the antecedents of effectuation: an empirical study within
TRANSCRIPT
The antecedents of effectuation:
an empirical study within an incubator
Supervising committee
dhr. dr. R.C.W. van der Voort, Thesis supervisor
dhr. dr. J.J. Ebbers, Co-reader
Date of submission: 1st of June 2017
Master thesis
Joint degree Entrepreneurship
Olivier Lieshout
10406638
Abstract
Entrepreneurs frequently face uncertainty while starting a new venture
which can influence entrepreneurial behavior. The conceptual work on
effectuation theory suggest that entrepreneurs will be influenced by
uncertainty and their own experience. Nevertheless empirical evidence
supporting the possible antecedents of effectual behavior is lacking.
Therefore, this thesis tries to explain which factors might influence the
decision-making of an entrepreneur. This research analyses the effect of
the growth stage of a firm on the entrepreneurial behavior, which has
thus far not been empirically tested. The antecedents of effectuation were
studied using multiple regression on a sample of 110 owners and
founders of firms housed within a Dutch incubator. The results suggest
that a causal approach is more common within the incubator and that the
market dynamism does not affect this. Furthermore, entrepreneurial
experience has been found to have a negative relationship with effectual
behavior, whereas the growth stage of the firm is positively related to the
use effectuation.
Statement of Originality
This document is written by student Olivier Lieshout who declares to take full responsibility
for the contents of this document. I declare that the text and the work presented in this
document is original and that no sources other than those mentioned in the text and its
references have been used in creating it. The University of Amsterdam and de Vrije
Univeristeit Amsterdam are responsible solely for the supervision of completion of the work,
not for the contents.
Table of Contents
1. Introduction ........................................................................................................................ 4
2. Theoretical framework ....................................................................................................... 5
2.1 Effectuation and causation processes ............................................................................... 5
2.3 Uncertainty and effectuation ............................................................................................ 7
2.4 Experience and Effectuation ............................................................................................ 9
2.5 Life stages and Effectuation .......................................................................................... 11
3 Methods…………………………………………………………………………………13
3.1 Sample............................................................................................................................ 13
3.2 Operationalizations......................................................................................................... 14
Antecedents ...................................................................................................................... 15
Dependent variables ......................................................................................................... 16
Control variable ................................................................................................................ 17
3.3 Method of analysis ......................................................................................................... 17
4. Results .............................................................................................................................. 18
Dynamism ......................................................................................................................... 20
Experience ........................................................................................................................ 20
Education level ................................................................................................................. 21
Growth stage ..................................................................................................................... 21
5. Further analysis of the results and discussion .................................................................. 22
Incubators ......................................................................................................................... 23
Growth stage ..................................................................................................................... 24
Measurement of entrepreneurial behavior ........................................................................ 25
6. Conclusion ........................................................................................................................ 27
Limitations ........................................................................................................................ 27
Suggestions for future research ........................................................................................ 28
Appendix .................................................................................................................................. 29
Appendix 1- Results testing hypotheses............................................................................... 29
Appendix 2 – Results further analysis ................................................................................. 32
Appendix 3 - Questionnaire ................................................................................................. 34
Bibliography ............................................................................................................................ 39
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1. Introduction
In the process of starting a new venture, entrepreneurs often have to make decisions facing a
lot of uncertainty (Alvarez and Barney, 2005). The behavior of the entrepreneur in these
environments is important for the understanding of the entrepreneurship and the economy as
a whole (Chandler et al., 2011), as entrepreneurship and the innovation it brings can have a
positive effect on economic growth (Wennekers & Turik, 1999).
In the understanding of entrepreneurial behavior, effectuation initiated a shift in the paradigm
(Perry et al., 2012). Sarasvathy (2001) proposed effectuation as a new perspective on how
economical artifacts emerge. The theory follows logics opposing the traditional economic
logic of analyzing and planning (Read et al., 2005). Effectuation is built on the perspective
that in the uncertainty, which is inherent to entrepreneurship, is not suited for an analytical
approach. This would cause entrepreneurs to use non-predictive logics (Fisher, 2012).
Perry et al. (2012) state that the literature on effectuation is in need of more empirical
research. However, most of the empirical research conducted over the last years has a strong
focus on performance (Cai et al, 2016; Deligianni et al. 2017; Roach et al., 2016). The
antecedents of effectual behavior on the other hand have often been neglected. Although the
conceptual work suggests strong effects of the environment in which an entrepreneur is active
and the experience of an entrepreneur on his entrepreneurial behavior, empirical research thus
far does not reinforce these relationships. As effectuation is often seen as a superior type of
entrepreneurial behavior (Read et al., 2005), it is important to understand what factors might
lead to this behavior. Therefore, the goal of this study is to empirically test what factors
influence the decision-making logics used by entrepreneurs.
In this research I will study whether market dynamism, entrepreneurial experience and the
growth stage of a firm affects entrepreneurial behavior. This adds to the existing literature on
effectuation in three ways. Firstly, in this studies the effect of the growth stage on the
entrepreneurial behavior is tested. This is valuable since it has been theorized that effectual
behavior might diminish as a company grows. Earlier empirical research has so far only used
age of a firm and size to study the effects of maturity of a firm on decision-making logics.
Secondly, the data used in this study was collected in an incubator. The specific
characteristics of the environment in which the companies are active might have an effect on
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the behavior of entrepreneurs. Therefore, it is relevant to study the antecedents in this
context, as studying the behavior in different environments can help deepen the
understanding of effectuation. Thirdly, the findings of this studies add to the empirical data
on the antecedents of effectual behavior. As empirical data on the interaction of these factors
is scarce this research adds by testing the interaction between the different proposed
components that cause effectual behavior.
The research is built up as follows; in section 2 the theoretical background of effectuation and
causation is discussed and hypotheses concerning the antecedents are deducted from the
theory. In section 3 the data and the methodology used will be explicated. The results of the
tests will be presented and interpreted in section 4. In section 5 the findings will be discussed
and further analyzed to test the robustness of the results. and the results with suggestions for
further research. Section 6 discusses the limitations of the study, suggestions for future
research and concludes the research.
2. Theoretical framework
In this section first effectuation and causation will be presented an discussed. This is
followed by an overview and discussion of the possible antecedents of effectual behavior that
are described in the literature. Based on this overview hypotheses will be formulated for the
possible effect of market dynamism, entrepreneurial experience and growth stage of a form
on decision-making logics.
2.1 Effectuation and causation processes
Sarasvathy (2001) tried to challenge the traditional theory on entrepreneurial behavior, often
labeled causation, by juxtaposing it to effectuation. Causation builds on the traditional
economic logics (Fisher, 2012), where an assessment of the potential costs and benefits over
time is fundamental. An entrepreneur is assumed to first set a goal and afterwards allocate the
means to achieve this goal (Dew et al., 2009). This is done via a consecutive process of
discovering, evaluating and exploiting opportunities (Shane & Venkataraman, 2000).
Causation processes are driven by intense analysis of an opportunity in terms of return and
risk (Read et al., 2009) The success of emerging firms is argued to be dependent on the
ability to analyze and select business opportunities (Chandler et al, 2011).
