Studying the Relationship between Social Influence, Productivity and Efficiency amongst Researchers in Knowledge Management from a Scientometric Perspective

Document Type : Original Article

Authors

1 Associate Professor, School of Social Sciences, Yazd University, Yazd, Iran

2 Associate Professor, School of Social Sciences, Yazd University, Yazd, Iran.

3 Associate Professor, Payame Noor University, Kermanshah, Iran

4 M.A. in Scientometrics, Yazd University, Yazd, Iran

Abstract

Purpose: The purpose of this study is to investigate the relationship between social influence (measured by centrality scores), performance (measured by the number of citations), and productivity (measured by the number of articles) among researchers in the field of knowledge management. In other words, this study aims to examine the application of centrality measures in the scientific evaluation of researchers.
Method: This is an applied scientometric study that employs co-authorship and social network analysis techniques. The statistical population comprises all articles in the field of knowledge management indexed in the Web of Science from 1900 to 2018, encompassing 23,258 records authored by 37,697 individuals. To calculate researchers’ social influence, a square co-authorship matrix was created using BibExcel software. The centrality components of social influence, including closeness, degree, and betweenness, were calculated using UCINET software. The co-authorship network of this field was visualized using NetDraw software. For data analysis, both descriptive and inferential statistics were employed using LISREL and SPSS software. In this study, the number of authored articles was considered an indicator of productivity, while the number of citations received served as an indicator of performance. To examine the relationships among research variables, a regression analysis was conducted, given the normal distribution of the data.
Findings: An analysis of the growth trend of articles in the field of knowledge management from 1990 to 2018 in the Web of Science database reveals a significant increase, particularly since the 2000s, peaking in 2017. The findings indicate that among the 37,697 contributing authors, Chen Yi stands out with 139 articles, making him the most productive author, while Gatzchak, with 2,398 citations, ranks highest in terms of performance. Regarding social influence indicators, the results show that Wang Yi holds a more stable and prominent position compared to other authors in the
field. Overall, the researchers' social network is coherent and well-integrated. Findings related to
co-authorship patterns reveal that the authors’ network consists of a large connected component and a few isolated nodes. The main co-authorship network demonstrates a coherent structure among authors. In examining the relationship between social influence and authors’ productivity and performance, the findings indicate a positive and significant correlation: authors with higher scientific output and more citations tend to have higher centrality scores.
Conclusion: The analysis of these relationships shows that researchers with more central roles in co-authorship networks also tend to have higher levels of productivity and performance. In other words, the higher the centrality score, the greater the productivity and performance. Therefore, based on the study’s findings, in the expanding field of knowledge management, greater social influence contributes positively to an author’s scientific output and impact. It can thus be concluded that Social Network Analysis is a powerful tool for evaluating researchers’ scientific activities and can potentially serve as an alternative to traditional evaluation indicators.

Keywords

Main Subjects

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Sciences and Techniques of Information Management
Volume 11, Issue 1
February 2025
Pages 109-134

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