Research On Game Analysis And Incentive Mechanism Of Scientific Crowdsourcing From The Perspective Of Knowledge Flow

Recently,in the context of accelerating the construction of an innovative nation,it is particularly urgent to vigorously develop advanced scientific and technological forces and strategic pillar industries.Large-scale scientific research tasks often require people with different professional backgrounds and knowledge to work together.It is difficult to meet the complexity of the problem and the diversified requirements of customers by only relying on internal R&D investment.Scientific crowdsourcing,as a new approach of contemporary activities,is a product of technological development and the popularization of the Internet.The initiators of scientific crowdsourcing gather the wisdom and knowledge from a large number of scientific researchers and institutions for the purpose of scientific discovery and solving the challenges faced by science and technology,and conduct knowledge transfer and technical collaboration through the scientific crowdsourcing platform.Essentially,scientific crowdsourcing is a process of knowledge gathering,knowledge dissemination,knowledge absorption and knowledge application.Therefore,studying the scientific crowdsourcing mode and operating mechanism under the knowledge flow is a subject of practical significance.So far,few domestic scholars have conducted in-depth analysis of scientific crowdsourcing,especially theoretically exploring the game relationship between participants and their knowledge transfer quality,and there is few quantitative and systematic model to guide companies on how to make better use of scientific crowdsourcing,including the solver’s competition strategy,the knowledge transfer quality between initiator and solver,and the incentive mechanism for sustainable crowdsourcing.Therefore,how to effectively establish and develop a scientific crowdsourcing mode from the perspective of knowledge flow is a problem that needs to be resolved urgently for both enterprises and academia.Based on this,through case study,game modeling,and comparative analysis,this dissertation takes the scientific crowdsourcing from the perspective of knowledge flow as the research object,and the main research work are as follows:(1)Through an in-depth analysis of the Kaggle platform,the scientific crowdsourcing mode,operating mechanism and core elements from the perspective of knowledge flow are sorted out.This dissertation briefly introduced the initiator and solvers on Kaggle platform,and summarized the “CDCD” modules,including“Competition”,“Discussions”,“Courses” and “Datasets”.Kaggle’s platform contains a complete knowledge flow process,including knowledge incubation and sharing,knowledge integration and innovation,knowledge transfer and commercialization.This dissertation elaborates scientific crowdsourcing value co-creation model and its three core aspects including crowdsourcing competition,crowdsourcing quality and crowdsourcing incentive,then proposes Kaggle’s “beyond-crowdsourcing” ecosystem.This dissertation summarizes the key points for conducting scientific crowdsourcing under the knowledge flow as three core aspects: crowdsourcing competition among solvers,knowledge transfer quality between initiator and solvers,and incentives mechanism for sustainable scientific crowdsourcing.(2)From the perspective of the solver,the solver’s competitive strategy in scientific crowdsourcing is researched.Based on the Hotelling model,this dissertation analyzes the knowledge transfer and solver’s knowledge pricing strategy in scientific crowdsourcing,and studies how the four key elements including knowledge utility,knowledge transfer cost,knowledge distance and knowledge transaction cost,to impact solver’s knowledge pricing and crowdsourcing profit.Based on salience theory and under the uniform pricing and discriminative pricing situations,this dissertation analyzes the impact of initiator’s sensitivity on knowledge utility and knowledge price to solver’s profit.The research results show that increasing the utility of knowledge,controlling the cost of knowledge transfer,shortening the knowledge distance and reducing the knowledge transaction cost are effective approaches to win the scientific crowdsourcing tasks.The research results also indicate that initiator’s salience has positive impact to solver’s profit,while solver has higher possibility to get more profit when participating in crowdsourcing competition hosted by initiator with more knowledge utility sensitivity.The research results provide theoretical basis and practical guidance for the solvers to participate in the scientific crowdsourcing competition.(3)From the perspective of the initiator,the knowledge transfer strategy and the revenue distribution mechanism between the initiator and solver under the scientific crowdsourcing are investigated.This dissertation establishes the centralized decisionmaking model with benefit-sharing and the decentralized decision-making model with Stackelberg master-slave game,which includes “without cost sharing between initiator and solver”,“unilateral cost sharing by initiator” and “with bilateral cost sharing”situations.This dissertation analyzes and compares the initiator’s knowledge absorbing ability,the solver’s knowledge dissemination ability as well as the knowledge coupling degree between the two parties to the impact of knowledge transfer quality and crowdsourcing revenue under the two models.The research results show that the knowledge transfer quality and the total revenue under the centralized decision-making model with benefit-sharing are higher than the ones obtained from the decentralized decision-making model with Stackelberg master-slave game,and revenue distribution coefficient is a key factor for initiator to select the proper model.Furthermore,the knowledge coupling degree between the initiator and solver can affect knowledge transfer quality and the total crowdsourcing revenue.The research findings help the initiator determine the best scientific crowdsourcing cooperation model to improve the knowledge transfer quality and the crowdsourcing revenue.(4)From the perspective of sustainable crowdsourcing for solver’s continuous participation,an appropriate incentive mechanism for solvers to participate in the knowledge transfer innovation process is designed.Based on the principal-agent model in two scenarios including both single motivation incentive and multiple motivations incentive,this dissertation analyzes the influence factors such as the solver’s effort level,effort cost,and exogenous environmental variables on the initiator’s revenue and the solver’s incentive coefficient under the asymmetrical information situation.The research results show that the solver’s work quality positively affects the crowdsourcing revenue and incentive coefficient,while the external environmental uncertainty and the solver’s effort cost present a negative correlation with the monetary incentive coefficient.The research results provide a theoretical basis for quantifying the initiator’s expected profit and designing an appropriate incentive plan for the solver.It also provides practical guidance for the enterprise to construct a sustainable crowdsourcing incentive plan from the perspective of the solver’s incentives.(5)As for the scientific crowdsourcing from the perspective of knowledge flow,the following managerial insights can be obtained: 1)The key points of the establishment of the scientific crowdsourcing model from the perspective of knowledge flow are the evaluation mechanism of the contractor’s knowledge,the knowledge transfer model of the initiator and solver,and the sustainable scientific crowdsourcing incentives mechanism.2)Crowdsourcing solvers should pay more attention to nonmonetary elements when participating in scientific crowdsourcing and try to shorten the knowledge distance between solver and initiator.3)The core enterprise,acts as initiator,needs actively explore the model of benefit sharing with the participants and adjust the revenue distribution coefficient to satisfy both enterprise and the participants so that they can achieve relatively reasonable returns and participants can be motivated with better enthusiasm.4)The initiators shall fully consider both monetary incentives and non-monetary incentives to establish sustainable knowledge flow based scientific crowdsourcing.