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THE POSITIVE SIDE OF COGNITIVE BIASES:
CHALLENGING THE MODEL
OF KAHNEMAN AND TVERSKY
by
MAURICE RAYMOND VAN DER PAUW
University of Groningen
Faculty of Economics and Business
MSc Business Administration – Business Development
First supervisor: Dr. J.D. van der Bij Second supervisor: Drs. H.P. van Peet
January 2012
Haddingedwarsstraat 2B4 9711 KA Groningen (06) 24380148 m.r.van.der.pauw@student.rug student number: 17531262
Abstract
Guided by insights from cognitive theories, this study explores the link between entrepreneurial performance and the ideal level of cognitive biases. We test the traditional assumption of Kahneman and Tversky that biases arising from the use of heuristics are disadvantageous and should be minimized. By drawing upon the profile deviation approach, we identified levels of cognitive biases that are associated with superior entrepreneurial performance. Hypotheses were tested using data from 289 entrepreneurs. Our findings indicate that the role of cognitive biases for entrepreneurial performance is positive rather than negative. The normative, theoretically optimal levels of cognitive biases tend to be lower than the empirically derived ideal profile. Deviation from the normative profile tends to have a beneficial effect for entrepreneurial performance, while deviation from the empirical profile is detrimental in case of the availability heuristic and not significant for the representativeness heuristic. Thus, our results shed light on the bright side of being biased.
Keywords: Serial entrepreneurs, entrepreneurial performance, cognitive biases, availability heuristic,
representative heuristic
1. Introduction
Human judgment deviates from rationality because of the concepts of bounded rationality and satisficing behavior (Simon, 1955, 1956). Simon (1990) relates these fundamental limitations to a scissor with one blade being the complex structure of task environments (external) and the other blade being the computational capabilities of the actor (internal) (for an extended research on this topic, see Todd and Gigerenzer, 2003). Although we attempt to make the optimal decisions in the rational model, due to time limits and cost constraints, often information is scanty leaving the problem statement, goals and relevant criteria unclear or incomplete. This is strengthened by intelligence limitations and perceptual errors of the individual, making it fairly impossible to calculate the perfect alternative (Bazerman and Moore, 2009). With a range of alternatives to choose from, we often simplify the process by searching for a satisfactory solution. Satisficing behavior implies forgo of the maximized solution in favor of an acceptable alternative, thereby knowingly abandoning the search for optimal choice (Simon, 1956; Bazerman and Moore, 2009).
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People facing such a complicated judgment or decision, often rely on heuristics to simplify the task. In this paper, heuristics are defined as the simplifying strategies, or general rules of thumb, that drive human intuition and help us cope with the complex environment surrounding decisions (Kahneman and Tversky, 1973; Tversky and Kahneman, 1973, 1974; Barnes, 1984; Schwenk 1984; Busenitz and Barney, 1997; Bazerman and Moore, 2009). Citing Tversky and Kahneman (1974) whose work has led to our modern understanding of the social-cognitive psychology of intuitive judgment and decision making; “[…] people rely on a limited number of heuristic principles which reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations”. In a more practical manner, heuristics are defined as the experienced-based set of techniques for problem solving, learning and discovery. There has been a general discussion whether the simplification of a complex process is deliberately chosen or that it happens intuitively and therefore unwittingly. This paper will follow the latter alternative, which is in line with the theories of Kahneman and Tversky.It is generally acknowledged that heuristics are helpful (e.g., Kahneman and Tversky, 1973; Tversky and Kahneman, 1974; De Kort and Vermeulen, 2008, Bazerman and Moore, 2009) and in many cases “yield very close approximations” to the optimal answers suggested by normative theories (Plous, 1993). This is in line with the ideas of Kahneman and Tversky, who state that heuristics usually yield acceptable decisions to problems (Busenitz and Barney, 1997). In today’s markets, due to time constraints and uncertainty, it has been argued that entrepreneurs are especially liable to use heuristics (Busenitz and Barney, 1997; Burmeister and Schade, 2007).
However, this intuitive driver of human behavior is prone to several (cognitive) biases. Biases and cognitive biases are used interchangeable throughout this paper. The traditional literature translates these biases (or fallacies) into inevitable errors in judgment (e.g. Kahneman, 2003; Forbes, 2005), that appear because the target attribute and the heuristic attribute are not the same (Kahneman and Frederick, 2002). The associated biases are seen as imperative evil that may lead to underestimated risk taking and unnecessary failures of entrepreneurial firms (Camerer and Lovallo, 1999; Simon et al., 2000; Keh et al., 2002). Although the majority of research has focused on the negative aspects of biases, recent research shows that some researchers are placing question marks to the presumed negative aspect of biases (e.g. Bingham and Eisenhardt, 2011). Another school of thought therefore postulates that at least some cognitive biases don’t have a negative impact on entrepreneurial behavior. For example, the extensively described overconfidence bias is positively linked to radical product innovations in the empirical research of Simon and Houghton (2003). This makes sense because overconfidence is related to risk taking, but overconfidence also decreases the approximate survival chances of nascent entrepreneurs (Koellinger et al., 2007). Furthermore, entrepreneurs who are (over)confident and start a subsequent venture might be better positioned, due to the resilience and second order effects (Hayward et al., 2010).
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All in all, the ideal levels of biases are unclear and the relationship with entrepreneurial performance has never been empirically tested to the best of our knowledge. In the words of Simon et al. (2000); “if future research suggests that biases lower performance, managers may want to minimize their biases” (emphasis added). Therefore, the optimal or ideal levels of cognitive biases need to be further examined in comparison to entrepreneurial performance. By drawing upon the profile deviation approach (Drazin and Van de Ven, 1985; Ventrakaman and Prescott, 1990; Doty et al., 1993; Hill and Birkinshaw, 2008), we intend to fill this gap. In this study, the ideal theoretical or normative profiles of cognitive biases are derived from literature and are contrasted with the empirical derived ideal profiles. The ideal level of biases of the empirical profile is derived from a list of award winning entrepreneurs. In this study, the dependent variable total number of ventures founded by the entrepreneur is used to test the hypothesis. Guided by cognitive biases theories, four theoretically derived hypotheses are presented. The hypotheses are embedded in the concepts of the availability and representativeness heuristic.This study seeks to make several conceptual and empirical contributions. The positive side of cognitive biases will be further explored by investigating the relationship between performance of the individual entrepreneur and a set of cognitive biases, namely; hindsight bias, illusory correlation, overconfidence bias, base-rate fallacy, illusion of control, regression fallacy and sample size fallacy. This chosen set of biases is in line with the work by Song et al. (in production). By contrasting the theoretically normative profiles with empirical profiles derived using a set of award winning entrepreneurs, we will explore what levels of cognitive biases should be considered as optimal. Furthermore, this will shed light on whether cognitive biases should be treated as inevitable errors that should be minimized.
The paper is structured as follows. In the next section, insights from cognitive theories suggesting links between the performance of individual entrepreneurs and the availability and representativeness heuristic are used to derive four hypotheses. An empirical dataset is used to test the hypotheses and to compare existing theories to this empirical set. This is followed by the data collecting and research method, which entail the measurements of the individual biases and the profile deviation analysis procedure. Results are then reported, followed by the key findings, discussion and limitations. Conclusions are presented lastly.
