Relational Mechanisms in Team Self-Assembly: A Network and Computational Approach

Twyman, Marlon

doi:https://doi.org/10.21985/n2-333x-j394

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Relational Mechanisms in Team Self-Assembly: A Network and Computational Approach

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This dissertation combines perspectives from social networks and teams research to advance understanding of team self-assembly. Across three substantive chapters, I explore team member search behaviors and invitation patterns in contexts where individuals exercise agency to select team members. First, I consider the search for team members in a social network and the resultant team characteristics. During team assembly, how does the prevalence of homophily in a network affect the search for team members and impact team diversity? Next, I investigate invitation patterns that emerge when people invite one another to teams in a technology platform developed to facilitate team assembly. From the investigation, the main question I answer is, “To what extent does the information contained in online recommendations affect teammate invitations when the potential target of a teammate invitation is someone whom you already know?” In other words, how do online recommendations influence whether an individual will invite a prior collaborator? Lastly, I study how invitation patterns impact the evolution of team relationships throughout the span of collaboration. Specifically, how do teammate invitation patterns affect the subsequent evolution of communication and leadership networks in teams? The team self-assembly patterns under investigation exemplify the typically opaque invitation and search behaviors performed when people look for and select teammates. As such, I sharpen insights into the ways in which people engage with one another when deciding with whom to form teams—making social networks a helpful perspective to guide the research conducted in this thesis. The dissertation leverages social network analysis techniques such as exponential random graph modeling (ERGM) and stochastic actor-oriented models for network dynamics (also known as SIENA models) to analyze empirical social networks related to invitations as well as agent-based modeling (ABM) and simulation of search behaviors in social networks. In the first substantive chapter, ABMs are employed to generate insights regarding the search for team members and the effects on team diversity. By varying levels of homophily in networks and manipulating problem complexity, I investigate the effectiveness of two different search strategies in identifying team members and also the effect of search on the expertise that exists within teams. The advantages of enacting a more information-intensive search strategy increase as problems become more complex and difficult, while homophily in networks helps in identifying team members that closely match problem requirements. In the next chapter, I observe two samples of students assembling interdisciplinary project teams by using a technology platform for team assembly. Using digital trace data from the platform, I conduct ERGM to explain the mechanisms responsible for generating the invitation networks that emerge as determine who to invite to their teams. Online recommendations are a notable feature of the platform’s interface and positively influence the likelihood of sending an invitation to a potential teammate, but prior collaborations are a boundary condition for the effect of online recommendations; specifically, online recommendations are less likely to be heeded when there is prior collaboration. Finally, the third substantive chapter extends the previous to investigate the implications of invitation patterns on dynamics of assembled teams. By investigating the longitudinal coevolution of communication and leadership within teams using SIENA modeling, there are new insights developed that explain how team self-assembly in a technology platform contributes to the emergence of team relationships. Leadership and communication influence the evolution of each other as project teams collaborate over time, and the invitation network that emerges during team self-assembly has a positive influence on communication but a negative effect on leadership within teams. However, teammate recommendations generated by the online platform do not have an effect on the coevolution of the team networks. Other important effects in explaining coevolution of the team relationships are endogenous network structures, such as popularity, reciprocity, and transitive closure. The study identifies the limits of employing technology-enabled team assembly as a tool to explain the coevolution of emergent team relationships. In its totality, this dissertation deepens understanding of invitation and search behaviors that occur during team self-assembly as well as the implications of such behaviors on team characteristics and dynamics.

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