Making the Crowd Wiser: (Re)combination through Teaming in Crowdsourcing

Abstract

Firms are increasingly adopting crowdsourcing contests to acquire innovative solutions to challenging prob- lems. As problems become increasingly complex, no individual may have the full range of requisite knowledge to develop an effective solution. There is a paucity of theory on the process that combines contestants’ diverse expertise via teaming. In this paper, we systematically explore: a) with whom to team up; b) when and how contestants should form teams; and c) the outcome of strategic teaming to develop a comprehensive theory from a (re)combination perspective. Using simulation experiments and empirical validation, we find that collaboration among contestants with different expertise increases team performance albeit condition- ally depending on the extent of knowledge overlap between contestants and timing of team formation. More interestingly, there is a misalignment between contestant-level and platform-level outcomes. These findings provide new insights on contestant performance and crowdsourcing quality and have implications for the design of crowdsourcing platforms.

Keywords: Crowdsourcing contests, Teaming, Team Performance, NK fitness landscapes model, simulation, empirical validation