (Roy & Elango, 2000) of entrepreneurs, and their influence on both surviving and non-surviving firms. Past studies (Hmieleski & Baron, 2009; Corbett & Hmieleski, 2007; Busenitz & Barney, 1997; Venkataram, 1997) focus mainly on isolated effects and we still know little about multilevel effects. Moreover, these studies appear to be affected by survival bias: scholars have mostly focused on differences among surviving entrepreneurial firms, not on non-survivors, leaving us with little knowledge about differences between surviving and nonsurviving firms. Finally, entrepreneurship research on cognition has studied some biases and largely neglected others. Research on entrepreneurÕs overconfidence (Busenitz & Barney, 1997; Cooper et al., 1988; Forbes, 2005; McCarthy, Schoorman, & Cooper, 1993; Olson, 1986) has mostly shown negative effects: high cost to individuals, society and the economy (Moore & Healy, 2008). Overconfidence is overestimation of oneÕs accuracy, or, alternatively, an overestimation of ability relative to others, and links with increased failure risk of firms (Ucbasaran Westhead, & Wright, 2006; Hayward, Shepherd, & Griffin, 2006). The failure risk increases when entrepreneurs overestimate their accuracy and control and underestimate risks (Simon et al., 2000). However, overconfidence is not the only cognitive bias associated with entrepreneurial firm creation and failure in the literature. Entrepreneurs also score high on optimism bias (Dosi & Lovallo, 1997; Fraser & Greene, 2006; Lovallo & Kahneman, 2003; Lowe & Ziedonis, 2006; Simon et al., 2000) and research shows a curvilinear relationship with performance (Brown & Marshall, 2001). In other words, some degree of optimism appears good, but optimism bias is negative. Yet no study has identified a cognitive bias that, unlike overconfidence and optimism, acts positively both on firm creation and survival. Distrust is emerging as an important construct in research on failure avoidance in high consequence industries (Burns et. al., 2006; Conchie & Donald, 2007; Kramer, 1999), but entrepreneurship research has largely neglected the distrust construct regardless of its early detection in entrepreneurs through psychoanalysis (Kets de Vries, 1985, 2003). When non-routine strategies are needed, those who

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distrust perform better, with the reverse being true when routine strategies are optimal (Schul, Mayo, & Burnstein, 2008). Starting a business is a non-routine affair and thus distrusting entrepreneurs might do better than optimistic entrepreneurs at steering their firms away from failure. However, little if any research exists that has tested distrust of entrepreneurs and firm survival using a multilevel perspective. Understanding how biases affect entrepreneurÕs decisions and influence outcomes is a key question in strategic entrepreneurship: if bias is about incorrect judgment it can lead to mistakes in decisions and firm failure (Camerer & Lovallo, 1999). We develop a model that advances our understanding of this less-investigated and under-theorized multilevel perspective by unpacking cognitive biases that influence firm development from conception to maturity or demise. Our model describes the different cognitive make-up of entrepreneurs, offering a way to resolve knowledge about isolated cognitive biases and their collective influence on the entrepreneurial firm.

Theory and hypotheses We begin this section by briefly examining cognitive biases: overconfidence, followed by optimism bias and distrust. We then examine how these cognitive biases influence the organization and survival of firms by discussing delegation, financial orientation, and opportunity orientation.

Overconfidence Overconfidence defined as ‘‘the positive difference between confidence and accuracy’’ (Schaefer, Williams, Goodie, & Campbell, 2004: p. 473) is systemic: ‘‘The more confident people are, the more overconfident they are, and, overall, confidence tends to exceed accuracy’’ (Klayman, Soll, Gonzalez-Vallejo, & Barlas, 1999: p. 217). Overconfidence, researched in terms of negative consequences, influencing conflicts, stock market crashes, and even wars (Moore & Healy 2008), is also a widely recognized context in entrepreneurship (Busenitz & Barney, 1997; Cooper et al., 1988; McCarthy et al., 1993). Since entrepreneurs are generally speaking overconfident (Cooper et al., 1988) it is the degree and form of overconfidence which matters. Griffin & Varey (1996) propose two forms of overconfidence: personal (dispositional) and predictive (situational). Personal overconfidence is sticky but situational overconfidence shifts according to context. We make personal overconfidence (Bertrand & Schoar, 2003; Griffin & Varey, 1996; Malmendier & Tate, 2005) the focus of our research. Larrick, Burson, and Soll (2007:p. 88) found ‘‘ . . . a positive relationship between better-than average perceptions and overconfidence. Higher perceptions of ability relative to others predicted greater degrees of overconfidence (Koellinger, Minniti & Schade, 2007). This relationship held across individuals and across domains.’’ Thus, overconfidence as overestimation of oneÕs own ability relative to others affects the expected outcomes of task execution. Overconfident entrepreneurs tend to largely ignore the competition, the strengths of direct competitors (Moore & Cain, 2007), to introduce riskier products with lower success rates (Simon et al., 2000), to under-resource the venture, to

280 engage less in legitimacy gaining activities, and to rely less on external networks for relational resources (Hayward et al., 2006), all of which are considered critical for firm survival. Overconfidence and survival are therefore negatively associated (Camerer & Lovallo, 1999). Hypothesis 1. Overconfidence is negatively associated with firm survival.

Optimism bias Unrealistic optimism, a cognitive bias, leads to overrating the likelihood of good events, underrating the likelihood of bad events (Zacharakis & Shepherd, 2001) and having positive outcome expectation in situations of no direct control (Koellinger et al., 2007). Entrepreneurs may,due to optimism bias, have high self-esteem, feel less vulnerable, and experience less emotional distress (Perloff, 1988; Weinstein, 1982) leading to fewer precautions to reduce risk (Harris et al., 1994; Weinstein, 1980, 1984, 1987). In other words, optimism bias may stimulate overconfidence in some entrepreneurs (Dubra, 2004; Schaefer et al., 2004; Williams, 1992; Wolfe & Grosch, 1990) since some individuals who overrate good outcomes of events that are not under their control will also overrate outcomes of tasks that are under their control (Koellinger et al., 2007). This is like the progression of confidence to overconfidence by someone having repeated success in the stock market during favorable economic conditions, thinking that his or her ability is the chief cause behind the success, and believing that the economy will continue to grow infinitely (optimistic bias) justifying larger and larger risks (overconfidence). Trevelyan (2008: p. 987) argued that ‘‘not only are optimism and overconfidence distinct from each other but they also have divergent interactions with other constructs.’’ Clearly, optimism bias and overconfidence are not two sides of the same coin, but can act together in synchronization, and are therefore related. Based on the evidence discussed so far we consider optimism and overconfidence to be clearly separate constructs, but optimism bias to be positively associated with overconfidence in entrepreneurs. Hypothesis 2a. Optimism bias is positively associated with overconfidence. Optimism bias is often seen as a positive trait in entrepreneurs. However, Hmieleski and Baron (2009: p. 475) argued that the majority of entrepreneurs would ‘‘fall into the portion of the optimism-performance function beyond the inflection point’’. In other words, optimism bias has a mainly negative influence on performance. This is in line with earlier research such as Perloff (1988), who also underlined the harmful effects of optimism bias. Further along this line Gartner (2005) argued that the primary reason behind high incidence of failure among start-ups was optimism bias. Optimism bias may help the entrepreneur to face obstacles, but it may preclude decisions that prepare firms for adversity (Gartner, 2001) and consequently has a negative association with survival (Hmieleski & Baron, 2009).

