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Greiner, R., Su, X., Shen, B., & Zhou, W. (2005). Structural extension to logistic regression: discriminative parameter learning of belief net classifiers. Machine Learning, 59, 297-322. doi:10.1007/s10994-005-0469-0

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• TITLE: Modelling Entrepreneurial Attitudes in Women Entrepreneurs with Bayesian Networks

  AUTHORS: Jorge López, Alicia Ramírez, Pilar Casado

  KEYWORDS: Entrepreneurship; Attitudes; Bayesian Networks; Women; Model Comparisons

  JOURNAL NAME: Psychology, Vol.3 No.3, March 20, 2012

  ABSTRACT: The entrepreneurial attitude model is nowadays getting more attention as a framework to explain and describe 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 feasibility 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 included 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 feasibility 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.