Shared accommodation in historic districts enriches tourists' local cultural experiences and supports urban regeneration. However, how objective spatial conditions are perceived and associated with satisfaction remains insufficiently explained. This study examines historic-district shared accommodation through a two-stage design integrating large language models and fsQCA. Study 1 uses Tujia reviews, TopicGPT and aspect-based sentiment analysis to identify perceived quality dimensions and classify their Kano roles. Study 2 combines platform-structured and multi-source geospatial data to compare subjective evaluations and objectively observable conditions in high-satisfaction configurations. Results show two core patterns in both subjective and objective configurations: location–service synergy and landscape–service complementarity, alongside partial divergence related to experiential factors such as service interaction. Findings inform differentiated operation and sustainable regeneration of historic districts.
This study proposes a machine learning (ML) framework enhanced with an active learning (AL) algorithm to replicate the decision-making processes of experienced revenue managers and improve hotel price forecasting. Using real sales data from a five-star hotel in Malaysia, four ML models are integrated with AL to enhance pricing accuracy. Model performance is assessed through revenue validation and feasibility analysis. To interpret model predictions and identify key drivers of pricing decisions, this study employs Pearson correlation analysis and SHAP analysis. Results indicate that the AL-enhanced ML model effectively learns and simulates revenue managers’ pricing adjustment behaviors, reduces prediction bias, and increases revenue. This research advances AI-driven dynamic pricing in hotel revenue management and offers practical insights for academia and industry.
Wild animals around the world are profoundly impacted by tourism activities, much of which endangers their lives. This paper builds on growing scholarship demonstrating how wildlife sanctuaries can advance animal welfare and species conservation–– prerequisites for truly sustainable wildlife tourism. It assesses the extent to which Costa Rican sanctuaries generate positive impacts for native non-human primates. To do so, it applies a Conservation Welfare Assessment Framework as a mixed-method, field-based approach. Findings reveal that sanctuaries can improve primate welfare and conservation outcomes while navigating trade-offs at the nexus of welfare, conservation, and sustainability. Insights are offered to advance sustainable wildlife tourism scholarship and practice.
This note investigates the complex relationship between corruption and tourism demand, employing a dynamic panel threshold model in the spirit of Seo and Shin (2016). Using a panel dataset of international tourism destinations over the period 2008–2022 and two corruption measures reflecting actual control of corruption and perceived corruption in the public sector, we unravel a non-monotonic relationship between corruption and tourism demand. The relevant curvature (‘U-shaped’ or ‘Inverted U-shaped’) depends on the corruption indicator used in the analysis. Policymakers should account for the nonlinear effects to ensure that anti-corruption measures effectively boost tourism demand alongside necessary institutional reforms.
This article analyses representations of Ukraine and Ukrainians in four travelogues spanning 1994-2024: Applebaum's Between East and West (1994), Nicolay's The Humorless Ladies of Border Control (2016), Brumme's Im Schatten des Krieges (2022), and Orth's Couchsurfing in der Ukraine (2024). Combining imagological theory with reflexive thematic analysis, the study traces hetero-images of Ukrainian national identity across postSoviet, post-Maidan, and wartime periods and reframes Ukraine through liminality rather than the East-West binary. Recurring hetero-images include hospitality, resilience, and cultural distinction from Russia, alongside a diachronic shift from fragmented post-Soviet identity toward war-forged national consolidation. Tourism implications are integrated throughout: the travelogues' destination imaginaries are read alongside Ukraine's emerging memory-tourism infrastructure, including the State Agency for Tourism Development's Places of Memory route. The study contributes to comparative literature, imagology, and tourism scholarship on travel writing, with practical relevance to destination image management and responsible tourism in conflict-affected contexts.
While tourism demand forecasting has seen rapid methodological advances in academia, its uptake in practice remains limited. Drawing on semi-structured interviews with 15 tourism practitioners and a systematic review of 752 international studies, this research employs thematic analysis to compare practitioner-driven forecasting practices with academic paradigms. The study identifies an academic-practice gap: business pragmatism on the ground and methodological myopia in research. Practitioners prioritize forecasting approaches that are timely, cost-effective, and "good enough" to support adaptive operations. In contrast, academic models often pursue technical refinement at the expense of contextual relevance. This study contributes to a more grounded understanding of forecasting as a socio-technical system and advocates for research paradigms that are both methodologically robust and practically meaningful.
