Search Results (396)

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

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

Rural communities face persistent transport disadvantages due to low population density, limited-service availability, and high operational costs, restricting access to essential services and exacerbating social inequality. Autonomous public transport systems offer a transformative solution by enabling flexible, cost-effective, and inclusive mobility options. This study investigates the operational determinants for autonomous road-based transit systems in rural and peri-urban South-East Queensland (SEQ), employing a structured survey of 273 residents and analytical approaches, including General Additive Model (GAM) and Extreme Gradient Boosting (XGBoost). The findings indicate that small shuttles suit flexible, non-routine trips, with leisure travelers showing the highest importance (Gain = 0.473) and university precincts demonstrating substantial influence (Gain = 0.253), both confirmed as significant predictors by GAM (EDF = 0.964 and EDF = 0.909, respectively). Minibus shuttles enhance first-mile and last-mile connectivity, driven primarily by leisure travelers (Gain = 0.275) and tourists (Gain = 0.199), with shopping trips identified as a significant non-linear predictor by GAM (EDF = 1.819). Standard-sized buses are optimal for high-capacity transport, particularly for school children (Gain = 0.427) and school trips (Gain = 0.148), with GAM confirming their significance (EDF = 1.963 and EDF = 0.834, respectively), demonstrating strong predictive accuracy. Hybrid models integrating autonomous and conventional buses are preferred over complete replacement, with autonomous taxis raising equity concerns for low-income individuals (Gain = 0.047, indicating limited positive influence). Integration with Mobility-as-a-Service platforms demonstrates strong, particularly for special events (Gain = 0.290) and leisure travelers (Gain = 0.252). These insights guide policymakers in designing autonomous road-based transit systems to improve rural connectivity and quality of life. Full article

The growth of tourism in coastal destinations has attracted academic attention due to the link between tourists’ motivations and their likelihood of recommending the destination. This study explores changes in tourist motivations, satisfaction, and recommendation behaviours in a coastal destination during the summers of 2019 (pre-COVID-19) and 2020 (during the pandemic). Employing quantitative analysis with Confirmatory Factor Analysis and Structural Equation Modelling, data from 394 pre-pandemic and 468 pandemic-period visitors were analysed. The findings reveal a shift in the tourist profile during the pandemic, with a predominance of younger visitors from nearby regions. Despite heightened uncertainty, satisfaction and the intention to recommend remained relatively high, albeit lower than pre-pandemic levels. The study underscores the importance of adapting marketing and management strategies to evolving tourist preferences, emphasising safety and sustainability in response to global crises. These results highlight the need for resilient policies to ensure positive visitor experiences and long-term growth in coastal tourism, contributing to the broader understanding of how external disruptions impact destination dynamics and tourist behaviour. Full article

This exploratory study investigates how youths aged 18–25 perceive and prioritize elements of the Integrated Digital Storytelling for Social Media (IDSM) framework in cultural heritage tourism contexts, addressing critical gaps between theoretical frameworks and contemporary social media engagement requirements. Through purposive sampling at cultural heritage tourism sites in Bangkok, Thailand, questionnaires were distributed to 100 participants to examine their preferences for cultural tourism video content and validate framework elements. Cultural authenticity emerged as the paramount consideration among participants, while traditional storytelling elements demonstrated sustained relevance when adapted for social media contexts. Youth participants preferred authentic mobile phone recordings over professional production, with optimal video durations and caption-dependent storytelling for mobile consumption. TikTok emerged as the primary motivational platform despite moderate usage frequency patterns. This exploratory study contributes preliminary empirical assessment of an integrated framework specifically designed for social media applications in cultural heritage tourism contexts. The findings provide evidence-based guidelines to help practitioners develop platform-optimized content strategies that effectively engage youth audiences while maintaining cultural authenticity. Full article

This paper explores the role of personalisation and predictive marketing in Croatian tourism and analyses how behavioural strategies can enhance the tourist experience and increase visitor loyalty. In today’s competitive tourism market, personalised marketing strategies have become a key factor in attracting and retaining tourists. Based on the theoretical framework and previous research, hypotheses were formulated on various aspects of behavioural marketing, such as the impact of personalisation on destination choice, the perception of marketing effectiveness by different demographic groups and the correlation between tourists’ satisfaction and their willingness to revisit a destination. The aim of this study was to test six hypotheses relating to the personalisation of marketing messages, demographic factors and their correlation with tourists’ willingness to revisit the destination. Using a quantitative methodology, a survey was conducted among 415 tourists who had visited the Croatian city of Vodice. A total of 257 questionnaires were completed. The data was analysed using descriptive statistics, correlation analysis and regression models to identify the most important factors influencing tourists’ behaviour and preferences. The results show that personalised marketing messages have a positive impact on tourists’ decision to visit the destination. In addition, this study shows that demographic factors such as gender, age and education significantly influence the perception of marketing effectiveness. Tourists who received predictive marketing messages expressed higher satisfaction with their stay and showed a greater willingness to visit the destination again than those who did not receive personalised offers. These results underline the importance of integrating behavioural strategies into marketing activities to build tourist loyalty and improve the overall tourism experience. Based on the research findings, further application of these marketing approaches is recommended to increase competitiveness and attract more loyal tourists. Full article

