Int Entrep Manag J DOI 10.1007/s11365-008-0075-2
A theory of entrepreneurial learning from performance errors Antoaneta P. Petkova
# Springer Science + Business Media, LLC 2008
Abstract This paper develops a theory of entrepreneurial learning from performance errors. The paper explains how entrepreneurs generate outcomes, and based on these, detect and correct errors in their own knowledge about the activities involved in creating and operating a new venture. The model developed in this paper reflects the major cognitive functions leading to outcome generation, error detection and error correction. We draw testable propositions about the effects of entrepreneurs’ domain-specific knowledge and cognitive ability on each stage of the learning process, which ultimately determine how much the entrepreneurs can learn from a given performance error. Keywords Entrepreneurial learning . Performance errors . Profitable opportunities
“…entrepreneurship is a process of learning and a theory of entrepreneurship requires a theory of learning” (Minniti and Bygrave 2001: 7) Entrepreneurship research defines entrepreneurs as individuals who discover, evaluate, and exploit profitable opportunities (Shane and Venkataraman 2000: 218). Thus, entrepreneurs often need knowledge that does not exist in a useful or tested form but instead it must be created (Aldrich 2000). For example, a newly started venture needs profit, power, visibility, and market share, which present the entrepreneurs with the problem how to achieve all these desirable goals while avoiding negative experiences (Weick 1979). In order to achieve these goals, entrepreneurs need to learn how to supply the new venture with resources, such as
A. P. Petkova (*) College of Business, San Francisco State University, 353 Business Building, 1600 Holloway Avenue, San Francisco, CA 94132, USA e-mail:
[email protected]
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financial capital, qualified personnel, technology, strategic partnerships, and customer goodwill (Zimmerman and Zeitz 2002). According to Block and McMillan (1985: 2), “Starting a new business is essentially an experiment. Implicit in the experiment are a number of hypotheses (commonly called assumptions) that can be tested only by experience.” Therefore, the entrepreneurial process has been conceptualized as an inherently dynamic process of experimentation and learning (Cope 2005; Harrison and Leitch 2005). Although extant entrepreneurship research focuses primarily on organizational leaning at the level of new ventures or even populations of new ventures (Caves 1998; Dutta and Crossan 2005; Lumpkin and Lichtenstein 2005; Pakes and Ericson 1998), scholars have also pointed to the need to analyze the process of entrepreneurial learning at the level of the individual entrepreneur (Cope 2001; Cope 2005; Corbett 2005; Krueger 2007; Politis 2005). Researchers have looked at individual differences (Corbett 2005) and critical “learning events”, such as significant successes and failures (Cope 2001, 2005; Minniti and Bygrave 2001; Reuber and Fischer 1999) that can impact substantively the entrepreneurial learning process. We extend this research by focusing in greater depth on errors as one type of event that occurs quite often during the startup process, because the task novelty and the lack of experience often put entrepreneurs in a situation of high potential for errors. For example, the high mortality rates of young firms observed by entrepreneurship scholars (Aldrich 2000; Reynolds and White 1993) suggest that many entrepreneurs either fail to learn during the start-up process or they learn too late. Given that the entrepreneurial process is intertwined with ongoing mistakes and learning on part of the entrepreneurs, it is critically important for both researchers and practitioners to understand what factors trigger entrepreneurial learning, how exactly entrepreneurs learn, and what conditions determine how much they can learn from a given experience. A careful review of the literature on learning in psychology and management and organization theory suggests three major sources of learning: (a) learning by repetition of efficient practices (“learning by doing”), (b) memorizing new information as a result of training or tutoring, and (c) replacement of incorrect knowledge and practices with new ones based on negative feedback. First, behavioral learning theories suggest that an individual’s experience in a given problem-solving domain increases efficiency (the so called ‘learning-by-doing’ models). By performing the same task multiple times, individuals have the opportunity to find the most efficient way of performing the task and to achieve mastery in the skills necessary for performing the task (Anzai and Simon 1979). Such learning involves some experimentation but at the same time requires repetition of a particular task over and over again. Usually the outcomes of such tasks are well defined and measurable, which allows for clear feedback and evaluation of the level of efficiency (Anzai and Simon 1979; March 1991). Experiential learning models represent the most widely adopted perspective in organization and management theory, because they are well-suited for explaining the emergence and change of organizational routines, as well as other processes of organizational learning (Cyert and March 1963; March 1991). This perspective reflects the learning processes of established organizations and their members but may have limited applicability to entrepreneurial learning,
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because the number of tasks entrepreneurs perform is typically high and the chances of repeatedly performing the same task are relatively lower compared to the typical manager or employee in an established organization. Although some entrepreneurial activities may be performed multiple times (for example, after recruiting several people an entrepreneur may become better at selection and recruitment), such activities are fewer than the more novel activities performed by entrepreneurs. However, the behavioral learning theories have been embraced by prior entrepreneurship research for their focus on action as a trigger of learning. Indeed, entrepreneurship scholars converge around the idea that entrepreneurs learn by doing, because the startup process in and of itself is a process of trial and error (Cope and Watts 2000; Cope 2005; Politis 2005; Smilor 1997). We extend these ideas by elaborating on the role of negative outcomes (as one particular result of entrepreneurial action) in the process of entrepreneurial learning. Second, researchers in education and psychology have focused on developing more effective instruction methods and motivating subjects to learn. Scholars have looked at improvement in performance speed and/or level of memorizing when performing relatively simple tasks, such as reading-comprehension, arithmetic skills, geometric proofs, and computer programming (see Glaser and Bassok 1989 for a review). At the theoretical level, researchers have tried to explain the processes of encoding of new information and its retrieval from memory (see Horton and Mills 1984 for a review). Although these studies are interesting and informative, they provide little insights into how entrepreneurs learn from real life experiences. In contrast to the controlled simple task environment of the training laboratory, where students encounter the same problems over and over again, the typical problemsolving situation faced by an entrepreneur is characterized by high level of uncertainty and ambiguity, ill-defined goals, difficult to interpret outcomes, and, most importantly, no information regarding “the right answer”. Therefore, the mainstream education and psychology research on learning is not directly applicable to studying entrepreneurial learning. Third, a