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The Relationship between Social Influence and the Scientific Performance of Authors in the co-Authorship Network in the Field of Medical Images

Document Type : Original Article

Authors

1 Professor, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Assistant Professor, Medical Physics, Radiology and Nuclear Medicine Department,, School of Paramedical, Kermanshah University of Medical Sciences, Kermanshah, Iran

3 PhD Student, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, ,Ahvaz, ,Iran

Abstract

Introduction: The co-authorship network contributes to sharing of knowledge and experience, increases efficiency, innovates, and develops scientific achievements. This study endeavors to identify the pattern of participation and evaluate the relationship between social influence and the scientific performance of authors in the co-authorship network in medical images.
Methods: This descriptive study was done using both the approach of scientometrics and social network analysis. The search was implemented in the Core Collection Web of Science (WOS) in February 2021. The cases included 37,190 articles divided into the three-time periods from 1991-2000, 2001-2010, and 2011-2020. The stages of data extraction, matrix construction, and computation of the co-authorship network centrality metrics were performed through a software. The effect of scientific performance evaluation indicators on the measures of centrality (social influence) was examined by multivariate regression analysis.
Results: Patterns of participation of one to three authors were decreasing. The number of articles, the number of citations, the H index, and normalized citations of each author have a direct and significant relationship with rank centrality, distance centrality, and betweenness centrality, and inverse and significant relationship with closeness centrality. Centrality variables explain 27% of the changes in the number of articles, 21% of the changes in the H index variable, 0.2% of the changes in the average citation of each article, and 0.6% of the changes in the normalized citation variable, respectively.
Conclusion: Considering the increase in researchers' tendency towards collaborative research, the use of centrality metrics and a combination of researchers' performance indicators in the field of medical images can provide a logical criterion for predicting and evaluating their performance while facilitating the identification of influential researchers.

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References
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Health Information Management
Volume 19, Issue 3 - Serial Number 85
September and October 2022
Pages 114-121
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