Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes

Abstract

This research article presents a novel approach to examining the utilization of artificial intelligence (AI) in sustainable supply chains (SSCs) by applying social network analysis (SNA). In contrast to previous literature that primarily relied on bibliometric analysis, this study investigates the intricate relationships among AI applications in SSCs using SNA. The primary objective is to promote collaborative research and offer valuable insights into influential authors, international co-authorship networks, collaborations among scientific institutions, and research categories identified through keyword analysis.Data were collected from the Web of Science (WOS) database to conduct this research, enabling a comprehensive co-authorship network analysis. This network revealed the collaboration patterns of 1400 authors and identified the most significant authors in this field using centrality measures and the TOPSIS technique.The study also explores institutional co-authorship networks, highlighting India's National Institute of Technology (NIT) as the most active institution. By considering geographical proximity and research specialization, the institutional network was divided into distinct clusters. A keyword analysis and thematic map also shed light on Sustainability and Supply Chain Management themes, revealing their connections with AI methodologies. Based on the thematic map, E-waste, Green supply chain management (GSCM), and Green innovation themes are potential future trends. The analysis of the country network showcased varying levels of development and scientific output, with China leading the field of AI applications in SSC research. Countries were further classified into clusters based on their investment in AI for sustainable supply chains, facilitating knowledge exchange and collaboration.The outcomes of this research contribute significantly to the field by utilizing social network analysis to explore co-authorship networks, leading to a comprehensive understanding of collaborative dynamics and knowledge dissemination. The insights gained from this study can inform decision-making, investments, and strategic collaborations in the domain of AI applications for sustainable supply chains, ultimately driving progress toward sustainability objectives.