Social influence, research productivity and performance in the social network co-authorship: A structural equation modelling

Abstract

Social influence refers to the interaction of one person with other researchers in the/a social network and is calculated by the analysis of co-authorship networks and centrality indices. The purpose of the present study was to investigate the relationship between social influence and productivity with the performance of the researchers in the area of throbbing headaches. Bibliometric indicators, social network analysis techniques and structural equation modeling (SEM) were employed. The population included 35050 records of throbbing headaches indexed in the Web of Science from 2005 to 2017. Analysis of the relationship between social influence scores and the researchers' performance showed a positive correlation between the degree and betweenness centrality with the performance of the researcher and no correlation between closeness centrality and performance; meaning the greater the degree and betweenness centrality of the authors', the greater effectiveness. Variance regression analysis revealed nearly 56 percent of the researchers' productivity variance was determined by the degree and betweenness centrality. In addition, the results indicated a correlation between social influence and ideational influence indicators, meaning the researchers with the higher social influence possess higher ideational influence. Based on the findings of the present study, using a combination of indicators to examine the effectiveness of an author in terms of productivity and performance is argued whether it can help identify a successful researcher in a scientific field in a more realistic and creative way.