sub-stream of psychology research focuses on error-based training and on learning triggered by performance errors (Gully et al. 2002; Ohlsson 1996; Stiso and Payne 2004). Scholars from this perspective have developed models of learning characterized by: (1) a well defined task, (2) clear standards for determining how appropriate the answers/outcomes are, and (3) immediate specific feedback to students, including what they did right or wrong and what the correct answer is. As discussed above, these conditions hardly hold in any entrepreneurial situation. However, the major concepts and cognitive processes identified by these studies provide useful grounds for developing a model of entrepreneurial learning from performance errors. In sum, this review of the extant learning literature shows that error learning has been largely overlooked by both entrepreneurship research and management and organization theories, thus making performance errors the least studied source of learning. The wide adoption of behavioral learning models and the relative neglect of error-based learning models could be explained with the fact that most organizational members work in rather predictable environments and hardly encounter numerous discrepancies between expectations and outcomes. However, errors provide important learning opportunities for entrepreneurs, because discrep-
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ancies between expectations and actual experiences serve as major triggers for reevaluation of previously held assumptions (Gatewood et al. 2002; Naffziger et al. 1994) and for development of new knowledge (Daft and Weick 1984). Because entrepreneurs are often involved in innovation and experimentation (Jenkins and Johnson 1997), they are more likely to encounter unexpected outcomes than the members of established organizations. Past entrepreneurship research suggests that entrepreneurs face situational factors such as high uncertainty, high novelty, time pressure and information overload (Baron 1998). According to Aldrich (2000: 96), “During the founding process, founders must cope with information overload and uncertainty, severe time pressures and high level of emotional involvement”. Such dynamic environments put pressure on entrepreneurs to act fast rather than correct, which in turn increases dramatically the likelihood of errors. As Shaver and Scott (1991: 35) explain: “… before there can be a new organization, the founder-to-be must at minimum develop and test prototypes, conduct appropriate market research, create the standard financial projections, and construct a business plan suitable for securing venture capital. Rarely is each of these activities completed to the founder’s satisfaction on the first pass.” Given the high uncertainty of most entrepreneurial activities coupled with the high likelihood of errors under conditions of high uncertainty, it is reasonable to assume that entrepreneurs are more prone to making errors than managers or employees in established organizations. If this is the case, errors may provide a much more important source of entrepreneurial learning than currently acknowledged. Therefore, entrepreneurial learning from performance errors may be an important yet understudied issue that merits research attention. Specifically, it is important to understand how entrepreneurs can learn from their errors—a process that often goes hand in hand with the acquisition of new skills and capabilities in novel and uncertain situations. This paper addresses the following research question: How can entrepreneurs learn from their own performance errors? We answer this question by developing a model of entrepreneurial learning from performance errors, which explains how entrepreneurs generate outcomes, and based on them can detect and correct flaws in their own knowledge regarding the activities involved in creating and operating a new venture. The model describes the major cognitive functions leading to outcome generation, error detection and error correction. Figure 1 illustrates the proposed model and relationships. The model developed in this paper extends psychology Domain-Specific Knowledge Structures
General knowledge
Specialized knowledge
P1
P4
P6
Error Detection Formulate the goal
Activate possible actions
Select a course of action
Interpret the outcomes
Compare outcomes to expectations
P7, P8, P9
Error Correction
Detect error
P2, P3
Actions
Revised knowledge
Outcomes - Importance - Magnitude
Fig. 1 A model of entrepreneurial learning from performance errors
Assign blame
P5
Attributional style
Attribute bad outcomes
Revise faulty knowledge structure
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models of error-based learning by proposing that entrepreneurs’ prior knowledge and cognitive biases can play a significant role at each stage of the learning process and may determine whether the processes of error detection and error correction that lead to learning will actually occur. This paper makes several important contributions to understanding entrepreneurial learning. First, it draws attention to performance errors as a major source of learning for entrepreneurs, an issue that has remained largely unexplored by past research. Second, the model developed in this paper extends the current state of knowledge by providing a deeper understanding of how entrepreneurs can learn from their performance errors and by articulating the factors that determine to what extent entrepreneurs would learn from a given error. Third, the model developed in this paper incorporates basic cognitive processes identified by psychology researchers together with cognitive biases found in the context of entrepreneurship to develop specific testable propositions regarding the factors that may influence the process of entrepreneurial learning. The paper proceeds with a brief explanation of the major concepts relevant for understanding the process of entrepreneurial learning—errors, prior knowledge, and cognitive biases. Next, we develop a process-model of entrepreneurial learning from performance errors, describing the stages of: (1) generation of entrepreneurial outcomes, including the choice and performance of entrepreneurial actions, (2) error detection, preceded by interpretation of outcomes and comparison of outcomes to expectations, and (3) error correction, including blame assignment, attribution of bad outcomes, and revision of faulty knowledge structures. We describe each of these functions as a step-by-step process and draw propositions about the impact of entrepreneurial knowledge and cognition on the learning process and outcomes.1 The paper concludes with a discussion of some implications of the proposed model and directions for future research.
Factors influencing entrepreneurial learning Performance errors as triggers of learning Assuming that people generate knowledge through experience, scholars have proposed that past entrepreneurial experience can serve as a major source of learning for entrepreneurs (Aldrich 2000; Minniti and Bygrave 2001). However, across various samples and empirical settings, studies consistently report nonsignificant effects of founders’ past entrepreneurial experience on the performance of subsequent ventures (Chandler and Jansen 1992; Davidsson and Honig 2003; Westhead and Wright 1998; Wright et al. 1997; Shane and Stuart 2002). Most surprisingly, entrepreneurs who succeed with their first venture often fail with the second one (Starr and Bygrave 1992), which suggests that entrepreneurs may not learn simply by doing things. These controversial findings call for a more careful examination of the major triggers of entrepreneurial learning. One potential 1
In reality the learning process can be more complicated if possible feedback loops are taken into account. However, this is beyond the scope of the current paper.
