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]
Int Entrep Manag J
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,
Int Entrep Manag J
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-
Int Entrep Manag J
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
Int Entrep Manag J
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.
Int Entrep Manag J
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-
Int Entrep Manag J
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;
Int Entrep Manag J
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
Int Entrep Manag J
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.
Int Entrep Manag J
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.
Int Entrep Manag J
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
Int Entrep Manag J
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.
Int Entrep Manag J
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
Int Entrep Manag J
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
Int Entrep Manag J
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.
Int Entrep Manag J
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
Int Entrep Manag J
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
Int Entrep Manag J
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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