Search Results (171)

With the arrival of an aging society and the continuous extension of the human lifespan, the quality of life has not improved in a corresponding manner. People’s demand for happiness and health is increasing. As a result, a model emerged that integrates tourism and medical services, which is health tourism. This growing demand has prompted many service providers to see it as a business opportunity and enter the market. Tourism can help travelers release work stress and restore physical and mental balance; meanwhile, health check-ups and disease treatment can help them regain health. Consumers have long favored health and medical tourism because it helps relieve stress and promotes overall well-being. As people age, some consumers experience a gradual decline in physical functions, making it difficult for them to participate in regular travel services provided by traditional travel agencies. Therefore, this study aims to explore the service needs of health and medical tourism customers (tourists/patients) and the interrelationships among these service needs, so that health and medical tourism service providers can develop more customized and diversified services. This study identifies four key drivers of medical tourism services: medical services, medical facilities, tour planning, and hospitality facilities. This study uses the APA (attention and performance analysis) method to assess each dimension and criterion and utilizes the DEMATEL method with the NRM (network relationship map) to identify network relationships. By combining APA and NRM techniques, this study develops the APA-NRM technique to evaluate adoption strategies and identify suitable paths for health tourism services, providing tailored development strategies and recommendations for service providers to enhance the service experience. 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

This study explores residents’ perceptions of tourism development with a particular emphasis on the economic dimension of sustainability, focusing on how economic benefits, costs, and related factors shape local support in Serbia, Kazakhstan, and Hungary. By analyzing perceived advantages and disadvantages, the study aims to assess the extent of local support for tourism and the moderating effects of travel frequency and contact with tourists. In parallel, tourist arrival forecasts for 2025–2030 provide context on the anticipated dynamics of tourism growth, with Hungary showing the highest projected increase. Using advanced statistical techniques, including Multi-Group Analysis (MGA), structural equation modeling (SEM), and machine learning methods, key factors driving tourism support were identified. Positive perceptions of economic benefits and cultural identification significantly enhance support for tourism, while perceived costs act as inhibitors. The application of Random Forest and XGBoost (version 1.7.x) models improved predictive accuracy, while K-means clustering and principal component analysis (PCA) clarified relationships among constructs. The findings provide actionable insights for developing sustainable tourism strategies that prioritize economic outcomes and community engagement, particularly in culturally and economically diverse settings. Full article

This study explores how rural tourism destinations in the Eastern Carpathians of Romania have recovered in the aftermath of the COVID-19 pandemic. Using data from 2016–2019 and 2021–2023, five core indicators—tourist arrivals, overnight stays, accommodation capacity, occupancy rates, and active units—were analyzed at the local level. Based on these indicators, a cluster analysis was conducted for us to identify groups of communes with similar tourism performance profiles. After clustering, composite indicators were calculated to track how each group evolved over time. The findings show that recovery has not been uniform: while some destinations bounced back or even improved, others continue to face structural challenges. These results suggest that local infrastructure, destination type, and governance capacity all play a role in shaping recovery paths. The paper offers a spatial overview of rural tourism dynamics and highlights the value of using data-driven tools for understanding uneven development in post-crisis contexts. Full article

Tourism is a core sector of Singapore’s economy, contributing significantly to Gross Domestic Product (GDP) and employment. Accurate tourism demand forecasting is essential for strategic planning, resource allocation, and economic stability, particularly in the post-COVID-19 era. This study develops a SARIMAX-based forecasting model to predict monthly visitor arrivals to Singapore, integrating web search data from Google Trends and external factors. To enhance model accuracy, a systematic selection process was applied to identify the effective subset of external variables. Results of the empirical experiments demonstrate that the proposed SARIMAX model outperforms traditional univariate models, including SARIMA, Holt–Winters, and Prophet, as well as machine learning-based approaches such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs). When forecasting the 24-month period of 2023 and 2024, the proposed model achieves the lowest Mean Absolute Percentage Error (MAPE) of 7.32%. Full article

