A theory of entrepreneurial learningfrom performance errors

Antoaneta P. Petkova

# Springer Science + Business Media, LLC 2008

Abstract This paper develops a theory of entrepreneurial learning from perfor-mance errors. The paper explains how entrepreneurs generate outcomes, and basedon these, detect and correct errors in their own knowledge about the activitiesinvolved in creating and operating a new venture. The model developed in this paperreflects the major cognitive functions leading to outcome generation, error detectionand error correction. We draw testable propositions about the effects of entrepre-neurs’ domain-specific knowledge and cognitive ability on each stage of the learningprocess, which ultimately determine how much the entrepreneurs can learn from agiven performance error.

Keywords Entrepreneurial learning . Performance errors . Profitable opportunities

“…entrepreneurship is a process of learning and a theory of entrepreneurshiprequires 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 testedform but instead it must be created (Aldrich 2000). For example, a newly startedventure needs profit, power, visibility, and market share, which present theentrepreneurs with the problem how to achieve all these desirable goals whileavoiding negative experiences (Weick 1979). In order to achieve these goals,entrepreneurs need to learn how to supply the new venture with resources, such as

Int Entrep Manag JDOI 10.1007/s11365-008-0075-2

A. P. Petkova (*)College of Business, San Francisco State University, 353 Business Building, 1600 Holloway Avenue,San Francisco, CA 94132, USAe-mail: apetkova@sfsu.edu

financial capital, qualified personnel, technology, strategic partnerships, andcustomer goodwill (Zimmerman and Zeitz 2002). According to Block and McMillan(1985: 2), “Starting a new business is essentially an experiment. Implicit in theexperiment are a number of hypotheses (commonly called assumptions) that can betested only by experience.” Therefore, the entrepreneurial process has beenconceptualized as an inherently dynamic process of experimentation and learning(Cope 2005; Harrison and Leitch 2005).

Although extant entrepreneurship research focuses primarily on organizationalleaning at the level of new ventures or even populations of new ventures (Caves1998; Dutta and Crossan 2005; Lumpkin and Lichtenstein 2005; Pakes and Ericson1998), scholars have also pointed to the need to analyze the process ofentrepreneurial learning at the level of the individual entrepreneur (Cope 2001;Cope 2005; Corbett 2005; Krueger 2007; Politis 2005). Researchers have looked atindividual differences (Corbett 2005) and critical “learning events”, such assignificant successes and failures (Cope 2001, 2005; Minniti and Bygrave 2001;Reuber and Fischer 1999) that can impact substantively the entrepreneurial learningprocess. We extend this research by focusing in greater depth on errors as one typeof event that occurs quite often during the startup process, because the task noveltyand the lack of experience often put entrepreneurs in a situation of high potential forerrors. For example, the high mortality rates of young firms observed byentrepreneurship scholars (Aldrich 2000; Reynolds and White 1993) suggest thatmany entrepreneurs either fail to learn during the start-up process or they learn toolate. Given that the entrepreneurial process is intertwined with ongoing mistakes andlearning on part of the entrepreneurs, it is critically important for both researchersand practitioners to understand what factors trigger entrepreneurial learning, howexactly entrepreneurs learn, and what conditions determine how much they can learnfrom a given experience.

A careful review of the literature on learning in psychology and management andorganization theory suggests three major sources of learning: (a) learning byrepetition of efficient practices (“learning by doing”), (b) memorizing newinformation as a result of training or tutoring, and (c) replacement of incorrectknowledge and practices with new ones based on negative feedback. First,behavioral learning theories suggest that an individual’s experience in a givenproblem-solving domain increases efficiency (the so called ‘learning-by-doing’models). By performing the same task multiple times, individuals have theopportunity to find the most efficient way of performing the task and to achievemastery in the skills necessary for performing the task (Anzai and Simon 1979).Such learning involves some experimentation but at the same time requires repetitionof a particular task over and over again. Usually the outcomes of such tasks are welldefined and measurable, which allows for clear feedback and evaluation of the levelof efficiency (Anzai and Simon 1979; March 1991). Experiential learning modelsrepresent the most widely adopted perspective in organization and managementtheory, because they are well-suited for explaining the emergence and change oforganizational routines, as well as other processes of organizational learning (Cyertand March 1963; March 1991).

This perspective reflects the learning processes of established organizations andtheir 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 chancesof repeatedly performing the same task are relatively lower compared to the typicalmanager or employee in an established organization. Although some entrepreneurialactivities may be performed multiple times (for example, after recruiting severalpeople an entrepreneur may become better at selection and recruitment), suchactivities are fewer than the more novel activities performed by entrepreneurs.However, the behavioral learning theories have been embraced by prior entrepre-neurship research for their focus on action as a trigger of learning. Indeed,entrepreneurship scholars converge around the idea that entrepreneurs learn bydoing, because the startup process in and of itself is a process of trial and error (Copeand Watts 2000; Cope 2005; Politis 2005; Smilor 1997). We extend these ideas byelaborating on the role of negative outcomes (as one particular result ofentrepreneurial action) in the process of entrepreneurial learning.

Second, researchers in education and psychology have focused on developingmore effective instruction methods and motivating subjects to learn. Scholars havelooked at improvement in performance speed and/or level of memorizing whenperforming relatively simple tasks, such as reading-comprehension, arithmetic skills,geometric proofs, and computer programming (see Glaser and Bassok 1989 for areview). At the theoretical level, researchers have tried to explain the processes ofencoding of new information and its retrieval from memory (see Horton and Mills1984 for a review). Although these studies are interesting and informative, theyprovide little insights into how entrepreneurs learn from real life experiences. Incontrast to the controlled simple task environment of the training laboratory, wherestudents encounter the same problems over and over again, the typical problem-solving situation faced by an entrepreneur is characterized by high level ofuncertainty and ambiguity, ill-defined goals, difficult to interpret outcomes, and,most importantly, no information regarding “the right answer”. Therefore, themainstream education and psychology research on learning is not directly applicableto studying entrepreneurial learning.

