Explainable AI Transparency, Algorithmic Trust, and Privacy Calculus in Mobile Banking: A Generation Z Study of Continuance Intention and Digital Financial Well-Being in the United States

Generative artificial intelligence (AI) is driving a shift in mobile banking from static, transaction-focused systems to dynamic engines that generate synthesized financial guidance, rationale, and suggestions tailored to consumers. Research exploring the factors driving mobile banking continuance usage intention and digital financial well-being remains nascent; however, little is known about the role that artificial intelligence system disclosure transparency, end user trust in automated decision-making (“algorithmic trust”), and perceived privacy risk–benefit trade-offs (referred to as “privacy calculus”) play in shaping those critical behavioral outcomes. This study seeks to fill that gap by empirically testing a model of the relationships among explainable AI (XAI) transparency, algorithmic trust, privacy calculus benefits, privacy calculus risk, mobile banking continuance intention, and digital financial well-being among Generation Z (Gen Z) mobile banking consumers in the United States. A quantitative, cross-sectional survey research design was used to collect data from 377 Generation Z (ages 18–29) respondents using a standardized online questionnaire. The data were analyzed using descriptive statistics, Pearson’s correlation, multivariate analysis of variance (MANOVA), and tests of between-subjects effects using IBM SPSS software version 31.0. The results find that XAI transparency significantly predicts both continuance intention (F = 8.69, p = 0.003) and digital financial well-being (F = 66.22, p < 0.001), though in opposite directions across the two outcomes. Algorithmic trust was a statistically significant predictor of both digital financial well-being (F = 320.13, p < 0.001) and continuance intention (F = 10.78, p = 0.001), while privacy calculus risk was a statistically significant, though positive rather than the hypothesized negative, predictor of both continuance intention (F = 36.04, p < 0.001) and digital financial well-being (F = 165.69, p < 0.001); however, because the privacy calculus risk scale showed weak internal-consistency reliability (α = 0.47) and all four predictors were severely intercorrelated (variance inflation factors of 9.19–26.79), these individual coefficients should be interpreted with caution rather than as evidence of four independently distinguishable psychological mechanisms. Privacy calculus benefits significantly predicted digital financial well-being (F = 14.78, p < 0.001) but not continuance intention (F = 2.97, p = 0.085). A very strong correlation was found between continuance intention and digital financial well-being (r = 0.908, p < 0.001). This study discusses theoretical contributions to and managerial implications for the design of explainable AI systems, the generation of algorithmic trust, and privacy considerations in fintech. These findings are timely given the accelerating deployment of large language model (LLM) banking assistants, agentic financial automation, and the regulatory push toward mandatory AI explainability (e.g., the EU AI Act), all of which make XAI transparency an increasingly central, rather than peripheral, construct in digital financial behavior. Because the sample was recruited online and skewed toward heavy neobank and fintech app users, these findings should be understood as applying most directly to similarly engaged Gen Z mobile banking consumers rather than being generalized to the entire U.S. Gen Z population.