This study examines the intersection between AI-enabled technologies and Human Resource Management (HRM) by analyzing 288 peer-reviewed articles from the Elsevier Scopus and Clarivate Web of Science databases, published over the last two decades, within the context of a digitized economy.
Objective
This research clarifies AI-enabled technologies’ role in HRM using the SPAR-4-SLR framework and a three-stage methodology with RStudio, VOSviewer, and Excel.
Methods
The methodology systematically examines articles by performance mapping, co-word analysis, co-citation analysis, and bibliographic coupling to unveil knowledge clusters, trends of research productivity, and significant contributions to the AI-HRM literature.
Results
The results highlight the most prevalent themes related to scientific production, leading publications, and influential researchers. Four knowledge clusters are identified using co-word analysis, three using co-citation analysis, and five using bibliographic coupling, reflecting prevailing trends in the subject. Based on this synthesis, we propose a conceptual framework that advances the theoretical understanding of the intersections between AI and HRM.
Implications
This study offers valuable insights for researchers and HR professionals by highlighting the evolving dynamics in the AI-HRM relationship, thereby enriching an integrative understanding of the discipline. The study identifies research gaps and suggests future investigation directions to enhance knowledge on AI-based HRM.
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