Search Results (2,540)

This study aims to identify the key destination-related factors associated with tourists’ revisit behavior in peri-urban rural areas within Chinese metropolitan regions in the context of short-video marketing, and to reveal the structural relationships and hierarchical characteristics among these factors. First, the LDA topic model was employed to conduct text mining on authentic tourist-generated comments posted on Douyin, through which the core factors related to revisit behavior were identified and conceptually standardized based on tourists’ expressions. Building on this process, 153 experts in relevant fields were invited to evaluate the direction and strength of the relationships among these factors. An integrated DEMATEL–ISM–MICMAC approach was then applied to analyze their causal attributes, hierarchical structure, and systemic roles. The results indicate that the identified factors do not operate independently but instead form a multilayered structure with clear hierarchical characteristics. Among them, rural visual imagery, escape-oriented experience, rural lifestyle experience, and rural industry integration occupy deeper structural levels and exert relatively strong structural influences on factors located at intermediate and surface levels. The findings further suggest that the sustained attractiveness of rural tourism destinations in metropolitan regions cannot rely solely on short-video exposure or isolated “internet-famous” attractions; rather, it requires coordinated alignment among digital communication content, rural industries, lifestyle experiences, and tourism supply. By integrating tourist-generated content, natural language processing, and expert-based structural assessment, this study extends research on short-video tourism marketing from a systems perspective and provides practical insights for peri-urban rural destinations in Chinese metropolitan regions seeking to optimize the structural configuration of tourism resources, products and services, and marketing communication. Full article

Accommodation establishments constitute a core component of tourism infrastructure, and their location choices directly affect water resource utilization, land pressure, and the spatial equilibrium of tourism development—issues that are particularly acute in vast arid regions. Yet the spatial organization of accommodation supply across extensive drylands characterized by fragmented oasis distribution, and the reasons why standard and non-standard accommodation follow divergent location logics, remain poorly understood. This study addresses three questions: (1) How are nine accommodation categories, differentiated by type and quality, distributed across Xinjiang? (2) Do directional spatial associations exist among categories that are consistent with hierarchical, path-dependent development? (3) Which factors drive these patterns, and do their effects exhibit the nonlinearity and threshold behavior predicted by location theory? Drawing on 12,073 accommodation establishments from the Ctrip platform, we construct a staged analytical framework in which each technique answers a specific question: the nearest-neighbor index and standard deviational ellipse characterize global patterns; kernel density estimation and OPTICS clustering identify local agglomerations; directional local co-location quotients measure asymmetric spatial associations; and XGBoost–SHAP isolates nonlinear drivers and threshold effects. Results reveal a highly concentrated “single-core, multi-center” structure anchored by Urumqi, Yining, and Kashgar, with rapid expansion toward the Ili Valley, Kashgar, and Altay since 2019. Standard accommodation tracks urban centrality and transport nodes, while non-standard accommodation tracks tourism resource endowments, consistent with location-theoretic expectations. Directional co-location analysis reveals hierarchical spatial associations among categories, and driving factors exhibit pronounced nonlinear threshold effects. From a sustainability perspective, the identified thresholds—elevation (1360 m), water-body proximity, and distance to rural tourism demonstration sites (3 km)—constitute quantifiable, spatially explicit sustainability indicators that can be incorporated into planning tools to monitor and steer accommodation development away from ecologically sensitive zones. Global Moran’s I diagnostics of model residuals (reduction of 83–99.7%) suggest that these findings are unlikely to be artifacts of spatial autocorrelation; this diagnostic, however, complements rather than replaces spatially blocked validation. The study contributes category-differentiated, spatially directed evidence for policies balancing tourism expansion against water security and ecosystem integrity, serving sustainable tourism development in arid-region destinations. Full article

