Psychology
2012. Vol.3, No.3, 265-271
Published Online March 2012 in SciRes (http://www.SciRP.org/journal/psych) http://dx.doi.org/10.4236/psych.2012.33037
Modelling Entrepreneurial Attitudes in Women Entrepreneurs
with Bayesian Networks*
Jorge López1, Alicia Ramírez2, Pilar Casado2
1Department of Human and Social Sciences, Universidad de Almería, Almería, Spain
2Department of Company Direction and Management, Universidad de Almería, Almería, Spain
Received December 17th, 2011; revised January 15th, 2012; accepted February 17th, 2012
The entrepreneurial attitude model is nowadays getting more attention as a framework to explain and de-
scribe new business creation. In short, the attitude model posits that the entrepreneurial behavior is a
planed action conditioned by the potential entrepreneur’s attitudes which depends on desirability and fea-
sibility beliefs. We have developed and compared three different Bayesian net models taking into account
the principles of the Shapero’s entrepreneurial event model. We have also modeled feasibility using two
different dimensions. Firstly, we considered opportunity feasibility dimension referring to the degree in
which a business would be successful attending to the market opportunities or demands. Secondly, we in-
cluded a dimension of resources feasibility referring to the feasibility of the business in terms of the
availability of possible resources to make the company a reality. The first model contained both feasibi-
lity dimensions whereas the other two only contained one dimension. Our results show that the Bayesian
model containing the two forms of feasibility is better to predict entrepreneurial intentions. Implications
in the context of promoting entrepreneurial attitudes and behaviors amongst women are finally discussed.
Keywords: Entrepreneurship; Attitudes; Bayesian Networks; Women; Model Comparisons
Introduction
Entrepreneurship is a key phenomenon in post-industrial
economies to understand its social realities and it is said that it
yields positive effects in the global and local productive mar-
kets. It seems that the time in which huge corporations made
their livings using cheap labor force in some regions is over. As
suggested by Shapero (1981), the old model of economy de-
velopment based on exploiting low qualified human resources
in some regions is less desirable in a long run as compared with
a self-renewing local economical system supported by small
and new created firms. In spite of the drawbacks concerning the
study of entrepreneurship from a methodological point of view
(i.e., Hébert & Link, 1989; Rogoff & Lee, 1996), the pheno-
menon is seen as an issue needing careful attention from so-
cial sciences because of its social usefulness. For example, Sha-
pero (1985) stated that “entrepreneurship provides communities
with the diversity and dynamism that not only assures continu-
ous development, but also an environment in which personal
freedom and individual rights can flourish” (p. 5) more than
three decades ago.
Entrepreneurial Attitudes
The concept of entrepreneur is quite elusive and difficult to
apprehend. As noted by Rogoff & Lee (1996), we can perceive
the effects of entrepreneurship but we cannot clearly see the
nature or essence of the phenomenon. The concept of entre-
preneur could be rooted to the Irish economics theorist Richard
(Hayek, 1985). From Cantillon (1755-2010) point of view, the
entrepreneur is the key to understand the dynamic of an eco-
nomical system because the entrepreneur is a social agent cop-
ing efficiently with uncertainty and making the system evolve.
Considering the entrepreneur as a social actor who flexibly
faces uncertainty is also an idea stressed by recent theorists (i.e.,
Hébert & Link, 1989; Polopolus & Emerson, 1991; Samue-
lson, 1970).
There are a wide range of definitions of entrepreneur in so-
cial science research literature. For example, Hébert & Link
(1989) using a synthetic and historical approach stated that an
entrepreneur is “someone how specializes in taking responsibi-
lity for and making judgmental decisions that affect the location,
the form, and the use of goods, resources or institutions” (p. 39).
We can find a different definition in Gartner (1988) who, from
a functional point of view, considers that an entrepreneur is “a
role that individuals undertake to create organizations” (p. 30).
From a methodological point of view, Huefner, Hunt & Robin-
son (1996) defined entrepreneurs as “those who said they were
entrepreneurs and had owned and managed one or more busi-
ness” (p. 62). In any case, and generally speaking, we could
accept the definition introduced by McKenzie, Ugbah, &
Smothers (2007) and consider that an entrepreneur is person or
group of people who seek to exploit an economic opportunity.
The traits model is probably the most commonly used in the
research field of entrepreneurship. The model states that an
entrepreneur person is that one who has or exhibits a set of
stable psychological traits. Although the model has received
several critics (i.e., Bird, 1988; Gartner, 1985, 1988), it was the
reference framework until the mid eighties to address the re-
search topic of entrepreneurship (i.e., Fuller-Love, 2006; Mc-
Kenzie et al., 2007; Thompson, 2004). For example, McCle-
*This research contains complementary results to those presented in the 4th
I
nternational Conference of Education, Research and Innovation (López,
Ramírez, & Casado, 2011) generated by the Woman Entrepreneur Research
Team at the Universidad de Almería.

