One of the most fundamental and universal shifts in modern science and technology is the flourishing of teams in all areas of science, scholarship, invention and entrepreneurship as solitary researchers vanish. Teams constitute the social engines that drive new developments with an increasing dominance in science and technology. Nevertheless, little is known about the process through which teams succeed and fail as the vast majority of studies are based on observation and analysis of successful teams alone. For example, most team research is restricted to teams that successfully formed in the first place, resulting in a joint publication or patent. In reality, most teams fail, sometimes in a spectacular manner. This fact suggests that our current understanding of teams suffers from systematic selection bias where failed teams have largely been ignored because the data that trace them are much less abundant. Prior investigations have documented the career advantages teams confer on their members, but not how they influence scientific discovery and technological invention. Here by analyzing teams that fail alongside their successful counterparts across many domains and outcome metrics, this project will uncover empirically-grounded insights regarding why, how, and when teams fail. Without analyzing the many ways in which teams fail, researchers remain unable to identify robust factors associated with success. This project examines team success and failure across a broad array of science and technology-related contexts, ranging from biological, social and natural science and scholarship to technology, software, and entrepreneurship. Teams can be large or small, more or less structurally integrated, and involve distinct combinations of member roles or mixtures of prior experience. The project involves a two-stage research program to understand how successful teams of different sizes and shapes "think differently" and can be designed to accelerate scientific and technological development. First, the project evaluates success and failure outcomes for than 100 million R&D teams over 100 years in terms of team size, network structure, role composition and experience. Second, insights developed from this investigation will enable the launch of large-scale online team experiments to isolate the causal mechanisms driving these effects. These experiments will bring certainty about critical team mechanisms and facilitate recommendations for policy that can be used to design teams optimized for specific purposes in advancing science and technology. Overall, this research promises to dramatically improve our ability to trace, assess, predict, nurture and design high-impact and highly disruptive teams.
Specifically, our project first involves (1) massive data cleaning and linkage between data on teams from a variety of domains in science, invention, and entrepreneurship. Then (2) team success and failure is measured at many stages, including the failure to secure funding, publish papers and prosecute patents, inject the frontier with novelty, attract scientific and technical attention, remain robust to replication, and achieve persistent influence. Next, the research (3) analyzes the impact of team size and complexity on success and failure by examining the size, complexity, role structure and diversity of experiences within teams. The project uses insights from this investigation to (4) deploy online team experiments to causally identify the influence of team characteristics on success and failure outcomes. Finally, (5) optimal teams are recommended, as also optimal team alterations or adjustments based on desired science and technology outcomes. Results from this work could influence global science and technology policy by increasing appreciation for the benefits of distinct types of teams -- small and large, simple and complex, diverse and similar -- relative to the science and technology outcomes they support, including disruption and collective advance.
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.