2. Theory and hypotheses development
In this section, hypotheses are presented that concern the relationship between the availability heuristic and entrepreneurial performance, and the relationship between the representativeness heuristic and entrepreneurial performance.
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There is no finite list of heuristic attributes (e.g. Kahneman and Frederick, 2002; Kahneman 2003), but three heuristics attributes are most commonly described in the cognitive heuristics literature; (i) the availability heuristic, (ii) the representative heuristic and (iii) the anchoring and adjustment heuristic (Tversky and Kahneman, 1974; Kahneman et al., 1982; Shaver and Scott, 1991; Plous, 1993; Gilovich et al., 2002). Anchoring effects are judgments which are influenced by “temporarily raising the accessibility of a particular value of the target attribute, relative to other values of the same attribute” (Kahneman, 2003), but will be excluded in line with Kahneman (2003) because the definition of heuristics by attribute substitution does not well coincide with the original theory offered by Tversky and Kahneman (1974).The two main heuristic attributes will each be discussed in detail individually but first, this section will start with a short theoretical background of serial entrepreneurs and discuss the associated benefits. This is because the dependent variable, the number of ventures founded by the entrepreneur, and the related experience of serial entrepreneurs are an important part of the logic reasoning that lead to the hypotheses. The measures of the dependent variable are discussed in the methods section.
Serial entrepreneurs are defined as individuals who own one business after another, but effectively own only one business at a time (Hall, 1995). Following the debate of recent decades that questioned the relevance of entrepreneurial experience has resulted in very different opinions.
It is often assumed and it might even “seem intuitively obvious” (Rerup, 2005), that the firms of entrepreneurs which have prior business start-up experience will outperform the firms that have been founded by novice entrepreneurs. Consequently, some authors argue that the experience gained from these ‘projects’ are significantly improving the survival chances of a new business (for example, Taylor, 1999). In particular, industry-specific experience and self-employment experience may lessen the liability of newness of the new venture (Bruederl et al., 1992; Cooper et al., 1994; Taylor, 1999; Headd, 2003). This might be due to acquired skills, having an edge in resource acquisition (Shane and Cable, 2002), the contacts which have been made or more reasonable expectations that can be made. In the words of Cooper et al. (1994), “[…] industry-specific human capital is a strong predictor of future success”. However, no empirical study has yet been able to find a definitive positive relationship between prior start-up experience and the performance of businesses (Birley and Westhead, 1993; Alsos and Kolvereid, 1998; Westhead and Wright, 1999, Carter Ram, 2003; Rerup, 2005).
Although the experience gained by entrepreneurs therefore can’t be directly linked to firm performance, it is still credible that experienced entrepreneurs can use their experience in various complex situations where heuristics play a major role. In other words, experienced entrepreneurs are then better able to cope with heuristics and the accompanying biases due to the opportunity of being able to
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cope with biases for a long period. The next two subsections will further investigate this assumption on the basis of heuristics and corresponding biases, resulting in four hypotheses to test.2.1 Availability heuristic
The availability heuristic is a rule of thumb in which individuals estimate the frequency of an event or the likelihood of its occurrence “by the ease with which instances or occurrences can be brought to mind” (Tversky and Kahneman, 1974). Whether instances or associations can be related to the target attribute depends therefore on the degree to which that event is readily ‘available’ in memory (e.g. Lichtenstein et al., 1978). The availability heuristic uses associative distance and strengthening by repetition as a basis for the judgment of frequency (Tversky and Kahneman, 1973) and is considered useful because instances or events of greater frequency are easier to remember than rare events, often leading to accurate judgment (Plous, 1993; Bazerman and Moore, 2009).
However, the availability of information is affected by various factors (such as vividness) that are unrelated to the frequency or probability of the judged event (Tversky and Kahneman, 1973; Carroll, 1978; Bazerman and Moore, 2009). For instance, some events are more available because they have taken place recently, are inherently easier to think about or are highly emotional to the individual (Plous, 1993). These factors can inappropriately affect the perceived frequency and the subjective probability when the availability heuristic is applied; consequently leading to predictable and systematic biases (Tversky and Kahneman, 1973, 1974, 1983; Plous, 1993). Literature suggests that there are many biases emanating from the availability heuristic, but three biases, which primarily originate from the availability heuristic, will be elaborated in this paper; the hindsight bias, illusory correlation and the overconfidence bias (e.g., Tversky and Kahneman, 1974; Kahneman, 2003; Russo and Schoemaker, 1992).
The hindsight bias appears if, after a certain event occurs, subjects tend to remember their predictions
about the event as being more accurate than they actually were. It will be defined as an “unjustified increase in the perceived probability of an event, due to outcome knowledge” (Agans and Shaffer, 1994). So, there is an inconsistency in the prediction of an outcome before and after knowing the actual outcome of the event (Slovic and Fischhoff, 1977). In hindsight, people tend to assign higher likelihoods to outcomes that actually had occurred but also exaggerate what they could have anticipated in foresight. They tend to view events as having appeared relatively “inevitable” before they happened (Fischhoff, 1975, 1976, 1982). The relevance of this bias is discussed in the work of Fischhoff, who cautions us for two potential problems; 1) having not foreseen the event, which was clearly evident in hindsight, may judge decision makers as incompetent and 2) decision makers may not learn much from the past because of the overestimation of knowledge in hindsight.
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Illusory correlation refers to the phenomenon of seeing a co-occurrence between two events in a
set of data, when no such co-occurrence exists (Tversky and Kahneman, 1974). Let us consider the following; an entrepreneur would come to the conclusion, when remembering stories from professional journals and newspapers, that high technology innovativeness is associated with high venture performance. Because he would believe in this association, that in reality does not exist (Song et al., 2008), he would invest in technologies that are highly innovative. This bias has a high persistence in the face of contradictory reality (Chapman and Chapman, 1969) and additionally, it “prevented judges from noticing relationships that were in fact present” (Tversky and Kahneman, 1974). Other researchers have found that illusory correlations, even with training, are hard to eliminate (Golding and Rorer, 1972).
Overconfidence refers to the failure to know the limits of one’s knowledge, resulting in
unjustified confidence in one’s own judgment and one’s certainty regarding facts (e.g. Fischhoff et al., 1977; Russo and Schoemaker, 1992; Zacharakis and Sheperd, 2001; Keh et al, 2002, Curşeu et al., 2008). Decision makers can become overconfident because they base their certainty on the ease with which they can recall reasons for confidence (i.e. the availability heuristic). The tendency to seek supporting evidence instead of disconfirming evidence will often result in insufficient adjustment of the initial estimates after receiving new data (Simon et al., 2000; Keh et al., 2002). Examples of overconfidence are numerous, and especially applicable in ‘ill-structured decision situations’ such as product introductions (Simon and Houghton, 2003). Although overconfidence is not universal, it is however prevalent and again, difficult to eliminate (Griffin and Tversky, 1992). The overconfidence bias lowers a decision makers’ perception of the different risks, due to the inability of associate risks with assumptions that are treated like facts. In the words of Plous (1993, p. 217), “No problem in judgment and decision making is more prevalent and more potentially catastrophic than overconfidence”.
