Search Results (530)

Most research on domestic dog (Canis familiaris) behavior has focused on pets with restricted movement. However, free-ranging dogs exist in diverse cultural contexts globally, and their interactions with humans are less understood. Tourists can facilitate unrestricted dog movement into wilderness areas, where they may negatively impact wildlife. This study investigated which stimuli—namely, voice, touch, or food—along with inherent factors (age, sex, sociability) motivate free-ranging dogs to follow a human stranger. We measured the distance (up to 600 m) of 129 free-ranging owned and stray dogs from three villages in southern Chile as they followed an experimenter who presented them one of the above stimuli or none (control). To evaluate the effect of dog sociability (i.e., positive versus stress-related or passive behaviors), we performed a 30 s socialization test (standing near the dog without interacting) before presenting a 10 s stimulus twice. We also tracked whether the dog was in the company of other dogs. Each focus dog was video-recorded and tested up to three times over five days. Generalized linear mixed-effects models revealed that the food stimulus significantly influenced dogs’ motivation to follow a stranger, as well as a high proportion of sociable behaviors directed towards humans and the company of other dogs present during the experiment. Juveniles tended to follow a stranger more than adults or seniors, but no effects were found for the dog’s sex, whether an owner was present, the repetition of trials, the location where the study was performed, or for individuals as a random variable. This research highlights that sociability as an inherent factor shapes dog–stranger interactions in free-ranging dogs when food is given. In the context of wildlife conservation, we recommend that managers promote awareness among local communities and tourists to avoid feeding dogs, especially in the context of outdoor activities close to wilderness. Full article

The study investigates the contribution of technology (TECH), quantified by Internet penetration, in influencing tourism performance (TP) among the top ten touristic nations in Europe: France, Spain, Italy, Turkey, the United Kingdom, Germany, Greece, Austria, Portugal, and the Netherlands. Using panel data from 2000–2022, the study includes additional structural controls like environment quality, gross domestic production (GDP) per capita, exchange rate (ER), and safety index (SI). The Method of Moments Quantile Regression (MMQR) is employed to capture heterogeneous effects at different levels of TP, and Driscoll–Kraay standard error (DKSE) correction is employed to make the analysis robust against autocorrelation as well as cross-sectional dependence. Spectral–Granger causality tests are also conducted to check short- and long-run dynamics in the relationships. Empirical results are that TECH and SI are important in TP at all quantiles, but with stronger effects for lower-performing countries. Environmental quality (EQ) and GDP per capita (GDPPC) exert increasing impacts at upper quantiles, suggesting their importance in sustaining high-level tourism economies. ER effects are limited and primarily short-term. The findings highlight the need for integrated digital, environmental, and economic policies to achieve sustainable tourism development. The paper contributes to tourism research by providing a comprehensive, frequency-sensitive, and distributional analysis of macroeconomic determinants of tourism in highly developed European tourist destinations. Full article

With the arrival of an aging society and the continuous extension of the human lifespan, the quality of life has not improved in a corresponding manner. People’s demand for happiness and health is increasing. As a result, a model emerged that integrates tourism and medical services, which is health tourism. This growing demand has prompted many service providers to see it as a business opportunity and enter the market. Tourism can help travelers release work stress and restore physical and mental balance; meanwhile, health check-ups and disease treatment can help them regain health. Consumers have long favored health and medical tourism because it helps relieve stress and promotes overall well-being. As people age, some consumers experience a gradual decline in physical functions, making it difficult for them to participate in regular travel services provided by traditional travel agencies. Therefore, this study aims to explore the service needs of health and medical tourism customers (tourists/patients) and the interrelationships among these service needs, so that health and medical tourism service providers can develop more customized and diversified services. This study identifies four key drivers of medical tourism services: medical services, medical facilities, tour planning, and hospitality facilities. This study uses the APA (attention and performance analysis) method to assess each dimension and criterion and utilizes the DEMATEL method with the NRM (network relationship map) to identify network relationships. By combining APA and NRM techniques, this study develops the APA-NRM technique to evaluate adoption strategies and identify suitable paths for health tourism services, providing tailored development strategies and recommendations for service providers to enhance the service experience. Full article