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explanation of these disappointing findings may be that prior research has not distinguished between positive and negative experiences. Specifically, it may be the case that entrepreneurs learn more from failure than from success. Although errors are often associated with stress, frustration, and perception of helplessness (Ivancic and Hesketh 1995/1996; Nordstrom et al. 1998), they may play an important role in the process of entrepreneurial learning, because errors can alert entrepreneurs of incorrect assumptions and beliefs (Daft and Weick 1984; Smith et al. 1997) and can trigger a process of elaborate analysis that leads to the development of new knowledge. Past research provides indications that errors can play an important role in stimulating entrepreneurial learning, because negative outcomes force people to reevaluate previously held knowledge and expectations (Fiske and Taylor 1991; Gatewood et al. 2002). Entrepreneurship scholars have identified “near-to-failure experience” (Guth et al. 1991) and “major setbacks” (Reuber and Fischer 1993) as powerful incentives for entrepreneurs to reconsider their assumptions and adjust their expectations. For example, Naffziger et al. (1994) propose that negative outcomes cause changes in entrepreneurs’ behavior and may even lead to discontinuation of entrepreneurial activities. Similarly, Gatewood et al. (2002) find that subjects lower their expectations regarding future startups after receiving negative feedback. Positive outcomes, on the other hand, lead entrepreneurs to persist with their selected course of action (Naffziger et al. 1994). Further, entrepreneurs tend to overexploit actions that initially have generated desirable outcomes (Minniti and Bygrave 2001), which may lead to overgeneralization and a failure to adapt to more dynamic situations. This conclusion is consistent with Sitkin’s (1992) idea that continuous success might be a liability because “failure to fail” can restrict individuals from exploring alternatives, inhibit risk taking, and lead to complacency (Gully et al. 2002). Work by Dormann and Frese (1994) also indicates that avoidance of errors may reduce exploratory behavior and development of new knowledge. Together these studies suggest that outcomes meeting or exceeding entrepreneurs’ expectations reassure entrepreneurs that they are doing well and provide limited learning incentives, because positive outcomes make entrepreneurs overconfident in what they are doing. Errors, on the other hand, may trigger learning, because negative outcomes call for change and provide incentives for entrepreneurs to reconsider their current beliefs and courses of action. Entrepreneurs’ prior knowledge and domain-related knowledge structures Each entrepreneur enters the startup process with an individual (idiosyncratic) stock of knowledge, accumulated through past experience (Cope 2005; Politis 2005; Reuber and Fischer 1999). This individual knowledge is organized into knowledge structures. A knowledge structure is “a mental template that individuals impose on an information environment to give it form and meaning” (Walsh 1995: 281). Different knowledge structures refer to different domains of activity (Fiske and Taylor 1991; Walsh 1995). When dealing with a specific problem, people evoke the knowledge structures that are most closely related to the problem domain. Therefore, previously developed domain-specific knowledge structures determine what infor-
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mation will be attended in a novel situation, as well as how the new information will be interpreted and incorporated into individuals’ memory (Fiske and Taylor 1991). It is important to note that in this paper we treat as distinct concepts (a) the experience of an entrepreneur, (b) the knowledge acquired by the entrepreneur as a result of certain experience, and (c) the learning process itself, consistent with Politis (2005). Entrepreneurial decisions are a function of two types of knowledge: specialized and generalized. Specialized knowledge refers to technical aspects of the chosen market—it can be both product-specific and industry-specific (Minniti and Bygrave 2001). Generalized knowledge refers more broadly to the domain of entrepreneurial activities that are similar across markets and determines to what extent an entrepreneur knows “how to be entrepreneurial” (Minniti and Bygrave 2001). Although many entrepreneurs may possess both generalized knowledge about entrepreneurship and entrepreneurial activities (Aldrich 2000) and specialized knowledge about a particular technology, a resource, or a customer need (Hayek 1945), it is likely that entrepreneurs differ in the degree to which they possess each of these two types of knowledge. Specialized knowledge possessed by entrepreneurs has a profound effect on their search and discovery processes, as well as on their decisions to exploit an opportunity (Venkataraman 1997). Specialized knowledge determines the types of opportunities that entrepreneurs discover and the ways they organize their new ventures to exploit those opportunities (Azoulay and Shane 2001; Shane 2000). For example, many high technology new ventures are started by leading engineers from established firms, who were involved in the invention and subsequently formed a new enterprise to explore the opportunity, based on this invention (Christensen and Bower 1996). Generalized knowledge guides entrepreneurs in the non-technical aspects of the startup process. According to Harrison and Leitch (2005), such nontechnical entrepreneurial knowledge includes general awareness of the existing market opportunities, competences in acquiring venture financing, and capabilities to manage the enterprise from startup to maturity. Both specialized and generalized knowledge can influence entrepreneurs’ decisions and actions and their subsequent learning. Prior research on the role of generalized and specialized knowledge suggests that entrepreneurs need both types of knowledge. We extend these ideas to develop more specific arguments about the effects of generalized versus specialized knowledge in each stage of the learning process. Specifically, in the context of entrepreneurial learning generalized knowledge provides flexibility and a broader range of applicability of domain-related knowledge structures, while specialized knowledge assures depth and specificity when analyzing the reasons for an error. Therefore, we propose that generalized knowledge may facilitate the detection of errors (e.g., when interpreting the outcomes), whereas specialized knowledge may be helpful for appropriate attribution of the reasons for the errors to occur. Cognitive biases Entrepreneurs do not follow rational (normative) thinking models but rather tend to use cognitive shortcuts called heuristics (Baron 1998; Mitchell et al. 2007). The use of heuristics can vary among individuals, as well as from one situation to another, depending on factors such as urgency and cognitive constraints (Bazerman 2001;
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Fiske and Taylor 1991). Sometimes the use of heuristics can be beneficial for entrepreneurs, because heuristics help entrepreneurs economize on cognitive efforts and may still lead to superior decisions (Mitchell et al. 2007). However, when used inappropriately, heuristics may become biases that lead to inaccurate processing of information and suboptimal decision making (Bazerman 2001). Prior studies have found that entrepreneurs exhibit various biases such as overconfidence, illusion of control, reasoning by analogy, and the law of small numbers (Keh et al. 2002; Simon and Houghton 2002). Entrepreneurship scholars explain entrepreneurs’ susceptibility to cognitive biases with situational factors such as high uncertainty, high novelty, time pressures, information overload, and high level of emotional involvement (Aldrich 2000; Baron 1998). For example, Simon and Houghton (2002) find that entrepreneurs acting under high uncertainty—i.e., in smaller, younger, and pioneering ventures—are more likely to exhibit illusion of control, reasoning by analogy, and the low of small numbers biases. Further, Krueger (2007) has argued that prior entrepreneurial experience and training (education) can alleviate or reinforce some of these biases. Two types of individual biases are particularly relevant for the model developed in this paper because of their potential effects on the error-correction stage: the selfserving attribution bias and the individual attributional style (Fiske and Taylor 1991). The self-serving attribution refers to individuals’ propensity to attribute positive outcomes to their own merits, while blaming negative outcomes to uncontrollable external factors (Bazerman 2001; Fiske and Taylor 1991). Attributional style refers to individuals’ tendency to make similar causal inferences over time and across different situations (Metalsky and Abramson 1981). Entrepreneurs with external locus of control are more likely to attribute the outcomes of a given activity to external factors outside of their control, whereas entrepreneurs with internal locus of control are more likely to attribute the same outcomes to their own decisions and actions (Jenkins and Johnson 1997; McClelland 1987). Importantly, such biases tend to persist and change only to a limited degree as a result of experience (Krueger 2007; Parker 2007). For example, Parker (2007) found that entrepreneurs give much greater weight to their prior beliefs than to new information when forming their expectations. Further, Krueger (2007) argues that prior success can lead to even stronger internal attributions among entrepreneurs. Therefore, biases are likely to affect the way entrepreneurs interpret their errors and the possibility to learn from those errors, as explained in the following section.
