A Meta-Analytic Review of Effectuation and Venture Performance

Abstract

Though much research in entrepreneurship makes the fundamental assumption that opportunities are found, new work is emerging which questions this core tenet. Effectuation, for example, positions the entrepreneur as co-creator of opportunities, together with committed stakeholders. In this study, we conduct a meta-analysis of the articles published in the Journal of Business Venturing, summarizing data on 9897 new ventures to connect three of the principles of effectuation positively with new venture performance. In so doing, we offer both specific insight into precisely measuring effectuation and a general method for extracting variables from prior work to measure new constructs.

Full text

A meta-analytic review of effectuation and venture performance
Stuart Read
a,
⁎, Michael Song
b,1
, Willem Smit
a,2
a
IMD, Chemin de Bellerive 23, P.O. Box 915, CH-1001 Lausanne, Switzerland
b
Charles N. Kimball, MRI/Missouri Endowed Chair in Management of Technology and Innovation & Professor of Marketing, 318 Bloch School,
5110 Cherry Street, Henry W. Bloch School of Business and Public Administration, University of Missouri-Kansas City, Kansas City,
MO 64110-2499, United States
Received 1 November 2006; received in revised form 1 February 2008; accepted 1 February 2008
Abstract
Though much research in entrepreneurship makes the fundamental assumption that opportunities are found, new work is
emerging which questions this core tenet. Effectuation, for example, positions the entrepreneur as co-creator of opportunities,
together with committed stakeholders. In this study, we conduct a meta-analysis of the articles published in the Journal of Business
Venturing, summarizing data on 9897 new ventures to connect three of the principles of effectuation positively with new venture
performance. In so doing, we offer both specific insight into precisely measuring effectuation and a general method for extracting
variables from prior work to measure new constructs.
© 2008 Elsevier Inc. All rights reserved.
Keywords: Effectuation; Discovery; Creation; Performance; Meta-analysis
1. Executive Summary
It is useful, when interpreting the findings from entrepreneurship research, to understand the underpinnings of the
work. One of the core assumptions common to much published research on entrepreneurship is that the task of the
entrepreneur is to discover opportunities and exploit them. When new venture creation is viewed from that perspective,
it is easy to see the importance of ideas such as entrepreneurial alertness and entrepreneurial orientation. However,
there is new thinking that approaches the challenge of venture creation from a different perspective. Effectuation, for
example, assumes not that opportunities are waiting to be discovered, but that opportunities emerge when created by an
entrepreneur and her partners. In this context, a series of different ideas become important in understanding new
venture creation. Ideas such as what each player brings to the opportunity creation process, how each player manages
risk, and how flexible all players are when faced with the surprises that challenge a start-up, offer insight to the aspiring
entrepreneur.
A
vailable online at www.sciencedirect.com
Journal of Business Venturing xx (2008) xxx –xxx
JBV-05457; No of Pages 19
⁎Corresponding author. Tel.: +41 21 618 01 11; fax: +41 21 618 07 07.
E-mail addresses: stuart.read@imd.ch (S. Read), songmi@umkc.edu (M. Song), willem.smit@imd.ch (W. Smit).
1
Tel.: +1 816 235 5841; fax: +1 816 235 6529.
2
Tel.: +41 21 618 01 11; fax: +41 21 618 07 07.
0883-9026/$ - see front matter © 2008 Elsevier Inc. All rights reserved.
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In this study, we seek to measure the relationship between effectual principles and new venture performance. We do
this by examining every study presented in every issue of this journal and carefully selecting variables that measure one
of the effectual principles presented in Table 1 of this article. Our effort yields two useful practical results. The first is a
precise measurement of effectuation. Starting with the core theoretical principles from Table 1, we have refined the
operationalization of effectuation to a level where independent researchers can systematically identify specific venture
features as effectual. In addition to offering clear constructs to researchers conducting future study of effectuation, this
precision can also aid the practical entrepreneur in implementing an effectual strategy. From our investigation, we
highlight the following refinements to the core definition of effectuation in Table 1:
•Means: while each individual is endowed with a wide range of means, only those that are relevant to the venture
constitute effectual means and should be considered when measuring new venture performance against effectual
strategy.
•Partnerships: an entrepreneur or a venture may build many relationships, but only those in which both parties share
the risk of the venture and benefit from the success of the venture constitute effectual partnerships.
•Affordable Loss: what matters in affordable loss is not the risk inherent in the industry or the individual venture, but
whether the entrepreneur manages that risk by attempting to measure upside opportunity potential, or effectually
considering the worst-case scenario.
•Leverage Contingency: having a business plan does not imply a lack of ability to leverage contingency –the
important issue is the entrepreneur's willingness to change when confronted with new information, means or
surprises.
The second useful practical result of this study is a quantitative analysis of the relationship between effectual
principles and new venture performance. Our findings from a sample of 9897 new ventures spanning industries,
geographies, time and individual founders indicate that all the heuristics which describe effectuation except Design,
which we were not able to measure, and Affordable Loss, which returned insignificant results, are positively and
significantly related to new venture performance.
2. Introduction
While much study in the area of entrepreneurship seems focused on “finding”and exploiting existing opportunities,
Sarasvathy (2001a) offers the alternative view that opportunities come to be when they are “co-created”by the
entrepreneur and her committed stakeholders. The notion of effectuation opens intriguing potential to rethink how we
teach and research entrepreneurship, but there is currently no information on whether it actually generates positive
outcomes for startups. The consequent research question for our study is a deceptively simple one: is there a general
connection between effectuation and new venture performance? Though easily stated, the complexity around our question
emerges from two sources. The first is that effectuation, although well developed theoretically, has yet to be measured
empirically. To complete our investigation, we would have to learn how to operationalize effectuation precisely. The
second is that we seek to measure effectuation against new venture performance generally –across time, individuals,
industries and geographies –to offer a generalizable result. In order to assemble a data set of the scale that matches our
aspirations, we would have to create a way to benefit from prior work of our colleagues in the entrepreneurship domain.
The presentation of our investigation includes three basic elements. We begin by reviewing the entrepreneurship
literature through the “found versus made”lens to determine whether effectuation offers a genuinely novel basis for
entrepreneurial study. Consistent with Alvarez and Barney (2005), we find the historical focus of the entrepreneurship
literature centered on the process of discovery (Kirzner, 1979). This effort enables our first contribution, a clear picture of
the foundations of entrepreneurship research along the dimensions of positioning (where opportunities are found) and
construction (where opportunities are made).
