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Bishakha Majumdar
Title: Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal
Configurational Explanation
Journal: Human Resource Management 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.
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