Differential Game Analysis of Scientific Crowdsourcing on Knowledge Transfer

Wang, Guohao; Yu, Liying

doi:10.3390/su11102735

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…According to the research of Malhotra et al [23], it is easier to create a smooth knowledge flow process for an internal crowd than external crowd participants. Wang et al [24] analysed knowledge transfer decision-making behaviour for the initiator and solver in scientific crowdsourcing. Fang et al [25] made use of crowdsourcing to resolve unlabelled data for modelling creation.…”

Section: Literature Reviewmentioning

confidence: 99%

The Game Equilibrium of Scientific Crowdsourcing Solvers Based on the Hotelling Model

Wang

2019

Journal of Open Innovation: Technology, Market, and Complexity

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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.

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Section: Literature Reviewmentioning

confidence: 99%

The Game Equilibrium of Scientific Crowdsourcing Solvers Based on the Hotelling Model

Wang

2019

Journal of Open Innovation: Technology, Market, and Complexity

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Dynamic Incentive Mechanism of Collaboration in Prefabricated Building Supply Chain Based on Differential Game

Wang,

Liu,

Qiao

2024

Buildings

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Supply chain collaboration is an important guarantee for improving the performance of prefabricated construction projects and exerting its advantages. The aim of this study was to explore the level and effect of supply chain collaboration under a no-cost-sharing contract, a cost-sharing contract, and a centralized decision-making contract. A dynamic incentive model of prefabricated building supply chain collaboration based on the differential game model was proposed. Considering the impact of BIM on collaboration, the BIM application level was also introduced into the incentive model. Through the comparative analysis of model solution results and numerical simulation, it was found that, in the centralized decision-making contract, the optimal collaborative effort and the optimal total revenue of enterprises in the prefabricated building supply chain were the largest; the optimal collaborative effort of enterprise in the prefabricated building supply chain reduced first and then tended to be stable; and the improvement of the BIM application level could promote collaboration in the prefabricated building supply chain. The results of this study are helpful for managers to select and formulate a reasonable collaboration incentive contract in the prefabricated building supply chain.

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How Can Scientific Crowdsourcing Realize Value Co-Creation? A Knowledge Flow-Based Perspective

Qiu,

Wang,

et al. 2024

Systems

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Presently, the practice of scientific crowdsourcing still suffers from user loss, platform operational inefficiency, and many other dilemmas, mainly because the process mechanism of realizing value co-creation through interaction between users and platforms has not yet been elaborated. To fill this gap, this study takes Kaggle as the research object and explores the realization process and internal mechanism of scientific crowdsourcing value co-creation from the perspective of knowledge flow. The results show that the operation process of Kaggle-based scientific crowdsourcing can be decomposed into five progressive evolutionary stages, including knowledge sharing, knowledge innovation, knowledge dissemination, knowledge application, and knowledge advantage formation. The knowledge flow activates a series of value co-creation activities of scientific crowdsourcing, forming a dynamic evolution and continuous optimization of the value co-creation process that includes the value proposition, value communication, value consensus, and all-win value. Institutional logic plays a key role as a catalyst in the value co-creation of scientific crowdsourcing, effectively facilitating the realization of value co-creation by controlling and guiding the flow of knowledge. The study unlocks the “gray box” from knowledge flow to value co-creation, providing new theoretical support and guidance for further enhancing the value co-creation capacity and accelerating the practice of scientific crowdsourcing.

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Differential Game Analysis of Scientific Crowdsourcing on Knowledge Transfer

Cited by 3 publications

References 32 publications

The Game Equilibrium of Scientific Crowdsourcing Solvers Based on the Hotelling Model

The Game Equilibrium of Scientific Crowdsourcing Solvers Based on the Hotelling Model

Dynamic Incentive Mechanism of Collaboration in Prefabricated Building Supply Chain Based on Differential Game

How Can Scientific Crowdsourcing Realize Value Co-Creation? A Knowledge Flow-Based Perspective

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