The second element articulates our research method. Our desire to use the work of our colleagues suggested meta-
analysis. A meta-analytic approach enables researchers to summarize the results of numerous studies investigating the
same phenomena. However, effectuation had not been investigated before we set off on our quest. Solving this problem led
us to our second contribution, the articulation of a systematic methodology for selecting variables from prior studies to
measure new constructs. Combining meta-analysis with the idea of inter-rater reliability (James et al., 1984) and learning
and holdout samples, we developed and documented an approach that yielded 94 variables from 48 studies. The results of
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our analyses of these data suggest positive relationships between new venture performance and all the effectual constructs
we were able to measure, except Affordable Loss. These findings contribute to the entrepreneurship discussion, offering
insight into the utility of effectuation in particular, and offering the first meta-analysis of new venture performance factors
we believe the field has seen.
Perhaps more important than the quantitative results is the third element of our investigation, a description of what we
learned in the process of measuring effectuation. Effectuation touches ideas that have been part of the entrepreneurship
discussion for years, demanding that operationalization of effectuation be thoughtful and precise in order to distinguish it
clearly from prior work. Our contribution in this area is a specific set of guidelines for what empirically represents each of
the theoretical heuristics associated with effectuation. It is our intention that these guidelines will benefit anyone
investigating effectuation, regardless of method, enabling the field to advance quickly in the study of effectuation in
specific and of the “made”view of entrepreneurship in general.
3. Literature review
It is broadly acknowledged that the search for a distinctive theory of entrepreneurship (Shane and Venkataraman, 2000;
Phan, 2004) continues. One of the explanations for why scholars have been able to gain little ground on a theory of
entrepreneurship may rest in the underlying “found or made”question (Alvarez and Barney, 2005, in press; Miller, 2007).
While researchefforts that assumeopportunities are found and exploited by alert entrepreneurs (McMullen and Shepherd,
2006) can be traced back to Kirzner (1973), Alvarez and Barney contend that, in reality, most entrepreneurship effort is
undertaken in an uncertain environment (Knight, 1921), where entrepreneurial strategies of creation are at work. They
further argue that entrepreneurship research has concentrated on discovery, and that an insufficient body of knowledge
exists about the potentially more relevant issue of how entrepreneurs create opportunities.
In an effort to examine the veracity of Alvarez and Barney's claim, we seek to project the major themes within the
entrepreneurship literature against a backdrop that will let us evaluate an emphasis on a discovery or creation-oriented
foundation inrelation to existing work. Fortunately, the effort ofempirically determining seven major areas of convergence
within the entrepreneurship literature (Gregoire et al., 2006) has already been done, so we build upon that effort, focusing
on the theoretical foundations of each convergence area. Below, wetrace in more detail the theoretical roots specific to each
major convergence area so that we may determine assumptions that pervade the entire area.
To construct a backdrop, we identified a recent framework used by Wiltbank et al. (2006) to review the strategic
management literature along the dimensions of control and prediction. This approach enables a clear distinction between
positioning strategies, intended to orient a firm within an exogenous environment (opportunities found), and construction
strategies, intended to shape an endogenous environment (opportunities made). This framework is relevant not only because it
enables us to orient existing work along the dimensions of discovery and creation, but also because Wiltbank et al. (2006)
explicitly draw a positive theoretical connection between construction strategies and uncertain environments such as those
faced by entrepreneurs. The result offers a descriptive summary of the foundations of the major convergence areas in
entrepreneurship research today, organized according to theoretical foundation across the dimensions of control and
prediction.
We proceed with a brief discussion of the seven major convergence areas in entrepreneurship research (Gregoire,
et al., 2006), positioning each within the Wiltbank framework, and directing the reader interested in more detail to
Gregoire et al.'s (2006) thorough treatment of the topic (areas ordered alphabetically).
Dynamics of new venture emergence: starting with the foundation of Penrose (1959), this stream of entrepreneurship
research has combined her work with that of Schumpeter (1942) to develop two areas of entrepreneurship theory,
connected by a common foundation. These are the resource base view (Barney, 1991) and the population ecology view
(Aldrich and Auster, 1986). Despite their differences, these views share common assumptions in that neither is reliant
on prediction or control to account for advantage. Whether a firm possesses superior resources or not, advantage is
derived largely from the adaptability of the organization in this formulation.
Factors and dynamics of new venture performance: on the strength of Porter's (1980) corporate strategy work, the
theme common to this convergence area is the role industry plays in venture success (Hobson and Morrison, 1983).
Upon that belief, strategies for best exploiting current and future industry structure are proposed and prediction offers a
key lever for the entrepreneur.
Firm-level behaviors:Schumpeter's (1934) treatise can be traced forward to current discussions regarding the
construct of Entrepreneurial Orientation (Lumpkin and Dess, 1996). The foundation of this view casts the entrepreneur
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as uniquely able to “carry out new combinations”(Schumpeter, 1934), though those new combinations are created to
fulfill existing needs. Therefore, while the entrepreneur is good at predicting what will be successful, she is also good at
constructing a solution. As such, we place this convergence area near the middle of our framework.
Identification and exploitation of opportunities: starting with Kirzner (1973, 1979), a body of scholars has
developed the notion of Entrepreneurial Alertness (Kaish and Gilad, 1991). The similarity in name to Entrepreneurial
Orientation is deceptive, as Entrepreneurial Alertness views the entrepreneur as a gifted and perceptive identifier of
opportunity that exists within the environment, and a determined pursuer of that existing opportunity.
Individual characteristics: early study in the field was based on McClelland's (1961) expectation that there must be
some significant psychological difference between entrepreneurs and the general population, but current
entrepreneurship researchers have largely abandoned this view. The lack of empirical evidence for such a notion
has shifted focus elsewhere. For the purposes of our review, however, if such evidence were found, it would rely on
neither prediction nor control to explain the entrepreneurial process.
Social networks, social capital:Aldrich and Zimmer (1986) highlight the importance of social networks to
management in general, and Birley (1985) has applied the concept to entrepreneurship in particular. And while interest
in social networks has waxed and waned over the years, the foundation, rooted in the sociology literature, has
remained. This foundation suggests that what entrepreneurs learn from social networks provides them with an
advantage in positioning for an existing opportunity.
Venture capital: the final area of convergence in the entrepreneurship literature, initiated by MacMillan et al. (1985),
seeks to understand the role of venture capital in the entrepreneurial process. Like the social networks area, this stream
suggests that entrepreneurs gain innovation and network advantages from association with venture capitalists, which
enable entrepreneurs to effectively position for an existing opportunity.
