Search Results (832)

The continuous growth of the tourism industry has made the issue of tourists’ environmentally responsible behavior (ERBR) an urgent issue in both academic and policy terms. This research aims to develop a conceptual model through the integration of the Theory of Planned Behavior (TPB), the Norm Activation Model (NAM), and environmental factors to analyze the mechanisms that influence tourists’ intentions and behaviors to be environmentally responsible. Data were collected from 400 Thai tourists and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The results indicate that the research model can comprehensively explain tourists’ behaviors. Internal mechanisms such as attitudes, subjective norms, perceived behavioral control, and personal norms all have significant influences on environmentally responsible behavioral intention and environmentally responsible behaviors. In particular, the norm internalization process shows that subjective norms can be systematically transformed into internal ethical values, which is key to fostering long-term sustainable behavior. This model provides a comprehensive theoretical understanding of environmentally responsible tourism behavior and can be used to effectively design policies and proactive activities to promote environmentally responsible tourist behaviors in the long term. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

AI Recommender Systems (RecSys) function as personalised forecasting engines, predicting user preferences to reduce information overload. However, the efficacy of these systems is often bottlenecked by the “Last Mile” of forecasting: the end-user’s willingness to adopt and rely on the prediction. While the existing literature often assumes that algorithmic accuracy (e.g., low RMSE) automatically drives utilisation, empirical evidence suggests that users frequently reject accurate forecasts due to a lack of trust or cognitive friction. This study challenges the utilitarian view that users adopt systems simply because they are useful, instead proposing that sustainable adoption requires a state of Cognitive Absorption—a psychological flow state enabled by the Cognitive Economy of trust. Grounded in the Motivation–Opportunity–Ability (MOA) framework, we developed the Trust–Absorption–Intention (TAI) model. We analysed data from 366 users of a major predictive platform using Partial Least Squares Structural Equation Modelling (PLS-SEM). The Disjoint Two-Stage Approach was employed to model the reflective–formative Higher-Order Constructs. The results demonstrate that Cognitive Trust (specifically the relational dimensions of Benevolence and Integrity) operates via a dual pathway. It drives adoption directly, serving as a mechanism of Cognitive Economy where users suspend vigilance to rely on the AI as a heuristic, while simultaneously freeing mental resources to enter a state of Cognitive Absorption. Affective Trust further drives this immersion by fostering curiosity. Crucially, Cognitive Absorption partially mediates the relationship between Cognitive Trust and adoption intention, whereas it fully mediates the impact of Affective Trust. This indicates that while Cognitive Trust can drive reliance directly as a rational shortcut, Affective Trust translates to adoption only when it successfully triggers a flow state. This study bridges the gap between algorithmic forecasting and behavioural adoption. It introduces the Cognitive Economy perspective: Trust reduces the cognitive cost of verifying predictions, allowing users to outsource decision-making to the AI and enter a state of effortless immersion. For designers of AI forecasting agents, the findings suggest that maximising accuracy may be less effective than minimising cognitive friction for sustaining long-term adoption. To solve the cold start problem, platforms should be designed for flow by building emotional rapport and explainability, thereby converting sporadic users into continuous data contributors. Full article

With the increasing prominence of sustainable consumption and the rising influence of Generation Z in the fashion market, secondhand fashion e-commerce platforms have become essential carriers of green fashion. Although AI-assisted recommendation mechanisms are widely embedded in these platforms, their psychological and behavioral effects on users’ continuous use and social engagement remain insufficiently examined. To address this gap, this study incorporates the Stimulus–Organism–Response (SOR) framework to investigate the psychological reaction pathways and behavioral intentions of Generation Z users within Human-AI Collaboration-enabled green e-commerce environments. Three AI-driven service stimuli—Human-AI Collaborative Recommendation Perception, AI Interaction Transparency, and Perceived Personalization—were conceptualized as stimulus variables; Psychological Immersion, Emotional Triggering, Cognitive Engagement, and Platform Trust were modeled as organism variables; and Continuous Use Intention and Social Sharing Intention served as behavioral response variables. Based on 498 valid samples analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results demonstrate strong empirical support for all proposed hypotheses. Specifically, AI-driven stimuli significantly and positively influence psychological responses, which subsequently strengthen users’ continuous usage and social sharing intentions. This research provides theoretical insights for developing Human-AI Collaboration-enabled service systems that balance efficiency and emotional resonance on green e-commerce platforms, and offers practical implications for promoting sustainable fashion values among younger consumers. Full article

