Despite a plethora of literature is available surrounding understanding of Entrepreneurial intention and behaviour, the complex psychological make-up of individuals have made the entire journey from intention to action very fickle. Past research have also laid more emphasis on intrinsic and behavioural factors in contrast to external environment. This study employs a sophisticated hybrid Structural Equation Modeling-Artificial Neural Network (SEM-ANN) analytical framework to investigate the psychological antecedents of entrepreneurial intention among undergraduate students. Drawing upon the Theory of Planned Behavior, the research examines how personal attitude, perceived behavioral control, and entrepreneurial motivation influence entrepreneurial aspirations through both linear and non-linear modeling approaches.

The pilot investigation involved 100 participants across diverse academic disciplines, utilizing rigorous measurement instruments with demonstrated reliability (Cronbach's α ranging from 0.866 to 0.935). The SEM analysis revealed that personal attitude emerges as the strongest predictor of entrepreneurial intention (β = 0.405, p < 0.001), followed by perceived behavioral control (β = 0.390, p = 0.017), while entrepreneurial motivation demonstrated no significant influence (β = 0.068, p = 0.618). The model collectively explained 46.8% of variance in entrepreneurial intention, indicating moderate predictive power.

The complementary ANN analysis validated these findings through methodological triangulation, confirming the primacy of attitudinal factors while revealing potential non-linear relationships. These results challenge conventional assumptions about motivation's role in entrepreneurial decision-making and provide empirical evidence for targeted interventions in entrepreneurship education. The hybrid methodology offers valuable insights for educators, policymakers, and researchers seeking to understand the complex psychological mechanisms underlying entrepreneurial intention formation.