Exploring users’ negative emotional factors in the adoption of AI agents for medical consultation services: evidence from China

Abstract

Users’ negative emotions are crucial in human-computer interactions with healthcare decision support systems, as they affect mental well-being and the success of artificial intelligence (AI) medical services. However, this emotional aspect remains underexplored. This study addresses this gap through an empirical investigation in China (n = 603), where AI-powered healthcare is rapidly growing. By extending cognitive appraisal theory (CAT), we develop a framework to examine relationships among users’ negative emotions, cognitive appraisals, and their antecedents. The framework is tested using Partial-Least-Squares Structural Equation Modeling (PLS-SEM) and Artificial-Neural-Network (ANN) analyses. The results show that cognitive appraisals differ in importance when triggering users’ negative emotions, including sadness, frustration, dislike, fear, and anger. The findings also indicate that cognitive appraisals are shaped by perceived usefulness, ease of use, social influence, facilitating conditions, habitual experience, hedonic value, and monetary value in AI agent-based medical consultation services. These findings contribute to future research on AI healthcare adoption and provide practical insights for policymakers and practitioners. They also help address psychological barriers to innovative services and support the broader integration of AI agents into healthcare systems for societal benefit.

Ähnliche Arbeiten