S.V. Gudmundsson, C. Lechner Hypothesis 2b. Optimism bias is negatively associated with survival.

Distrust Distrust, understood as the psychological state of not trusting other people and their abilities (Kramer, 1999) is defined as having confident negative expectations about the behavior and abilities of others (Lewicki, McAllister, & Bies, 1998). We draw a clear distinction between distrust and pessimism, the latter being frequently examined in conjunction with optimism. This is because pessimists lack biasfor-action, a trait strongly associated with entrepreneurs (Trevelyan, 2008). We found no research that shows a link between inaction and distrust in others. Distrust has been studied as a social relational phenomenon (Burt, 1999) and as a rational choice perspective (calculated distrust) related to risk (Conchie & Donald, 2007; Kramer, 1999), but underlying these perspectives distrust is a psychological state that varies among individuals (Sorrentino, Holmes, Hanna & Sharp, 1995; Gurtman, 1992). Distrust (i.e. in others) in conjunction with high self-trust (i.e. in oneself) has been associated with opportunity-oriented entrepreneurs who monitor risk and vulnerabilities (Lewicki et al., 1998). In this sense self-trust and distrust are distinctive constructs that can coexist (Burns et al., 2006; Lewicki et al., 1998). A distrusting entrepreneur is reluctant to delegate tasks to others and an overconfident entrepreneur will not feel the need to seek assistance from others regardless of task difficulty (Gino & Moore, 2007). So if the entrepreneur is both distrusting and overconfident it will intensify miscalibration of tasks; the entrepreneur has to do more on his or her own (reluctance to delegate), and with less input from those with knowledge and experience (feeling of little need to seek assistance). So, if an entrepreneur is distrustful in others and overconfident, it will lead to excessive self-reliance (Koellinger et al., 2007; Larrick et al., 2007; Schaefer et al., 2004) and both the number and variance of errors is magnified. Hypothesis 3a. Distrust in others is positively associated with overconfidence. Recent research on high risk activities (oil platforms, investment banking, medical surgery, aircraft piloting, nuclear powerplants, etc.) shows that distrust is related to failure avoidance (Conchie & Donald, 2007; Burns et. al., 2006). Specifically, when non-routine strategies are needed, distrusting persons perform better, with the reverse being true when routine strategies are best (Schul et al., 2008). In an entrepreneurial firm the threat of failure is ever present in a non-routine context, implying ‘‘a substantial learning situation’’for the entrepreneur (Gibb & Ritchie, 1982). Thus, firms of distrusting entrepreneurs are more likely to survive because of greater focus on failure avoidance through more sensible task selection, and more analysis (Teach, Schwartz, & Tarpley, 1989). Kets de Vries (1985), using psychoanalysis, associates distrust with advantages such as keeping the entrepreneur alert to potential moves

Cognitive biases, organization, and entrepreneurial firm survival of competitors, suppliers and customers. Distrusting entrepreneurs do not discount negative events, and are more likely to engage in control mechanisms (Davis, Schoorman, & Donaldson, 1997; Kets de Vries, 1985; Lewicki et al., 1998). We can say that excessively optimistic entrepreneurs seek projects perceived as most likely to succeed (Higgins, 1998), but distrusting entrepreneurs seek projects felt to be least likely to fail (Trevelyan, 2008). It is all about attitude to risk: optimists are risk tolerant and pleasure seeking, selecting intermediate task difficulty, while, distrusting entrepreneurs focus on preventing failure and are less risk tolerant, wanting to analyze decisions critically and select easier tasks (McGraw, Mellers, & Ritov, 2004). So by focusing on risk and task selection, distrusting entrepreneurs would do better on average, while optimistic entrepreneurs would do worse. Thus, distrust leads to greater precaution and therefore increases chances of entrepreneurial firm survival. Hypothesis 3b. Distrust in others is positively associated with survival. The cognitive make-up of entrepreneurs influences firm organization. The following sections cover how cognitive biases influence financial orientation, opportunity orientation, and the propensity to delegate.

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2011). If true, it would prevent the best use of resources during the decisive early years of an entrepreneurial, and thus resource-constrained, firm. But, entrepreneurial firms especially in the early resource-constrained years need strong priority-setting for resource use to reduce failure risk (Thornhill & Amit, 2003). This entitles us to suppose that the entrepreneurÕs willingness to delegate will lessen the firmÕs chances of survival. Hypothesis 4c. A propensity to delegate is negatively associated with survival.

Financial orientation Control is rule setting and monitoring to achieve goals (Green & Welsh, 1988). While trust reduces perceived outcome risk, distrust increases perceived outcome risk (Das & Teng, 2001). People who distrust others will engage in control mechanisms (Davis et al., 1997), which are behavioral and output-control oriented (Ouchi & Maguire, 1975). Output control such as accounting and financial planning reduces risk perception (Das & Teng, 2001). Entrepreneurs who distrust involve themselves in control functions, feeling best qualified to attract and control resources. As a result, distrust positively influences financial orientation among entrepreneurs.

Delegation Giving up control through delegation calls for trust in the delegates, so the personal trait to trustis positively associated with willingness to delegate (Aggarwal & Mazumdar, 2008). In this sense trust and control are substitutes. Fear of losing control and distrust in others are two of the prime reasons of a managerÕs reluctance to delegate (Cuba & Melburn, 1982). Distrust thus increases the need for control and lessens the willingness to delegate. Hypothesis 4a. Distrust in others is negatively associated with the propensity to delegate. As we argued above, if unrealistic optimists overrate positive outcomes for non-controllable events (Hmieleski & Baron, 2008) they are also likely to overrate the abilities of others. In fact, optimism in general is associated with agreeableness, which is characterized by trust rather than suspicion (Sharpe, Martin, & Roth, 2011).Therefore, optimism bias is positively related to the delegation of tasks. Hypothesis 4b. Optimism bias is positively associated with delegation. The literature suggests the entrepreneurial firm needs, over time, to change and become more professional to survive and grow (Hofer & Charan, 1984), calling for delegation to handle expansion of tasks (Greiner, 1972). However, no relationship was found between entrepreneurÕs agreeableness (trusting versus suspicious) and the long-term survival of ventures (Ciavarella, Buchholtz, Riordan, Gatewood, & Stokes, 2004). This raises the question as to whether delegation, trusting others to carry out tasks, is associated with lack of direction, lack of involvement, and avoidance of confrontation (Alkahtani, Abu-Jarad, Sulaiman, & Nikbin,

Hypothesis 5a. Distrust of others is positively associated with financial orientation. Unlike distrust, optimism bias may cause less risk awareness (Simon et al., 2000; Zacharakis & Shepherd, 2001) and therefore less need to implement control (Das & Teng, 2001). Financial orientation implies a need for control by the entrepreneur. For that reason, optimism bias, associated with less risk awareness, is negatively related to financial orientation. Hypothesis 5b. Optimism bias is negatively associated with financial orientation. Davila and Foster (2007) argue that for attracting resources and controlling the firm, individuals build financial control systems, to control risk and avoid failure. However, entrepreneurial firms often lack managerial and financial resources, inhibiting the implementation of control systems (Bianchi, 2002). Nonetheless, a predisposition for financial orientation, feeling at ease dealing with financial issues, may promote this control function in a simple but important way at the birth of the start-up, increasing the likelihood of survival. In fact, Reynolds (1987) found that firm survival was dependent on how attentive small business owners were to financial matters. Hypothesis 5c. Financial orientation is positively associated with survival.

