A DEFINITION OF POTENTIAL ENTREPRENEUR FROM A PROBABILISTIC POINT OF VIEW
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Actas del XI Congreso de Metodología de las Ciencias Sociales y de la Salud ISBN 978-84-613-7589-9
A DEFINITION OF POTENTIAL ENTREPRENEUR FROM A
PROBABILISTIC POINT OF VIEW
Jorge López Puga1, Juan García García1, Carlos J. Cano1, Ana B. Gea2 y Leticia de la Fuente1
1
Universidad de Almería
2
Fundación Mediterránea-Universidad de Almería-Empresa
Entrepreneurship “is a role that individuals undertake to create organizations” and
entrepreneurial activity has been related to organizational leadership. We propose defining
entrepreneur in a probabilistic way. In our view, a person can be classified as an entrepreneur
depending on the probability s/he shows a set of traits. To test this hypothesis we have
concentrated on the definition of potential entrepreneurs: undergraduate students who think
they might be entrepreneurs in the future but have not yet owned and managed a business.
We asked a sample of undergraduate students (n = 1,111; age average = 23.37, SD = 4.28,
range = 17-56; male = 35%, female = 64.6%) to fill a questionnaire on attitudes towards
entrepreneurship. The test had ten dimensions. We built a Naïve Bayes Net Classifier and a
convergent Bayesian network in order to assess the influence of the test dimensions on the
attitude towards organizations creation. Our results show that the convergent model is able to
predict more than the 87% of the entrepreneurial tendency. We conclude that our probabilistic
model is highly efficient predicting entrepreneurship. As a result, our framework considering
entrepreneurship as a matter of probabilistic nature has been reinforced.
The fact that entrepreneurship plays an important role in the productive system has been emphasized in
several contexts (Corman, Lussier, and Nolan, 1996). Entrepreneurs have a double effect on economy.
On the one hand, entrepreneur people have the power to regulate employment, introduce innovation
or make economy more dynamic and those changes can be measured from a microeconomic point
of view. On the other hand, from a macroeconomic point of view, they enrich the business web of
countries.
The entrepreneur tries to make a profit from a creative view of the world while a manager earns a
living from a non innovative activity. More specifically, the entrepreneur is a person or group who tries
to exploit a business opportunity (McKenzie, Ugbah, and Smothers, 2007). Samuelson (1970) noted
that entrepreneurs are characterized by a vision, originality, courage and tendency to introduce instead
of inventing things. From Gartner’s (1989) point of view, an entrepreneur “is a role that individuals
undertake to create organizations”. Secondly, entrepreneurial activity has been related to organizational
leadership (Antonakis and Autio, 2006; Bjerke and Hultman, 2003). Thus, the entrepreneur is a kind
of leader who assumes the creation of organizations. However, the definition of entrepreneur is elusive
and Rogoff and Lee (1996) noted that entrepreneurship has confused researchers in social sciences the
way subatomic particles have puzzled physicists.
We propose defining entrepreneur in a probabilistic way instead of describing it as an all-or-
nothing phenomenon. In our view, a person can be classified as an entrepreneur depending on the
probability s/he shows a set of traits. To test this hypothesis we have concentrated on the definition of
potential entrepreneurs suggested by Huefner, Hunt and Robinson (1996): undergraduate students who
think they might be entrepreneurs in the future but have not yet owned and managed a business. We
used Bayesian networks as analytic tools to model entrepreneurship. More specifically, we built two
types of models (Naïve Bayes Classifier and a convergent Bayesian network) to assess the predictive
power of the dimensions of a scale on attitudes towards business creation. Our results show that the
convergent Bayes net is far more predictive than the Naïve Classifier. Overall, convergent model was
577Actas del XI Congreso de Metodología de las Ciencias Sociales y de la Salud ISBN 978-84-613-7589-9
able to predict above 87% of the tendency to entrepreneurship. As a result, our theoretical framework
of considering entrepreneurship as a matter of probabilistic nature has been reinforced.
