Unveiling the Collaborative Patterns of Artificial Intelligence Applications in Human Resource Management: A Social Network Analysis Approach (2308.09798v1)

Abstract: The integration of 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.

• The paper demonstrates that employing social network analysis combined with TOPSIS effectively identifies central figures and collaborations in AI-HRM research.
• It analyzes an extensive dataset involving 43,789 researchers and 81,891 collaborations, highlighting regional research clusters led by China and the USA.
• The study offers actionable insights for advancing interdisciplinary partnerships and future innovations in AI-enabled human resource management.

Social Network Analysis of AI Applications in HRM

The research paper "Unveiling the Collaborative Patterns of Artificial Intelligence Applications in Human Resource Management: A Social Network Analysis Approach" presents a rigorous examination of co-authorship networks, focusing on the interplay between AI and human resource management (HRM). Using Social Network Analysis (SNA), the authors analyze an extensive dataset of 43,789 researchers and 81,891 scientific collaborations, drawn from articles indexed in the Web of Science (WOS) database from 2000 to 2023.

Objective and Methodology

The paper aims to address the fragmented research landscape in the AI-HRM domain by using SNA to evaluate collaboration patterns among researchers. By leveraging SNA indicators such as centrality measures, the paper provides insights into the productivity, performance, and community structures within AI-HRM research. This comprehensive approach identifies key individuals, institutions, and countries contributing to the field, as well as emerging trends and dominant topics.

An innovative aspect of this research is the application of the TOPSIS technique, which aggregates multiple criteria to identify the most central authors in the AI-HRM co-authorship network. This methodological advancement allows for a multi-faceted evaluation of influence and collaboration within the research community.

Key Findings

The findings reveal significant collaborative networks among authors, institutions, and nations. Pedrycz Witold, identified as the most influential author across all centrality measures (degree, closeness, and betweenness), serves as a pivotal connector in the AI-HRM network. Prominent authors such as Chen, C.L. Philip, and Herrera Francisco also emerge as central figures, highlighting their substantial contributions to the domain.

Institutionally, the Chinese Academy of Sciences leads in collaboration, underscoring China's influential role in AI-HRM research. The paper categorizes institutional collaborations into distinct communities, such as "AI Research Powerhouses in Asia" and "AI in Top US Universities," illustrating regional clustering in scientific engagement.

Nationally, the research identifies China, the USA, and Canada as key players in the AI-HRM research landscape. The paper further uncovers three major collaborative communities—global HR applications, AI-powered workforce management, and emerging market HR innovation—that are indicative of differing regional research emphases and collaborations.

Implications and Future Directions

This paper underscores the importance of interdisciplinary and cross-geographical collaboration in advancing AI applications within HRM. The identification of major players and trends provides a roadmap for future research and potential partnerships. The analysis suggests that nations and institutions leverage their influential positions to drive forward both theoretical and practical advancements in the field.

The use of SNA demonstrates its efficacy in uncovering hidden collaboration patterns and research gaps. This methodology can be further expanded to explore other interdisciplinary fields where AI can have a transformative impact.

Conclusion

Overall, the paper makes a compelling case for the application of social network analysis to understand and enhance research collaborations in AI and HRM. By highlighting the pivotal roles of certain individuals and institutions, and identifying key trends, the research opens up new avenues for scholarly inquiry and practical application in leveraging AI for efficient HRM practices. Future research might continue to explore these dynamics, focusing on more granular levels of analysis, such as the impact of specific AI technologies on HRM sub-functions.