Opportunity orientation Opportunities are product changes, creation of new products, discovery of new markets, discovery of new materials,

282 new methods of production, and new ways of organizing (Eckhardt & Shane, 2003). Entrepreneurs take risks in the pursuit of opportunity (Timmons, 1994) because an opportunity means a possibility to realize valued interests (DiMaggio, 1988; Maguire, Hardy, & Lawrence, 2004). Hills (1995), comparing a successful group with a representative group of entrepreneurs, found that more than 85 percent in both groups saw opportunity as a process rather than a onetime happening. In other words chasing opportunities is seen as a disposition, a nexus between the individual and the opportunity (Shane & Venkataraman, 2000). Dispositional distrust in the abilities of others is associated with the pursuit of opportunities but for different reasons compared to optimism bias. For instance Teach et al. (1989) found that some entrepreneurs favored systematic approaches to opportunity recognition, while others formed companies based on ideas going through little screening. In our view this difference corresponds well to the different cognitive processes characterizing unrealistically optimistic versus distrusting entrepreneurs: the former group is prone to overrate outcomes of events that are not under their control (Koellinger et al., 2007) and do little screening, while the latter group tends to factor in the negative influence of the external environment and engage proactively in implementing control mechanisms (Davis et al., 1997; Lewicki et al., 1998) through systematic approaches. Hence, both groups are geared to opportunity recognition but apply different cognitive processes to the same goal. A distrusting entrepreneur is likely to analyze available information systematically (Teach et al., 1989) and feel better able than others to pursue an opportunity. Hypothesis 6a. Distrust in others is positively associated with opportunity orientation. Optimism bias and opportunity orientation are related. Overrating the chances of positive outcomes dampens perceived risk in chasing an opportunity and artificially inflates expectations (Shepperd, Ouellette, & Fernandez, 1996). Entrepreneurs are over optimistic (Cooper et al., 1988); irrespective of how well-prepared they may or may not be (Hmieleski & Baron, 2008) and have a tendency to select information that confirms beliefs (Johnson-Laird, 1999), i.e. confirmation bias. Thus, optimism bias, in the literature, is associated with opportunity orientation (Baron, 2004). Hypothesis 6b. Optimism bias is positively associated with opportunity orientation. Brush, Greene, Hart, and Edelman (1997), Brush, Greene, and Hart (2001) argue that small businesses fail because of misalignment between resources and opportunities. Opportunities need resources that entrepreneurs may have to create to continue the firm. Some opportunities need more resources than others and the ability of the entrepreneur to align resources to opportunities constitutes a management skill. Not all entrepreneurs are good at both identifying opportunities and finding resources. If we assume the failure rate of start-ups and projects is as high as some argue (Nobel, 2011), being opportunity-oriented should rather associate with failure than non-failure. Thus,

S.V. Gudmundsson, C. Lechner entrepreneurs with a strong tendency to act on opportunities are opportunity-oriented, and run the risk, on average, of fragmenting limited resources and threatening the survival of their firms, especially in the early years (Thornhill & Amit, 2003). Simon et al. (2000: p. 127), state in their work: ‘‘Ironically, the very processes that increase the likelihood of starting a venture may actually decrease performance.’’ Some entrepreneurs may even neglect one start-up while pursuing an opportunity by starting another. Although opportunity orientation is at the heart of entrepreneurship, it is also at the heart of entrepreneurial risk-taking and therefore associated with firm failure once a venture is created. Hypothesis 6c. Opportunity orientation is negatively associated with survival.

Methodology Sample Our study is based on a survey of Icelandic entrepreneurial firms. The economic and social characteristics of the country are comparable to those of developed economies on most measures.1,2 Icelandic culture is characterized by low power distance, flat hierarchies, informality, optimism, individualism (Eyjolfsdottir & Smith, 1997) and high trust in social institutions (Olafsson, 1996). We drew two set of samples randomly from the national registry of enterprises: one among bankrupt firms that had operated for at least three years consecutively in a-ten year period (t1  t10) counted backward from the year the study was performed; and another among non-bankrupt companies operating for at least three years in the same ten-year period and still existing in the year the sample was drawn (t10). The sample consisted of 335 firms (153 = bankrupt; 182 = non-bankrupt) with an effective response rate of 115 (34%) firms: 45 (29%) responses from bankrupt and 70 (38%) responses from non-bankrupt firms. Bankruptcy was indicated in the registry by a bankruptcy filing and de-registration. Over this ten-year period the ratio of bankruptcies to start-ups was 38 percent, 2595 bankrupt firms and 6882 new firms. The proportion of bankruptcies in the first half of the sample period was approximately 46 percent compared to 56 percent in the second half, thus showing a reasonably stable proportion over the entire sample period. A different questionnaire was created for each group reflecting necessary differences in wording and tense to reflect the existing or non-existing state of the firms. Otherwise all questions in the two instruments mirrored each other. We took precautions to avoid social-desirability bias, 1 Using the year 2009 as reference: GDP(PPP) calculated by the OECD was ranked 17th, income dispersion was ranked 1st (GINI 25.0), and the country was ranked 3rd (0.969) in the UN Human Development Index (Human Development Report, 2009). 2 The Icelandic economy experienced a major setback in 2008 due to the Credit Crunch and a large increase in the number of bankruptcies followed. However, our survey was performed well before the financial crises, and is therefore not biased by such an extraordinary event. Besides, we researched entrepreneursÕ dispositions, which are relatively stable traits over time.

Cognitive biases, organization, and entrepreneurial firm survival given the sensitive nature of bankruptcy, by emphasizing on the face of the questionnaire that the responses would remain strictly confidential and could not be traced back to specific individuals or firms (Zahra & Covin, 1995). Anonymity of respondents was accomplished through blind returns: a card with a response number was mailed separately from the questionnaire by the respondent, indicating that a questionnaire had been returned. Follow up calls were made to all respondents not returning the cards. Any study relying on the recall of past experiences is subject to hindsight bias. Christensen-Szalanski and Willham (1991) show through meta-analysis that although 58% of the 85 studies sampled report non-significant results for hindsight bias, they found significant average weighted effect size, lending support to the existence of hindsight bias (cognitive or motivational). If hindsight bias is present in our study, the question becomes one of whether its effect size is potentially large enough to render our results invalid. Going back to Christensen-Szalanski and Willham (1991), the effect size constitutes a ‘‘small’’ effect resulting in the conclusion that it does not attain a conventional measure of practical significance. Further, a concern about the time from when a firm existed and when the questionnaire was administered might also raise concerns. In other words, does the length of time between the two events intensify hindsight bias? A recent study (DenBoer, 2006) found no support for the time hypothesis between original answers and recall estimates, concluding that no hindsight bias was displayed. Based on the evidence we have presented we feel that hindsight bias is not a reason for concern in our study. We used a key informant approach (Brush & Vanderwerf, 1992; Chandler & Hanks, 1993; Huber & Power, 1985) by contacting only the founders of the firms. Not all respondents met the criteria when examining returns: 85 percent (n = 98) were founders, while 15 percent were either hired CEOs (n = 4), bought the company (n = 12), or joined the company after creation as co-owner (n = 1). However, all the responding entrepreneurs fall within the definition of entrepreneurship we used (see Davidsson, 2006), which covers start-ups, new product launches, market expansion and firm revitalization. We included questions to assess respondentÕs personal and business objectives to separate between small business managers and entrepreneurs (Carland, Hoy, Boulton, & Carland, 1984): 57 percent (n = 66) of the respondents found the business growth objective important to very important, while 97 percent (n = 112) found the profit objective important to very important. These objectives, growth and profit, are associated with entrepreneurs rather than small business managers (Carland et al., 1984), the latter group being associated with furthering personal goals. However, on further scrutiny our respondents did not differ: personal fulfillment as an objective was deemed important to very important by 79 percent (n = 91) of respondents, and to have good personal income was deemed important to very important by 84 percent (n = 97) of the respondents. We were therefore not able to distinguish between small business managers and entrepreneurs. Since the majority of the respondents were the founders of the firms, we chose to use the term entrepreneur as opposed to small business manager.

