Attracting solutions in crowdsourcing contests: The role of knowledge distance, identity disclosure, and seeker status

Abstract

We investigate in the context of crowdsourcing how seekers can increase open innovation performance, measured as received solver attention, by making two strategic decisions: selecting innovation tasks that are well suited for crowdsourcing and choosing between the potentials of status signalling through identity disclosure versus enjoying the benefits of anonymity. Drawing on uncertainty reduction theory, we suggest that a well-articulated problem statement reduces uncertainties of potential solvers and increases their willingness to participate. We argue that the ability of seekers to draft high-quality problem statements depends on the distance between the problem domain and their current knowledge stock. An analysis of 637 crowdsourcing projects finds that problem-seeker knowledge distance and received solver attention are curvilinear related such that moderate levels of knowledge distance maximize solver participation. However, high-status seekers who engage in identity-based status signalling are able to benefit from crowdsourcing across all levels of problem-seeker knowledge distance.