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Navigating the intellectual terrain of AI–HRM integration: a bibliometric review (2000–2024)

Muhammad Usman et al.

Journal of Organizational Effectiveness2026https://doi.org/10.1108/joepp-08-2025-0670article
AJG 2ABDC B
Weight
0.50

Abstract

Purpose The academic discourse has increasingly recognized the critical role of artificial intelligence (AI) in transforming human resource management (HRM), leading to a surge of interest in its integration and implications. This study offers a thorough bibliometric analysis of AI integration in HRM from 2000 to 2024. Design/methodology/approach This study employs a data-driven approach to identify emerging patterns among publications, the most cited documents, the co-occurrence network, authors production, most cited nations, thematic evolution, trending topics and the relationships between authors and countries with various themes in the field of AI–HRM research, as indexed in the Scopus repository. Findings According to the data, scholarly interest increased significantly after 2017, with technologically sophisticated economies contributing the most. Ethical considerations, AI-driven recruitment, performance analytics and decision-making are important thematic clusters. Co-occurrence network and thematic mapping reveal a shift away from simple automation-focused research and toward strategic themes like digital transformation, talent optimization and sustainability. Practical implications This study offers insightful guidance for academics, practitioners and policymakers navigating this quickly changing field by synthesizing the intellectual landscape of AI in HRM, identifying substantial research gaps and suggesting future approaches. Originality/value This study provides researchers and practitioners with practical recommendations on how to manage the digital transformation of HRM by using fragmented literature. The unique contribution ties AI with HRM and lays the foundation for future research and development.

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https://doi.org/https://doi.org/10.1108/joepp-08-2025-0670

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@article{muhammad2026,
  title        = {{Navigating the intellectual terrain of AI–HRM integration: a bibliometric review (2000–2024)}},
  author       = {Muhammad Usman et al.},
  journal      = {Journal of Organizational Effectiveness},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/joepp-08-2025-0670},
}

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Navigating the intellectual terrain of AI–HRM integration: a bibliometric review (2000–2024)

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.