Uncovering the Strategies and Dynamics of Research Fields Using Network Science: Structural Evidence from a Decade of Privacy Research

Charles Perez (), Karina Sokolova () and Vincent Dutot ()
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Charles Perez: PSB: Paris School of Business
Karina Sokolova: PSB: Paris School of Business
Vincent Dutot: Strategy and Management, IPAG Business School

The Review of Socionetwork Strategies, 2022, vol. 16, issue 2, 573-597

Abstract: Abstract Objective This study aims to better understand the dynamics, organisation and collaboration strategies of a research community through social network analysis. While network is particularly adapted to handling the complexity of social interactions between groups, it is currently underused in many research fields. The topology of the social graph and collaboration strategies is rarely investigated or discussed despite its potential usefulness in understanding group dynamics. The author-strategic diagram, based on social network analysis metrics, is proposed and discussed. Methods Our analysis considers 10 years of co-authored research publications on privacy. First, we explore the dynamics of the community by analysing the incoming, remaining and departing authors, and the authors’ publishing lifetimes. Second, we focus on the dynamics using social network analysis metrics: distance, modularity, node centralities, local clustering coefficient, assortativity. Results Despite the multi-disciplinary nature of privacy research, collaborations exhibit cohesion and a small-world nature. However, the research community is hierarchical, the domain being structured around a few leaders and sub-leaders performing bridging roles between sub-communities. Authors collaborate with others in a similar position in the network except for the leaders, who collaborate a lot, but not with each other. Our author-strategic diagram shows the coexistence of divergent collaboration strategies within a single community related to multiple visions of social capital. Transition phases seem to be present; these could indicate future leaders of the community. Conclusion Our results highlight the disparity within the research community and the differing connection types that leaders and other researchers have. Our results show the usefulness of network science in understanding a research field’s community and its dynamics. Comparable analyses of other research communities could help to uncover similar patterns and to tie micro and macro theories together as we show for the theory of social capital.

Keywords: Network science; Social network; Privacy; Co-authorship; Collaborations (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12626-022-00111-1

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