Search Results (6,247)

Modular living walls (MLWs) are increasingly adopted as biophilic façade interventions in high-end commercial environments, yet their behavioural effects at luxury retail entrances remain underexplored. Grounded in the stimulus–organism–response framework, this study investigates how MLWs integrated into luxury retail façades influence visitors’ perceptual, emotional, and behavioural responses at the Beauty Galleria of The Exchange TRX, Kuala Lumpur. Using a field-based survey and structural equation modelling with 400 visitors, the study examines the relationships among MLW presence, perceived naturalness, pleasure, arousal, perceived restorativeness, façade attractiveness, and behavioural intentions. The results show that MLWs significantly enhance perceived naturalness, pleasure, arousal, and perceived restorativeness. These organismic responses positively influence perceived façade attractiveness, which in turn strongly predicts both approach intention and photo-taking intention. Façade attractiveness emerged as a central mediating mechanism, while the indirect effect of MLWs on behavioural intentions was significant. The model explained a substantial proportion of variance in façade attractiveness and confirmed that consumer responses to biophilic façades are primarily shaped through sequential perceptual and affective processes rather than direct stimulus effects. This study extends the application of the S–O–R framework to façade-level biophilic design and provides practical evidence that MLWs can enhance first impressions and consumer engagement in luxury retail environments. Full article

Background/Objectives: Elevated central adiposity (ECA) in childhood is associated with early cardiometabolic risk and hemodynamic alterations. However, evidence in Spanish schoolchildren regarding the relationship between eating behavior traits and central adiposity is limited, particularly across developmental stages. This study aimed to examine the association between Children’s Eating Behaviour Questionnaire (CEBQ) subscales and ECA, and to explore potential differences by age group. Methods: A cross-sectional study was conducted in 496 rural schoolchildren aged 6–15 years. ECA was defined using the waist-to-height ratio (WHtR) and sex-specific cut-offs validated for the Spanish pediatric population. Eating behavior was assessed with the CEBQ (Z-scores), and diet quality was measured using the KIDMED index. Multivariable logistic regression models were adjusted for sex, KIDMED score, and maternal education. Analyses were subsequently stratified by age (6–9 and 10–15 years). Results: The prevalence of ECA was 45.90%. In fully adjusted models, higher Food Responsiveness (FR) was associated with increased odds of ECA, while Satiety Responsiveness (SR) acted as a protective factor; sex also showed an independent association. After stratification, sex remained the only significant predictor in children aged 6–9 years. Among those aged 10–15 years, FR was significantly associated with ECA (p = 0.008), while Slowness in Eating (SE) showed a borderline positive association in the adjusted model (p = 0.049) and was therefore interpreted cautiously. SR and Emotional Undereating (EU) showed protective trends near significance (p = 0.081 and p = 0.082, respectively). Conclusions: The association between eating behavior traits and ECA varies by age. In older children, FR showed a robust association with ECA, whereas no behavioral predictors were observed in younger children. The protective role of SR in the global model and the emergence of behavioral predictors in older participants highlight the importance of targeted interventions during late childhood. Full article

Background: Adventist spirituality has been identified as a relevant psychosocial resource for emotional well-being; however, evidence on its relationship with self-control and perceived stress in specific religious populations remains limited. Objective: The aim of this study was to examine the associations between Adventist spirituality, self-control, and perceived stress in a sample of adults belonging to the Seventh-day Adventist Church and residing in coastal regions of Peru. Methods: A cross-sectional study was conducted between December 2025 and January 2026 with 506 Seventh-day Adventist adults who completed an online questionnaire. Adventist spirituality was assessed using the Mission Commitment Questionnaire, which captures religious–spiritual commitment through three dimensions: personal devotion, participation, and witnessing. Self-control and perceived stress were measured using standardized scales. Data were analyzed using partial least squares structural equation modeling. Results: The constructs showed adequate internal consistency, with Cronbach’s alpha values ranging from 0.875 to 0.951 and composite reliability values ranging from 0.906 to 0.956. Adventist spirituality was positively associated with self-control (β = 0.479, p < 0.001) and negatively associated with perceived stress (β = −0.457, p < 0.001). Personal devotion showed the strongest contribution to the higher-order spirituality construct. The model explained 22.9% of the variance in self-control and 20.9% of the variance in perceived stress. Conclusions: Adventist spirituality, particularly personal devotion, was associated with higher self-control and lower perceived stress. Although the cross-sectional design does not allow causal inference, the findings support the relevance of Adventist spirituality as a psychosocial resource linked to emotional well-being in this religious population and justify future longitudinal studies. Full article

