Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration

2023-08-18

IEEE Access

AI Summary

This study investigates the global research collaboration landscape and thematic evolution in AI-empowered human resource management (AI-HRM). Addressing the lack of dynamic, network-informed syntheses in the field, we construct a co-authorship network from 102,000 authors and 288,000 collaborations indexed in Web of Science. Integrating social network analysis (SNA), centrality measures, community detection, and the TOPSIS multi-criteria decision method, we introduce a methodological innovation: simultaneous identification of thematic clusters and geographically anchored collaboration communities. Results reveal four core research themes—AI system identification and control, HR data analytics and performance optimization, machine learning–driven classification and prediction, and AI-augmented strategic decision-making—as well as three national clusters (U.S., China, EU) and five high-impact institutional communities. The study systematically characterizes the structural features of knowledge production and collaborative governance in AI-HRM, advancing beyond static literature review paradigms.

Abstract

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.

Problem

• Artificial Intelligence
• Human Resource Management
• Research Collaboration Network

Innovation

• Social Network Analysis
• Artificial Intelligence in Human Resource Management
• Global Research Trends