Exploring the Duality of Perceptions: Insights into Uncertainties, Aversion and Appreciation Towards Algorithmic HRM

Abstract

The human resource management (HRM) function has witnessed the rapid integration of algorithms into incumbent processes; however, significant employee resistance and aversion to algorithmic decision‐making have also been reported. Research on algorithmic HRM practices indicates an underlying duality of perceptual responses by HRM professionals towards this technology. We seek to understand how HRM professionals experience algorithmic HRM use and determine if there are bright sides to its organizational integration. We undertake a qualitative, open‐ended study based on written responses to open‐ended questions from 58 respondents in the United Kingdom and the United States of America. The data were thematically analyzed using grounded theory, which revealed four themes representing HRM professionals' overarching perspectives on why algorithmic HRM precipitates aversion or appreciation. The first two themes highlight HRM professionals' perceived subjective uncertainty regarding algorithmic HRM and its perceived negative effects on the organization. The third theme acknowledges the positive effect of algorithmic HRM, and the final theme discusses three critical coping strategies (embrace, avoid, and collaborate) that HRM professionals adopt to counteract their experienced fears. Our findings suggest that HRM professionals adopt a cautiously fearful rather than a wholly adverse outlook towards algorithmic HRM, wherein aversion and appreciation appear to emerge simultaneously. We contend this existence of a duality of perceptual responses to algorithmic HRM may be a precursor to setting a harmonious collaboration between humans and algorithms in the HRM domain, contingent on appropriate levels of oversight and governance. Implications for theory and managerial practice are also discussed.

Publication DOI: https://doi.org/10.1002/hrm.22263
Divisions: College of Business and Social Sciences > Aston Business School > Work & Organisational Psychology
College of Business and Social Sciences > Aston Business School
Funding Information: This work was supported by the Liikesivistysrahasto (Finnish Foundation for Economic Education), Finland [grant number 220209]. Funding:
Additional Information: Copyright © 2024 The Author(s). Human Resource Management published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: algorithmic aversion,qualitative,human resources,algorithmic human resource management,employee
Publication ISSN: 1099-050X
Last Modified: 27 Mar 2025 08:11
Date Deposited: 07 Jan 2025 17:26
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Related URLs: https://onlinel ... .1002/hrm.22263 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-12-22
Published Online Date: 2024-12-22
Accepted Date: 2024-10-23
Authors: Tandon, Anushree
Dhir, Amandeep
Malik, Ashish
Budhwar, Pawan (ORCID Profile 0000-0001-8915-6172)
Kaur, Puneet

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