Artificial intelligence is increasingly adopted by tourism organisations, yet its relationships with creativity, innovation, and organisational growth intention remain insufficiently understood across national contexts. This study examined relationships among current organisational AI use, intention to use or expand the use of AI, perceived usefulness of AI for tourism service development, creativity at workplace, organisational innovation, and organisational growth intention in Lithuania, Poland, and Sweden. Survey responses, provided by 436 representatives of tourism organisations, were analysed using structural equation modelling, bias-corrected bootstrapping, and exploratory multi-group comparisons. Current organisational AI use was positively associated with creativity at workplace in the pooled model, but the association was modest and was not robust in country-specific bootstrap analyses. Perceived usefulness of AI for tourism service development showed a more robust positive association with organisational innovation, while creativity at workplace was strongly associated with organisational innovation and organisational innovation was positively associated with organisational growth intention. Intention to use or expand the use of AI was positively associated with organisational growth intention in the hypothesised structural model, but this relationship was specification-dependent and did not remain statistically significant after controlling for main activity group and number of employees. Significant indirect associations were also identified, and country-specific analyses indicated heterogeneity in selected relationships. The findings support distinguishing the three AI-related dimensions and suggest that creativity-supportive and innovation-management practices remain important alongside AI-related practices. Full article

This study addresses the research gap concerning how rural cultural–tourism integration contributes to regional economic resilience, a relationship that remains underexplored despite growing policy interest in rural revitalization. Using cross-sectional data for 285 Chinese prefecture-level cities in 2020, the study constructs a multidimensional ACT (agriculture–culture–tourism) indicator system and employs XGBoost regression with SHAP (SHapley Additive exPlanations) analysis to examine the nonlinear associations between ACT factors and economic resilience. Spatial analysis reveals significant positive spatial autocorrelation (Moran’s I = 0.412) and pronounced regional disparities. Machine learning results indicate that traditional village numbers and primary industry value-addedness are the most important predictors, with SHAP dependence analysis revealing threshold effects and diminishing marginal returns. An illustrative micro-level case study of 140 social media posts from Guangzhou provides interpretive evidence on how low-intensity, leisure-oriented tourism experiences mediate the translation of ACT resources into economic outcomes. The findings suggest that ACT integration is associated with stability-oriented resilience buffering rather than transformative growth, highlighting the importance of functional integration, product diversification, and institutional capacity for enhancing regional economic resilience. Full article

Heritage tourism has become an important driver of tourism development, particularly in Asia, where countries actively promote national and cultural identities to attract visitors. Although individual studies have examined heritage identity in relation to tourism, existing reviews have not systematically synthesised the theoretical perspectives, methodological approaches, and empirical evidence on how tangible and intangible heritage identities are utilised as tourism development resources across South and Southeast Asia. Accordingly, this systematic review addresses three questions: (i) how research on heritage identity in heritage tourism has evolved in South and Southeast Asia, including its publication trends, theoretical perspectives and methodological approaches; (ii) which forms of tangible and intangible heritage identity are most utilised in South and Southeast Asia; and (iii) what conceptual and practical challenges and future research directions emerge from the literature. This study presents a systematic literature review with narrative synthesis of 44 publications to analyze how tangible and intangible heritage identities are used to promote heritage tourism in the region. The findings indicate that most studies focus on tangible heritage such as monuments, archaeological sites and colonial architecture, while intangible heritage, including cultural festivals and living traditions, remains comparatively underrepresented. The review further shows that heritage identity plays a significant role in strengthening national identity, enhancing visitor experiences and supporting local economic development across South and Southeast Asia. At the same time, several challenges remain, including the underutilisation of heritage resources, limited recognition of intangible heritage and the need to improve visitor management and sustainable tourism practices. The study concludes that Asian countries have significant untapped potential to further integrate heritage identity into tourism strategies, particularly through more innovative uses of intangible cultural heritage and improved heritage governance and management. Full article

