The game equilibrium of scientific crowdsourcing solvers based on the hotelling model

• Published in: Journal of open innovation Vol. 5; no. 4; pp. 1 - 14
• Main Authors: Wang, Guohao, Yu, Liying
• Format: Journal Article
• Language: English
• Published: Basel MDPI 2019; Elsevier Ltd; Elsevier Limited; Elsevier
• Subjects: Collaboration; Competition; Crowdsourcing; Decision making; Equilibrium; game model; Initiators; Innovations; Internet; Knowledge acquisition; knowledge flow; Knowledge management; Product development; scientific crowdsourcing; Solvers; scientific crowdsourcing; equilibrium; game model; knowledge flow
• ISSN: 2199-8531; 2199-8531
• DOI: 10.3390/joitmc5040089

Scientific crowdsourcing, which can effectively obtain wisdom from solvers, has become a new type of open innovation to address worldwide scientific and research problems. In the crowdsourcing process, the initiator should satisfy his own research needs by selecting a proper solver from the crowd, and the solver must have multiple competitions in order to obtain scientific research tasks from the initiator. The participants in the scientific crowdsourcing are based on the knowledge flow to realize the value added of knowledge. This paper discusses a few factors, including knowledge utility, knowledge transfer cost, knowledge distance, and knowledge trading cost, which all affect the solvers to achieve game equilibrium and win tasks in scientific crowdsourcing. By referring to the concept of Hotelling model, this paper constructs a game model with the solvers as the participants, and analyses solvers' behaviours in scientific crowdsourcing and their profit impacts by each of the key elements. The results show that from a crowdsourcing solver's point of view, increasing knowledge utility, controlling knowledge transfer cost, shortening knowledge distance to the initiator, and leveraging with a knowledge trading cost are four effective approaches to wining the competition of a scientific crowdsourcing task. The research conclusions provide a theoretical basis and practice guidance for crowdsourcing solvers to participate in scientific crowdsourcing from the perspective of the knowledge flow process.