3.1. Existing literature focused on positioning
Our findings in the literature review are consistent with scholars who have noted a research bias toward opportunity
discovery in entrepreneurship research (Alvarez and Barney, 2005). Clearly, there is existing work that would fall into
the construction half of Wiltbank et al.'s (2006) framework. But that work is more likely to represent individual novel
ideas as opposed to significant bodies of research where numerous scholars have converged around a core foundation
(Gregoire et al., 2006). Our next question revolves around whether convergence on positioning is appropriate. Is the
positioning half of the framework where significant aspects of entrepreneurial advantage can be explained? Is there any
advantage at all to be considered in the construction half of the framework?
3.2. Effectuation
In an effort to pursue that question, we introduce effectuation. Effectuation was induced from empirical studies
of entrepreneurship as a form of expertise (Sarasvathy, 2001a) under uncertainty (Knight, 1921). Drawn from
Simon's (1981) work in The Sciences of the Artificial, the effectual process of non-predictive design positions the
manager of a new venture as discounting prediction, as it does not account for the future impact of her actions on
her new venture. She seeks to shape the future of her product, firm and market in conjunction with her partners and
through her own actions. Described as a set of heuristics for decision making in uncertain environments, effectual
Table 1
Basic principles of effectual thought (Sarasvathy and Dew 2005)
Issue Effectual principle
View of the future Design. The future is contingent on actions by willful agents.
Givens Means provide the basis for decisions and new opportunities. 3 subconstructs: —What I know —Who I am —
Whom I know.
Attitude toward others Partnership. Build your market together with customers, suppliers and even prospective competitors.
Predisposition toward risk Affordable Loss. Calculate downside potential and risk no more than you can afford to lose.
Predisposition toward
contingencies
Leverage Contingency. Surprises can be positive. Leverage them into new opportunities.
Underlying logic To the extent that we can control the future, we don't need to predict it.
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reasoning consists of strategies that combine available means with unanticipated contingencies to construct a series
of stakeholder commitments. Effectuation has seen gathering interest in theoretical discussions relating to
management (Augier and Sarasvathy, 2004; Sarasvathy, 2001a)aswellaseconomics(Dew et al., 2004)and
psychology (Sarasvathy, 2003). And although effectuation was developed around the new venture creation setting,
it has more recently been extended to address finance (Sarasvathy and Wiltbank, 2002) and innovation questions
(Dew and Sarasvathy, 2001).
3.3. Principles of effectuation
The principles of effectuation are presented in Table 1. Each of the five principles represents an approach to decision
making that does not rely on prediction, instead assuming the impact of willful individual creation.
From a practical standpoint, effectuation is an appealing lens in the new venture setting as it provides normative
approaches to problem solving designed to be functional in uncertainty. In contrast, strategies rooted on the positioning
side of Fig. 1 require the decision maker to a) have access to historical information or analogous situations that allow
her to anticipate a likely future, and b) bet that the future will be sufficiently like the past so predictions based on
historical data will be well informed. The effectual principle of Design guides the entrepreneur to incorporate the
impact of her actions on the ultimate outcome of the environment. Means provide the decision-maker with a basis for
direction, suggesting that opportunities emerge from the knowledge, contacts and resources at hand. Partnership
suggests that new opportunities may be created as a result of the additional means provided by new stakeholders.
Affordable Loss encourages entrepreneurs to incorporate the possible downside in evaluating alternatives so that
opportunity failure will not result in greater venture or personal failure. And where the future is not predictable, the
entrepreneur should seek to Leverage Contingency, finding new possibilities from surprises –even negative surprises.
Several useful thought experiment examples of how firms are effectually created are available in Sarasvathy's (2001a)
theory exposition.
Fig. 1. Theoretical convergence areas in entrepreneurship research, across dimensions of prediction and control.
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3.4. Effectual model
As effectuation provides heuristics specifically intended for uncertainty, an environment in which many
entrepreneurs operate (Alvarez and Barney, 2005; Sarasvathy, 2001b), we start with the expectation that effectual
strategies should benefit new venture performance. Further, effectuation was derived from a model of expert
entrepreneurial action. The psychology literature suggests that experts –individuals who have acquired unique pattern
matching and pattern recognition skills (Chase and Simon, 1973) through years of deliberate practice (Ericsson et al.,
2006)–outperform the general population within their domain (Ericsson and Lehmann, 1996). Given that effectuation
reflects the heuristics of expert entrepreneurs within their domain, our central hypothesis posits a positive link between
effectual approaches and new venture performance. Our model is presented in Fig. 2.
4. Method
It was our objective to assemble a data set of relationships between effectual constructs and new venture
performance measures across time, geographies, industries and individual founders. Wanting to benefit from and
summarize the extensive empirical effort in the entrepreneurship area, we were attracted to meta-analysis (Lipsey and
Wilson, 2001). Meta-analysis offers a heuristic for estimating effect size relationships between effectual constructs and
new venture performance using previous study results. As effectuation had not been explicitly measured, we devised an
approach for selecting existing studies from the literature, a method that had to be a) effective: the criteria for study
selection had to identify the data necessary to analyze the relationship between the individual principles behind
effectuation and their relationship with new venture performance; b) transparent: both process and criteria had to be
clear enough so that the reader interested in measuring the construct of effectuation could reliably repeat what we did,
and so that a researcher interested in using the approach to measure some other new construct could also use the
generalized process; and c) measurable: there had to be some quantitative way to know whether we had “gotten it
right,”a measure which would stand the scrutiny of scientific rigor.
3
Our approach, diagrammed in Fig. 3, integrates
the idea of inter-rater reliability (James et al., 1984) with the idea of learning and holdout samples in order to establish a
systematic way of building a meta-analysis using variables from prior studies to measure new constructs.
As illustrated in Fig. 3, our approach was to consider the entire body of work included in every issue of the Journal
of Business Venturing. We split the volumes into two groups: 2007–1996, representing the learning sample against
which we would refine our initial search criteria; and 1995–1985, representing the holdout sample against which we
would test our final search criteria.
Fig. 2. Theoretical model of an effectual approach on new venture performance. Notes: 1. Hypothesized relationships are marked with a (+) 2. Meta-
analytic effect sizes are presented in parentheses inside construct boxes 3. We were not able to measure constructs denoted in boxes with dashed lines
4. Significance annotated as: * (p≤.05), ** (p≤.01), *** (p≤.001).
3
We would like to express our appreciation to two anonymous reviewers and the editor who encouraged us to develop and document a rigorous
and measurable approach to study selection, and we hope it will be of value to future researchers interested in measuring and testing new constructs
using existing variables from the literature.