As sustainable tourism gains global momentum, extended reality (XR) technologies have emerged as important tools for enhancing visitor experiences at overburdened World Heritage Sites while mitigating physical deterioration through non-consumptive engagement. However, existing research on immersive technologies in heritage tourism has largely relied on single-cultural samples and has paid limited attention to theoretically grounded boundary conditions in post-adoption behaviour. To address these gaps, this study extends the Expectation–Confirmation Model (ECM) by incorporating cultural distance (CD) and prior visitation experience (PVE) as moderating variables, and empirically tests the proposed framework using a mixed domestic–international sample exposed to an on-site XR application at the Badaling Great Wall World Heritage Site. Data were collected immediately after the XR experience and analysed using structural equation modelling. The results validate the core relationships of ECM while identifying significant moderating effects. Cultural distance attenuates the positive effects of confirmation on perceived usefulness as well as the effect of perceived usefulness on continuance intention, while prior visitation experience weakens the influences of enjoyment and visual appeal on satisfaction. These findings establish important boundary conditions for ECM in immersive heritage contexts. From a practical perspective, the study demonstrates that high-quality, culturally responsive XR can complement physical visitation and support sustainable conservation strategies at large-scale linear heritage sites. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

This study examines the recent diversification of the Korean golf market into traditional field, popular virtual reality (VR), and park golf, which is rapidly expanding among older adults. Comparing participants’ psychological characteristics and behavioral intentions across golf types is essential for sustainably developing the golf industry. Therefore, differences were investigated in participation satisfaction (physical, mental, and social), switching intention, loyalty, and continuous participation intention among regular participants in all three golf types in urban Korea. Data were analyzed from 327 adults aged 20 years or older (Field: 98, VR: 132, Park: 97) in Korea using on/offline surveys, and a multivariate analysis of variance with post hoc tests was implemented to compare psychological and behavioral differences across the three golf types. The findings showed that, first, physical and mental satisfaction were significantly higher in the park golf group than in the rest of the groups. Second, switching intention was higher in the field golf group than in the VR golf group. Third, loyalty and continuous participation intention were highest in the park golf group. Each golf type thus offers unique experiential value, with park golf particularly effective in fulfilling participants’ physical and psychological needs. Conversely, field golf faces potential risks of participant attrition because of cost and time burdens. The findings provide useful implications for predicting demand and developing differentiated marketing and management strategies tailored to generational needs. Full article

Digital retail is undergoing a paradigm shift driven by the deep integration of artificial intelligence (AI) and augmented reality (AR). Although prior studies have examined the independent effects of AI-based personalized recommendation (cognitive path) and AR-enabled immersion (experiential path), how their integration systematically shapes user behavior through internal psychological mechanisms remains an important unresolved theoretical gap. To address this gap, this study develops an integrated model grounded in the stimulus–organism–response (S-O-R) framework and trust transfer theory. Specifically, the model examines how personalized recommendation, as a dynamic external stimulus, influences users’ cognitive state (perceived usefulness) and experiential state (immersion); how the overall trust of users in the integrated platform can be used as a key boundary condition to adjust the transformation efficiency from the above stimulus to the internal state; and how the above cognitive and experiential states can ultimately drive the continued usage intention through the mediation of positive emotional response. Based on survey data from 400 Chinese consumers with AR shopping experience on Taobao, analyzed using structural equation modeling (SEM), the results indicate that (1) personalized recommendation positively affects both immersion and perceived usefulness; (2) platform trust significantly and positively moderates the effects of personalized recommendation on both immersion and perceived usefulness; (3) both cognitive and experiential states stimulate positive emotions, which in turn enhance continued usage intention, with perceived usefulness exerting a stronger effect; (4) a key theoretical finding is that there is a significant positive correlation between perceived usefulness and immersion, revealing the coupling of psychological paths in an integrated environment; however, immersion does not moderate the effect of personalized recommendation on emotional responses, suggesting that the current integration mode emphasizes the formation of a stable psychological structure rather than real-time interaction. This study makes three contributions to the existing literature. First, it extends the application of S–O–R theory in a complex technological environment by analyzing the “organism” as a parallel and related cognitive-experience dual path and confirming its coupling relationship. Second, it elucidates the enabling role of trust as a moderating mechanism rather than a direct antecedent, thereby enriching micro-level evidence for trust transfer theory in the context of technology integration. Finally, by contrasting path coupling with process regulation, this study provides a more detailed distinction for understanding the theoretical connotations and boundaries of AI–AR technology integration, which may mainly be a kind of structural integration. Full article