Third, a sub-stream of psychology research focuses on error-based training and onlearning triggered by performance errors (Gully et al. 2002; Ohlsson 1996; Stiso andPayne 2004). Scholars from this perspective have developed models of learningcharacterized by: (1) a well defined task, (2) clear standards for determining howappropriate the answers/outcomes are, and (3) immediate specific feedback tostudents, including what they did right or wrong and what the correct answer is. Asdiscussed above, these conditions hardly hold in any entrepreneurial situation.However, the major concepts and cognitive processes identified by these studiesprovide useful grounds for developing a model of entrepreneurial learning fromperformance errors.

In sum, this review of the extant learning literature shows that error learning hasbeen largely overlooked by both entrepreneurship research and management andorganization theories, thus making performance errors the least studied source oflearning. The wide adoption of behavioral learning models and the relative neglectof error-based learning models could be explained with the fact that mostorganizational members work in rather predictable environments and hardlyencounter 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 re-evaluation 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 outcomesthan the members of established organizations. Past entrepreneurship researchsuggests that entrepreneurs face situational factors such as high uncertainty, highnovelty, time pressure and information overload (Baron 1998). According to Aldrich(2000: 96), “During the founding process, founders must cope with informationoverload and uncertainty, severe time pressures and high level of emotionalinvolvement”. Such dynamic environments put pressure on entrepreneurs to act fastrather than correct, which in turn increases dramatically the likelihood of errors. AsShaver and Scott (1991: 35) explain: “… before there can be a new organization, thefounder-to-be must at minimum develop and test prototypes, conduct appropriatemarket research, create the standard financial projections, and construct a businessplan suitable for securing venture capital. Rarely is each of these activities completedto the founder’s satisfaction on the first pass.” Given the high uncertainty of mostentrepreneurial activities coupled with the high likelihood of errors under conditionsof high uncertainty, it is reasonable to assume that entrepreneurs are more prone tomaking errors than managers or employees in established organizations. If this is thecase, errors may provide a much more important source of entrepreneurial learningthan currently acknowledged. Therefore, entrepreneurial learning from performanceerrors may be an important yet understudied issue that merits research attention.Specifically, it is important to understand how entrepreneurs can learn from theirerrors—a process that often goes hand in hand with the acquisition of new skills andcapabilities in novel and uncertain situations.

This paper addresses the following research question: How can entrepreneurslearn from their own performance errors? We answer this question by developing amodel of entrepreneurial learning from performance errors, which explains howentrepreneurs generate outcomes, and based on them can detect and correct flaws intheir own knowledge regarding the activities involved in creating and operating anew venture. The model describes the major cognitive functions leading to outcomegeneration, error detection and error correction. Figure 1 illustrates the proposedmodel and relationships. The model developed in this paper extends psychology

P1 P4 P6 P7, P8, P9

Error Detection Error Correction

P2, P3 P5

Domain-Specific Knowledge Structures

Interpret the

outcomes

Compare outcomes to expectations

Detect error

Assign blame

Attribute bad

outcomes

Revise faulty knowledge structure

Revised knowledge General knowledge Specialized knowledge

Attributional style

Activate possible actions

Select a course of

action

Formulate thegoal

Outcomes- Importance- Magnitude

Actions

Fig. 1 A model of entrepreneurial learning from performance errors

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models of error-based learning by proposing that entrepreneurs’ prior knowledge andcognitive biases can play a significant role at each stage of the learning process andmay determine whether the processes of error detection and error correction that leadto learning will actually occur.

This paper makes several important contributions to understanding entrepreneur-ial learning. First, it draws attention to performance errors as a major source oflearning for entrepreneurs, an issue that has remained largely unexplored by pastresearch. Second, the model developed in this paper extends the current state ofknowledge by providing a deeper understanding of how entrepreneurs can learnfrom their performance errors and by articulating the factors that determine to whatextent entrepreneurs would learn from a given error. Third, the model developed inthis paper incorporates basic cognitive processes identified by psychologyresearchers together with cognitive biases found in the context of entrepreneurshipto develop specific testable propositions regarding the factors that may influence theprocess of entrepreneurial learning.

The paper proceeds with a brief explanation of the major concepts relevant forunderstanding the process of entrepreneurial learning—errors, prior knowledge, andcognitive biases. Next, we develop a process-model of entrepreneurial learning fromperformance errors, describing the stages of: (1) generation of entrepreneurialoutcomes, including the choice and performance of entrepreneurial actions, (2) errordetection, preceded by interpretation of outcomes and comparison of outcomes toexpectations, and (3) error correction, including blame assignment, attribution of badoutcomes, and revision of faulty knowledge structures. We describe each of thesefunctions as a step-by-step process and draw propositions about the impact ofentrepreneurial knowledge and cognition on the learning process and outcomes.1

The paper concludes with a discussion of some implications of the proposed modeland directions for future research.