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The introduction of effectuation was guided by the discrepancy between the environment
implied by causation and the actual environment in which entrepreneurs are active. An
entrepreneur often faces uncertainty and ambiguity rather than risk. In a situation that is
characterized by risk, the amount of possible outcomes is limited (Knight, 1921). On the
other hand, in a situation of uncertainty there is a unlimited amount of possible outcomes
(LeRoy & Singell, 1987).
Alvarez and Barney (2007) argue that a situation of risk might be better approached in an
analytical way. However, in a situation of ambiguity and uncertainty people will make
decisions differently, since they lack the possibility to thoroughly analyze the opportunity
(Sarasvathy, 2001 ). As analyzing and forecasting a new and uncertain market is problematic,
entrepreneurs will focus on the effects they can create with their own means, rather than
selecting means to reach a goal (Sarasvathy, 2008).
2.2 Differences between Causation and Effectuation
The difference between the two approaches can be described with a simplified example used
by Sarasvathy (2001). In the process of preparing a meal for dinner, one could take either a
causal approach or an effectual approach. When one first defines what to cook and
consecutively buys all the ingredients needed to cook exactly what they decided to prepare, it
is a process of causation. On the other hand, when the starting point would be the ingredients
one has already and how they can be used to make a meal, it would be an effectual approach.
Effectuation incorporates five main principles which contrast with the theory of causation
(Sarasvathy, 2008).
First, effectual behavior has a strong focus on the means of the entrepreneur, rather than his/
her goal, which is the main driver in causation. In a situation of uncertainty it is challenging
to define the future outcomes and therefore the focus will be on the means at hand. In
effectuation the goal of the entrepreneur is not clearly defined or easily adjusted (Fisher,
2012). In the causal logic the goal is important and the entrepreneur will allocate the means
needed to reach these goals.
Second, using effectual decision-making the ‘affordable loss’ is essential, whereas causation
is guided by expected returns. The principle of affordable loss states that an entrepreneur will
not invest more than he is willing to lose, regardless the payoff (Dew et al., 2008). Taking the
affordable loss as a main driver of investments, eliminates the need for clear prediction of the
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future (Saravathy, 2008). Furthermore, the principle of affordable loss diminishes of the role
of uncertainty in early stage funding decisions (Sarasvathy, 2008). A causal approach relies
on estimating the return on an investment, where the risk of the investment has to correspond
with the return.
Third, in effectuation emphasis is on strategic collaboration rather than competitive analysis.
Due to innovative nature of entrepreneurship competition is hard to define (Fisher, 2012).
Furthermore, the focus on partnerships and precommitments of stakeholders can reduce the
uncertainty that comes with emergence of a new venture (Perry et al., 2012). By bringing
extra stakeholders in the consequences of a failure for the firm itself might be lower which
can ease operating in new markets (Alvarez and Barney, 2005). Following a causal approach
an entrepreneur would try to gain a competitive advantage analyzing the competition
intensively.
Fourth, effectuation has a focus on exploiting contingencies instead of avoidance of
contingencies. Using an effectual approach one is expected to remain flexible and therefore
able to adjust to changes in the market and environment, whereas with a causal approach the
goal is set, which results in an avoidance of uncertainty (Chandler, et al 2011). Entrepreneurs
using effectual behavior will see uncertainty as a possibility rather than something you have
to refrain from.
Fifth, effectuation follows a non-predictive logic, whereas causation follows a predictive
logic. The rationale of effectuation can be described as ‘to the extend we can control the
future we do not need to predict it’ (Sarasvathy, 2008, p. 91). A non-predictive logic allows
entrepreneurs to adapt to the uncertain environment in which they are active. Firms focusing
on control will be able to reduce the need to predict the future and might therefore be more
successful in uncertain situations (Wiltbank et al. 2006).
2.3 Uncertainty and effectuation
As already described in the previous section, the uncertain environment in which starting
firms operate is often theorized to lead to effectual behavior (Sarasvathy 2001; 2008; Fisher
2012; Harms and Schiele, 2012). Minzberg (1993) argues that environments are always
unpredictable and that therefore long term planning will give poor results. When an
entrepreneur faces uncertainty the logics of effectuation allow him/her to stay in control of
the outcome (Chandler et al., 2011).
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Uncertainty is something constructed rather than absolute (Daft and Weick, 1984) and
therefore the perception of the entrepreneur is really important. An entrepreneur with
extensive knowledge of a market might perceive the uncertainty of the market different than
an entrepreneur lacking this knowledge (Dew et al. 2008).
Empirical research has been done on the link between uncertainty and effectuation. Chandler
et al. (2011) found that uncertainty has a negative effect on causal behavior. On the other
hand, they did not find a significant relationship between uncertainty and effectual behavior.
However, they did find a positive relation between uncertainty and ‘experimentation’ which
they argue is effectual behavior. Uncertainty was measured using four statements about the
ease of decision-making and adapting to a dynamic environment. Applying the same
measurement for uncertainty, Alsos et al. (2016) found a strong link between effectuation and
uncertainty.
Besides this, also other approaches to define the antecedents of effectuation have been used.
Fisher (2012) studied the decision-making logics of six internet based ventures that were
founded around 2000. The analysis was based on extensive qualitative data concerning the
development and growth of the firms. Fisher (2012) argues that firms within this market are
likely to experience uncertainty due to the dynamism in the market. Dynamic markets can be
characterized by changes in technologies, customer preferences, demand and competition
(Volberda & van Bruggen, 1997). Fisher (2012) found that these firms indeed follow a more
effectual logic than a causal logic. However, the approaches are not mutually exclusive, since
in some cases both effectual and causal logic where used simultaneously (Fisher, 2012).
Harms and Schiele (2012) focused on internationalizing high growth firms when studying the
antecedents of effectual behavior. They defined the uncertainty of the expansion as the
perceived dynamism of the international market that a firm was entering. Harms and Schiele
(2012) found that more dynamic markets are more likely entered using effectual behavior, on
the other hand the effect of causation was not significant. This however could be due to the
small sample (N=65) used in their studies (Harms and Schiele, 2012). Mthani and Urban
(2014) found that the use of effectuation processes in large high tech firms are not related to
the dynamism of the market in which they operate. As the firms were heterogeneous in
maturity they controlled for age and size of the firm. Gruber (2007) found that market impact
of business planning is negative in dynamic environments. Which suggests that dynamic
markets would optimally not be approached using causation.
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Although conceptual work on effectuation suggest strong links between uncertainty and
effectual behavior, empirical studies so far do not show a clear link between uncertainty and
effectuation. Perry et al. (2012) suggest that more empirical research needs to be done to
deepen the understanding of effectuation processes and its antecedents. In this research the
perceived dynamism of the market is used to study the effect of uncertainty of the market on
both causation and effectuation. As in a dynamic market the changes in demand, supply and
technology are frequent, dynamic environments can be characterized as uncertain.
Building on the work conceptual work of Sarasvathy (2001, 2008) and the empirical work
Fisher (2012) and Harms and Schiele (2012) the influence of market dynamism is expected to
be positive on effectual behavior. Contrasting, a more dynamic environment is expected to
have a negative effect on the use of causation.
