Mapping the Knowledge Base for the Impact of Artificial Intelligence on Human Resources Management: A Bibliometric Study

John William Kasubi, Lazaro A. Kisumbe and Wakara I. Nyabakora

SAGE Open, 2025, vol. 15, issue 3, 21582440251377298

Abstract: The rapid integration of artificial intelligence (AI) into organizational processes presents both opportunities and challenges for human resource management (HRM), yet a systematic understanding of its scholarly development remains limited. This bibliometric review examines the “influence of artificial intelligence†(AI) on “human resources management†(HRM) literature, following the PRISMA protocol to identify knowledge gaps and guide future inquiry. Data from “the Scopus database†is analyzed by VOSviewer software to visualize the research landscape. By analyzing publication trends, citation networks, and thematic clusters, this review uncovers key research themes, influential authors, and emerging directions in AI-HRM studies. The results highlight the growing integration of AI knowledge in HRM practices, showcasing both their potential advantages and the challenges they pose. The key themes center on talent acquisition, performance analytics, ethical concerns, and decision-making automation. Influential authors and sources were mapped, and emerging research frontiers identified. The study concludes that AI is reshaping HRM practices, but ethical, legal, and human-centered implications remain underexplored. Future research should address cross-cultural perspectives, long-term impacts on workforce dynamics, and the development of inclusive AI systems in HRM. This thorough analysis provides valuable understandings for practitioners, policymakers, and researchers, supporting the development of AI-driven HRM strategies and developing a deeper consideration of AI’s evolving role in the workplace.

Keywords: AI; artificial intelligence; bibliometric review; HRM (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251377298

DOI: 10.1177/21582440251377298

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