In recent years, the importance of developing the tourism sector and diversifying income sources has grown in the Gulf Cooperation Council (GCC) countries. This paper estimates the impact of tourism industry development on income equality in the GCC region from the first quarter of 2014 to the fourth quarter of 2023. Furthermore, this paper evaluates the existence of the Kuznets curve and its implications for income distribution. To achieve these objectives, this study employs panel cointegration tests and the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model. The dataset combines quarterly data from the World Bank and national statistical agencies, including indicators such as tourism revenue, international arrivals, government effectiveness, and education expenditure (used as a proxy for income equality). The results indicate that tourism revenue (TOU) has a significant and positive long-run effect on income equality (0.14%). In the short run, the squared term of tourism revenue (TOU2) becomes significant and positive (0.01%), but the findings do not support the Kuznets curve hypothesis. Furthermore, the number of international travelers (TRAV) has a negative and significant effect in the long run, while government effectiveness (GE) is negative and significant in both the long and short run. A key limitation of the study lies in the use of education expenditure as a proxy for income equality, due to the unavailability of consistent inequality metrics across the GCC countries. Full article

Angling tourism is becoming increasingly important at Central Europe’s largest lowland reservoir, Lake Tisza. The lake, created in the 1970s, covers 127 km² and has been increasingly used for recreational and nature conservation purposes recently. This study seeks to identify anglers’ site selection preferences at Lake Tisza, considering hydrological and ecological aspects, in support of sustainable site management. In order to achieve this, an in-person questionnaire survey was carried out covering the whole area during springtime 2024. During the survey period, a total of 224 anglers provided answers about their preferred location and recreational characteristics. Data processing was carried out using SPSS 26 and ESRI ArcGIS version 10.4.1. Based on the created catchment area map, it was found that a significant proportion (74%) of anglers arrive from within a 50 km radius, but the lake also has appeal outside the country. A total of 53.1% of respondents visit the lake several times per month, typically for fishing purposes. In addition, cycling, walking and picnicking are popular recreational activities among anglers. The respondents considering different landscapes (pictures showed by the interviewers) for angling prefer shaded areas with vegetation and a narrow view of the wide expanse of water (62%). The results of the open-ended questions indicate that site selection is primarily based on the existence of a shadow (49.5%), and suitability for fishing is only the second aspect (40.6%). Our study also highlighted the international trend that anglers are more interested in leisure activities in a green environment. In addition, the results have practical significance for more successful recreation planning and sustainable site management. Full article

This study examines the relationship between carbon dioxide (CO₂) emissions and gross domestic product (GDP) in the tourism industry, using annual time series data from 1955 to 2019 for Greece and 1930 to 2019 for Italy. The Autoregressive Distributed Lags (ARDL) model was used to determine the threshold point at which the tourism-induced Environmental Kuznets Curve (EKC) emerges in each stage of tourism, as indicated by the Tourism Area Life Cycle (TALC) model. The existence and direction of causality between CO₂ emissions, GDP, and tourist arrivals was determined using the Toda–Yamamoto Granger causality test. The analysis emphasizes the complex relationship between economic growth, environmental degradation, and the tourism industry. The EKC hypothesis is supported by empirical findings that show a strong long-term cointegration between tourist arrivals, GDP, and CO₂ emissions. This suggests that, while tourism growth initially contributes to environmental degradation, it also fosters conditions for sustainable development as economies progress, providing critical insights for policy-makers seeking to promote environmentally sustainable tourism. Full article

The impact of tourism on economic growth is a subject of interest to researchers as well as policy makers. Numerous studies have explored this relationship, often arriving at varying conclusions depending on the methods employed. Most of these studies, however, assume a symmetric relationship between tourism and economic growth. In this study, the Hatemi-J asymmetric causality test was used to test for short-run asymmetric causality between tourism receipts and economic growth in Turkey for the period 1990–2023, and unidirectional causality was found between the increase in tourism incomes and economic growth and between the decrease in tourism incomes and economic contraction. Additionally, the hidden co-integration test was applied to examine the asymmetric relationships between them in the long run, and the results reveal that an increase in tourism revenues provides resilience to the economy by mitigating contraction during economic downturns. This study contributes to the field by addressing the interaction of tourism and the economy in Turkey from an asymmetric perspective and by revealing previously unobserved relationships. The results provide partial support for the tourism-led growth hypothesis. In the long term, it is recommended that policymakers design tourism strategies aimed at enhancing resilience to economic shocks, thereby also strengthening the national economy. Diversified markets and products, well-structured incentives, and sustainable tourism practices are key elements in achieving this goal. Full article