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Most of the respondents (72 percent – n = 83) were between the ages of 36 and 55, with an average age of 44. About 27 percent of respondents (n = 31) had a university degree, 40 percent (n = 46) had a technology education, and 33 percent of respondents (n = 38) had high school or lower levels of education. Average experience in a similar domain before the start-up was 7.9 years. The average firm size was 5.1 employees and average firm age was 8.5 years. The small size of the firms implies the owner-founder had a dominant influence. The sample was broad with firms drawn from all industries: construction 6% (n = 7), manufacturing 17% (n = 20), commerce 19% (n = 22), services 25% (n = 29), transport and travel 6% (n = 7), fisheries 4% (n = 4) and other unclassified industries 23% (n = 26). Our sample was therefore not biased by industry, as no single industry was overrepresented. Finally, we analyzed non-response bias using means analysis of the first and the last group of respondents (Armstrong & Overton, 1977) showing a non-significant difference between the groups. The last group of respondents (n = 18) was obtained from a telephone follow-up among nonrespondents.

Measures Our dependent variable consisted of one item: observed survival. It was measured as 1 = bankrupt and 0 = not bankrupt. The observed survival data were recorded directly from the national registry of enterprises. Thus, the dependent variable and the independent variables in the models were sourced separately, partially controlling for common method bias (Podsakoff, MacKenzie, Jeong-Yeon Lee, & Podsakoff, 2003). Bankrupt companies in the sample had operated for at least three years consecutively, in the sample frame, before bankruptcy. The non-bankrupt companies were established in the same sample frame as bankrupt companies, existing at least three years consecutively, and still operating when the survey instrument was administered. Overconfidence Overconfidence was measured in an experimental setting by how accurate one thinks oneÕs knowledge is compared to actual fact (Forbes, 2005). However, such a measure of overconfidence was beyond the context of our research, which focused on real events. Thus, our overconfidence measure used miscalibration of knowledge and abilities in a real context. The measures reflect an entrepreneur who feels little need to seek assistance and advice from others, even if suggested (see Larrick et al., 2007). The respondents of both active and failed firms3 were asked ‘‘What could have been done better in the past to improve performance?’’ Overconfidence was measured through three items on a five-point scale ranging from 1, ‘‘would have made a great difference’’, to 5 ‘‘would not have made any difference’’. The items were partially derived from Parks (1977) and dealt with miscalibration: knowledge miscalibration, worded as ‘‘to seek assistance to solve problems’’, financial miscalibration, worded as ‘‘to calculate costs more accurately to 3 All questions reported in this section were harmonized in the questionnaires given to the two respective groups.

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estimate necessary margins’’, and planning miscalibration, worded as ‘‘to prepare the start-up better’’. Since all the firms in the sample had operated for three years or more, miscalibration rooted in overconfidence was likely to be evident to the entrepreneur by that stage, both for bankrupt and surviving firms. We assume the entrepreneurÕs awareness of miscalibration (overconfidence) is associated with performance over time (Murphy & Winkler, 1977). The reliability coefficient for this scale was 0.81 and the average variance extracted (AVE) was 0.60.

to seize opportunities. Subjects responded to how well certain statements described them, measured via two items on a five-point scale ranging from 1, ‘‘describes me very well,’’ to 5, ‘‘describes me very poorly.’’ The items covered opportunity orientation such as quick follow-up on ideas, worded as ‘‘I execute my ideas promptly’’, and tendency to act on many opportunities leading to many simultaneous projects (Segerstrom & Solberg, 2006), worded as ‘‘I usually have many projects going at the same time’’. The reliability coefficient for this scale was 0.82 and AVE was 0.70.

Optimism bias Optimism bias was measured as the difference between expectations over events not under the control of the entrepreneur and real outcomes as defined by Koellinger et al. (2007). Although optimism is usually measured through the ‘‘life orientation test’’ (Scheier, Carver, & Bridges, 1994), we followed a suggestion by Colvin and Block (1994) using the difference between expectancy and later experience of external institutions and clients (Radcliffe & Klein, 2002). We believe using future-oriented scales on individuals having experienced bankruptcy would be more prone to error than asking the same individuals to contrast what they expected and what was experienced, which is backward-oriented. The question asked of respondents was worded as follows: ‘‘Compared to your expectations when starting the business, what factors had a negative influence?’’ The items were mostly taken from Parks (1977): negative influence of financial institutions (expectation of supportive financial institutions) and the negative influence of non-paying customers (expectation of on-time payments), measured on a scale ranging from 1, ‘‘very high influence’’, to 5, ‘‘no influence’’. These items, we believe, reflect well the notion of external non-controllable negative factors affecting the entrepreneurial firm: excessively optimistic entrepreneurs often discount negative real-life information (Geers & Lassiter, 2002). The composite reliability coefficient for the optimism bias scale was 0.81 and AVE was 0.68.

Delegation Delegation is a personÕs willingness to assign authority and responsibility to another person. The use of delegation to impel a person to carry out activities for the delegator shows confidence in a personÕs ability. It motivates him or her and stimulates communication between the delegate and the delegator (Be ´nabou & Tirole, 2003). Delegation measures were taken from Parks (1977), and measured via three items on a five-point scale ranging from 1, ‘‘describes me very well,’’ to 5, ‘‘describes me very poorly.’’ The statements covered ease of delegation, worded as ‘‘I find it easy to assign tasks to others’’, ease of communication, ‘‘I find it easy to communicate with others’’, and ease of praising staff, worded as ‘‘I often praise people I manage’’. The composite reliability coefficient for this scale was 0.79 and AVE was 0.56.

