How and when AI-driven HRM promotes employee resilience and adaptive performance: A self-determination theory

Hoa Do, Lin Xiao Chu and Helen Shipton

Journal of Business Research, 2025, vol. 192, issue C

Abstract: Despite growing research on AI in HRM, gaps remain, particularly in understanding the mechanisms through which AI-driven HRM influences employee outcomes. This study addresses this gap by developing a conceptual model to examine how AI-driven HRM impacts employee resilience and adaptive performance. Based on self-determination theory, the model proposes that employee exploration mediates the relationships between AI-driven HRM and employee outcomes. Additionally, trust in AI moderates these relationships. Two studies were conducted to test the hypotheses: Study 1 developed and validated a 12-item AI-driven HRM scale across three samples: 50 managers, 150 employees for exploratory factor analysis (EFA), and 150 employees for confirmatory factor analysis (CFA). Study 2, with data from 274 US employees through a three-wave survey, explored the effects of AI-driven HRM on resilience and performance. Results from Study 2 supported all proposed relationships, thereby offering important implications for both theory and practice in the AI-driven HRM field.

Keywords: AI-driven Human Resource Management; Trust in AI; Exploration; Resilience; Adaptive performance; Self-determination theory (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:192:y:2025:i:c:s014829632500102x

DOI: 10.1016/j.jbusres.2025.115279

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