Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation

Shubhabrata Basu, Bishakha Majumdar, Kajari Mukherjee, Surender Munjal*, Chandan Palaksha

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process.

Original languageEnglish
Article number100893
Number of pages16
JournalHuman Resource Management Review
Issue number1
Early online date14 Mar 2022
Publication statusPublished - Mar 2023


  • Artificial intelligence
  • Fuzzy set qualitative comparative analysis
  • HRM
  • Systematic review
  • Thematic causal configurations


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