How Healthqual drives willingness to pay a premium and revisit intention in health-care sector: the moderation role of AI-enabled service

Abstract

Purpose: This study aims to examine how perceived health-care service quality (Healthqual), comprising tangibles (TAN), reliability (REL), responsiveness (RES), assurance (AS) and empathy (EM), influences patient trust, satisfaction and behavioral outcomes within artificial intelligence (AI)-enabled health-care environments. Grounded on the Stimulus–Organism–Response (S-O-R) model, the research analyzes the intermediary functions of trust and satisfaction and examines how AI-enabled services moderate these relationships. Design/methodology/approach: A structured questionnaire was administered to 477 patients in urban Vietnam who had recently used AI-supported health-care services. The data were examined through Partial Least Squares Structural Equation Modeling (PLS-SEM) by SmartPLS 3.0. Both repeated-indicators and two-stage methods were used to validate the hierarchical component model. Findings: Healthqual (HQ) positively influences patient trust and satisfaction, especially through responsiveness and tangibles. Trust and satisfaction partially mediate the effects of service quality on willingness to pay and revisit intention, confirming their central role. However, AI-enabled services weaken these effects, highlighting the emotional gap and cultural misalignment in tech-driven health-care interactions. Originality/value: This research contributes theoretically by validating Healthqual as a second-order construct, expanding upon the S-O-R framework in health care. In addition, AI-enabled services are positioned as a central contributor to perceived health-care quality, offering a novel extension to Healthqual by explaining how AI transforms patient trust, satisfaction and behavioral intentions.