Abstract
The rapid diffusion of artificial intelligence (AI) into the insurance value chain has fundamentally reshaped how consumers search for, evaluate, and purchase health insurance products. Drawing upon the Technology Acceptance Model (Davis, 1989), the extended Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2012), and trust-based models of online exchange (Gefen et al., 2003; McKnight et al., 2002), this study develops and empirically tests an integrated model linking AI-driven personalization, AI service quality, and perceived usefulness to health insurance purchase intention through the sequential mediating mechanisms of customer experience and digital trust. Survey responses from 412 health insurance consumers were analysed using a two-step approach (Anderson & Gerbing, 1988) comprising exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and partial least squares structural equation modelling (PLS-SEM) with 5,000 bootstrap subsamples. The measurement model demonstrated strong reliability and validity (Cronbach’s α = 0.822–0.871; CR = 0.876–0.905; AVE = 0.640–0.704), and discriminant validity was established through both the Fornell–Larcker criterion and the HTMT ratio. All nine hypothesised paths were supported. Customer experience (β = 0.328, p < .001) and digital trust (β = 0.362, p < .001) emerged as the strongest proximal drivers of purchase intention, with the model explaining 42.5% of the variance in purchase intention. The findings underscore that AI capabilities translate into purchase behaviour primarily when they enhance the experiential and trust-related dimensions of the digital insurance journey, offering actionable guidance for insurers, insurtech platforms, and policymakers.