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

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  • Do, Hoa
  • Chu, Lin Xiao
  • Shipton, Helen

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.

Suggested Citation

  • Do, Hoa & Chu, Lin Xiao & Shipton, Helen, 2025. "How and when AI-driven HRM promotes employee resilience and adaptive performance: A self-determination theory," Journal of Business Research, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:jbrese:v:192:y:2025:i:c:s014829632500102x
    DOI: 10.1016/j.jbusres.2025.115279
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