Information Systems Research 2021
Making the Crowd Wiser: (Re)Combination Through Teaming in Crowdsourcing
Abstract
Open innovation and crowdsourcing have become standard tools for governments, foundations, and firms seeking solutions to hard problems. The implicit assumption is that more participation and more collaboration produce better outcomes. This research complicates that picture. Using agent-based simulation calibrated against one of the world’s largest crowdsourcing platforms, we show that spontaneous team formation during contests trades off parallel exploration for collaborative depth and that this tradeoff is not always favorable. For simple, well-defined problems, enabling teaming at the right moment improves the odds of finding the best solution. For complex, interdependent problems, precisely the kind that public innovation challenges tend to target, poorly timed or signal-driven teaming can suppress the diversity of search and reduce the likelihood of breakthrough outcomes. Policymakers designing grand challenge competitions, innovation prizes, or open problem-solving initiatives should attend carefully to the structural features of their platforms: what information is made visible, when teaming is permitted, and how incentives interact with collaboration norms. The design of the information environment is not neutral; it shapes who collaborates with whom, and ultimately whether the crowd delivers on its promise.
- DOI
- 10.1287/isre.2023.0556
- Sources
- semanticscholar openalex