Affiliation:
1. School of Economics and Management China University of Petroleum (East China) Qingdao China
2. School of Economics and Management Shandong Youth University of Political Science Jinan China
3. School of Management and Economics Beijing Institute of Technology Beijing China
Abstract
AbstractDespite numerous enterprises embracing crowdsourcing to access several innovative solutions, the prevalence of information asymmetry among different participants has led to an increase in the submission of low‐quality solutions and payment disputes. To improve the efficiency of crowdsourcing solutions for innovation, this study aims to employ an evolutionary game model to capture the dynamic interaction and decision‐making process of the requesters, platforms, and solvers. Initially, we dissect the relevant factors influencing the behavioral decisions of participants to construct a tripartite evolutionary game model. Subsequently, we analyze five potential evolutionarily stable strategies and conditions. Ultimately, we simulate the dynamic evolution of participant decision‐making behavior and the sensitivity of related parameters. The simulation results depict that the initial selection probabilities of populations bear no correlation to the system stability, which only influences the time required to reach equilibrium. The participant's behaviors are affected by price, loss, penalty, compensation, cost, and reputation recognition. Reward and punishment mechanisms help effectively mitigate the emergence of free‐riding and collusion. These findings provide important implications for the sustainable development of crowdsourcing solutions for innovation.
Funder
National Natural Science Foundation of China