In response to the European Union’s revised Urban Wastewater Treatment Directive, which mandates enhanced monitoring and advanced treatment of micropollutants, this study was conducted. It took place within the Coastal Landscape Park (CLP), a Natura 2000 protected area in northern Poland. The focus was on the municipal wastewater treatment plant in Jastrzębia Góra, located in a region exposed to seasonal tourist pressure and discharging effluent into the Czarna Wda River. A total of 90 wastewater samples were collected during five monitoring campaigns (July, September 2021; February, May, July 2022) and analysed for 13 pharmaceuticals and personal care products (PPCPs) using ultra-high-performance liquid chromatography tandem mass spectrometry with electrospray ionisation (UHPLC-ESI-MS/MS). The monitoring included both untreated (UTWW) and treated wastewater (TWW) to assess the PPCP removal efficiency and persistence. The highest concentrations in the treated wastewater were observed for metoprolol (up to 472.9 ng/L), diclofenac (up to 3030 ng/L), trimethoprim (up to 603.6 ng/L) and carbamazepine (up to 2221 ng/L). A risk quotient (RQ) analysis identified diclofenac and LI-CBZ as priority substances for monitoring. Multivariate analyses (PCA, HCA) revealed co-occurrence patterns and seasonal trends. The results underline the need for advanced treatment solutions and targeted monitoring, especially in sensitive coastal catchments with variable micropollutant presence. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

Tourist satisfaction of nighttime urban lakes as scenic areas, such as the Daming Lake, is influenced by multiple factors, which are crucial for tourists’ experiences and the sustainable development of these areas. This paper explores the factors impacting nighttime visitor satisfaction at the Daming Lake Scenic Area. Basing our studies on analysis of the literature and questionnaire surveys, the study constructs a visitor satisfaction evaluation index system based on the Expectancy-Disconfirmation Theory. Utilizing the revised importance-performance analysis method, the study identifies several significant influencing factors including the distinctive features of nighttime shopping products, the rich variety of nighttime tourscape and entertainment products, the aesthetically pleasing design of nighttime lighting products, the affordable price of nighttime dining products, and the diverse methods, reasonable pricing, and multimodal transit options of nighttime transportation. Furthermore, it finds the main factors that reduce tourists’ satisfaction in nighttime urban lakes include: premium pricing of nighttime shopping and dining products, transport infrastructure deficiencies, the cultural connotation of tourism products, and the safety of nighttime tourscape and entertainment products. This research provides insights to enhance satisfaction in urban lake scenic areas and expands the application of the tourist satisfaction theory. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

Understanding and promoting guest satisfaction is central to the economic sustainability of the hospitality industry. Satisfaction influences consumers’ booking intentions, hotel choice, loyalty, and the reputation and performance of accommodation establishments. Thus, accurate decision making by hotel managers relies on trustworthy and easily accessible information on the variables that affect guest satisfaction. Nowadays, this information is available through reviews and ratings provided by online platforms, such as TripAdvisor. Indeed, much research into guest satisfaction uses TripAdvisor reviews. However, this study aims to analyse guest satisfaction using only TripAdvisor ratings. These ratings can be more succinct and tractable indicators than reviews. A sample of 118 hotels in Cape Verde and the Azores, two archipelagos belonging to Macaronesia, and a descriptive, predictive, and discriminant methodology are employed for this purpose. Four main results are obtained. First, the rated items on TripAdvisor are consistent with the scientific literature on this topic. Second, TripAdvisor ratings are valid and reliable. Third, TripAdvisor ratings can predict guest satisfaction based on the perceived quality of hotel services. Fourth, there are significant differences in ratings depending on the tourism destination chosen. These results are of interest to researchers, tourists, as well as hotel, destination, and platform managers. Full article

