A DEFINITION OF POTENTIAL ENTREPRENEUR FROM A PROBABILISTIC POINT OF VIEW Jorge López Puga 1, Juan García García 1, Carlos J. Cano 1, Ana B. Gea 2 y Leticia de la Fuente 1 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-ornothing 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 577
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. Participants Method 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. 578
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 -11.413-25.943 579
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. 580
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. 581