TY - JOUR
T1 - Finding useful solutions in online knowledge communities
T2 - A theory-driven design and multilevel analysis
AU - Liu, Xiaomo
AU - Alan Wang, G.
AU - Fan, Weiguo
AU - Zhang, Zhongju
N1 - Funding Information:
History: Sabyasachi Mitra, Senior Editor; Wendy Duan, Associate Editor. Funding: W. Fan is partially supported by the National Natural Science Foundation of P.R.C. [Grants 71531013, 71729001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2019.0911.
Publisher Copyright:
© 2020 INFORMS.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Argument quality
KW - Information usefulness
KW - Online knowledge community
KW - Source credibility
KW - Text mining
KW - Theory-driven design science
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U2 - 10.1287/ISRE.2019.0911
DO - 10.1287/ISRE.2019.0911
M3 - Article
AN - SCOPUS:85092891149
SN - 1047-7047
VL - 31
SP - 731
EP - 752
JO - Information Systems Research
JF - Information Systems Research
IS - 3
ER -