Profiling Social Entrepreneurship Orientation Through Machine Learning Predictive Modeling

ABSTRACT This study examines the prediction of Social Entrepreneurship Orientation (SEO) in youth using machine learning techniques to generate a robust predictive model. Drawing on a large cross‐country survey of European youth, results indicate that cognitive–attitudinal indicators—such as concern for environmental goals, purpose‐driven motivation, and positive perceptions of entrepreneurship, emerge as the most powerful determinants, providing a stronger predictive signal for SEO than demographic or background variables such as gender, education or nationality. Feature‐importance analyses quantify each predictor's contribution. These insights suggest a shift in youth entrepreneurship from economic self‐interest toward socially and environmentally conscious innovation. The implications are substantial for educators, policymakers, and impact investors aiming to identify, support, and scale socially oriented entrepreneurial potential. This paper offers both a methodological contribution to forecasting social change and a strategic tool for anticipating human capital trends in mission‐driven innovation.