J. LÓPEZ ET AL.
lland (1955, 1961) pointed out that certain psychological traits
like need for achievement were associated with the process of
business creation. Traits like achievement motivation, intelli-
gence, risk tolerance, self-efficacy, optimism or locus of control
have been powerfully related with the phenomenon of entre-
preneurship (i.e., Gottfredson, 1998; Huefner et al., 1996; Stan-
worth, Stanworth, Granger, & Blyth, 1989). However, as stressed
by Robinson, Stimpson, Huefner & Hunt (1991), we could cri-
ticize four aspects of the traits model. First, the model may be
criticized because the methodologies based on psychological
traits research were not adapted to the specific field of entre-
preneurship. As a result, the measurement instruments gener-
ally used in psychological research were used in the entrepre-
neurship context without adapting its contents threatening con-
tent validity standards. Secondly, personality theories were nei-
ther adapted from psychology to the entrepreneurship area. On
the third place, some problems with convergent validity indexes
were observed because the heterogeneity of scales used to
measure the same construct correlated poorly. Finally, the traits
model did not pay too much attention to the interactive theories
which were beginning to develop in those days.
A new perspective originated in the context of social psy-
chology research suggested that the new business creation
could be explained as a conscious and intentional process in
constant interaction with social environment (i.e., Bird, 1988;
Krueger & Brazeal, 1994; Krueger & Carsrud, 1993; Krueger,
Reilly, & Carsrud, 2000; Liñán, Battistelli, & Moriano, 2008).
Licht & Siegel (2006) point out that the origin of that frame-
work could be traced to the Shapero & Sokol’s (1982) pioneer-
ing work. Shapero & Sokol (1982) proposed that business crea-
tion was an activity culturally conditioned based on the system
of values encouraged by society. In Shapero & Sokol’s opinion,
human regular behavior is guided by a kind of inertia. Never-
theless, that inertia is, under certain circumstances, broken
suddenly in a person’s life span causing a type of displacement
which forces the person to choose between different behavioral
alternatives (Shapero, 1975). In the Shapero & Sokol’s “entre-
preneurial event” model, the decision to create a new business
depends on its desirability and feasibility (Shapero & Sokol,
1982). The model was optimized approximately a decade later
by Krueger & Carsrud (1993) and Krueger & Brazeal (1994)
who joined the Shapero & Sokol’s entrepreneurial event model
with the Theory of Reasoned Action (Ajzen & Fishbein, 1980;
Fishbein & Ajzen, 1975). As a result, the new business creation
process was understood as an intentional action mediated by the
subjective perceptions of the potential entrepreneur.
Bayesian Ne ts
Bayesian networks originated in the field of artificial intelli-
gence as statistical tools to model and manage uncertainty.
From a technical point of view, Bayes nets (also referred as
probabilistic causal nets, Bayesian expert systems, probabilistic
expert systems, causal nets, belief nets or influence diagrams)
are statistical tools belonging to the family of Highly Structured
Stochastic Systems (Cowell, Dawid, Lauritzen & Spiegelhalter,
1991) and they can represent both the quantitative and qualita-
tive dimension of reality to solve problems or to make deci-
sions under uncertainty.
Firstly, attending to its qualitative dimension, a Bayes net is
a graph which means it is a graphical representation of a prob-
lem. Although there is no widely accepted consensus about the
definition (i.e., Harary, 1969; Gould, 1988; Spirtes, Glymour,
& Scheines, 2000; Tutte, 1984; Xiang, 2002), we could say a
graph is a pair G = (V, E) where V is a set of vertices, nodes or
variables and E is a set of edges. Additionally, a Bayes net is a
specific type of graph called directed acyclic graph or DAG.
The directionality of the graphical structure refers to the fact
that links between variables or nodes are directed and it is rep-
resented by arrows. As a result, an arrow from a node A that
point to another B means that B depends (statistically) on A. On
the other hand, in a DAG cycles or loops are not allowed, that
is say if you begin a directed path from a particular node you
could never come to the initial point. Taking into account these
constraints, three types of basic connections are allowed in a
Bayesian network: serial (also known as causal-chain), diverg-
ing (also known as common-cause model) and converging (also
called common-effect model). These three basic structures be-
have differently when propagating probabilities and provide a
robust architecture to update the model’s parameters based on
the principle of conditional independence.
After specifying qualitative structure it is necessary to deter-
mine the quantitative component in a Bayes net (Cowel et al.,
1999). We could define the quantitative structure of a Bayesian
network attending to three different aspects. First, probability is
considered as a degree of belief about an event as opposed to
the classical or frequentists view (i.e., Dixon, 1964; Heckerman,
1995). Secondly, a Bayes net entails a set of conditioned prob-
ability functions. That is to say, every variable in a Bayesian
net is defined parametrically by a conditional probability func-
tion in the form of a conditional probability table (CPT) which
represents the variable’s state’s probability as a function of
others variables’ states. Finally, Bayes’ theorem is the basic
rule to make inferences and to update probabilities in a Bayes-
ian network. It comes from the concept of conditional probabil-
ity applied to the intersection of related sets and it is due to the
contributions of mathematician Thomas Bayes (1763). Bayes’
rule is useful when we want to know something about an un-
certain event by taking into account evidence from another
related event.
