Innovation Platform Users’ Knowledge-Sharing Decision Mechanisms: A Network Evolutionary Game Model for Enterprises in China
Xiangshan Jin · SAGE Open · 2025
The complexity and diversity of users’ knowledge-sharing behaviors on crowdsourcing innovation platforms make their decision selection an important subject for scientific research. This article aims to understand decision selection by exploring the factors that influence users’ knowledge-sharing behavior. Based on the Wuli-Shili-Renli (WSR) system approach, this study constructs traditional and network evolutionary game models and compares and analyzes the results through simulations and calculations. The results show that heterogeneous knowledge ratio, knowledge quality, platform service quality, contribution incentive coefficient, and knowledge absorption ability have a positive impact on the evolution depth of knowledge sharing among platform users, while the cost of knowledge contribution (sunk cost) has a negative impact on the evolution depth of knowledge sharing among platform users. In addition, the risk of knowledge contribution does not affect the depth of evolution, but it does have a negative effect on the speed of evolution. Therefore, crowdsourcing innovation platforms should focus on improving knowledge and service quality while providing effective incentives to promote knowledge sharing. Simultaneously, they should reduce the cost of knowledge contribution to avoid negative impacts on sharing behavior. These findings provide theoretical support and practical guidance for platform operators, helping them optimize platform design and promote knowledge sharing among users.