Distrust Distrust is a personÕs predisposition not to have confidence in others (Kramer, 1999) and was developed as an interpersonal measure (Gurtman, 1992) of general distrust in others and general dissatisfaction with the solutions of others. The statements were worded as follows ‘‘I trust few and keep an eye on my staff’’ and ‘‘I am usually dissatisfied with the solutions of others’’. These items were measured on a five-point scale ranging from 1, ‘‘describes me very well,’’ to 5, ‘‘describes me very poorly.’’ In building this scale we distinguish between low trust and high distrust as suggested by Lewicki et al. (1998): if low trust is characterized by no hope, no confidence, passivity and hesitance, high distrust signifies skepticism, wariness, watchfulness and vigilance. We believe the scale captures well the watchfulness and vigilance of a distrusting entrepreneur. The reliability coefficient for this scale was 0.78 and AVE was 0.63. Opportunity orientation Opportunity orientation is a personÕs predisposition to adapt quickly to new situations and to take action promptly

Financial orientation Financial orientation is a personÕs predisposition to deal with financial matters. Respondents were asked to what extent they liked to deal with the financial aspects of the business, worded as ‘‘Individuals feel differently about dealing with various aspects of the business. How do you like working on the following tasks:’’ accounts receivables, worded as ‘‘dealing with debtors’’, banks and other lending institutions, worded as ‘‘dealing with lenders’’. The items were measured on a five-point scale ranging from 1, ‘‘Like very much,’’ to 5, ‘‘Dislike very much.’’ The reliability coefficient for this scale was 0.80 and AVE was 0.67. Controls Controls firm age has been used as a standard control assuming older firms have overcome teething problems in the start-up process (Ciavarella et al., 2004; Thornhill & Amit, 2003). In addition, we used education level (Ciavarella et al., 2004), which may help survival because of better knowledge of business management. We included experience because entrepreneurial experience does not necessarily follow the age of the entrepreneur, so we controlled for the ownerÕs age separately from experience. We controlled for firm size, measured as number of employees: having a greater number of employees, not only means more ‘‘management’’ for the entrepreneur but also a resource to grow. Finally, we controlled for gender, as studies have shown women to be more risk averse than men (Jianakoplos & Bernasek, 1998) and therefore possibly less prone to failure. There has been very little research including this factor (Kalleberg & Leight, 1991).

Cognitive biases, organization, and entrepreneurial firm survival

Methods The isolation of statistical effects for single variables has reached a remarkable degree of sophistication in the social sciences. However, we understand that ‘‘effects do not occur in a vacuum’’ (Norem & Chang, 2002: p. 995) and entrepreneurship scholars are calling for greater understanding of complex organizational processes by taking a multilevel perspective (Hitt, Beamish, Jackson, & Mathieu, 2007). Thus, to examine complex relationships, using the multilevel perspective, we build a partial least squares (PLS) path model using Smart-PLS (Ringle, Wende, & Will, 2005). PLS is a method that assumes that variables have been measured neither free of error (Fornell & Bookstein, 1982) nor that they are normally distributed (Cassel, Hackl & Westlund, 1999; Chin, Marcolin, & Newsted, 2003; Fornell & Bookstein, 1982). The factors driving our selection of PLS specifically (see Chin, 1995, 1998; Chin & Newsted, 1999; Lee & Tsang, 2001) were smaller sample size than is usually recommended for covariate-based SEM methods (Marsh, Hau, Balla, & Grayson, 1998), several variables that were not normally distributed, and the formative nature of the inner model: PLS as a technique meets these requirements well. Concerns are increasingly raised about reflective versus formative constructs (Bollen, 2007; Diamantopoulos & Siguaw, 2006; Howell, Breivik, & Wilcox, 2007a; Howell, Breivik, & Wilcox, 2007b; Kim, Shin, & Grover, 2010). For example, Kim et al. (2010: 358) point out that formative measurement is susceptible to both interpretational confounding and external inconsistency, making formative measurement a less attractive alternative to reflective measurement, so that researchers should opt for reflective measurement whenever possible (Howell et al., 2007a; Howell et al., 2007b; Kim et al., 2010). We can distinguish between formative and reflective measurement approaches for latent, unobservable constructs such as cognitive biases. Formative means that the explanatory indicators create (form) the latent construct while reflective means that the latent construct produces its observable measurements, i.e. the measurements reflect the various degrees of the latent construct (Fornell & Bookstein, 1982). The difference between the two approaches is that reflective measurements are thematic and thus interchangeable (with high internal consistency) while formative measurements each contribute (possibly to different degrees) to the latent construct. The choice of approach depends on the research objectives, even if formative measurement approaches are recognized to be more problematic (Kim et al., 2010). All the items in the measurement model (outer model) were reflective. We used perceptual measures that reflect the degree of existence of cognitive biases or other predispositions; theoretically, and based on psychological research, it appears difficult to envisage measurements that influence the level (potentially with a different degree and with low internal consistency) of cognitive biases and the other predispositional constructs. For example, the tendency not to seek advice from others does not create distrust but is a consequence of distrust (and thus reflective); the difference between expectations and subsequent real outcomes does not form optimism bias but are consequences of optimism bias (and thus reflective). In both cases, there is temporal precedence of the latent variables

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and internal consistency is high (see Wilcox, Howell & Breivik, 2008). Hence, all the constructs in our measurement model were deemed reflective, implying that the observed indicators (items) are caused by an underlying construct and not vice versa (Fornell & Bookstein, 1982).

Results Reliability, validity, and common method bias The means, standard deviations, factor correlations and reliability estimates are reported in Table 1. In our study, composite reliability was used to measure internal consistency of items (Cortina, 1993; Raykov, 1998) rather than CronbachÕs alpha (Cronbach, 1951). The latter weights all indicators equally and is suitable for summated rating scales. We preferred to compute the latent variable score as a weighted sum of the indicators, by the use of partial least squares. Composite reliabilities should be greater than 0.60 in exploratory studies (Chin, 1998) and greater than 0.70 in confirmatory studies (Chin, 1998; Fornell & Larcker, 1981). The composite reliability values of all constructs exceeded the recommended minimum of 0.70 (range 0.77 to 0.82). To test for multicollinearity we calculated Variance Inflation Factors (VIF). The variables had VIF values ranging from 1.2 to 2.2, well below the cut-off value of 10, showing that multicollinearity was not an issue in the data. In view of the number of cases, we performed power analysis (Cohen, 1988) to test the adequacy of our sample size (Goodhue, Lewis, & Thompson, 2006; Chin & Newsted 1999). The post hoc computed power (1-b err prob.) for the theoretical model was 0.99, which is above the recommended minimum of 0.80 for business research (Hair, Anderson, Tatham, & Black, 1995). Internal consistency, measured through item loadings on latent variables, was above the recommended minimum value of 0.50 (range 0.65 to 0.93) (Barclay, Higgins, & Thompson, 1995; Tabachnick & Fidell, 2000). To test discriminant validity we used the square-root of AVE (Carmines & Zeller, 1979; Fornell & Larcker, 1981; Hulland, 1999) and cross loadings (Chin, 1998; Gefen, Staub, & Boudreau, 2000). Discriminant validity is assumed if the square-root of AVE for a particular latent variable exceeds the correlation of that variable and any other latent variable. In all cases (see Table 1, off-diagonal of the matrix) the square-root of AVE was considerably higher than the bivariate correlations between the latent variables. The cross-loadings test showed that no manifest variables loaded higher on any other latent variable than their associated latent variable. These two tests showed strong discriminant validity. Convergent validity (AVE) exceeded the threshold value of 0.50 for all constructs (range 0.56 to 0.70) (Fornell & Larcker, 1981). Although we saw no theoretical justification for using formative constructs in our measurement model, we tested whether misspecification could alter our results. We reversed the constructs one by one from reflective to formative and observed no significant changes from the original results, except for optimism bias to financial orientation, which led to a significance change (p < .05 to p < .01) and optimism bias to survival (p < .05 to p < .01). This test demonstrates that our results would have remained the same

