Collaborative Research: HNDS-R: Advancing Diverse Viable Effective Networked Teams (ADVENT)

Embracing individual differences can show measurable benefits for teams and organizations. Prior research shows that the degree to which team members differ in their identity, experience, and background can positively influence team functioning by increasing information, skills, ability, and knowledge. Diverse teams are essential in organizations since they enable individuals to transcend the limits of their specialization, integrate diverse viewpoints, generate innovative ideas, and solve wicked problems. Although organizations have promoted heterogeneity in their units for decades, several workers are less likely to engage with teams when forced to work together. And despite the expanded training in organizations, workers’ attitudes and behaviors toward individual difference are hard to change. How can organizations simultaneously assemble high-performance and high-viability teams with high heterogeneity and affinity levels? The project identifies ideal team combinations based on team members’ existing social connections. In doing so, this research provides innovative solutions to optimize team performance and viability. The "Collaborative Research: HNDS-R: Advancing Diverse Viable Effective Networked Teams (ADVENT)" project addresses the challenge of forming high-performance, high-viability teams with significant heterogeneity and affinity levels. This project aims to create an open-source computational network model to optimize team formation by integrating functional individual difference and member affinity. The researchers train and test this computational model using empirical data from four distinct teaming contexts: scientific collaborations, open-source software development, space crews simulating long-duration space exploration missions, and project teams among students in educational and executive development programs. Utilizing the Multitheoretical Multilevel (MTML) framework, the researchers explore how teams within social networks can form at various levels (individual, dyad, triad, and group) and seek to maximize heterogeneity in certain characteristics while enhancing members’ affinity in others. Key goals include developing an MTML model to elucidate team assembly mechanisms, implementing a data-driven computational model to estimate team performance and viability based on member and network attributes, conducting virtual experiments to explore the impacts of various team configurations, and publishing an interactive web-based exploratorium for users to simulate team performance and viability dynamically. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.