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4.1. Initial search criteria
Before searching any articles, two of the authors agreed on the following initial search criteria:
Search the Journal of Business Venturing between 1996 and 2007 on all combinations of the following performance
terms (performance, ROI, return on investment, sales growth, revenue growth, ROA, survival, return on assets, return
on equity, ROE, employee growth); firm description terms (new, small, early, early stage, fledgling, emerging); and
terms for firms (venture, firm, startup, company, companies). Next, search the reference lists of all the articles identified
during the first step as a means of both identifying other relevant work within the Journal of Business Venturing and
validating the relevance of work chosen. This initial search must identify every article that meets two criteria:
1) the dependent variable in the piece operationalizes some aspect of venture performance; and
2) there is a correlation table (that includes both independent and dependent variables).
Then identify the studies from that set that operationalize an independent variable matched to one of the effectual
constructs taken from Sarasvathy and Dew (2005) and described in Table 1.
Against these criteria, two of the authors independently reviewed the 403 articles in the Journal of Business Venturing
between the years 1996 and 2007. One of the authors identified 47 studies, and the other identified 16, where every one of the
16 overlapped with the 47. As the results revealed weak inter-rater agreement (James et al., 1984)of0.51ontheoverlapin
Fig. 3. Process model for extracting existing variables into analysis of a new construct.
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articles selected, the authors identified numerous areas where the initial search criteria could be refined. These refinements are
described in the results section. Based on the revised criteria, both authors agreed to select 35 of the studies from the learning
sample and proceeded to review the holdout sample independently −290 articles in the Journal of Business Venturing
between the years 1995 and 1985. One of the authors identified 13 studies and the other identified 12, where every one of the
12 selected overlapped with the set of 13. The authors agreed to use all 13. The refined criteria, detailed in the results section,
resulted in strong inter-rater agreement (James et al., 1984) of 0.96 in the holdout sample.
4.2. Data staging
We then organized the 48 studies (35 studies from 1996−2007, plus 13 studies from the holdout sample, 1985−1995) into
different groups around effectual constructs, and combined the results from different studies using meta-analysis (Hunter and
Schmidt, 1990; Lipsey and Wilson, 2001). To measure the association between new venture performance and one of the
principles, we used a standard meta-analysis statistic, the correlation coefficient. In indicated instances, to preserve statistical
independence, we calculated an average correlation from a single citation that provided two or more correlations with
performance and similar measures of a given effectual approach (Hunter and Schmidt, 1990: 476). We did the same for studies
that provided correlations of an effectual approach with more than one performance measure meeting our criteria. Following
data organization, the literature suggests a next step of making corrections to the individual observations to ensure consistency
in measurement across studies (Hedges and Olkin, 1985; Hunter and Schmidt, 1990; Wolf, 1986). We applied corrections to
all independent and dependent variables that were based on subjective measures.
4.3. Reliability correction for subjective measures
As we were able to obtain reliability measures for every study in which subjective measures were used, we corrected
directly for variable measurement error in correlation using Hunter and Schmidt's (1990) construct validity correction
according to the following formula:
r¼
r0
a1a2
where: rdenotes corrected correlation; r
0
denotes the raw Pearson correlation between variable 1 and variable 2; a
1
denotes the value of Cronbach's αfor reliability of variable 1; a
2
denotes the value of Cronbach's αfor reliability of
variable 2.
4.4. Mean correlation: random-effects model
Continuing to follow Hunter and Schmidt (1990), we computed a mean correlation for each meta-analysis using the
random-effects model, weighting each study according to sample size and using a random effects model as follows:
P
Y¼
PWcYc
PWc
where: Y
¯denotes population effect size across studies in the analysis; W
c
denotes the reciprocal of individual study
effect size variance; Y
c
denotes individual study effect size.
The random-effects model is more conservative than the alternative fixed-effects model, and we report our numbers using
a random-effects model. We ran the same analyses using the fixed-effects model and found all results unchanged.
4.5. Dependent measure validation test
We were concerned that the wide range of dependent variable measures used across our set of studies might bias the
results, so we conducted a test to validate our findings. We eliminated all studies with perceptual or subjective measures
of financial firm performance, and with measures not specific to quantitative firm performance. We then ran our meta-
analyses on just the subset. The results for meta-analyses of Means—What I know (relevant and irrelevant), Means—
Who I am (relevant and irrelevant), and Means—Whom I know were unchanged. Our measure of Partnership was
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reduced to seven studies and became non-significant; our measure of Leverage Contingency was reduced to three
studies, also returning non-significant results. In both cases, effect size was still positive, but the analyses lacked power.
Affordable Loss remained non-significant. Overall, this test provides some assurance that our results were not biased by
performance measures that reflect too broad a set of outcomes.
4.6. Observed variable measurement error validation test
Scholars with significant experience in meta-analytic methods have suggested that observed variables (not latent
constructs) might not be 100% reliable. In order to conduct a test that assumes there is measurement error in our
observed variables, we recalculated all correlations between observed dependent and independent variables using an
assumed average accuracy of 0.80 (Dalton et al., 2003) and ran all our meta-analyses again. While several outcomes
shifted in significance from (pb0.001) to (pb0.01), our results were not significantly changed, excepting Contingency,
which lost significance, moving from (p= 0.049) to (p= 0.067) giving us some assurance that the accuracy of observed
variable measurement did not generate bias in our meta-analyses.
5. Results
For each effectual construct, we discuss both the quantitative results of the meta-analysis as well as the qualitative
elements learned about measuring effectuation. Table 2 presents a summary of the meta-analyses, organized by
effectual principle. As we were unable to identify existing measures of the effectual principle of Design, we present
meta-analyses of the effectual principles of Means (all three aspects), Partnership,Affordable Loss and Leverage
Contingency.InTable 3, we report performance measures, specific measures of the effectual construct and number of
observations in a study, and the corrected correlation between the construct and performance.
5.1. Means
Following effectuation, we measure and analyze the three aspects of means, articulated as what I know, who I am
and whom I know, starting with the first.
