医疗服务管理智能化工程中AIGC客服用户使用意愿影响因素实证研究

Empirical Study on the Influencing Factors of AIGC Customer Service Users’ Willingness to Use in the Intelligent Engineering of Medical Service Management

摘要: 针对全球医疗资源紧缺与传统医疗服务模式的痛点，AIGC驱动的医疗智能客服成为医疗服务转型升级的重要方向。文章以整合型技术接受模型(UTAUT)为核心，融合创新扩散理论(DOI)与社会临场感理论，引入感知兼容性、相对优势、拟人化等变量，并纳入互联网隐私担忧这一情境变量，构建医疗智能客服用户使用意愿影响因素模型。以寻医问药网“寻医小Q”为研究对象，通过问卷调研收集数据，运用偏最小二乘结构方程模型(PLS-SEM)进行实证分析。结果表明，绩效期望、努力期望、相对优势、拟人化、人机交互感知质量均对用户使用意愿产生显著正向影响，对互联网隐私担忧产生显著负向影响，感知信任在绩效期望与使用意愿间发挥中介作用，感知兼容性与认知度则正向影响绩效期望。研究结论为优化医疗智能客服系统设计、提升用户接受度提供理论依据与实践策略，助力缓解医疗资源供需矛盾。

Abstract: Against the backdrop of the global shortage of medical resources and the pain points of the traditional medical service model, AIGC-driven medical intelligent customer service has become an important direction for the transformation and upgrading of medical services. Taking the Unified Theory of Acceptance and Use of Technology (UTAUT) as the core, this study integrates the Diffusion of Innovation Theory (DOI) and the Social Presence Theory, introduces variables such as perceived compatibility, relative advantage and anthropomorphism, and incorporates the contextual variable of internet privacy concern, so as to construct a model of influencing factors on users’ adoption intention of medical intelligent customer service. Taking “Xunyi Xiao Q” of XYWY.com as the research object, this study collects data through questionnaire surveys and conducts an empirical analysis using the Partial Least Squares Structural Equation Model (PLS-SEM). The results show that performance expectancy, effort expectancy, relative advantage, anthropomorphism, and perceived quality of human-computer interaction all have significant positive effects on users’ adoption intention, while internet privacy concern exerts a significant negative effect. Perceived trust plays a mediating role between performance expectancy and adoption intention, and perceived compatibility and awareness positively affect performance expectancy. The research conclusions provide a theoretical basis and practical strategies for optimizing the design of medical intelligent customer service systems and improving user acceptance, thus helping to alleviate the contradiction between the supply and demand of medical resources.