Abstract
The rapid integration of Artificial Intelligence (AI) into human resource management has created new imperatives for understanding how AI-driven Sustainable HRM (AI-SHRM) practices shape employee outcomes. This study examines the impact of AI-SHRM on employee performance (EP) and work-life balance (WLB) and the mediating role of employee well-being (EWB). Grounded in the Job Demands-Resources (JD-R) model and Conservation of Resources (COR) theory, a conceptual framework was developed and tested using primary survey data from 805 employees across seven industry sectors in India, including banking, manufacturing, information technology, healthcare, retail, education and government. Partial Least Squares Structural Equation Modelling (PLS-SEM), via Smart PLS 4.0, was used for analysis. The measurement model showed strong reliability and validity, with Cronbach’s alpha above 0.89, Average Variance Extracted above 0.70 and satisfactory model fit. Data were analysed using IBM SPSS Statistics for Windows, Version 26.0(IBM Corp., Armonk, NY, USA).AI-SHRM exerted significant positive direct effects on well-being, performance and work-life balance and well-being significantly influenced both performance and work-life balance. Mediation analysis showed that well-being partially and complementarily mediated the relationships between AI-SHRM and both outcomes. The model showed strong explanatory power, with R² values of 0.946, 0.896 and 0.917 for EP, EWB and WLB respectively. Employee well-being is identified as the primary transmission mechanism through which AI-SHRM investments generate downstream employee outcomes, with theoretical and managerial implications for the design and governance of AI-augmented HR systems.
Keywords: AI-driven Sustainable HRM, Employee Performance, Employee Well-being, Job Demands-resources Model, Work-life Balance.