Factors influencing entrepreneurial learning

Performance errors as triggers of learning

Assuming that people generate knowledge through experience, scholars haveproposed that past entrepreneurial experience can serve as a major source oflearning for entrepreneurs (Aldrich 2000; Minniti and Bygrave 2001). However,across various samples and empirical settings, studies consistently report non-significant effects of founders’ past entrepreneurial experience on the performance ofsubsequent ventures (Chandler and Jansen 1992; Davidsson and Honig 2003;Westhead and Wright 1998; Wright et al. 1997; Shane and Stuart 2002). Mostsurprisingly, entrepreneurs who succeed with their first venture often fail with thesecond one (Starr and Bygrave 1992), which suggests that entrepreneurs may notlearn simply by doing things. These controversial findings call for a more carefulexamination 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 notdistinguished between positive and negative experiences. Specifically, it may be thecase that entrepreneurs learn more from failure than from success. Although errorsare often associated with stress, frustration, and perception of helplessness (Ivancicand Hesketh 1995/1996; Nordstrom et al. 1998), they may play an important role inthe process of entrepreneurial learning, because errors can alert entrepreneurs ofincorrect assumptions and beliefs (Daft and Weick 1984; Smith et al. 1997) and cantrigger a process of elaborate analysis that leads to the development of newknowledge.

Past research provides indications that errors can play an important role instimulating entrepreneurial learning, because negative outcomes force people toreevaluate previously held knowledge and expectations (Fiske and Taylor 1991;Gatewood et al. 2002). Entrepreneurship scholars have identified “near-to-failureexperience” (Guth et al. 1991) and “major setbacks” (Reuber and Fischer 1993) aspowerful incentives for entrepreneurs to reconsider their assumptions and adjusttheir expectations. For example, Naffziger et al. (1994) propose that negativeoutcomes cause changes in entrepreneurs’ behavior and may even lead todiscontinuation of entrepreneurial activities. Similarly, Gatewood et al. (2002) findthat subjects lower their expectations regarding future startups after receivingnegative feedback.

Positive outcomes, on the other hand, lead entrepreneurs to persist with theirselected course of action (Naffziger et al. 1994). Further, entrepreneurs tend tooverexploit actions that initially have generated desirable outcomes (Minniti andBygrave 2001), which may lead to overgeneralization and a failure to adapt to moredynamic situations. This conclusion is consistent with Sitkin’s (1992) idea thatcontinuous success might be a liability because “failure to fail” can restrictindividuals from exploring alternatives, inhibit risk taking, and lead to complacency(Gully et al. 2002). Work by Dormann and Frese (1994) also indicates thatavoidance of errors may reduce exploratory behavior and development of newknowledge. Together these studies suggest that outcomes meeting or exceedingentrepreneurs’ expectations reassure entrepreneurs that they are doing well andprovide limited learning incentives, because positive outcomes make entrepreneursoverconfident in what they are doing. Errors, on the other hand, may triggerlearning, because negative outcomes call for change and provide incentives forentrepreneurs 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) stockof knowledge, accumulated through past experience (Cope 2005; Politis 2005;Reuber and Fischer 1999). This individual knowledge is organized into knowledgestructures. A knowledge structure is “a mental template that individuals impose onan information environment to give it form and meaning” (Walsh 1995: 281).Different knowledge structures refer to different domains of activity (Fiske andTaylor 1991; Walsh 1995). When dealing with a specific problem, people evoke theknowledge 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 willbe interpreted and incorporated into individuals’ memory (Fiske and Taylor 1991). Itis important to note that in this paper we treat as distinct concepts (a) the experienceof an entrepreneur, (b) the knowledge acquired by the entrepreneur as a result ofcertain experience, and (c) the learning process itself, consistent with Politis (2005).

Entrepreneurial decisions are a function of two types of knowledge: specializedand generalized. Specialized knowledge refers to technical aspects of the chosenmarket—it can be both product-specific and industry-specific (Minniti and Bygrave2001). Generalized knowledge refers more broadly to the domain of entrepreneurialactivities that are similar across markets and determines to what extent anentrepreneur knows “how to be entrepreneurial” (Minniti and Bygrave 2001).Although many entrepreneurs may possess both generalized knowledge aboutentrepreneurship and entrepreneurial activities (Aldrich 2000) and specializedknowledge about a particular technology, a resource, or a customer need (Hayek1945), it is likely that entrepreneurs differ in the degree to which they possess eachof these two types of knowledge.

Specialized knowledge possessed by entrepreneurs has a profound effect on theirsearch and discovery processes, as well as on their decisions to exploit anopportunity (Venkataraman 1997). Specialized knowledge determines the types ofopportunities that entrepreneurs discover and the ways they organize their newventures to exploit those opportunities (Azoulay and Shane 2001; Shane 2000). Forexample, many high technology new ventures are started by leading engineers fromestablished firms, who were involved in the invention and subsequently formed anew enterprise to explore the opportunity, based on this invention (Christensen andBower 1996). Generalized knowledge guides entrepreneurs in the non-technicalaspects of the startup process. According to Harrison and Leitch (2005), such non-technical entrepreneurial knowledge includes general awareness of the existing marketopportunities, competences in acquiring venture financing, and capabilities to managethe enterprise from startup to maturity. Both specialized and generalized knowledgecan influence entrepreneurs’ decisions and actions and their subsequent learning.

Prior research on the role of generalized and specialized knowledge suggests thatentrepreneurs need both types of knowledge. We extend these ideas to develop morespecific arguments about the effects of generalized versus specialized knowledge ineach stage of the learning process. Specifically, in the context of entrepreneuriallearning generalized knowledge provides flexibility and a broader range ofapplicability of domain-related knowledge structures, while specialized knowledgeassures depth and specificity when analyzing the reasons for an error. Therefore, wepropose that generalized knowledge may facilitate the detection of errors (e.g., wheninterpreting the outcomes), whereas specialized knowledge may be helpful forappropriate attribution of the reasons for the errors to occur.

