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.
A conceptual framework of personalized HRM
Strategic HRM refers to “the pattern of planned human resource deployments and activities intended to enable an organization to achieve its goals” (Wright & McMahan, 1992). Most of the previous research on strategic HRM has focused on the differences in HRM across different organizations, whereas personalized HRM centers on the differences in HRM within organizations.
Several seminal studies have explored the variations in HRM within organizations. Pearce, Tsui, Porter, and Hite (1995) showed that multiple types of employment modes can exist within firms. Lepak and Snell (1999) further developed the concept of HR architecture to capture four employment codes, employment relationships, and HR configurations that are based on the value and uniqueness of human capital. Previous research on strategic HRM differentiation (Becker & Huselid, 2006; Huselid & Becker, 2011; Zhou, Zhang, & Liu, 2012) has suggested that HRM practices are not always applied consistently to all groups of employees and that such a differentiation in the application of HRM may lead to the differences noted in HRM quality across organizations.
While companies, such as FANG, use personalization to attract and retain customers, and more and more organizations are introducing personalized HRM to better attract, develop, and retain their best employees. Personalized HRM represents the next generation of HRM, which is characterized by the adoption of advanced HR analytics and AI to optimize the quality of HRM as well as its ROI. Altogether, this paper advances the strategic HRM literature by providing a conceptual framework of personalized HRM and discussing its theoretical and managerial implications. We have introduced a two-level causal conceptual framework explaining the causal mechanisms that link personalized HRM and firm financial performance. Building on the theories of individual differences and person-organization fit, we have proposed and explained why personalized HRM outperforms traditional HRM approaches in terms of enhancing employee ability and motivation, productivity, HR climate, flexibility, the ROI of HRM, and consequently, the firm's financial performance. We have argued that personalized HRM conveys a unique and sustained competitive advantage for organizations by offering the positive effects of additional beneficial performance on top of the positive impacts of HPWPs. Lastly, we have discussed the theoretical and managerial implications and outlined how HR analytics and AI can be used in developing and maintaining a personalized HRM system. Thus, this conceptual paper provides the basis for future empirical studies on personalized HRM.