Modeling Employee Performance in AI-Enabled Hybrid Work Systems: A Statistical and Machine Learning Approach

Abstract

This study examines the impact of work conditions and AI technical support on employee performance in hybrid work settings among Indian professionals. Data from 210 respondents were analyzed using regression, mediation, and machine learning techniques. Role clarity, psychological wellbeing, social cohesion, and hybrid adaptability emerged as the strongest predictors of performance. AI support showed an indirect, enabling influence. Mediation results confirmed the role of social cohesion between role clarity and performance. Non-linear models outperformed linear regression, indicating complex interaction effects. The findings highlight the importance of human and organizational factors in AI-enabled hybrid work systems.