Cognitive biases

Entrepreneurs do not follow rational (normative) thinking models but rather tend touse cognitive shortcuts called heuristics (Baron 1998; Mitchell et al. 2007). The useof 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 forentrepreneurs, because heuristics help entrepreneurs economize on cognitive effortsand may still lead to superior decisions (Mitchell et al. 2007). However, when usedinappropriately, heuristics may become biases that lead to inaccurate processing ofinformation and suboptimal decision making (Bazerman 2001). Prior studies havefound that entrepreneurs exhibit various biases such as overconfidence, illusion ofcontrol, reasoning by analogy, and the law of small numbers (Keh et al. 2002; Simonand Houghton 2002). Entrepreneurship scholars explain entrepreneurs’ susceptibilityto 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 thatentrepreneurs acting under high uncertainty—i.e., in smaller, younger, and pioneer-ing 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 priorentrepreneurial experience and training (education) can alleviate or reinforce some ofthese biases.

Two types of individual biases are particularly relevant for the model developedin this paper because of their potential effects on the error-correction stage: the self-serving attribution bias and the individual attributional style (Fiske and Taylor 1991).The self-serving attribution refers to individuals’ propensity to attribute positiveoutcomes to their own merits, while blaming negative outcomes to uncontrollableexternal factors (Bazerman 2001; Fiske and Taylor 1991). Attributional style refersto individuals’ tendency to make similar causal inferences over time and acrossdifferent situations (Metalsky and Abramson 1981). Entrepreneurs with externallocus of control are more likely to attribute the outcomes of a given activity toexternal factors outside of their control, whereas entrepreneurs with internal locus ofcontrol are more likely to attribute the same outcomes to their own decisions andactions (Jenkins and Johnson 1997; McClelland 1987). Importantly, such biases tendto persist and change only to a limited degree as a result of experience (Krueger2007; Parker 2007). For example, Parker (2007) found that entrepreneurs give muchgreater weight to their prior beliefs than to new information when forming theirexpectations. Further, Krueger (2007) argues that prior success can lead to evenstronger internal attributions among entrepreneurs. Therefore, biases are likely toaffect the way entrepreneurs interpret their errors and the possibility to learn fromthose 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 thepsychological literature on errors and failure-driven learning (Berkson andWettersten 1984; Gully et al. 2002; Ohlsson 1987; Schank 1986; Stiso and Payne2004). 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 negativeoutcomes tend to be more salient to entrepreneurs than positive ones (Reuber andFischer 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 areerrors, compare errors to correct actions, and update knowledge structures according-ly” (Stiso and Payne 2004: 3). The model developed in this section incorporates themajor cognitive processes that lead to error detection and error correction. We furtherextend the ideas of psychology scholars by proposing that certain characteristics ofentrepreneurs’ prior knowledge and cognitive biases can influence the differentstages of the learning process. These arguments are summarized in specific testablepropositions.

Generation of entrepreneurial outcomes

According to prior research, learning from performance errors can occur only aftersome 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 actionthat can lead to unexpected or undesirable outcomes. According to Jenkins andJohnson (1997), an entrepreneurial outcome represents a desired level of financialperformance in the business. More generally, entrepreneurial outcomes could be bothtangible, such as organization creation, value creation, innovation, growth, profit,sales, and market share (Gartner 1990; Kuratko and Hornsby 1997; Shane andVenkataraman 2000), and intangible, such as entrepreneurs’ intrinsic rewards(Kuratko and Hornsby 1997).

Goal formulation Entrepreneurship by definition is a purposeful, goal directed typeof activity, associated with the exploitation of potentially profitable opportunitiesthat are relevant for the entrepreneur (Naffziger et al. 1994; Shane and Venkataraman2000). Consequently, entrepreneurs initiate a particular course of action with certainexpectations of the desirable outcomes. Prior research has found that entrepreneurspursue both extrinsic goals (e.g., income, personal wealth, and other materialrewards) and intrinsic goals (e.g., satisfaction, independence, excitement, andchallenge) (Kuratko and Hornsby 1997; Naffziger et al. 1994). Entrepreneurialgoals can vary in their specificity and complexity, depending on the individualcharacteristics of the entrepreneur who formulates them, as well as on the situationalfactors (Shane and Venkataraman 2000).

It is important to note that the situation for which the entrepreneurial goals areformulated usually involves a certain degree of novelty for the entrepreneur. Ifentrepreneurs set out to achieve a goal that is entirely familiar and well defined, thechances of error are much lower and there would be limited learning opportunities. Onthe other hand, when entrepreneurs face a novel or unfamiliar situation, they need toengage in cognitive efforts in order to select an appropriate course of action.Furthermore, the idea of desirable outcomes that entrepreneurs have is largelydependent on their prior knowledge and cognitive characteristics. Faced with exactlythe same objective situation, entrepreneurs may perceive different profitableopportunities, which respectively would lead them to set different goals and expect-ations (Shane 2000). Depending on the specific goals that are set, entrepreneurs canthen 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 possiblecourse of action. Therefore, the choice of action involves cognitive representationsof possible alternatives and selection among them. In order to select a course ofaction, entrepreneurs first need to activate cognitively the possible alternatives byeliciting them from the knowledge structures in which the relevant information isorganized. Knowledge structures hold the repertoire of available alternatives, whichentrepreneurs have to recall and then decide to what extent they are applicable to thecurrent situation. The cognitive activation of alternatives, also referred to ascognitive search, allows people to evaluate the potential outcomes of variousalternatives without actually taking the actions and bearing the consequences ofthem (Gavetti and Levinthal 2000). Entrepreneurs can evaluate different alternativesbased on their understanding of the environment and the expected consequences ofengaging in a particular type of action. Although cognitive search provides theopportunity to explore a broad set of alternatives (Gavetti and Levinthal 2000),empirical evidence suggest that entrepreneurs do not consider all possible choicesbut instead tend to search within a relatively small amount of information (Kaish andGilad 1991).

