Search Results (135)

Southern Europe is a climate change hotspot, with rising temperatures threatening the region’s tourism industry. While existing research has focused on supply-side adaptations and macro-level demand shifts, this study examines the demand side—tourist experience and adaptive behaviors at the micro-experiential level, with implications for broader discussions of resilience and adaptive capacity. The study analyzed 6466 TripAdvisor reviews from 11 open-air heritage sites in Italy, Greece, Spain, and Malta, linking reviews to site-relative thermal exposure tertiles and applying dictionary-based text matching with mixed-effects models. Under hotter conditions, tourists were significantly more likely to mention heat, thermal discomfort, and coping resources such as shade and drinking water, with co-occurrence patterns indicating that discomfort and relief-oriented infrastructure are narrated together. Yet overall satisfaction ratings remained uniformly high and statistically unaffected by temperature, revealing a paradox wherein experiential strain intensifies while evaluative scores stay stable. These findings suggest patterns consistent with behavioral and cognitive adaptive responses, whereby positive evaluations are maintained despite heightened thermal stress, and indicate that narrative-based indicators may capture experiential shifts that conventional satisfaction metrics miss. Full article

Over the past 20 years, trends in the construction or renovation of football stadiums have undergone rapid transformation. Simple sports venues are constantly evolving into multifunctional facilities and play a decisive role in shaping cities’ image. To date, significant emphasis has been placed on developing stadiums as venues for sporting events, with a focus on supply-side perspectives, particularly in relation to design, marketing, and sustainability. However, we know relatively little about how the direct consumers of this product, the visitors to these facilities, experience and perceive these infrastructures, especially outside of match days. This paper follows a framework for researching this perspective, focusing on the services provided as key points of interest in stadium tourism, and on the written reactions on social networks. This framework is implemented by employing a set of well-known single-word themes, matching each review to these themes, and finally measuring the sentiment of the collected short texts as an implicit indicator of sentiment on the studied themes. Its realization is based on natural language processing, semantic similarity analysis, and sentiment evaluation to identify dominant themes, recurring lexical patterns, and emotional tones in visitor comments. The study concerns thirteen major European stadiums and reviews posted on Google and TripAdvisor. The research findings highlight the themes that shape a unique tourist experience, capturing tourist interests in stadium tourism in the post-COVID-19 era. Finally, the individual evaluation of the themes provides practical and clear tools for stadium managers, tourism operators, destination managers and legislators who seek to maximize visitor engagement and multiply the overall socio-economic value of these iconic infrastructures for the benefit of the wider urban environment that hosts them. Full article

Digital cultural heritage platforms face a dual challenge: preserving rich historical information while engaging an audience with declining attention spans. This paper addresses that challenge by proposing a modular information architecture designed to mitigate cognitive overload in cultural heritage tourism applications. We begin by examining evidence of diminishing sustained attention in digital user experience and its specific ramifications for cultural heritage sites, where dense content can overwhelm users. Grounded in cognitive load theory and principles of user-centered design, we outline a theoretical framework linking mental models, findability, and modular information architecture. We then present a user-centric modeling methodology that elicits visitor mental models and tasks (via card sorting, contextual inquiry, etc.), informing the specification of content components and semantic metadata (leveraging standards like Dublin Core and CIDOC-CRM). A visual framework is introduced that maps user tasks to content components, clusters these into UI components with progressive disclosure, and adapts them into screen instances suited to context, illustrated through a step-by-step walkthrough. Using this framework, we comparatively evaluate personalization and information structuring strategies in three platforms—TripAdvisor, Google Arts and Culture, and Airbnb Experiences—against criteria of cognitive load mitigation and user engagement. We also discuss how this modular architecture provides a structural foundation for human-centered, explainable AI–driven personalization and recommender services in cultural heritage contexts. The analysis reveals gaps in current designs (e.g., overwhelming content or passive user roles) and highlights best practices (such as tailored recommendations and progressive reveal of details). We conclude with implications for designing cultural heritage experiences that are cognitively accessible yet richly informative, summarizing contributions and suggesting future research in cultural UX, component-based design, and adaptive content delivery. Full article

