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

Application 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. Whereas prior studies have focused primarily on the independent impacts of content and source characteristics, we propose a content-context congruence perspective with a focus on the role of congruence between an answer’s content and the answer’s contextual cues. Using a unique data set from WebMD Answers, we find that an answer is 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. The insight for management: Most user-generated content platforms offer guidelines for content contributors to guide their writing in ways more conducive to being helpful, and these guidelines typically prescribe a simplistic formula centered around characteristics of the “ideal” content without regard to context. Instead, we advise Q&A sites to educate content contributors about the pitfalls of following simple formulas and the diversity of readers looking for answers of different questions. 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, de