H1a: Market dynamism has a positive effect on the use of effectuation principles
H1b: Market dynamism has a negative effect on the use of causation principles
2.4 Experience and Effectuation
Besides the dynamism of the market, it is also suggested that entrepreneurial experience is an
antecedent of effectual behavior (Read et al., 2005; Dew et al. 2009). This is based on the
perception that effectuation is a form of entrepreneurial expertise and therefore positively
related to the performance of a firm (Chandler et al. 2011). Read et al. (2005) linked the
processes of effectuation with the literature on expertise in general. Their studies suggests
that successful and experienced entrepreneurs are more likely to use effectual approaches
(Read et al., 2005). An important part of the expertise process is learning and mastering a
certain behavior. One of the components of gaining expertise is knowledge on the domain
(Shanteau, 1992), which can be gained through entrepreneurial experience (Dew et al., 2009).
Cope and Watts (2000) argue that entrepreneurs learn and gain experience by encountering
incidents in the entrepreneurial process and the reflection on those incidents. Johannisson et
al. (1998) suggest that entrepreneurial experience helps entrepreneurs when making business
decisions under uncertainty, as the earlier involvement in entrepreneurship has provided tacit
knowledge. Therefore, the level of experience of an entrepreneur can influence the behavior
since the learning process might guide an entrepreneur towards effectuation.
In addition to conceptual studies, empirical research has been done to test the relation
between experience and effectuation. Cai et al. (2016) found that the use of effectuation and
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exploratory learning has a positive effect on the performance of a firm. Politis (2008) found
that habitudinal entrepreneurs are slightly more likely to use effectual behavior than novice
entrepreneurs.
Another effect of entrepreneurial experience can be increased self-confidence as an
entrepreneur. Inexperienced entrepreneurs have been found more likely to use effectuation
logics if they are more confident in their entrepreneurial capabilities (Engel et al., 2014).
Furthermore, experience influences the probability that one will engage in entrepreneurship
as it changes the perception of feasibility of projects and ventures (Kreuger, 1993). Harms
and Schiele (2012) found a positive effect of international experience on the use of
effectuation in the internationalization process. Johnson and McKelvie (2012) studied the use
of decision making logics in a corporate context and found only partial support for a positive
effect of experience on the use of effectuation logics.
Altogether, the studied literature suggests that a more experienced entrepreneur is the more
likely he/she is to use of effectual logic. Following this reasoning the following hypotheses
can be formulated.
H2a: The use of effectual behavior is positively affected by the entrepreneurial experience of
the entrepreneur
H2b: The use of causal behavior is negatively affected by the entrepreneurial experience of
the entrepreneur
Furthermore, Harms and Schiele (2012) suggest that experience and the perceived dynamism
of the market have an interacting effect. An experienced entrepreneur is expected to be more
likely to use effectual behavior in general. However, in situations where the future is
predictable, they might use a predictive logic since this can be beneficial in these situations
(Harms and Schiele, 2012). Effectuation might not be superior behavior in each situation. For
example, Brews and Hunt (1999) found, studying a sample of firms more than 4 years old, a
positive relation between financial performance and the amount of formal planning. In the
situation that one wants to reach a clear goal effectuation will not be optimal, as the effectual
nature will draw the focus away from the goal (Sarasvathy, 2008). On the other hand,
inexperienced entrepreneurs might find it straightforward to use a more text-book logic in an
uncertain environment, which would make them less likely to adopt effectuation logic.
Therefore, it is proposed that the perceived uncertainty might have a moderating effect on the
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influence of entrepreneurial experience. Harms and Schiele (2012) tested this in a context of
internationalizing firms and found partial support for the hypothesis. Therefore, it is
interesting to see if this effect is suggested by the data that is collected within a incubator,
H3a: The perceived uncertainty has a moderating effect on the relation between experience
and the extent to which effectuation is used: The higher the perceived uncertainty in a market,
the stronger the relation between experience the extent to which effectuation is used.
H3b: The perceived uncertainty has a moderating effect on the relation between experience
and the extent to which causation is used: The lower the perceived uncertainty in a market,
the stronger the relation between experience the extent to which causation is used.
2.5 Life stages and Effectuation
Apart from the individual experience of the entrepreneur and the perceived uncertainty of the
environment, the characteristics of a firm might influence the decision-making logic used. A
small and starting venture is likely to use different logics than a stable and large company due
to the large contrast in maturity of the venture.
It has been proposed that firms follow a path of different life stages, which can be used to
define distinct phases through which a firm moves while growing (Greiner, 1972; Churchill
and Lewis, 1983). There has been a large effort to define and measure different stages of such
a growth path (McMahon, 1998). A multitude of stages that firms go through in their life
cycle have been proposed, reaching form 3 to 10 stages (Hanks et al., 1993). A common way
to define different stages is investigating the problems that a company faces at a certain time,
for these will differ between various stages (Kazanjian, 1989). Besides examining the most
urgent challenges a company has, it is also important to take into account that not every
company wants to grow (Stanworth and Curran, 1976). Some entrepreneurs might not persue
growth as they prefer the freedom they have owning a smaller firm (Davidsson, 1989).
Therefore, the growth ambition of the entrepreneur is an important factor in the growth of a
firm. Furthermore, some companies might reach a point at which they are no longer able to
grow due to the resources they have or market they are active in (Hanks et al. 1993).
Notwithstanding, later growth stage firms on average have more employees, are older and
have more complex management systems (Hanks et al. 1993).
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In the literature on effectuation the growth of a firm has also been discussed. Sarasvathy
(2008) proposes that over time the decision-making process might evolve towards a causal
approach, as a firm will have more possibility to analyze the opportunities at hand.
‘I would predict that most enduring high-growth firms, particularly firms that transformed
industries and opened up new markets, would have begun effectually. In other words, if we
look closely at the early stage histories of enduring firms, we should be able to trace patterns
of effectual actions in their origins. But as they survive and grow, their management will
need to become more causal, particularly in exploiting the new markets they have created
and building on long term competitive advantages’ (Sarasvathy, 2008; p. 132)
The extra knowledge about markets, products and clients might make a more goal focused
logic possible (Sarasvathy, 2001). Laaksonen et al. (2010) present support for a change from
effectual behavior when a company is starting and resource poor, towards causal behavior as
a new venture gets more mature. Mature companies that have more resources will be more
likely to follow manage these resources in an effectual way (Sarasvathy, 2008). Furthermore,
during the process of growing a firm will become more familiar with the customers and
suppliers which will allow for more predictive logics. Following this reasoning behavior is
expected to differ in different growth stages of a firm. In most empirical research the size and
age are used as controls to filter the effects that might be caused by this proposed relationship
(Mthanti & Urban, 2014; Harms and Schiele, 2012). However, due to the diversity in growth
paths of firms measuring size and age is not an optimal measure of its growth (Delmar et al.,
2003). Therefore, it is important to define growth stages, to be able to measure the effect of
growth of a firm on effectual behavior. Based on this logic it can be expected that firms that
find themselves in later stages might use a more causal approach.
H4a: Early growth stage firms will be more likely to use effectuation than later growth stage
firms
H4b: Early growth stage firms will be less likely to use effectuation than later growth stage
firms.