A model of entrepreneurial learning from performance errors The model of entrepreneurial learning proposed in this paper draws on the psychological literature on errors and failure-driven learning (Berkson and Wettersten 1984; Gully et al. 2002; Ohlsson 1987; Schank 1986; Stiso and Payne 2004). According to this literature, learning is triggered by negative feedback, expressed in undesirable or unexpected outcomes of certain actions. Consequently, errors made by entrepreneurs are likely to trigger learning because negative outcomes tend to be more salient to entrepreneurs than positive ones (Reuber and Fischer 1993). Experimental psychology also suggests that, before individuals can
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learn from their errors, they have to “recognize errors, understand why errors are errors, compare errors to correct actions, and update knowledge structures accordingly” (Stiso and Payne 2004: 3). The model developed in this section incorporates the major cognitive processes that lead to error detection and error correction. We further extend the ideas of psychology scholars by proposing that certain characteristics of entrepreneurs’ prior knowledge and cognitive biases can influence the different stages of the learning process. These arguments are summarized in specific testable propositions. Generation of entrepreneurial outcomes According to prior research, learning from performance errors can occur only after some unexpected outcomes are generated (Ohlsson 1996; Stiso and Payne 2004). Thus, before discussing how entrepreneurs learn from their own performance errors, we briefly describe the process of choosing and performing a given course of action that can lead to unexpected or undesirable outcomes. According to Jenkins and Johnson (1997), an entrepreneurial outcome represents a desired level of financial performance in the business. More generally, entrepreneurial outcomes could be both tangible, such as organization creation, value creation, innovation, growth, profit, sales, and market share (Gartner 1990; Kuratko and Hornsby 1997; Shane and Venkataraman 2000), and intangible, such as entrepreneurs’ intrinsic rewards (Kuratko and Hornsby 1997). Goal formulation Entrepreneurship by definition is a purposeful, goal directed type of activity, associated with the exploitation of potentially profitable opportunities that are relevant for the entrepreneur (Naffziger et al. 1994; Shane and Venkataraman 2000). Consequently, entrepreneurs initiate a particular course of action with certain expectations of the desirable outcomes. Prior research has found that entrepreneurs pursue both extrinsic goals (e.g., income, personal wealth, and other material rewards) and intrinsic goals (e.g., satisfaction, independence, excitement, and challenge) (Kuratko and Hornsby 1997; Naffziger et al. 1994). Entrepreneurial goals can vary in their specificity and complexity, depending on the individual characteristics of the entrepreneur who formulates them, as well as on the situational factors (Shane and Venkataraman 2000). It is important to note that the situation for which the entrepreneurial goals are formulated usually involves a certain degree of novelty for the entrepreneur. If entrepreneurs set out to achieve a goal that is entirely familiar and well defined, the chances of error are much lower and there would be limited learning opportunities. On the other hand, when entrepreneurs face a novel or unfamiliar situation, they need to engage in cognitive efforts in order to select an appropriate course of action. Furthermore, the idea of desirable outcomes that entrepreneurs have is largely dependent on their prior knowledge and cognitive characteristics. Faced with exactly the same objective situation, entrepreneurs may perceive different profitable opportunities, which respectively would lead them to set different goals and expectations (Shane 2000). Depending on the specific goals that are set, entrepreneurs can then generate possible alternative courses of action and can choose among them. Therefore, goal formulation serves as the initial stage in the proposed learning model.
