Structural Equation Modeling and Machine Learning Analysis of Factors Influencing Students’ Entrepreneurial Intention
DOI:
https://doi.org/10.55583/invest.v7i1.2126Keywords:
Entrepreneurial Intention, Social Capital, Structural Equation Modeling, Machine Learning, Higher EducationAbstract
Entrepreneurial intention has become a critical issue in higher education, yet previous studies have primarily relied on explanatory models without integrating predictive approaches to identify the most influential determinants. This study aims to examine the relationships among organizational experience, campus support, entrepreneurial training, risk perception, social capital, and entrepreneurial intention by integrating Structural Equation Modeling (SEM) and Machine Learning (ML). Data were analyzed using covariance-based SEM to examine structural relationships and ML techniques to validate predictive performance and identify the most important predictors. The findings reveal that social capital is the only variable with a significant positive effect on entrepreneurial intention, whereas organizational experience, campus support, entrepreneurial training, and risk perception do not show significant direct effects. ML analysis consistently confirms social capital as the strongest predictor of entrepreneurial intention, supporting the robustness of the SEM findings while providing complementary predictive insights. The integration of SEM and ML represents the main contribution of this study by combining explanatory and predictive perspectives to obtain a more comprehensive understanding of entrepreneurial intention. These findings suggest that universities should prioritize strengthening students' social capital through collaborative networks, mentoring, and community engagement to foster entrepreneurial intention more effectively.
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