A bibliometric analysis of artificial intelligence and machine learning applications for human resource management
Abstract This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyz...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Future Business Journal |
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| Online Access: | https://doi.org/10.1186/s43093-025-00602-x |
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| author | Güler Koştı İsmail Kayadibi |
| author_facet | Güler Koştı İsmail Kayadibi |
| author_sort | Güler Koştı |
| collection | DOAJ |
| description | Abstract This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research. |
| format | Article |
| id | doaj-art-ed4facc4c8f842c6a8f3897cf05123c7 |
| institution | Kabale University |
| issn | 2314-7210 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Future Business Journal |
| spelling | doaj-art-ed4facc4c8f842c6a8f3897cf05123c72025-08-20T04:02:55ZengSpringerOpenFuture Business Journal2314-72102025-07-0111111910.1186/s43093-025-00602-xA bibliometric analysis of artificial intelligence and machine learning applications for human resource managementGüler Koştı0İsmail Kayadibi1Department of Scientific Research Projects, Rectorate Unit, Afyonkarahisar Health Sciences UniversityDepartment of Management Information Systems, Afyon Kocatepe UniversityAbstract This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.https://doi.org/10.1186/s43093-025-00602-xHuman resources managementArtificial intelligenceMachine learningBibliometric analysisDigital transformationResearch evolution |
| spellingShingle | Güler Koştı İsmail Kayadibi A bibliometric analysis of artificial intelligence and machine learning applications for human resource management Future Business Journal Human resources management Artificial intelligence Machine learning Bibliometric analysis Digital transformation Research evolution |
| title | A bibliometric analysis of artificial intelligence and machine learning applications for human resource management |
| title_full | A bibliometric analysis of artificial intelligence and machine learning applications for human resource management |
| title_fullStr | A bibliometric analysis of artificial intelligence and machine learning applications for human resource management |
| title_full_unstemmed | A bibliometric analysis of artificial intelligence and machine learning applications for human resource management |
| title_short | A bibliometric analysis of artificial intelligence and machine learning applications for human resource management |
| title_sort | bibliometric analysis of artificial intelligence and machine learning applications for human resource management |
| topic | Human resources management Artificial intelligence Machine learning Bibliometric analysis Digital transformation Research evolution |
| url | https://doi.org/10.1186/s43093-025-00602-x |
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