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Generation of alternatives Entrepreneurs usually have more than one possible course of action. Therefore, the choice of action involves cognitive representations of possible alternatives and selection among them. In order to select a course of action, entrepreneurs first need to activate cognitively the possible alternatives by eliciting them from the knowledge structures in which the relevant information is organized. Knowledge structures hold the repertoire of available alternatives, which entrepreneurs have to recall and then decide to what extent they are applicable to the current situation. The cognitive activation of alternatives, also referred to as cognitive search, allows people to evaluate the potential outcomes of various alternatives without actually taking the actions and bearing the consequences of them (Gavetti and Levinthal 2000). Entrepreneurs can evaluate different alternatives based on their understanding of the environment and the expected consequences of engaging in a particular type of action. Although cognitive search provides the opportunity to explore a broad set of alternatives (Gavetti and Levinthal 2000), empirical evidence suggest that entrepreneurs do not consider all possible choices but instead tend to search within a relatively small amount of information (Kaish and Gilad 1991). If an entrepreneur can find analogical previous occasions, he or she may apply directly the available knowledge template to the new situation, because entrepreneurs tend to choose actions that replicate, or are closely related to, the ones they have already taken (Minniti and Bygrave 2001). However, since most entrepreneurial situations contain novel or unfamiliar circumstances, it is likely that the available knowledge structures will not apply directly to the current situation. In such cases, entrepreneurs can recall the most similar prior experience and can judge by approximation what course of action they should take (Fiske and Taylor 1991; Rosch and Lloyd 1978). Such approximation might be rather coarse-grained, because the likelihood of encountering exactly the same problem or situation is much lower for an entrepreneur than for a manager in an established organization. Consequently, the lack of a readily available knowledge structure that fits perfectly the new situation may lead entrepreneurs to recall less appropriate knowledge structures or to apply incorrectly a knowledge structure that appears relevant. In both cases, the approximation process increases the chances of error. This is a critical difference between the model developed in this paper and the existing models of error training, which assume identical conditions and elimination of the error with repetition of the same task. Unlike students in training situations, who are provided with the correct answer and become less likely to make the same mistake over time, entrepreneurs often lack information about the “correct answer”, so they may not even notice when something goes wrong. Moreover, the fact that each entrepreneurial situation differs from the previous ones increases the chances of new errors to occur. To account for these important differences between entrepreneurial contexts and experimental/ training conditions, we treat both error detection and error correction as probable rather than certain events and we analyze the specific conditions that determine whether these events will occur. Selection of a course of action Once various alternatives are considered, the next step is to select a particular course of action. Such selection could be based on the most economically-desirable expected outcomes, the most reasonable alternative
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given the resources available to the entrepreneurs, or some other relevant criteria. In general, the greater the fit between the action–outcome linkages in the entrepreneurs’ knowledge structures and the “objective” reality, the more efficient the selected actions are likely to be (Gavetti and Levinthal 2000). However, entrepreneurs often face novel problems or situations in which the action–outcome linkages still need to be created. Theoretically, when people are confronted with an unfamiliar situation, they try to be accurate rather than fast (Thorngate 1976). However, entrepreneurship research shows consistently the opposite pattern: because entrepreneurs face time constraints simultaneously with high novelty, uncertainty, and information overload, they often make decisions fast, which makes them susceptible to numerous errors (Aldrich 2000; Baron 1998). Action implementation Once the course of action is selected, entrepreneurs have to act upon their goals in order to achieve the desirable outcomes. Prior research suggests that entrepreneurial activities are complex and usually involve a number of mutually related actions for producing a single outcome (Aldrich 2000; Block and McMillan 1985; Carter et al. 1996; Reynolds 1997). To implement a particular action, entrepreneurs need relevant practical knowledge about the respective domain of entrepreneurial activity (Ryle 1949). Even though the entrepreneurs may be confident in the type of action necessary to achieve a particular goal, lack of practical knowledge may lead to incomplete or unsuccessful implementation of the selected course of action (Ryle 1949). Therefore, the specialized knowledge possessed by entrepreneurs is likely to influence the degree to which they can implement effectively a selected course of action. Error detection Contrary to earlier research, which assumes that entrepreneurial intentions lead to desired outcomes (Lafuente and Salas 1989), recent research suggests that entrepreneurial intentions and outcomes are often disconnected, with intentions leading to better or worse than the expected outcomes (Jenkins and Johnson 1997; Naffziger et al. 1994). In their study, Jenkins and Johnson (1997) find that nondeliberate emerging strategies can change the initially intended course of action, resulting in unintended entrepreneurial outcomes. Further, Naffziger and colleagues (1994) propose that entrepreneurial outcomes may be below, equal to, or above expectations. Discrepancies between expectations and outcomes offer learning opportunities for entrepreneurs (Daft and Weick 1984), provided that the entrepreneurs become aware of these discrepancies. According to Fisher and Lipson (1986), errors reveal the existing cognitive representations of a problem-solving strategy and expose its flaws so that the individuals can understand the cause of error. Thus, it is to the entrepreneurs’ advantage to discover as many sources of error as possible, so that they can deepen their knowledge and minimize the number of subsequent errors. An entrepreneur can detect an error if something in the outcome indicates that there is a discrepancy between the intended and the actual results of a particular action. The process of error detection involves three steps: observing and interpreting the outcomes, comparing the outcomes to the expectations, and detecting an error.
