开放式创新社区用户信息有用性识别研究

Identifying Useful Information from Open Innovation Community

【目的】探究如何从信息数量庞杂冗余、内容质量参差不齐的开放式创新社区中识别出高度有用的用户反馈信息。【方法】以信息采纳模型为理论基础,抓取小米MIUI社区官方论坛23 137条Bug反馈信息作为研究对象,利用二元逻辑回归,从信息质量和信息源可信性两个维度探讨影响开放式创新社区信息有用性的因素。【结果】在信息质量方面,信息及时性对信息有用性有正向影响,信息完整性对信息有用性有影响,信息语义性对信息有用性有负向影响;在信息源可信性方面,用户先前经验不会影响信息有用程度,但用户主动贡献程度对信息有用性有正向影响。【局限】仅研究一个社区的一个版块,结果欠缺普适性。【结论】本研究提取的信息有用性关键影响因素能够有效提升开放式创新社区中用户信息有用性识别的效率与准确度。

[Objective] The paper aims to identify useful message from open innovation community with numerous redundant and low quality information. [Methods] First, we retrieved 23,137 users’ comments on programming bugs from the official Xiaomi MIUI Forum based on the information adoption model. Then, we applied binary logistic regression method to explore factors affecting the usefulness of these comments. [Results] The timeliness of information had positive impact on their usefulness, the integrity of information also affected their usefulness, and the semantics of information had negative effects on their usefulness. The users’ previous experience did not influence the usefulness of information. However, users’ previous contribution had positive effects on the usefulness of information.[Limitations] The research data was collected from small portion of one community, which might yield biased results. [Conclusions] This paper could help us effectively identify usefulness information from open innovation communities.