Heritage sites are designed to foster in-depth cultural experiences for tourists at destinations and are well suited for promoting slow travel. However, cultural differences and tourist’ limited awareness often hinder its practice. While augmented reality (AR) is typically used to provide immediate destination information, its potential to build cultural competence and influence slow travel intentions has not been examined. Grounded in zone of proximal development theory, this study reveals that the perceived enjoyment of AR is positively associated with cultural competence, but only for highly engaged users. For less engaged users, personal innovativeness moderates the relationship between perceived advantage and cultural competence. Across both groups, cultural competence strengthens both empathy and slow travel intentions. These findings offer guidance for implementing slow travel initiatives at cultural heritage sites.
Illicit transaction detection in blockchain networks is of substantial practical value for anti-money-laundering supervision, financial risk control, and on-chain fund tracing. However, suspicious transactions in evolving blockchain graphs are difficult to identify because laundering behaviors are often mixed with normal transfers and evolve through directional propagation, branching, local mixing, and cross-temporal reorganization. Existing graph neural networks and temporal models still struggle to model transaction-flow directionality, local mismatch, and structural disturbance within a unified framework. To address this problem, we propose Hydro-TGT, a flow-aware graph neural network (GNN)-Transformer framework that integrates three flow-aware statistical proxy fields, a dual-stream GNN encoder, and an evolutionary time-gated Transformer guided by Proxy-Guided Flow Sequence (PGFS). The proxy fields are constructed from observable transaction attributes and local graph statistics to summarize transaction-flow position, local-neighborhood mismatch, and structural disturbance, thereby providing compact flow-aware cues beyond the raw features alone. On the public Elliptic benchmark, Hydro-TGT achieves 73.17% full-span F1-score (F1) and 77.48% area under the precision-recall curve (PR-AUC) over the T35–49 test horizon. Further validation on Ethereum Fraud achieves 94.10% F1 and 97.15% PR-AUC, and additional validation on Ethereum Phishing achieves 86.52% F1 and 92.48% PR-AUC under a pooled T33–T40 test protocol. These results indicate that Hydro-TGT can provide effective support for illicit transaction identification and practical blockchain AML analysis under evolving on-chain environments.
Initial trust in public-facing medical consultation AI is formed during early encounters, yet the safety judgments underlying this process remain insufficiently understood. Using a vignette-based survey of prospective users from the general public (N = 525) and partial least squares structural equation modeling , this study tested a dual-safety model in which perceived clinical safety (PCS) and perceived data security (PDS) were specified as two proximal safety judgments associated with initial trust (IT) and advice-following intention (INT). PCS and PDS were both positively associated with IT, and IT was positively associated with INT. Perceived explainability and key-evidence visibility were positively associated with PCS, whereas perceived system performance and stability was strongly associated with PDS and privacy invasion concern was negatively associated with PDS. Structural assurance showed an unexpected negative association with PCS, and scenario risk perception was not significantly associated with PCS or IT despite successful risk manipulation. The core PCS/PDS–IT pathways remained stable in supplementary robustness analyses. These findings clarify dual safety judgments as proximal correlates of initial trust and provide human-factors-oriented evidence for future work on safety communication, data-use explanation, and calibrated trust in public-facing medical AI.
As generative artificial intelligence (AI) expands in cultural tourism souvenir design and disclosure policies evolve, evidence remains limited on how AI authorship disclosure shapes responses across nationality groups and products. Drawing on signaling theory, two studies compared consumers from China and the U.S. across three disclosure conditions: unlabeled, human–AI co-created, and AI-generated. Study 1 used tourism postcard stimuli (N = 1110), and Study 2 used mug and tourism tote bag stimuli (N = 1255). Both studies found that disclosure affected purchase intention and interacted significantly with nationality. Differences across disclosure conditions were significant among participants from the U.S. but not among those from China. In Study 1, the unlabeled and human–AI co-created conditions did not differ, and both exceeded the AI-generated condition. In Study 2, the unlabeled condition exceeded both disclosed conditions for both products, whereas the latter two did not differ. AI-generated disclosure was consistently associated with lower purchase intention than no disclosure, whereas the relative position of human–AI co-created disclosure differed between studies. Parallel mediation analyses showed indirect effects through perceived effort and perceived authenticity. Perceived effort effects were significant for both comparisons with the unlabeled condition across three examined products, whereas the human–AI co-created versus unlabeled authenticity effect was nonsignificant in Study 1 but significant in Study 2. Nationality moderated some, but not all, indirect effects. Thus, firms should not assume that human–AI co-creation enhances responses and should consider clarifying human contribution and pretesting disclosure strategies across products and national markets.
Although money and time are essential resources that influence consumer decision-making, research has mostly ignored how resource concept activation affects tourists' decisions in the context of tourism. Drawing on mental simulation theory, this study investigates how activating time versus money resource concepts influences tourists' preferences for different types of tourism activities. Across three experiments, the results show that when the concept of time is activated, tourists report stronger preferences for challenging activities, whereas when the concept of money is activated, they report stronger preferences for relaxing activities. Outcome-focused simulation mediates the relationship between time activation and challenging activity preference, whereas process-focused simulation mediates the relationship between money activation and relaxing activity preference. These indirect pathways are further conditioned by maximization: the time-related pathway becomes stronger as maximization increases, whereas the money-related pathway becomes weaker. The results offer practical implications for tourism destinations that can tailor marketing strategies based on tourists’ decision-making tendencies.