Measuring Means—What I know: our review revealed two potential categories of Means—What I know −those that
might be relevant to the focal venture and those that are not. Our interpretation of effectuation is that only the means in
the former category, the type of knowledge that can be classified as domain-specific expertise (Ericsson et al., 2006), is
relevant to an effectual approach. For the purposes of our study selection and meta-analysis, we included both
categories of means, coding those related to the focal venture as “relevant”and the rest as “irrelevant.”The means we
coded as “relevant”included: a) entrepreneurial experience, b) experience in the industry where the startup is operating,
c) experience in the functional area where the individual is operating in the startup, d) partner expertise (Lu and
Beamish, 2006), and e) human capital if it was related to entrepreneurship, the industry in which the startup is operating
or the individual's functional area. All other experience was coded as “irrelevant”to the focal venture. “Irrelevant”
Table 2
Summary of meta-analysis of new venture performance and each effectual principle
Effectual principle Basic statistics Correlation and 95% confidence
interval
Test of null (2 tail)
Number of studies Total of samples from studies Point estimate Lower
limit
Upper
limit
Z-value P-value
Means—What I know (relevant) 24 5145 0.115 0.040 0.190 2.994 0.003
Means—What I know (irrelevant) 8 1095 0.098 0.039 0.157 3.213 0.001
Means—Who I am (relevant) 10 1892 0.230 0.109 0.344 3.679 0.000
Means—Who I am (irrelevant) 12 1814 0.085 0.029 0.140 2.961 0.003
Means—Whom I know 14 2329 0.112 0.043 0.179 3.194 0.001
Partnership 14 3196 0.169 0.003 0.326 1.998 0.046
Affordable Loss 4 783 −0.019 −0.208 0.172 −0.193 0.847
Leverage Contingency 5 712 0.074 0.000 0.148 1.967 0.049
All models are random-effects, results are unchanged using fixed-effects models, and significance values are based on two-tailed tests.
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Table 3
Detailed inventory of new venture performance and effectual principle measures for meta-analysis
Study Performance measures Effectual principle measures R/I⁎nCorrected
effect
Means (What I know)
(Barney et al., 1996) Firm performance (revenue/employee at first
round financing)
Industry experience R 205 −0.120
(Beckman et al., 2007) Firm size Startup and exec experience R 161 0.060
(Begley, 1995) Growth, ROA and debt Industry experience R 114 0.015
(Box et al., 1994) Growth in employment Startup experience R 103 0.238
(Carter et al., 1997) Venture termination Startup and industry experience 203 0.185
(Chaganti and Schneer,
1994)
Return on assets and total sales Number of years of industry experience R 345 0.070
(Chandler and Jansen,
1992)
Profitability and growth Startup experience R 38 0.071
(Ciavarella et al., 2004) Venture survival Startup and industry experience R 111 0.040
(Davidsson and Honig,
2003)
Sales, profitability and completion Previous startup experience R 380 0.165
(De Clerq and Sapienza,
2006)
Perceived performance (sales and market share) CEO experience in new ventures R 298 −0.050
(Dimov and Shepherd,
2005)
Homerun and strikeout percent Law, finance, consulting and
entrepreneur experience
R112−0.060
(Doutriaux, 1992) Corporate sales in year 8 Functional area experience R 30 0.080
(Florin, 2005) Growth, wealth and tenure Startup and industry experience R 277 0.0967
(Haber and Reichel,
2007)
Employees, revenues and tourism Previous entrepreneurial experience R 305 −0.040
(Higashide and Birley,
2002)
Perceptual measure Entrepreneurial team competencies
(6 items)
R 57 0.680
(Jones et al., 2001) Market, financial and product performance Internally available resources R 188 0.533
(Lerner and Haber, 2001) Profitability (perceptual) Domain-specific experience R 53 0.290
(Lerner et al., 1997) Gross revenues Startup and industry experience R 218 0.320
(Lichtenstein et al.,
2007)
Positive cash flow Early activity (specific to venture) R 109 0
(Lu and Beamish, 2006) Longevity and profitability Partners' host country experience R 522 −0.100
(McGee and Dowling,
1994)
Sales growth Technical and industry experience R 210 0.220
(Ohe et al., 1992) Success index Marketing, manufacturing and technology R 38 0.129
(Shane and Delmar,
2004)
Venture failure (reverse coded) Startup and industry experience R 223 0.008
(Thornhill, 2006) Revenue growth Technical staff as percent of workforce R 845 −0.020
(Anna et al., 2000) Sales Human and economic competences I 103 0.165
(Beckman et al., 2007) Firm size Team functional diversity I 161 0.083
(Begley, 1995) Growth, ROA and debt Education I 114 0.070
(Chandler and Hanks,
1998)
Earnings and growth Human capital I 102 0.150
(Chandler and Jansen,
1992)
Profitability and growth Experience, education and competence I 38 0.110
(De Clerq and Sapienza,
2006)
Perceived performance (sales & market share) CEO experience (trichotomous) I 298 0.100
(Honig, 1998) Profit and employees Education I 215 0.080
(Honig, 2001) Log of annual profit Work, education and college I 64 0.058
Means (Who I am)
(Bamford et al., 2006) Net interest margin Assets at founding R 798 0
(Carter et al., 1997) Venture termination (reverse coded) Access to credit resources R 203 −0.010
(Chandler and Hanks,
1998)
Growth and earnings Initial capital R 102 0.110
(Doutriaux, 1992) Corporate sales in year 8 Initial capital R 30 0.640
(Honig, 1998) Profit and employees Initial capital R 215 0.265
(Honig, 2001) Log of annual profit Capital R 64 0.490
(McGee and Dowling, 1994) Sales growth Assets R 210 0.120
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Table 3 (continued)
Study Performance measures Effectual principle measures R/I⁎nCorrected
effect
Means (What I know)
(Ohe et al., 1992) Success index Capital R 38 0.250
(Zahra and Bogner,
2000)
Investors' return on equity R&D investment R 116 0.350
(Zahra and Bogner, 2000) Sales, growth and profit Internal R&D investments R 116 0.290
(Chaganti and Schneer, 1994) Sales and ROA Firm age I 345 0.040
(Barney et al., 1996) Firm performance (revenue/employee at first
round financing)
Overall team tenure I 205 0.010
(Begley, 1995) Growth, debt and ROA 2 personality attributes and firm age I 114 0.008
(Begley and Boyd,
1987)
Firm size and firm age 5 personality attributes I 147 0.002
(Box et al., 1994) Employment growth Locus of control and achievement I 103 0.039
(Carter et al., 1997) Venture termination (reverse coded) Firm age I 203 0.088
(Chandler and Hanks,
1994)
Revenue and growth Firm age and capabilities I 155 0.288
(Ensley et al., 2002) Sales, growth and profit Firm age, size and TMT size I 192 0.070