Although Ward Edwards (1998) pointed out more than a
decade ago that Bayesian networks had promising perspectives
for psychology, these techniques are not very common in psy-
chology literature as data analysis tools. However, there has
been an increasingly amount of papers trying to highlight Bayes
nets potential to serve as a normative reference model of human
and animal causal cognition (i.e. Glymour, 2001, 2003; Gopnik,
Glymour, Sobel, Schulz, Kushnir, & Danks, 2004; Gopnik &
Schulz, L, 2004; Holyoak & Cheng, 2011; Penn & Povinelli,
2011). Additionally, despite the fact that a number of papers
have been published dealing with an underlying substantive
psychological point of view, most of them were focused on the
computational perspective. Thus, efforts have been made to
develop student models in the field of Intelligent Tutoring Sys-
tems (i.e., Conati, Gertner, & VanLehn, 2002; Martin & Van-
Lehn, 1995; Mislevy & Gitomer, 1996), in psycholinguistics
(i.e., Narayan & Jurafsky, 1998, 2002; Jurafsky, 1996), in psy-
chological diagnostics (Mani, McDermmott, & Valtorta, 1997),
and to predict long-term consumers behaviors (Baesens et al.,
2004).
Objectives and Hypothesis
Our main objective is to model entrepreneurial attitudes in
266

J. LÓPEZ ET AL.
women using Bayesian networks. Although the woman entre-
preneur is not a new phenomenon (Gartner, 1985), and in spite
of the fact that most of the research in the field try to study the
role of the woman in the process of a new venture creation (i.e.
Veciana, Aponte & Urbano, 2005), it is also true that the phe-
nomenon has received less attention than the generic study of
entrepreneurship (Gewin, 2012; López, Ramirez, & Casado,
2011). On the other hand, we want to use Bayesian networks as
a relatively new technique to model entrepreneurial attitudes
which provides some advantages compared with classical
methods (i.e., López & García, in press; López, García, Cano,
Gea, & De la Fuente, 2010).
As stated above, the Shapero’s entrepreneurial event model
identifies feasibility and desirability as the two main constructs
affecting the intention to set up a new business (Krueger &
Brazeal, 1994; Krueger & Carsrud, 1993; Shapero, 1982). As a
result, we expect a Bayesian network representing those phe-
nomena will predict new business creation intention reliably.
Secondly, we propose to introduce a differentiation in the con-
struct feasibility to accommodate earlier proposals of the entre-
preneurial event model and recent theoretical developments.
Specifically, as Shapero (1981) pointed out, we suggest consid-
ering feasibility as a composed construct integrated by a part
referred to resources and another to opportunity (Cohen &
Winn, 2007; McMullen & Shepherd, 2006). In that sense, en-
trepreneurial intention would depend on desirability and feasi-
bility involving the latter one two components: opportunity
(referred to how the new business is feasible in terms of creat-
ing new products or services relative to the potential compe-
tence) and resources (alluding to the degree in which the entre-
preneur considers the set up of the new business will be possi-
ble in terms of economic, knowledge, and social resources). We
propose it is practically useful to differentiate between these
dimensions and we hypothesize the best model would be that
one considering the two aspects of feasibility at the same time.
Method
Participants
A sample of 140 women entrepreneurs was asked to fill in an
electronic questionnaire. Their ages ranged between 19 and 66
years (M = 42.38, SD = 9.12) and they all were from Andalusia
Autonomic Region in Spain. The object population for that
research came from a directory of Andalusian woman entre-
preneurs published in 2009 as a special issue in the magazine
Mujer Emprendedora (Vol. 107, September, ISSN: 1575-9377).
The original database contained 808 records of businesses
managed by women in the Autonomic Region of Andalusia
(Spain). After correcting repeated records and invalid informa-
tion a final data set containing 587 records was used in the
study. As a result, the response rate was 23.85%.
Instruments
An electronic questionnaire developed with the LimeSurvey
platform (Fa. Carsten Schmitz) was used. The questionnaire
contained a set of individual items and scales that were used to
create three different Bayesian networks. The items were taken
from Morales’ (2008) research about the academic entrepre-
neur.
To assess the relatives and peers influence we used two di-
chotomic items for which participants had to answer yes or not:
my close relatives were or have been entrepreneurs, and I knew
cases of other women in my cl ose environment who had created
their own business. Entrepreneurial intention was operational-
ised with the item which of the following statements best de-
scribes your case? There were three options in that item: 1) The
decision to create a business was an unexpected one, it de-
pended on the circumstances; 2) I had sometimes thought about
creating my own business but I considered it as an unlikely
possibility; 3) I had always had the idea of setting up a business
in mind; however the first two alternatives were recoded into
one category to differentiate between the true type of entrepre-
neur defined by Shane (2004).
