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BACKGROUND Online Health Community is of great significance to improve the level of medical and health services. How to determine doctors' quality and choose appropriate one for online consultation is a crucial issue. Multiple participants (patients, doctors, and platform managers)on online OHC send out signals reflecting doctors' quality from different perspectives to patients. How to determine a doctor's service quality through these signals is a critial issue for patients and very few research have investigated this questions before. OBJECTIVE In this paper, based on signaling theory, online-feedback mechanism and trust theory, we explored the impact of multi-source signals (patient-generated content, doctor-generated content, and system-generated content) on patients’ decision-making, and the moderating effect of disease risk on the relationship between patient-generated content, doctor-generated content, and system-generated content and patients’ decision-making. METHODS Selecting 20 kinds of disease types, we crawled 6659 doctors'information and 743498 review comments from Haodaifu online. We constructed a multiple linear regression model, using spss22.0 to analyze the data. RESULTS The results revealed that: (1) patient,doctor and system-generated content have positive impact on patient decision-making; (2) patient-generated content has a substitute effect on doctor-generated content and system-generated content; (3) disease type positively moderates the relationship between patient-generated content and patients' consultation, while does not significantly moderate the relationship between doctor-generated content, system-generated content and patients'consultation. CONCLUSIONS The research results have enriched relevant literature of patients'consultation behavior in online health community by considering the substitute effect of multiple participants generated content and the moderating effect of disease types. It enriches our understanding of online health community’s operating mechanism and help patients choose appropriate doctors for self health management. It also provides practical support for platform managers on how to design recommendation strategy to increase doctors’ consultations.
Wei et al. (Mon,) studied this question.