Towards understanding the characteristics of successful and unsuccessful collaborations: a case-based team science study

Abstract Scientific breakthroughs for complex, large-scale problems require a combination of contributory expertize, disciplinary expertize, and interactional expertize, or socialized knowledge. There is, however, little formal recognition of what expertize is important for team success, and how to evaluate different types of contributions. This is problematic for the field of the Science of Team Sciences (SciTS). Funding is increasing for team science globally, but how do we know if teams are collaborating in meaningful ways to meet their goals? Many studies use bibliometric and citation data to understand team development and success; nevertheless, this type of data does not provide timely metrics about collaboration. This study asks: Can we determine if a team is collaborating and working together in meaningful ways in a process evaluation to achieve their goals and be successful in an outcome evaluation, and if so, how? This exploratory longitudinal, mixed-methods, case-based study, reports on eight interdisciplinary scientific teams that were studied from 2015–2017. The study used six different methods of data collection: a social network analysis at three-time points, participant observation, interviews, focus groups, turn-taking data during team meetings, and outcome metrics (publications, award dollars, etc.). After collecting and analyzing the data, a Kendall Rank Correlation was used to examine which development and process metrics correlated with traditional outcome metrics: publications, proposals submitted, and awards received. Five major implications, practical applications, and outputs arise from this case-based study: (1) Practicing even turn-taking is essential to team success. (2) The proportion of women on the team impacts the outcomes of the team. (3) Further evidence that successful team science is not about picking the right people, but on how to build the right team for success. (4) This article presents process metrics to increase understanding of successful and unsuccessful teams. (5) Teams need to engage in practices that build relationships for knowledge integration. This case-based study represents an early step to more effectively communicate how teams form and produce successful outcomes and increase their capacity for knowledge integration. The results contribute to the knowledge bank of integration and implementation by providing additional evidence about evaluation for scientific teams, including the know-how related to everyday interactions that lead to goal attainment. This study provides further evidence that to create new knowledge, scientific teams need both contributory and interactional expertize.