With the rise in experience economy and the popularization of digital technology, user-generated content (UGC) has become a core data source for understanding tourist needs and evaluating the service quality of venues. As a landmark venue that combines science education, interactive experience, and landscape viewing, the service quality of the Macau Science Center directly affects tourists’ travel experience and word-of-mouth dissemination. However, existing studies mostly rely on traditional questionnaire surveys and lack multi-technology collaborative analysis. In order to accurately identify the factors affecting satisfaction, this study uses 788 valid UGC data from five major platforms, namely Google Maps reviews, TripAdvisor, Sina Weibo, Xiaohongshu (Rednote), and Ctrip, from January 2023 to November 2025. It integrates word frequency analysis, semantic network analysis, latent Dirichlet allocation (LDA) topic modeling, and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment computing to construct a systematic research framework. The study found that (1) the core attention dimensions of users cover the needs of parent–child and family visits, exhibitions and interactive experiences, ticketing and consumption services, surrounding environment and landscape, emotional evaluation, and recommendation intention. (2) The keyword association network has gradually developed from a loose network in the early stage to a comprehensive experience-dense network. (3) LDA analysis identified five main potential demand themes: comprehensive visiting experience and scenario integration, parent–child interaction and characteristic scenario experience, core venue facilities and ticketing services, visiting value and emotional evaluation, and transportation and surrounding landscapes. (4) User emotions were predominantly positive, accounting for 82.7%, while negative emotions were concentrated in local service details, and the emotional scores showed a fluctuating upward trend. This study provides targeted suggestions for the service optimization of the Macau Science Center and also provides a methodological reference for UGC-driven research in similar cultural venues. Full article

The tourism industry is currently experiencing a substantial digital transformation, with Online Travel Agencies (OTAs) committed to integrating innovations such as artificial intelligence (AI) in order to enhance service delivery and personalize user experiences. This study investigates the relationship between the utilization of AI and the technical quality scores of tourism websites, aiming to identify significant associations and variances in the critical conversion phase. An exploratory research design is employed to evaluate the technical quality of three prominent international tourism websites, i.e., Booking.com, Airbnb.com, and Tripadvisor.com. The investigation uses Google Lighthouse, with Performance, Accessibility, Best Practices, and Search Engine Optimization (SEO) as variables analyzed across both desktop and mobile versions, as well as on pages with and without AI functionality. Data analysis is performed using JASP (version 0.19.3), including linear regression analysis to quantify the predictive relationship. The analysis confirms that the Performance variable is the most sensitive to the influence of AI. AI integration demonstrates a significant positive influence on the Performance score of travel websites. The regression model indicates that AI usage explains 78.9% of the variation in the Performance score (${\mathrm{R}}^2$ = 0.789, p < 0.001), indicating a substantial correlation with technical sustainability. Nevertheless, there remains an ongoing necessity for optimization, particularly with regard to the enhancement of overall performance and improvement of scores for mobile devices. The study acknowledges certain limitations related to the sample size of AI applications and the accessibility of specific AI versions in particular geographic regions. Full article

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts. Full article

The COVID-19 pandemic reshaped travel patterns and customer expectations, generating profound challenges for the hotel industry. This study analyzes 50,000 TripAdvisor reviews of New York hotels to examine how customer satisfaction with hotel selection attributes shifted before and during the pandemic. BERTopic was applied to extract eight key attributes, while VADER, PRCA, and Asymmetric Impact–Performance Analysis (AIPA) were used to capture asymmetric effects and prioritize improvements. Comparative analyses by hotel classification, travel type, and customer residence reveal significant shifts in food and beverage, location, and staff, particularly among lower-tier hotels, business travelers, and international guests. The novelty of this study lies in integrating BERTopic and AIPA to overcome survey-based limitations and provide a robust, data-driven view of COVID-19’s impact on hotel satisfaction. Theoretically, it advances asymmetric satisfaction research by linking text-derived attributes with AIPA. Practically, it offers actionable guidance for hotel managers to strengthen hygiene, expand contactless services, and reallocate resources effectively in preparation for future crises. In addition, this study contributes to sustainability by showing how data-driven analysis can enhance service resilience and support the long-term socio-economic viability of the hotel industry under global crises. Full article

