Finding useful solutions in online knowledge communities: A theory-driven design and multilevel analysis

Xiaomo Liu, G. Alan Wang, Weiguo Fan, Zhongju Zhang

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)731-752
Number of pages22
JournalInformation Systems Research
Issue number3
StatePublished - 2020


  • Argument quality
  • Information usefulness
  • Online knowledge community
  • Source credibility
  • Text mining
  • Theory-driven design science

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


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