文献类型：期刊文献

英文题名：The dark side of AI-enabled HRM on employees based on AI algorithmic features

作者：Zhou, Yu[1];Wang, Lijun[2];Chen, Wansi[2]

机构：[1]Renmin Univ China, Sch Business, Beijing, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China

年份：2023

卷号：36

期号：7

起止页码：1222

外文期刊名：JOURNAL OF ORGANIZATIONAL CHANGE MANAGEMENT

收录：;WOS:【SSCI(收录号:WOS:001106314200001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant number 72072180) endowed to Yu Zhou and Ministry of Education Humanities and Social Science Fund Project (Research on the Condition Configuration of TMT Bottom Line Mentality and Its Impact on the Transformation of Digital Business Models) granted by Wansi Chen.

语种：英文

外文关键词：Artificial intelligence; Human resources management; AI algorithmic features; Dark sides; Theoretical mechanisms; Perceived justice

摘要：PurposeAI is an emerging tool in HRM practices that has drawn increasing attention from HRM researchers and HRM practitioners. While there is little doubt that AI-enabled HRM exerts positive effects, it also triggers negative influences. Gaining a better understanding of the dark side of AI-enabled HRM holds great significance for managerial implementation and for enriching related theoretical research.Design/methodology/approachIn this study, the authors conducted a systematic review of the published literature in the field of AI-enabled HRM. The systematic literature review enabled the authors to critically analyze, synthesize and profile existing research on the covered topics using transparent and easily reproducible procedures.FindingsIn this study, the authors used AI algorithmic features (comprehensiveness, instantaneity and opacity) as the main focus to elaborate on the negative effects of AI-enabled HRM. Drawing from inconsistent literature, the authors distinguished between two concepts of AI algorithmic comprehensiveness: comprehensive analysis and comprehensive data collection. The authors also differentiated instantaneity into instantaneous intervention and instantaneous interaction. Opacity was also delineated: hard-to-understand and hard-to-observe. For each algorithmic feature, this study connected organizational behavior theory to AI-enabled HRM research and elaborated on the potential theoretical mechanism of AI-enabled HRM's negative effects on employees.Originality/valueBuilding upon the identified secondary dimensions of AI algorithmic features, the authors elaborate on the potential theoretical mechanism behind the negative effects of AI-enabled HRM on employees. This elaboration establishes a robust theoretical foundation for advancing research in AI-enable HRM. Furthermore, the authors discuss future research directions.