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Figure 1 – The antecedents of effectual and causal behavior
1. Figure 1 shows the antecedents that are hypothesized to have an effect on the entrepreneurial behavior that is used.
Each antecedent is connected to an different aspect of the a venture. As a mediating relation is suggested of the
uncertainty of the market on the effect of entrepreneurial experience, these two are also connected with a line.
3. Methods
In this section I will first describe the sample used for this studies. As the data is collected
within an incubator the implications of the environment for that data are also discussed.
Subsequently the operationalizations of the variables will be explained followed by a detailed
description of method of analysis.
3.1 Sample
Perry et al. (2012) argue that effectuation literature is now in an intermediate state. They
propose, based on a review of the literature, that survey data will help to further test
conceptualized relationships. Therefore, surveys were conducted to study the effect of
dynamism, entrepreneurial experience and the growth stage of a firm on decision-making by
entrepreneurs. The collection of the data was done within a Dutch incubator. The setting in
which the data was collected is interesting since incubators are an important pillar in the start-
up ecosystem (WEF, 2014). Incubators can help new ventures by creating a supporting
environment (Peters et al., 2004), by offering favorable rental conditions (Bergek and
Norrman, 2008), and by the reducing the cost through sharing and co-location of services
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(Bergek and Norrman, 2008). For example, sharing the overhead cost of cleaning, printing
and supporting personnel (Allen and Raman, 1985). Moreover, business incubators often
provide access to expert business advice and facilitate networking for starting ventures
(Bøllingtoft and Ulhøi, 2005).
These characteristics of an incubator might influence the entrepreneurial behavior of
entrepreneurs. For example, the natural way of sharing housing, personnel and other costs
with other companies might cause the focus to be less on competitive analysis and more on
how companies can help each other. The large amount of companies within one building
might also encourage partnerships and collaboration between companies.
Apart from that the guidance of expert entrepreneurs may affect the entrepreneurial behavior
of the companies within an incubator. As it has been found that entrepreneurial expertise and
effectuation are related it could be that the guidance will encourage on effectual behavior.
Therefore, it could be that more effectuation is used within an incubator than in a regular
environment.
The data was be collected in B. Amsterdam, which is an incubator that houses 216 start-ups.
B. Amsterdam as an incubator has a focus on facilitation of networking and creating an
entrepreneurial and inspiring environment (B. Amsterdam, n.d.). Collecting data within an
incubator is appealing due to the highly entrepreneurial environment. Furthermore, it is
interesting to see whether the results from companies within an incubator are similar to
earlier found results. The data was collected from the owners, founders or general managers
of the firms. Three companies were left out since the founder, manager and owner did not
work within the office in the incubator. The survey was first send by e-mail, after which
every company was also approached in person to ensure a high response rate. The final
sample consists of 110 respondents, managers or founders and therefore the response rate is
50.9%. The companies are diverse in size, industries and growth phase. The survey data
consists of measurements of the dynamism of the market, the use of effectuation and
causation principles, the growth stage of the company, the growth ambitions and
entrepreneurial experience of the entrepreneur.
3.2 Operationalizations
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Antecedents
Dynamism of the market is measured using a scale provided by Volberda et al. (2012) This
measure of dynamism is based on the perspective of the respondent. The perception of the
entrepreneur is important since his/her behavior is based on this, as uncertainty is not
absolute but constructed (Sarasvathy, 2001). This is measured with 6 statements about the
perceived frequency of changes and the impact of changes in the market in which the
entrepreneur is active. These questions were answered on a 7 point Likert scale (Likert,
1932). The full questionnaire is presented in appendix 3. To evaluate the reliability of the
constructs, the Cronbach’s α (Cronbach, 1951) has been computed for each scale variable.
The descriptive statistics and the Cronbach’s alpha’s of all used variables can be found in
Table 1. For this scale the Cronbach’s α .884, which is a high test score that suggests no
problems with the reliability of the scale (Murphy & Davidshofer, 1988).
Entrepreneurial experience is measured by the number of firms the person has been founder
of before starting with the current company. Read and Sarasvathy (2005) argue that an
entrepreneur who has started multiple companies has found out that the effectual approach is
more effective. Read and Sarasvathy (2005) also state that the experience in the starting
phase rather than the managing phase is important. Since encountering problems and the
reflection on these problems are essential in entrepreneurial learning (Cope & Watts, 2000),
also unsuccessful attempts setting up a company will bring additional entrepreneurial
experience (Nanda & Sorensen, 2010). Therefore, the amount of companies founded will be a
suiting measure of the previous entrepreneurial experience of the entrepreneur.
The growth-stage measure is based on the four different stages defined by Kazanjian (1988).
The stages are defined by measuring how apparent different types of problems, which are
specific to each stage, are at that point in time. This was done using a scale measuring the
relevance of problems, on a 7 point Likert scale, in 20 statements each covering stage specific
problems (Kazanjian, 1988) However, to improve the taxonomy extra data has been used to
clearly define the growth stage. Since the personal ambition of an entrepreneur is important
for the growth of a company data on the ambition of the entrepreneur have been included.
Therefore it is possible to that a company that is not a large or an old company is still at the
end of its growth cycle. This has been measured by asking the entrepreneur for the size of the
company now in FTE and the ideal size of the firm in 5 year in FTE. Furthermore, data on the
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age and size have been used to increase the quality of the classification. The stages are then
coded as follows: conceptualization & development =1, commercialization and early
growth=2, growth =3 and stability =4.
Dependent variables
The measurement of the effectuation and causation principles is based on Alsos, Clausen and
Solvoll (2014). This scale covers the different aspects of effectual and causal behavior. The
scale measures effectuation and causation by asking respondents to state whether they agree
or disagree with ten statements on a 7 point Likert-scale. Five statements concern effectual
behavior and five statements concern causal behavior. As it has been found that both
decision-making logics can be used simultaneously (Fisher, 2012; Chandler et al. 2011) it is
important to use measures for both effectuation and causation Alsos et al (2016). Therefore,
both separately will be used as dependent variables.
Murphy and Davidshofer (1988) state that a Cronbach’s alpha below 0.6 would be of an
unacceptable level for research. They argue that a score between 0.6 and 0.7 is low but
acceptable. Unfortunately, the score of the scale for effectuation was below this threshold (α
= .584). To increase the consistency two statements were removed. ‘Instead of calculating
how much profit we will gain when we invest, we invest based on the resources we have at
our disposal’ and ‘We base our cooperation with others on informal agreements, which are
changed depending on what they can offer’. This increases the Cronbach’s alpha to 0.649,
which is an acceptable level according to Murphy and Davidshofer (1988).A reason that
measurement of effectuation does not provide data with a high internal consistency, might be
that the scale only uses one statement for each principle of effectuation. As effectuation is a
construct that is made up out of separate factors that do not necessarily all have to be
effective at the same time (Sarasvathy, 2008). Chandler et al. (2012) also found that the five
constructs can vary, they argue that ‘strategic collaboration and making precommitments’ is
not specific effectual behavior, as they found that this also fits within the causal logic.
Therefore, it would have been better to increase the reliability by asking multiple questions
on each principle. On the other hand, the scale provided data with a Cronbach’s α of 0.74
when used by Alsos et al. (2016).