The availability heuristic was given in terms of the ease with which instances or occurrences can be brought to mind. It is likely that lots of experience will improve the ease to bring instances to mind, because more situations are dealt with in the past of the entrepreneur and repetition cultivates familiarity and pattern recognition (Baron and Ensley, 2006). In a wider perspective, heuristics can be viewed as the experienced-based set of techniques for problem solving, learning and discovery. This implies that the experience of award winning entrepreneurs has helped them to refine or strengthen these techniques. Due to the opportunity of being able to cope with the associated biases for a long period, it is likely that these entrepreneurs have the ‘ideal’ level of the biases. For the empirical profile, it is therefore hypothesized that;
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Hypothesis 1A. Deviation from the empirical profile of the ideal levels of cognitive biases emanating from the availability heuristic will be negatively related to entrepreneurial performance.
Throughout the work of Kahneman and Tversky, biases are treated as inevitable errors driven by heuristic thinking (e.g. Kahneman, 2003). Also the majority of existing research up till now had focused on the different problems arising due to biased decision making. This concerns both biases emanating from the availability heuristic as well as the representativeness heuristic. While the call for future research on the exact role of cognitive biases in entrepreneurial decision making is given by multiple authors (e.g. Simon et al., 2000; Forbes, 2005), it is still dubious to how positive or negative these biases actually are. For the normative profile, we will follow the line of Kahneman and Tversky therefore, and theoretically derive the ‘ideal’ level of biases. Summarizing these arguments leads to the following hypothesis;
Hypothesis 1B. Deviation from the normative profile of the ideal levels of cognitive biases emanating from the availability heuristic will be negatively related to entrepreneurial performance.
2.2 Representativeness heuristic
Representativeness is an assessment of the degree to which the heuristic attribute is similar to or resembles the target attribute (Kahneman and Tversky, 1972, 1973; Tversky and Kahneman, 1983, 1984, Bar-Hillel and Neter, 1993). The heuristic assists people to predict intuitively the outcome that appears most representative of the evidence, thereby ignoring the reliability of the evidence as well as prior probabilities, violations in the logic of statistical prediction (Kahneman and Tversky, 1973). The degree of representativeness is determined by (i) the similarity in essential characteristics to the parent data, (ii) the reflection of salient features of the process by which it is generated and (iii) the reflection of previous experience with a particular event (Kahneman and Tversky, 1972). The downside of using this heuristic and the associated biases are extensively documented. Tversky and Kahneman (1974) argue that people order the possibilities of an outcome “by probability and by similarity [or representativeness] in exactly the same way”, causing people to make erroneous predictions of extreme values if these happen to be representative (Kahneman and Tversky, 1973). We will focus on four important biases that emanated from the representativeness heuristic; base-rate fallacy, illusion of control, regression fallacy and sample size fallacy.
Base-rate fallacy occurs when irrelevant information is used to make a probability judgment, ignoring
available statistical information about prior probabilities (the base-rate frequency) (Tversky and Kahneman, 1974). People use base-rate data correctly and understand its relevance when no specific
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evidence is given (Kahneman and Tversky, 1972; Tversky and Kahneman, 1974), but tend to disregard such data when some kind of evidence is given (Bazerman and Moore, 2009). While qualitative or individuating data can be very valuable, the bias becomes apparent when worthless evidence is taken into account. Fischhoff and Bar-Hillel (1984) argue that this might be because of the lack of appreciation given to base-rates in judgment under uncertainty. Neglect of the base-rate yields some unfortunate implications, for instance when a novice entrepreneur wants to start a new venture and is convinced of his positive personal input but looses sight of the base-rate for business failures in calculating the risks.Illusion of control, or unrealistic control, occurs when people perceive that objectively
chance-determined events, or uncontrollable events, are within their control (Langer, 1975; Langer and Roth, 1975; Zuckerman et al., 1996). The associated bias refers to the overestimation of one’s skills, and consequently his or her ability to predict and cope with future events (Schwenk, 1984; Simon et al., 2000). There are two reasons for this bias, which both tend to have a degree of self reinforcement because individuals tend to seek supporting information while ignoring disconfirming evidence (Schwenk, 1986). The first reason is that people are motivated to control their environment and believe that, should difficulties arise, additional effort would suffice to steer the strategy into the right direction (Langer, 1975; Schwenk, 1984; Keh et al., 2002). Secondly, the phenomenon has to do with the difference between skills and luck, and is clearly put in the study of Zuckerman et al. (2004, p.227) that discusses; “participants with high UCB scores [unrealistic control beliefs] did not distinguish between skill and chance tasks in terms of success estimates”. This implies that decision makers will underestimate risk, because of their belief in anticipation and preventing negative outcomes.
Although some research has discussed that illusion of control may be a part of the overconfidence bias (Schwenk, 1986), we differentiate between the overestimation of one’s certainty regarding their
metaknowledge and the overestimation of their skills to influence future events (Russo and Schoemaker,
1992; Keh et al., 2002).
Regression fallacy is an erroneous causal interpretation of regression to the mean (Tversky and
Kahneman, 1974), that leads people to naïvely develop “predictions based on the assumption of perfect correlation with past data” (Bazerman and Moore, 2009). Statistically, regression to the mean is a phenomenon taking place when one looks at two related measurements. The first measurement is either extremely high or extremely low and therefore naturally attracts attention. In this case, the second measurement is likely to move closer to the mean than the first measurement. As such, regression to the mean is a statistical phenomenon, caused by chance. However, when one erroneously tries to explain this phenomenon by a causal mechanism, we speak about regression fallacy. In the entrepreneurial setting, this could mean that an individual would assume that this year’s sales are directly predictable from last year’s sales.
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Sample size fallacy, also known as belief in the law of small numbers, concerns with the usage of
limited amount of data, for instance a small sample of information, to draw firm conclusions. People often make judgments on the basis of sample proportion because large random samples are rarely available or too costly to retrieve (Busenitz and Barney, 1997; Keh et al., 2002), but with this phenomenon, people erroneously do not take into account the size of the sample that crucially influences the reliability of the sample proportion value (Tversky and Kahneman, 1971, 1974). Thus, people’s intuitive judgments are dominated by sample proportion, and not by sample size. The representativeness heuristic leads people to the incorrect believe that a limited number of informational inputs are representative for the whole population. Entrepreneurs with high level of sample size fallacy would generalize from the small samples, without being aware of the confidence intervals that information based on these small samples has. Decision makers are especially liable to the sample size fallacy when only very few vividly described cases are available (Schwenk, 1984); more positive information is proportionately published and failures exist only for a short period, resulting in an overly optimistic prospect of the future and thus diminishing perceived risk (Kahneman and Lovallo, 1993; Simon et al., 2000).
