We used a repeated measures design and Social Network Analysis (SNA) to examine the nature and quality of collaboration in an NSF Growing Convergence Research project on addressing freshwater salinization (Grant et al., 2022; Misra et al., 2024). Academic experts (n=20) with different disciplinary backgrounds (Engineering and Applied Sciences, Natural and Physical Sciences, Social Sciences and Humanities), and varying levels of experience (faculty, post-doctoral researchers, and graduate students) participated in a repeated measures study on collaboration activities, interactions, and processes during the course of the five-year convergence research project.

Using SNA, we traced patterns of information exchange and collaborative ties between team members to identify key actors and knowledge brokers who facilitated cross-disciplinary knowledge integration. This analysis addressed the following research questions concerning knowledge flow, collaboration dynamics, integrative capacity (Salazar et al., 2012), and transdisciplinary orientation (Misra et al., 2015).

(a) Knowledge Flow: Do key actors who act as hubs or intermediaries, identified using measures of centrality (degree, betweenness, and closeness) also report a clearer understanding of the team’s direction, purposes, and goals?

(b) Collaboration Dynamics: Are collaboration clusters in the network heterophilic or homophilic? In a heterophilic network individuals collaborate with people who are different than themselves by one or more measures, but in a homophilic network individuals are more likely to work with members who are more like them by one or more measures. Are the different types of collaboration clusters within the team, identified using cohesion measures (network density, clustering coefficients, structural equivalence, and brokerage), positively correlated with the diversity of perspectives in these clusters? Are members of these clusters more likely to be influenced by ideas from members’ belonging to disciplines different from their own?

(c) Integrative Capacity: Is the distribution of expertise and collaborative ties (network’s range, tie strength, network centralization measure) positively associated with team members’ perceived ability to synthesize knowledge from different disciplines?

(d) Transdisciplinary Orientation: Do team members who report a high transdisciplinary orientation also more likely to have greater connectivity within the network and exert more influence as knowledge brokers, assessed using centrality measures (degree and betweenness)? Additionally, we examine whether changes in transdisciplinary orientation scores correspond with shifts in individuals’ network positions and the overall network structure, offering insights into how transdisciplinary mindsets contribute to creativity and cross-disciplinary collaboration over time.

To provide contextual nuance to the quantitative data, we analyzed participants’ open-ended responses to understand the potential barriers and facilitators of convergence research, including patterns of collaboration, communication norms, disciplinary barriers, accountability mechanisms, and competing priorities (Bammer, 2008; Godemann, 2008; Klein, 2021). By highlighting both facilitators and inhibitors of collaborative dynamics, this study offers practical insights for the design and management of cross-disciplinary teams. The findings of this study can be used by team leaders, boundary spanners, and funding agencies to support convergence research projects and programs.