This study investigates how tourism employment affects life satisfaction - not only for workers themselves, but also for their partners - using European Social Survey data from 30 countries (2002-2022). Tourism workers report lower life satisfaction, which is largely attributable to socioeconomic factors. However, partners of tourism workers experience an even larger reduction in life satisfaction - a 'partner penalty' exceeding the estimated effect of divorce. This life satisfaction penalty for partners is concentrated among men and families with children, suggesting that gender norms and caregiving responsibilities play a central role. Notably, dualtourism couples do not exhibit this penalty, consistent with shared occupational understanding. Proxy indicators suggest time-based work-family conflict, including disrupted family time and reduced social interactions, as a plausible mechanism.
After decades of benign price increases, inflation has resurfaced as a concern for firms and investors in the tourism and hospitality sectors. Characterised by large capital investment needs, significant labour costs, and heavy demand for raw materials, both tourism and hospitality may be particularly susceptible to inflation. This paper examines links between shocks to inflation and the returns of tourism and hospitality-focused firms. Empirical findings indicate a transitory relationship between inflation shocks and tourism and hospitality returns, focused predominantly at short horizons and which, when present, tends to be negative. The relationship weakens once the market influence is removed from the analysed stocks, indicating a limited specific impact of inflation on the sector. Some distinctions emerge in findings for the analysed subsectors, with inflationary shocks having a more durable influence on casinos and gambling stock returns.
Small island tourism destinations face a paradox: prosperity depends on the natural assets that tourism threatens. Current compensation policies fail by treating tourists identically through single-stage frameworks that conflate participation barriers with contribution intensity. Using stated preference data from 70,930 Canary Islands tourists, hurdle models with bootstrap validation separate participation (Stage 1: complementary log-log) from contribution intensity (Stage 2: OLS) across four compensation levels (<5% to >20% of trip expenditure). Education increases participation yet reduces contribution intensity (€ − 0.23 to €-6.63); near-zero residual correlation (ρ = 0.0004, p = 0.955) confirms distinct mechanisms govern each stage. Progressive taxation aligned with spending capacity generates €770.4 M versus €267.9 M from flat rates—a 188% revenue increase—transforming compensation into a self-financing mechanism requiring expenditure proxies and machine learning for full implementation.
The tourism business is increasingly encountering disruptions due to external shocks, fluctuating demands, and changing tourist sentiments. Whereas conventional risk prediction models are mostly static and less responsive. The study aims to design a real-time decision support tool through a novel CRISP-LSTM11Cascading Risk-Integrated Strategic Planning using Long Short-Term Memory model, integrating hierarchical LSTM structures with attention-driven multimodal fusion of tourism statistics, online reviews, and GDELT22Global Database of Events, Language, and Tone events. The results reveal that CRISP-LSTM performs better than conventional models, with better precision, recall, F1-score, and AUC (0.86, 0.90, 0.88, and 0.92, respectively). Simulations on high-risk scenarios indicates reduced economic loss (40%) and recovery time (six months). The real-time CRISP-LSTM significantly enhances strategic results with extreme disruption scenarios. Thus, the study offers a practical and novel approach for tourism enterprise resilience.
Tourism research often struggles to demonstrate its tangible influence on governance. This commentary presents a case in Valais, Switzerland, where sustained collaboration between academic institutions, public authorities, and industry translated scientific insights into tourism policy and practice. Several studies conducted in the region, including research on summer glacier skiing, contributed to climate adaptation, risk management, and destination resilience. Academic teams acted as trusted intermediaries by connecting research and decision-making through workshops, media engagement, and advisory roles. This long-term cooperative model supported knowledge transfer, informed cantonal climate strategies, guided tourism diversification, and stimulated public debate, illustrating how research can foster innovative governance and resilience in mountain regions.
This study applies a hierarchical pattern analysis to examine how tourists' carbon offset willingness intersects with behavioural profiles. Using FP-Growth on data from nearly 80,000 visitors to the Canary Islands (2022-2023), it shows that carbon offsetting is shaped not only by environmental values but also by cognitive engagement, planning autonomy, and dependence on mass-tourism infrastructures. The analysis identifies four tourist profiles, from non-compensators with routinized behaviours to highly proactive tourists integrating sustainability into their consumption. The study proposes targeted policies such as opt-out defaults, symbolic eco-incentives, loyalty schemes, and participatory governance. Findings highlight the need for multi-layered sustainability strategies that move beyond binary classifications of environmental commitment.