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Control variable
Education level is included as a control variable. Education level is often used as a proxy for
human capital, which has been found an important influence on entrepreneurial behavior
(Davidsson & Honig, 2003). A possible effect is that due to the more managerial and
analytical nature of causation, causal behavior might be used more frequently by higher
educated. Whereas the more hands on approach adopted in lower level education, might fit
better with effectual principles. To measure the level of education all entrepreneurs were
asked for the highest level of education they’ve completed. Educational level has been
divided in 5 different levels; Community College, Higher Vocational Education, University
Bachelor, University Master and PHD. These have respectively have been coded with the
numbers 1 to 5.
3.3 Method of analysis
First off, to get a better understanding of the collected data, a correlation matrix will be
computed. As linear regression will be used to test the hypothesis the correlation matrix is
important. When the correlation between different independent variables is to high this could
cause multi-collinearity. Besides that, the levels of skewness and kurtosis will be inspected to
evaluate whether normality of the variables can be assumed. Consequently, to test whether
the data is homoscedastic a Glejser test (Glejser, 1969) will be performed. This test regresses
the estimated errors of the regression on the independent variables. If significant relations are
found the hypothesis of homoscedasticity should be rejected. This is important as one of the
assumptions when using OLS is that no heteroscedasticity and autocorrelation is present.
Moreover, a paired t-test is done to see whether there is a difference in the average level of
effectuation and causation used by the companies in the sample.
To be able to study whether the described variables have an effect on the entrepreneurial
behavior of the respondents, I will use linear regression. This regression will be done using
Ordinary Least Squares which gives an estimation of the ‘best fitting line’ within the data.
The regression coefficients are tested for significance with a t-test. As it has been found that
causation and effectuation are not opposites (Chandler et al., 2011), it is better to use both
causation and effectuation as dependent variables in separate linear regressions (Harms and
Schiele, 2012).
18
First to estimate whether market dynamism and entrepreneurial experience have an effect on
the use of effectuation and causation we will use the equations (1a) and (1b). These equations
do not yet include the variable that measures the growth stage. The first estimate is done
without the growth stage, as this variable has not been used in earlier research before. By
doing so it is possible to see if the addition of this variable changes the findings. If adding
this variable to the equation does not change the estimations of (1a) and (1b) this will add to
the robustness of these results.
(1a) Effectuation = β0 + β1 * dynamism + β2 * experience + β3 * education + ε
(1b) Effectuation = β0 + β1 * dynamism + β2 * experience + β3 * education + ε
To evaluate the influence of the growth stage of a company on the entrepreneurial behavior,
we will use equations (2a) and (2b). These include the variable growth stage and the other
variables that were already included in (1a) and (1b). The growth stage variable is added to
test the theorized decrease in effectual behavior and increase in causal behavior when a firm
grows.
(2a) Effectuation = β0 + β1 * dynamism + β2 * experience + β3 * education + β4 * Growth stage + ε
(2b) Causation = β0 + β1 * dynamism + β2 * experience + β3 * education + β4 * Growth stage + ε
To verify whether the effect of experience moderates the effect of market dynamism, an
interaction variable is created. This variable, dynamism*experience, will be added to the
regressions equations (2a) and (2b) which results in equations (3a) and (3b). The interaction
variable will be used to determine if the effect of experience differs when the market changes.
(3a) Effectuation =β0 + β1 * dynamism + β2 * experience +β3 * education + β4 * Growth stage + β5 * dynamism*experience +ε
(3b) Causation = β0 + β1 * dynamism + β2 * experience +β3 * education + β4 * Growth stage + β5 * dynamism*experience +ε
4. Results
In this section the results of the will be presented and discussed. This will be done following
the same sequence as in which the hypotheses were formulated.
Table 2 shows the correlations between the variables. Although these correlations will not be
used to test the hypotheses, it useful to get a good insight on the collected data. The
correlation between effectuation and causation is negative and significant at the 1% level.
This implies that when an entrepreneur uses more effectual behavior he/she is likely to use
19
less causation. Nevertheless, this does not imply that the two are complete opposing logics,
since the coefficient is not -1.
Furthermore, the correlation matrix shows that the growth stage is significantly correlated
with the size and age of the firm, which follows the logic that a more mature company is
normally bigger and older. However, due to these significant correlations, age and size will
be left out as independent variables in the regressions when the variable growth stage is
included. This is done to ensure that there will be no multi-collinearity in the regression. The
relation of growth stage with effectuation and causation will be discussed with the regression
results.
When using OLS it is important that the variables can be assumed to be normally distributed.
To evaluate this the skewness and kurtosis of each variable can be inspected. These are
provided in table 1 in appendix 1. When these values are between -2 and 2 the variables can
be assumed to be suitable for OLS (George & Mallery, 2010). The values for all the variables
used in the regressions for both skewness and kurtosis fall between -2 and 2. The high values
of skewness and kurtosis of both age and size of the firm are no obstacle, as these variables
were solely used to improve the taxonomy of the growth stages.
Moreover, it is essential that the data is homoscedastic to estimate using OLS. Table 3 shows
the results of the Glesjer test performed. This was done for the main equations testing the
antecedents of entrepreneurial behavior, namely (2a) and (2b). The results show no
significant relationships between the absolute unstandardized residuals and the independent
variables. Therefore, it can be assumed that the variables are homoscedastic.
Table 4 shows the results of a paired t-test on the difference between effectuation and
causation. This test is interesting, since it can help determine whether the means of two
variables significantly differ. Due to the entrepreneurial environment in which the data was
collected it is interesting to see how the these values compare. Based on the earlier described
function of incubators it could be that within with this entrepreneurial environment more
effectuation would be used than causation. Nonetheless, the results from table 3 show that
over the whole sample the level of causation used is significantly higher than effectuation.
This can be seen from the significant negative mean of the difference between effectuation
and causation. Although this finding is interesting it is not enough to draw any conclusions
concerning the hypothesis. The regression analysis can help determine what factors explain
the high value of causal behavior within the incubator.
20
Table 5 and 6 show the results of the regressions ran including the different independent
variables. Table 5 shows the results of the regression with effectuation as dependent variable
whereas Table 6 shows the results of the regression with causation as dependent variable.
Dynamism
The results suggest that dynamism of the market does not influence the entrepreneurial
behavior. For neither causation nor effectuation any significant effect of market dynamism is
found. Although theoretically this relation has been described multiple times the data does
not provide evidence of this relationship. It could be that the dynamism of the market does
not have a direct effect on the behavior of entrepreneurs, as other empirical studies did not
find a significant effect or only a partial effect of market dynamism on effectuation (Mthani
and Urban, 2014; Harms and Schiele, 2012).
Experience
The results in table 5 reveal a significant negative effect of entrepreneurial experience on
effectuation in regression (1a) and (1b), (β = .132, P= 0.077; β = .125, P = 0.092). The
results from table 6 indicate a significant positive effect of experience on causation in
regression (1b) and (2b), respectively (β = .118, P= 0.067; β = .106, P = 0.082). This suggests
that the more experienced an entrepreneur is the more he/she will rely on causal behavior. As
hypothesis 1A and 1B suggest that experience will have a negative effect on causal behavior
and a positive relation with effectual behavior, these findings run counter the hypothesis.
