Robotaxi services represent a major step in the commercialization of autonomous driving, offering efficiency, consistency, and safety benefits. However, despite technological advances, their large-scale adoption is far from guaranteed. Most urban users already rely on mature ride-hailing platforms such as Didi and Uber, making the real behavioral question not whether to adopt Robotaxi, but whether to migrate from existing services. Prior studies based on TAM, UTAUT, or trust models have primarily examined users’ initial adoption decisions, overlooking the substitution behavior that better captures how people shift between competing mobility services in real contexts. This study addresses this gap by applying the Push–Pull–Mooring (PPM) framework to examine users’ migration from ride-hailing to Robotaxi services, based on survey data collected from 1206 respondents across four Chinese cities (Beijing, Shanghai, Guangzhou, and Wuhan). The model was tested using structural equation modeling and multi-group analysis (SEM–MGA). Push factors reflect negative experiences with ride-hailing, including social anxiety and insecurity caused by drivers’ behaviors; pull factors emphasize Robotaxi’ autonomy and service reliability; while mooring factors capture habitual ride-hailing use and perceived Robotaxi risk. Findings indicate that push and pull factors significantly promote migration intentions, whereas mooring factors hinder them. Among all factors, perceived risk exerted the strongest negative effect (β = −0.36), underscoring its critical role as a barrier to Robotaxi migration. Gender differences are also evident, with women more sensitive to risks and men more influenced by reliability. By situating adoption within a migration context, this study enriches high-risk innovation theory and offers practical guidance for designing gender-sensitive and user-specific promotion strategies.

We investigate how generative AI tools function in smart learning by estimating a structural path model that combines the Stimulus–Organism–Response (SOR) framework with Self-Determination Theory (SDT). Using survey data from N = 540 university students and covariance-based SEM, we examine whether perceptions of these tools—usefulness (PU), ease of use (PEU), creative benefit (PCB), and personalization (PP)—align with SDT’s motivational states of perceived autonomy (PA) and perceived competence (PC) and, in turn, relate to creative confidence (CC) and creative engagement (CE). All four perceptions show positive links to PA and PC, with PP exhibiting the largest association with PA. PA precedes PC, indicating a sequential motivational route. At the behavioral level, PC relates more strongly to CC, whereas PA shows a comparatively larger association with CE. In aggregate, the results support integrating SOR with SDT to explain students’ psychological responses to generative AI tools and inform course designs that cultivate autonomy and competence to sustain creative confidence and engagement in smart-learning contexts.

Integrated programs combining two or more interventions, such as cognitive training and exercise, are attracting attention as a non-pharmacological treatment of dementia. Challenges in implementation, along with a lack of strategies to sustain engagement and motivation, have limited integrated programs in Korea. This study developed an integrated program using gamification strategies to improve cognition and physical strength in older people and evaluated its effectiveness. The study was conducted for 8 weeks in the form of stepping training using the Stroop effect on 24 adults aged 65 years or older. With this intervention, cognitive function significantly increased (p = 0.007); in particular, memory (p = 0.005) and language function (p = 0.042) showed significant improvement. Physical performance also significantly improved (p = 0.001), and the reward strategy had the highest satisfaction with 4.29 points in the user satisfaction survey. Thus, an integrated program is effective in improving cognition and physical performance in aging adults, and gamification has a positive effect on increasing persistence and motivation in older people. This study can be a useful reference in developing cognitive enhancement programs.

Traditional paradigms of interface evaluation, ranging from heuristic inspections to post-hoc usability testing, exhibit significant limitations in scalability, adaptability, and sensitivity to contextual user behaviors. Recent advances in automated evaluation, often powered by machine learning, provide computational efficiency but remain constrained by their inability to capture tacit, experiential, and affective dimensions of usability. This study advances a usability-driven optimization framework underpinned by a human-in-the-loop (HITL) paradigm, wherein iterative human feedback dynamically informs algorithmic adaptation throughout the evaluation cycle. The proposed approach operationalizes usability as a multidimensional construct, integrating quantitative performance indicators (task completion latency, error propagation rate, and interaction efficiency) with qualitative indices (perceived workload, cognitive friction, and affective resonance). A hybrid evaluation pipeline is conceptualized in which algorithmic models perform baseline assessments, while human evaluators inject corrective signals, constraint refinements, and context-aware judgments to recalibrate optimization trajectories. It has been demonstrated through empirical prototyping that this symbiotic evaluation mechanism improves the strength and ecological genuineness of interface evaluations and provides statistically significant changes in the System Usability Scale (SUS) scores and interaction achievement scales. The research creates a methodological intersection between automated evaluation architecture and user-centered design epistemologies by foregrounding usability as the goal to be optimized instead of a diagnostic of the design, which occurs after the design. The paradigm has referential to adaptive interface engineering, human-computer symbiosis, and the incorporation of affective computing in the next-generation usability analytics.

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems.

With advances in artificial intelligence and computer vision, pose recognition-based feedback systems are increasingly being introduced into dance classes to support movement understanding and error correction. However, how learners interpret and adopt such feedback during actual classroom use, and how this process shapes their intention to continue using the system, remains insufficiently understood. This study develops and validates a three-layer “System–Psychological–Experience” model, with learning experience positioned as the key mechanism linking antecedent factors to usage intention. A sequential mixed-methods design was employed. In Phase 1, interviews were conducted with 20 dance majors to identify factors relevant to classroom integration. In Phase 2, structural equation modeling was performed using 398 valid survey responses. In Phase 3, explanatory interviews with five students and five teachers were conducted to further interpret the underlying mechanisms. The results showed that learning experience was the strongest direct predictor of usage intention (β = 0.409, p < 0.001). At the system layer, perceived usefulness and perceived ease of use contributed to usage intention mainly by strengthening learning experience. At the psychological layer, system trust and perceived value showed the same indirect pattern through learning experience. Teaching context also played an important role. Teaching compatibility directly enhanced learning experience, while teacher influence both strengthened learning experience and indirectly contributed to usage intention by increasing system trust and perceived value. Overall, the findings suggest that learners’ intention to continue using pose recognition-based feedback systems is closely associated with their overall learning experience in teacher-mediated classroom practice, and the study offers implications for classroom-oriented design.