Method
Participants
We asked a sample of 1,111 (male: 35% and female: 64.6%) undergraduate students from University
of Almería to fill a questionnaire on attitudes towards entrepreneurship. The participants’ age ranged
from 17 to 56 and the averaged age was 23.37 (SD: 4.28). All the degrees studied at the Universidad
de Almería were tested and they were classified into three clusters (a) Human and Low Sciences,
b) Technical Sciences and c) Business Sciences) to carry out a no-probabilistic stratified sampling
procedure.
Materials
We used a test (ACEMP) about attitudes towards business creation we have developed in previous
researches (i. e., Cano, García, and Gea, 2003). The test had 29 multiple-choice items with a Likert
response scale with four options. There were 13 items in a negative sense so these items were recoded
before getting the final score on attitude to entrepreneurship. After inverting the negative items the
scale sense is positive (ranging from 29 to 116), indicating a higher value a more positive attitude
towards business creation. The test had ten dimensions: negotiation, perseverance, independence,
creativity, risk taking, internal locus of control, competitiveness, risk tolerance, self-confidence and
self-organization.
Procedure
The scale towards business creation was into a booklet containing other scales aimed to collect
information under a wide research project program. The booklet was provided to the students, previous
consent of the professor, before or after sessions of compulsory subjects in their classrooms. The test
was self-administrated in groups and participants neither receive any reward nor payment for filling in
the questionnaire but a few words of thanks were given to them.
Data Analysis
We built a Naïve Bayes Classifier (it is also called Simple Bayes Classifier and divergent Bayesian
network) and a convergent Bayesian network in order to assess the influence of the test dimensions on
the attitude towards organizations creation. We used Netica 4.08 (Norsys Software Corp.) to build the
models and the parameters were estimated using the maximum likelihood procedure corrected with
Laplace’s rule. The variable of convergence or divergence, depending on the model, was the answer
to the question Do you wish to set up your own business? The answer to this question only took two
possible values, Yes or Not.
578Actas del XI Congreso de Metodología de las Ciencias Sociales y de la Salud ISBN 978-84-613-7589-9
Results
On average, the models predict the desirability of setting up a business with an 80.26% of accuracy.
However, as can be seen on Figure 1, the convergent Bayesian network produces a better rate of correct
classifications.
Figure 1. Comparison between models
These differences remain when we use statistics for the goodness of fit. In Table 1 you can
see the logarithmic loss, quadratic loss and the spherical payoff for each model. As can be seen, the
convergent model obtains better values in all of these parameters.
Table 1. Goodness of fit for the models
Model Logarithmic Loss Quadratic Loss Spherical payoff
Simple Bayes Classifier 0.5696 0.3816 0.7864
Convergent Bayesian Network 0.4332 0.2598 0.8632
As regards to the fit of the test dimensions considered independently, we can see on Table 2 that
the dimensions of negotiation and perseverance got the best values whereas the traits of confidence and
organization reach the worst values.
Table 2. Goodness of fit for each node
Deviation rate compared to class node
Node % of hits Naïve Convergent
Negotiation 87.71 14.72 0.19
Perseverance 80.94 7.95 -6.58
Independence 60.82 -12.17 -26.7
Creativity 60.02 -12.97 -27.5
Risk taking 57.08 -15.91 -30.44
Internal locus of control 56.46 -16.53 -31.06
Competitiveness 55.30 -17.69 -32.22
Risk tolerance 53.61 -19.38 -33.91
Confidence 52.09 -20.9 -35.43
Organization 51.74 -21.25 -35.78
Average -11.413 -25.943
579Actas del XI Congreso de Metodología de las Ciencias Sociales y de la Salud ISBN 978-84-613-7589-9
Discussion
We have modelled propensity to entrepreneurial activity with Bayesian networks and found that our
model is highly efficient. Comparatively, the convergent Bayesian network has reached a better level of
accuracy and fit. However, in terms of the average of goodness of fit, taking into account the deviation
accuracy in the features variables, the simple Bayesian classifier generates better values (Table 2).