The representativeness heuristic referred to the degree to which the heuristic attribute is similar to or resembles the target attribute. Although it is discussed that the use of this heuristic is easily coupled with violations in the logic of statistical prediction, and especially the underestimation of risk, it is also plausible that experience will help entrepreneurs not to make the same mistake twice. This would imply that experienced entrepreneurs are not biased after a while, but this is certainly not the case. When recalling a former occurrence that resembles the current situation, all entrepreneurs accept the fact that they don’t know all the parameters that lead towards success or failure, i.e. uncertainty can not be taken out. Some parts are inevitably similar to the new situation, the reason the representative heuristic plays a role, and other parts remain uncertain. When you accept that not all things are within control, you realize that the former situation also dealt with uncertainty and that biases were in play as well. Experienced entrepreneurs are aware of their biased decision making and chose to continue on the basis of this awareness. The simplification of complex situations, and the coherent underestimation of risk, makes experienced entrepreneurs especially liable to see opportunities that others don’t.
Summarizing these arguments, experience will lead to closer rates to the ‘ideal’ level of the biases. This discussion suggests the following hypothesis;
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Hypothesis 2A. Deviation from the empirical profile of the ideal levels of cognitive biases emanating from the representativeness heuristic will be negatively related to entrepreneurial performance.
For the normative profile, we will again follow the line of Kahneman and Tversky, and theoretically derive the ‘ideal’ level of biases. This suggests the following hypothesis;
Hypothesis 2B. Deviation from the normative profile of the ideal levels of cognitive biases emanating from the representativeness heuristic will be negatively related to entrepreneurial performance.
3. Data collection and research method
3.1 Sample, data collection and respondents
This study is based on the data which was drawn from three sources (i) a list of the one hundred greatest entrepreneurs of the last 25 years, compiled by the venture capitalist, David Silver; (ii) the list of national winners of the Entrepreneur of the Year award, compiled by Ernst and Young, and (iii) a list of 6,359 founders of venture-backed firms provided by Venture One. The latter is a leading venture capital research company and gathers information through direct contacts and surveying VC firms.
The questionnaire and supplemental data was sent by priority mail to 1500 selected entrepreneurs. Adjusted due to undeliverable addresses, the final sample was compiled of 1,176 randomly selected entrepreneurs. After this first package, four follow-up mailings were sent to the entrepreneurs and, follow-up letters were sent one week later. In the end of the data-collected process, we received completed questionnaires from 289 entrepreneurs, representing a response rate of 26.6% (289/1176). The dataset consists of relative experienced entrepreneurs, with an average age of 42 and entrepreneurial experience of 14.5 years. Serial entrepreneurs accounted for about 90% in the dataset.
3.2 Measures and ideal levels of cognitive biases
The following measures were taken from the study of Song et al. (in production). This paper used existing cases and scales from literature if possible, and strengthened their measurements by developing additional entrepreneurial cases based on the already existing cases.
Hindsight bias occurs when entrepreneurs remember their predictions about a former event more accurately than they actually were. In the beginning of our survey we asked entrepreneurs to answer a number of questions and rate with what probability their answers on these questions were correct. The questions were difficult general knowledge questions with two answer options: correct and incorrect. At
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the end of the survey, we gave respondents the correct answers on the knowledge questions and asked them to remember their estimates for correctness in hindsight (without looking at the first page of the survey). The hindsight bias manifests itself when respondents originally gave the incorrect answer and lowered their estimate for correctness in hindsight. The larger the difference between the original estimate and the estimate in hindsight, the larger the bias. We follow Bukszar and Connolly (1988) and Slovic and Fischhoff (1977) with this procedure. According to Campbell and Tesser (1983), there should be at least 30 min. between an original judgment and the hindsight judgment. It took the participants about 40-45 min. to fill out our questionnaire, therefore the potential memory bias is not a problem in our study. In case the memory bias would still be significant, it would only make our findings more conservative due to the decreased variation of the construct. We used a 3-item scale for hindsight bias. Theoretically, the optimal level of the hindsight bias is zero.Illusory correlation takes place when entrepreneurs see a co-occurrence between two events, when no such co-occurrence exists. Our items are based on the ideas of Tversky and Kahneman (1974). We used common myths about co-occurrences between for example, the fact that a cat has been spayed or neutered and its weight, and between university licenses and the larger size of a company. Since the facts that we used were false, from the theoretical point of view entrepreneurs should strongly disagree with all the items (1 on the scale of 1 to 7).
Overconfidence concerns not knowing the limits of one’s knowledge and having unwarranted certainty in one’s judgments. We also used the aforementioned general knowledge questions and the estimates of the probability that the answers are correct, to measure overconfidence. We followed the procedure of Forbes (2005), and Brenner et al. (1996) to develop a 3-item scale for overconfidence; however we used other knowledge questions, because questions from literature are somewhat out-dated. The more certain entrepreneurs are that they gave the correct answer when in fact they are wrong, the higher level of overconfidence they have. Thus, an entrepreneur will be oftentimes wrong when he thinks he is right in case he is overconfident. Because theoretically no overconfidence should be exercised, the ideal level of this bias is zero.
Base-rate fallacy occurs when irrelevant case information is used to make judgments in favor of available statistical information. We used two cases to measure the base-rate fallacy, which both are based on Lynch and Ofir (1989). The first case is about high-tech firms. Respondents have to make an estimate of the probability that a given high-tech firm will fail within the first five years. We start the case description by giving statistical information (the base-rate) about high-tech firms' failures (60%). We also give irrelevant information about the founder's hobbies and social life. When respondents deviate in their predictions from 60%, they exhibit base-rate fallacy. The second case is about purchasing a five-year old car. Similarly, we start by statistical information: "Consumer Reports" suggest that there is a 50%
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probability that such a car will require major repairs in the 6th year. We also give irrelevant case information concerning the color and the interior of the car. Respondents are asked to predict the likelihood that the car requires major repairs during the next year. When respondents deviate from 50%, they show a base-rate fallacy. The more they deviate, the higher the bias. Thus, theoretically the optimum level of the base-rate fallacy is zero.Illusion of control means that people perceive that objectively uncontrollable events are within their control. We measured this construct by a 5-item scale based on Simon et al. (2000) and Zuckerman et al. (1996). Items concern, for instance, the accuracy of predictions of future market developments and the perception that everything that happens is a result of the respondent's own doing. The more respondents think that they can accurately predict the market, or that what's happening is always a result of their own doing, the higher level of illusion of control they exhibit. On the other hand, the other extreme of the scale is feeling that one cannot influence anything at all, which is also bad. Therefore, the normatively optimal level of this bias should be neutrality (4 on the scale of 1 to 7).
Regression fallacy concerns an erroneous causal interpretation of regression to the mean. In such situations, there are always two related measurements: one that is extreme and therefore attracts attention and another that is closer to the mean. We measured regression fallacy by a case that is based on an example of Kahneman and Tversky (1973). The case describes a stable economic environment, which is not likely to grow naturally. The firm's sales increased by 15% two years ago and decreased by 5% one year ago, thus bringing the sales closer to the mean. In order to grow further, the firm increased its advertising budget last year by 25%. As we know, despite that, its sales decreased by 5% due to regression to the mean. Without the advertising campaign, the firm's sales could have decreased by even more than 5%. When respondents conclude that advertising was not effective, they give a causal interpretation of sales decrease in the last year and therefore exhibit regression fallacy. Thus, from the theoretical point of view the entrepreneurs should ideally refrain from establishing a negative link between their actions and the outcome.