1 0.81 0.76 0.79 0.80 0.82 0.81 0.68 0.63 0.56 0.67 0.70 0.60 7.64 2.76 0.89 0.31 0.83 8.07 1.05 0.77 0.69 1.10 0.81 0.91 0.49 5.08 8.49 3.37 1.90 1.98 7.90 3.02 3.75 2.23 3.45 2.50 4.11 0.39 1. Firm size 2. Firm age 3. Age owner 4. Gender 5. Education 6. Experience 7. Optimism bias 8. Disp. distrust 9. Delegation 10. Financialorientation 11. Opport.Orient. 12. Overconfidence 13. Survival

a

(.77) 0.39 (.84) 0.16 0.01 (.81) 0.04 0.01 0.28 (.74) 0.23 0.18 0.11 0.19 (.79) 0.17 0.01 0.22 0.24 0.04 (.82) 0.13 0.16 0.11 0.19 0.39 0.37 1 0.11 0.05 0.08 0.1 0.03 0.11 0.02 1 0.1 0.05 0.11 0.01 0.1 0.08 0.01 0.04 1 0.31 0.04 0.16 0.08 0.01 0.05 0.02 0.02 0.01 0.11 1 0.07 0.05 0.04 0.16 0.18 0.18 0.01 0.03 0.12 0.05 0.29 1 0.06 0.03 0.08 0.07 0.05 0.07 0.04 0.04 0.17 0.12 0.05 0.15

1 0.17 0.05 0.17 0.01 0.08 0.06 0.05 0.05 0.02

12 11 10 9 8 7 6 5 4 3 2 1 C.R. AVE S.D. Mean

Factor correlations, average variance extracted, and reliabilities. Table 1

Correlations > |.19| are significant at p < .05; n = 115; Diagonal elements in parentheses are square roots of average variance extracted (Hulland, 1999); C.R. = composite reliability; AVE = average variance extracted.

S.V. Gudmundsson, C. Lechner 13

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even if any or all constructs had been misspecified as reflective. The explanatory power of our model was good (R2 = 0.41) in comparison to previous studies (see Hmieleski & Baron, 2009). To further quality-test our model, we followed Tennenhaus et al. (2005) suggesting a goodness-of-fit (GoF) measure applicable to PLS path modeling, defined as the geometric mean of the average communality and average R2 for the endogenous constructs.4Wetzels, OdcerkerkenSchro ¨der, and Van Oppen (2009) propose baseline values for GoF; small = 0.1, medium = 0.25, large = 0.36. For the theoretical model, a GoF value of 0.51 was obtained which exceeds the base value for large effect sizes of R2, indicating good model performance. Acquiring measurements of the predictors and the criterion variable from the same sources is apt to cause common method bias (Podsakoff et al., 2003). In our study the dependent variable, survival, was collected separately from the key informants, directly from archival resources. However, we had reason to believe that three constructs drawn from the same block of questions in the instrument might be subject to common method bias. To test whether this was the case we followed a procedure suggested by Liang, Saraf, Hu, and Xue (2007) for PLS based on a common method factor (Podsakoff et al., 2003; Williams, Edwards, & Vandenberg, 2003). The common factor included all the principal indicators of the model constructs. Then we named separate constructs for all indicators with paths to the method factor and calculated the variances explained for both the substantive and the method factor loadings (see Appendix 1). The average variance explained by the substantive indicators was 0.64, and the average variance by the method indicators was 0.01. No method factor loadings were significant and the ratio between the method and the substantive variance was high. We thus infer that method is not a concern for this study.

Findings Table 2 and Figure 1 report the results of the analysis. The control model includes only control variables, and the theoretical model includes all tested relationships. For the sake of completeness, we base our discussion on the theoretical model. We entered six control variables: gender, firm size, owner age, firm age, previous experience, and education. Only firm age was significant in this test. The overall model R2 for the control model was low (.11) and the delta between the two models was large (DR2 = .32, p < .05, F = 8.64) allowing us to assumethat the control variables alone do not adequately explain the variance in the dependent variable. The non-significance of the education control supports Baumol, Shilling, and Wolff (2009), in that entrepreneurship in general may not be sensitive to educational attainment. However, educational attainment might be associated with constructs such as financial orientation. To test this we performed a post hoc test by running a direct path from education attainment to financial orientation and found a significant positive relationship (b = .11, p < .05) with negligible change in other model parameters. Thus, financially 4

GoF =

p

(AVE x R2).

Cognitive biases, organization, and entrepreneurial firm survival Table 2

287

Results of PLS path analysis for survivala.

Hypothesis

Path from

To

Coefficient (t) Control model

Firm age Firm size Age Gender Education Previous experience Overconfidence Optimism bias Optimism bias Distrust Distrust Distrust Optimism bias Delegation Dispositional distrust Optimism bias Financial orientation Distrust Optimism bias Opportunity orientation

H1 H2a H2b H3a H3b H4a H4b H4c H5a H5b H5c H6a H6b H6c

Survival Survival Survival Survival Survival Survival Survival Overconfidence Survival Overconfidence Survival Delegation Delegation Survival Financial orientation Financial orientation Survival Opportunity orientation Opportunity orientation Survival

Model R2 DR2(F) Pseudo Model GoFb

.28 .13 .08 .02 .03 .02

e

(4.82) (1.34) (0.80) (0.22) (0.31) (0.22)

.11

Theoretical model .26e(4.65) .05 (0.59) .09 (1.24) .04 (0.54) .01 (0.07) .04 (0.65) .35e (4.58) .36e (4.26) .17c (2.12) .20c(2.03) .14c (1.68) .20c (1.91) .19c (2.01) .17c (2.28) .05 (0.56) .16c(2.12) .28e (4.00) .20d (2.77) .16c (1.91) .13 (1.39) .41 .32e (8.64) .51

Note: Insignificant paths have been removed. a Values of t were calculated through bootstrapping with 500 resamples and 115 cases per sample. b Goodness of Fit Measure (see Tennenhaus, et al., 2005). c p < 0.05. d p < 0 .01. e p < 0 .001.

H4a (-.20*) Distrust

Delegation H4c (-.17*)

H3a (.20*)

H3b (.14*)

H6a (.20**)

H1 (-.35***)

Overconfidence

Firm survival

H5a (ns) H4b (.19*)

H2b (-.17*) H5c (.28***)

H2a (.36***)

Optimism bias

H5b (-.16*)

Financial orientation H6c (ns)

H6b (.16*)

Figure 1

Opportunity orientation

Results from path analysis.

oriented entrepreneurs do indeed have higher educational attainment. Another relationship we decided to test for post hoc was industry influence. We had assumed, a priori, that since no one industry was dominant in our sample it would make industry effects negligible on our results. To

be sure, however, we carried out a test with industry as a control variable and found a non-significant relationship (b = .033, ns). The results support most of the hypotheses (see Figure ure1), except hypotheses H5a and H6c, which had non-significant paths. In Hypothesis 1 we proposed that overconfidence was negatively related to survival. The coefficient was negative (b = .35) and significant (p < .001), supporting the hypothesis. A strong relationship was revealed for Hypothesis 2a where we proposed that optimism was positively related to overconfidence. In the model the coefficient was positive (b = .36) and significant (p < .001), supporting the hypothesis. Regarding the effects of optimism bias on survival, Hypothesis 2b, we proposed a negative relationship. The coefficient was negative (b = .17) and significant (p < .05), supporting the hypothesis. In Hypothesis 3a we proposed that distrust was positively related to overconfidence. The coefficient was positive (b = .20) and significant (p < .05), supporting the hypothesis. In Hypothesis 3b we proposed that distrust was positively related to survival. Although with a weaker relationship than with overconfidence, the coefficient was positive (b = .14) and significant (p < .05). Also, in Hypothesis 4a we proposed that distrust was negatively related to delegation. The

