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

Abstract

People are increasingly searching for health information from online sources such as question-and-answer (Q&A) services. The health information obtained this way can have significant impacts on people’s health decisions and lives, but what constitutes a helpful answer in the medical domain remains insufficiently explored. While prior studies examining antecedents of readers’ helpfulness evaluation of answers 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. Specifically, we leverage the unique setting of medical Q&A sites to identify two types of critical contextual cues — 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 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