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Observing and interpreting the outcomes Outcome interpretation is a subjective evaluation process guided by the individual knowledge structures of each entrepreneur. Knowledge structures serve as a framework, which influences the manner in which relevant information is assimilated (Stotland and Canon 1972; Weick 1979). In the absence of objective measures of outcomes, entrepreneurs’ generalized knowledge structures are likely to be their main source of judgment as to whether an outcome is favorable or unfavorable (Ohlsson 1996; Rosch and Lloyd 1978). Generalized knowledge can provide entrepreneurs with more flexible and encompassing ways of understanding the problem and interpreting its outcomes. For example, in a laboratory experiment, Boland et al. (2001) found that exposure to abstract knowledge facilitates managerial decision making on a complex task. Generalized knowledge is more helpful than specialized knowledge for making sense of ambiguous situations (Hill and Levenhagen, 1995), which makes generalized knowledge particularly valuable when the entrepreneurial outcomes are ambiguous, loosely defined, and difficult to interpret. Therefore, we propose that entrepreneurs’ generalized knowledge can help them interpret the outcomes more effectively. Proposition 1: The greater an entrepreneur’s generalized knowledge, the more precise outcome interpretations (s)he is likely to make Comparing outcomes to expectations Environments vary in the ease and accuracy with which cause–effect or means–ends relations can be perceived and enacted in them (Weick 1979), which makes entrepreneurs unlikely to notice and judge as an error every discrepancy between their expectations and the actual outcomes. For example, if non-entrepreneurial intentions (such as sustained profitability) lead to entrepreneurial outcomes (such as sales growth), as demonstrated by some of the entrepreneurs in Jenkins and Johnson’s (1997) study, entrepreneurs may not consider errors the actions that have led to such unexpected outcomes. Clearly, if the outcomes are better than expected, the entrepreneurs are likely to feel satisfied and to continue with the selected course of action (Naffziger et al. 1994). Therefore, outcomes that exceed expectations provide low incentives for entrepreneurs to analyze the reasons why the outcomes occurred. However, if the outcomes deviate from expectations in a negative direction, and particularly if the deviation is significant, entrepreneurs are likely to perceive a discrepancy between outcomes and expectations that can motivate them to search for an explanation. Therefore, when comparing outcomes to expectations, if no discrepancy is detected, the analytical process will stop and no learning will take place. Only if an error is actually detected, the entrepreneurs are likely to look for the causes of the error and potentially to learn from them. The role of outcomes for error detection Errors are experienced as discrepancies between what the entrepreneur expected to happen and what appears to be the case. As the previous discussion suggests, errors are detected by comparing the actual with the expected outcomes. According to the subjective view of errors, actions are not correct or incorrect by themselves, but under certain circumstances some actions are more efficient than others (Ohlsson 1996). Consequently, a crucial condition at the error-detection stage is that a particular course of action is judged as error due to
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its unsatisfactory, unacceptable, or otherwise negative outcomes, as compared to the entrepreneurs’ expectations. Since not all outcomes are equally salient, the probability of error detection can increase with the magnitude of outcomes (Fiske and Taylor 1991). Prior entrepreneurship research has argued that significant “events” (either positive or negative) serve as major triggers of learning (Cope 2005; Reuber and Fischer 1999). For example, failure to raise money from a venture capitalist or other potential investors is a negative outcome of greater magnitude than receiving a lower than the expected amount of money. Therefore, errors of greater magnitude will be more salient to entrepreneurs and, accordingly, more likely to be detected. These arguments lead to the following proposition: Proposition 2: The greater the magnitude of a negative outcome, the more likely the entrepreneur to detect an error Errors can occur at any stage of the entrepreneurial process, when performing activities such as looking for capital to start a venture based on an idea/opportunity, looking for resources to set up production, looking for customers or distributors, etc. (Aldrich 2000; Shane and Venkataraman 2000). Each of these activities may be perceived by the entrepreneurs as more or less critical for the development and survival of their ventures.2 As a result, the (negative) outcomes of these activities are likely to vary in their importance for each entrepreneur depending on the entrepreneur’s priorities and personal valuation systems (Kuratko and Hornsby 1997). For example, a failure to recruit the foremost accounting authority may appear negative, but it may not be as crucial from the entrepreneur’s perspective as the failure to obtain financial or other resources. Because of their limited span of attention, entrepreneurs are likely to pay greater attention to activities of higher priority for them (Fiske and Taylor 1991) by monitoring more carefully the progress and outcomes of those activities. Therefore, entrepreneurs may be able to identify errors easier in areas perceived as critically important for the survival and success of the new venture, which leads to the following proposition: Proposition 3: The relative importance that an entrepreneur attributes to a given action will be positively related to the likelihood of error-detection in the domain of this action The role of prior knowledge for error detection Entrepreneurs’ prior knowledge can play the role of a baseline for evaluating the outcomes of an action, as well as for judging the action as correct or error. On the one hand, people tend to interpret (or misinterpret) new information in ways consistent with their existing knowledge structures (Fiske and Taylor 1991). On the other hand, inconsistent information creates a sense of conflict, which stands out as a salient event and is, therefore, more likely to attract entrepreneurs’ attention (Fiske and Taylor 1991). However, the knowledge required to recognize an error is often more complex and not everybody 2
It should be noted that the relative importance of different activities and outcomes as perceived by an entrepreneur may not necessarily correspond to the actual impact of those activities on the success of the new venture. In fact, one could argue that the ability to recognize what is most critical for a new venture is a rather complex skill which is not possessed by all entrepreneurs.
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possesses it (Ohlsson 1996). Specifically, one must have previous knowledge about the range of reasonable outcomes from a given course of action in order to recognize that a particular outcome is undesirable. Given the novelty of many entrepreneurial activities, the lack of relevant prior knowledge presents a major challenge to entrepreneurs when evaluating certain outcomes. Depending on the task novelty and the knowledge previously accumulated, an entrepreneur’s prior knowledge in a given domain may be more or less relevant to a particular situation. If the task at hand appears similar to a previously performed one, the entrepreneur is more likely to recall and apply an existing knowledge structure to the new situation. When applying an existing knowledge structure, entrepreneurs can use analogical reasoning to understand and interpret the outcomes of their actions and to detect an error. If the prior knowledge possessed by entrepreneurs is very general or distant from the current domain of action, it may be difficult to apply the existing knowledge structures to the new situation. If so, the entrepreneurs may not be able to detect an error because the existing knowledge structures would not allow them to understand and evaluate properly the outcomes of their actions. Specialized knowledge is likely to help entrepreneurs detect an error by providing more fine-grained cognitive representations of the desired or expected outcomes. Such representations make the patterns of similarity or dissimilarity more salient (Rosch 1975) and lead to noticing particular relevant attributes (Fiske and Taylor 1991). Entrepreneurs may vary widely in the extent to which they have developed knowledge about each particular domain of activity. For example, in comparison to a novice founder, a serial founder is likely to have better developed and more elaborate domain-specific knowledge structures in many domains of entrepreneurial activity (Politis 2005). If entrepreneurs have already developed certain knowledge structures, and if the new facts that they face fit with these structures, they will be able to understand better the relationships between different concepts by building new relationships among previously existing concepts (Fiske and Taylor 1991). In case the new facts are inconsistent with previously held knowledge, the discrepancy will attract the entrepreneurs’ attention, because conflicting cues are more salient than consistent ones and people tend to devote their limited attention to the most salient cues (Fiske and Taylor 1991; Rosch and Lloyd 1978). Better developed and more elaborate knowledge structures would allow for easier detection of such discrepancies, because such structures provide a greater number of attributes based on which the level of fit (or misfit) between expectations and outcomes can be evaluated. Therefore, a higher level of specialization of entrepreneurs’ knowledge is likely to facilitate the process of error detection: Proposition 4: The more specialized an entrepreneur’s prior knowledge in the domain of the chosen action is, the higher the probability of error detection by the entrepreneur Error correction People try to understand the causality of events in order to predict and control the outcomes of their actions (Fiske and Taylor 1991). If an action is perceived as incorrect, a logical conclusion to make is that the practical knowledge on which the