288 coefficient was negative (b = .20) and significant (p < .05), supporting the hypothesis. Testing the effect of optimism bias on delegation we found a positive significant relationship (b = .19, p < .05), supporting Hypothesis 4b. Similarly, we hypothesized that delegation was negatively related to survival, and the results showed a negative significant relationship (b = .17, p < .05), supporting Hypothesis 4c. We hypothesized that financial orientation was positively related to distrust, but the coefficient (b = .05) was not significant, and Hypothesis 5a is not supported. Interestingly though, in Hypothesis 5b we proposed a negative relationship between optimism bias and financial orientation (b = .16), which was supported (p < .05). We argued that financial orientation was positively related to survival and we found a significant positive relationship (b = .28; p < .001), supporting Hypothesis 5c. We argued that both optimism and distrust were associated with opportunity orientation in Hypotheses 6a and 6b. The results support both hypotheses. For Hypothesis 6a the coefficient was positive (b = .20) and significant (p < .01) and for Hypothesis 6b the coefficient was positive (b = .16) and significant (p < .05). However, in Hypothesis 6c we assumed that opportunity orientation was negatively related to survival, whereas the coefficient was positive and non-significant (b = .13, ns). Thus, Hypothesis 6c is not supported.

Discussion and conclusions By specifying a multilevel theoretical model we show how entrepreneurÕs cognitive biases shape the organization of firms and influence survival, an important contribution to entrepreneurship research. By drawing up a more finegrained picture we show that overconfidence is influenced by both optimism bias and distrust, while optimism bias– overconfidence and distrust–overconfidence describe two distinctive cognitive types of entrepreneurs associated with non-survival. In other words, overconfidence is a central theme in entrepreneurial firm non-survival and its effects are magnified in combination with other cognitive biases. Our results in the entrepreneurial domain clearly confirm prior research in the general domain pointing to the disastrous effects of overconfidence (Plous, 1993). Those who are overconfident are likely to have high core self-evaluations and think too highly of their abilities (Judge, Locke, & Durham, 1997) causing decisional errors to be made: the greater the risk, the graver the consequences of overconfidence bias. Although both optimism bias and distrust are associated with overconfidence, these cognitive biases have markedly different links with survival when considered separately. Without the influence of overconfidence, those who are distrusting are more likely to be associated with surviving firms in contrast with unrealistic optimists who are associated with non-surviving firms. Thus both overconfidence and unrealistic optimism, separately and in combination, pose a recipe for failure in the entrepreneurial domain. Lastly, cognitive biases shape organization factors (propensity to delegate, opportunity orientation, and financial orientation) in a distinctive way, with one exception, namely

S.V. Gudmundsson, C. Lechner opportunity orientation, that appears common to all entrepreneurs. Entrepreneurs are therefore similar in some ways and distinctive in others, they seem generally overconfident and opportunity-oriented, but either optimistic or distrusting, so we can state that entrepreneurs vary in their cognitive make-up: a fairly unmapped territory in entrepreneurship research. Contributions First, we show that overconfidence, which has been commonly linked to unrealistic optimism, is also associated with other biases (Townsend, Busenitz, & Arthurs, 2010). Our findings strongly support the assumption that overconfidence increases the risk of non-survival among entrepreneurial firms: entrepreneurs clearly overestimate their accuracy and control of situations, and underestimate risks (Simon et al., 2000). In other words, overconfidence seems to be a decisive factor in flawed decisions and actions (Larrick et al., 2007), and other cognitive biases, such as optimism bias and distrust, are associated with overconfidence and can magnify its effects. Overconfidence is thus not only contingent on context but also on other psychological factors that act with overconfidence and that influence organization and firm survival. Second, we complement research on overconfidence and optimism by showing that starting and failing of firms can be traced back to the same cognitive biases. Optimism bias has a negative effect on firm survival, strengthening arguments on low risk perception and resultant propensity to fail. Previous research (Hmieleski & Baron, 2008) suggests a positive link between optimism bias and firm performance. However, similar to a recent study carried out by Hmieleski and Baron (2009), we found a negative link between optimism bias and firm survival, suggesting that entrepreneurship performance research is often affected by a survival bias. The relationship between distrust, optimism bias and performance is likely to be curvilinear, meaning that performance is sensitive to the degree of optimism or distrust. Although we could not test the curvilinear relationship, our model demonstrates that overly optimistic and distrusting entrepreneurs are likely to be overconfident and their firms less likely to survive. For practical purposes, we have shown that overconfidence and optimism bias are doublesided: both are potential drivers of firm creation and of firm failure. Third, we contribute to an understanding of cognitive entrepreneurship by introducing distrust as a new and distinct influence on overconfidence. According to our findings, distrust is positively associated with survival of entrepreneurial firms, yet it also has a positive relationship with overconfidence, which in turn is strongly associated with non-survival. We have shown that reasonable distrust is good but too much may promote excessive self-reliance beyond oneÕs capabilities. We contribute by showing that entrepreneurs are not all created the same when it comes to cognitive biases; their cognitive type can differ, with profound influence on their firms. This underexplored relationship opens a new perspective in exploring entrepreneurÕs cognitive biases. Our findings harmonize with research on high consequence industries identifying distrust as failurepreventing in non-routine risky situations (Burns et. al., 2006; Conchie & Donald, 2007). Although research on dis-

Cognitive biases, organization, and entrepreneurial firm survival trust in entrepreneurs is still rare, a positive direct relationship with survival supports the notion that distrusting entrepreneurs are more alert to their environment (Kets de Vries, 1985, 2003), although extreme self-reliance, and loss of proportion, could magnify the effects of overconfidence. Our results suggest that sensible distrust fosters a more down-to-earth view of risks by entrepreneurs, resulting in a more reasonable selection of tasks and better analysis of opportunities, increasing chances of firm survival. Fourth, an important contribution of our research was to look into the association of our key measures with several important organizational dimensions often observed in entrepreneurship research. We theorized for instance that distrusting entrepreneurs were likely to handle work on their own rather than to delegate, and our findings supported this hypothesis. Conversely, the willingness to delegate is significantly influenced by optimism bias as it lessens the perceived need for control (as partially expressed by a weak financial orientation). Thus, optimism bias may favour a ‘‘laissez-faire’’ style of management. We wondered whether optimists simply delegate more because they grow their firms too fast, leading to a lower chance of survival. We therefore ran an ad hoc test on optimism bias to see if firm size was significant as a direct control variable. However, the relationship was not significant (b = 0.065, n.s.) and we can state that firm size as a proxy for growth did not explain higher propensity to delegate among highly optimistic entrepreneurs. We also tested if overconfidence and distrust in combination would magnify reluctance to delegate, but found a non-significant change in the R2 of delegation (DR2 = 0.008, n.s.) when running a path from overconfidence to delegation. Distrust increases reluctance to trust others with tasks (delegating to the agents), while overconfidence increases the feeling of not needing assistance (input from the agents) when doing tasks. Thus, overconfidence does not affect delegation in combination with distrust. Both distrust and optimism bias acted positively on opportunity orientation. This is an important finding as opportunity orientation is at the heart of entrepreneurship and our findings support the theory that both optimism bias and distrust are associated with starting entrepreneurial firms. In other words, the different cognitive make-up of entrepreneurs does not affect opportunity orientation. From the extant literature we hypothesized that opportunity orientation might fragment resources and scatter the attention of the entrepreneur to too many projects, decreasing survival chances of the firm. We based our theorizing on Brush et al. (1997), Brush et al. (2001), who argued that small businesses fail because of misalignment between resources and opportunities. Our results did not support the hypothesis. One possible explanation is that the negative and positive elements of opportunity orientation, characterized by multitasking ability and rapid action potential, may balance out in response to non-routine situations and adversity. Furthermore, our findings show that distrusting entrepreneurs, contrary to our expectations, are neither strong nor weak in financial orientation, while optimism bias is associated with weak financial orientation as expected. Thus, an unrealistically optimistic entrepreneur is more likely to neglect this function in comparison to a distrusting entrepreneur: a consideration for financial stakeholders of

