Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis

• 成果类型: Article
• 署名作者: Liu, Xiaomo; Wang, G. Alan; Fan, Weiguo; Zhang, Zhongju
• 署名单位: Virginia Polytechnic Institute & State University; University of Iowa; Arizona State University; Arizona State University-Tempe
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0911
• 发表日期: 2020
• 页码: 731-752
• 关键词: word-of-mouth; perceived usefulness; consumer reviews; user reviews; INFORMATION; helpfulness; sales; MODEL; contribute; expertise

摘要

Online communities and social collaborative platforms have become an increasingly popular avenue for knowledge sharing and exchange. In these communities, users often engage in informal conversations responding to questions and answers, and over time, they produce a huge amount of highly unstructured and implicit knowledge. How to effectively manage the knowledge repository and identify useful solutions thus becomes a major challenge. In this study, we propose a novel text analytic framework to extract important features from online forums and apply them to classify the usefulness of a solution. Guided by the design science research paradigm, we utilize a kernel theory of the knowledge adoption model, which captures a rich set of argument quality and source credibility features as the predictors of information usefulness. We test our framework on two large-scale knowledge communities: the Apple Support Community and Oracle Community. Our extensive analysis and performance evaluation illustrate that the proposed framework is both effective and efficient in predicting the usefulness of solutions embedded in the knowledge repository. We highlight the theoretical implications of the study as well as the practical applications of the framework to other domains.