As generative AI services become increasingly integrated into consumer decision making, concerns have grown regarding their influence on consumer autonomy—the extent to which individuals retain independent control over AI-assisted decisions. Although these services offer efficiency and convenience, they can simultaneously constrain consumer decision making, potentially impacting trust, satisfaction, and usage intention. This study investigates the role of perceived consumer autonomy in shaping consumer responses, specifically examining how task difficulty (Study 1) and AI service design elements—explainability, feedback, and shared responsibility (Study 2)—influence autonomy perceptions and subsequent consumer outcomes. Using two scenario-based experiments involving a total of 708 participants, the results reveal that perceived autonomy significantly enhances consumer trust, particularly in contexts involving high task difficulty. Among the tested AI design interventions, shared responsibility emerged as most effective in enhancing perceived autonomy, trust, satisfaction, and long-term engagement, whereas explainability and feedback alone showed limited impact. These findings underscore the importance of designing AI services that actively support consumer agency through user-involved decision-making frameworks rather than relying solely on passive informational transparency. Theoretical implications for consumer autonomy in AI interactions are discussed, along with practical recommendations for designing consumer-centered AI services.

Understanding how trust in artificial intelligence evolves is crucial for predicting human behavior in AI-enabled environments. While existing research focuses on initial acceptance factors, the temporal dynamics of AI trust remain poorly understood. This study develops a temporal trust dynamics framework proposing three phases: formation through accuracy cues, single-error shock, and post-error repair through explanations. Two experiments in financial advisory contexts tested this framework. Study 1 (N = 189) compared human versus algorithmic advisors, while Study 2 (N = 294) traced trust trajectories across three rounds, manipulating accuracy and post-error explanations. Results demonstrate three temporal patterns. First, participants initially favored algorithmic advisors, supporting “algorithmic appreciation.” Second, single advisory errors resulted in substantial trust decline (η² = 0.141), demonstrating acute sensitivity to performance failures. Third, post-error explanations significantly facilitated trust recovery, with evidence of enhancement beyond baseline. Financial literacy moderated these patterns, with higher-expertise users showing sharper decline after errors and stronger recovery following explanations. These findings reveal that AI trust follows predictable temporal patterns distinct from interpersonal trust, exhibiting heightened error sensitivity yet remaining amenable to repair through well-designed explanatory interventions. They offer theoretical integration of appreciation and aversion phenomena and practical guidance for designing inclusive AI systems.

This study has three principal aims: to classify consumers by their retirement preparation planning and behavior; to examine the financial status of retirement preparation in each country; and to find ways to help consumers effectively secure financial sustainability after retirement by practicing retirement preparation. Adopting the planner–doer model, consumers were classified into four types: ‘indifferent,’ ‘just planner,’ ‘just doer,’ and ‘ideal doer.’ This study used data collected by Korea Life Insurance Association to compare the level of retirement preparation in Korea, Japan, Germany, the U.S., and the U.K. The data were analyzed using the chi-square test, analysis of variance (ANOVA), and binary logistic regression analysis. The major findings are three-fold: first, the largest percentage of consumers in every country were ‘just doers’; second, ‘ideal doers’ are older than the other types, but have a low expected retirement age and high level of retirement preparedness; and third, ‘ideal doers’ demonstrated distinct variations in their characteristics in different countries. These findings indicate the need to help consumers recognize the necessity to prepare effectively for financial sustainability in their post-retirement lives; this requires the development of policy and gradual education programs, including effective practical suggestions.

The rapid growth of mobile financial services provides significant opportunities for enhancing digital financial inclusion among older adults. However, elderly consumers often lag in adoption and sustained usage due to psychological barriers (e.g., technology anxiety) and factors related to prior experience and comfort with technology (e.g., technology familiarity). This study investigates how technology anxiety and technology familiarity influence elderly consumers’ continuance intention toward mobile banking, while examining age as a moderator by comparing younger older adults (aged 60–69) and older adults (aged 70+). Using data from an online survey of 488 elderly mobile banking users in South Korea, we conducted hierarchical regression analyses. The results show that technology anxiety negatively affects continuance intention, whereas technology familiarity positively enhances sustained usage. Moreover, age significantly moderated these relationships: adults aged 70+ were notably more sensitive to both technology anxiety and familiarity, highlighting distinct age-related psychological differences. These findings underscore the importance of targeted digital literacy initiatives, age-friendly fintech interfaces, and personalized support strategies. This study contributes to the fintech literature by integrating psychological dimensions into traditional technology adoption frameworks and emphasizing age-specific differences. Practically, fintech providers and policymakers should adopt tailored strategies to effectively address elderly consumers’ unique psychological needs, promoting sustained adoption and narrowing the digital divide in financial technology engagement.

Digital financial services have transformed consumer financial behavior, yet their effects on sustainable development outcomes remain poorly understood. This study examines how mobile financial services (MFS) usage influences financial behaviors across temporal dimensions and investigates the moderating role of financial literacy from a systemic sustainability perspective. Drawing on Construal Level Theory, Dual Process Theory, and Social Cognitive Theory, we analyze data from 21,757 U.S. adults from the 2021 National Financial Capability Study to explore relationships between MFS usage, financial literacy dimensions—objective knowledge (OK), subjective knowledge (SK), and perceived ability (PA)—and both short-term and long-term financial behaviors. The results reveal a dual temporal pattern: MFS usage negatively affects short-term behaviors, including spending control and emergency preparedness, while positively influencing long-term behaviors such as retirement planning and investment participation. Financial literacy dimensions demonstrate differential moderating effects, with OK providing protective benefits against short-term risks, while PA can paradoxically exacerbate these adverse short-term effects. These findings highlight complex implications for sustainable development, demonstrating how individual behaviors aggregate to influence systemic financial resilience and progress toward Sustainable Development Goals related to poverty reduction, economic growth, and inequality reduction. Policymakers should adopt behaviorally informed regulatory approaches that address temporal trade-offs. Educators should design digital-specific literacy programs emphasizing realistic risk assessment alongside confidence-building, thereby promoting sustainable financial behaviors in increasingly digital environments.