Are the 'big ponds' in the core? - Overlap of the core/periphery collaboration structure and ‘big pond’/’small pond’ segregation
Dominika Czerniawska | University of Warsaw | Poland
Keywords: core/periphery, scientific networks, 'big ponds'/'small ponds'
The main aim of the paper is verifying to what extend the core/periphery collaboration structure in science overlaps with ‘big pond’ vs. ‘small pond’ segregation. Both of these concepts are widely applied in social network in science. We expect that ‘big ponds’ with resources they control will attract more productive scientists, which will result in more dense collaboration networks affiliated with ‘big ponds’. The analysis is conducted based on Polish scientific community. Publication database covers years 2000-2015 with 200 000 scientists and 400 000 publications. ‘Big pond’/’small pond’ classification is based on the size of institution operationalised as total amount of research funds obtained for fundamental research, a number of active scientists and government evaluation conducted every four years.
Assessing the effects of structural constraints on the relationship between collaboration and performance in research-oriented teams
Paola Zappa | Maynooth University | Ireland
Innovation, teams, healthcare
Interunit teams are important entities through which organizations generate new ideas and, ultimately, innovate. Research on team-based innovation has repeatedly examined the relationship between team composition and team performance and the extent to which they jointly affect team stability. Relatively less is known on how this relationship can be mediated by the effect of structural constraints, i.e. formal relations between the organizational units which can force their members to engage in and persist in team collaboration, if when it is poorly successful.
We draw on theories of team-base collaboration and performance feedback to develop an integrated understanding of this phenomenon. We test our hypotheses using longitudinal data on collaboration in research projects among around 100 physicians, who are members in a large research-oriented hospital.
Relationship of co-authorship networks and citations: analysis based on Google Scholar
Nataliya Matveeva | National Research university Higher School of Economics | Russian Federation
Cooperation between researchers is an essential feature of scientific activity. It assumes a work of several researchers on a scientific problem, during which scientists exchange ideas, discuss problems and results, and generate new ideas. This kind of partnership can increase performance of future projects and results of this cooperation generally appear in joint publications. The cooperation between researchers can be analyzed by co-authorship network, where authors of publications are nodes and their joint publications are links.
In this study, we analyze the relationship between the co-authorship network parameters (total number of links (joint publications), degree centrality, closeness centrality) and citation characteristics (total number of citations, h-index, i10 – index, average citation of co-authors). For this study 110 000 profiles of scientists registered in Google Scholar from various countries and scientific fields were used. We divided sample on scientific fields since it has various citation indicators and we take into account the time effect of citation because the presence in sample of authors with various scientific tenures affects their bibliometric indicators.
In contrast to Web of Science (WoS) and Scopus, Google Scholar is more appropriate prototype of co-authorship social network because it provides the opportunity for the users to organize their scientific profiles and to manage their co-authors lists.
To estimate relation between scientist collaboration and their bibliometric characteristic we used regression analysis. In empirical models were evaluated networks characteristics of 34 thousand author’s profiles taking into account links with all network participants. Limitations caused by the fact that information about year of the first citation is available only after 2007 year in GS (for the data that were collected in the Jan 2014) that leads to inclusion tenure of citation in explanatory variables only for profiles with first citation after 2007.
There were found that the highest average values of citations, indexes Hirsch and i10 are in the fields of biomedicine, physics and chemistry, and the smallest indexes - in economics and finance. The average number of co-authors for all disciplines is 6, this indicator is above average in the natural sciences, below average is in economics & finance. For full sample closeness centrality is 0.185 it is above average in computer science, below average – in economics & finance and physics & chemistry.
There is a positive correlation between scholar’s citation counts and number of co-authors, between citations and the author’s centrality, and between scholar’s citations and the average citation of co-authors. The h-index and i10 index are correlated significantly with the number of co-authors and average citation of co-authors.
Thus scientists who maintain more contacts and more active than others have better bibliometric indicators on the average. These findings are valid both for young and senior scholars as well as for researchers from various disciplines.