If an entrepreneur can find analogical previous occasions, he or she may applydirectly the available knowledge template to the new situation, because entrepre-neurs tend to choose actions that replicate, or are closely related to, the ones theyhave already taken (Minniti and Bygrave 2001). However, since most entrepreneur-ial situations contain novel or unfamiliar circumstances, it is likely that the availableknowledge structures will not apply directly to the current situation. In such cases,entrepreneurs can recall the most similar prior experience and can judge byapproximation what course of action they should take (Fiske and Taylor 1991; Roschand Lloyd 1978). Such approximation might be rather coarse-grained, because thelikelihood of encountering exactly the same problem or situation is much lower foran entrepreneur than for a manager in an established organization. Consequently, thelack of a readily available knowledge structure that fits perfectly the new situationmay lead entrepreneurs to recall less appropriate knowledge structures or to applyincorrectly a knowledge structure that appears relevant. In both cases, theapproximation process increases the chances of error. This is a critical differencebetween 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 thesame task. Unlike students in training situations, who are provided with the correctanswer and become less likely to make the same mistake over time, entrepreneursoften lack information about the “correct answer”, so they may not even notice whensomething goes wrong. Moreover, the fact that each entrepreneurial situation differsfrom the previous ones increases the chances of new errors to occur. To account forthese important differences between entrepreneurial contexts and experimental/training conditions, we treat both error detection and error correction as probablerather than certain events and we analyze the specific conditions that determinewhether these events will occur.

Selection of a course of action Once various alternatives are considered, the nextstep is to select a particular course of action. Such selection could be based on themost economically-desirable expected outcomes, the most reasonable alternative

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given the resources available to the entrepreneurs, or some other relevant criteria. Ingeneral, the greater the fit between the action–outcome linkages in the entrepreneurs’knowledge structures and the “objective” reality, the more efficient the selectedactions are likely to be (Gavetti and Levinthal 2000). However, entrepreneurs oftenface novel problems or situations in which the action–outcome linkages still need tobe created. Theoretically, when people are confronted with an unfamiliar situation,they try to be accurate rather than fast (Thorngate 1976). However, entrepreneurshipresearch shows consistently the opposite pattern: because entrepreneurs face timeconstraints 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 toact upon their goals in order to achieve the desirable outcomes. Prior researchsuggests that entrepreneurial activities are complex and usually involve a number ofmutually related actions for producing a single outcome (Aldrich 2000; Block andMcMillan 1985; Carter et al. 1996; Reynolds 1997). To implement a particularaction, entrepreneurs need relevant practical knowledge about the respective domainof entrepreneurial activity (Ryle 1949). Even though the entrepreneurs may beconfident in the type of action necessary to achieve a particular goal, lack ofpractical knowledge may lead to incomplete or unsuccessful implementation of theselected course of action (Ryle 1949). Therefore, the specialized knowledgepossessed by entrepreneurs is likely to influence the degree to which they canimplement effectively a selected course of action.

Error detection

Contrary to earlier research, which assumes that entrepreneurial intentions lead todesired outcomes (Lafuente and Salas 1989), recent research suggests thatentrepreneurial intentions and outcomes are often disconnected, with intentionsleading 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 non-deliberate 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 aboveexpectations. Discrepancies between expectations and outcomes offer learningopportunities for entrepreneurs (Daft and Weick 1984), provided that theentrepreneurs become aware of these discrepancies. According to Fisher and Lipson(1986), errors reveal the existing cognitive representations of a problem-solvingstrategy and expose its flaws so that the individuals can understand the cause oferror. Thus, it is to the entrepreneurs’ advantage to discover as many sources of erroras possible, so that they can deepen their knowledge and minimize the number ofsubsequent errors. An entrepreneur can detect an error if something in the outcomeindicates that there is a discrepancy between the intended and the actual results of aparticular action. The process of error detection involves three steps: observing andinterpreting the outcomes, comparing the outcomes to the expectations, anddetecting an error.

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Observing and interpreting the outcomes Outcome interpretation is a subjectiveevaluation process guided by the individual knowledge structures of each entrepre-neur. Knowledge structures serve as a framework, which influences the manner inwhich relevant information is assimilated (Stotland and Canon 1972; Weick 1979). Inthe absence of objective measures of outcomes, entrepreneurs’ generalizedknowledge structures are likely to be their main source of judgment as to whetheran outcome is favorable or unfavorable (Ohlsson 1996; Rosch and Lloyd 1978).Generalized knowledge can provide entrepreneurs with more flexible and encom-passing ways of understanding the problem and interpreting its outcomes. Forexample, in a laboratory experiment, Boland et al. (2001) found that exposure toabstract knowledge facilitates managerial decision making on a complex task.Generalized knowledge is more helpful than specialized knowledge for making senseof ambiguous situations (Hill and Levenhagen, 1995), which makes generalizedknowledge 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 moreprecise outcome interpretations (s)he is likely to make

Comparing outcomes to expectations Environments vary in the ease and accuracywith which cause–effect or means–ends relations can be perceived and enacted inthem (Weick 1979), which makes entrepreneurs unlikely to notice and judge as anerror every discrepancy between their expectations and the actual outcomes. Forexample, if non-entrepreneurial intentions (such as sustained profitability) lead toentrepreneurial outcomes (such as sales growth), as demonstrated by some of theentrepreneurs in Jenkins and Johnson’s (1997) study, entrepreneurs may not considererrors the actions that have led to such unexpected outcomes. Clearly, if theoutcomes are better than expected, the entrepreneurs are likely to feel satisfied and tocontinue with the selected course of action (Naffziger et al. 1994). Therefore,outcomes that exceed expectations provide low incentives for entrepreneurs toanalyze the reasons why the outcomes occurred. However, if the outcomes deviatefrom expectations in a negative direction, and particularly if the deviation issignificant, entrepreneurs are likely to perceive a discrepancy between outcomes andexpectations that can motivate them to search for an explanation. Therefore, whencomparing outcomes to expectations, if no discrepancy is detected, the analyticalprocess will stop and no learning will take place. Only if an error is actuallydetected, the entrepreneurs are likely to look for the causes of the error andpotentially to learn from them.