Forest tourism visitation in U.S. national forests has grown by approximately 8 percent over the past decade (from 2014 to 2022) from 147 million to 158.7 million visits per year, indicating a clear upward trajectory in demand for nature-based leisure experiences, yet the determinants of traveler satisfaction in this context remain insufficiently understood. Existing studies have primarily emphasized destination attributes, overlooking the interplay between psychological motivations, lifestyle orientations, and physical conditions. This omission is critical because it limits a holistic understanding of forest traveler’s experiences, which prevents us from fully capturing how internal dispositions, everyday life contexts, and well-being concerns interact with destination attributes to shape satisfaction. Therefore, the purpose of this study is to explore how motivation, lifestyle, and physical condition jointly shape satisfaction in forest tourism, drawing on Push–Pull Theory and environmental psychology. A dataset of 10,792 TripAdvisor reviews of U.S. national forests was analyzed using LIWC 2022 for psycholinguistic feature extraction and Ordered Logit Regression for hypothesis testing. Results show that positive emotional tone, leisure-oriented language, health references, and reward motivation significantly enhance satisfaction, while negative tone, illness, and work-related language reduce it. Curiosity and risk motivations were non-significant, and allure exerted only a marginal effect. These findings extend the Push–Pull framework by incorporating lifestyle and physical condition as moderating variables and validate emotional tone in user-generated content as a proxy for subjective evaluations. The study refines motivation theory by revealing context-specific effects of motivational dimensions. The results offer actionable insights for destination managers, service providers, marketers, and policymakers aiming to enhance forest travel experiences and promote sustainable tourism development. Full article

In this article, we study a directed version of Douglas–Rachford splitting method in real Hilbert spaces. By using new, self-contained, and simplified techniques, we prove its weak convergence. The major innovation is that we exploit the firm non-expansiveness of the Douglas–Rachford operator for the first time to derive the best possible upper bounds on direction factors, assuming that the involved factors remain constant. We give a new rare feature selection model equipped with the TripAdvisor hotel-review dataset. Numerical results confirm the user-friendliness and efficiency of directed Douglas–Rachford splitting in solving this model. Full article

This study examines how hotel characteristics and urban spatial context influence tourist satisfaction in Seoul, South Korea, by integrating sentiment analysis of online reviews with regression modeling. Drawing on 4500 TripAdvisor reviews from 75 hotels, sentiment scores were extracted using aspect-based sentiment analysis, and two regression approaches, ordinary least squares (OLS) and spatial autoregressive combined models, were applied to evaluate how hotel specific features, such as the age and scale of the hotels and room rates, and their geographic characteristics, such as the proximity to airports and cultural landmarks, affect both emotional sentiment and formal hotel ratings. The OLS model for sentiment scores identified the scale and rating of the hotels as well as the proximity to the airports as key predictors. Additionally, the spatial autoregressive combined model was also statistically significant, suggesting spatial spillover effects. A separate model for the traditional rating revealed weaker associations, with only the hotel’s opening year reaching significance. These findings highlight a divergence between emotional responses and structured ratings, with sentiment scores more sensitive to spatial context. This study offers practical implications for hotel managers and urban planners, emphasizing the value of incorporating spatial factors into hospitality research to better understand the tourist experience. Full article

