Search Results (2,633)

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, and Google Earth Engine (GEE) were used to assess flood-prone zones across North Macedonia’s watersheds. The presented GEE-based assessment was accomplished by a custom script that automates the FFPI calculation process by integrating key factors derived from publicly available sources. These factors, which define susceptibility to torrential floods, include slope (Copernicus GLO-30 DEM), land cover (Copernicus GLO-30 DEM), soil type (SoilGrids), vegetation (ESA World Cover), and erodibility (CHIRPS). The spatial distribution of average FFPI values across 1396 small catchments (10–100 km²) revealed that a total of 45.4% of the area exhibited high to very high susceptibility, with notable spatial variability. The CHIRPS rainfall data (2000–2024) that combines satellite imagery and in situ measurements was used to estimate peak 24 h runoff and discharge. To improve the accuracy of CHIRPS, the data were adjusted by 30–50% to align with meteorological station records, along with normalized FFPI values as runoff coefficients. Validation against 328 historical river flood and flash flood records confirmed that 73.2% of events aligned with moderate to very high flash flood susceptibility catchments, underscoring the model’s reliability. Thus, the presented cloud-based scenario highlights the potential of the GEE’s efficacy in scalability and robustness for flash flood modeling and regional risk management at national scale. Full article

This article aims to explore and analyze current issues and features of environmental protection in managing local tourism destinations based on the principles of sustainable development through the relevant literature and thus to provide an insight into major environmental measures and activities that should be implemented in practice, emphasizing the importance of environmental sustainability as a key factor in the development and success of local tourist destinations in today’s business environment. Qualitative methods were used, with the literature review based on content analysis by keywords. This particularly affects the business process efficiency and the participation of destination stakeholders and in many cases leads to a low level of environmentally sustainable destination practices. In addition to this theoretical approach, this study also has direct managerial implications for destination environmental business operations. An attractive and well-preserved environment is the primary factor of tourism and local tourism destination development and its success, as well as an integrated part of the tourism product. This study addresses a critical gap in the existing literature on environmental sustainability at local destinations, where prior work has often overlooked the integration of actionable, practice-oriented frameworks tailored for both researchers and practitioners. While theoretical insights into sustainable practices abound, there remains a scarcity of holistic analyses that bridge scholarly understanding with implementable strategies for on-the-ground application. To fill this void, our research provides a comprehensive overview and systematic analysis of current practices, with targeted emphasis on co-developing scalable frameworks for improving environmentally sustainable practices at local destinations. Full article

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

Understanding the factors that drive green buying intentions has become critical, as environmental issues continue to rise globally. The study investigates the influence of environmental commitment and green motivation on environmental identity and green purchasing intentions. Additionally, it assesses the mediating role of environmental identity in the relationships between environmental commitment, green motivation, and green purchasing intentions. Moreover, it examines the moderating effect of eco-conscious behaviour on the relationships between environmental commitment, green motivation, green identity, and green purchasing intentions. A total of 440 participants, who stayed in high-rate hotels in Sharm el-Sheikh, were asked to fill out the survey distributed. (PLS-SEM) was used to analyze data. The study outcomes confirmed that environmental commitment and green motivation significantly affect green identity and purchasing behavior. Besides, the results showed the essential mediator contribution of the environmental identity between environmental commitment and green motivation. In addition, it explains eco-conscious behavior as a moderator between the previously mentioned variables. The study contributes to the existing tourism literature by demonstrating the impact of green commitment and environmental motivation on making choices to buy eco-friendly products. Moreover, the results hold significant implications for researchers, policymakers, and tourism stakeholders. Full article

As large language models (LLMs) gain traction among researchers and practitioners, particularly in digital marketing for tasks such as customer feedback analysis and automated communication, concerns remain about the reliability and consistency of their outputs. This study investigates annotation bias in LLMs by comparing human and AI-generated annotation labels across sentiment, topic, and aspect dimensions in hotel booking reviews. Using the HRAST dataset, which includes 23,114 real user-generated review sentences and a synthetically generated corpus of 2000 LLM-authored sentences, we evaluate inter-annotator agreement between a human expert and three LLMs (ChatGPT-3.5, ChatGPT-4, and ChatGPT-4-mini) as a proxy for assessing annotation bias. Our findings show high agreement among LLMs, especially on synthetic data, but only moderate to fair alignment with human annotations, particularly in sentiment and aspect-based sentiment analysis. LLMs display a pronounced neutrality bias, often defaulting to neutral sentiment in ambiguous cases. Moreover, annotation behavior varies notably with task design, as manual, one-to-one prompting produces higher agreement with human labels than automated batch processing. The study identifies three distinct AI biases—repetition bias, behavioral bias, and neutrality bias—that shape annotation outcomes. These findings highlight how dataset complexity and annotation mode influence LLM behavior, offering important theoretical, methodological, and practical implications for AI-assisted annotation and synthetic content generation. Full article