This study investigates the environmental and socioeconomic dimensions of domestic cycling tourism in Slovenia, focusing on “double pollution” and “greenhushing” practices. The aim is to evaluate the sustainability of cycling tourism by examining its indirect environmental impacts, particularly emissions from ancillary travel behaviours such as car usage to reach cycling destinations. Utilizing data from 2011 to 2021, this research employs factor analyses using the principal component analysis (PCA) extraction method and vector autoregression (VAR) modelling to explore relationships between key socioeconomic, environmental, and tourism-related variables. This study identifies three common factors influencing cycling tourism: (1) socioeconomic and urban dynamics, (2) tourism-driven environmental factors, and (3) climatic sustainability challenges. Results highlight that cycling tourism contributes to emissions due to associated car travel, counteracting its eco-friendly image. Findings reveal that favourable economic conditions and urbanisation drive tourism demand, while increased tourist arrivals correlate with higher emissions. This study also uncovers greenhushing, where stakeholders underreport the environmental costs of cycling tourism, leading to mistaken perceptions of its sustainability. This study concludes that, while domestic cycling tourism supports economic growth and health, its environmental benefits are compromised by ancillary emissions. Transparent environmental reporting, enhanced public transport, and local bike rental systems are recommended to mitigate these challenges and align cycling tourism with Slovenia’s sustainability goals. Full article

Tourism still represents a means of generating revenues in the coastal areas in the Mexican Caribbean, despite the growing concern about the social and environmental impacts. The Nichupte Lagoon System (NLS), the most representative lagoon of Quintana Roo State for being in the middle of Cancun’s hotel development, has experienced a continuous drop-off in its water quality due to several factors, including dredging and wastewater discharges from different anthropogenic activities, which modify the flux of nutrients, increase the number of pathogenic microorganisms, and promote physicochemical changes in this ecosystem. Three sampling campaigns (2018, 2019, and 2020) were carried out in the NLS in August, which is the month of greatest tourist occupancy. To evidence the presence of anthropogenic wastewater in the NLS, the caffeine tracer was used, and to determine the water quality, 43 sampling stations were monitored for “in situ” physicochemical parameters (salinity and dissolved oxygen), and water samples were collected for the quantification of nutrients (NO₂⁻ + NO₃⁻, NH₄⁺, SRP and SRSi) and chlorophyll-a (Chl-a). For data analysis, the lagoon was subdivided into five zones (ZI, ZII, ZIII, ZIV, and ZV). Caffeine spatial and time variation evidence (1) the presence of anthropogenic wastewater in all areas of the NLS probably resulting from the tourist activity, and (2) wastewater presence is directly influenced by the coupling of the hydrological changes driven by anomalous rain events and the number of tourists. This same tendency was observed for nutrients that increased from 2018 to 2019 and the trophic state changed from oligotrophic to hypertrophic in all areas, as a result of previous anomalous precipitations in 2018, followed by normal precipitations in 2019. From 2019 to 2020, the nutrients decreased due to the drop in tourism due to COVID-19, promoting fewer nutrients in the lagoon, but, also coupled with an anomalous precipitation event (Cristobal storm), resulted in a dilution phenomenon and an oligotrophic state. The cluster analysis indicated that the least similar zones in the lagoon were the ZI and ZV due to their geomorphology that restricts the connection with the rest of the system. Principal component analysis revealed that wastewater presence evidenced by the caffeine tracer had a positive association with dissolved oxygen and chlorophyll-a, indicating that the arrival of nutrients from wastewater amongst other sources promotes algal growth, but this could develop into an eutrophic or hypertrophic state under normal precipitation conditions as seen in 2019. This study shows the relevance of monitoring in time of vulnerable karstic systems that could be affected by anthropogenic contamination from wastewater inputs, stressing the urgent need for efficient wastewater treatment in the area. The tourist industry in coastal karstic lagoons such as the NLS must have a Wastewater Treatment Program as a compensation measure for the anthropic pressure that is negatively changing the water quality of this highly relevant socio-environmental system. Full article

