The Impact of Work Automation on Human Resource Decision Making: The Mediating Role of Employee Performance

This study investigates the intricate relationship between work automation, employee performance, and human resource decision-making in the context of increasing artificial intelligence (AI) adoption. Drawing on social systems theory, contingency theory, and cognitive load theory, we propose a conceptual framework that explores the direct and indirect effects of work automation on HR decision-making, with employee performance as a mediating factor. A quantitative survey of 122 managerial-level employees from technology, manufacturing, and financial sectors across 18 countries was conducted. Using Partial Least Squares Structural Equation Modelling, we tested hypotheses examining the relationships among variables. Results reveal that work automation indirectly influences HR decision-making through employee performance. The study introduces the concept of Emotionally Aware AI Decision Making (EA-AIDM) as a critical factor in leveraging AI for effective HR decision-making. EA-AIDM emerges as a significant mediator between work automation and HR decision-making, offering a novel approach to integrating AI technology with human factor considerations in HR practices. Our findings suggest that organisations should adopt a holistic approach to work automation implementation in HRM, balancing technological advancements with employee performance considerations. This research contributes to the growing body of literature on AI in HRM by providing empirical evidence on the mediating role of EA-AIDM and offers practical insights for organisations navigating the evolving landscape of work in the age of AI.