289

entrepreneurial firms. To conclude, distrust and optimism bias influence different organization characteristics, but act in tandem on opportunity orientation. Finally, an important contribution of our work is that we went beyond existing research on entrepreneurÕs cognitive biases, which has mostly focused on nascent entrepreneurship (Townsend et al., 2010), while performance outcome studies, like the one described here, are rare and usually affected by survival bias (Hmieleski & Baron, 2008).

Implications From a practical point of view, we think the results can have various implications for entrepreneurs and stakeholders in small firms. Entrepreneurs can become aware of their psychological dispositions and set up counter balancing or self-regulatory mechanisms (Hmieleski & Baron, 2008). Such mechanisms could be awareness of decision biases through training but could also involve selecting staff with different dispositions as a balancing act. Training of unrealistic optimists should stimulate the motivation to manage finances, to take advice, not to leave matters up to chance, and to understand the value of healthy distrust in oneself and others in non-routine situations. Training of distrusting entrepreneurs should aim at building trust in others in routine situations. Training of overconfident entrepreneurs should aim at creating awareness of miscalibration in decision making. Again, entrepreneurs with overconfidence dispositions could select confident staff but with low overconfidence tendencies as a counterbalance. One group of interested parties for whom the results would be relevant is providers of finance: venture capitalists, business angels and banks might find it useful to include the psychological characteristics of the CEO not only in their risk assessments but also when developing support services for entrepreneurs. Recruitment firms serving entrepreneurs might develop tests that identify the cognitive make-up of candidates to balance the various decision biases through staff selection policies.

Limitations and future research There are some limitations to our study. First, we only captured the perception of the founder and not of the whole team, which drives both organization and survival. On the other hand, our sample is mainly comprised of micro-firms in which the founder remains a central figure. Second, the national sample used might be culturally specific and therefore limit the generalization of our findings to other populations. However, much entrepreneurial research is subject to this potential bias. We nevertheless believe the findings can be generalized to high-trust cultures (see Delhey & Newton, 2005; Fukuyama, 1995). To test if our findings may be replicable in low-trust cultures, we believe that a comparison study is necessary. Third, our overconfidence measures derived from real settings differs from laboratory measures used in many other studies. Despite this difference, we believe that our findings are unlikely to be critically biased as individuals have been found to be less overconfident and to sense higher risks in a real setting (Bukszar, 2003). Fourth, we could not test a trust construct in parallel with distrust, based on the premise that they are not opposites on the

290 same scale (Conchie & Donald, 2007; Dimoka, 2010). Some entrepreneurs might show both trust and distrust while others might be predominantly either trusting or distrusting. The influence of trust and distrust in combination on overconfidence, optimism bias and organization could be collectively explored. Fifth, the relationship of distrust and optimism bias is likely to be curvilinear, meaning the degrees of optimism and distrust each have an effect on survival. We could not test this effect in our study, but hope that future research may test the curvilinear relationships directly to cast a better light on entrepreneurial firm survival. Sixth, in our study we could not use a longitudinal approach (Low & MacMillan, 1988) because of limited resources. Other research could survey firms at different time points, starting at founding stage, until a representative number of bankrupt firms exist for comparison with non-bankrupt firms in the sample. Finally, while behavioural research often shows direct links with firm outcomes, our approach suggests that future research could explore in greater depth the extent to which entrepreneur cognition shapes the firm and outcomes.

Conclusions Our study provides an important input to develop further strategic entrepreneurship theory, by pointing out how entrepreneurs with different cognitive make-up develop their firms (Shane et al., 2003) with different results. We have shown that all entrepreneurs are not created the same and there are entrepreneurs with a cognitive make-up that is less likely to be associated with failure. If optimism is about discounting the negative signals from the environment in the face of adversity, distrust is about decoding the signals and developing contingency approaches to overcome adversity. If it was not for cognitive biases, start-ups would probably not take place as often as we observe. For that reason, after carrying out this research, we are aware that our advice on training programs to deal with entrepreneurial biases might be overly optimistic, perhaps largely

Appendix 1

S.V. Gudmundsson, C. Lechner fruitless. What we should rather advise entrepreneurs is to balance their organizations (recruitment firms could specialize in this domain), to make sure that there is room for different points of view, to create an external network of diverse advisers (not only admirers or ‘‘yes’’ people), to make room for insiders to question and influence major decisions, and finally and most importantly to make sure that someone is around to raise a red flag when overconfidence sets in. Entrepreneurs will continue to drift to the extremes of optimism, distrust and overconfidence, to take risks, to fail and to succeed, regardless of the business environment or the period we live in. However, to take a decision not having listened and to fail, is often a surprise; to take a decision despite having listened and to fail, may be enlightening, but no surprise; whereas to take a decision having listened could have sparked enough precaution and preparation to prevent failure. We hope to have stimulated further questions and increased the awareness of academics and entrepreneurs alike about the influence of cognitive biases, and the organization effects and the survival outcomes associated with them. In carrying out this research we had the privilege to learn from many entrepreneurs some of whom had tasted sweet success and others bitter failure, yet behind it all there is perhaps something that most entrepreneurs share, in the words of Rousseau: There is only one man who gets his own way – he who can get it single handed; therefore freedom, not power is the greatest good. That man is truly free who desires what he is able to perform, and does what he desires. Jean-Jacques Rousseau, LÕEmile, 1762

Acknowledgments ´ lafsson, Herve The authors would like to thank Stefa ´n O ´ Laroche and the anonymous European Management Journal reviewers for their helpful comments and insights on earlier versions of this article.

Cognitive biases, organization, and entrepreneurial firm survival

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CHRISTIAN LECHNER is currently senior professor strategy and entrepreneurship, and Director of the Research Center for Entrepreneurship and Growth Strategies, Toulouse Business School. He earned his PhD from University of Regensburg, Germany. He has spent visiting periods at University of Regensburg, Rutgers University, Libera Universita ` di Bolzano, ESSEC, and WHU. His research interests cluster around: Entrepreneurship, network organizations, regional and small firm networks, high-tech clusters, strategic networks, cooperative competition, and growth strategies.