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action is based may be faulty. Whereas the action itself cannot be corrected after the fact, the faulty knowledge structure can be revised and improved. Error correction refers to removing flaws from the underlying knowledge structures in order to improve future actions. Error correction consists of three cognitive processes: blame assignment, attribution of bad outcomes, and revision of faulty knowledge structures (Ohlsson 1996). Blame assignment The first step toward error correction is to identify the reasons for the error to occur. Often entrepreneurs realize post-fact that they have lacked some relevant information that could have affected their choice of action. For example, at the outset of a new venture, many entrepreneurs start with less than adequate knowledge about how to perform various activities, such as selection and recruitment of key personnel, raising financial resources from venture capitalists and other investors, and building relationships with customers or partners. Since the performance of complex tasks typically involves a large number of actions, the fact that an error is identified implies that at least one of these actions was wrong. The term “blame assignment” refers to the process of identifying the factors that have contributed to unfavorable outcomes in a particular context (Ohlsson 1996). Entrepreneurs could blame an error on their own lack of ability, insufficient efforts, task difficulty, bad luck, or outside impediments (Fiske and Taylor 1991; Shaver and Scott 1991; Weiner et al. 1978). At this stage of the process the entrepreneurs’ attributional style is critically important for determining whether or not they will take responsibility for the undesired outcome and learn from their error. Entrepreneurs with external locus of control are more likely to attribute bad outcomes to external factors outside of their control, whereas entrepreneurs with internal locus of control are more likely to attribute the outcomes to their own correct or incorrect decisions and actions (Jenkins and Johnson 1997; McClelland 1987). For example, an entrepreneur may believe that all the necessary actions were correct, but the market crashed, as was the case with the Internet bubble in 2000. Another entrepreneur may blame herself/himself for not being vigilant enough to sensor the approaching crisis and take action accordingly. If the entrepreneur attributes the bad outcomes to external uncontrollable factors, (s)he is unlikely to perceive error in her/his own actions and no learning will take place. If the entrepreneur takes responsibility for the outcome and continues analyzing and looking for specific reasons for the error, learning is more likely to occur. Therefore, we propose that: Proposition 5: Entrepreneurs who blame the error on their own faulty actions are more likely to learn than entrepreneurs who blame the error on external factors beyond their control Attribution of bad outcomes When outcomes depart from expectations and intentions, people normally try to explain to themselves what went wrong (Ohlsson 1996)—e.g., was the action faulty in itself or was it inappropriate for the particular situation? Explanations can vary in their complexity: some explanations are simple and straightforward, while others involve complex reasoning and require a large amount of knowledge about the domain of action. Often the action itself has a reasonable potential to produce good outcomes, but inappropriate execution can lead
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to unsatisfactory outcomes. When entrepreneurs solve a problem for the first time, they are guided by general rules rather than specialized practical knowledge that can account for all the characteristics of a particular situation. In this case, errors may occur due to overly generalized practical knowledge (Ohlsson 1996). Alternatively, entrepreneurs may incorrectly apply highly specialized and well-developed knowledge structures, which have been constructed from seemingly analogous situations. However, because entrepreneurial situations are rarely similar enough, such analogical reasoning may be inappropriate. If so, the error should be attributed to the entrepreneur’s failure to take into account the applicability constraints of the available knowledge structure, not to the over-generality of prior knowledge. At this stage, it is crucial for entrepreneurs to recognize what exactly was wrong with the knowledge and the assumptions that led to the incorrect action. Specialized knowledge structures are likely to influence the way entrepreneurs infer causality, because people search among the causal linkage they know (Fiske and Taylor 1991). Therefore, entrepreneurs can benefit from possessing more specialized knowledge, because specialized knowledge allows them to identify and use more relevant attributes for evaluation of the reasons for error. Thus, the more specialized knowledge entrepreneurs possess, the more relevant attributes they can use for evaluation. These arguments lead to the following proposition: Proposition 6: The more specialized knowledge an entrepreneur possesses regarding the domain of action, the higher the likelihood of correct attribution of the reasons for error Revision of faulty knowledge structures Learning of complex knowledge and skills involves qualitative restructuring and modification of the existing knowledge structures (Glaser and Bassok 1989). After detecting an error and attributing it to a particular action, entrepreneurs may try to repair the faulty knowledge by uncovering domain-specific knowledge which, if available earlier, would have prevented the error (Glaser and Bassok 1989). The way a faulty knowledge structure is revised depends on the flaws that are identified. As already mentioned, entrepreneurial errors can be due to applying overly generalized knowledge structures or to inappropriate application of specialized knowledge structures. Entrepreneurs often begin with general or intuitive knowledge, which is refined as learning occurs and entrepreneurs understand their environment better (Hill and Levenhagen 1995). If the practical knowledge that has led to a performance error was over-generalized, the knowledge structure needs to be refined through specialization (Anzai and Simon 1979; Anderson 1987; Langley 1985). A knowledge structure becomes more specialized by incorporating more information about the applicability conditions of a particular action to a given situation (Ohlsson 1996), meaning that new domain-specific knowledge is added to the existing knowledge structure. As a result, the old knowledge structure undergoes a transformation, expressed in a progression toward a more sophisticated knowledge structure, which is more adequate for the particular problem domain and accounts for more factors and relationships in that domain. Consequently, errors due to overly generalized knowledge structures can be corrected by specializing the old knowledge structures so that they become active only in situations for which they are appropriate.
