More than Words in Medical Question-and-Answer Sites: A Content-Context Congruence Perspective

Chih-Hung Peng (), Dezhi Yin () and Han Zhang ()
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Chih-Hung Peng: College of Commerce, National Chengchi University, Taipei 11605, Taiwan
Dezhi Yin: Muma College of Business, University of South Florida, Tampa, Florida 33620
Han Zhang: Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308

Information Systems Research, 2020, vol. 31, issue 3, 913-928

Abstract: Given the popularity and prevalence of medical question-and-answer (Q&A) services, it is increasingly important to understand what constitutes a helpful answer in the medical domain. Prior studies on user-generated content have examined the independent impacts of content and source characteristics on reader perception of the content's value. In the setting of medical Q&A sites, we propose a novel content-context congruence perspective with a focus on the role of congruence between an answer’s content and the answer’s contextual cues. Specifically, we identify two types of contextual cues critical in this unique setting—the language attributes (i.e., concreteness and emotional intensity) of the question’s content, and the acuteness of the disease to which the question is related. Building on the priming literature and construal-level theory, we hypothesize that an answer will be perceived as more helpful if the language attributes of the answer’s content are congruent with those of the preceding question, and if they are congruent with the disease’s acuteness. Analyses of a unique data set from WebMD Answers provide empirical evidence for our theoretical model. This research deepens our understanding of readers’ value judgment of online medical information, demonstrates the importance of considering the congruence of content with contextual cues, and opens up exciting opportunities for future research to explore the role of content-context congruence in all varieties of user-generated content. Our findings also provide direct practical implications for knowledge contributors and Q&A sites.

Keywords: medical Q&A; answer helpfulness; user-generated content; content-context congruence; fit; concreteness; emotional intensity; construal level (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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