Entrepreneurial intentions have been a major focus of research that have been studied using generic models. The paper will use Bayesian Networks to model entrepreneurial intentions as it provides an advantage over classical methods.
A cross-sectional study was conducted among a random sample of 324 Emirati University students by implementing the Unsupervised Structural Learning algorithm to build the Model.
Entrepreneurial intentions are highly affected by attitude, self-efficacy, subjective norms, and opportunity feasibility. Whereas obstacles and university opportunity feasibility are the variables whose influence on entrepreneurial intention is less.
This study looked at entrepreneurship intention and attitudes among students who are not yet entrepreneurs using Bayesian networks as a new technique and see how this can affect their intention in stating a business. Conclusions are stemming from the existing Emirati social construct (people-centric society of the Arab world, rather than system-centric society of the Western world). This has created value-added contribution of the paper to the research questions.
