Search Results (604)

The growth of cultural tourism is one of the key areas supporting Indonesia’s policy direction for 2025–2030. This focus aligns with Pillar 8 of the Sustainable Development Goals (SDGs), which promotes decent work and economic growth. Based on previous observations, the factors influencing cultural tourism marketing are inherently multivariate, making it feasible to construct a model based on multivariate analysis. Several multivariate analysis methods have been frequently employed in prior studies, including Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Principal Component Analysis (PCA), Logistic Regression, and Cluster Analysis, among others. Another significant factor influencing cultural tourism is the growing interconnectedness of information technology services, such as various web-based information system applications including Geographic Information System (GIS), which are often used as tools in cultural tourism marketing strategies. This systematic literature review formulates a hypothesis regarding the integration of multivariate analysis with GIS, suggesting that combining multivariate analysis models with GIS provides a more comprehensive spatial understanding of the distribution of tourist interests and enhances the planning of sustainable cultural tourism marketing strategies. Full article

Traditional foods (TFs) represent a key component of regional cultural identity and gastronomic heritage, particularly within the hospitality sector. The growing interest of tourists in authentic, locally sourced and sustainable food underscores the importance of understanding the perceptual and socio-demographic factors that shape their preferences and choices regarding TFs. The aim of this study is to identify and explain the factors that influence tourist attitudes toward dishes prepared with TFs in the hospitality sector, as well as to examine the extent to which socio-demographic characteristics predict tourists’ purchasing decisions. For this purpose, the Tourist Perception and Preferences Model in the Context of Traditional Foods (TPP-TF model) and the Perceptual Factors Scale for Traditional Food Consumption (PFS-TFC) were developed. The research was conducted on a sample of 507 respondents in the A.P. Vojvodina (Republic of Serbia), employing both exploratory and confirmatory factor analyses, which identified the following three key factors: socio-cultural, ecological, and economic. The results of the logistic regression analysisshowed that income level and place of residence significantly influenced the decision to purchase dishes based on traditional foods (TFs). Tourists with higher income levels were substantially more likely to purchase TFs, highlighting the role of economic affordability in shaping consumer choices. Conversely, individuals residing in urban areas showed a significantly lower likelihood of purchasing TFs compared to rural respondents, suggesting that traditional food consumption remains more rooted in rural environments and is closely associated with cultural proximity. Full article

Cultural and touristic information is increasingly available through a multitude of heterogeneous sources, including official repositories, community platforms, and open data initiatives. While prominent landmarks are typically covered across sources, less-known attractions are also documented with varying degrees of detail, resulting in fragmented, overlapping, or complementary content. To enable integrated access to this wealth of information, harvesting and consolidation mechanisms are required to collect, reconcile, and unify distributed content referring to the same entities. This paper presents a machine learning-driven framework for harvesting, homogenizing, and augmenting cultural and touristic data across multilingual sources. Our approach addresses entity resolution, duplication detection, and content harmonization, laying the foundation for enriched, unified representations of attractions and points of interest. The framework is designed to support scalable integration pipelines and can be deployed in applications aimed at tourism promotion, digital heritage, and smart travel services. Full article

The southern Anhui region is home to numerous atypical traditional villages. These settlements serve not only as vital spaces for residents’ daily lives but also as core venues for the transmission and dissemination of traditional culture. Consequently, effectively identifying and classifying these deteriorating traditional villages to formulate corresponding conservation and revitalization strategies has become a critical issue in cultural heritage preservation and utilization. This study focuses on 40 traditional villages in Yixian County, Huangshan City, Anhui Province, as its research subjects. Based on Points of Interest (POI), media sources, village archives, and field surveys, a systematic analysis of these traditional villages was conducted. By integrating a literature review with expert consultation within a multidisciplinary framework, we constructed a three-tiered evaluation system comprising 15 indicators across three major domains: resident experience, tourist engagement, and village environmental quality. The clustering results indicate that 33 out of the 40 villages are atypical. Based on indicator aggregation, these atypical villages are further categorized into two types: B1 (endangered-type) and B2 (resident-dominant/tourist supplementary type). Using this classification system, we propose tailored development strategies for each village type. The findings offer both theoretical and practical guidance for broader traditional village conservation efforts. Full article

The issue of tourism in protected natural areas is becoming central to defining new patterns of use, so managers, policy makers, and local businesses have an interest in improving visitor experiences and the promotion of sustainable tourism. This study analysed the factors influencing tourists’ choices regarding the role of ecosystem services provided by protected natural areas by studying the behaviour of 400 visitors to the Gran Paradiso National Park (Italy). The results identified the key motivations driving tourists’ choices and behaviour, categorising them into distinct visit patterns in relation to the role air quality, consumption of local products, and biodiversity. Furthermore, certification systems were found to be central in defining the level of appreciation of local products among visitors. These results can provide valuable insights into improving visitor experiences and promoting sustainable tourism and highlights the potential of ecosystem services as a key driver for conservation-oriented tourism strategies. Full article

