Publication date: Available online 18 May 2017
Author(s): A. Natalicchio, A. Messeni Petruzzelli, A.C. Garavelli
Crowdsourcing initiatives are increasingly spreading among organisations aiming at outsourcing the development of solutions to internal innovation problems to external problem solvers. However, while knowledge about crowdsourcing is growing, a complete understanding of the underlying dynamics of these initiatives is still lacking. This study aims at elucidating this topic by investigating the influence exerted by the interplay between the characteristics of innovation problems, individuals developing solutions (problem solvers), and crowdsourcing platforms on the related problem solving performance. Specifically, we use NK fitness landscapes to simulate the search for solutions conducted by problem solvers in several scenarios, depending on the decomposability and accuracy of delineation of the innovation problems, the degree of bounded rationality of the solvers, and the cooperation policies of the crowdsourcing platforms. Our findings contribute to the development of the theory on search for solutions in crowdsourcing initiatives, by revealing the characteristics of problem solvers and the types of platforms that maximise the performance of the problem solving process, as the quality of the best solution provided and the time required to elaborate on it, according to specific innovation problems. Furthermore, our findings promote the formulation of guidelines for organisations using crowdsourcing to solve their innovation problems, and for the crowdsourcing platforms’ managers.