Food tourism is growing in travel experiences, with tourists increasingly drawn to culturally authentic and aesthetically engaging food. Understanding the psychological and experiential factors shaping these preferences is crucial for enhancing tourist visit intentions. Therefore, this study examines the factors such as attitudes (ATT), subjective norms (SN), perceived behavioural control (PBC), and food aesthetic experience (FAE) that influence tourists’ behavioural intentions towards food preferences (BIFP) in Goa, India. The research extends the theory of planned behaviour (TPB) by incorporating food aesthetic experiences as a mediating factor. A quantitative approach was employed, involving a structured questionnaire based on a five-point Likert scale, administered to 217 tourists visiting Goa, India, who were selected through a random sampling method. Data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) through SmartPLS 4.0. Results show that attitudes, subjective norms, perceived behavioural control, and food aesthetic experience significantly influence tourists’ behavioural intentions towards food preferences. Food aesthetic experience emerged as a strong mediator, particularly between attitudes, perceived behavioural control, and tourists’ behavioural intentions towards food preferences. These findings highlight the role of sensory appeal in shaping culinary choices. The study concludes that a holistic approach, supported by cultural education and stakeholder collaboration, can enrich tourist experiences and support food tourism development. Full article

Recommendation systems applied to tourism are widely recognized for improving the visitor’s experience in tourist destinations, thanks to their ability to personalize the trip. This paper presents a hybrid approach that combines Machine Learning techniques with the Ordered Weighted Averaging (OWA) aggregation operator to achieve greater accuracy in user segmentation and generate personalized recommendations. The data were collected through a questionnaire applied to tourists in the different points of interest of the Special, Tourist and Cultural District of Riohacha. In the first stage, the K-means algorithm defines the segmentation of tourists based on their socio-demographic data and travel preferences. The second stage uses the OWA operator with a disjunctive policy to assign the most relevant cluster given the input data. This hybrid approach provides a recommendation mechanism for tourist destinations and their cultural heritage. Full article

Background/Objectives: Although nutrition is widely recognized as a key factor in post-event recovery in sports, little attention has been given to how its cultural and social dimensions—embodied in local cuisine—intersect with the needs of traveling athletes, for whom food often also serves as a medium of cultural immersion and sensory exploration. Poland, with its rich regional culinary traditions and numerous international running events, offers a compelling context in which to explore these interactions. This study aims to understand the role of local cuisine in the experiences of foreign runners participating in the Poznan Half Marathon 2025, with particular attention on cultural engagement, tourist motivations, and post-exercise recovery processes. Methods: This study was based on a qualitative approach, utilizing semi-structured in-depth interviews conducted with 12 international runners from the United Kingdom, Germany, and Ukraine. The participants possessed a minimum of two years’ experience in traveling for sports. Results: The findings identified three main areas of the significance of food: (1) food as an element of cultural exploration, (2) local cuisine as a motivator or barrier when choosing a race, (3) food as a symbolic reward and structured recovery practice supporting nutritional and psychological processes. Approaches varied by nationality—British participants preferred spontaneous taste discovery, Ukrainians valued culinary comfort similar to home, and Germans planned their culinary experiences with greater awareness. Conclusions: Local cuisine plays a multifaceted role in international running events, serving not only nutritional needs but also emotional and cultural functions that shape the overall participant experience. Both event organizers and local restaurants should consider offering diverse and culturally sensitive food options to enhance recovery, satisfaction, and the appeal of sports tourism destinations. Full article

This study investigates the impact of cultural openness on intertemporal consumption behavior in the Middle East and North Africa (MENA) region, using panel data from 14 countries spanning 2010 to 2022. Unlike prior research that primarily focused on lifestyle shifts or product preferences, this study explores how cultural globalization influences the trade-off between present consumption and future savings, as captured by the consumption-to-savings ratio (LCESR). Cultural openness is operationalized using the Cultural Globalization General Index (LCGGI), and its effect is analyzed alongside key control variables including Internet penetration, real GDP per capita, inflation, and tourism. To address endogeneity and unobserved heterogeneity, this study employs the system Generalized Method of Moments (GMM) estimator, supported by robustness check models. The findings reveal a significant positive relationship between cultural openness and LCESR in both the short and long run, indicating that increased exposure to global cultural flows enhances consumption tendencies in the region. Internet penetration and inflation negatively affect saving behavior, while GDP per capita shows a positive effect. Tourist arrivals exhibit limited influence. This study also highlights the importance of historical consumption behavior, as the lagged dependent variable strongly predicts the current LCESR. Robustness checks confirm the consistency of the results across all models. These insights suggest that cultural openness, digital infrastructure, and macroeconomic stability are pivotal in shaping consumption/saving patterns. The results carry important implications for financial education, digital consumption governance, and cultural policy strategies in the MENA region and similar emerging markets undergoing rapid cultural integration. Full article

