Managing Innovation in a Crowd

Crowd innovation is an emerging technology where innovation is sourced out to the public through an open call. At the center of crowd innovation is a resource allocation problem: there is an abundance of workers but a scarcity of high skills, and an easy task assigned to a skilled worker is a waste of resources. This problem is complicated by the fact that the exact difficulties of innovation tasks may not be known in advance, so tasks that require high-skill labor cannot be identified and allocated ahead of time. More problematically, worker skills in a crowd environment are their own private information, and they may have an incentive to misrepresent that information. Taken together, these aspects imply that the firm has to solve the challenging problem of how to assign tasks to workers even though it knows neither the difficulties of the tasks nor the skills of the workers. We first relax the constraint that the firm does not know the skills of the workers (and that workers participate voluntarily) and examine the resulting assignment problem. We show that the solution to this problem takes the form of a skill hierarchy, where tasks are first attempted by low-skill labor, and high-skill workers only engage with a task if less skilled workers are unable to finish it. We derive this structure of the optimal matching by studying a dynamic programming formulation. The most distinctive property of the optimal matching is that it ensures that the heterogeneous skills of the available workers are efficiently utilized.