Research On The Knowledge Sharing Efficiency Evaluation And Promotion Path Of Open Innovation Community

In the platform economy,open innovation communities(OICs)have become an important platform for interaction between enterprises and users,and enterprises increasingly are using OICs to attract users to participate in innovation and improve organizational performance through knowledge sharing.However,as the scale of the community continues to grow,OICs also face management problems such as resource loss,low overall quality of participants and low willingness to share knowledge.For OICs,the degree of knowledge sharing is affected by the quality of knowledge and the degree of participation of the participants,indeed,the knowledge sharing efficiency of OICs is also affected,thus it is important to evaluate the knowledge sharing efficiency scientifically.This thesis firstly constructed an evaluation index system for the knowledge sharing efficiency of OICs based on knowledge sharing theory and relevant literature research,captured the data of 12 types of circles in Xiaomi communities from 2020 to 2022,and combined the three-stage DEA method and window model to measure the knowledge sharing efficiency,evaluate and analyze the changes and effectiveness of the knowledge sharing efficiency of communities before and after excluding environmental factors.Secondly,a research model of the knowledge sharing efficiency enhancement path of OICs was constructed based on theoretical perspectives and literature induction.Finally,the fs QCA method was applied to investigate the grouping path of multi-conditional interactions to improve community knowledge sharing efficiency,and based on this,recommendations were proposed to improve community knowledge sharing efficiency and enhance enterprise innovation performances.The results of the study show that:(1)the efficiency of knowledge sharing in OICs was not DEA effective,and after adjustment,the combined technical efficiency of 75% of "circles" and the scale efficiency of 83.33% of "circles" had significantly decreased,and the pure technical efficiency had slightly increased.The lower scale efficiency was the main reason for the low overall technical efficiency of OIC Phase 3.The overall knowledge sharing efficiency of OICs can be classified into three categories: "double high","high low" and "double low".(2)The differences in the overall technical efficiency of knowledge sharing in OICs before and after adjustment by similar SFA models were large,indicating that environmental factors and random interference had a strong influence on the knowledge sharing efficiency of OICs,among which the number of user posts,the number of followers,employee participation and the proportion of certified users had a significant negative influence on input redundancy,while the frequency of user posts and community size had a positive influence on input redundancy.(3)The fs QCA method was used to explore the optimisation paths of knowledge sharing efficiency in open innovation communities.The analysis of the high knowledge sharing efficiency group shows that the effect of the five antecedent variables on high knowledge sharing efficiency differs under different paths,and it can be seen that the knowledge sharing efficiency of open innovation communities is influenced by the linkage of different combinations of individual-level and environment-level factors.Analysis of the non-high knowledge sharing efficiency grouping shows that user interaction and outcome expectations are the core elements that lead to low knowledge sharing efficiency,and it is evident that individual-level elements have a more significant impact on knowledge sharing efficiency.The comparison reveals that the factors affecting the efficiency of knowledge sharing in open innovation communities are "asymmetrical",i.e.the factors that cause low knowledge sharing efficiency are not the exact opposite of those that cause high knowledge sharing efficiency.Based on the empirical research,three types of strategies,namely "external technical support type","user expectation-led type" and "comprehensive development type",have been developed to provide a basis for open innovation communities to make management strategies according to their own characteristics and different development stages.This provides a basis for open innovation communities to choose their management strategies according to their own characteristics and different stages of development.