An explanation for the positive effect of experience on the use of causation could be that
experience has provided the entrepreneur with more ability to use causal behavior in a start-
up scenario. As an entrepreneur has more experience he might have a better understanding of
the long term possibilities for a venture and is more likely to determine what is needed to get
towards that goal. When one is more experienced with forecasting and analysis, it could be
that this does become more useful for a starting company (Brews and Hunt, 1999).
Another reason for these unexpected findings could be the reduction of uncertainty that
experience brings for entrepreneurship as a whole. Fraser and Greene (2006) argue that extra
entrepreneurial experience reduces the uncertainty concerned with starting a business.
Therefore, it could be that more experienced entrepreneurs perceive less uncertainty within
the opportunity as a whole. This could, due to reduced uncertainty, lead to more causal
21
behavior. At the same time, this would explain the negative effect of experience on
effectuation. This proposed diminished ambiguity is likely not to be included in the
dynamism variable for it measures the dynamism of the market and not the overall
uncertainty of the opportunity.
The interaction variable experience*dynamism does not show any significant effect on
effectuation or causation. This variable was included in the multiple regressions (3a) and (3b)
to test whether there is a moderating effect of market dynamism on the effect that experience
has on the entrepreneurial behavior. The intuition behind this hypothesis was that a more
experienced entrepreneur is more likely to adjust well to the dynamics of the market.
However, for both effectuation and causation this coefficient has not been found significant.
This could be due to the lack of relation between the level of dynamism in the market and
effectual behavior.
Education level
The control variable education level does not have a significant effect on either effectuation
or causation. This suggests that the behavior of entrepreneurs is not affected by the education
they have had.
Growth stage
Regression (2a) in Table 5 shows a significant effect positive effect of growth stage on
effectual behavior. The coefficient, .299, is significant at a 10% level. Besides that Table 6
shows a highly significant negative effect of growth stage on the use of causation. The
negative coefficients of the regression (2b) and (3b) are both significant at a 1% level. As it
was hypothesized that firms in later stages would use more causation and less effectuation,
these findings contradict the formulated hypotheses. The significant negative effect of growth
stage on the level of causation used is a remarkable finding, as it has often been theorized that
companies will evolve towards a more analytical logic as they grow. This finding however
can be interpreted multiple ways.
First off, it could be that companies that have grown have used less causal logic. While they
were growing may have relied on effectual behavior and as they grew because of the success
it has brought in earlier stages, they did not change this behavior. It could be that companies
that relied on causation a lot were less successful and therefore might not have reached
22
further stages. This would explain why the companies in later stages use more effectuation.
However, since the data is not collected over time from the same company it is no insight is
given on how the use of entrepreneurial logic evolved over time.
Second, the use of effectual logics might be learned as a company grows. As a company
grows the entrepreneur might gain entrepreneurial experience, which could add to the
probability that an entrepreneur will focus use effectuation. Experience gained in current
company is not included in the variable experience, as that variable measures the experience
before starting the newest venture. Therefore, it could be that the growth stage also measures
a part of the entrepreneurial experience an entrepreneur has.
5. Further analysis of the results and discussion
In this section I will discuss the results found, test possible other relationships and analyze
whether the results found are robust.
First of all, as the dynamism of the market has not been found to have an effect on the
behavior of the entrepreneur. It could be that the uncertainty faced by entrepreneurs that
affects behavior is not only related to the market in which they are active. The perception of
the dynamism of the market might be a good indication of the uncertainty related to
competition and client demands. However, besides this uncertainty measure there could be
other ambiguities that influence the behavior. One of the things that is not is taken into
account is the perception of the own ability compared to the competitor. It might be that
entrepreneurs in a very dynamic market who feel that they better at innovating than
competitors use different behavior than a venture that does not perceive themselves superior
to the rest of the market. As the confidence one has in his/her own entrepreneurial capabilities
could influence the entrepreneurial behavior (Engel et al. 2014), the perceived dynamism
might not measure the whole uncertainty concerned with a start-up.
Furthermore, a venture can also be active in a not dynamic market, which it is trying to
transform with an uncertain technology or an innovative business model. While the market in
which the company is active can be rather mature and not dynamic, the companies
proposition might still cause a lot of uncertainty. An example that might clarify this process
would be the emergence of Netflix in the late ‘90s. As Netflix was active in the video rental
23
market it could be argued that they did not face much uncertainty. However, Netflix used a
subscription model which in which clients pay month rather than per DVD and the DVDs
were delivered at home rather that rented at a store (Mayfield, 2006). Although, they were
able to analyze competitors such as Blockbusters and could study the demand for video rental
in each city. However, this might not be the favorable approach, as they still faced a lot of
uncertainty due to their innovative business model.
As we do not measure the innovativeness of the company in this studies, we are not able to
say whether this is also the case in this sample. If such relations are also applicable for the
collected data it could be that the dynamism of the market is not the best measure. Therefore,
it would be interesting to study whether the level of innovativeness of a venture has an
influence on the entrepreneurial behavior. This might help explain the lack of significant
relationship between dynamism and entrepreneurial behavior.
The results of this studies do not suggest relationships that would be expected based on the
literary research done. Most of the findings run counter the earlier hypothesized relationships.
This could be due to the environment in which the studied firms were active. To the best of
my knowledge this is the first time the antecedents of effectuation have been empirically
tested with data collected within an incubator. Therefore, it could be that relationships might
be affected by the environment in which the companies are active.
Incubators
Incubators often offer services such as advice from experienced entrepreneurs, training
programs and network events (Bergek and Norrman, 2008). The support within the incubator
and the entrepreneurial environment could help reduce the uncertainty inherent to a new
venture. It might be that a start-up within an incubator feels more confident than a new
venture that is not housed in an incubator. Therefore, it would be interesting to see how the
perceptions of uncertainty differ between companies inside and outside an incubator. If being
housed in an incubator influences the relation that an entrepreneur has to the uncertainty in a
market, the relationship might also differ for this sample. This would explain why no effect is
found for dynamism on entrepreneurial behavior.
Besides that the environment might affect the uncertainty perceived by the entrepreneur, it
could also influence the experience of the entrepreneur (Hughes et al., 2007). As discussed
earlier, incubators have a supporting function for starting entrepreneurs and often supply
24
advice and support from more experienced entrepreneurs. This might reduce the difference
between an experienced and an unexperienced entrepreneur. If entrepreneurs without
experience have access to this knowledge and support this might therefore not be an essential
variable when explaining effectual behavior.
In other words, the incubation of a company might have an influence on the use of
effectuation due to the environment and support within the incubator. Although the data
available does not allow comparison with companies outside the incubator, it is possible to
see if an effect can be found using the total hours one has spent within the incubator. If
spending time within an incubator has an effect on the decision-making logics, the
entrepreneurs who have spent less time within the incubator would be expected to use less
effectual logics. The total time spend in the incubator was measured by asking how many
hours a week one spends on average in the incubator and how long they have been housed
within the building. By multiplying these we measure the total hours spend within the
incubator. This variable was added to regression (2a) and (2b) to see if spending more time
within the incubator has an effect on the entrepreneurial behavior. As can be seen from table
7 & 8 in Appendix 2, this variable has no significant effect on either causation or
effectuation. Furthermore, adding this variable does not change the earlier found significant
relationships.