The opportunity feasibility scale contained four items asking
how important were relevant opportunity situations (for exam-
ple, the discovery of a new method of production) when con-
sidering the creation of a new business (Chiesa & Piccaluga,
2000; Heirman & Clarysse, 2004). Resources feasibility was
measured with eight Likert-type items from Autio & Kauranen
(1994) plus an item about personal assets. Six items were used
to get a measure of desirability taken from Autio & Kauranen
(1994) and Radosevich (1995). A set of eleven potential obsta-
cles (for example, creating a distribution network, raising
money) were used to measure the degree in which woman en-
trepreneurs considered they faced difficulties when they created
the business. All those scales were answered in a four point
Liker-type scale where the options ranged from Not important
at all (1) to Very important (4). Finally, a scale of perceived
risk containing three items was used to evaluate the degree in
which the entrepreneur considered the venture creation a risky
choice for the company itself, personal capital and professional
career development. Items were answered in a four point an-
swer scale (0 = Nothing at all, 3 = A lot). Table 1 shows de-
scriptive statistics for the scales scores.
Procedure
The directory was tabulated and incorporated to a database of
potential participants in the LimeSurvey platform. Then, three
different emails templates were developed to manage the invi-
tation and data collection process. A personalized invitation
mail was written for each participant indicating the name of the
study and the aim of the research. The email of the survey de-
signer was available in case any participant had a question or
inquiry. Potential participants could access to the online ques-
tionnaire just by clicking in a hyperlink. This invitation email
also contained a hyperlink to allow potential participants to
Table 1.
Descritive statistics and internal consistency coefficients.
Statistics
Scales M SD LLαa αb ULαc
Opportunity feasibility 11.212.41 0.468 0.5700.659
Resources feasibility 19.534.70 0.669 0.7280.782
Desirability 13.013.99 0.727 0.7770.822
Risk 8.06 2.14 0.625 0.7050.771
Obstacles 24.385.13 0.674 0.7330.787
aLower limit confidence interval (90%) for Cronbach’s α coefficient of internal
consistency (Feldt, Woodruff, & Salih, 1987); bCronbach’s α coefficient of in-
ternal consistency; c. Upper limit confidence interval (90%) for Cronbach’s α
coefficient of internal consistency (Feldt et al., 1987).

J. LÓPEZ ET AL.
delete their record from our database and to avoid receiving
more emails related with the research. A confirmation email
was also written which was sent automatically from the server
once the participant had successfully completed the question-
naire. Finally, a reminder email was also written to remain po-
tential participants to fill in the survey. Two and four weeks
after the first invitation email, a reminder email was sent to
those participants who had not filled in the questionnaire. The
final database contained information collected during a period
of six weeks. No reward, apart from verbal acknowledges at the
end of de questionnaire and in the confirmation email, was
given to participants for participating in the study.
Data Analysis
The first step to take when defining a Bayesian network con-
sists in defining its qualitative structure (Cowell et al., 1999).
Bayesian network structural specification maybe defined using
automatic procedures in the form of computational algorithms
(i.e., Cooper & Herskovits, 1992; Cowell et al.,1999; Spirtes et
al., 2000) or based on the judgments of experts (i.e., Edwards,
1998; Nadkarny & Shenoy, 2004). We have followed a theory-
driven method to specify the models. Specifically, our models
are based on the Shapero’s entrepreneurial event model which
suggests that entrepreneurial intention depends on feasibility
and desirability (Krueger & Brazeal, 1994; Krueger & Carsrud,
1993; Shapero, 1975; Shapero & Sokol, 1982). As a result, we
tested three different models depicted in Figure 1. Model 1
could be considered the complete model in which entrepreneu-
rial intention depends on desirability and either opportunity
feasibility and resources feasibility. Additionally, desirability
would depend on perceived risk, peer influence and relatives
influence whereas the two types of feasibility would depend on
perceived obstacles. Model 2 and 3 consider alternatively the
relevance of opportunity and resources feasibility indepen-
dently.
Scales total scores were discretised using percentile 33 and
66 in all the cases and parameter were assessed using the ma-
ximum likelihood estimation (or observed frequency estimation,
OFE) corrected with the Laplace Law of succession (Greiner,
Su, Shen, & Zhou, 2005; Ng & Jordan, 2002). We used Netica
Application for Microsoft Windows (Norsys Software Corp.)
version 4.16 to run the analysis. To evaluate the goodness of fit
we used the hit classification rate, and scoring measures like the
logarithmic loss, quadratic loss and spherical payoff (Pearl,
1978). The logarithmic loss ranges between zero and infinity
indicating zero the best fit, the quadratic loss measure ranges
between zero and two indicating zero the best fit whereas the
spherical payoff varies between zero and one where one refers
to the best fit. We also carried out a sensitivity analysis to
evaluate the impact of each variable in the model on the entre-
preneurial intentions variable. Entropy reduction (or mutual
information) is referred to the expected reduction in the query
variable (intentions in our case) due to a finding in any other
variable of the model (Pearl, 1991). It varies between zero
(meaning complete independence between the query and the
instantiation variable) and the entropy value of the query with-
out any evidence about the model. Secondly, we will also
compute the variance of node belief and the RMS change of
belief (Neapolitan, 1990). Both statistics range from zero to one
where the closer the value to zero, the strongest the indepen-
dence between the query and instantiation variable.
