管理评论 ›› 2026, Vol. 38 ›› Issue (8): 188-200.

• 市场营销 • 上一篇    

健康科普文章的医患共同语言对医生在线问诊服务绩效的影响研究

江红艳1, 孙雪凌1,2, 张梦婷1, 刘恬1   

  1. 1. 中国矿业大学经济管理学院, 徐州 221116;
    2. 无锡太湖学院物联网工程学院, 无锡 214063
  • 收稿日期:2025-06-13 发布日期:2026-09-11
  • 作者简介:江红艳,中国矿业大学经济管理学院教授,博士生导师,博士;孙雪凌(通讯作者),中国矿业大学经济管理学院博士研究生;张梦婷,中国矿业大学经济管理学院博士研究生;刘恬,中国矿业大学经济管理学院博士研究生。
  • 基金资助:
    国家自然科学基金面上项目(72572159;72072172);江苏省社会科学基金项目(24GLB020);中央高校基本科研业务费专项资金资助项目(2021ZDPYYQ006);国家社会科学基金重大项目(22ZD&137)。

The Impact of Doctor-Patient Shared Language in Health Popular Science Articles on Physicians’ Online Service Performance

Jiang Hongyan1, Sun Xueling1,2, Zhang Mengting1, Liu Tian1   

  1. 1. School of Economics and Management, China University of Mining and Technology, Xuzhou 221116;
    2. IoT Engineering School, Wuxi Taihu University, Wuxi 214063
  • Received:2025-06-13 Published:2026-09-11

摘要: 在线健康社区(OHCs)是我国互联网医疗消费和健康知识传播的重要场景,然而以往研究通常将在线问诊和健康科普视为两个独立的研究问题,针对二者潜在关联的研究较为有限。鉴于此,本研究在社会资本理论框架下引入信号传递视角构建研究模型,通过机器学习方法从OHCs平台数据中筛选细粒度的研究数据,并基于自然语言处理技术提取出反映医生健康科普文章易懂性的“医患共同语言”变量,考察其如何对医生在线服务绩效产生影响。结果发现,健康科普文章中的医患共同语言对医生在线问诊服务绩效产生积极影响,而且医患互惠反馈在这一过程中发挥中介作用,同时医生的职业资历显著调节医患互惠反馈对医生在线问诊服务绩效的影响。研究结果不仅扩展了社会资本理论的内涵与解释范畴,而且为医生如何提供优质科普知识以及在线健康社区的高效运营等提供了重要的实践启示。

关键词: 医患共同语言, 在线问诊服务绩效, 医患互惠反馈, 职业资历, 社会资本理论

Abstract: Online health communities (OHCs) are an important platform for internet-based medical services and knowledge dissemination in health education in China. However, previous studies often treat online consultation and health promotion as two separate research questions, with limited research on their potential associations. Given this research gap, this study constructs a research model by integrating the signaling perspective within the framework of social capital theory and employs text mining and statistical modeling methods to explore the multidimensional effects of physicians’ social capital in the context of health knowledge popularization in OHCs, and examine how it affects physicians’ online service performance. For sample selection, this study takes China’s most representative online health community platform (HaoDF Online) as the research context and obtains abundant research data from it. The researchers first train text classification models using machine learning methods to automatically identify the topics of doctors’ popular science articles, which greatly improves the accuracy and coverage of sample selection. Next, this study employs word vector techniques to compare the similarity between doctors’ health popular science articles and patients’ consultation text, and quantitatively extracts the “doctor-patient shared language” variable that reflects the readability of physicians’ health popular science articles. Based on the above work, this study constructs an empirical dataset with accurate mapping relationships between popular science article topics and online consultation diseases, which lays the data foundation for subsequent fine-grained causal relationship verification. Finally, this study adopts multi-level regression models to simultaneously examine the interaction effects among independent, mediating and moderating variables. The results show that doctors’ use of doctor-patient shared language in health popular science writing has a positive impact on their online consultation volume, and doctor-patient reciprocal feedback mediates this process. Furthermore, doctors’ professional qualifications can significantly moderate the effect of doctor-patient reciprocal feedback on online consultation volume. These findings extend the theoretical connotation and interpretation of social capital theory. They also provide important references for doctors to provide high-quality popular science knowledge and promote efficient OHCs operation.

Key words: doctor-patient shared language, physicians’ online service performance, doctor-patient reciprocal feedback, professional qualifications, social capital theory