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基于区块链的知识社区中通证激励分配垄断对用户知识贡献的影响

李志宏, 谢永靖, 许小颖   

  1. 华南理工大学工商管理学院, 广州 510000
  • 收稿日期:2022-01-06 修回日期:2022-04-08 出版日期:2022-06-25 发布日期:2022-07-29
  • 通讯作者: 许小颖,Email:bmxyxu@scut.edu.cn.
  • 基金资助:
    国家自然科学基金(72071083,72171089),广东省自然科学基金(2019A1515011370,2021A1515012003),广州市哲学社科规划2021年度课题(2021GZQN09)资助课题.

李志宏, 谢永靖, 许小颖. 基于区块链的知识社区中通证激励分配垄断对用户知识贡献的影响[J]. 系统科学与数学, 2022, 42(6): 1362-1374.

LI Zhihong, XIE Yongjing, XU Xiaoying. An Analysis of the Influence of Token Incentive Allocation Monopoly on User Knowledge Contribution in Blockchain-Based Knowledge Communities[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(6): 1362-1374.

An Analysis of the Influence of Token Incentive Allocation Monopoly on User Knowledge Contribution in Blockchain-Based Knowledge Communities

LI Zhihong, XIE Yongjing, XU Xiaoying   

  1. School of Business and Management, South China University of Technology, Guangzhou 510000
  • Received:2022-01-06 Revised:2022-04-08 Online:2022-06-25 Published:2022-07-29
区块链通证激励的兴起,为解决用户内容创作动力不足的问题提供了新思路,但其在实际落地过程中仍面临着诸多挑战.文章以基于区块链的知识社区Steemit为研究对象,通过采集区块数据,从激励平等性和知识贡献效率两个维度分析了社区通证激励机制的现状及问题,从而揭示社区通证激励分配垄断的现象,并探究了通证激励分配垄断对用户知识贡献的影响.研究结果表明:社区的通证激励分配被少部分头部用户所垄断,且社区的激励分配机制不能有效反映用户的知识贡献水平;通证激励分配的不平等性降低了用户的内容生产和内容发现水平.
The rapid development of blockchain token incentives provides a new perspective to solve the problem of insufficient motivation for user content creation, but it still faces many challenges in the actual implementation process. This paper takes Steemit, a knowledge community based on blockchain, as the research object.By collecting block data, this paper analyzes the situations and problems existed in the token incentive mechanism of community from two aspects, including incentive equality and knowledge contribution efficiency, thus revealing the problem of token incentive allocation monopoly. Moreover, this paper identifies the influence of token incentive allocation monopoly on user knowledge contribution. The results show that the token incentive distribution in the community is monopolized by a small number of top users, and the incentive distribution mechanism in the community cannot effectively reflect the users' knowledge contribution levels. The inequality of token incentives allocation results in the decrease of users' content production and content discovery levels.

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