Background/Objectives: To evaluate construct (convergent and divergent) validity and conduct confirmatory factor analysis (CFA) of the National Center on Health, Physical Activity and Disability (NCHPAD) Wellness Assessment (NWA) tool. Methods: A cross-sectional survey validation study utilizing secondary data. We assessed Spearman correlations between NWA and 36-Item Short Form Health Survey (SF-36), NWA and Godin Leisure-Time Exercise Questionnaire (GLTEQ) and NWA and Modified Fatigue Impact Scale (MFIS) scores to determine construct validity. A CFA was conducted to test the appropriateness of a three-factor model for NWA. Results: Data from 149 participants were used to assess construct validity and from 180 participants for CFA. Both correlations between NWA mental wellness domain and SF-36 mental component scores and between NWA emotional/spiritual wellness domain and SF-36 emotional well-being scores were 0.61 (p < 0.001 for both). The correlation between NWA physical wellness domain and SF-36 physical component score was −0.06 (p = 0.45). The correlations of NWA with GLTEQ overall and with health contribution scores were 0.26 and 0.30, respectively (p < 0.001 for both). The correlations of all NWA domain and MFIS subscale scores ranged between −0.42 and −0.25 (p < 0.05). The CFA model’s comparative fit index was 0.90. Conclusions: The NWA physical wellness domain did not demonstrate strong convergent validity, as mental and emotional/spiritual wellness domains did. All domains showed strong divergent validity, and CFA showed evidence supporting a three-factor model. Future efforts will emphasize refining and reevaluating the physical wellness domain until it achieves strong psychometric properties. Full article

Conceptual ambiguity in People–Place Relationships (PPR) research limits consistent operationalization and cross-context comparability, particularly in under-represented cultural settings. This study develops an integrated, context-sensitive framework for assessing PPR in residential environments and empirically examines its measurement structure. The framework is applied in Damascus as a pilot context to assess its structural validity, internal consistency, and applicability. The methodological approach comprised two stages: conceptual development and empirical validation. First, two rounds of case-study analysis derived from a prior systematic literature review synthesized environmental (social and urban) and relational (cognitive, affective, attachment) dimensions into a coherent framework. Second, the framework was operationalized and tested using survey data from 1610 residents across Damascus districts. Six first-order indices and one composite PPR index were constructed and evaluated using exploratory factor analysis and Cronbach’s alpha with item–total correlation analysis. Results demonstrate a stable multidimensional structure that integrates evaluative environmental conditions with relational processes, moving beyond emotion-dominant interpretations of attachment. The framework advances existing approaches by linking theoretical constructs to empirically tested measurement dimensions. While further validation in diverse contexts is required, the results indicate that the model provides a coherent and adaptable basis for assessing residential PPR in socio-culturally complex urban environments. Full article

Digital game-based learning environments (DGBLEs) are increasingly integrated into classrooms as learning tools, yet limited research exists regarding the impact of students’ discrete emotions on digital gameplay performance. This study examined the role of emotions and arousal in predicting performance outcomes during digital gameplay. Thirty-two grade 5 students (M_age = 10.99, 62.5% male) played four digital games (two math; two identically designed non-math). During gameplay, real-time heart rate and affective data were collected and analyzed using an interpretable machine learning approach (XGBoost). Results suggest that students performed better on non-math games, as compared to math games. Real-time anger was associated with lower performance, particularly in games, whereas other emotions and physiological measures were not significant predictors. This pilot investigation suggests that discrete emotions, particularly anger, may play a more important role in performance during math gameplay than in comparable non-math activities. The results highlight the importance of supporting emotional regulation during digital math learning, as unmanaged anger may impact performance. This study contributes to the growing literature on affective dynamics in digital game-based learning. Full article

This study investigates how two key characteristics of AI-enabled chatbots in mobile banking applications—perceived intelligence and perceived anthropomorphism—influence users’ cognitive and hedonic evaluations, namely perceived usefulness, confirmation, and perceived enjoyment, and how these evaluations subsequently shape user satisfaction and continuance intention. Grounded in the Expectation–Confirmation Model (ECM), the study also examines the moderating role of users’ need for interaction with service employees in these relationships. Using a quantitative research design, data were collected through a structured survey from 402 users of AI-enabled mobile banking applications in Türkiye. The proposed model was tested using partial least squares structural equation modeling (PLS-SEM), and moderated mediation effects were analyzed using Hayes’ PROCESS Macro (Model 58). The results reveal that perceived intelligence positively affects perceived anthropomorphism, perceived usefulness, perceived enjoyment, and confirmation, while perceived anthropomorphism further reinforces these effects. Cognitive and emotional evaluations significantly enhance user satisfaction, which in turn strongly predicts continuance intention toward chatbot usage. Moreover, the need for interaction with service employees significantly moderates the indirect effects of perceived usefulness, perceived enjoyment, and confirmation on satisfaction and continuance intention. By extending the expectation–confirmation model with both cognitive and emotional dimensions, this study offers novel insights into user-centered chatbot design in mobile banking and highlights the importance of individual differences in shaping sustained technology use. Full article

