Empirical Study of Economic Impact Analysis of Physician's Online Response in the Physician-Driven Online Health Community

Abstract

Physician-driven online health communities (OHCs) have become an increasingly important mechanism through which patients seek answers to their medical questions and subsequently select physicians for paid medical services. In this platform, physicians can voluntarily contribute their knowledge in responses to any health-related questions from patients, whether it falls within or outside their specialty domain. Yet we do not know to what extent physicians’ knowledge contribution in these online responses relates to patients’ subsequent subscription to their paid medical services, which are available on the same platform. Prior research has focused on physicians’ identity-based information (e.g., professional status) and behavioral information (e.g., responding to patients’ consultations), while neglecting how the specific content of these communications affects patients’ subscription intention. To gain a holistic view of how physicians’ online responses impact medical service subscription, we extend the persuasive communication literature to the physician-driven OHC context and conduct two studies to examine the informative and persuasive effects of physician-generated content.

Study 1 examines the informative effect of physician-generated content in physician-driven OHCs. Theoretical arguments in the informativeness literature are drawn upon to propose a consolidated framework whereby physicians earn more economic returns through medical service subscriptions by providing specialized or generalized messages to health-related questions. We further integrate category perception theory with the informativeness literature to argue that physicians’ labels (professional status and boundary spanning) in OHCs could influence the effect of physician-generated content provided by medical specialists and generalists.

Study 2 examines the persuasive effect of physician-generated content in physician-driven OHCs, and proposes that in their online responses to patients’ questions, physicians could increase patients’ medical service subscriptions by making rational appeals in the form of technical messages, and emotional appeals in the form of caring messages. Furthermore, the efficacy of these two types of message appeals is contingent on two aspects of source credibility, manifested by a physician’s professional status for the rational appeal, and smile expression in their profile image that, along with caring messages, significantly adds to their emotional appeal.

Data are collected from a leading physician-driven OHC in China. Hypotheses are empirically examined using machine learning techniques to analyze the information structure (generalized and specialized messages) and communication style (rational and emotional appeals) in online responses made by 653 physicians providing their professional status and profile images.

Results of Study 1 reveal that specialized message is insignificantly associated with medical service subscriptions, and is significant for specialists with high professional status only. Generalized messages and generalized sources (boundary spanning) are significantly negatively associated with the medial service subscriptions, where its interaction effect is also significant. Results of Study 2 indicate that emotional appeal (caring messages) in physicians’ online responses positively impacts their economic return in the form of paid medical subscriptions, while rational appeals (technical messages) do not. The impact of emotional appeal in online responses is more substantial for physicians with smiling faces in their profile images, while the effect of rational appeal becomes positively significant for physicians with a higher professional status. The findings advance the OHC literature on physicians’ active participation and make important contributions to the informativeness and persuasion literature. This study also provides practical guidelines to the platform owners and participants.

Date of Award	21 Nov 2022
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Yulin FANG (Supervisor), Vogel DOUG (External Supervisor), Juhee KWON (Supervisor) & Kai H. LIM (Supervisor)