Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis-Reference-Cited by-同舟云学术

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

Published:2020-09 Issue:3 Volume:31 Page:731-752
ISSN:1047-7047
Container-title:Information Systems Research
language:en
Short-container-title:Information Systems Research

Author:

Liu Xiaomo¹^ORCID,Wang G. Alan²^ORCID,Fan Weiguo³^ORCID,Zhang Zhongju⁴^ORCID

Affiliation:

1. S&P Global Ratings, New York, New York 10041;

2. Department of Business Information Technology, Pamplin College of Business, Virginia Tech, Blacksburg, Virginia 24061;

3. Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242;

4. W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287

Abstract

In this study, we utilize a kernel theory of knowledge adoption model and propose a novel text analytic framework to classify the usefulness of solutions in online knowledge communities. The study combines multiple disciplines (behavioral, empirical, design science, and technical) to tackle an important and relevant business problem: how to effectively manage an online knowledge repository and identify useful solutions. Our framework can be implemented in online knowledge communities to improve users’ experience of searching for useful knowledge. The proposed framework has the potential to guide the development of customer-facing chatbots, which understand human-language questions and return helpful answers immediately.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

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

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