Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration
As artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10816603/ |
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| author | Mehrdad Maghsoudi Motahareh Kamrani Shahri Mehrdad Agha Mohammad Ali Kermani Rahim Khanizad |
| author_facet | Mehrdad Maghsoudi Motahareh Kamrani Shahri Mehrdad Agha Mohammad Ali Kermani Rahim Khanizad |
| author_sort | Mehrdad Maghsoudi |
| collection | DOAJ |
| description | As artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes remains lacking. This research employs social network analysis (SNA) to examine the co-authorship network within AI applications in HRM research, providing insights into collaboration dynamics and identifying key research directions. Through analysis of centrality measures and application of the TOPSIS method, the study identifies influential authors, institutions, and emerging research themes. Analysis of 102,296 authors and 287,799 collaborations reveals distinct communities focusing on specific aspects of AI-HRM across regions. The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. The country co-authorship network analysis uncovers three main communities: Global HR Applications, HRM in the Middle East and Asia, and Global Integration of AI in HRM, reflecting shared regional challenges. Institutional collaboration patterns indicate five distinct communities, from established Asian AI research centers to emerging research hubs in developing economies. These findings provide valuable insights for researchers exploring collaboration opportunities, practitioners implementing AI solutions, and policymakers developing strategic frameworks for AI adoption in HRM. This research contributes to understanding the evolving landscape of AI-HRM research and offers practical guidelines for leveraging AI in HR practices. |
| format | Article |
| id | doaj-art-d63c616b8f8b4ef6996df9fb357a5281 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d63c616b8f8b4ef6996df9fb357a52812025-01-07T00:02:13ZengIEEEIEEE Access2169-35362025-01-01133090311410.1109/ACCESS.2024.352343710816603Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research CollaborationMehrdad Maghsoudi0https://orcid.org/0000-0002-1896-1825Motahareh Kamrani Shahri1https://orcid.org/0009-0006-3941-3111Mehrdad Agha Mohammad Ali Kermani2https://orcid.org/0000-0002-2972-5852Rahim Khanizad3https://orcid.org/0000-0002-8031-8507Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, IranDepartment of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranDepartment of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranDepartment of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranAs artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes remains lacking. This research employs social network analysis (SNA) to examine the co-authorship network within AI applications in HRM research, providing insights into collaboration dynamics and identifying key research directions. Through analysis of centrality measures and application of the TOPSIS method, the study identifies influential authors, institutions, and emerging research themes. Analysis of 102,296 authors and 287,799 collaborations reveals distinct communities focusing on specific aspects of AI-HRM across regions. The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. The country co-authorship network analysis uncovers three main communities: Global HR Applications, HRM in the Middle East and Asia, and Global Integration of AI in HRM, reflecting shared regional challenges. Institutional collaboration patterns indicate five distinct communities, from established Asian AI research centers to emerging research hubs in developing economies. These findings provide valuable insights for researchers exploring collaboration opportunities, practitioners implementing AI solutions, and policymakers developing strategic frameworks for AI adoption in HRM. This research contributes to understanding the evolving landscape of AI-HRM research and offers practical guidelines for leveraging AI in HR practices.https://ieeexplore.ieee.org/document/10816603/Artificial intelligence (AI)human resource management (HRM)social network analysis (SNA)co-authorship networkresearch collaboration |
| spellingShingle | Mehrdad Maghsoudi Motahareh Kamrani Shahri Mehrdad Agha Mohammad Ali Kermani Rahim Khanizad Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration IEEE Access Artificial intelligence (AI) human resource management (HRM) social network analysis (SNA) co-authorship network research collaboration |
| title | Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration |
| title_full | Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration |
| title_fullStr | Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration |
| title_full_unstemmed | Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration |
| title_short | Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration |
| title_sort | mapping the landscape of ai driven human resource management a social network analysis of research collaboration |
| topic | Artificial intelligence (AI) human resource management (HRM) social network analysis (SNA) co-authorship network research collaboration |
| url | https://ieeexplore.ieee.org/document/10816603/ |
| work_keys_str_mv | AT mehrdadmaghsoudi mappingthelandscapeofaidrivenhumanresourcemanagementasocialnetworkanalysisofresearchcollaboration AT motaharehkamranishahri mappingthelandscapeofaidrivenhumanresourcemanagementasocialnetworkanalysisofresearchcollaboration AT mehrdadaghamohammadalikermani mappingthelandscapeofaidrivenhumanresourcemanagementasocialnetworkanalysisofresearchcollaboration AT rahimkhanizad mappingthelandscapeofaidrivenhumanresourcemanagementasocialnetworkanalysisofresearchcollaboration |