Differential Game Analysis of Scientific Crowdsourcing on Knowledge Transfer release_rev_8b557052-5310-421f-b62e-67db973503fa

Abstract

Scientific crowdsourcing based on knowledge transfer between enterprises has drawn wide attention. This paper constructs the Stackelberg master–slave game model and the benefit sharing model. Through the model comparison and numerical simulation, the knowledge transfer behavior and the revenue distribution mechanism of crowdsourcing initiator and solver in the context of scientific crowdsourcing are studied. The research shows that the knowledge transfer quality and the crowdsourcing total revenue under the benefit sharing state are better than the Stackelberg master–slave game under the leadership of the crowdsourcing initiator and when the revenue distribution coefficient between the crowdsourcing initiator and solver is within a certain range. The final revenue for each party in the benefit sharing state is higher than the one in the Stackelberg master–slave game state. In addition, the research finds that the knowledge coupling degree between the initiator and the solver has a positive impact on knowledge transfer and crowdsourcing benefits. The conclusions of this paper provide a theoretical basis for enterprises, especially for large-scale high-tech business to business enterprises, to transfer knowledge and distribute revenue and eventually improve their scientific crowdsourcing quality.
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