This study aims to investigate how tourists articulate their ecotourism experiences through online reviews of coastal Karnataka. As one of India’s most favored domestic destinations since the 1960s, coastal Karnataka is renowned for its scenic shoreline, unique biodiversity, and diverse ecotourism experiences along a 320 km stretch of the Arabian-Sea rim spanning across the Uttara Kannada, Udupi, and Dakshina Kannada districts. Despite the growing popularity of ecotourism and sustainable travel, limited research has examined how tourists share ecotourism concepts via online reviews. Employing content analysis through the Leximancer tool, this study examines 1843 online reviews from 80 eco-resorts and homestays listed on TripAdvisor^® to identify emerging themes from interconnected concepts and assess the extent to which ecotourism values are reflected. The findings highlight several themes out of which “place”, “food”, “resort”, and “rooms” were the dominant ones. Notably, this study also revealed minimal references to coastal conservation or sustainability. The analysis offers several theoretical contributions to the ecotourism literature by demonstrating the implication of construal level theory (CLT) and the use of Leximancer in analyzing large-scale travel data. This study also provides meaningful insights for the tourism service providers to use online review data to enrich tourist experiences and outlines actionable strategies for strengthening the visibility of coastal conservation efforts and promote ecotourism advocacy. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article

Online reviews increasingly shape experiential travel decisions. This study investigates how structural and linguistic features of user-generated content influence peer endorsement in wine tourism. While prior research has explored review valence and credibility, limited attention has been paid to how micro-level textual and identity cues affect social approval metrics such as likes. Grounded in the Elaboration Likelihood Model, the analysis draws on 7942 TripAdvisor reviews using automated web scraping, readability metrics, and multivariate regression. Results indicate that location disclosure significantly increases likes, while higher textual complexity reduces endorsement. Title length and reviewer contributions function as peripheral cues, with an interaction between complexity and title length compounding cognitive effort. Findings refine dual-process persuasion theory and offer practical insights for content optimization in post-pandemic tourism engagement. Full article

Templestay programs in South Korea represent a unique convergence of Buddhist ritual, cultural immersion, and wellness tourism. While often treated as niche cultural experiences, their broader significance within sustainable wellness tourism remains underexplored. This study examines participant reflections from the Beomeosa Templestay program through thematic analysis of over 600 reviews sourced from TripAdvisor, Google Reviews, and handwritten guestbooks. Using a triangulated framework combining Grounded Theory, Symbolic Interactionism, and the Wellness Tourism Model, the research identifies four recurring experiential themes: spiritual development, emotional healing, cultural immersion, and conscious consumption. Findings reveal cross-cultural variations: non-Korean participants emphasized spiritual exploration and cultural learning, while Korean participants prioritized emotional renewal and reconnection with heritage. Yet, across all groups, participants reported transformative outcomes, including heightened clarity, inner calm, and enhanced self-awareness. These results suggest that Templestays serve as accessible, culturally grounded wellness retreats that align with rising global demand for intentional, mindful travel. This study contributes to sustainable tourism scholarship by framing Templestays as low-impact, spiritually resonant alternatives to commercialized wellness retreats. Practical recommendations are offered to expand participation while maintaining program authenticity and safeguarding the spiritual and cultural integrity of monastic hosts in an increasingly globalized wellness landscape. Full article

The subject of this article is the centuries-old religious heritage of Vilnius. The aim of the article is to analyse this heritage and its reflection in the gaze of tourists. In particular, it focuses on selected Catholic, Orthodox, Protestant, Jewish, and Karaite sites. The methods used in the empirical study include the analysis of reviews posted on the Tripadvisor website by tourists from different countries who visited five selected sites: (1) St. Anne’s Church, (2) Holy Spirit Orthodox Church, (3) Evangelical Lutheran Church, (4) Vilnius Choral Synagogue, and (5) Kenesa. The authors employed the method of desk research, which involves the analysis of existing data. The selection of objects was made by indicating the most commented sites of a given religious tradition for which the most comments were received. In the light of the pervasive influence of social media, it is noteworthy to observe the contemporary representation of multi-religious Vilnius that is disseminated through this medium. Urban sacred spaces are not only places of worship of interest to religious people, including local and foreign pilgrims. Furthermore, they constitute an attractive urban heritage for a significant number of cultural tourists. Committed tourists, including cultural tourists, meticulously document their impressions in various forms of narrative, offering either endorsement or criticism of a particular object. In this manner, they also interpret elements of the heritage in the local urban space. Full article