Amid growing global economic uncertainty, strengthening tourism economic resilience is critical to the economic sustainability of tourism destinations. Using panel data for 288 Chinese prefecture-level cities from 2010 to 2024, this study uses the staggered designation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment and applies a staggered difference-in-differences (DID) model to examine whether artificial intelligence (AI) development promoted by the pilot-zone initiative enhances tourism economic resilience. Results show that the pilot-zone initiative significantly enhances city-level tourism economic resilience, and this finding remains robust across a series of endogeneity and robustness checks. Mechanism analysis identifies data factor utilization, technological innovation, and industrial structure upgrading as three parallel channels. Moderation analysis shows that the resilience-enhancing effect of the pilot-zone initiative is stronger in cities with more developed digital infrastructure, higher levels of marketization, and greater human resources. Heterogeneity analysis reveals overall regional heterogeneity and a stronger effect in resource-based cities. These findings clarify the mechanisms and boundary conditions linking AI development promoted by the pilot-zone initiative to tourism economic resilience and provide implications for technology-enabled sustainable tourism development. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article

This study provides a comprehensive quantitative assessment of the carbon footprint of a four-star hotel in Istanbul, Türkiye, spanning the period from 2022 to 2024, to identify critical operational emission drivers. Methodologically grounded in ISO 14064-1 and the Greenhouse Gas Protocol, the research integrates direct (Scope 1), indirect energy (Scope 2), and selected value-chain (Scope 3) emissions. The operational data underwent rigorous uncertainty and sensitivity analyses, alongside multi-tier benchmarking against global, climatic, and regional datasets. Findings reveal that total emissions increased from 1444.93 tCO₂e in 2022 to 1592.61 tCO₂e in 2024, predominantly driven by grid-connected electricity, which accounted for approximately 47.1% of the total footprint in 2024. Key operational hotspots include the continuous baseload energy demands of mechanical rooms and lifts, fugitive R-404A refrigerant leakage, and Scope 3 impacts from red meat procurement and organic waste. Furthermore, sensitivity analyses confirmed the footprint’s high elasticity to the national grid’s carbon intensity. Despite the facility outperforming benchmarked peers in overall eco-efficiency, the study concludes with actionable, stratified recommendations for facility engineers, hotel operators, and tourism policymakers to overcome technical, supply-chain, and grid-level barriers to decarbonization. Full article

Destinations increasingly shift from promotional branding toward holistic and inclusive identity strategies, where cultural heritage operates as both an anchor of local distinctiveness and a catalyst for creative place-making. This study presents a mixed-methods Triple Bottom Line (TBL) assessment of four Greek cultural heritage institutions, a regional theatre, a municipal gallery, a university-affiliated foundation, and a regional film festival, to examine how governance structures, resource availability, and organisational capacity shape environmental, social, and economic sustainability performance. Through document analysis and semi-structured interviews, the research identifies eleven recurring thematic variables across the TBL dimensions and systematically compares their implementation across distinct governance models. Findings show that social sustainability, particularly accessibility, education, and community engagement, constitutes a structurally universal baseline across all institutions, regardless of governance type or resource level. In contrast, environmental and economic performance diverge sharply according to governance model, building ownership, and access to capital. As cultural institutions function as experiential landmarks within cultural and creative tourism, their inclusive practices enhance community wellbeing while co-producing destination identity as socially responsible and culturally authentic. The study concludes with targeted policy recommendations on reporting standards, capacity building, and structural constraints, offering transferable insights for creative place-making and sustainable destination branding in Greece and other Mediterranean contexts. Full article