The role of outcomes for error detection Errors are experienced as discrepanciesbetween what the entrepreneur expected to happen and what appears to be the case.As the previous discussion suggests, errors are detected by comparing the actualwith the expected outcomes. According to the subjective view of errors, actions arenot correct or incorrect by themselves, but under certain circumstances some actionsare more efficient than others (Ohlsson 1996). Consequently, a crucial condition atthe 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 theentrepreneurs’ expectations. Since not all outcomes are equally salient, theprobability of error detection can increase with the magnitude of outcomes (Fiskeand 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 venturecapitalist or other potential investors is a negative outcome of greater magnitude thanreceiving a lower than the expected amount of money. Therefore, errors of greatermagnitude will be more salient to entrepreneurs and, accordingly, more likely to bedetected. These arguments lead to the following proposition:

Proposition 2: The greater the magnitude of a negative outcome, the more likelythe entrepreneur to detect an error

Errors can occur at any stage of the entrepreneurial process, when performingactivities 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 beperceived by the entrepreneurs as more or less critical for the development andsurvival of their ventures.2 As a result, the (negative) outcomes of these activities arelikely to vary in their importance for each entrepreneur depending on theentrepreneur’s priorities and personal valuation systems (Kuratko and Hornsby1997). For example, a failure to recruit the foremost accounting authority mayappear negative, but it may not be as crucial from the entrepreneur’s perspective asthe failure to obtain financial or other resources. Because of their limited span ofattention, entrepreneurs are likely to pay greater attention to activities of higherpriority for them (Fiske and Taylor 1991) by monitoring more carefully the progressand outcomes of those activities. Therefore, entrepreneurs may be able to identifyerrors easier in areas perceived as critically important for the survival and success ofthe new venture, which leads to the following proposition:

Proposition 3: The relative importance that an entrepreneur attributes to a givenaction will be positively related to the likelihood of error-detection in thedomain of this action

The role of prior knowledge for error detection Entrepreneurs’ prior knowledge canplay the role of a baseline for evaluating the outcomes of an action, as well as forjudging the action as correct or error. On the one hand, people tend to interpret (ormisinterpret) new information in ways consistent with their existing knowledgestructures (Fiske and Taylor 1991). On the other hand, inconsistent informationcreates a sense of conflict, which stands out as a salient event and is, therefore, morelikely to attract entrepreneurs’ attention (Fiske and Taylor 1991). However, theknowledge 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 anentrepreneur may not necessarily correspond to the actual impact of those activities on the success of thenew venture. In fact, one could argue that the ability to recognize what is most critical for a new venture isa rather complex skill which is not possessed by all entrepreneurs.

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possesses it (Ohlsson 1996). Specifically, one must have previous knowledge aboutthe range of reasonable outcomes from a given course of action in order to recognizethat a particular outcome is undesirable. Given the novelty of many entrepreneurialactivities, the lack of relevant prior knowledge presents a major challenge toentrepreneurs when evaluating certain outcomes. Depending on the task novelty andthe knowledge previously accumulated, an entrepreneur’s prior knowledge in agiven domain may be more or less relevant to a particular situation. If the task athand appears similar to a previously performed one, the entrepreneur is more likelyto recall and apply an existing knowledge structure to the new situation. Whenapplying an existing knowledge structure, entrepreneurs can use analogicalreasoning to understand and interpret the outcomes of their actions and to detectan error. If the prior knowledge possessed by entrepreneurs is very general or distantfrom the current domain of action, it may be difficult to apply the existingknowledge structures to the new situation. If so, the entrepreneurs may not be able todetect an error because the existing knowledge structures would not allow them tounderstand and evaluate properly the outcomes of their actions.

Specialized knowledge is likely to help entrepreneurs detect an error by providingmore 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 Taylor1991). Entrepreneurs may vary widely in the extent to which they have developedknowledge about each particular domain of activity. For example, in comparison to anovice founder, a serial founder is likely to have better developed and moreelaborate domain-specific knowledge structures in many domains of entrepreneurialactivity (Politis 2005). If entrepreneurs have already developed certain knowledgestructures, and if the new facts that they face fit with these structures, they will beable to understand better the relationships between different concepts by buildingnew relationships among previously existing concepts (Fiske and Taylor 1991). Incase the new facts are inconsistent with previously held knowledge, the discrepancywill attract the entrepreneurs’ attention, because conflicting cues are more salientthan consistent ones and people tend to devote their limited attention to the mostsalient cues (Fiske and Taylor 1991; Rosch and Lloyd 1978). Better developed andmore elaborate knowledge structures would allow for easier detection of suchdiscrepancies, because such structures provide a greater number of attributes basedon which the level of fit (or misfit) between expectations and outcomes can beevaluated. Therefore, a higher level of specialization of entrepreneurs’ knowledge islikely to facilitate the process of error detection:

Proposition 4: The more specialized an entrepreneur’s prior knowledge in thedomain of the chosen action is, the higher the probability of error detection bythe entrepreneur

Error correction

People try to understand the causality of events in order to predict and control theoutcomes of their actions (Fiske and Taylor 1991). If an action is perceived asincorrect, 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 thefact, the faulty knowledge structure can be revised and improved. Error correctionrefers to removing flaws from the underlying knowledge structures in order toimprove future actions. Error correction consists of three cognitive processes: blameassignment, 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 forthe error to occur. Often entrepreneurs realize post-fact that they have lacked somerelevant information that could have affected their choice of action. For example, atthe outset of a new venture, many entrepreneurs start with less than adequateknowledge about how to perform various activities, such as selection andrecruitment of key personnel, raising financial resources from venture capitalistsand other investors, and building relationships with customers or partners. Since theperformance of complex tasks typically involves a large number of actions, the factthat an error is identified implies that at least one of these actions was wrong. Theterm “blame assignment” refers to the process of identifying the factors that havecontributed 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 andScott 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 takeresponsibility for the undesired outcome and learn from their error. Entrepreneurswith external locus of control are more likely to attribute bad outcomes to externalfactors outside of their control, whereas entrepreneurs with internal locus of controlare more likely to attribute the outcomes to their own correct or incorrect decisionsand actions (Jenkins and Johnson 1997; McClelland 1987). For example, anentrepreneur may believe that all the necessary actions were correct, but the marketcrashed, as was the case with the Internet bubble in 2000. Another entrepreneur mayblame herself/himself for not being vigilant enough to sensor the approaching crisisand take action accordingly. If the entrepreneur attributes the bad outcomes toexternal uncontrollable factors, (s)he is unlikely to perceive error in her/his ownactions and no learning will take place. If the entrepreneur takes responsibility forthe 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 actionsare more likely to learn than entrepreneurs who blame the error on externalfactors beyond their control

Attribution of bad outcomes When outcomes depart from expectations andintentions, people normally try to explain to themselves what went wrong (Ohlsson1996)—e.g., was the action faulty in itself or was it inappropriate for the particularsituation? Explanations can vary in their complexity: some explanations are simpleand straightforward, while others involve complex reasoning and require a largeamount of knowledge about the domain of action. Often the action itself has areasonable 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 canaccount for all the characteristics of a particular situation. In this case, errors mayoccur due to overly generalized practical knowledge (Ohlsson 1996). Alternatively,entrepreneurs may incorrectly apply highly specialized and well-developedknowledge structures, which have been constructed from seemingly analogoussituations. However, because entrepreneurial situations are rarely similar enough,such analogical reasoning may be inappropriate. If so, the error should be attributedto the entrepreneur’s failure to take into account the applicability constraints of theavailable knowledge structure, not to the over-generality of prior knowledge. At thisstage, it is crucial for entrepreneurs to recognize what exactly was wrong with theknowledge and the assumptions that led to the incorrect action. Specializedknowledge 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 relevantattributes for evaluation of the reasons for error. Thus, the more specializedknowledge entrepreneurs possess, the more relevant attributes they can use forevaluation. These arguments lead to the following proposition:

Proposition 6: The more specialized knowledge an entrepreneur possessesregarding the domain of action, the higher the likelihood of correct attributionof the reasons for error

Revision of faulty knowledge structures Learning of complex knowledge and skillsinvolves qualitative restructuring and modification of the existing knowledgestructures (Glaser and Bassok 1989). After detecting an error and attributing it to aparticular action, entrepreneurs may try to repair the faulty knowledge by uncoveringdomain-specific knowledge which, if available earlier, would have prevented theerror (Glaser and Bassok 1989). The way a faulty knowledge structure is reviseddepends on the flaws that are identified. As already mentioned, entrepreneurial errorscan be due to applying overly generalized knowledge structures or to inappropriateapplication of specialized knowledge structures.

Entrepreneurs often begin with general or intuitive knowledge, which is refined aslearning occurs and entrepreneurs understand their environment better (Hill andLevenhagen 1995). If the practical knowledge that has led to a performance errorwas over-generalized, the knowledge structure needs to be refined throughspecialization (Anzai and Simon 1979; Anderson 1987; Langley 1985). Aknowledge structure becomes more specialized by incorporating more informationabout the applicability conditions of a particular action to a given situation (Ohlsson1996), meaning that new domain-specific knowledge is added to the existingknowledge structure. As a result, the old knowledge structure undergoes atransformation, expressed in a progression toward a more sophisticated knowledgestructure, which is more adequate for the particular problem domain and accounts formore factors and relationships in that domain. Consequently, errors due to overlygeneralized knowledge structures can be corrected by specializing the old knowledgestructures 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 specializedknowledge structure developed from a seemingly analogous but not identicalsituation. In this case, the old knowledge structure can be revised by discarding theknowledge components that have proven to be inapplicable to the new situation andreplacing them with new more relevant ones. Unlike the knowledge specializationprocess discussed above, knowledge updating may not produce a more complexknowledge structure. In fact, the new knowledge structure could be less complexthan the previous one, if only a few new concepts are incorporated to replace theones that have been discarded as invalid (Fiske and Taylor 1991).

