How to become an important player in scientific collaboration networks?

Highlights

• Prolific mid-career researchers with a lot of direct connections and financial power can control the flow of knowledge and access to the resources.

• Local influencers are mostly found to be mid-career productive researchers who have a lot of direct connections.

• Highly connected industrial researchers who are on average less productive tend to collaborate in knit groups.

Abstract

Scientific collaboration is one of the important drivers of research progress that supports researchers in the generation of novel ideas. Collaboration networks and their impact on scientific activities thus already attracted some attention in the research community, but no work so far studied possible factors which can influence the network positions of the researchers at the individual level. The objective of this paper is to investigate various characteristics and roles of the researchers occupying important positions in the collaboration network. For this purpose, we focus on the collaboration network among Canadian researchers during the period of 1996 to 2010 and employ multiple regression models to estimate the impact on network structure variables. Results highlight the crucial role of past productivity of the researchers along with their available funding in determining and improving their position in the co-authorship network. It is shown that researchers who have great influence on their local community do not necessarily publish high quality works. We also find that highly productive researchers not only have more important connections but also play a critical role in connecting other researchers. Moreover, although mid-career scientists tend to collaborate more in knit groups and on average have higher influence on their local community, our results specifically highlight the important role of young researchers who occupy mediatory positions in the network which enable them to connect different communities and fuel information transmission through the network.

Introduction

Recent progress in information technologies has cut the world-wide distances enabling researchers to get in contact easier. Hence, nowadays no specific border can be defined for scientific activities in a way that researchers have formed a global community aiming to advance the level of knowledge. Concurrently, the nature of the science has become more complex and inter-disciplinary which encourages scientists to be more collaborative in order to increase their scientific productivity, to get access to new knowledge and financial resources, etc. Katz and Martin (1997) define scientific collaboration as the process through which researchers with a common goal work together to produce new scientific knowledge. The importance of collaborative research is now acknowledged in scientific communities (Brad Wray, 2006). Through collaboration researchers get access to an often informal network of scientists that may facilitate knowledge and skill diffusion (Tijssen, van Leeuwen, & Korevaar, 1996; Tijssen, 2004). Although it is not easy to quantify scientific collaboration, co-authorship has become the standard way of measuring collaboration since it is considered as a better sign of mutual scientific activity (De Solla Price, 1963, Ubfal and Maffioli, 2011). Co-authorship networks, as one of the main forms of scientific collaboration (Abbasi, Altmann, & Hwang, 2010), evolve over time. This evolution might reflect the growth/decay of a research subject, community or even a scientific field (Huang, Zhuang, Li, & Giles, 2008). This evolution and changes can be also seen at the nodes level (i.e. researchers in the co-authorship networks) where researchers’ positions and their importance within their community and/or the whole collaboration network might also change over time. Position of a node in a network depends both on its direct and indirect connections with the other nodes (Mattsson & Johanson, 1992).

Due to the growing large number of researchers and their co-authorship links, scientific collaboration networks are among the complex ones (Abbasi, Hossain, & Leydesdorff, 2012). Role of a researcher (node) in a network can bring some advantages to the researcher (e.g. better access to knowledge sources, political factors, awareness of potential projects, etc.), and the surrounding community. This becomes more interesting as one notes that the roles of nodes in a network might change over time (Abbasi et al., 2012). Barabási and Albert (1999) showed that a new node in a network will be linked to the other nodes with large number of connections (higher degree centrality) with a higher probability. This indicates the importance of the highly connected nodes in a network. This is also confirmed by Moody (2004) who showed that authors who are new in a scientific network are more likely to get connected to highly reputable authors with many collaborators thus making the surrounding community of the reputable researcher denser. On the other hand, there exist studies indicating that getting connected to high performing nodes (researchers, organizations, etc.) can affect the performance of the connecting node. For example, Mote (2005) analyzed the impact of inter-organizational complexity on the research output of 20 projects in national labs and found that groups that were connected to prolific organizations also showed higher performance. All of this highlights the importance of structural collaboration network positions in scientific and technological activities. Thus this paper specifically focuses on researchers’ roles in their collaboration networks and assesses the impact of influencing factors.

The remainder of the paper proceeds as follows: Section 2 discusses the gaps in the literature and objectives of the research; Section 3 presents the data, methodology and the models; Section 4 presents the empirical results and interpretations; Section 5 concludes; and Section 6 discusses the limitations.