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Beyond Single-Platform Adoption: Unpacking Doctors' Cross-Channel Behavioral Mechanisms in Omni-Channel Medical Practice
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DOI：10.3390/healthcare14070923.png)
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Abstract
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Highlights What are the main findings? Doctors' adoption of the OCOM service mode is mainly driven by effort expectancy, social influence, habit, and patient volume. Performance expectancy is no longer a significant factor in the pandemic context. Social platform use promotes doctors' engagement with professional platforms. What are the implication of the main findings? Platforms should prioritize ease of use and reduce operational complexity. Enhancing social influence and cross-platform integration can promote OCOM adoption.Highlights What are the main findings? Doctors' adoption of the OCOM service mode is mainly driven by effort expectancy, social influence, habit, and patient volume. Performance expectancy is no longer a significant factor in the pandemic context. Social platform use promotes doctors' engagement with professional platforms. What are the implication of the main findings? Platforms should prioritize ease of use and reduce operational complexity. Enhancing social influence and cross-platform integration can promote OCOM adoption.Abstract Background: As digital healthcare ecosystems evolve, doctors are no longer confined to a single platform for online medical services. Instead, a new omni-channel service mode has emerged, integrating professional medical platforms with widely used social platforms. This shift raises important questions about how doctors adopt and navigate multi-platform environments. The purpose of this study is to explore the behavioral mechanisms that shape doctors' adoption of omni-channel online medical services in the post-pandemic era. Methods: Drawing on the Unified Theory of Acceptance and Use of Technology, complemented by channel effect and technology transfer perspectives, we conducted a survey of 958 Chinese doctors. A structural equation model was employed to test the relationships among effort expectancy, social influence, patient volume, habitual use, platform experience, future expectancy, and adoption intention. Results: The analysis revealed that effort expectancy, social influence, patient volume, and habitual use exert significant positive effects on adoption intention. Platform experience enhances doctors' perceptions of ease of use and usefulness, while future expectancy indirectly shapes adoption intention through these perceptions. In contrast, performance expectancy no longer emerges as a decisive factor in the post-pandemic context, suggesting that external motivations may be overshadowed by practical experience and social dynamics. Moreover, doctors' engagement with social platforms positively influences their use of professional platforms, highlighting cross-channel spillover effects that reinforce adoption across service types. Conclusions: This study extends technology adoption theory by situating doctors within dynamic, multi-platform service environments and demonstrating the importance of cross-channel influences. The findings provide practical guidance for platform designers and policymakers on how to effectively integrate professional and social platforms to enhance digital healthcare delivery.
Keywords:
Omni-Channel Online Medical (OCOM) service mode
UTAUT
channel effect
technology transfer
structural equation model
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