用户契合视角下知识分享社区专家贡献度评价研究

Research on the Evaluation of Experts’ Contribution in Knowledge Sharing Community from the Perspective of User Engagement

[目的/意义]近年来,用户流失已成为阻碍知识分享社区发展的重要因素。从用户契合视角出发评价社区用户的贡献,有助于吸引潜在用户、增强用户粘性,进而减少用户流失。据此,文章从实现知识分享社区用户契合的视角,对社区专家用户的贡献度进行评价。[方法/过程]首先基于顾客契合理论构建专家贡献度评价体系,再以百度知道日报作为研究情景,获取其精选栏目下的专家贡献行为数据,接着利用熵权法计算各评价指标的熵权,最后根据TOPSIS评价方法得到专家贡献度。[结果/结论]结果显示,不同类型契合行为均对专家贡献度产生了影响,且评价结果与平台自身的用户热度评价结果具有一定的重合。同时,专家贡献度分布不平衡,呈现出"马太效应"。此外,不同用户特征(如专家类型等)的专家其贡献度也具有显著差异。最后,本文立足用户契合视角提出了针对性的建议,从而帮助知识分享社区提升专家用户的贡献水平。

[Purpose/significance]In recent years,the loss of users has become an important factor hindering the development of knowledge sharing community.Evaluating the contribution of community users from the perspective of user engagement will help attract potential users,increase user stickiness,and reduce user loss.Accordingly,this article evaluates the contribution of community expert users from the perspective of improving user engagement.[Method/process]First,we built an expert contribution evaluation system based on customer engagement theory,then used Baidu Know Daily as the research scenario to obtain expert contribution behavior data under its selected columns,and then used the entropy weight method to calculate the entropy weight of each evaluation index.Finally,the expert contribution degree was obtained according to the TOPSIS evaluation method.[Result/conclusion]The results show that,different types of engagement behavior have an impact on expert contribution.Also,evaluation results overlap with the popularity of platform users nearly in half.Meanwhile,the distribution of expert contribution is unbalanced,showing the phenomenon of"Matthew Effect".In addition,experts with different user characteristics(such as expert types,etc.)also have different contribution level.Finally,this article proposed a number of strategies based on the perspective of user engagement to help the knowledge sharing community improve the contribution level of expert users.