Digital cultural-heritage (DCH) platforms are integral to achieving UN SDG Target 11.4, yet their long-term sustainability is compromised by a systemic vulnerability: the rapid decay of user engagement once the initial “novelty effect” fades. To address the theoretical anomaly of the “null effect” regarding value perception found in prior studies, this paper develops a competitive dual-path model to determine whether information-centric or experience-centric strategies effectively foster sustainable continuance intention. Drawing on the stimulus–organism–response (S-O-R) framework, interactivity is modelled as a high-order managerial investment. A quantitative survey of 407 DCH users was analysed using covariance-based structural equation modelling (CB-SEM). The results resolve the strategic dilemma: while interactivity enhances both pathways, a chi-square difference test $\left(\Delta {\chi }^2\left(1\right)=26.207\right.$, p < 0.001) confirms that affective value exerts a significantly stronger impact on cultural identity than epistemic value, supporting the affective primacy hypothesis. Crucially, cultural identity serves as the essential mediator that translates user experience into “emotional stickiness.” By demonstrating that narrative-driven affective engagement is superior to mere information dissemination, this study provides a validated blueprint for virtual–real symbiosis. The findings offer actionable guidance for managers to build digital resilience and safeguard heritage transmission across generations. Full article

A global decline in students’ motivation and academic performance poses a serious threat to future competence supply, particularly in knowledge-driven economies such as Sweden. Despite higher education’s growing importance for economic and social mobility, the number of students pursuing such education continues to fall. This study employs a mixed-methods design using an explanatory sequential approach to explore how teachers’ leadership identity influences their aspirational engagement in shaping students’ beliefs and intentions to pursue higher education and future career opportunities. The results show that teachers who identify strongly with their leadership role exhibit a type of leadership that influences aspirational engagement with students. This, in turn, may promote students’ beliefs in their potential and intentions to pursue higher education through (1) aspirational engagement in individual dialogues with students, (2) aspirational engagement when introducing new subject areas in whole-class communication, and (3) aspirational engagement related to practical work experience (PRAO). This study demonstrates an understanding of the important potential of teachers’ contributions to elevate society’s future competence supply. Full article

This study examines the determinants of talent retention in the hotel sector of Extremadura, a peripheral European region facing depopulation, labour scarcity and structural limitations that threaten the sustainability of its human capital base. Grounded in the Job Demands–Resources (JD-R) theory, the research analyses how a set of key labour resources, specifically professional training, organisational trust, job satisfaction and sustainability commitment, influence employees’ intention to remain in their organisations. These resources are conceptualised as organisational and motivational mechanisms that enhance employees’ capacity to cope with job demands and reinforce their attachment to the organisation. A quantitative survey was conducted with hotel-sector employees in Extremadura; 255 questionnaires were validated, and the proposed structural model was tested using SEM. The findings show that organisational trust is the strongest predictor of retention, followed by professional training and sustainability commitment, while job satisfaction also exerts a significant, though more moderate, effect. These results indicate that enhancing fairness perceptions, strengthening continuous training pathways and integrating sustainability-oriented values are essential strategies for retaining qualified personnel in territories with limited external opportunities. Rather than measuring human capital sustainability directly, the study shows that talent retention operates as a central empirical mechanism through which the sustainability of human capital can be supported in peripheral tourism economies. It concludes by highlighting the need for managerial practices that support transparent leadership, structured professional development and participatory sustainability initiatives, and encourages future research to incorporate longitudinal designs and direct measures of human capital sustainability. Full article

