Convenience, Trust, and Customer Satisfaction with AI-Supported Visa Digital Payments in Bangladesh

Digital payment satisfaction is an important post-adoption outcome, yet users may evaluate payment services through several overlapping functional and relational criteria. This study investigates how perceived convenience, trust, expectation confirmation, performance expectancy, and effort expectancy are associated with satisfaction among users of Visa-enabled digital payments in Bangladesh. Drawing on expectation-confirmation theory and research on technology acceptance and digital trust, a cross-sectional analysis was conducted using 385 populated survey records from the supplied Group-6 workbook. Three-item composite scores were calculated for each construct. Ordinary least squares regression was estimated with five simultaneous predictors, and a separate one-way analysis of variance examined satisfaction across self-reported AI-feature experience categories. The regression explained 24.3% of satisfaction variance (adjusted R² = .233; F(5,379) = 24.33, p < .001). Performance expectancy (B = .179), effort expectancy (B = .167), trust (B = .160), and convenience (B = .143) showed positive conditional associations; confirmation did not meet the .05 threshold (B = .086, p = .077). Satisfaction did not significantly differ across AI-experience groups (F(3,381) = .386, p = .763). However, internal consistency estimates were below conventional thresholds for every focal three-item scale (α = .452-.526), materially limiting construct-level inference. The findings provide a transparent preliminary assessment of payment satisfaction and identify measurement validation, respondent provenance, and AI-specific operationalization as priorities before confirmatory interpretation or journal submission.