Zanzibar has witnessed fast growth in tourism due to its natural beauty and unique cultural values. On average, about 600,000 tourists arrive annually, creating demand for more hotels, which significantly adds to the generation of waste streams on the island. Food waste is a multifaceted issue and a cross-sectoral problem. However, existing research on food waste management in hospitality focuses mainly on operational and managerial perspectives, while overlooking the role of stakeholder engagement and their social interactions, creating a gap in understanding the relational and context-specific factors shaping sustainable practices, particularly in small island destinations. Therefore, collaborative efforts from different stakeholders are required to ensure sustainable waste management. This study aims to map the key stakeholders and analyze engagement dynamics and structural patterns of social networks to improve hotel food waste management as part of a sustainable tourism strategy in Zanzibar. Stakeholder mapping and analysis, and social network analysis, were applied to examine both the dynamic and interaction patterns. Semi-structured interviews were conducted with different stakeholders related to tourism and waste management operations to solicit their roles, responsibilities, interests, knowledge, interaction, information sharing, influence, and power in decision-making. The results revealed that waste management is the responsibility of local authorities, and there was limited interaction, information sharing, and coordination among stakeholders and across sectors. Building collaborative relationships is important and can be achieved by stimulating interactions through active communication platforms, including social media and online webinar sessions. Moreover, the study proposes a context-specific model for analyzing small-scale stakeholder interactions regarding food waste management in tourist hotels that can inform future stakeholder coordination and policy interventions. Full article

Understanding how different population groups interact with urban environments is essential for analyzing spatial dynamics and informing urban planning, especially in cities experiencing high visitor pressure. This study presents a methodological framework for the spatial and temporal delineation of urban areas based on user-generated location-based data. By collecting nearly 1 million Google Maps reviews in the municipality of Donostia-San Sebastián, we identify and classify user profiles based on their spatiotemporal behavior. First, we collect points of interest (POIs) and associated reviews, including profile identifiers and timestamps. Then, we perform user-level webscraping to reconstruct review histories, enabling us to infer the predominant geographical origin of each user. Users are classified as residents or tourists using both spatial prevalence and temporal activity patterns. The resulting data is aggregated onto a hexagonal grid for geostatistical analysis. Using the Getis-Ord Gi* statistic and Mann-Kendall trend tests, we identify hotspots and long-term trends of activity for different population segments. Additionally, we propose novel indicators such as predominant periods of activity and diversity of geographical origin per cell to characterize heterogeneous patterns of urban use. Our results reveal distinct behavioral patterns, highlighting a more evenly distributed use of urban space by residents, with spatially overlapping yet temporally offset activities across central areas where tourists tend to concentrate their interactions. This spatiotemporal concentration is intensified as the tourists’ origin becomes more distant, suggesting that proximity shapes urban engagement. The proposed methodology offers a replicable strategy for urban analysis using publicly accessible user-generated data and contributes to the understanding of sociospatial dynamics in tourism-intensive cities. Full article

Fun plays a pivotal role in unlocking positive outcomes. Tourists can fall head over heels for a destination or lose interest as they immerse themselves in their journey. This study examined the mediating role of fun in the relationship between service excellence and place love and further investigated how expectation congruence moderates this effect. Our findings reveal that service excellence exerts significant influences on all dimensions of fun. Moreover, the four dimensions of fun—social vigor, emotional spark, psychological zest, and flow–significantly affect place love. Additionally, expectation congruence significantly moderates the effect of service excellence on flow and emotional spark. Full article

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments. Full article

The Mediterranean coast is known for its great tourist attractions, concentration of population, and economic activities. Specifically, in the autonomous regions like Catalonia and Valencia, more than half of the population lives in coastal counties, and the population during the summer months increases due to the influx of tourists. Furthermore, in this stretch of coast, there are some areas of natural interest such as the Delta del Ebro or the Albufera, which are two of the most important wetland areas in the Mediterranean. However, according to studies by Day Today, the retreat of the coastline has increased in recent years, and this influences management of coastal territory both directly and indirectly, mostly harming all sectors with low levels, creating spaces with significant problems. It is for this reason that reporting on climate change and the impact on the coasts is assuming an important role in society, because they are essential tools for planning and management costs. In this thesis, the ground that would be affected by a +1 m, +2 m, and +3 m increase in average sea level, as simulated by the existing flood simulator, has been quantified. And a methodology has been developed for determining the vulnerability of the land based on flooding provided by terrain elevations, and each area studied was evaluated with different degrees of vulnerability: very high, high, moderate, or low. Finally, a first estimate has been made of economic loss that could involve a meter rise in the average sea level for Catalan beaches, and major damage to natural parks, urban areas, and major infrastructure has been identified. This study shows that there are nine areas with high vulnerability due to the low heights of their territory, and the majority of the flooded land is concentrated in the Ebro Delta and the Albufera, which jointly dominate the totals across scenarios. Full article