Sample size fallacy (the law of small numbers bias) arises when people make their judgments on the basis of a (small) sample, while not taking into account the actual size of this sample. The 4-item scale is based on Simon et al. (2000) and Mohan-Neill (1995). Items concern basing strategic decisions on the opinion of closest friends and colleagues, on only one source of information, or not basing such decisions on large scale market research. The higher respondents score on these items, the greater the law of small numbers bias they exhibit. Similar to the illusion of control, the other opposite for this bias involves another extreme when entrepreneurs rely exclusively on the large scale research. Therefore, from the theoretical point of view the entrepreneurs should engage in both types of market research and thus score neutrally on this construct (4 on the scale of 1 to 7).
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3.3 MeasuresThe empirical profile for cognitive biases is created on the basis of entrepreneurs who had won the Entrepreneur of the Year award, a list created by Ernst and Young. This experienced group, with an average of 5.08 ventures founded, is presumed to perform well because of the award that they have won. Therefore, this paper assumes that this group has the ‘ideal’ level of biases, and deviation from these levels is then undesirable.
The dependent variable, total number of ventures founded by the entrepreneur, is used as a single measure of entrepreneurial performance. This measure of the extent of entrepreneurial experience is in line with for example Ucbasaran et al. (2009). However, the general criticism in the entrepreneurial literature is that it’s not widely agreed-upon what measures of (individual) entrepreneurial success or performance should be considered. On the one hand, serial entrepreneurs have a special role since they can potentially build upon their experience and start more successful ventures. On the other hand, being a serial entrepreneur does not automatically mean that a more successful venture can be founded (Westhead and Wright, 1998). A recent meta-analysis of Song et al. (2008) on new venture performance, found no direct association between prior start-up experience and venture performance. The main arguments that discuss the advantages of serial entrepreneurs seem to go along two lines: efficiency and innovativeness (Podoynitsyna et al., in production). First, being a serial entrepreneur tends to improve efficiency of operations. For example, for entrepreneurs that owned up to 4.5 businesses, business ownership experience was positively associated with opportunity identification (Ucbasaran et al., 2009). Second, serial entrepreneurs are more likely to come up with product innovations and exploit more innovative opportunities (Westhead and Wright, 1998; Ecbasaran et al., 2009).
For these reasons, and regarding the highly experienced entrepreneurs in our dataset, we concluded that serial entrepreneurs are a very valuable population of entrepreneurs concerning our research.
Finally, age and gender of the entrepreneur were used as control variables in the analysis.
Listed in Table 1 are the resulting empirical ideal profile, the theoretical profile and the actual range in the total sample. The normative levels of cognitive biases are fundamentally different from the empirically derived ideal profiles.
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Table 1: Theoretical (normative) and empirical profiles for cognitive biases
Min in the total sample Max in the total sample Theoretical ideal profile Empirical profile award winners (n=13) 1. Base-rate fallacy 0 4.5 0 2,9231 2. Hindsight bias 0 3 0 1,359 3. Illusion of control 1.2 7 4 4,9538 4. Illusory correlations 1.25 7 1 4,4615 5. Overconfidence 0.67 10 0 6,5897 6. Regression fallacy 0 10 0 5,9231 7. Sample size fallacy 1 7 4 5,2308
8. Ventures founded 1 19 N/A 5,08
3.4 Validity and reliability
The Cronbach alpha’s for the cognitive biases ranged between 0.73 and 0.87, demonstrating good reliability. In order to validate the measures of cognitive biases, we conducted a confirmatory factor analysis. Chi-Square was 448.49, df were 186, CFI was 0.95, NFI was 0.92, standardized RMR was 0.055, and finally RMSEA was 0.070. All item loadings were highly significant; the lowest standardized value was 0.55, while the majority was between 0.65 and 0.85.
A second-order confirmatory factor analysis was executed to determine whether the seven factors (biases) load reasonable on one of the two higher-order factors (heuristics). Chi-Square was 434.92, df were 177, CFI was 0.95, NFI was 0.92, standardized RMR was 0.073, and finally RMSEA was 0.065. All standardized loadings were greater than 0.5 at a highly significant level, except for base-rate fallacy, that is found to be insignificant.
All in all, this demonstrates a good model fit and provides evidence for the convergent and discriminant validity. Moreover, it empirically demonstrates that biases either emanate from the availability heuristic, or from the representativeness heuristic.
3.5 Profile deviation analysis procedure
The profile deviation approach originally stems from the gestalt view on organizations suggesting that firms aligning their strategies with their environment achieve better performance. In this study, we draw upon the profile deviation approach in order to determine the profiles with ideal levels of cognitive biases (Drazin and Van de Ven, 1985; Ventrakaman and Prescott, 1990; Doty et al., 1993; Hill and Birkinshaw, 2008).
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The profile deviation approach involves a broad conceptualization of alignment between several characteristics and environment. Its advantages include the ability to retain the complex, interrelated nature of linkages. The systemic and holistic view is therefore maintained (Venkatraman and Prescott, 1990). This holistic nature of the profile deviation approach accurately corresponds to the theoretical positioning of the intuitive thinking. Heuristics and biases are one of the main outputs of intuitive thinking. This intuitive thinking is a system that operates in an automatic, holistic and associationistic manner (Epstein, 1994, Kahneman, 2003). It is primarily non-verbal and intimately associated with affect. As opposed to that, the rational thinking is primarily conscious analytical system that functions by a person’s understanding of conventionally established rules of logic and evidence (Epstein, 1994; Kahneman, 2003). It is intentional, analytic, primarily verbal and relatively affect-free. Thus, we can conclude that the use of the profile deviation approach is justified in this case.The approach distinguishes three steps. First, we determined the empirical ideal profile of cognitive biases using the award winning entrepreneurs and the normative ideal profile was derived from literature. Both the empirical and normative ideal profiles were given earlier in the methods section and are shown in Table 1. The second step calculates the Euclidian distance for each entrepreneur using the following formula;
DIST = ∑
where Xis is the ideal score on the sth cognitive bias and Xjs is the score of the jth focal entrepreneur on the
sth cognitive bias. A negative relationship between the Euclidian distance metric and entrepreneurial performance indicates that deviation from the ideal profile has consequences for performance. In other words, if coalignment with a certain optimum level of biases has significant performance implications, the distance metric should be negative and significant with the measure of entrepreneurs’ performance. At this time, the seven cognitive biases were split into the availability and representativeness heuristic and merged for each heuristic. Finally, a linear regression analysis was performed to test the significance of the relationship between the Euclidian distance metric and the entrepreneurs’ performance.
4. Results
The correlations, means, and standard deviations can be found in table 2. To test the hypothesized relationships, the profile-deviation analysis is used. Table 3 presents a summary of the results of two regression models. For both the theoretical and empirical models, the Euclidian distance metric was used. Both models excluded the national winners of Entrepreneurs of the Year as described earlier in the Methods section.
