Co-authorship networks and scientific performance: an empirical analysis using the generalized extreme value distribution

Abstract

This paper aims to explore the effects of collaborative behaviour on scholar scientific performance. Individual network measures related to scholar centrality as well as attitude to collaborate with others are derived from co-authorship networks in a given scientific community (i.e. Italian academic statisticians). Co-authorship information have been collected from three data sources of national-based, discipline-based, and international-based high-impact publications. Both network and individual covariates are used to model individual h-index by generalized extreme value distribution. Results show a positive association between performance and actors' central position in the network. Having a large number of co-authors and occupying central positions are likely to positively affect scientific performance

Outstanding and ordinary scientists’ co-authorship networks in the early career phase

“…On the other hand, a low betweenness centralized network is a closed network. One study of the research networks of Italian statisticians found that closed networks had a negative effect on individual scientists' performance (De Stefano and Zaccarin 2015). Research has also found out that central authors have a high level of control, receive more citations (Ortega 2014), and a high g-index (Abbasi, Chung, and Hossain 2012;Abbasi, Wigand and Hossain 2014) in sparse ego networks.…”

“…First, little research has examined scientists' productivity and impact at the same time (with the exception of Eisend and Schmidt's 2014 study) despite the fact that scientists' performance includes productivity (the number of papers), impact (citation counts, Oguz, Kingsley and John 2014), and the order of authors' names on publications (Abramo, Cicero and D'Angelo 2013). Second, although researchers have analyzed co-authorship networks to describe knowledge flow among co-authors, until recently, most studies have ignored the effect of the characteristics of ego-centered co-authorship networks on scientist performance (Letina 2016;De Stefano and Zaccarin 2015). An ego-centered co-authorship network is a network of a specific scientist (ego) and his/her co-authors (actors) (Letina 2016).…”

“…Third, little research has employed longitudinal designs (He Geng and Campbell-Hunt 2009) in examining the evolution of scientists' ego-centered co-authorship networks and their relationship with scientists' career development (Gonzalez-Brambila, Veloso, and Krackhardt 2013). Finally, some important factors for career development such as work experience, age (De Stefano and Zaccarin 2015), gender (Letina 2016), scientific discipline, funding amount, and research team size have generally been ignored in this area. Letina (2016) suggested that such factors should be included in future network models of researchers' performance because being linked with a productive researcher has benefits for the co-author's own productivity.…”

Community Structure in Co-authorship Networks: The Case of Italian Statisticians

“…In the last decades social network analysis (SNA) has become a widespread methodological approach to study scientific collaboration. As stated in several studies [5,8], scientific collaboration is a crucial factor to enhance publication productivity and research quality. The role of scientific collaboration allowing a fertile ground for the development of new ideas is also recognized in research funding European programmes as well as national projects.…”

Scientific Production and Productivity in Curriculum Vitae Characterisation: Simple and Nested H Indices that Support Cross-Disciplinary Comparisons

“…This first step will be to differentiate networking from scientific activity (Cainelli et al, 2012(Cainelli et al, , 2015Schubert, 2012) by highlighting anomalies in a CV that potentially arise from the questionable practice of unjustified cross-citations by authors. Indeed, networking might be beneficial if it increases the cultural exchanges between organizations (De Stefano & Zacccarin, 2016), but it is detrimental if it affects researchers' attitudes towards scientific interests and publishing (McCarty & Jawitz, 2013). In other words, each organization should be in a position to transparently evaluate the networking activity of each researcher.…”