Research on the diffusion of green innovation behavior based on complex network evolutionary game
Xiaoya Chen,
Weiwei Yang,
Renjie Zhang
Abstract:The strategic choice of green innovation subjects and the evolution of green innovation networks have a two‐way feedback effect. This study aims to construct a comprehensive framework for analyzing the evolution of green innovation networks from micro to macro levels. The gravity model demonstrates the structural complexity of green innovation networks. Additionally, an evolutionary game model based on green innovation networks is constructed to discuss the network's diffusion mechanism of green innovation beh… Show more
Despite 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.
Despite 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.
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