Exploring the Duality of Perceptions: Insights into Uncertainties, Aversion and Appreciation Towards Algorithmic HRM - Tandon, Anushree; Dhir, Amandeep; Malik, Ashish; Budhwar, Pawan; Kaur, Puneet (Human Resource Management, 2025)
ABSTRACTThe 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.