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Table 2: Correlations, means and standard deviations for the total sample (N=289)1 2 3 4 5 6 7 8 9 10 1 # of ventures 2 Age 0,033 3 Gender 0,018 0,05 4 Base-rate fallacy 0,018 -0,082 -0,029 5 Hindsight bias ,601** 0,022 0,027 0,027 6 Illusion of control ,465** -0,015 0,069 ,271** ,287** 7 Illusory correlations ,261** 0,011 0,023 -0,002 ,217** ,339** 8 Overconfidence bias ,584** -0,024 0,057 0,082 ,887** ,238** ,195** 9 Regression fallacy ,455** -0,085 -0,01 -0,029 ,390** ,301** ,164** ,428** 10 Sample size fallacy ,534** -0,053 0,009 -0,061 ,448** ,217** ,318** ,406** ,373** 11 Mean 4,48 42,33 1,28 2,62 1,04 5,17 4,87 5,80 5,62 4,67 12 Std. Deviation 2,86 12,71 0,45 0,99 0,94 1,25 1,18 2,42 2,49 1,30
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Table 3: Regression models for the profile deviation analysis ‡
Model A: # of ventures founded - NP Model B: # of ventures founded - EP Control variables Age 0,076 0,048 Gender 0,014 0,073 Biases Misalignment Availability Heuristic 0,469*** -0,254*** Representativeness Heuristic 0,283*** 0,078 N 276 276 F 50,838*** 4,634** R 0,655 0,253 R Squared 0,429 0,064 Adj R Square 0,420 0,050 † P<0,10; * p<0,05; ** p<0,01; *** p< 0,001 ‡ Based on standardized scores
NP is normative profile, EP is empirical profile
4.1 Hypotheses 1: Availability heuristic
The biases misalignment associated with the empirical profile of the availability heuristic is negative and significant for the model excluding the award winning entrepreneurs. This provides strong support for hypothesis 1A. In contrast, the normative profile of the ideal levels of cognitive biases emanating from the availability heuristic (model A) is significant at the p<0.001 level but shows a positive result. This evidence provides no support for hypothesis 1B.
4.2 Hypotheses 2: Representativeness heuristic
The model gives a different view regarding the misalignment of biases emanating from the representativeness heuristic. The regression model of the empirical profile (model B) is not significant, providing no support for hypothesis 2A. The biases misalignment associated with the normative profile of the representativeness heuristic is positive and significant. This again shows no support for hypothesis 2B.
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5. Discussion
5.1 Key findings and implications
In this study we focused on the relationship between performance of individual entrepreneurs and seven cognitive biases: hindsight bias, illusory correlations, overconfidence, base-rate fallacy, illusion of control, regression fallacy, and sample size fallacy. To the best of our knowledge, this is the first study to investigate so many cognitive biases simultaneously. As a result, we were able to explore the bright side of being biased using the profile deviation approach (Drazin and Van de Ven, 1985; Hult et al., 2006; Venkatraman, 1990; Venkatraman and Prescott, 1990; Vorhies and Morgan, 2003). The overall proposition of this research is that there is a certain configuration of non-zero cognitive biases delivering optimal entrepreneurial performance, in contradiction to the theories of Kahneman and Tversky.
The results show some clear evidence that cognitive biases should not be treated as a negative certainty, thereby challenging the model of Kahneman and Tversky. Consistent with hypothesis 1A, the empirical ideal profile is highly significant and negatively related to the entrepreneurial performance. This means that the further away entrepreneurs’ cognitive biases are from these profiles, the worse it is for their performance. In its extent, misalignment with the theoretical or normative ideal profile is strongly positive related to the number of ventures founded by the entrepreneur.
An important notion to make is that the negative correlation of the empirical profile is only significant for the availability heuristic and not for the representativeness heuristic. A possible explanation for this finding is that the representative heuristic is made of a very heterogeneous set of biases; the base rate fallacy, illusion of control, regression fallacy and sample size fallacy. A confirmatory regression analysis of the independent biases confirms this explanation, making it in turn logical that the final result is insignificant in our model.
Our results have three main implications. First, cognitive biases should not be treated as an inevitably evil phenomenon, at least in entrepreneurial decision-making. Our findings indicate that the role of cognitive biases is positive rather than negative. The beneficial effects are particularly strong for the empirical ideal profile of cognitive biases emanating from the availability heuristic, defined in terms of number of ventures founded. Second, the levels of cognitive biases justified by the theories derived from Kahneman and Tversky are not optimal for entrepreneurs, especially for serial entrepreneurs. The third main implication is related to the situation vs. trait debate in the entrepreneurial cognition literature. The dominant explanation in the cognition literature is that the cognitive biases are evoked by various situational characteristics (Busenitz and Barney, 1997; Forbes, 2005). However, this study shows that differences in cognition do primarily stem from differences between entrepreneurs with respect to
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personal traits instead of the situational explanation. Another added value of this study is therefore uncovering the more stable part of cognitive biases.5.2 Limitations and avenues for future research
Inevitably this study is associated with several limitations, which may entail future research opportunities. First of all, our dataset consisted of relatively experienced entrepreneurs, with a mean number of years of entrepreneurial experience of 14.5 and about 90% serial entrepreneurs. This means that the findings of this study may be only applicable to experienced entrepreneurs and further research should explore whether these findings hold for less experienced entrepreneurs.
Furthermore, about 70% of the entrepreneurs in our dataset were involved in two or three ventures at the same time. These so-called portfolio entrepreneurs are a different group of habitual entrepreneurs, and we were unable to include these in our analysis due to time constraints and robustness constraints. Further research is necessary to investigate the correlation between the portfolio en serial entrepreneurs.
Thirdly, in our research design, the questions about overconfidence and hindsight bias were intentionally connected to the same context in order to be able to compare their effects directly. In particular, answers on the hindsight bias are coupled to answers on the overconfidence bias. However, no significant multicollinearity effects were found between the two constructs. Moreover, the overconfidence and hindsight bias questions were placed as far away from each other as possible within the questionnaire. It took the respondents around 40-45 minutes to fill out the questionnaire, so the time lag between answering the overconfidence questions and answering the hindsight bias questions was regarded sufficient (Campbell and Tesser, 1983). However, future research with a measure of the hindsight bias with a greater time lag is necessary in order to validate our findings. Alternatively, completely independent measures of overconfidence and hindsight biases may be used.
References
Agans, R.P., Shaffer, L.S. (1994). The hindsight bias: the role of the availability heuristic and perceived risk, Basic and Applied Social Psychology, 15 (4), pp.439-449
Alsos, G. A., Kolvereid, L. (1998). The business gestation process of novice, serial, and parallel business founders, Entrepreneurship, Theory and Practice, pp. 101–114.
Bar-Hillel, M., Neter, E. (1993). How alike is it versus how likely is it: a disjunction fallacy in probability judgments. Journal of Personality and Social Psychology, 65 (6), pp. 1119-1131
Barnes, J. H. Jr. (1984). Cognitive biases and their impact on strategic planning, Strategic Management
21
Baron, R.A., Ensley, M.D. (2006). Opportunity recognition as the detection of meaningful patterns: evidence from comparison of novice and experienced entrepreneurs, Management Science, 52, pp. 1331-1344Bazerman, M.H., Moore, D. (2009). Judgment in managerial decision making, Hobogen, New Jersey: John Wiley & Sons Ltd.