In terms of the dimensions studied, we have noted that negotiation and perseverance are the best for
predicting entrepreneurship but confidence and organization are the worst. In general, our framework
considering entrepreneurship as a matter of probabilistic nature has been reinforced.
The results from this research are significant in two ways, methodologically and theoretically.
On the one hand, we have reinforced the idea of using Bayesian networks as analytic tools and we
have shown it is useful in the context of researching the key features of entrepreneurship (i. e., García,
López, Cano, Gea y De la Fuente, 2006; López, 2009). Secondly, our results can be considered as new
evidence on the validity of our scale to measure attitudes towards business creation (i. e., Cano et. al.,
2003; García, Cano y Gea, 2005). On the other hand, from a theoretical point of view, our results could
be useful to characterise the profile of potential entrepreneur (Huefner et. al., 1996). And that could
be useful to guide local, regional and national policies regarding entrepreneur activity promotion or to
design training and optimization programs in order to improve entrepreneur’s abilities.
References
Antonakis, J. and Autio, E. (2006). Entrepreneurship and leadership. In J. B. Baum, M. Frese, R. Baron
(Eds), The Psychology of Entrepreneurship (pp. 189-207). Mahwah, NJ: Laurence Erlbaum.
Bjerke, B. and Hultman C.M. (2003). A dynamic perspective on entrepreneurship, leadership and
management as a proper mix for growth. International Journal of Innovation and Learning, 1, 72-93.
Cano, C. J., García, J. and Gea, A. B. (2003). Actitudes emprendedoras y creación de empresas en los
estudiantes universitarios. Almería: Servicio de Publicaciones de la Universidad de Almería / Consejo
Social de la Universidad de Almería.
Corman, J., Lussier, R. and Nolan, K. G. (1996). Factors that encourage entrepreneurial start-ups and
existing firm expansion: a longitudinal study comparing recession and expansion periods. Academy of
Entrepreneurship Journal, 1, 43-55.
García, J., Cano, C. J. y Gea, A. B. (2005). Actitudes emprendedoras en estudiantes universitarios y
empresarios. Evidencias de validez de un instrumento. Iberpsicología, 10 (8), art. 12.
García, J., López, J., Cano, C. J., Gea, A. B. y De la Fuente, E. I. (2006, Septiembre). Aplicación de
las redes bayesianas al modelado de las actitudes emprendedoras. Comunicación presentada en el IV
Congreso de Metodología de Encuestas. Pamplona.
Gartner, W. B. (1988). “Who is an entrepreneur?” Is the wrong question. American Journal of Small
Business, 12 (4), 11-32.
Huefner, J. C., Hunt, H. K., and Robinson, P. B. (1996). A comparison of four scales predicting
entrepreneursihp. Academy of Entrepreneurship Journal, 1, 56-80.
580Actas del XI Congreso de Metodología de las Ciencias Sociales y de la Salud ISBN 978-84-613-7589-9
López, J. (2009). Modelos predictivos en actitudes emprendedoras: análisis comparativo de las
condiciones de ejecución de las redes bayesianas y la regresión logística. Tesis doctoral no publicada,
Facultad de Psicología, Universidad de Almería.
McKenzie, B., Ugbah, S. and Smothers, N. (2007). “Who is an entrepreneur” is still the wrong
question? Academy of Entrepreneurship Journal, 13, 23-43.
Rogoff, E. G., and Lee, M. S. (1996). Does firm origin matter? An empirical examination of types of
small business owners and entrepreneurs. Academy of Entrepreneurship Journal, 1, 1-17.
Samuelson, P. A. (1970). Economics (8ª ed.). New York: McGraw-Hill.
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