Tourism offers many economic benefits but can have long-lasting ecological effects when improperly managed. Tourism can cause overwhelming pressure on wastewater treatment systems, as in Belize, where some of the over 400 small islands (cayes) that were once temporary sites for fishermen have become popular tourist destinations. An overabundance of nitrogen, in part as a result of incomplete wastewater treatment, threatens human health and ecosystem services. The tourism industry is a complex and dynamic industry with many sectors and stakeholders with conflicting goals. In this study, a systems thinking approach was adopted to study the dynamic interactions between stakeholders and the environment at Laughing Bird Caye National Park in Belize. The project centered on nutrient discharges from the caye’s onsite wastewater treatment system. An archetype analysis approach was applied to frame potential solutions to nutrient pollution and understand potential behaviors over time. “Out of control” and “Underachievement” were identified as system archetypes; “Shifting the Burden” and ‘‘Limits to Success’’ were used to model specific cases. Based on these results, upgrading of the wastewater treatment system should be performed concurrently with investments in the user experience of the toilets, education on the vulnerability of the treatment system and ecosystem, and controls on the number of daily tourists. Full article

The field of tourism economics has consistently attracted big attention from scholars across various countries. Tourism is inherently linked to economic aspects. Concurrently, Indonesia has relocated its Ibu Kota Negara/National Capital City, now named “IKN”, from Jakarta to East Kalimantan. In addition to extensive public infrastructure development, the Indonesian government is also working to revitalize the tourism sector in IKN. To assess the economic feasibility of this sector, an in-depth study is necessary. This research aims to examine labor absorption, tourist visits, and economic growth as indicators of successful tourism economic performance. It also analyzes the variables that influence these indicators, including (1) wages, (2) occupancy rates, (3) room rates, (4) food and beverage facilities, (5) inflation, (6) hotel and lodging taxes, (7) restaurant and eating-house taxes, and (8) investment. The regression testing method employs Ordinary Least Squares (OLS). According to the data analyzed from 2013 to 2024, the authors identified three main points: First, tourist visits and inflation have positive and significant impacts on labor absorption. Second, labor absorption, wages, occupancy rates, economic growth, and investment positively and significantly influence tourist visits. Third, tourist visits, room rates, food and beverage facilities, and inflation have positive and significant effects on economic growth. The implications of this research can be enlightening for regulators and future initiatives. This is particularly important for guiding further empirical investigations and policy planning aimed at accelerating economic development in the tourism sector. Full article

Traditional Jiangnan water towns in China serve as important cultural heritage sites and tourist destinations. Existing studies have revealed a potential connection between environmental elements and spatial perception in these towns. However, there remains a lack of research systematically investigating whether and how these environmental elements influence subjective evaluation indicators, such as spatial attractiveness, and the mechanisms underlying the interactions between these elements. To further understand these mechanisms, we used Nanxun Old Town as our study site, employed computer vision techniques to perform semantic segmentation on street-view images, extracted the visual proportions of environmental elements, and conducted quantitative correlation analysis with subjective attractiveness evaluations. The findings indicate that different environmental elements in water towns shape spatial imagery in diverse ways, thereby influencing perceived attractiveness. Firstly, though space-defining elements such as buildings and water generally contribute positively to perceived attractiveness, their proportions should be controlled within a reasonable range to maintain a spatial scale that aligns with the traditional imagery of water towns. Secondly, foreground elements like boats and lanterns, although occupying a smaller proportion, can effectively enhance the space when properly combined. Finally, the influence of elements such as bridges and buildings depends on the specific viewing distance and angle. These findings, based on an interpretable analytical framework, reveal that the effects of environmental elements on spatial attractiveness are context-dependent and nonlinear, varying with their proportions, combinations, and perspectives. This approach offers a more comprehensive understanding of the mechanisms by which environmental elements shape spatial attractiveness, providing a scientific foundation for regulating key visual components and optimizing spatial composition for sustainable traditional water town environment management. Full article