The rapid development of the low-altitude economy is reshaping tourism activities, infrastructure provision, and landscape resource use, yet national-scale research on low-altitude tourism bases remains limited. Using data on 1247 bases in China, this study applies average nearest-neighbor analysis, kernel density estimation, and spatial inequality measures to characterize multi-scale patterns. The study further employs an optimal parameters-based geographical detector within a geographical nature framework to identify influencing factors and interactions. The results reveal significant clustering and regional inequality, with a pronounced east–west divide along the Heihe–Tengchong Line. A diamond-shaped core bounded by Beijing, Hangzhou, Chengdu, and Sanya, together with its 200 km buffer zone, contains 89.17% of all bases. Domestic tourism revenue, the number of low-altitude industry enterprises, general aviation airports, national tourist resorts, and domestic tourist arrivals constitute the leading factors, although the dominant factor combinations vary across base types. Factor interactions are dominated by two-factor and nonlinear enhancement. The three natures form a coupled pathway: first nature provides environmental suitability, second nature transforms resource potential into marketable tourism products, and third nature regulates implementation. General aviation infrastructure acts as an operational interface linking environmental conditions, tourism demand, industrial support, and policy arrangements. The findings provide a basis for differentiated regional zoning and type-specific facility planning, thereby informing the sustainable development of low-altitude tourism. Full article

Tourism is a complex economic activity all over the world shaped by distinct local resources: culture, nature, industrial heritage, urban ambiance, place-based uniqueness, and geographical accessibility. The simultaneous governance and management of economic growth motives, preservation of the cultural heritage base, and respect for nature and ecological quality calls for an evidence-based and multi-faceted policy analysis that seeks to achieve sustainable development among conflicting policy objectives in tourist areas. The present paper seeks to explore the feasibility of a sustainable balance for various heterogeneous cultural heritage areas in Europe (‘urban pilot regions’), with particular attention to sustainable local development characterized by circular economic objectives and an ecological balance strategy based on the principle of stakeholders’ co-creation. The paper addresses the knowledge gap between generic policy aims and site-specific views of actors or visitors. To that end, an extensive survey experiment was administered in the urban regions concerned, in which a wide range of relevant management issues/questions related to environmental preferences and perceptions were posed to stakeholders and visitors. The data were analyzed by means of a novel respondent-oriented multivariate statistical tool, viz. Generalized Q-Analysis, which is suitable for handling big databases with many respondents. The paper shows that the application of Generalized Q-Analysis to common survey data enriches the results from the application of a conventional Q-Analysis. Furthermore, the study also highlights that, based on the views expressed by the surveyed visitors, the tourist areas concerned are quite different from each other in attracting specific classes of visitors. Therefore, functional specialization in the tourist sector seems to be an important anchor point for effective governance of urban tourism, not only in Europe, but also elsewhere in the world. Full article

Henan Province is one of China’s most culturally resource-rich regions, yet the spatial organization and driving mechanisms of its high-grade tourism resources remain insufficiently understood. This study investigates the spatial distribution characteristics and influencing factors of 2396 high-grade tourism resource sites across 157 county-level units in Henan Province. An integrated analytical framework combining GIS-based spatial analysis (nearest neighbor index, kernel density estimation, and spatial autocorrelation), geographic detector methods, and a spatial error model is employed to examine the roles of natural, economic, social, and cultural factors. The results indicate that high-grade tourism resources exhibit significant spatial clustering, forming a pronounced core–periphery structure with a dual-center pattern centered on the main urban area of Zhengzhou and Dengfeng. Strong positive spatial autocorrelation is observed, characterized by contiguous hot spots in central and northwestern Henan and cold spots in the eastern plains. The spatial differentiation of high-grade tourism resources is driven by multiple interacting factors, among which topography and material cultural heritage are the primary drivers, while economic development and intangible cultural heritage act as secondary facilitators. These findings provide empirical evidence for understanding inland tourism spatial structures and offer practical insights for heritage-led, cluster-based tourism planning, aimed at promoting sustainable regional development. Full article