Tourism development in protected areas often generates public debate, yet the social drivers of community acceptance remain underexplored. Using survey data from 1000 Australians and a latent-class choice model, this study examines how trust in institutions, perceptions of distributional fairness, and emotional responses shape support for tourism development. Trust, driven by confidence in governance and procedural fairness, along with perceptions and emotions about distributional fairness, significantly influenced public support. Latent class analysis revealed distinct segments that differ in preferences regarding ownership models and management approaches. Findings highlight that public support is context-dependent and driven by the nature of the development. Transparent, inclusive governance and equitable benefit distribution can strengthen social licence, reduce conflict, and promote sustainable tourism in protected areas.
This study makes a pioneering empirical contribution to tourism research by exploring the complex emotion of awe within virtual reality (VR) tourism experiences. Drawing on twenty semi-structured interviews and thematic analysis, it identifies awe as a multidimensional construct encompassing perceptions (altered time and space, hyperrealism), feelings (self-diminishment, connectedness), and emotions (confusion, fear, surprise). These dimensions appear to influence tourists' behavioural intentions, including destination visit intentions and technology adoption. Moving beyond dominant quantitative paradigms, this research offers a qualitative conceptualisation of awe in VR tourism. The findings provide empirical insights into how awe manifests in VR tourism and suggest practical implications for destination marketers, VR developers, and tourism strategists, advancing understanding of immersive emotional engagement and psychological responses to emerging technologies.
Digital cultural tourism is rapidly expanding, yet relevant research on tourist satisfaction predominantly relies on questionnaire surveys or unimodal textual analysis, failing to capture the comprehensive and multidimensional evidence embedded in multimodal User-Generated Content. To effectively leverage multimodal data, this study proposes a multimodal analytical framework for tourist satisfaction applied to four digital cultural tourism projects in Changsha. By integrating topic modeling, sentiment analysis, image recognition, and interpretable machine learning techniques, the analysis reveals the complex, asymmetric effects of textual drivers and the distinct impacts of visual features on satisfaction. This study deepens the understanding of mechanisms of tourist satisfaction in digital tourism contexts and provides data-driven insights for destination managers.
The study examines how cultural tourism development interacts with new-type urbanization in China through panel data of 31 provinces (2012-2022), an improved entropy method, a coupling coordination model, and an obstacle analysis framework. Results show a steady rise in national coordination, with a brief decline in 2020 due to the COVID-19 pandemic, followed by rapid recovery. Spacially, it follows a gradient of eastern > central > western > northeast, and the western is now above the northeast. All regions show fluctuating upwards trends and narrowing of regional disparities and the system resilience is also increased. Key constraints to further improvement include the optimization of cultural tourism industry structure, the efficiency of cultural resource utilization, and the level of economic urbanization.
This study examines how outdoor recreation and nature-based tourism businesses in rural Sweden navigated the COVID-19 pandemic, with a particular focus on the role of food in fostering resilience. Survey and interview data were combined to analyze how providers responded to crisis conditions and developed long-term strategies. Using the Resource-Based View and Dynamic Capabilities frameworks, findings show that businesses leveraged place-based assets to attract domestic visitors, redesign offerings for smaller groups, and integrate food and nature as educational tools. These adaptations were not only temporary fixes but became enduring shifts that strengthened social cohesion, reinforced local food systems, and contributed to rural development. Rather than “bouncing back,” businesses exemplified “bouncing forward,” transforming crisis into opportunities for innovation and community benefit.
This study investigates spatial interactions in tourism across 11 Silk Road countries (2002-2019) by combining a nested Constant Elasticity of Substitution utility function with Bayesian Spatial Durbin Models. It compares three spatial structures: inverse-distance, cultural proximity, and trade-intensity matrices. The results reveal competition and destination substitution among geographically and culturally proximate countries, while trade-based linkages play a relatively limited role. An increase in tourism arrivals in one destination tends to reduce tourism flows to neighboring destinations. At the same time, improvements in economic fundamentals-GDP and rule of law-in surrounding countries lead to an increase in inbound tourism to the country under consideration. This indicates that although destination choice is competitive, regional institutional improvements can yield mutual benefits.
Tourism destinations, particularly those characterized by mass tourism, face ongoing challenges in promoting public transport as a key component of destination sustainability and competitiveness. Public transport reduces dependence on private vehicles, enhances visitor mobility, and broadens the array of accessible attractions. This study, based on a survey conducted at Costa Daurada (n = 1954), a Mediterranean coastal destination in South Catalonia, examines the determinants influencing visitors' choices between private vehicles and public transport for excursions. The multivariate probit model reveals a persistent preference for private vehicles, highlighting structural barriers to public transport adoption. The findings contribute to understanding how behavioural shifts and contextual factors impact tourist mobility choices and offer insights for promoting sustainable transportation.
