Understanding the effect of professional and social signals on physician performance on online health platforms
Purpose Drawing on signaling theory, this study investigates the interaction between professional signals (professional capital) and social signals (peer and patient recognition) in shaping physician performance on online health platforms (OHPs). Design/methodology/approach The research model is tested using panel data from a leading OHP. We employed the correlated random-effects model to test the research hypotheses. Findings Results confirm that professional capital, peer recognition and patient recognition each positively associate with physician performance. Both peer recognition and patient recognition, as online-generated social signals, substitute for professional capital, a signal generated and certified offline but displayed online. Crucially, the three-way interaction effect confirms that social signals jointly substitute for professional signal. Originality/value This study contributes to the online health platform and digital platform signaling literature by theorizing and empirically testing how institutionally generated professional signals interact with peer- and patient-generated social signals. Its primary contribution is the identification and testing of a joint substitution mechanism: peer and patient recognition together reduce the marginal value of professional capital. The study also highlights an alternative platform-supported career path, especially for junior physicians, and offers implications for platform governance, physician reputation management and equitable online health ecosystem design.
Authors
- Guorui Fan (ORCID: https://orcid.org/0000-0001-8586-1755)
- Zhaohua Deng (ORCID: https://orcid.org/0000-0002-7744-7818)
- Di Wang (ORCID: https://orcid.org/0009-0003-9266-3409)
Institutions
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Industrial Management & Data Systems
- Published
- 2026-10-31
- DOI
- https://doi.org/10.1108/imds-07-2025-1053
- Primary Topic
- Expert finding and Q&A systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00