This study examines the career preferences of Generation Z (Gen Z) recent graduates in India through a data-driven predictive modelling framework. With the rapid transformation of the labour market driven by digitalization, remote work adoption, and entrepreneurial growth, understanding the evolving career choices of Gen Z has become increasingly important for organizations, educational institutions, and policymakers. The research focuses on three core dimensions of career preference: industry and sector choice, remote work orientation, and entrepreneurial inclination. Primary data was collected from 154 Gen Z graduates aged 22–26 years across India using a structured online questionnaire. The study employs descriptive statistics, Chi-square tests, and supervised machine learning algorithms—Logistic Regression and Random Forest—to analyse and predict career preference outcomes. The findings indicate that Information Technology and Software is the most preferred sector, while hybrid work arrangements dominate workplace preferences. Notably, 59.1% of respondents expressed entrepreneurial intentions, with starting a business emerging as the most common career goal. Among the tested hypotheses, risk tolerance was found to be a statistically significant predictor of entrepreneurial inclination. Furthermore, Random Forest outperformed Logistic Regression in predictive accuracy, identifying entrepreneurial self-concept, career growth orientation, age, and work experience as key influencing variables. The study contributes to the extension of Social Cognitive Career Theory and the Theory of Planned Behaviour in the Indian context. It also offers practical insights for recruiters, academic institutions, and policymakers to align strategies with Gen Z expectations. The integration of survey-based analytics with machine learning provides a robust framework for predictive career decision analysis in emerging economies.
Ameen et al. (Fri,) studied this question.