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返回卷期清單 (28(4) / 2023 / 12)
28(4)
/
2023 / 12
/
pp. 598 - 610
Personalized human resource management via HR analytics and artificial intelligence: Theory and implications
161
作者
Xiaoyu Huang
(Jack H. Brown College of Business and Public Administration, California State University San Bernardino, San Bernardino, CA, USA)
Fu Yang
(School of Business Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China)
Jiaming Zheng
(School of Labor and Human Resources, Renmin University of China, Beijing, China)
Cailing Feng *
(College of Public Administration, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu, 210095, China)
Lihua Zhang
(School of Labor and Human Resources, Renmin University of China, Beijing, China)
Xiaoyu Huang
Jack H. Brown College of Business and Public Administration, California State University San Bernardino, San Bernardino, CA, USA
Fu Yang
School of Business Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan Province, China
Jiaming Zheng
School of Labor and Human Resources, Renmin University of China, Beijing, China
Cailing Feng *
College of Public Administration, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu, 210095, China
Lihua Zhang
School of Labor and Human Resources, Renmin University of China, Beijing, China
英文摘要

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.

英文關鍵字

Personalized HRM; Strategic HRM; HR differentiation; HR analytics; Artificial intelligence