To sum up, faulty knowledge can be revised through specialization of overlygeneralized knowledge structures or through updating of already complexspecialized knowledge structures. In both cases, it is crucial that new facts aregathered during the process of revision of faulty knowledge structures and that thesefacts 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 morelikely it is to be revised through specialization

Proposition 8: The more specialized the prior knowledge structure is, the morelikely 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 theincorrect action, the higher the likelihood of appropriate revision of the faultyknowledge structure

In conclusion, when learning from performance errors, entrepreneurs can acquireadditional practical knowledge in the respective domain of entrepreneurial activityeither through specialization or extension of their pre-existing knowledge structures.The entrepreneurs’ knowledge structures can become increasingly complex, as theycombine some of the prior knowledge with the newly incorporated knowledge aboutfacts and relationships among them (Fiske and Taylor 1991). During the learningprocess the balance between generalized and specialized knowledge may change,especially when the entrepreneurs start with a limited level of specializedknowledge. It is important for an entrepreneur to posses both generalized andspecialized knowledge, because generalized knowledge provides flexibility and abroader range of applicability of domain-related knowledge structures, whilespecialized knowledge assures depth and specificity when analyzing the reasonsfor the error.

Discussion

This paper develops a theory of entrepreneurial learning from performance errors byintroducing concepts from psychology research on individual learning to the domainof entrepreneurial activity. It extends prior research by proposing that the process ofentrepreneurial learning is influenced by the characteristics of the entrepreneurs’prior knowledge and by their cognitive biases. The proposed model incorporatesthree main cognitive processes—outcome generation, error detection and error

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correction. According to this model, entrepreneurial learning results in a revision offaulty knowledge structures, leading either to specialization of overly generalizedknowledge structures or to refinement and extension of specialized knowledgestructures.

This paper makes several important contributions to entrepreneurship theory andpractice. First, it draws attention to performance errors as a major source of learningfor 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 surroundingthe startup process and early life of firms. Given that errors are unavoidable, theyshould be examined more closely by both scholars and practitioners, in order tocapture the learning opportunities they provide.

Second, the model developed in this paper extends the current state of knowledgeby providing a more thorough and precise picture of how exactly entrepreneurslearn. Specifically, our model articulates the processes that take place and the factorsthat impact the extent to which entrepreneurs can learn from a given error.Importantly, unlike psychological research that treats each step of the learningprocess as predetermined, our model depicts each step as a probability event, thelikelihood of which is determined by the entrepreneurs’ domain-specific knowledgeand attributional style.

Third, this paper integrates concepts from prior research in cognitive psychology,entrepreneurship, management, and organization that have not been related before ina coherent model of entrepreneurial learning. Psychology research has studied theprocesses of error-detection and error-correction in laboratory (experimental) settingswhere these processes occur by design. Therefore, this research has not analyzed thefactors that may influence the likelihood that the learning processes actually takeplace in a real-life situation characterized by high uncertainty and ambiguity.Entrepreneurship scholars, on the other hand, have identified numerous problemsfaced by entrepreneurs, such as high uncertainty, time pressures, task novelty andcomplexity, that lead to cognitive biases and performance errors (Baron 1998;Jenkins and Johnson 1997) but have not looked at these errors as learningopportunities. This paper brings the two bodies of research together and takes astep further to examine under what conditions and how exactly entrepreneurs canlearn from their performance errors.

Future research directions

An important direction for future research is to empirically examine the proposedprocesses and learning outcomes at different stages of the model developed in thispaper. In testing this model, researchers have to be aware of several potentialchallenges. First, researchers need to identify the appropriate methods for capturingthe different elements of the model, which describe both individual entrepreneurs’strategic choices, individual-level cognitive processes, and their aggregate effects onthe accumulated entrepreneurial knowledge. To address this challenge, werecommend that researchers use multi-method approaches, since different method-ologies 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 anexcellent means for examining complex processes that are diffused over time andplace (Hargadon and Douglas 2001; Rindova and Kotha 2001; Rindova et al. 2007).Laboratory experiments that have been used by cognitive psychology scholars tostudy error-learning by students (Ohlsson 1996) can also be fruitfully deployed instudying entrepreneurial cognition and learning. Computer simulations provide a setof tools for studying how the processes we discuss unfold over time (Adner 2002).Because the model we propose breaks down the cognitive processes through whichentrepreneurs learn from performance errors into discrete stages and outcomepossibilities, the model enables the design of relatively straightforward computersimulations that can examine patterns of entrepreneurial learning under differentdegrees of prior knowledge and levels of discrepancy between outcomes andexpectations. Another empirical challenge with testing the proposed model arisesfrom the unobservable variables included in the model, such as knowledgestructures, interpretation of outcomes, and attributional style. The unobservablevariables discussed in the model can be operationalized and measured using variousmethods 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 ofthe model developed in this paper to a variety of entrepreneurial contexts, includingboth startup and corporate entrepreneurship contexts, because according to thebroader definition of entrepreneurship, managers in established firms can alsoengage in entrepreneurial activities (Covin and Miles 1999, 2007; Kuratko et al.2005; Morris et al. 2008). Therefore, the model developed in this paper may alsoapply to entrepreneurs in existing organizations—i.e., managers and otherorganizational members who perform novel tasks or otherwise engage inentrepreneurial behaviors. For example, the members of a research and developmentteam who work on developing a new technology are likely to make multiple errorsalong the way, so the model of entrepreneurial learning developed in this paper mayapply to them as well. Similarly, in high velocity markets characterized by highlevels of innovation activity (Eisenhardt 1989), managers may use failed newproduct introductions (as one instance of error) to learn from them. More generally,similar error learning processes may occur in relatively unstructured or uncertainsituations, in which individuals engage in creative or novel activities. For example, ascholar starting new research project or using new analytical methods may encounternumerous unexpected and unpredictable problems, thus behaving as an entrepreneurrather than as a manager of the project. Therefore, future research should test themodel proposed here in a variety of entrepreneurial situations in order to establishthe scope of its validity and applicability.

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