This study examined associations between problematic Internet use, video game addiction, physical activity, perceived physical fitness, and psychological distress in pre-service teachers. A cross-sectional survey was completed by 404 education students from the University of La Laguna using validated measures (CERI, Vela Test, IPAQ-short, IFIS, GHQ-28). Descriptive, correlational, group-comparison, and binary logistic regression analyses were conducted. The prevalence of video game addiction was low (4%), and problematic Internet use was rare (1%), although 25.3% showed moderate-risk Internet use. Within the small group of students with video game addiction, male students showed relatively higher risk scores; however, this was not significantly associated with physical or emotional well-being. Problematic Internet use was positively associated with psychological distress (r = 0.267, p < 0.001) and poorer physical health indicators. Perceived physical fitness was inversely associated with distress (r = −0.248, p < 0.001) and, together with problematic Internet use, emerged as the strongest predictors with clinically relevant distress (GHQ ≥ 13) in the logistic model. A focus group (n = 4) provided exploratory qualitative insights into participants’ perceptions of digital behaviours, particularly regarding perceived loss of control and its potential associations with academic, physical, and emotional well-being. Full article

As social robots are increasingly deployed in domains such as healthcare, education, and entertainment, there is growing demand for affective agents that can interpret users’ affective states and respond in contextually appropriate ways. Existing work has established strong foundations for emotion generation and appraisal, but the step that connects generated emotion to behavioral execution still relies heavily on model-specific rules or implicit links. We frame this issue as a Mechanism Gap and propose an Emotional BDI framework that introduces Frijda’s action tendency as an intermediate representation layer between the Affective Core and the Belief–Desire–Intention (BDI) Executor. Rather than mapping emotion directly to concrete behavior, the framework first transforms affective state into a directional action tendency and then lets BDI reasoning realize that tendency according to role and context. This creates an explicit emotion-to-behavior mediation structure through which the same emotion can be expressed differently across situations and roles. In an exploratory user evaluation with 26 participants, the proposed model received more favorable ratings than an Emotion-Driven Agent in satisfaction ($p=0.010$) and appropriateness ($p=0.002$). Compared with a Cooperative Agent, the proposed model showed a significant advantage only in satisfaction ($p=0.030$). These findings suggest that the proposed framework offers a useful architectural direction for affective decision making beyond direct mapping or unconditional compliance. Full article

In this study, we examined how disinformation and trustworthy news differ in their narrative construction across nine theoretically motivated dimensions. We address the following research question: how do disinformation and trustworthy news differ in narrative organisation and epistemic grounding? We analysed 610 English-language news articles (308 pro-Kremlin disinformation and 302 trustworthy articles) covering selected international events from 2015 to 2023, using data derived from the EUvsDisinfo dataset. Narrative elements were extracted using a hybrid pipeline combining large language models and knowledge graphs, resulting in article-level representations for comparative analysis. Ordinal scores (1–5) were assigned for emotional intensity, cultural complexity, conspiracist structure, source diversity, crisis intensity, evidence support, media control, solutions orientation and memory work. Non-parametric comparisons showed significant differences in eight of these nine dimensions. Disinformation articles revealed stronger conspiracist structuring and greater meta-media hostility, as well as significantly lower source diversity, evidence support, cultural complexity and weaker memory work. Emotional intensity did not differ reliably across disinformation and trustworthy news. A simple additive NarrativeRisk score, which we designed as a transparent and interpretable summary measure, showed between-group differences in both parametric and non-parametric tests. As a univariate discrimination indicator, NarrativeRisk achieved ROC AUC ≈ 0.84. Cluster analysis identified three recurrent narrative profiles, including one dominated by disinformation, one by trustworthy news and one mixed profile. These findings indicate that disinformation is distinguished not only by factual unreliability but also by different patterns in narrative organisation. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

Social media platforms have become critical communication environments during disasters, where individuals express emotions, share information, and engage in public discourse. These platforms also reflect heterogeneous communication patterns shaped by different actor groups. However, existing studies predominantly focus on emotion classification and often overlook the combined role of actor identity and conflict dynamics. To address this gap, this study proposes an integrated AI-based analytical framework for actor-aware emotion and conflict analysis in post-disaster social media. An expert-annotated Turkish tweet dataset was constructed based on Ekman’s emotion model, including anger, fear, sadness, happiness, and surprise, along with an additional irrelevant/off-topic category and conflict-level labels. A Transformer-based model (BERTurk) was fine-tuned for multi-class emotion classification. Experimental results show that the proposed model achieves strong classification performance, with an accuracy of 0.931 and an F1-score of 0.912, outperforming conventional machine learning and deep learning baselines. Actor-based analysis reveals systematic differences in emotional and conflict patterns across groups. Scientists, journalists, and individual users exhibit higher levels of conflict and more pronounced negative emotional expressions, whereas institutionally oriented actors display comparatively balanced and supportive communication patterns. In addition, a web-based decision support system was developed to enable interactive visualization and actor-level exploration of emotional and conflict dynamics. Overall, the proposed framework provides a scalable, analytically robust approach to understanding social media discourse in disaster contexts and offers practical implications for AI-driven crisis communication and decision-support systems. Full article