MODEL I
Desirability
Perc ei ved Risk
Peers Influence
Relatives InfluenceEnt repreneurial Intent i on
Resources Feasibility
Opportunity Feasibility
Obst acles
MODEL II
Desirability
Perc ei ved Risk
Peers Influence
Relat ives InfluenceEnt repreneurial Intent i on
Resourc es F eas i bi li ty
Obst acles
MODEL III
Desirability
Perc ei ved Risk
Peers Influence
Relat ives InfluenceEntrepreneurial Intent i on
Opportunity Feasibility
Obst acles
Figure 1.
Alternative models.
Results
Table 2 depicts the measurements of fit for the three used
models. As can be seen, Model I containing variables referred
to resources and opportunity feasibilities produces the better
results. That model is able to correctly classify woman entre-
preneurs in 75% of the cases. Additionally, logarithmic loss and
quadratic loss measurements are the lower and the spherical
payoff is the higher compared with the other two models. On
the other hand, Model II (considering resources feasibility
alone) and Model III (modeling opportunity feasibility) yield
similar goodness of fit parameters. These results suggest that
Model I considering feasibility as a composite construct of two
different types of feasibilities (opportunity and resources) is a
better model than those taking into account either of the dimen-
sions separately.
In order to explore the relative influence of each variable in
Model I on entrepreneurial intention, we carried out a sensitiv-
ity analysis whose results are shown in Table 3. As can be ap-
preciated, and as it is predicted by the theory, opportunity fea-
sibility, desirability and resources feasibility are the three most
influential variables in the model. There are a second set of
variables, obstacles and relatives influence, accounting for a
relatively important degree of influence whereas perceived risk
and peers influence are the variables whose influence on entre-
preneurial intention is lower.
Discussion
The most striking result in our research is that the differen-
tiation between opportunity and resources feasibility is useful to
predict entrepreneurial intention. Given that Model I produced
better goodness of fit parameters, we could conclude feasibility
is, at least for women entrepreneur, integrated by a dimension
of opportunity and another one of resources. That evidence is
268

J. LÓPEZ ET AL.
Table 2.
Models goodness of fit.
Model
Statistics I II III
Hit rate 75.00% 71.74% 72.83%
Logarithmic loss 0.5406 0.5888 0.5689
Quadratic loss 0.3596 0.4002 0.3826
Spherical payoff 0.7983 0.7739 0.7847
Table 3.
Sensitivity test results.
Statistics
Variable ENTa VARb RMSc
Opportunity Feasibility 1.07 × 10–2 3.67 × 10–3 6.06 × 10–2
Desirability 7.29 × 10–3 2.49 × 10–3 4.99 × 10–2
Resources Feasibility 1.12 × 10–3 3.84 × 10–4 1.96 × 10–2
Obstacles 7.60 × 10–4 2.60 × 10–4 1.61 × 10–2
Relatives Influence 1.90 × 10–4 6.53 × 10–5 8.08 × 10–3
Perceived Risk 7.00 × 10–5 2.39 × 10–5 4.89 × 10–3
Peers Influence 3.00 × 10–5 9.00 × 10–6 3.00 × 10–3
aEntropy reduction; bVariance of beliefs; cRMS change of belief.
compatible with previous research on entrepreneurial attitude
models (Krueger & Brazeal, 1994; Krueger & Carsrud, 1993;
Krueger et al., 2000) and earlier theoretical proposals about op-
portunity feasibility (Shapero, 1981). Additionally, our results
agree with recent emphasis on the opportunity facet of entre-
preneurship (McMullen & Shepherd, 2006) and future research
should shed light on that phenomenon in an attitudinal holistic
model of entrepreneurial intention.
The fact that our study was focused on the figure of the
woman entrepreneur is a mixed blessing—it is worth research-
ing the profile of woman entrepreneur given it seems that it had
been a relatively neglected topic in the past research agenda
(López et al., 2011), but we cannot discern if the observed re-
sults can be generalized to men. As regards to the first argu-
ment, we consider education as a key point to intervene in order
to enhance the role of women in business creation and to
strength our knowledge of the relationship between gender and
business creation. For example, it has been recently shown that
early school experiences have a determinant impact on entre-
preneurial motivation amongst women (Díaz-Pérez & Gon-
zález-Morales, 2011) so knowing and understanding the proc-
ess underling the development of entrepreneurial attitudes in
women will enhance our chances to intervene and improve the
phenomenon. In that sense, the study of the potential entrepre-
neur woman is one of the strategic points we should consider in
the research agenda. Finally, we would like to suggest the pos-
sibility women have in the new field of sustainable entrepre-
neurship (Shepherd & Patzelt, 2011) because, as observed by
Glodež, Hribar & Dolinšek (2011), women seem to show better
attitudes and predispositions to engage themselves in environ-
mentally concerned jobs. As a consequence, future studies
should try to elucidate if there exist any relationship between
women entrepreneurs and environmental and social entrepre-
neurship.
Finally, we would like to stress our study is innovative in the
sense Bayes nets are not quite common in psychological re-
search as a statistical modeling tool. For example, Aguilera et al.
