Two-Staged SEM-ANN Approach based Investigation of the Impact of Employee Turnover on Organizational Efficiency in Indian IT Companies

Abstract

Employee turnover is a critical challenge in the fast-paced and competitive Indian IT industry, significantly impacting organizational efficiency. This study employs an innovative Two-Staged Structural Equation Modeling-Artificial Neural Network (SEM-ANN) approach to explore the relationship between employee turnover and organizational efficiency, focusing on the mediating roles of employee engagement, retention policies, and management effectiveness, along with the moderating effect of organizational size. Data were collected from 556 employees in prominent IT companies using a stratified random sampling method and analyzed using both linear and non-linear modeling techniques. The findings reveal that employee turnover negatively impacts organizational efficiency (β = -0.391, p < 0.05), while employee engagement (β = 0.778), retention policies (β = 0.631), and management effectiveness (β = 0.877) positively influence it. Mediation analysis demonstrates that these variables mitigate turnover’s adverse effects, with management effectiveness having the strongest mediating impact. Organizational size moderates the turnover-efficiency relationship, with larger organizations experiencing more pronounced negative effects. By integrating SEM and ANN, this study provides a comprehensive understanding of both direct and indirect relationships, offering actionable insights for practitioners to develop targeted HR strategies and enhance organizational performance.