An information ecology approach to preferences for online medical consultation platforms in China: a discrete choice experiment

Objectives Online medical consultation (OMC) platforms have become a critical component of digital public health infrastructure in China, yet their sustainability and equitable accessibility are constrained by limited user willingness to pay (WTP). This study aims to quantify preferences and relative WTP for OMC attributes within an information ecology framework, and to examine how these preferences vary across user characteristics and disease contexts. Methods A discrete choice experiment with seven attributes was administered via an online survey. Mixed logit models were estimated to derive attribute utilities and relative marginal WTP, with moderation effects and subgroup heterogeneity systematically examined. Results A total of 417 valid responses were collected from Chinese adults. Among respondents, social recommendation and physician-generated articles were the most influential attributes, followed by reply count and network environment, while physician rating was not significant. Disease severity was the strongest source of preference heterogeneity: WTP for articles and social recommendation increased substantially from mild to severe conditions. Trust significantly attenuated reliance on external signals, while mobile health literacy reduced price sensitivity. Prior OMC experience consistently elevated relative WTP across most attributes. Conclusion This study supports the applicability of information ecology theory to OMC payment decisions in this sample. Social recommendation and physician-generated articles were most valued; trust and mobile health literacy served as key moderators; disease severity fundamentally reshaped preferences. These findings suggest that OMC platform design and payment-related strategies may need to be context-sensitive, particularly with respect to disease severity. Future research and policy discussions may need to consider differential support for users with lower literacy.