  The integration of artificial intelligence (AI) into human resource
management (HRM) strategies has become increasingly common due to technological
advancements. This has spurred a new field of research focused on evaluating
the impact of AI adoption on business and individual outcomes, as well as how
to evaluate AI-enabled HRM practices. However, there is limited
cross-disciplinary research in this area, causing a fragmented body of
knowledge. To address this issue, social network analysis has been recognized
as a tool for analyzing and researching large-scale social phenomena in HRM.
The study of scientific co-authorship networks is one application of social
network analysis that can help identify the main components and trends in this
field. Using social network analysis indicators, the current study examined the
AI&HRM co-authorship network, which consists of 43,789 members and 81,891
scientific collaborations. The study analyzed articles related to AI&HRM
published between 2000 and 2023 extracted from the WOS citation database.
Through centrality measures, the most important members of the "AI&HRM"
co-authorship network were identified using the TOPSIS method, which identified
twenty prominent researchers in this field. The study also examined the
keywords "AI&HRM" and the scientific cooperation network of nations,
universities, and communities. Overall, this study highlights the importance of
cross-disciplinary research and social network analysis in understanding the
implications of AI adoption in HRM.