This is not an optimal way to test whether the incubator has an effect, since it leaves out the
actual activities and interactions within the incubator and does not compare with companies
outside the incubator. Nevertheless, it is interesting to see that the time spent within the
incubator does not affect the behavior significant. Besides that it adds to the robustness of the
earlier found results as adding the new variable does not change the earlier found
relationships.
Growth stage
The results show that the growth stage has a negative effect on causation. An explanation
could be that as a company grows the entrepreneurs finds out that causal logics are not
optimal. Although this would be based on extra experience as an entrepreneur, the variable
experience does not capture this experience, as it only measures the experience one has
before starting the new venture. However, one could argue that the gained experience within
the current venture will have the same effect. If this would be the case it could be suggested
25
that there is be a moderating effect of previous experience on the growth stage. This follows
the logic that if an entrepreneur is already experienced he will not learn as much from an
extra start up, whereas an inexperienced entrepreneur will learn more from the growth
process of the recent firm. To test this the variable growth stage*experience was added to
(2a) and (2b). The results of this regression are presented in Appendix 2 table 7 & 8. The
data does not support this hypothesized relationship. For both effectuation (P= 0.643) and
causation (P= 0.410) this variable was not significant, which suggests that there is no
moderating relationship growth stage on the effect of previous experience.
This implies that the effect of experience does not differ when a company is in a later growth
stage. This could be explained following two different logics. Either the growth stage of the
company does not affect the experience of the entrepreneur, and therefore the effect of
experience does not change in different stages. On the other hand, the lack of interaction
could be because the growth of the current venture adds to the experience of the entrepreneur
regardless of his previous experience. However, due to the available data it is not possible to
further investigate the relationship.
Measurement of entrepreneurial behavior
As discussed in section 3.2 the measurement for effectuation developed by Alsos et al. (2014)
did not provide data with a high Cronbach’s alpha. Therefore two statements within the
measurement were deleted to improve the internal consistency. However, to test the
robustness of the results found, the same multiple regressions have been performed with the
original scale. Although the Cronbach’s alpha of the original scale is 0.58 if all the five
questions are included, this does not necessarily mean that is does not measure the behavior.
Chandler et al. (2011) argue that effectual behavior does not always consist of simultaneous
use of all principles. Hence, the Cronbach’s alpha might suggest that the measurement is
inconsistent, whereas this might also be due to the nature of the behavior. Consequently, the
results of the regressions using this new measure can be expected to be similar to the results
of the earlier tests. This new dependent variable will be referred to as effectuation*. The
hypotheses presented in figure 1 are tested again following the same methodology, however
using a different measure for effectuation since it now includes the statements that were
deleted earlier.
26
The results of these extra tests are presented in table 9 in appendix 2, and differ in some
aspects. With the new measure for effectuation the results still show no effect of market
dynamism on the use of effectuation. The results for entrepreneurial experience on the other
hand are different. When using the new dependent variable effectuation*, entrepreneurial
experience no longer has a significant effect on effectuation. Nevertheless, the non-
significant coefficient is still negative, which was also suggested by the results presented
earlier.
Interestingly, the control variable education level has a significant negative effect on
effectuation*. This proposes that the higher the education of the entrepreneur, the less likely
he is to use effectuation. This effect is significant at a 10% level, which is, looking at the size
of the sample a relevant relationship. It can be argued that effectuation runs counter the logic
of analyzing markets taught in business schools. Therefore, it could be that the use of
effectual approaches is rejected by higher educated entrepreneurs because they have learned
to do analysis in school. Since it has been found that effectuation has a positive effect on
performance (Read et al., 2009), it would suggest that a higher level of education might not
be beneficial to the entrepreneur. Nonetheless the data does not show a significant
relationship between causation and education level. This would propose that higher level
education does not make one more likely to use analytical approaches but just more likely to
reject non analytical approaches.
Moreover, the effect of growth stage is also no longer significant using effectuation* as a
dependent variable. This shows that not every significant relation found with causation also
implies a significant relation with effectuation. Harms and Schiele (2012) also found that a
positive relation with either effectuation of causation does not mean that the opposite relation
also appears from the data.
Altogether, the measurement of effectuation is essential to the findings as shown by these
extra tests. The significant findings of the antecedents on effectuation disappear as the other
measure for effectuation is used.
27
6. Conclusion
In this studies the antecedents of effectual have been researched using empirical data from
entrepreneurs that are housed within an incubator.
The results suggest that dynamism of the market does not influence the entrepreneurial
behavior of an entrepreneur. The entrepreneurial experience of an entrepreneur on the other
hand has a negative effect on the use of effectuation and a positive effect on causal behavior.
The growth stage a firm finds itself is found to have a positive effect on effectuation and a
negative effect on causation. Besides that, no mediating effect of entrepreneurial experience
was found relationship between market dynamism and entrepreneurial behavior.
Furthermore, the results were robust when controlling for the number of hours spent within
the incubator, which suggests that presence within an incubator does not influence the
entrepreneurial behavior.
Limitations
Firstly, the used measure for effectuation in this studies has not provided consistent data. As
the results found using both measures for effectuation differ, the generalizability of the results
will be limited. A more detailed scale that measures the different proposed logics of
effectuation separately might be useful to improve the understanding of this behavior.
Secondly, as a result of the timeframe in which the data was collected, it was not possible to
study how the behavior changes over time. Although the data suggests that entrepreneurial
behavior changes as a firm grows, this can only be really measured with longitudinal studies.
Therefore, based on this research it is not possible to rule out the effect of not growing
companies on the data, as it could be that only specific companies are able to grow.
Thirdly, due to the relatively small sample that was available for the study, it was not possible
to do further classification of companies and split the sample to increase the homogeneity of
the sample. This would have helped to further clarify relations between the antecedents and
the behavior. Furthermore, an increased sample size might have helped to find significant
relationship in the interaction effects. As the sample was small it might not be optimal to
measure more subtle interaction effects.
Fourthly, since all the data was collected within an incubator it was not possible to adequately
measure the effect of incubation on the use of decision-making logics. This study attempts to
explain the effect of incubation, by taking time spent within an incubator into account.
28
Nevertheless, this measure is not able to compare not incubated and incubated ventures.
Moreover, it lacks insight in how actively one benefits from incubation. For that reason, it is
not feasible to analyze and retrieve what part of the findings can be contributed to incubation
and the environment.
Fifthly, the used measure for experience is only able to measure the entrepreneurial
experience previous to the start of the current venture. This might therefore only partially
measure the experience of the entrepreneur. Consequently, the growth stage a venture might
also measure part of experience of the entrepreneur.
Suggestions for future research
Although some empirical research has been done to test the antecedents of effectual behavior,
there is still a lot unclear or unknown.
First of all, the uncertainty an entrepreneur faces might have different aspects. Therefore, it is
interesting to measure this more thoroughly including a measure of the entrepreneurs
confidence, the innovativeness of a product or service offered and other possible factors that
could influence the uncertainty faced by an entrepreneur. This could enlarge the
understanding of uncertainty as an antecedent of effectual behavior. This is needed since
earlier empirical findings do not unambiguously suggest a significant relations.