The COVID-19 pandemic severely impacted Japan’s inbound tourism, but recent recovery trends highlight the growing importance of Chinese tourists. Understanding their motivations is crucial for revitalizing the industry. Building on our previous framework, this study applies Transformer-based natural language processing (NLP) models and principal component analysis (PCA) to analyze large-scale user-generated content (UGC) and identify key motivational factors influencing Chinese tourists’ visits to Hokkaido. Traditional survey-based approaches to tourism motivation research often suffer from response biases and small sample sizes. In contrast, we leverage a pre-trained Transformer model, RoBERTa, to score motivational factors like self-expansion, excitement, and cultural observation. PCA is subsequently used to extract the most significant factors across different destinations. Findings indicate that Chinese tourists are primarily drawn to Hokkaido’s natural scenery and cultural experiences, and the differences in these factors by season. While the model effectively aligns with manual scoring, it shows limitations in capturing more abstract motivations such as excitement and self-expansion. This research advances tourism analytics by applying AI-driven methodologies, offering practical insights for destination marketing and management. Future work can extend this approach to other regions and cross-cultural contexts, further enhancing AI’s role in understanding evolving traveler preferences. Full article

The purpose of this study is to reveal the characteristics of wine tourists by determining their demographic and psychographic profiles, the effects of their travel motivations and lifestyles on their attitudes towards wine tourism, and the relationships between their demographic characteristics and attitudes. Based on a quantitative approach, study data were collected using a questionnaire. The sample consists of tourists who had visited a winery or participated in the vintage in a wine destination. Wine tourists’ principal motivations were pull factors and wine-related motivations. Their attitudes towards wine consumption had higher mean values than attitudes towards info-seeking, meaning that wine tourists preferred to taste wine and shop from wineries more than learn about wine. Regarding lifestyles, they were mostly innovators and experiencers. For destinations like Türkiye, wine tourism is a significant economic and sociocultural development tool. It is the first study to identify the wine tourists’ profile based on VALS-2, thereby providing a different perspective for the literature. Full article

Climate change poses a significant challenge to global tourism, influencing destination choices and traveller behaviour. This study examines the awareness of individuals regarding the impact of climate change on tourism destinations and their decision-making processes. Through a qualitative research design, semi-structured interviews were conducted and analysed in NVivo 15 to explore the extreme natural events, environmental concerns, and sustainability considerations that can shape travel preferences. Findings from the literature and 20 interviews indicate that climate change is increasingly perceived as a barrier to destination selection, with tourists prioritising safety, environmental resilience, and sustainability. Additionally, transportation choices reflect a balance between efficiency and ecological impact, with a growing inclination toward sustainable travel alternatives. This research highlights the need for policy frameworks that address climate-related risks in the tourism sector, with a focus on adaptation strategies and responsible travel practices. The study contributes to the discourse on climate change and tourism by highlighting behavioural trends and proposing measures for enhanced awareness and sustainable decision-making. Full article

Tourist behavior is evolving rapidly, and local restaurants must develop strategies to capture the attention of these changing tourists. This study, conducted in Thailand, investigates the relationships between perceived authenticity, consumer self-expansion, tourists’ preference for local restaurants, positive word-of-mouth (WOM), and intention to visit local eateries. It also examines whether self-expansion mediates the relationship between perceived authenticity and restaurant preference. Data were collected from 497 tourists through self-administered questionnaires distributed on-site and analyzed using structural equation modeling (SEM). The results show that perceived authenticity has a significant positive effect on consumer self-expansion (β = 0.902), which, in turn, strongly predicts preference for local restaurants (β = 0.870). Preference also significantly influences both positive WOM (β = 0.976) and visit intention (β = 0.949). Notably, perceived authenticity does not directly affect restaurant preference, indicating a partial mediation effect through self-expansion. These findings highlight the critical role of self-expansion in enhancing the influence of perceived authenticity on tourist behavior, offering practical insights for hospitality marketers aiming to promote local dining experiences. Full article

Tourist walkability is essential for sustainable tourism in traditional villages, where walking is often the primary mode of exploration. However, few studies have examined walkability from tourists’ perspectives, especially in village settings. This study investigates how immediate built environment perceptions, shareability (defined as the capacity of a place to encourage social media sharing), and personal attributes affect tourist walkability in traditional villages. A questionnaire survey was conducted in two traditional villages in Yongzhou, Hunan, to explore these relationships. The results reveal that the perceived quantity of traditional architecture strongly influences tourist walkability, while among built environment features, artificial features exert a greater overall impact than natural ones. Moreover, shareability plays a significant role in enhancing walkability, whereas personal attributes, though influential, have a relatively smaller effect. As the majority of survey participants were aged 18–24, these findings are particularly relevant to understanding the tourism preferences of Generation Z, a cohort with growing influence in the tourism market. These insights provide valuable guidance for designers, tourism developers, and authorities aiming to enhance walkability, promote sustainable tourism, and revitalise culturally rich traditional villages. Full article