Social Network Analysis and Visualization of Big Data Research Adoption: A Scientometrics Approach

Abstract

The research focuses on analyzing big data research papers to advance the theoretical understanding of big data and development planning in enterprises and management. Five thousand one hundred forty-eight (5148) research papers published between 2014 and 2024 were gathered from the “Web of Science Core Collection” database. In our proposed study we use two social network analysis and visualization tools, namely CiteSpace and VOSviewer to extract and present data, including knowledge graphs illustrating authors, journals, publication growth, institutions, countries, and keyword clusters. Significant collaborations and citations among governments, institutions, and authors were identified through scientometric analysis. The USA, China, and the Czech Republic were identified as the leading countries with the most published papers. The study emphasizes the importance of visually analyzing emerging trends, structural changes, and research hotspots in big data research using scientometrics. By uncovering keyword co-occurrence networks, prominent authors, key research themes, breakthrough publications, and research development over time, the study proposes a research agenda for further exploration and deeper insights into big data. The study has limitations that can be overcome using different dataset websites and other scientometric tools, as mentioned in the conclusion and future work.