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The dark side of AI-enabled HRM on employees based on AI algorithmic features

Yu Zhou (School of Business, Renmin University of China, Beijing, China)
Lijun Wang (School of Business, East China University of Science and Technology, Shanghai, China)
Wansi Chen (School of Business, East China University of Science and Technology, Shanghai, China)

Journal of Organizational Change Management

ISSN: 0953-4814

Article publication date: 27 November 2023

Issue publication date: 11 December 2023

1154

Abstract

Purpose

AI 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/approach

In 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.

Findings

In 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/value

Building 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.

Keywords

Acknowledgements

Yu Zhou and Lijun Wang are co-first authors.

Lijun Wang and Wansi Chen are co-corresponding authors.

The authors acknowledge the valuable comments and discussions with Prof. Peter Cappelli, of the Wharton School at the University of Pennsylvania.

Citation

Zhou, Y., Wang, L. and Chen, W. (2023), "The dark side of AI-enabled HRM on employees based on AI algorithmic features", Journal of Organizational Change Management, Vol. 36 No. 7, pp. 1222-1241. https://doi.org/10.1108/JOCM-10-2022-0308

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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