Background: Information asymmetry causes barriers for the patient's decision-making in the online health community. Patients can rely on the physician's self-disclosed information to alleviate it. However, the impact of physician's self-disclosed information on the patient's decision has rarely been discussed. Objectives: To investigate the impact of the physician's self-disclosed information on the patient's decision in the online health community and to examine the moderating effect of the physician's online reputation. Methods: Drawing on the limited-capacity model of attention, we develop a theoretical model to estimate the impact of physician's self-disclosure information on patient's decision and the contingent roles of physician's online reputation in online healthcare community by econometric methods. We designed a web crawler based on R language program to collect more than 20,000 physicians’ data from their homepage in Haodf—a leading online healthcare community platform in China. The attributes of the physician's information disclosure are measured by the following variables: emotion orientation, the quantity of information and the semantic topics diversity. Results: The empirical analysis derives the following findings: (1) The emotion orientation in physician's self-disclosure information is positively associated with patient's decision; (2) Both excessive quantity of information and semantic topics diversity can raise barriers for patient's decision; (3) When the level of physician's online reputation is high, the negative effect of the quantity of information and semantic topics diversity are all strengthened while the positive effect of the emotion orientation is not strengthened. Conclusions: This study has a profound importance for a deep understanding of the impact of physician's self-disclosure information and contributes to the literature on information disclosure, the limited capacity model of attention, patient's decision. Also, this study provides implications for practice.
All Science Journal Classification (ASJC) codes
- Health Informatics
- Online health community
- Self-disclosed information
- Text mining