Learning A Bayesian Structure to Model Entrepreneurial Intentions and Attitudes Toward Business Creation among Emirati Students

Economic growth in most advanced countries is driven by small and medium-sized enterprises (SMEs), and most countries prioritize entrepreneurship for economic growth and innovation. This is very apparent in the United Arab Emirates (UAE), where an average of around 39 percent of adults want to start a business in the next three years. As such, entrepreneurial intentions have been a major area of focus in research, but they have always been studied using generic models. We use Bayesian networks as a relatively new technique to model entrepreneurial intentions as it provides an advantage over classical methods. Using the theory of planned behavior as a foundation, we conduct a cross-sectional study among a random sample of 324 Emirati university students in the UAE. We implement unsupervised structural learning within BayesiaLab using the SopEQ unsupervised algorithm to minimize the “minimum description length” score. Our model provides confirmation of and more robust statistical support for existing theoretical frameworks. It helps us find relationships among the different entrepreneurial factors and 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.