| Zhihu and other interactive Q&A knowledge sharing platforms adopt a "question answer reply communication" mode,where users achieve identity conversion between questioners and respondents.This sharing behavior is based on knowledge exchange,knowledge innovation,and creation.It can not only enrich users’ knowledge base and obtain additional substantive and virtual benefits,but also bring innovation efficiency and value reputation to the platform,promoting the overall development of the platform.The platform will use reward and punishment rules to manage the game behavior of participating users.Through incentives or punishments,intellectual property protection,and network effect regulation,it will protect high-quality opinion leader users from staying on the platform,increase the stickiness of ordinary knowledge seeking users with a large base,prevent user churn,and promote knowledge exchange and sharing behavior as widely as possible to maximize platform profits.The overall benefits of the entire knowledge sharing platform ecosystem are highly related to the decision-making of the three parties: the knowledge sharing platform,opinion leader users,and ordinary knowledge seeking users.However,there are contradictions in the needs and benefits of the three parties: opinion leader users,ordinary knowledge seeking users,and knowledge sharing platforms.They all hope that the other party will pay more while retaining the benefits as much as possible.In this situation,the revenue and cost demands among various entities cannot be met simultaneously,and the platform lacks an active management mechanism,which cannot gradually stabilize.Therefore,this scenario can be described as a game between the platform and different users within the knowledge sharing platform ecosystem.This study is based on the research method of evolutionary game theory,introducing collaborative innovation and network effect mechanisms,and focusing on parameters such as initial intention,reward and punishment intensity,and distribution coefficient.The aim is to explore the mechanism by which the three major participants achieve equilibrium and stability in the knowledge sharing platform ecosystem,And solve the following problems:(1)What is the equilibrium and stable state of the game among the three parties participating in the interactive question and answer type knowledge sharing platform ecosystem?(2)What are the impacts of rewards and punishments,knowledge allocation rules,knowledge redundancy,and network effects on the direction and size of stable equilibrium results in knowledge sharing platforms?(3)What management methods can knowledge sharing platforms use to promote the active and stable operation of the entire ecosystem?In response to the above issues,this study identified the tripartite entities in the core layer of the knowledge sharing platform ecosystem as the research objects,and used literature research,evolutionary game theory,and simulation methods to conduct analysis and research.Firstly,the relevant connotations and characteristics of knowledge sharing behavior within the platform were summarized,and the system conceptual model of this study was characterized;Secondly,an evolutionary game model of the knowledge sharing platform ecosystem was established and its stability was analyzed;Finally,Matlab software was used to simulate the evolution trend under different system conditions and parameter changes,thereby exploring governance strategies to promote ecological balance and stability.Thus,the following conclusions can be drawn:(1)Under normal circumstances,rewards and punishments for ordinary knowledge seeking users are actually more effective in promoting users to evolve in a positive direction;When the willingness of opinion leaders is extremely low,increasing rewards for opinion leader users has become the most effective measure.(2)Punishing and supervising opinion leader users and providing reward and supervision to ordinary knowledge seeking users can better enable knowledge sharing platforms to evolve towards incentive and supervision.(3)The willingness of opinion leaders and users to share is particularly important for knowledge sharing platforms.Compared to the low willingness of ordinary users,the low willingness of opinion leader users has a more significant impact on the evolution process,and the platform can make greater efforts to retain them through reward mechanisms,which can result in greater returns.(4)When the willingness of opinion leaders and users to share is extremely low,coupled with highly redundant knowledge sharing content that is not competitive,it will ultimately lead to the evolution of behavior strategies for ordinary knowledge seeking users towards passive free riding.(5)When the management willingness of the platform is extremely low,opinion leader users will need the platform’s protection more than ordinary knowledge seeking users because they will be more concerned about the adverse effects of knowledge leakage.(6)When the initial intention of opinion leader users is low,increasing the distribution of innovative knowledge benefits to opinion leader users will actually facilitate the evolution of ordinary users towards active reciprocity.(7)The network effect has a greater impact on opinion leader users,while its impact on ordinary knowledge seeking users is relatively insignificant.Based on the research conclusions,suggestions were proposed for optimizing the management of knowledge sharing platforms from four aspects: improving the platform’s reward and punishment mechanism,building knowledge expansion services,reasonably allocating innovative benefits,and strengthening the construction of interactive networks. |
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