Systematic assessment of the potential for realizing the value of ecological products in plateau lake basins, together with the identification of differentiated pathways, is essential for addressing structural tensions between ecological protection and regional development and for improving the precision of value-realization practices. Taking the nine major plateau lake basins in Yunnan Province as the study area, this study constructs a comprehensive indicator system covering three dimensions: supply potential, transformation conditions, and market demand. The k-means clustering method is then applied to classify basin types and identify dominant value-realization pathways. The results indicate that: (1) the VPPI ranges from 0.1594 to 0.7679, with the highest value in Dianchi Lake Basin and the lowest in Chenghai Lake Basin; the high-, medium-, and low-potential zones have mean VPPI values of 0.6928, 0.4033, and 0.2106, respectively; (2) four distinct types are identified, namely the regulatory service trading type, cultural tourism empowerment type, characteristic provisioning product premium type, and protection-priority reserve type; and (3) low-potential basins are mainly constrained by weak market demand, with a mean market-demand contribution of only 0.0386. These findings provide quantitative decision support for context-specific pathway design in plateau lake basins. Full article

Tourism-demand forecasting overwhelmingly targets aggregate volumes, leaving the question of where demand will come from largely unaddressed. This paper forecasts that question directly. We make three contributions. First, we apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to guest origin composition in large-scale platform booking data, which to our knowledge is the first use of Bayesian compositional time-series methods on booking-origin shares across multiple global destination regions. Second, we introduce seasonal structure in the Dirichlet precision parameter, and we isolate its contribution through an ablation against an otherwise identical constant-precision specification: seasonal precision lowers mean absolute error in all four destination regions, by between 13% and 35%. Third, the forecast target is the monthly composition of bookings indexed by booking date rather than stay date, which makes it observable ahead of realized arrivals and therefore usable for decisions with long lead times. Using proprietary Airbnb reservation data spanning 2017–2025 across four destination regions, we document substantial pandemic-era shifts in booking composition with heterogeneous recovery patterns. In rolling-origin evaluation, BDARMA achieves the lowest forecast error for EMEA, the most compositionally diverse region, reducing mean absolute error by 27% relative to naïve forecasts ($p<0.001$). Performance elsewhere is mixed: simple benchmarks remain hard to beat, and exponential smoothing on isometric log-ratio-transformed data attains the lowest error averaged across the four regions. The EMEA pattern suggests that direct compositional modeling is most valuable where several origin markets hold material shares, although four destination regions are too few to establish this as a general rule. The methodology yields probabilistic forecasts of source market shares that can inform marketing allocation, concentration-risk monitoring, and forward-looking operational planning. Full article

The spatial mismatch between tourism resources and tourism vitality represents one of the key constraints on the high-quality development of regional tourism. Taking 77 county-level administrative units in Heilongjiang Province as the study area, this research utilizes panel data from 2019 to 2024 to evaluate tourism resource endowment and tourism vitality through the entropy-weighted TOPSIS method. A coupling coordination degree model, quadrant classification approach, and spatial autocorrelation analysis are employed to identify spatial mismatch patterns. Furthermore, a progressive analytical framework integrating XGBoost-based nonlinear modeling, SHAP interpretability analysis, and Geodetector-based spatial validation is constructed to identify the main explanatory factors and nonlinear associations related to these mismatch patterns. The results reveal that: (1) significant spatial mismatches exist between tourism resources and tourism vitality across counties in Heilongjiang Province. The overall coupling coordination degree remains at a low coordination level and exhibits a distinct core–periphery spatial structure; (2) four categories of mismatch units are identified, including high high matching, resource-leading mismatch, vitality-leading mismatch, and low low matching types. These categories display pronounced spatial clustering characteristics, with a sharp contrast between the high-value clusters in the Harbin metropolitan area and border regions and the low-value clusters in western Suihua; (3) the number of hotels shows the highest explanatory contribution in the model, showing a clear threshold-like pattern, while border ports demonstrate a sparse but important association pattern; (4) strong interaction relationships exist among explanatory variables, with nearly 30% of the model’s explanatory power originating from synergistic multi-factor interactions; and (5) Geodetector analysis further confirms the spatial explanatory power of the major explanatory factors and the significance of multivariate interaction effects. This study provides policy references for optimizing the allocation of tourism resources and enhancing tourism vitality in underdeveloped border regions. Full article