Abstract: In the era of social media, the innovative communities(IC) have become important habitats of lead users. Therefore, how to identify leading users in the big data environment is a key issue for companies to capture value from innovation communities. In this paper, we propose a novel approach that relies on extracting linguistic-style cues from community posts to identify lead users. Text mining were used to extract positive emotional, collectivism and self-interest-oriented writing style cues from 47310 posts written by 355 members of 10 ICs. The results show that positive emotional and self-interest-oriented linguistic-style are significantly positive related to lead-userness, they can play signaling roles for identifying lead users. While, the relationship between collectivism linguistic-style and lead-userness are not significant. The theoretical and managerial implications of this novel approach of identifying lead users were concluded.

摘要： 在社会化媒体时代，如何在创新社区的海量数据环境中识别出领先用户，是企业从创新社区中获取价值的关键问题。本文从语言风格的视角出发，首先通过内容分析法探索了创新社区中领先用户表达的典型语言风格特征，即成就需求、未来导向、积极情绪和集体主义。其次，收集了355名创新社区用户所发表的47310篇帖子，利用自动文本分析方法对其中包含的积极情绪、集体主义和未来导向语言风格进行了测量，并验证了这四种语言风格与用户领先性的关系。研究结果表明，创新社区用户生成内容中所表现的语言风格起到了“信号”的作用：除了集体主义之外，成就需求、未来导向和积极情绪的语言风格都与用户领先性有显著的正向关系，可以作为识别领先用户的有效指标。最后，讨论了基于语言风格的领先用户识别机制的理论意义和实践价值。