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Alternatively, the error may be due to the incorrect application of a specialized knowledge structure developed from a seemingly analogous but not identical situation. In this case, the old knowledge structure can be revised by discarding the knowledge components that have proven to be inapplicable to the new situation and replacing them with new more relevant ones. Unlike the knowledge specialization process discussed above, knowledge updating may not produce a more complex knowledge structure. In fact, the new knowledge structure could be less complex than the previous one, if only a few new concepts are incorporated to replace the ones that have been discarded as invalid (Fiske and Taylor 1991). To sum up, faulty knowledge can be revised through specialization of overly generalized knowledge structures or through updating of already complex specialized knowledge structures. In both cases, it is crucial that new facts are gathered during the process of revision of faulty knowledge structures and that these facts are evaluated and incorporated into the revised knowledge structure. Together, the above arguments lead to the following propositions: Proposition 7: The more generalized the prior knowledge structure is, the more likely it is to be revised through specialization Proposition 8: The more specialized the prior knowledge structure is, the more likely it is to be revised through updating or adjustment to the new situation Proposition 9: The more new knowledge is acquired during the analysis of the incorrect action, the higher the likelihood of appropriate revision of the faulty knowledge structure In conclusion, when learning from performance errors, entrepreneurs can acquire additional practical knowledge in the respective domain of entrepreneurial activity either through specialization or extension of their pre-existing knowledge structures. The entrepreneurs’ knowledge structures can become increasingly complex, as they combine some of the prior knowledge with the newly incorporated knowledge about facts and relationships among them (Fiske and Taylor 1991). During the learning process the balance between generalized and specialized knowledge may change, especially when the entrepreneurs start with a limited level of specialized knowledge. It is important for an entrepreneur to posses both generalized and specialized knowledge, because generalized knowledge provides flexibility and a broader range of applicability of domain-related knowledge structures, while specialized knowledge assures depth and specificity when analyzing the reasons for the error.
Discussion This paper develops a theory of entrepreneurial learning from performance errors by introducing concepts from psychology research on individual learning to the domain of entrepreneurial activity. It extends prior research by proposing that the process of entrepreneurial learning is influenced by the characteristics of the entrepreneurs’ prior knowledge and by their cognitive biases. The proposed model incorporates three main cognitive processes—outcome generation, error detection and error
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correction. According to this model, entrepreneurial learning results in a revision of faulty knowledge structures, leading either to specialization of overly generalized knowledge structures or to refinement and extension of specialized knowledge structures. This paper makes several important contributions to entrepreneurship theory and practice. First, it draws attention to performance errors as a major source of learning for entrepreneurs—an issue that has remained largely unexplored by past research. Our theory suggests that errors are an important source of learning for entrepreneurs, because of their proliferation under the high uncertainty and ambiguity surrounding the startup process and early life of firms. Given that errors are unavoidable, they should be examined more closely by both scholars and practitioners, in order to capture the learning opportunities they provide. Second, the model developed in this paper extends the current state of knowledge by providing a more thorough and precise picture of how exactly entrepreneurs learn. Specifically, our model articulates the processes that take place and the factors that impact the extent to which entrepreneurs can learn from a given error. Importantly, unlike psychological research that treats each step of the learning process as predetermined, our model depicts each step as a probability event, the likelihood of which is determined by the entrepreneurs’ domain-specific knowledge and attributional style. Third, this paper integrates concepts from prior research in cognitive psychology, entrepreneurship, management, and organization that have not been related before in a coherent model of entrepreneurial learning. Psychology research has studied the processes of error-detection and error-correction in laboratory (experimental) settings where these processes occur by design. Therefore, this research has not analyzed the factors that may influence the likelihood that the learning processes actually take place in a real-life situation characterized by high uncertainty and ambiguity. Entrepreneurship scholars, on the other hand, have identified numerous problems faced by entrepreneurs, such as high uncertainty, time pressures, task novelty and complexity, that lead to cognitive biases and performance errors (Baron 1998; Jenkins and Johnson 1997) but have not looked at these errors as learning opportunities. This paper brings the two bodies of research together and takes a step further to examine under what conditions and how exactly entrepreneurs can learn from their performance errors. Future research directions An important direction for future research is to empirically examine the proposed processes and learning outcomes at different stages of the model developed in this paper. In testing this model, researchers have to be aware of several potential challenges. First, researchers need to identify the appropriate methods for capturing the different elements of the model, which describe both individual entrepreneurs’ strategic choices, individual-level cognitive processes, and their aggregate effects on the accumulated entrepreneurial knowledge. To address this challenge, we recommend that researchers use multi-method approaches, since different methodologies are better suited to capture different empirical phenomena. Case studies, already extensively used by entrepreneurship researchers, enable comprehensive
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analysis of the various relationships presented in the model because they provide an excellent means for examining complex processes that are diffused over time and place (Hargadon and Douglas 2001; Rindova and Kotha 2001; Rindova et al. 2007). Laboratory experiments that have been used by cognitive psychology scholars to study error-learning by students (Ohlsson 1996) can also be fruitfully deployed in studying entrepreneurial cognition and learning. Computer simulations provide a set of tools for studying how the processes we discuss unfold over time (Adner 2002). Because the model we propose breaks down the cognitive processes through which entrepreneurs learn from performance errors into discrete stages and outcome possibilities, the model enables the design of relatively straightforward computer simulations that can examine patterns of entrepreneurial learning under different degrees of prior knowledge and levels of discrepancy between outcomes and expectations. Another empirical challenge with testing the proposed model arises from the unobservable variables included in the model, such as knowledge structures, interpretation of outcomes, and attributional style. The unobservable variables discussed in the model can be operationalized and measured using various methods established by psychology research, such as verbal protocol analysis, questionnaires, and Likert-type scales (Fiske and Taylor 1991; Ohlsson 1996). Another important direction for future research is to explore the applicability of the model developed in this paper to a variety of entrepreneurial contexts, including both startup and corporate entrepreneurship contexts, because according to the broader definition of entrepreneurship, managers in established firms can also engage in entrepreneurial activities (Covin and Miles 1999, 2007; Kuratko et al. 2005; Morris et al. 2008). Therefore, the model developed in this paper may also apply to entrepreneurs in existing organizations—i.e., managers and other organizational members who perform novel tasks or otherwise engage in entrepreneurial behaviors. For example, the members of a research and development team who work on developing a new technology are likely to make multiple errors along the way, so the model of entrepreneurial learning developed in this paper may apply to them as well. Similarly, in high velocity markets characterized by high levels of innovation activity (Eisenhardt 1989), managers may use failed new product introductions (as one instance of error) to learn from them. More generally, similar error learning processes may occur in relatively unstructured or uncertain situations, in which individuals engage in creative or novel activities. For example, a scholar starting new research project or using new analytical methods may encounter numerous unexpected and unpredictable problems, thus behaving as an entrepreneur rather than as a manager of the project. Therefore, future research should test the model proposed here in a variety of entrepreneurial situations in order to establish the scope of its validity and applicability.
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