(in press) noted that health sciences, life sciences, sociology
and educative areas are far less concerned with using Bayesian
networks as modeling tools when compared with computer
science, mathematics or engineering. However, some contribu-
tions have been made to introduce Bayesian networks in the
research area of the entrepreneurship psychology (López &
García, in press; López & García, 2011; López, García, Cano &
De la Fuente, 2010). We propose to introduce progressively the
use of Bayesian networks in psychological research as a mod-
eling statistical tool and we hope our study presented here could
contribute to that end.
Acknowledgements
The authors would like to thank Sigried Lievens whose com-
ments and suggestions have inspired that research.
REFERENCES
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., & Salmerón,
A. (2011). Bayesian networks in environmental modelling. Envi-
ronmental Modeling and Software , 26, 1376-1388.
doi:10.1016/j.envsoft.2011.06.004
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predict-
ing social behavior. Englewood, NJ: Prentice-Hall.
Autio, E., & Kauranen, I. (1994). Technologist-entrepreneurs versus
nonentrepreneurial technologists: Analysis of motivational triggering
factors. Entrepreneurship y Regional Development, 6, 315-328.
doi:10.1080/08985629400000019
Baesens, B., Verstraeten, G., Van den Poel, D., Egmont-Petersen, M.,
Van Kenhove, P., & Vanthienen, J. (2004). Bayesian classifiers for
identifying the slope of the customer life cycle of long-life customers.
European Journal of Operational Re se arch, 156, 508-523.
doi:10.1016/S0377-2217(03)00043-2
Bayes, T. (1763). An essay towards solving a problem in the doctrine of
chances. Philosophical Transactions , 53, 370-418.
doi:10.1098/rstl.1763.0053
Bird, B. (1988). Implementing entrepreneurial ideas: The case for in-
tention. Academy of Management Review, 13, 442-453.
Cantillon, R. (2010). An essay on economic theory. Auburn, AL: Lud-
wig von Mises Institute.
Chiesa, V., & Piccaluga, A. (2000). Exploitation and diffusion of public
research: The chase of academic spin-offs companies in Italy. R & D
Management, 30, 329-339. doi:10.1111/1467-9310.00187
Cohen, B., & Winn, M. I. (2007). Market imperfections, opportunity
and sustainable entrepreneurship. Journal of Business Venturing, 22,
29-49. doi:10.1016/j.jbusvent.2004.12.001
Conati, C., Gertner, A., & VanLehn, K. (2002). Using bayesian net-
works to manage uncertainty in student modeling. Modeling and
User-Adapted Interaction, 12, 371-417.
doi:10.1023/A:1021258506583
Cooper, G. F., & Herskovits, E. (1992). A bayesian method for the
induction of probabilistic networks from data. Machine Learning, 9,
309-347. doi:10.1007/BF00994110
Cowell, R. G, Dawid, A. P. Lauritzen, S. L & Spiegelhalter, D. J. (1999).
Probabilistic networks and expert systems. Harrisonburg, VA: Sprin-
ger.
Díaz-Pérez, F. M., & González-Morales, O. (2011). Desire of a mana-
gerial position and entrepreneurial motivation: The influence of gen-
der and school type in the Canary Islands Young people. iBusiness, 3,
184-193.
Dixon, J. R. (1964). A programmed introduction to probability. New
York: John Wiley & Sons.
Edwards, W. (1998). Hailfinder. Tools for and experiences with bayes-
ian normative modeling. American Psychologist, 53, 416-428.

J. LÓPEZ ET AL.
doi:10.1037/0003-066X.53.4.416
Feldt, L. S., Woodruff, D. J., & Salih, F. A. (1987). Statistical inferences
for coefficient alpha. Applied Psychological Measurement, 11, 93-103.
doi:10.1177/014662168701100107
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behave-
ior: An introduction to theory and research. Reading, MA: Addison-
Wesley.
Fuller-Love, N. (2006). Management development in small firms. In-
ternational Journal of M a n ag e m e n t Reviews, 8, 175-190.
doi:10.1111/j.1468-2370.2006.00125.x
Gartner, W. B. (1985). A conceptual framework for describing the phe-
nomenon of new venture creation. Academy of Management Review,
10, 696-706.
Gartner, W. B. (1988). “Who is an entrepreneur?” Is the wrong ques-
tion. American Journal of S ma ll Business, 12, 11-32.
Gewin, V. (2012). Women in business: Finding a way in. Nature, 481,
103-105. doi:10.1038/nj7379-103a
Glodež, N., Hribar, T. & Dolinšek, S. (2011). Young people’s (and wo-
men in particular) priorities and choices related to science and tech-
nology education—Case of Slovenia. Proceedings of the 4th Interna-
tional Conference of Education, Research and Innovation, Madrid,
14-16 November 2011, 4778-4787.
Glymour, C. (2001). The mind’s arrows. Bayes nets and graphical
causal models in psychology. Cambridge, MA: MIT Press.