Secondly, the effect of each antecedent on the specific effectual logics could be studied in
more depth. As not all logics are applied at the same level (Chandler et al., 2011), it may be
studied how the different logics of effectuation are affected by its antecedents. Studying these
effects is useful as the literature at this stage does not provide a profound understanding of
the antecedents of effectual behavior.
Thirdly, the effect of growth stage on decision-making logics should be further explored.
Studies using longitudinal qualitative data might be able to provide insight in how
entrepreneurs change the decision-making logics as a company grows. Furthermore,
quantitative research might be able to use longitudinal data to improve the understanding of
the how antecedents change in influence over time.
Fourthly, the influence of an incubator and the services offered within an incubator on
effectual behavior and its antecedents should be explored. Since incubators environments that
try to enhance the entrepreneurial activity of the ventures housed, it is interesting to see
29
whether companies within this environment show different entrepreneurial behavior than
companies outside an incubator. Further research on these effects might also help to put the
results found in this study in another perspective.
Appendix
Appendix 1- Results testing hypotheses
Table 1
Descriptive variables including skewness, kurtosis and Cronbach’s alpha
Variables Minimum Maximum Mean Std. Deviation Skewness Kurtosis Cronbach’s α
Dynamism 1.67 7.00 4.520 1.248 -.389 -.425 .884
Education 1 5 3.06 1.078 -.218 -1.450
Effectuation 1.40 5.60 3.573 .938 -.062 -.539 .584
Causation 2 6.20 4.516 .965 -.410 -.278 .638
Experience 1 8 2.96 1.508 1.191 1.793
Size (# FTE) 0 500 12.09 48.338 9.643 97.585
Firm Age (in years) 0 28 4.964 5.348 2.028 4.753
Growth stage (1 to 4) 1 4 2.06 .707 .384 .242
The scales for dynamism, effectuation and causation were based on 7-point Likert scales. The total score has been divided by
the amount of questions within the scale, which brings the score back between 1 and 7. The age was measured in years and
when a firm was not a year old yet the score was 0.
Table 2
Correlation matrix
Measure 1 2 3 4 5 6 7 8
1. Dynamism -
2. Education -.103 -
3. Effectuation .43 -.091 -
4. Causation .120 .077 -.392** -
5. Experience .060 -.043 -.163 .177 -
6. Firm Age .128 .017 .142 -.304** -.081 -
7. Size (#FTE) .089 .089 -.033 -.001 -.119 .408** -
8. Growth Stage -.032 .078 .179 -.335** -.057 .679** .270** -
* is significant at 5% level
** is significant at 1% level
30
Table 3
Glejser tests for homoscedasticity
Dependent variable Residuals effectuation Residuals causation
Constant .387
(.374)
.732
(.305)
Dynamism .059
(.050)
-.033
(.041)
Experience .013
(.041)
.009
(.034)
Growth Stage .012
(.088)
.071
(.072)
Education level .064
(.058)
-0.020
(.047)
The first column with results shows the results of a linear regression with the absolute residuals of regression (2a) as the dependent variable,
whereas the second show the results of a linear regression with the absolute residuals of regression (2b) as the dependent variable.
Significant relationships between the residuals and the independent variables suggest heteroscedasticity in a Glesjer test.
* is significant at 5% level
** is significant at 1% level
Table 4
Paired T- test on the difference of the mean
Pair Mean Standard deviation T-value
Effectuation - Causation -1.28348** 1.82953 -7.391
* is significant at 5% level
** is significant at 1% level
31
Table 5
Regression results with Effectuation as dependent variable
Variables (1a) (2a) (3a)
(Constant)
3.719***
(.592)
3.108***
(.667)
2.962***
(1.013)
Dynamism
.041
(.090)
.045
(.089)
.081
(.206)
Education level
-.102
(.104)
-.116
(.103)
-.118
(.104)
Experience
-.132*
(.074)
-.125*
(.073)
-.072
(.288)
Growth stage
.299*
(.157)
.295*
(.159)
Dynamism*Experience
-.012
(.062)
* is significant at a 10% level
** is significant at a 5% level
*** is significant at a 1% level
Table 6
Regression results with Causation as dependent variable
Variables
(1b) (2b) (3b)
(Constant)
3.412***
(.509)
4.392***
(.548)
4.022***
(.832)
Dynamism
.098
(.078)
.092
(.073)
.182
(.169)
Education level
.092
(.089)
.115
(.085)
.111
(.085)
Experience
.118*
(.064)
.106*
(.060)
.241
(.236)
Growth stage
-.479***
(.129)
-.489***
(.131)
32
Dynamism*Experience
-.030
(.051)
Appendix 2 – Results further analysis
Table 7 and table 8 show the extra regressions used to deepen the understanding of the results of the main
hypotheses. The regressions were again done separate with effectuation and causation as dependent variable.
The extra variables were added to (2a) and (2b) to see if these variables have an effect on the entrepreneurial
behavior. Regression (4a) and (4b) include the interaction variable Experience*Growth whereas (5a) and (5b)
include the variable Hours spent within incubator.
Table 7
Extra regressions with effectuation as dependent variable
Variables
(2a) (4a) (5a)
Constant (β0)
4.060***
(.551)
3.837
(.732)
4.107
(.556)
Dynamism -.018
(.074)
-.023
(.075)
-.025
(0.075)
Education level
-.163*
(.085)
-.161*
(.086)
-.169*
(.086)
Experience
-.088
(.061)
-.004
(.190)
-0.087
(.061)
Growth stage
.161
(.130)
.273
(.275)
.142
(.132)
Exp*Growth stage
- -.040
(.643) -
Hours within incubator
- - 5.804E-5
(0.00)
* is significant at a 10% level
** is significant at a 5% level
*** is significant at a 1% level
33
Table 8
Extra regressions with causation as dependent variable
Variables
(2b) (4b) (5b)
Constant (β0) 4.392***
(.548)
4.787***
(.728)
4.363
(.553)
Dynamism
.092
(.073)
.100
0.074
.105
(.075)
Education level
.115
(.085)
.111
(0.85)
.117
(.085)
Experience .106*
(.060)
-.042
(.189)
.111
(.061)
Growth stage -.479***
(.129)
-.678**
(.273)
-.460***
(.132)
Exp*Growth stage
- .071
(.086)
-
Hours within Incubator
- - 0.00
(0.00)
* is significant at a 10% level
** is significant at a 5% level
*** is significant at a 1% level
Table 9
Regression results with Effectuation* as dependent variable
Variables
(1a) (2a) (3a)
Constant (β0) 4.389***
(.484)
4.060***
(.551)
3.729***
(.688)
Dynamism
-.020
(.074)
-.018
(.074)
-.073
(.170)
Education level
-.155*
(.085)
-.163*
(.085)
-.161*
(.086)
Experience -.092
(.061)
-.088
(.061)
-.170
(.238)
Growth stage
.161
(.130)
.167
(.131)
Dynamism*Experience
.018
(.051)
* is significant at a 10% level
** is significant at a 5% level
*** is significant at a 1% level
34
Appendix 3 - Questionnaire
35
36
37
38
39
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