Factor Analysis of the University Environment and Support System: A Bayesian Approach

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Abstract

University students represent a reservoir of entrepreneurial talent and an inherent source of creativity and innovation. One way to help unleash their talents as an engine of economic growth is by increasing our understanding of elements—internal or external, real or perceived—that lead to and influence the emergence of new ventures led by students. There is evidence in the literature that the university environment and support system (ESS) can affect the entrepreneurial intention of students attending the institution. The university ESS comprises the support mechanisms necessary for entrepreneurial activity and could motivate students to consider entrepreneurship a possible career choice. Kraaijenbrink et al. developed and validated a university ESS scale that helped them identify three motivational factors of the university ESS influencing entrepreneurial intention. Despite its increasing popularity, a detailed study and analysis of the Kraaijenbrink et al. university ESS scale are still lacking in the extant literature. This study fills that gap by conducting an extensive factor analysis of the university ESS scale using a Bayesian approach. To the best of our knowledge, this is the first time this scale has been the subject of such an extensive investigation. We capitalized on the added flexibility of the Bayesian approach to address novel substantive questions through the mathematical model. We used it to enrich our understanding of the university ESS and generate new ideas for possible scale and model modifications. Furthermore, this evaluation provided novel insight into the relationships among the many dimensions of the university ESS. In the future, similar studies conducted by aspiring entrepreneurial universities could help them interpret the efficacy of their efforts to promote entrepreneurial activities among students.

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APA

Bazan, C. (2023). Factor Analysis of the University Environment and Support System: A Bayesian Approach. SAGE Open, 13(4). https://doi.org/10.1177/21582440231218511

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