A Holistic Examination of Employee Retention using Machine Learing

Abstract

Employee retention remains a critical challenge for organizations seeking to maintain stability, enhance productivity, and reduce turnover costs in a highly competitive business environment. This study synthesizes insights from existing literature and empirical research to examine the multifaceted factors influencing retention, including corporate culture, employee empowerment, organizational learning, talent development practices, entrepreneurship education, cultural intelligence, and technological interventions such as artificial intelligence and machine learning. Various predictive modelling approaches, including Logistic Regression, Random Forest, deep learning, and Stacking-Based Transfer Learning, demonstrate the capacity to identify key drivers of attrition, forecast turnover, and inform proactive retention strategies. Empirical studies from diverse organizational contexts, including banking, startups, and multinational workplaces, highlight the mediating and moderating roles of protean career attitudes, work engagement, socialization tactics, and organizational size in shaping retention outcomes. The findings underscore the importance of integrating human-centric strategies with data-driven analytics, offering actionable insights for HR practitioners to design targeted interventions, enhance employee satisfaction, and sustain long-term workforce stability in dynamic and multicultural work environments.