Personalized human resource management via HR analytics and artificial intelligence: Theory and implications

Abstract

This conceptual paper theorizes the emerging concept of personalized human resource management (HRM), which refers to HRM programs and practices that vary across individuals within an organization. As a subset of high-performance work practices (HPWPs), personalized HRM is implemented at the individual level and represents the next generation of HRM, which is characterized by the adoption of advanced HR analytics and artificial intelligence (AI) to provide tailored HR solutions. We argue that personalized HRM constitutes a unique source of sustained firm competitive advantage and offers additional beneficial performance effects on top of other HPWPs. Drawing on the theories of individual differences and person-organization fit, we explain why personalized HRM outperforms traditional standardized HRM in terms of productivity, favorable HR climate, flexibility, return on investment of HRM, and firm financial performance. We also suggest that business strategy is a moderator of the relationship between HRM and firm performance. Building on the AI job replacement theory, we further propose that the mechanical and analytical intelligence (intuitive and empathetic intelligence) required for personalized HRM tasks is positively (negatively) related to the adoption of AI. Lastly, we elaborate on the implications and explain how advanced HR analytics and AI can facilitate the transition toward personalized HRM.