Crowdsourcing design decisions for optimal integration into the company innovation system

Abstract

Companies apply crowdsourcing as the digital approach to source external information into their innovation system. Rather than analyzing the crowdsourcing platform facilitating the connection of company and crowd, this paper is among the first to study systematically the structural design decisions for the integration of crowdsourcing into the company innovation system. Proper structural decision support concerns the design of purpose, specificity, and composition of crowdsourcing. The aim of this paper is to explore how these design decisions relates to innovation system outcome. The results are drawn from an agent-based simulation of competing companies to observe the outcome of different designs of crowdsourcing integration into the innovation system. Both local and distant knowledge environments for innovation are simulated. The main finding is that optimal crowdsourcing design into the innovation system is contingent on the targeted innovation environment. Secondly, the results also reveal a potential conflict between the crowdsourcing platform recommendations in literature and innovation system recommendations made in this paper. Task specificity may generate platform traffic but weakens innovation outcome. In addition, the decided combination of purpose and composition is delicate to ensure highest innovative outcome. Overall, the results provide useful guidelines to companies in their efforts of implementing crowdsourcing into the innovation system.