This study develops and tests factor-based and holistic theoretical models to explain the relationships between Spatial Quality Satisfaction (SQS), Overall Dormitory Satisfaction (ODS), and Place Attachment (PA) in student dormitories. Data collected from 450 students residing in five state-run dormitories in Kırklareli, Turkey, via three 5-point Likert-type scales (validated by expert review and a pilot study), were analyzed using structural equation modeling, following exploratory factor analysis, confirmatory factor analysis, and Cronbach’s alpha validations. Findings from the factor-based Model 1 indicate that eight of the ten SQS factors do not significantly influence ODS and PA when considered individually, whereas “Emotional and Psychological Atmosphere” and “Flexibility of Use” emerge as key determinants. Additionally, this model reveals that ODS has a strong and positive effect on PA. Results from the holistic Model 2 demonstrate that SQS, when treated as an integrated construct, has a robust and significant effect on both ODS and PA, with ODS playing a significant mediating role in the relationship between SQS and PA. Overall, the findings suggest that students perceive spatial quality as a whole. Therefore, improving student dormitories through a holistic SQS approach is more effective in enhancing PA and ODS than interventions focused on individual spatial dimensions. Full article

Global aging is accelerating, with the proportion of the population aged 60 and above projected to reach 22% by 2050. In cold-region communities, the visual landscape environment is closely associated with the health of older adults, particularly showing associations with blood pressure (BP) and emotion states. However, associations between these factors across different landscape spaces and potential gender differences remain underexplored. This study utilized eye-tracking experiments to collect visual attention data from older adults in three types of cold-region community spaces: inter-building spaces, walkways and squares. The ground, buildings, trees, lawn, and the sky were identified as the primary Areas of Interest (AOIs). The Profile of Mood States (POMS) scale was used to assess emotion during walking experiments, revealing suggestive gender–environment interaction characteristics. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) were measured, and a Mann–Whitney U test indicated that DBP in community squares exhibited significant environmental dependency (U = 114.5, p = 0.004, r = 0.44). Hierarchical Linear Models (HLMs) revealed that, after controlling for individual differences, the number of fixation points on ground was independently associated (i.e., independent of measured individual characteristics) with elevated SBP ($\gamma =0.31, p=0.011$), while fixation on trees was associated with reduced SBP ($\gamma =-0.24, p=0.018$). Furthermore, gender moderation effects were observed: the association between ground fixation and SBP was stronger in females ($\gamma =0.18, p=0.022$), whereas the association between sports facilities and DBP was stronger in males ($\gamma =0.29, p=0.009$). Based on these findings, evidence-based design strategies are proposed, including the optimization of ground safety, gender-differentiated planting configurations, and targeted layouts for sports facilities. These results provide empirical support for age-friendly community design in cold regions. Full article

This study examines the impact of explainable artificial intelligence (XAI) features on user satisfaction and purchase intention in Saudi mobile shopping applications, utilising the stimulus–organism–response (S–O–R) framework. With the increasing reliance on AI-driven decision support in e-commerce, enhancing transparency, fairness, trustworthiness, and interpretability has become crucial for shaping consumer perceptions and behavioural responses. The research employed a quantitative methodology using partial least squares structural equation modelling (PLS-SEM) to examine the relationships among stimulus factors, cognitive and affective states, consumer satisfaction, and purchase intention. In a survey of 597 respondents from Jeddah and Makkah, Saudi Arabia, the findings highlight that fairness and bias detection, trustworthiness, and transparency significantly influence consumers’ cognitive and affective states, which in turn enhance satisfaction and intention to purchase. Consumer satisfaction emerged as a critical mediator, reinforcing the role of positive emotional and cognitive experiences in driving purchase behaviours. However, interpretability showed limited impact, suggesting that consumers may prioritise fairness and trustworthiness over technical clarity of explanations. Theoretically, this study contributes to advancing knowledge on the role of XAI in consumer behaviour by integrating fairness, transparency, and affective responses into the S–O–R paradigm. From a managerial perspective, the results underscore the importance for mobile shopping platforms to design AI systems that foster trust, reduce perceived bias, and ensure transparency, thereby improving consumer engagement and purchase outcomes. By addressing gaps in interpretability and transparency, businesses can strengthen user trust and loyalty, ultimately enhancing competitive advantage in Saudi Arabia’s rapidly growing e-commerce sector. Full article