Urban delivery demand continues to rise, intensifying last-mile logistics challenges and accelerating the transition from manual delivery to autonomous delivery robots (ADRs). This study investigates the behavioral mechanisms underlying consumers’ migration toward ADRs. Grounded in the socio-technical systems perspective, we integrate the Push–Pull–Mooring (PPM) model with Social Cognitive Theory (SCT) to explain how technological and social stimuli shape switching and continuance intentions through cognitive and emotional pathways. Survey data from 786 Chinese consumers, analyzed using second-order structural equation modeling, support the proposed framework. The results indicate that dissatisfaction with manual delivery (push) and perceived benefits of ADRs (pull) significantly enhance both switching and continuance intentions. Outcome expectancy positively predicts switching intention but negatively predicts continuance intention. Technophobia reduces switching intention but does not significantly influence continuance. Moreover, social norms moderate key relationships, highlighting the role of external social influence in technology transition. This study extends PPM research into the smart logistics context, introduces socio-cognitive mechanisms into technology switching analysis, and conceptually distinguishes switching and continuance intentions as separate constructs. The findings offer practical guidance for ADR developers and policymakers by emphasizing strategies to reduce emotional resistance, enhance social endorsement, and promote the sustainable adoption of autonomous delivery technologies. Full article

Using the “Cloud Tour Dunhuang” digital museum as a case, this study integrates an enhanced TAM3 with UTAUT and introduces two external variables—cultural identity and technological innovation—to construct a comprehensive framework for users’ continuance intention. Based on 484 valid responses, we employ a sequential mixed-method design combining structural equation modeling (SEM), artificial neural networks (ANNs), necessary condition analysis (NCA), and grounded theory (GT). The results show that (1) cultural identity and technological innovation significantly promote behavioral intention and continuance behavior by strengthening perceived usefulness; (2) performance expectancy and social influence exert significant effects, whereas effort expectancy and facilitating conditions are comparatively weaker; and (3) the integrated model delivers superior explanatory power and predictive performance relative to single-path baselines. This research enriches user-behavior scholarship in digital cultural heritage and offers theory-informed, practical guidance for improving user retention and optimizing platform design. Full article

As mobile payments gain global prevalence, understanding the factors driving their sustained usage becomes imperative. WeChat Pay, integrated into China’s dominant social platform, provides a distinctive context for examining mobile payment continuance intention. This study extends the Technology Acceptance Model by incorporating trust, network externalities, and application-specific features (red envelopes and interface design) to explore users’ continuance intention toward WeChat Pay. We analyzed 650 valid responses from experienced WeChat Pay users using partial least squares structural equation modeling. The results demonstrated that trust and network externalities significantly shape continuance intention, while red envelopes and interface designs further encourage ongoing use. This study offers a comprehensive integrated framework for understanding the continuance of mobile payments and provides actionable guidance for product development and strategic planning. Full article

As artificial intelligence (AI) technologies and Virtual Exchange (VE) become increasingly embedded in higher education, AI-driven digital humans have begun to feature in design-oriented virtual international workshops, providing a novel context for examining learner behaviour. This study develops a structural model to examine the links between system support, interaction processes, self-efficacy, satisfaction, and international learning intention. Specifically, it investigates how perceived AI support, system ease of use, and interaction intensity influence students’ continuous participation in international learning through the mediating roles of learning self-efficacy, interaction quality, and satisfaction. Data were collected through an online questionnaire administered to undergraduate and postgraduate students who had participated in an AI-driven digital human–supported online international design workshop, yielding 611 valid responses. Reliability and validity analyses, as well as structural equation modelling, were conducted using SPSS 22 and AMOS v.22.0. The results show that perceived AI support, system ease of use, and interaction intensity each have a significant positive effect on learning self-efficacy and interaction quality. Both self-efficacy and interaction quality, in turn, significantly enhance learning satisfaction, which subsequently increases students’ intentions for sustained participation in international learning. Overall, the findings reveal a coherent causal chain: AI-driven digital human system characteristics → learning process experience → learning satisfaction → sustained participation intention. This study demonstrates that integrating AI-driven digital humans can meaningfully improve learners’ process experiences in virtual international design workshops. The results provide empirical guidance for curriculum design, pedagogical strategies, and platform optimization in AI-supported, design-oriented virtual international learning environments. Full article