(Honig, 2001) Log of annual profit Business age I 64 0.020
(Miner et al., 1995) Sales, growth and profit Patents (non-specific to venture) I 72 0.170
(Zahra and Bogner,
2000)
Sales, growth and profit Firm age and internal
corporate entrepreneurship
I 116 0.065
(Zahra and Garvis,
2000)
ROA, sales and profit Global scope, firm age and corp ent I 98 0.280
Means (Whom I know)
(Beckman et al., 2007) Firm size Team size at start 161 0.115
(Begley, 1995) Growth, debt and ROA Firm size 114 0.023
(Carter et al., 1997) Going out of business (W) Firm and startup team size 203 0.085
(Chaganti and Schneer,
1994)
Sales and ROA Firm size 345 0.046
(Davidsson and Honig,
2003)
Sales, profitability and completion Parents, friends and network 380 0.177
(Ensley et al., 2002) Growth, sales and profit Firm and startup team size 192 0.067
(Fombrun and Wally,
1989)
Profit and growth Firm size 95 0.03
(George et al., 2002) Net sales and products on the market Number of university links 147 −0.15
(Honig, 2001) Log of annual profit Number of employees 64 0.53
(Lerner et al., 1997) Size, profitability, revenues and income Advisors and networks 218 0.07
(Walter et al., 2006) Sales growth and sales per employee Network capabilities 149 0.27
(Zahra, 1996b) Profit and productivity R&D spending and quality (external) 47 0.144
(Zahra and Bogner,
2000)
Profitability and sales growth Number of employees 116 0.15
(Zahra and Garvis,
2000)
ROA, sales and profit Company size 98 0.125
Contingency
(Chaganti and Schneer,
1994)
ROA and sales Customization 345 0.08
(Ciavarella et al., 2004) Venture survival Openness and agreeableness 111 −0.05
(Covin and Slevin,
1990)
Performance Organicity 143 0.08
(Ensley et al., 2006) Sales, growth and employees Transformational leadership style 66 0.184
(Zahra, 1996b) Profitability and productivity Willingness to modify products 47 0.158
Partnership
(Bamford et al., 2004) Deposit and loan growth Outside members of the board 490 0.22
(Barney et al., 1996) Firm performance (revenue/employee at first
round financing)
Number of VC board seats 205 0.07
(continued on next page)
Means ( Who I am )
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Means —What I know identified in our literature review included: a) gender, b) personality, c) early activity, d)
consensus, e) monitoring, f), morale, g) strategic orientation (Durand and Coeurduroy, 2001), h) diversity, and i)
management skills (Ensley et al., 2006).
Results of Means—What I know: from our literature search, we identified 24 studies –representing an overall
sample size of 5145 firms –that measure the effect of “relevant”Means—What I Know on new venture performance.
Meta-analysis of these data showed that “relevant”Means—What I know were significantly (effect size = 0.115,
p= 0.003) and positively related to new venture performance, supporting our central hypothesis. The “irrelevant”
Means—What I know measures we identified in 8 studies, representing 1095 observations, were also significantly
(effect size = 0.098, p= 0.001) and positively related to new venture performance. While both results are highly
significant, we note with interest that the effect size for “relevant”Means—What I know is stronger than the effect for
“irrelevant.”
Measuring Means—Who I am: each individual possesses a certain assortment of resources, some of which enable
opportunities and some of which constrain opportunities. In the case of founding teams, we assumed individual means
accrue to the firm as a whole. For example, if one individual in a founding team holds a patent, that means is of use to the
firm. Means—Who I am that we found “relevant”in the context of an effectual approach to starting a new venture included:
a) capital, b) assets, c) technological capabilities in technology-related businesses, d) internal R&D investments, and
e) patents related to the business. Means—Who I am that we coded as “irrelevant”in the context of effectuation included:
a) firm age, b) global scope, c) international corporate entrepreneurship, d) locus of control, e) need for achievement,
f) patents in general, g) resource-based capabilities, h) risk taking propensity, i) tolerance of ambiguity, j) type A
personality, k) overall team tenure, and l) self-efficacy.
One of the questions we faced in coding Means—Who I am was whether means antithetical to effectuation should be
reverse scored and included. For example, efficacy in planning (Anna et al. 2000) contradicts the effectual heuristic of starting
with Means, and so efficacy in planning might represent weakness in using a means-based approach. It was our thinking,
however, that because planning could just as easily work alongside effectual heuristics as be antithetical to them, it would be
Table 3 (continued)
Study Performance measures Effectual principle measures R/I⁎nCorrected
effect
Means (What I know)
(Doutriaux, 1992) Corporate sales in year 8 Level of founder ownership 30 0.04
(Folta et al., 2006) Public and private equity offerings Number of alliances 789 0.61
(Fombrun and Wally,
1989)
Growth and profit Equity ownership 95 0.02
(George et al., 2002) Net sales and products on the market Number of links (alliances) 147 0.32
(Hatfield and Pearce,
1994)
Partner goals and satisfaction Overlap in partners' goals 60 0.232
(Higashide and Birley,
2002)
Perceptual measure Policy and goal conflict with VC
(reverse coded)
57 0.03
(Lu and Beamish, 2006) Longevity and profitability Partners' equity ownership 522 0.01
(McGee and Dowling,
1994)
Sales growth Cooperative partnerships 210 −0.22
(Weaver and Dickson,
1998)
Firm size and financial strength Number of alliances 252 0.12
(Zahra, 1996b) Profitability and productivity Commercialization with partners 47 0.13
(Zahra, 1996a) Return on assets Use of external technology sources 176 0.32
(Zahra and Bogner,
2000)
Investors' return on equity Reliance on external sources of
technology
116 0.3
Affordable Loss
(Bamford et al., 2004) Deposit and loan growth Liquidity and leverage risk position 490 −0.061
(Fombrun and Wally,
1989)
Profit and growth Risk taking (reverse scored) 95 −0.090
(Ohe et al., 1992) Success index Risk distribution 38 0.460
(Tan, 2007) Performance and profitability Defensiveness and (reverse coded)
risk taking
160 −0.220
⁎R= relevant to effectuation, I = irrelevant to effectuation (applies only to Means—What I know and Means—Who I am).
Partnership
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impossible to determine how and whether efficacy in planning related to effectuation, and consequently we excluded the
measure from our study.
