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Learning a Bayesian Structure to Model Entrepreneurial Intentions towards Business Creation

Learning a Bayesian Structure to Model Entrepreneurial Intentions Towards Business Creation

Abstract

Economic growth in most advanced countries is driven by small and medium enterprises, and most countries prioritize entrepreneurship for economic growth and innovation. This is very apparent in the United Arab Emirates, where an average of about 39% of adults want to start a business in the next three years. As such, Entrepreneurial intentions have been a major focus of research, but they have always been studied using generic models. We use Bayesian Networks (BN) to model entrepreneurial intentions as it provides an advantage over classical methods. To our knowledge, no study has used the BN framework to model entrepreneurial intentions within the UAE. Using the Theory of Planned Behavior (TPB) as a foundation, a cross-sectional study was conducted among a random sample of 324 Emirati University students in the UAE. We implemented Unsupervised Structural learning within BayesiaLab using the SopEQ unsupervised algorithm to minimize the “Minimum Description Length” (MDL) score. Our model provides confirmation of and more robust statistical support for existing theoretical frameworks. It helped not only find relationships among the different entrepreneurial factors but also assess the effects of changes in these variables on intentions. One of the strengths of our study is the inclusion of attitudes toward entrepreneurship and self-efficacy variables. Accordingly, the main conclusion that can be drawn from our model is that Entrepreneurial intentions are highly affected by attitude, self-efficacy, subjective norms, and opportunity feasibility. The results can be used by professionals for proposing new policies for university opportunities and government support.

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About the Presenter

Linda Smail, Ph.D.
Department of Mathematics & Statistics
College of Natural and Health Sciences
Zayed University, Dubai,
United Arab Emirates
linda.smail@zu.ac.ae

Linda Smail is an Associate professor in the Department of Mathematics and Statistics at Zayed University, Dubai, United Arab Emirates, where she teaches Mathematics and Statistics courses. She obtained her Ph.D. in Mathematics from Marne-La-Vallée University, France, in 2004. Her research interests are in inference, learning graphical models, and applications of Bayesian Networks in different fields, from Education to Health.


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