Predicting Entrepreneurial Intention Among Computer Science Students Using Structural Equation Modeling and Machine Learning

Abstract

Entrepreneurial intention (EI) among students are critical drivers of innovation, job creation, and economic growth in contemporary societie. This study examines the determinants of entrepreneurial intention (EI) among computer science and engineering students in Bangladesh by integrating structural equation modeling (SEM) and machine learning (ML). Survey data were collected from 929 students. The reflective measurement model estimated in SmartPLS 4 demonstrated strong reliability and validity, while the structural model explained 57.2% of the variance in Entreprenuerial Intention.. Complementary ML models optimized through nested cross-validation, confirmed the robustness of findings, XGBoost yielded the lowest error (RMSE ≈ 1.00; R2 ≈ .58. The integration of SEM and ML advances explanatory and predictive understanding of EI, suggesting that interventions should emphasize mastery-oriented training to enhance PBC, targeted knowledge development to strengthen EK, and orientation-building experiences to reinforce PA.