The city of Acapulco was impacted by two near-consecutive hurricanes. On 25 October 2023, Hurricane Otis made landfall, reaching the highest Category 5 storm on the Saffir–Simpson scale, causing extensive coastal destruction due to extreme winds and waves. Nearly one year later (23 September 2024), Hurricane John—a Category 2 storm—caused severe flooding despite its lower intensity, primarily due to its unusual trajectory and prolonged rainfall. Digital shoreline analysis of PlanetScope images (captured one month before and after Hurricane Otis) revealed that the southern coast of Acapulco, specifically Zona Diamante—where the major seafront hotels are located—experienced substantial shoreline erosion (94 ha) and damage. In the northwestern section of the study area, the Coyuca Bar experienced the most dramatic geomorphological change in surface area. This was primarily due to the complete disappearance of the bar on October 26, which resulted in a shoreline retreat of 85 m immediately after the passage of Hurricane Otis. Sentinel-1 Synthetic Aperture Radar (SAR) showed that Hurricane John inundated 2385 ha, four times greater than Hurricane Otis’s flooding (567 ha). The retrofitted QGIS methodology demonstrated high reliability when compared to limited in situ local reports. Given the increased frequency of intense hurricanes, these methods and findings will be relevant in other coastal areas for monitoring and managing local communities affected by severe climate events. Full article

This study examines the determinants of environmentally responsible behavior among tourists in the heritage villages of Paveh County, Iran, through an integrated theoretical framework that synthesizes place-related psychological constructs with the Theory of Planned Behavior (TPB). Employing structural equation modeling on data collected from 443 tourists across three heritage villages (July–November 2024), the investigation tested comparative theoretical models with differing explanatory capacities. The baseline TPB model confirmed significant positive effects of environmental attitudes (β = 0.388), environmental norms (β = 0.398), and perceived behavioral control (β = 0.547) on behavioral intentions, which subsequently influenced environmental behavior (β = 0.561). The extended model incorporating place-related variables demonstrated enhanced explanatory power, with the R² values increasing from 48.2% to 52.7% for behavioral intentions and from 49.2% to 54.7% for actual behavior. Notably, place identity exhibited dual psychological functions: moderating the intention–behavior relationship (β = 0.155) and mediating between place attachment and environmental behavior (β = 0.163). These findings advance sustainable tourism theory by illuminating the complex pathways through which place-based psychological connections influence environmental behavior formation in heritage contexts, suggesting that more sophisticated theoretical frameworks are required for understanding and promoting sustainable practices in culturally significant destinations. Full article

Economic growth, especially in emerging economies, has altered the composition of international tourism. It is therefore essential to possess the skills necessary to understand the influence of culture on human behaviour, thereby enabling an appropriate response to the traveller. This research aims to develop a tool for identifying openness, flexibility, awareness, and intercultural preparedness. It focuses on the metacognitive and cognitive aspects of cultural intelligence that shape the development of empathy in customer service staff in hotels in Quintana Roo. The variables were validated and incorporated into a quantitative study using multivariate analysis and inferential statistics. A sample of 77 questionnaires was analysed using simple random sampling under a proportional design. Multiple Correspondence Analysis (MCA) was employed as a discriminatory technique to identify the most significant independent variables. These were subsequently entered as regressors into ordinal logistic regression (OLR), along with age and work experience, in order to estimate the probabilities associated with each level of the dependent variable. The results indicated that age had minimal influence on the metacognitive and cognitive variables, whereas years of experience among tourism staff exerted a significant effect. Full article

