Paper Info
Title
Finding Useful Solutions In Online Knowledge Communities: A Theory-Driven Design And Multilevel Analysis
Abstract
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.
Year
DOI
Venue
2020
10.1287/isre.2019.0911
INFORMATION SYSTEMS RESEARCH
Keywords
DocType
Volume
online knowledge community, information usefulness, argument quality, source credibility, text mining, theory-driven design science
Journal
31
Issue
ISSN
Citations 
3
1047-7047
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaomo Liu11069.85
G. Alan Wang227119.89
Weiguo Fan32055133.38
Zhongju Zhang437421.01