Bingham, C.B., Eisenhardt, K.M. (2011). Rational heuristics: the ‘simple rules’ that strategists learn from process experience, Strategic Management Journal, 32, pp. 1437-1464
Birley, S., Westhead, P. (1993). A comparison of new businesses established by ‘‘novice’ and ‘‘habitual’’ founders in Great Britain, International Small Business Journal, 12, pp. 38–60.
Brenner, L. A., Koehler, D. J., Liberman, V., and Tversky, A. (1996). Overconfidence in Probability and Frequency Judgments: A Critical Examination. Organizational Behavior and Human Decision
Processes, 65 (3), pp. 212-219
Bruederl, J., Preisendoerfer, P., Ziegler, R. (1992). Survival chances of newly founded business organizations. American Sociological Review, 57, pp.227-242
Bukszar, E., and Connolly, T. (1988). Hindsight bias and strategic choice: some problems in learning from experience: Academy of Management Journal, 31 (3), pp. 628-641
Burmeister, K., Schade, C. (2007). Are entrepreneurs’ decisions more biased? An experimental investigation of the susceptibility to status quo bias, Journal of Business Venturing, 22, pp. 340-362
Busenitz, L.W., Barney, J.W., (1997). Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making, Journal of Business Venturing,
12 (1), pp. 9-30.
Camerer, C., Lovallo, D. (1999). Overconfidence and excess entry: an experimental approach, The
American Economic Review, 89 (1), pp. 306-318
Campbell J.D., Tesser, A. (1983). Motivational interpretations of hindsight bias: An individual difference analysis. Journal of Personality 51 (4): pp. 605-620
Carroll, J.S. (1978). The effect of imagining an event on expectations for the event: an interpretation in terms of the availability heuristic, Journal of Experimental Social Psychology, 14, pp. 88-96
Carter, S., Ram, M. (2003). Reassessing portfolio entrepreneurship, Small Business Economics, 21, pp. 371-380
Chapman, L.J., Chapman, J.P. (1969). Illusory correlation as an obstacle to the use of valid psychodiagnostic signs, Journal of Abnormal Psychology, 74 (3), pp. 271-280
Cooper, A.C., Gimeno-Gascon, F.J., Woo, C.Y. (1994). Initial human and financial capital as predictors of new venture performance. Journal of Business Venturing, 9, pp. 371-395
22
Curşeu, P.L., Vermeulen, P.A.M., Bakker, R.M. (2008). The psychology of entrepreneurial strategic decisions, in Vermeulen, P.A.M. and Curşeu P.L. (eds), Entrepreneurial strategic decision-making: a cognitive perspective (pp. 41-67), Cornwall, Great Britain: MPG Books Ltd.De Kort, M.J.J. and Vermeulen, P.A.M. (2008). Entrepreneurial decision-makers and the use of biases and heuristics, in Vermeulen, P.A.M. and Curşeu P.L. (eds), Entrepreneurial strategic decision-making: a cognitive perspective, Cornwall, Great Britain: MPG Books Ltd.
Doty, D.H., Glick, W.H., Huber, G.P. (1993). Fit, equifinality and organizational effectiveness: a test of two configurational theories, Academy of Management Journal, 36 (6), pp. 1196-1250
Drazin, R., Van de Ven, A.H. (1985). Alternative forms of fit in contingency theory, Administrative
Science Quarterly, 30, pp. 514-539
Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American
Psychologist, 49 (8), pp. 709-724
Fischhoff, B. (1975). Hindsight ≠ foresight: the effect of outcome knowledge on judgment under uncertainty, Journal of Experimental Psychology: Human Perception and Performance, 1 (3), pp. 288-299
Fischhoff, B. (1976). The effect of temporal setting on likelihood estimates, Organizational Behavior and
Human Performance, 15, pp.180-194
Fischhoff, B. (1982). Debiasing, in Kahneman, D., Slovic, P, Tverksy, A (eds), Judgment under uncertainty: Heuristics and biases (pp.422-444). New York: Cambridge University Press
Fischhoff, B., Bar-Hillel, M. (1984). Focusing techniques: a shortcut to improving probability judgments?, Organizational Behavior and Human Performance, 34, pp. 175-194
Fischhoff, B., Slovic, P., Lichtenstein, S. (1977). Knowing with certainty: the appropriateness of extreme confidence, Journal of Experimental Psychology: Human Perception and Performance, 3 (4), pp. 552-564
Forbes, D.P. (2005). Are some entrepreneurs more overconfident than others?, Journal of Business
Venturing, 20, pp. 623-640
Gilovich, T., Griffin, D., Kahneman D. (2002), Heuristics and biases: The psychology of intuitive judgment, New York: Cambridge University Press.
Golding, S.L., Rorer, L.G. (1972). Illusory correlation and subjective judgment, Journal of Abnormal
Psychology, 80 (3), pp. 249-260
Griffin, D., Tversky, A. (1992). The weighing of evidence and the determinants of confidence, Cognitive
Psychology, 24, pp. 411-435
Hall, P. (1995). Habitual owners of small businesses. Small Firms Partnership for Growth, London: Paul Chapman, pp. 217–230.
23
Hayward, M.L.A., Forster, W, Sarasvathy S., Fredrickson, B.L. (2010). Beyond hubris: how highlyconfident entrepreneurs rebound to venture again, Journal of Business Venturing, 25, 569-578
Headd, B. (2003). Redefining Business Success: Distinguishing Between Closure and Failure. Small
Business Economics, 21, pp. 51-61
Hill, S. A., Birkinshaw, J. (2008). Strategy-organization configurations in corporate venture units: impact on performance and survival, Journal of Business Venturing, 23, pp.423-444
Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality, American
Psychologist, 58 (9), pp. 697–720
Kahneman, D., Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive Judgment, in Gilovich, T., Griffin, D., Kahneman D. (eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 49-81). New York: Cambridge University Press.
Kahneman, D., Lovallo, D. (1993). Timid choices and bold forecasts: a cognitive perspective on risk taking, Management Science, 39 (1), pp. 17-31
Kahneman, D., Slovic, P., Tversky, A. (1982). Judgment under uncertainty: heuristics and biases. New York: Cambridge University Press.
Kahneman, D., Tversky, A. (1972). Subjective probability: a judgment of representativeness, Cognitive
Psychology, 3, pp. 430-454
Kahneman, D., Tversky, A. (1973). On the psychology of prediction, Psychological review, 80 (4), pp. 237-251
Keh, H.T., Foo, M.D., Lim, B.C. (2002). Opportunity evaluation under risky conditions: the cognitive processes of entrepreneurs, Entrepreneurship Theory and Practice, 27 (2), pp. 125-148
Koellinger, P., Minniti, M., Schade, C. (2007). “I think I can, I think I can”: overconfidence and entrepreneurial behavior, Journal of Economic Psychology, 28, pp. 502-527
Langer, E.J. (1975). The illusion of control, Journal of Personality and Social Psychology, 32 (2), pp. 311-328
Langer, E.J., Roth, J. (1975). Heads I win, tails it’s chance: the illusion of control as a function of the sequence of outcomes in a purely chance task, Journal of Personality and Social Psychology, 32 (6), pp. 951 – 955
Lichtenstein, S., Slovic, P., Fischhoff, B., Layman, M., Combs, B. (1978). Judged frequency of lethal events, Journal of Experimental Psychology: Human Learning and Memory, 4 (6), pp. 551-578
Lynch, J.G.J, Ofir, C. (1989). Effects of cue consistency and value on base-rate utilization. Journal of
Personality and Social Psychology, 56 (2), pp.170-181
Mohan-Neill, S. (1995). The influence of firm’s age and size on its environmental scanning activities.
