Joint vs. Separate Crowdsourcing Contests

Abstract

In a crowdsourcing contest, innovation is outsourced by a firm to an open crowd that competes in generating innovative solutions. Given that the projects typically consist of multiple attributes, how should the firm optimally design a crowdsourcing contest for such a project? We consider two alternative mechanisms. One is a joint contest, where the best solution is chosen from the joint solutions across attributes submitted by all contestants. The other is multiple separate parallel subcontests, with each dedicated to one attribute of the project. It is intuitive that the separate contest has the advantage of potentially creating a “cooperative” final solution contributed by different contestants. However, somewhat surprisingly, we show that the separate contest may reduce the incentive for the crowd to exert effort, resulting in the joint contest becoming the optimal scheme. The comparison of the expected best performances in the two contests depends on the project’s characteristics. For example, if contestants’ performances have a sufficiently high (respectively, low) level of randomness, the separate (respectively, joint) contest is optimal. If the number of contestants is large (respectively, small) enough, the separate (respectively, joint) contest is optimal. Moreover, we find that when the prize is endogenized, the optimal amount of the prize in the joint contest is no less than that in the separate contest. Finally, we extend the model to account for contestants with heterogeneous types. This paper was accepted by Gad Allon, operations management.