Abstract
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.
Highlights
As initiated by Jeff Howe [1], crowdsourcing, a type of “open innovation,” refers to an effort to leverage the expertise of a global pool of individuals and organizations, to as quickly and cost effectively as possible develop and implement creative solutions to innovation challenges
Introduce the game model into the scientific crowdsourcing equilibrium decision process and analyse solvers’ behaviours; Simulate a knowledge flow process based on scientific crowdsourcing with four key elements, which are knowledge utility, knowledge transfer cost, knowledge distance, and knowledge trading cost; Analyse how these key elements affect a solver’s profit in the scientific crowdsourcing process
− gi, the always to get higher than foreign solver j, even i’s crowdsourcing knowledge utility is less than always getcase, higher profitcost than solver even if solver i’s It knowledge utilitywritten is less than solverexpect j’s
Summary
As initiated by Jeff Howe [1], crowdsourcing, a type of “open innovation,” refers to an effort to leverage the expertise of a global pool of individuals and organizations, to as quickly and cost effectively as possible develop and implement creative solutions to innovation challenges. 2019, 5, 89 the initiator has the resources and funds, while the solver has the creativity and technology Both parties need to work together to improve the quality of knowledge transfer and make maximum profit. The knowledge flow decision-making behaviour of the crowdsourcing subjects can be understood as the process of a game between the initiator and the solver [15]. This paper constructs a game model to investigate how solvers participate in the competition to win the task, from a knowledge flow perspective. Introduce the game model into the scientific crowdsourcing equilibrium decision process and analyse solvers’ behaviours; Simulate a knowledge flow process based on scientific crowdsourcing with four key elements, which are knowledge utility, knowledge transfer cost, knowledge distance, and knowledge trading cost; Analyse how these key elements affect a solver’s profit in the scientific crowdsourcing process.
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