This paper evaluates botanical gardens in terms of their potential for tourist valorization, aiming to identify the garden with the highest tourist appeal and integration opportunities within the tourist market. Based on a literature review and established attractiveness criteria, a methodological framework using multi-criteria decision-making was developed to compare and rank the botanical gardens. The empirical part of the study focuses on botanical gardens in Split–Dalmatia County, including six gardens evaluated across nine criteria. Eight local tourism experts assessed the importance of these criteria and the gardens’ performance. The fuzzy SiWeC (SImple WEight Calculation) method was used to determine the importance of each criterion. The fuzzy TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) was used to measure the potential of botanical gardens. The main results obtained with this approach showed that the most important criteria are C4—Visitor content and C3—Biodiversity conservation. The Botanical Garden of Primary School Ostrog has the greatest potential, followed by the Botanical Garden Split. All observed botanical gardens have excellent tourist potential, with minimal differences in ranking among them. These findings demonstrate that botanical gardens play a key role in diversifying the tourist offer, reducing seasonality, and increasing the overall attractiveness of destinations. They also contribute to raising environmental awareness and emphasizing the importance of nature conservation and sustainable development, aligning with the increasing tourist interest in natural and ecologically responsible experiences. This study offers practical insights, as the results can assist garden management, tourism communities, and policymakers in developing and promoting strategies. Additionally, the framework proposed can be applied in other regional and international contexts. Full article

The unique hollowed-out catamaran hulls and complex environmental conditions in lake areas hinder traditional ranging algorithms (combining target detection and stereo matching) from accurately obtaining depth information near the center of ships. This not only impairs the navigation of electric tourist boats but also leads to high computing resource consumption. To address this issue, this study proposes a ranging method integrating improved ORB (Oriented FAST and Rotated BRIEF) with stereo vision technology. Combined with traditional optimization techniques, the proposed method calculates target distance and angle based on the triangulation principle, providing a rough alternative solution for the “gap period” of stereo matching-based ranging. The method proceeds as follows: first, it acquires ORB feature points with relatively uniform global distribution from preprocessed binocular images via a local feature weighting approach; second, it further refines feature points within the ROI (Region of Interest) using a quadtree structure; third, it enhances matching accuracy by integrating the FLANN (Fast Library for Approximate Nearest Neighbors) and PROSAC (Progressive Sample Consensus) algorithms; finally, it applies the screened matching point pairs to the triangulation method to obtain the position and distance of the target ship. Experimental results show that the proposed algorithm improves processing speed by 6.5% compared with the ORB-PROSAC algorithm. Under ideal conditions, the ranging errors at 10m and 20m are 2.25% and 5.56%, respectively. This method can partially compensate for the shortcomings of stereo matching in ranging under the specified lake area scenario. Full article

The awareness of tourism’s environmental impact has increased interest in sustainable alternatives such as green hotels, yet tourists often fail to translate pro-environmental attitudes into action, reflecting the attitude–behavior gap. This study extends the Theory of Planned Behavior (TPB) by incorporating Environmental Knowledge and Climate Change-Related Risk Perceptions (CC-RRPs) as background factors and testing their effects on Green Hotel Purchase Intentions (GHPIs) among Spanish travelers. Data from 1442 respondents were analyzed using covariance-based Structural Equation Modeling (SEM) with bootstrapped mediation testing. Results show that In-Group Norms are the strongest predictor of GHPIs, followed by Eco-Hotel Attitudes, while Perceived Behavioral Control (PBC) has a weaker but significant effect. Environmental Knowledge predicts all three mediators, and CC-RRPs predict Attitudes and Norms but not PBC. Crucially, both antecedents affect GHPIs only indirectly, supporting a mediation-based framework. These findings clarify the distinct roles of Environmental Knowledge as a cognitive antecedent and CC-RRPs as cognitive–affective evaluations that motivate attitudes and norms, while also highlighting the centrality of social influence in a Southern European context. Beyond theoretical contributions, the results underscore the importance of trust and authenticity: addressing greenwashing through transparent communication and credible certification frameworks is essential to ensure sustainable hospitality choices. Full article

Education is essential to empower communities towards sustainable destination development. While research has covered the importance of participation and empowerment in achieving sustainable tourism in communities, academic discourse on educating local communities on sustainable tourism, particularly education via non-formal means, is scarce. To address this gap, our research aimed to determine perceptions of local stakeholders on the efficacy of a sustainable tourism workshop in the Marico Biosphere Reserve, South Africa. Sustainability is the cornerstone of biosphere reserves, with this reserve choosing sustainable tourism as its main development route. A workshop on sustainable tourism was designed according to the needs identified and offered to stakeholders involved in the reserve. This was followed by qualitative research via focus group interviews to investigate the positive effects perceived following this non-formal education event. The data culminated in a model, wherein the greatest effect was increased awareness regarding sustainable tourism. The themes of increased positivity, interest, and empowerment in sustainable tourism, as well as a desire to participate more, were also evident. In addition, interesting linkages between certain themes and codes emerged, which emphasize the positive knock-on effects of non-formal education in sustainable tourism. While the findings cannot be generalized to all contexts, they suggest the success of this type of education in furthering sustainable development practices for the betterment of local communities and tourists, as well as the natural environment. Furthermore, this exploratory research can inform the achievement of certain Sustainable Development Goals and guide managers and stakeholders in different settings who want to implement education on sustainable tourism. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article