Artificial intelligence in human resource management: Publication growth, citation inequality, and international collaboration
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DOIhttp://dx.doi.org/10.21511/ppm.24(4).2026.16
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Article InfoVolume 24 2026, Issue #4, pp. 235–253
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Type of the article: Research Article
Abstract
This study aims to examine publication dynamics, citation inequality, author and country participation, international scientific collaboration, cited-reference relationships, and author-keyword structures in Scopus-indexed research on artificial intelligence in human resource management (HRM) published between 2000 and 2025. Following EID-level screening of 1,596 records, 659 publications were identified as thematically relevant to the topic of AI-HRM. The selected corpus accumulated 25,072 citations, averaging 38.05 citations per publication. Citation impact was highly concentrated, with a Gini coefficient of 0.7396 and 60.16% of citations attributable to the most-cited 10% of publications. Annual output increased from 16 publications in 2019 to 267 in 2025, corresponding to a compound annual growth rate of 59.86%. In addition, descriptive growth-model comparisons showed that the exponential model provided the strongest fit to the annual publication series (R² = 0.9945). International co-authorship was identified in 211 publications (32.02%). Country collaboration, cited-reference co-citation, and author-keyword networks were examined to characterize the intellectual and conceptual structure of the field. The study provides a traceable and reproducible bibliometric assessment of the documented indicators of publication growth, citation inequality, international collaboration, and thematic relationships within the selected AI-HRM corpus.
Acknowledgments
The research was supported through grant funding of the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant IRN AP23490443), “Human capital management, improving the quality of labor resources in the context of the development of the digital economy.”
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JEL Classification (Paper profile tab)J24, M12, M15, O33
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References42
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Tables10
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Figures6
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- Figure 1. Annual scientific production and comparative growth-model fit for the full Scopus retrieval and AI-HRM corpus (2000–2025)
- Figure 2. Citation inequality (Lorenz curves)
- Figure 3. International co-authorship network by country
- Figure 4. Cited-reference co-citation network in AI-HRM research
- Figure 5. Author-keyword co-occurrence network in AI-HRM research
- Figure 6. Author-keyword network by mean publication year
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- Table 1. Summary of the Scopus topical screening results (2000–2025)
- Table 2. Independently recalculated indicators for the full retrieval and AI-HRM corpus
- Table 3. Comparative goodness-of-fit statistics for publication-growth models and count-model diagnostics
- Table 4. Leading affiliation countries in the AI-HRM corpus (N = 659)
- Table 5. Leading Scopus Author ID entities in the AI-HRM corpus
- Table 6. Ten most-cited documents in the 659-publication AI-HRM corpus
- Table 7. Most frequently cited references in the AI-HRM corpus
- Table 8. Thematic interpretation and occurrence frequencies of author-keyword clusters identified by VOSviewer
- Table 9. Period-normalized frequencies of selected author keywords (N = 659)
- Table 10. Comparison with previous AI-HRM bibliometric studies
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