An evaluation of social networks within federally funded research projects

Abstract

United States federal agencies fund research to promote discovery and innovation. Most agencies require collaboration because teams promote productivity to a greater degree than singular researchers. However, the functionality and productivity of collaboration is poorly understood. The purpose of this study was to evaluate the collaborative structure of a federally funded entomology research team to determine the characteristics of the network structure and its impact on research collaboration using social network analysis (SNA) methodology. An online survey and interviews were used to collect data. The theories of social network, strong and weak ties, and scientific collaboration were employed to determine the degree of collaboration among team members. We found a low density pattern of collaboration that was associated with: (a) a centralized pattern, (b) the presence of sub-teams functioning like sub-networks, and (c) the presence of less interactive members. Our results confirm that the SNA approach was useful for evaluating network collaboration with innovative indicators to assess the dynamics of scientific collaboration. The study was limited by non-response. Future research should focus on collecting SNA data longitudinally of the whole network to determine how networking structure and benefits evolves over time, and how strong and weak ties impact scientific discovery.

Exploring the Dynamics of a Long-Term Research Network

“…Researchers' names were also triangulated throughout the three different projects (2015-2021) to determine the number of exits and entrances. Finally, data were pulled from a previous SNA study by Dossou Kpanou et al (2020) for data completeness. All of the names were changed to alpha numeric codes (R1) to ensure their privacy.…”

“…For instance, the SNA approach has been used to provide empirical evidence of the structure of scientific collaboration, its functionality and importance for productivity, and the achievement of research team objectives (Contandriopoulos et al, 2018;Defazio et al, 2009;Kaliva et al, 2015). Marabesi andDossou Kpanou et al (2020) used the SNA approach to better understand network structure among a horticulture research team and an entomology research team to gain insight into causality processes and hidden patterns of collaborative networks. Wu et al (2021) used SNA to study group interactions within the focus group setting.…”