Tourist destination classification is pivotal for enhancing the travel experience, supporting cultural heritage preservation, and enabling smart tourism services. With recent advancements in artificial intelligence, deep learning-based systems have significantly improved the accuracy and efficiency of landmark recognition. To address the limitations of existing datasets, we developed the Samarkand dataset, containing diverse images of historical landmarks captured under varying environmental conditions. Additionally, we created enhanced image variants by squaring pixel values greater than 225 to emphasize high-intensity architectural features, improving the model’s ability to recognize subtle visual patterns. Using these datasets, we trained two parallel YOLO11 models on original and enhanced images, respectively. Each model was independently trained and validated, preserving only the best-performing epoch for final inference. We then ensembled the models by averaging the model outputs from the best checkpoints to leverage their complementary strengths. Our proposed approach outperforms conventional single-model baselines, achieving an accuracy of 99.07%, precision of 99.15%, recall of 99.21%, and F1-score of 99.14%, particularly excelling in challenging scenarios involving poor lighting or occlusions. The model’s robustness and high performance underscore its practical value for smart tourism systems. Future work will explore broader geographic datasets and real-time deployment on mobile platforms. Full article

Wine tourism has emerged as a thriving activity within the international wine sector, evolving from simple winery visits to immersive and experiential engagements. This study explores the role of wine tourism in Galicia, emphasizing its integration into the circular economy and sustainability frameworks. By analyzing visitor expectations and leveraging Galicia’s unique attributes—such as its natural beauty, cultural heritage, and renowned gastronomy—this research aims to position Galicia as a leading wine tourism destination in Spain. The study employs surveys targeting wineries, accommodation providers, and wine tourists to assess the impact of sustainable practices on economic performance and community development. Key findings indicate that activities related to renewable energy and sustainable tourism significantly enhance winery revenues, while also contributing to local economic growth and population retention in rural areas. The research concludes that a collaborative approach between public and private sectors is essential for designing a sustainable and circular economy in wine tourism, ensuring long-term benefits for both the environment and local communities. Full article

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

Choosing the optimal path in planning is a complex task due to the numerous options and constraints; this is known as the trip design problem (TTDP). This study aims to achieve path optimization through the weighted sum method and multi-criteria decision analysis. Firstly, this paper proposes a weighted sum optimization method using a comprehensive evaluation model to address TTDP, a complex multi-objective optimization problem. The goal of the research is to balance experience, cost, and efficiency by using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to assign subjective and objective weights to indicators such as ratings, duration, and costs. These weights are optimized using the Lagrange multiplier method and integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. Additionally, a weighted sum optimization method within the Traveling Salesman Problem (TSP) framework is used to maximize ratings while minimizing costs and distances. Secondly, this study compares seven heuristic algorithms—the genetic algorithm (GA), particle swarm optimization (PSO), the tabu search (TS), genetic-particle swarm optimization (GA-PSO), the gray wolf optimizer (GWO), and ant colony optimization (ACO)—to solve the TOPSIS model, with GA-PSO performing the best. The study then introduces the Lagrange multiplier method to the algorithms, improving the solution quality of all seven heuristic algorithms, with an average solution quality improvement of 112.5% (from 0.16 to 0.34). The PSO algorithm achieves the best solution quality. Based on this, the study introduces a new variant of PSO, namely PSO with Laplace disturbance (PSO-LD), which incorporates a dynamic adaptive Laplace perturbation term to enhance global search capabilities, improving stability and convergence speed. The experimental results show that PSO-LD outperforms the baseline PSO and other algorithms, achieving higher solution quality and faster convergence speed. The Wilcoxon signed-rank test confirms significant statistical differences among the algorithms. This study provides an effective method for experience-oriented path optimization and offers insights into algorithm selection for complex TTDP problems. Full article