Results of Means—Who I am: from our literature search, we identified 10 studies –representing a sample size of 1892
firms –that measure the effect of “relevant”Means—Who I am on new venture performance. Our meta-analyses of
these data revealed that “relevant”Means—Who I am were significantly (effect size = 0.230, p= 0.000) and positively
related to new venture performance, lending further support to our central hypothesis. A meta-analysis of measures of
“irrelevant”Means—Who I am from 12 studies, representing 1814 firms, showed that the construct was also
significantly (effect size = 0.085, p= 0.003) and positively related to new venture performance. Again, we note with
interest that while both results are significant, the “relevant”Means—Who I am demonstrate a stronger main effect than
the “irrelevant”Means—Who I am.
Measuring Means—Whom I know: the third category of means articulated by effectuation is the founding team's
network, individuals and entities which might offer opportunities and resources to the venture. As with the previous
two categories of means, we focused on elements relevant to the focal venture. These included variables such as a)
entrepreneurial parents, b) friends in the business, c) business network, d) number of university links, e) social capital,
f) network capabilities, g) firm size, h) team size, and i) R&D partnerships for technology firms. We categorized all of
these as “relevant,”regardless of whether the venture started with these means or the stakeholders joined in the course
of building the venture. In this category, the difficulty was not in identifying “relevant”and “irrelevant”means, but in
distinguishing Means—Whom I know from the effectual construct of Partnership. Based on our interpretation of
effectuation, we separated Means—Whom I Know from Partnership based on whether success is dependent on the
other party (usually identified as money, equity or products having changed hands). In our understanding of
effectuation, the people a founder knows who provide access to other means and new opportunities defines a Means,
but financial commitment with risk and reward shared defines a Partnership.
Results of Means—Whom I know: from our literature search, we identified 14 studies –representing an overall sample
of 2329 firms –that measure the effect of Means—Whom I know on new venture performance. As all the studies we
identified presented means relevant to the new venture, and consistent with the effectual definition of Whom I know, we
conducted a meta-analysis of these data and found Means—Whom I know significantly (effect size= 0.112, p=0.001)and
positively related to new venture performance. This finding is consistent with our central hypothesis and lends support to
the overall effectual expectation of the importance of the effectual notion of Means to new venture performance.
5.2. Partnership
Effectuation departs somewhat from the mainstream literature on normative corporate strategy in its recommendation that
entrepreneurs minimize competitive orientation and instead build firm and market in partnership with committed external and
internal stakeholders. The end result of the creation effort is shaped and defined by the very addition of partners to the process.
Each partner brings new means and new opportunities that the effectual founder continues to sculpt into a coherent product,
firm or market.
Measuring Partnership: starting with the idea that in effectual Partnership both parties must share in the risk and the
gain from venture success, we realized that the construct could be applied to the firm both exogenously (example:
partnerships with other firms, customers, standards bodies, etc.) and endogenously (example: partnerships with
employees), and we included both in our analyses. We identified strictly transactional relationships (example: licensing
and/or purchase of technology (Jones et al., 2001)) as “irrelevant”because it was hard to evaluate whether these “arm's-
length”partners shared in both risk and reward.
Two elements from the literature we excluded from the construct of Partnership were balance between partners and
competitive aggressiveness (as an inverse). The idea of balance between partners (Pearce and Hatfield, 2002) offers insight
into the relationship in general, but does not address the effectual question of whether both parties share in risk and reward.
Coming to this conclusion initiated an interesting question about the nature of relationships of effectual players with other
effectual players, versus relationships between effectual players and causal players. This was not something we found in our
literature search, but we felt it would offer strong potential for future research. We discussed competitive aggressiveness
(Lumpkin and Dess, 2001)asameasureoftheinverseofPartnership but excluded it because it might be possible to be
competitively aggressive with some players while establishing collaborative partnerships with others.
Results of Partnership: from our literature search, we identified 14 studies –representing an overall sample size of
3196 firms –that measure the effect of Partnership on new venture performance. We conducted a meta-analysis of
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these data and found Partnership to be significantly (effect size = 0.169, p= 0.046) and positively related to new
venture performance, which supports our central hypothesis.
5.3. Affordable Loss
Effectuation suggests that instead of focusing on upside opportunity potential, expert entrepreneurs are more likely to
limit downside risk, effectively setting a level of Affordable Loss.
Measuring Affordable Loss: the studies we identified relating to Affordable Loss fell into one of two categories. One
category looked at how people proactively assume risk, generally described as risk taking propensity (Miller and
Friesen, 1983). The second looked at how people mitigate or distribute risk. As the first category embodies a natural
inverse of the heuristic described by effectuation, we analyzed both categories, evaluating them separately and together
to understand both sides of managing risk. More difficult to categorize were studies investigating product-specific risk.
For example, we were not able to include a study looking at product newness (Bruton and Rubanik, 2002) because it
was unclear how the entrepreneur managed the new product risk. Likewise, we excluded studies investigating product
radicality (Zahra and Bogner, 2000), as we were uncertain how the risk associated with product radicality had been
managed. However, future research might well learn more about risk and affordable loss by looking at product
decisions, provided the strategies underlying those decisions could be understood.
Results of Affordable Loss: our literature search identified 4 studies –representing an overall sample size of 783 firms –
that measure the effect of Affordable Loss on new venture performance. We conducted a meta-analysis of these data and
found the Affordable Loss construct not significantly (effect size= −0.019, p=0.847) related to new venture performance.
5.4. Leverage Contingency
Effectuation suggests Leverage Contingency as an alternative to formal plans based on prediction. In contrast to a
positioning strategy in which a founder pursues a specific goal, effectuation offers the possibility that the end result of the
process may look nothing like the initial idea that caused the founder to form the new venture. Instead, the result is shaped
through innovative applications of contingent alternatives that arise during the process of creation.
Measuring Leverage Contingency: constructs we coded as reflecting Leverage Contingency included: a) willingness to
modify products, and b) customization, as well as traits and approaches likely tobe associated with Leverage Contingency,
including c) openness, d) organicity, and e) transformational leadership style. Though Leverage Contingency implies the
willingness to shift strategy, we did not consider business planning to constitute the inverse of contingency. A business plan
might be used instrumentally to bring partners into the venture and subsequently be discarded, or it may actually be used to
guide the venture. Unless the study gave us some insight into whether plans, once created, were changed, used or
discarded, we had no way of knowing how to use the data. Though not analyzed here, future research may profitably
investigate contingency in the context of business model change.
Results of Leverage Contingency: we identified 5 studies containing constructs of relevance to the effectual principle of
Leverage Contingency, and these studies represented 712 firms. Our meta-analysis of these data indicated that the effectual
construct of Leverage Contingency was positively (effect size = 0.074, p= 0.049) and significantly related to new venture
performance.
