Perceived Security and Intention to Use AI-Enabled Visa Digital Payment Services in Bangladesh

Digital payment adoption depends on users’ assessments of functional value, security, trust, and service experience. This study examines intention to use and continue using AI-enabled Visa digital payment services in Bangladesh through an integrated acceptance, security, and continuance framework. A cross-sectional survey dataset yielded 385 usable observations after attention-check and completeness screening. Nine theory-relevant antecedents were examined using simultaneous multiple regression, supplemented by exploratory bootstrap indirect-effect and interaction analyses. Technology readiness was the largest standardized predictor of intention (β = .224, p < .001), followed by satisfaction (β = .143, p = .004), trust (β = .142, p = .004), and performance expectancy (β = .134, p = .005). Perceived security retained a positive association after adjustment for the other constructs (β = .101, p = .032). The model explained 32.4% of the variance in intention. Bootstrap intervals were consistent with small positive indirect associations through trust and satisfaction, whereas the perceived-security × technology-readiness interaction was not significant. Internal consistency was low across the focal three-item scales, restricting the strength of construct-level inference. The findings offer exploratory evidence that security perceptions operate alongside readiness, trust, satisfaction, and expected performance. They support further investigation of an integrated adoption framework with refined measures and longitudinal designs, rather than causal or confirmatory conclusions from the present data.