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

Background/Objectives: Violence is a significant occupational health issue for paramedics, yet underreporting limits efforts to identify and mitigate risk. Leveraging a novel, point-of-event violence reporting system, we aimed to identify characteristics of 9-1-1 calls associated with an increased risk of violence in a single paramedic service in Ontario, Canada. Methods: We retrospectively analyzed all electronic violence and patient care reports filed by paramedics in Peel Region and used logistic regression to identify call-level predictors of any violence and, more specifically, physical or sexual assault. Results: In total, 374 paramedics filed 974 violence reports, 40% of which documented an assault, corresponding to a rate of 4.18 violent encounters per 1000 9-1-1 calls. In adjusted models, the risk of violence was elevated for calls originating from non-residential locations (e.g., streets, hotels, bars), occurring during afternoon or overnight shifts, and involving young or working-age males. Presenting problems related to intoxication, mental health, or altered mental status were strongly associated with increased risk, with particularly high adjusted odds ratios for assault. Conclusions: These findings support the utility of near-miss and violence surveillance systems and highlight the need for multidisciplinary crisis response to high-risk calls, especially those involving mental health or substance use. Full article

As climate change intensified water scarcity in Southern Europe, tourism-dependent regions such as Portugal’s Algarve faced growing pressure to adapt their water management systems. This study investigated how hotel groups in the Algarve have adopted and communicated water reuse technologies—specifically desalination and greywater recycling—under environmental, institutional, and reputational constraints. A comparative qualitative case study was conducted involving three hotel groups—Vila Vita Parc, Pestana Group, and Vila Galé—selected through purposive sampling based on organizational capacity and technology adoption stage. The analysis was supported by a supplementary mini-case from Mallorca, Spain. Publicly accessible documents, including sustainability reports, media coverage, and policy frameworks, were thematically coded using organizational environmental behavior theory and the OECD Principles on Water Governance. The results demonstrated that (1) higher organizational capacity was associated with greater maturity in water reuse implementation; (2) communication transparency increased alongside technological advancement; and (3) early-stage adopters encountered stronger financial, regulatory, and operational barriers. These findings culminated in the development of the Maturity–Communication–Governance (MCG) Framework, which elucidates how internal resources, stakeholder signaling, and institutional alignment influence sustainable infrastructure uptake. This research offered policy recommendations to scale water reuse in tourism through financial incentives, regulatory simplification, and public–private partnerships. The study contributed to the literature on sustainable tourism and decentralized climate adaptation, aligning with UN Sustainable Development Goals 6.4, 12.6, and 13. Full article

(1) On-board accelerometers are increasingly employed in real-world biomechanics to monitor vibrations and shocks. This study assesses the accuracy, repeatability, and variability of three commercially available inertial measurement units (IMUs)—Xsens, Blue Trident, and Shimmer 3—in measuring vibration and shock parameters relevant to human motion analysis. (2) A controlled laboratory setup utilizing an electrodynamic shaker was employed to generate sine waves at varying frequencies and amplitudes, as well as shock profiles with defined peak accelerations and durations. (3) The results showed that Blue Trident demonstrated the highest accuracy in shock amplitude and timing, with relative errors below 6%, while Xsens provided stable measurements for low-frequency vibrations. In contrast, Shimmer 3 exhibited considerable variability in signal quality. (4) These findings offer critical insights into sensor selection based on specific application needs, ensuring optimal accuracy and reliability in dynamic measurement environments. This study lays the groundwork for improved IMU application in biomechanical research and practical deployments. Future research should continue to investigate sensor performance, particularly in angular motion contexts, to further enhance motion analysis capabilities. Full article

This study provides user-centered insights into how inclusive forest design can support the physical, emotional, and social well-being of older adults. It operationalizes universal design principles in natural settings and confirms their relevance through empirical evidence. With the acceleration of global population aging, adapting forest recreation environments to meet the specific needs of older adults is increasingly urgent. This study investigates how infrastructure influences both participation and emotional well-being among older visitors to forest recreation areas. Data were collected from 446 participants aged 65 and older, using a structured survey distributed through in-person contact and digital snowball sampling. Participants reported their infrastructure preferences and their emotional responses related to forest visits. The findings show that older adults highly value site cleanliness, shaded seating, accessible restrooms, and clear signage. Expectations varied significantly according to health status, age group, and visitation frequency. Emotional well-being was positively associated with both comfort and visit frequency. These results demonstrate how inclusive infrastructure plays a vital role in supporting older adults’ access to and enjoyment of forest environments. The study affirms that universally designed forests not only reduce barriers but also promote psychological health and active aging, contributing to developing more equitable and sustainable nature-based recreation areas. Full article