How Team Interlock Ecosystems Shape the Assembly of Scientific Teams: A Hypergraph Approach. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Lungeanu, Alina; Carter, Dorothy R.; DeChurch, Leslie A.; Contractor, Noshir S.
• Is Part Of: Communication methods and measures. Volume 12:Issue 2/3(2018)
• Page Start: 174
• Page End: 198
• Publication Date: 2018-04-03
• Subjects: Communication -- Methodology -- Periodicals; Communication -- Research -- Periodicals; Communication -- Study and teaching -- Periodicals; 302.2072
• DOI: 10.1080/19312458.2018.1430756
• Languages: English
• ISSNs: 1931-2458

Abstract

ABSTRACT: Today's most pressing scientific problems necessitate scientific teamwork; the increasing complexity and specialization of knowledge render "lone geniuses" ill-equipped to make high-impact scientific breakthroughs. Social network research has begun to explore the factors that promote the assembly of scientific teams. However, this work has been limited by network approaches centered conceptually and analytically on "nodes as people, " or "nodes as teams." In this article, we develop a " team-interlock ecosystem " conceptualization of collaborative environments within which new scientific teams, or other creative team-based enterprises, assemble. Team interlock ecosystems comprise teams linked to one another through overlapping memberships and/or overlapping knowledge domains. They depict teams, people, and knowledge sets as nodes, and thus, present both conceptual advantages as well as methodological challenges. Conceptually, team interlock ecosystems invite novel questions about how the structural characteristics of embedding ecosystems serve as the primordial soup from which new teams assemble. Methodologically, however, studying ecosystems requires the use of more advanced analytics that correspond to the inherently multilevel phenomenon of scientists nested within multiple teams. To address these methodological challenges, we advance the use of hypergraph methodologies combined with bibliometric data and simulation-based approaches to test hypotheses related to the ecosystem drivers of team assembly.