6. Discussion and future research
Based on our findings, there is initial empirical support for a positive relationship between an effectual approach to strategy
making and new venture performance. As this intriguing result may stimulate further research, we focus our discussion on
issues raised by our investigation, with the intention of guiding future efforts. We organize our discussion around specific
theoretical and methodological issues we encountered in our investigation, matched with suggestions for how to overcome
them in future research, and save more general comments for the conclusion.
6.1. The nature of prediction and control
We grounded this study in the theoretical question of whether opportunities are “made”or “found,”and the
associated implication that a strategy of control may be useful where opportunities are made. Our exposition highlights
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the efficacy of control-oriented effectual strategies in the new venture context, but leaves open the connection between
predictive strategy and finding opportunities, and the question of when control or prediction is more useful in new
venture strategy. Effectuation starts with the position that the future is contingent upon actions by willful agents seeking
to reshape the current environment and fabricate new ones. The essential characteristic of the future, in this view, is
uncertainty. Environments can be made stable for periods of time in certain areas. But these periods of stability tend to
be “artificial”exceptions designed by human action rather than the “natural”regularity of a predictive universe.
Perhaps the ultimate normative recommendation is to use both regularities and contingencies with a combination of
positioning and construction strategies, and with the application and level of each depending on the uncertainty of the
particular decision. A sophisticated study involving the juxtaposition of these constructs in a particular setting, with
control for context, is necessary to unravel this issue.
Suggestion 1.Design an experiment involving a scenario-based instrument that manipulates the predictability of a
situation. The objective would be to tease out differences that cause a subject to choose a positioning strategy over a
construction one and vice versa.
6.2. Measurement of design
We were only able to measure four of the five effectual constructs. This obvious gap presents a possible fruitful
avenue for future research. In the new venture setting, the principle of Design assumes the future is not determined by
the past, but by stakeholders in the venture shaping products, firms and entire markets. Subjective measures of Design
should seek to tap into the degree to which individuals approach decisions with an orientation toward whether they are
attempting to Predict the shape of the future environment or Design it.
Suggestion 2.Design can be measured through an individual's intent to shape the environment, or her actions
attempting to control an environmental outcome.
6.3. Ambiguity of Affordable Loss
Our non-finding on the measure of Affordable Loss only offers more fuel to the ongoing debates in the literature
regarding the issue of entrepreneurial risk propensity (Stewart and Roth, 2004). Risk is obviously a central
entrepreneurial issue, and clearly we have not yet created a meaningful approach for understanding its subtleties.
Effectuation guides us to consider Affordable Loss as an alternative to Expected Return and it may be interesting to
explore that difference in an experimental setting where meaningful differences in perceptions of risk as well as
strategies for dealing with risk might be explored. We expect the economics literature may offer experimental designs
that control for individual differences in this category of investigation. And as the issue of Affordable Loss is inherently
an economic calculus, we suggest searching that literature for designs to test Affordable Loss.
Suggestion 3.Employ experimental economic designs to measure Affordable Loss.
6.4. Decision making frame of effectual constructs
The results of our analyses demonstrate the significant role that, for example, an entrepreneur's means play in new
venture performance. However, the formulation of means employed in the literature is not completely in line with that
of effectuation. While effectuation recognizes the importance of possessing specific means, it recommends
entrepreneurs follow a means orientation in decision making, instead of committing to specific goals. Because we were
unable to locate studies that investigate the subtlety of a means orientation to strategy making, we applied the nearest
formulation available to us –the means available to the entrepreneur, assuming that the entrepreneur would put those
means –particularly relevant ones –to work. This difference suggests another interesting avenue for future research. A
significant contribution could be made by measuring and testing a means orientation to decision making, determining
the impact of existing means on the propensity to employ a means-oriented approach, and linking the entire construct to
new venture performance. More generally, this same issue applies to the effectual constructs of Partnership and
Leverage Contingency. From a purely effectual perspective, each should be operationalized not as quantities of
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Means or Partnerships possessed by the individual, but the degree to which the individual bases decisions on available
Means or existing Partnerships.
Suggestion 4.Measure effectual constructs of Means, Partnership and Leverage Contingency as a function of how
existing levels frame decisions, and measure the inputs to effectual constructs, such as means available, separately.
6.5. Performance construct consistency
As some results of our investigation lost significance when we excluded perceptual performance measures, we appreciate
the issue of the inconsistency of assessing performance across studies. While all the studies we employed in our analysis
introduce measurement of the construct of interest with respect to venture performance, virtually every one varies in the exact
metric used and in the collection of data to operationalize that metric. The lack of agreement regarding performance
measurement will hamper the development of literature in the field, constraining comparisons across predictor variables,
industries and other constructs of interest. We appreciate it is unlikely that a single measure will be appropriate for all situations
(Griffin and Page, 1996), but hope future research will compare different measures of venture performance, specifically to
determine correlation between the various measures and recommend a subset of measures researchers should focus on in
order to ensure results will be comparable across studies.
Suggestion 5.Search for homogenous dependent variables around performance.
6.6. Rigorous reporting
In order to integrate research across studies, the necessary statistics must be reported in the publication so that they are
accessible to future researchers. Our sample would have been significantly increased had researchers included correlation
tables with both independent and dependent variables.
Suggestion 6 (for editors).Set aside page space, if only in the appendix, for the necessary statistics (correlation
tables), detailed descriptions of study design and perceptual item constructs, so results can be easily integrated into
future research.
7. Conclusion
Is the debate on whether new ventures are positioned or constructed complete? Hardly. However, we hope that with this
study we encourage scholars to consider three important issues as they advance their research. The first is to be aware of the
weight of the theoretical foundations on existing work. We were surprised at the emphasis on positioning at the core of
entrepreneurship research. It made us wonder what other assumptions current scholars take for granted as a function of the
historical foundations of their work. We are not suggesting that all these assumptions may be misplaced or incorrect –only
that it may represent a useful devotion of time to catalog and appreciate those assumptions so we are aware of any biases that
may accompany them. The second is to appreciate the rich data that lie in the dusty volumes of past work. We are confident
that many new ideas can benefit from initial examination by extracting relevant constructs from the literature utilizing the
process we created for this study. And the third is to incorporate the possibility that creation may be at the root of some startup
processes. Doing so may be one of the keys to enabling our community to establish and communicate the distinctiveness of
entrepreneurship.
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