24
Plous, S. (1993). The psychology of judgment and decision making, New York, NY, England:McGraw-Hill Book Company
Podoynitsya, K., Van der Bij, H., Song, M. (in production). What is good about being biased?
Rerup, C. (2005). Learning from past experience: footnotes on mindfulness and habitual entrepreneurship,
Scandinavian Journal of Management, 21, pp.451-472
Rosenbusch, N., Brinckmann, J., Bausch, A. (2011). Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs, Journal of Business Venturing,
26, pp. 441-457
Russo, J.E., Schoemaker, P.J.H. (1992). Managing overconfidence, Sloan Management Review, 33 (2), pp. 7-17
Schwenk, C.R. (1984). Cognitive simplification processes in strategic decision-making, Strategic
Management Journal, 5 (2), pp.111-128
Schwenk, C.R. (1986). Information, cognitive and commitment to a course of action, Academy of
Management Review, 11 (2), pp. 298-310
Shane, S., Cable, D., 2002. Network ties, reputation, and the financing of new ventures. Management
Science, 48 (3), 364–381.
Shaver, K.G., Scott, L.R. (1991). Person, process, choice: the psychology of new venture creation,
Entrepreneurship Theory and Practice, 16 (2), pp. 23-45
Simon, H.A. (1955). A behavioral model of rational choice, The Quarterly Journal of Economics, 69 (1), pp. 99-118.
Simon, H.A. (1956). Rational choice and the structure of the environment, Psychological Review,63 (2), pp. 129-138.
Simon, H.A. (1990). Invariants of human behavior, Annual Review of Psychology, 41, pp.1-19
Simon, M., Houghton, S.M. (2003). The relationship between overconfidence and the introduction of risky products: evidence from a field study, Academy of Management Journal, 46 (2), pp.139-149
Simon, M., Houghton, S.M., Aquino, K. (2000). Cognitive biases, risk perception, and venture formation: how individuals decide to start companies, Journal of Business Venturing, 15, pp.113-134
Slovic, P., Fischhoff, B. (1977). On the psychology of experimental surprises, Journal of Experimental
Psychology: Human Perception and Performance, 3 (4), pp. 544-551
Song, M., Podoynitsyna, K., Van der Bij, H., Halman, J.I.M. (2008). Success factors in new ventures: a meta-analysis, Journal of Product Innovation Management, 25, pp. 7-27
Song, M., Podoynitsyna, K., Van der Bij, H., Weggeman, M. (in production). Risk-taking from the bottom of the heart: how rational and intuitive thinking form entrepreneurial risk-taking propensity
25
Taylor, M.P. (1999). Survival of the Fittest? An analysis of self-employment duration in Britain. TheEconomic Journal, 109, pp. 140-155
Todd, P.M., Gigerenzer, G. (2003). Bounded rationality to the world, Journal of Economic Psychology,
24, pp. 143-165
Tversky, A., Kahneman, D. (1971). Belief in the law of small numbers, Psychological Bulletin, 76 (2), pp.105-110
Tversky, A., Kahneman, D. (1973). Availability: a heuristic of judging frequency and probability,
Cognitive Psychology, 5, pp. 207-232
Tversky, A., Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases, Science, 185, pp. 1124-1131
Tversky, A., Kahneman, D. (1983). Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment, Psychological Review, 90 (4), pp. 293-315
Tversky, A., Kahneman, D. (1984). Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment, in Gilovich, T., Griffin, D., Kahneman D. (eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 19-48). New York: Cambridge University Press.
Ucbasaran, D., Westhead, P., Wright, M. (2009). The extent and nature of opportunity identification by experienced entrepreneurs, Journal of Business Venturing, 24, pp. 99-115
Venkatraman, N., Prescott, J.E. (1990). Environment-strategy coalignment: an empirical test of its performance implications, Strategic Management Journal¸ 11 (1), pp. 1-23
Vermeulen, P.A.M. and Curşeu P.L. (2008), Entrepreneurial strategic decision-making: a cognitive perspective, Cornwall, Great Britain: MPG Books Ltd.
Vorhies, D.W., Morgan, N.A. (2003). A configuration theory assessment of marketing organization fit with business strategy and its relationship with marketing, Journal of Marketing, 67, pp.100-115
Westhead, P., Wright, M. (1998). Novice, portfolio and serial founders: are they different?, Journal of
Business Venturing, 13, pp. 173-204
Westhead, P., Wright, M. (1999). Contributions of novice, portfolio, and serial founders located in rural and urban areas, Regional Studies, 33(2), pp. 157–173.
Zacharakis, A.L., Shepherd, D.A. (2001). The nature of information and overconfidence on venture capitalists’ decision making, Journal of Business Venturing, 16, pp. 311-332
Zuckerman, M., Knee, C.R., Kieffer, S.C., Gagne, M. (2004). What individuals believe they can and cannot do: explorations of realistic and unrealistic control beliefs, Journal of Personality
Assessment, 82 (2), pp. 215-232
Zuckerman, M., Knee, C.R., Kieffer, S.C., Rawsthome, L, Bruce, L.M (1996). Beliefs in realistic and unrealistic control: Assessment and implications, Journal of Personality, 64 (2), pp. 435-464
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Appendix I. The asterisk (*) denotes which questions were used in this thesis.
A Survey on Entrepreneurial Risk Taking
A research project sponsored by the Ewing Marion Kauffman Foundation
Principal Researchers:
Michael Song
The Kauffman Foundation Senior Faculty Fellow
Charles N. Kimball, MRI/Missouri Endowed Chair in Management of Technology and Innovation & Professor of Marketing
University of Missouri-Kansas City 5110 Cherry Street Kansas City, MO 64110-2499 Phone: +1 (816)235-5841 Fax: +1 (816)235-6529 songmi@umkc.edu Ksenia Podoynitsyna PhD Candidate
Faculty of Technology Management Eindhoven University of Technology
P.O. Box 513
5600 MB Eindhoven, the Netherlands Phone: +31 (40)247-3640
Fax: +31 (40)246-8054
k.s.podoynitsyna@tm.tue.nl
Hans van der Bij
Assistant Professor
Faculty of Technology Management Eindhoven University of Technology
P.O. Box 513
5600 MB Eindhoven, the Netherlands Phone: +31 (40)247-3702
Fax: +31 (40)246-8054
j.d.v.d.bij@tm.tue.nl
General Instructions:
This survey contains statements which may or may not apply to you. For each statement circle the answer that best represents your opinion. Please rely on your first impressions and work rapidly. Once you have given an answer to a question, please do not turn back to change the answer. If you cannot answer any specific questions for any reasons, please try to give your best judgment and proceed to the next question.
Confidentiality:
All responses will be held in the strictest confidence. Data will only be analyzed at the aggregate level. No individual responses will be released or disclosed. No one except the principal academic researchers will have access to the raw data.