Glymour, C. (2003). Learning, prediction and causal Bayes nets. Trends
in Cognitives Sciences, 7, 43-48.
doi:10.1016/S1364-6613(02)00009-8
Gopnik, A., & Schulz, L. (2004). Mechanisms of theory formation in
young children. Trends i n Cognitives Sciences, 8, 371-377.
doi:10.1016/j.tics.2004.06.005
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., &
Danks, D. (2004). A theory of causal learning in children: Causal and
bayes nets. Psychological Review, 111, 3-32.
doi:10.1037/0033-295X.111.1.3
Gottfredson, L. S. (1998). The general intelligence factor. Scientific
American Presents, 9, 24-29.
Gould, R. (1988). Graph theory. Menlo Park, CA: Benjamin/Cummings.
Greiner, R., Su, X., Shen, B., & Zhou, W. (2005). Structural extension
to logistic regression: Discriminative parameter learning of belief net
classifiers. Mach ine Lea rnin g, 59, 297-322.
doi:10.1007/s10994-005-0469-0
Harary, F. (1969). Graph theory. Philippines: Westview Press.
Hayek, F. A. (1985). Richard Cantillon. The Journal of Libertarian
Studies, 7, 217-247.
Hébert, R. F., & Link, A. (1989). In search of the meaning of entrepre-
neurship. Small Business Economics, 1, 39-49.
doi:10.1007/BF00389915
Heckerman, D. (1995). A tutorial on learning with bayesian networks
(Tech. Rep. MS-TR-95-06). Redmon, WA: Microsoft Research.
Heirman, A., & Clarysse, B. (2004). How and why do research-based
start-ups differ at founding? A resource-based configurational per-
spective. Journal o f Technology Transfer, 29, 247-268.
doi:10.1023/B:JOTT.0000034122.88495.0d
Holyoak, K. J., & Cheng, P. W. (2011). Causal learning and inference
as a rational process: the new synthesis. Annual Review of Psychol-
ogy, 62, 135-163. doi:10.1146/annurev.psych.121208.131634
Huefner, J. C., Hunt, H. K., & Robinson, P. B. (1996). A comparison of
four scales predicting entrepreneursihp. Academy of Entrepreneur-
ship Journal, 1, 56-80.
Jurafsky, D. (1996). A probabilistic model of lexical and syntactic
access and disambiguation. Cognitive Science, 20, 137-194.
doi:10.1207/s15516709cog2002_1
Krueger, N. F., & Brazeal, D. V. (1994). Entrepreneurial potential and
potential entrepreneurs. Entrepreneurship Theory and Practice, 18,
91-104.
Krueger, N. F., & Carsrud, A. L. (1993). Entrepreneurial intentions:
Applying the theory of planned behavior. Entrepreneurship and Re-
gional Development, 5, 315-330.
doi:10.1080/08985629300000020
Krueger, N. F., Reilly, M. D., & Carsrud, A. L. (2000). Competing
models of entrepreneurial intentions. Journal of Business Venturing,
15, 411-432. doi:10.1016/S0883-9026(98)00033-0
Liñán, F., Battistelli, A., & Moriano, J. A. (2008). Entrepreneurial in-
tentions in Europe. In J. A. Moriano, M. Gorgievski & M. Lukes
(Eds.), Teaching psychology of entrepreneurship: Perspectives from
six European countries (pp. 21-43). Madrid: National Open Uni-
versity (UNED).
López, J., & García, J. (2011). Comparative performance of Bayes net
and logistic regression to predict entrepreneurship. Communication
presented at the 12th European Congress of Psychology, Istanbul,
4-8 July 2011.
López, J., & García, J. (in press). Comparative study on entrepreneurial
attitudes modelled with logistic regression and Bayes nets. The
Spanish Journal of Psychology.
López, J., García, J., Cano, C. J., Gea, A. B., & De la Fuente, L. (2010).
A definition of potential entrepreneur from a probabilistic point of
view. Proceedings of the XI Congreso de Metodología de las Cien-
cias Sociales & de la Salud, Istanbul, 4-8 February 2010, 577-581.
López, J., Ramírez, A., & Casado, M. P. (2011). Business characteris-
tics correlating with the education level in women entrepreneurs.
Proceedings of the 4th International Conference of Education, Re-
search and Innovation, Madrid, 14-16 November 2011, 5049-5046.
Mani, S., McDermmott, S., & Valtorta, M. (1997). MENTOR: A baye-
sian model for prediction of mental retardation in newborns. Re-
search in Developmental Disabilities, 18, 303-318.
doi:10.1016/S0891-4222(97)00012-7
Martin, J., & VanLehn, K. (1995). Sutdent assessment using bayesian
nets. International Journal of Human-Computer Studies, 42, 575-591.
doi:10.1006/ijhc.1995.1025
McClelland, D. (1955). Some social consequences of achievement
motivation. In M. R. Jones (Ed.), Nebraska symposium on motivation.
Lincoln: University of Nebraska Press.
McClelland, D. (1961). The achieving society. New York: Free Press.
McKenzie, B., Ugbah, S., & Smothers, N. (2007). “Who is an entre-
preneur” is still the wrong question? Academy of Entrepreneurship
Journal, 13, 23-43.
McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and
the role of uncertainty in the theory of the entrepreneur. Academy of
Management Review, 31, 132-152.
doi:10.5465/AMR.2006.19379628
Mislevy, R. J., & Gitomer, D. H. (1996). The role of probability-based
inference in an intelligent tutoring system. User-Mediated and User-
Adapted Interaction, 128, 253-282.
Morales, S. (2008). The academic entrepreneur and its decision to cre-
ate a spin-off: An analysis of the Spanish case. Unpublished Doctoral
Dissertation, Facultat d’Economia, Universitat de Valencia, Valencia.
Nadkarni, S., & Shenoy, P. P. (2004). A causal mapping approach to
constructing bayesian networks. Decision Support Systems, 38, 259-
281. doi:10.1016/S0167-9236(03)00095-2
Narayan, S., & Jurafsky, D. (1998). Bayesian models of human sentence
processing. Proceedings of the 20th Annual Meeting of the Cognitive
Science Society. Madison, NJ: Lawrence Erlbaum Associates.
Narayan, S., & Jurafsky, D. (2002). A bayesian model predicts human
parse preference and reading times in sentence processing. Advances
in Neural Information Processin g, 14, 59-65.
Neapolitan, R. E. (1990). Probabilistic reasoning in expert systems:
Theory and algorithms. New York: Wiley & Sons.
Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative
classifiers: A comparation of logistic regresion and naive Bayes. Ad-
vances in Neural Information Pro ce ssing Systems, 14, 841-848.
Pearl, J. (1978). An economic basis for certain methods of evaluating
probabilistic forecasts. International Journal of Man-Machine Stud-
ies, 10, 175-183. doi:10.1016/S0020-7373(78)80010-8
Pearl, J. (1991). Probabilistic reasoning in intelligent systems: Networks
of plausible inference (2nd ed.). San Mateo, CA: Morgan Kaufmann.
Penn, D. C. & Povinelli, D. J. (2011). Causal cognition in human and
nonhuman animals: A comparative, critical review. Annual Review of
Psychology, 58, 97-118.
doi:10.1146/annurev.psych.58.110405.085555
Polopolus, L. C., & Emerson, R. D. (1991). Entrepreneurship, sanctions,
and labor contracting. Southern Journal of Agricultural Economics,
12, 57-68.
270

J. LÓPEZ ET AL.
Radosevich, R. (1995). A model for entrepreneurial spin-offs from pub-
lic technology sources. International Journal Technological Man-
agement, 10, 879-893.
Robinson, P. B., Stimpson, D. V., Huefner, J. C., & Hunt, H. K. (1991).
An attitude approach to the prediction of entrepreneurship. Entre-
preneurship Theory and Practice, 15, 13-31.
Rogoff, E. G., & Lee, M. S. (1996). Does firm origin matter? An empi-
rical examination of types of small business owners and entrepre-
neurs. Academy of Entrepreneurship Journal, 1, 1-17.
Samuelson, P. A. (1970). Economics (8th ed.). New York: McGraw-
Hill.
Shane, S. (2004). Academic entrepreneurship: University spinoffs and
wealth creation. Cheltenham, UK: Edward Elgar.
Shapero, A. & Sokol, L. (1982). The social dimensions of entrepre-
neurship. In C. Kent, D. Sexton, & K. H. Vesper (Eds.), The ency-
clopedia of entrepreneurship (pp. 72-90). Englewood Cliffs, NJ:
Prentice-Hall.
Shapero, A. (1975). The displaced, uncomfortable entrepreneur. Psy-
chology Today, 9, 83-133.
Shapero, A. (1981). Entrepreneurship. Key to self-renewing economies.
Economic Development Commentary, 5, 19-23.
Shapero, A. (1985). Why entrepreneurship? A worldwide perspective.
Journal of Small Business Management, 23, 1-5.
Shepherd, D. A. & Patzelt, H. (2011). The new field of sustainable
entrepreneurship: Studying entrepreneurial action linking “what is to
be sustained” with “what is to be developed”. Entrepr eneurship Theo-
ry and Practice, 35, 137-163. doi:10.1111/j.1540-6520.2010.00426.x
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction
and search (2nd ed.). Cambridge, MA: MIT Press.
Stanworth, J., Stanworth, C., Granger, B., & Blyth, S. (1989). Who
becomes an entrepreneur? International Small Business Journal, 8,
11-22. doi:10.1177/026624268900800101
Thompson, J. L. (2004). The facets of the entrepreneur: indentifying
entrepreneurial potential. Management Decision, 42, 243-258.
doi:10.1108/00251740410515861
Tutte, W. T. (1984). Graph theory. Cambridge: Cambridge University
Press.
Veciana, J. M., Aponte, M., & Urbano, D. (2005). University students’
attitudes towards entrepreneurship: A two countries comparison. In-
ternational Entrepreneurship and Management Journal, 1 , 165-182.
doi:10.1007/s11365-005-1127-5
Xiang, Y. (2002). Probabilistic reasoning in multiagent systems. A gra-
phical models approach. Cambridge: Cambridge University Press.
doi:10.1017/CBO9780511546938