Accurate forecasting of tourist volume is crucial for the sustainable development of the tourism industry. Deep-learning methods based on multivariate data can enhance the accuracy of tourism demand forecasting, enabling tourism management departments and enterprises to make evidence-based decisions. This study adopts an inverted transformer approach with a self-attention mechanism, which can improve the extraction of correlation features from the time series of multiple variables. Taking Zhejiang Province, a major tourist destination in China, and Hangzhou, a famous tourist city in China, as case studies, this research considers historical tourist volume, search engine data, weather data, date pattern data, and seasonal data in daily tourism volume forecasting. By comparing the forecasting results with three benchmark models, including CNN, RNN, and LSTM, the inverted transformer model’s effectiveness in forecasting the daily total visitors and overnight visitors is validated. This study’s findings can be applied to forecast the regional daily tourist arrivals, enabling decision-makers in the tourism sector to make more precise forecasts and devise more dependable plans. Full article

This study explores the evolution of cycling in Istria, a region located in parts of Slovenia and Croatia along the Northern Adriatic coast, recognised for its vibrant tourism sector. The research aims to evaluate the potential of cycling for both daily commuting and tourism while examining its economic, environmental, and social impacts. The study uses secondary data from 2010 to 2023, econometric methods, and interviews to assess how cycling contributes to economic growth, regional collaboration, employment rates, and greenhouse gas (GHG) emissions. In Slovenia, regression analysis indicates that bicycle imports positively correlate with tourist arrivals, which positively influences GDP. However, VAR analysis shows that employment has a negative impact on GHG emissions. In contrast, the increase in bicycles contributes to higher GHG emissions, suggesting the possibility of greenwashing or double pollution. In Croatia, regression coefficients were found to be insignificant, aligning with insights gained from interviews. Nonetheless, VAR analysis reveals that tourist arrivals and GDP positively reinforce each other, while GHG emissions negatively affect employment. Bicycles are shown to reduce GHG emissions, and tourist arrivals are also associated with lowered GHG emissions. The study concludes that cycling holds significant potential for fostering sustainable tourism and regional development; however, the differing impacts observed in Slovenia and Croatia necessitate tailored policy interventions. The hypothesis is partially confirmed in Slovenia, where bicycle imports positively impact tourist arrivals and GDP but are challenged by increased GHG emissions, while in Croatia, the hypothesis is not supported by direct economic gains from cycling. However, bicycles contribute to reducing GHG emissions. Full article

The level of success in tourism is gauged by several metrics; however, the most widely used is the level of tourist arrivals. However, this research answered the call for greater investigation of the impacts of qualitative factors and intangible cultural–heritage assets on destination performance. The primary research purpose was to analyze the effect of implementing a local well-being philosophy (Tri Hita Karana) on tourist revisit intentions for Bali and the mediation of destination quality and destination image. A research model was developed to examine the relationships among local wisdom (TKH), destination quality, destination image, and revisit intentions. Data were collected via a survey of 520 digital nomadic tourists and analyzed using SmartPLS 4. The results indicated that the implementation of THK positively and significantly affected revisit intentions, destination image, and destination quality. Destination image and destination quality had positive and significant effects on revisit intentions, and destination image and destination quality also significantly mediated the effect of THK implementation on revisit intentions. The findings suggested that implementing local wisdom values such as THK in the management of a destination makes visitors feel more favorably about the quality and image of the destination and they have the intention to revisit. Full article

Reports from the World Tourism Organization indicate that tourism activity has been increasingly booming; this sector is essential for economic growth and may affect the environment. Tourism is one of the key strategic sectors for planned growth in Saudi Arabia’s Vision 2030. This study is designed to evaluate the long-termning association between tourist arrivals, growth domestic product (GDP), and CO₂ emissions in the Kingdom of Saudi Arabia (KSA). The data related to these variables were assessed for the period 2010 to 2020. The autoregressive distributed lag (ARDL) bounds results revealed that there are long-established relations between tourist arrivals and growth domestic product and tourist arrivals and CO₂ emissions. The dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) model outcomes were compatible with the outcomes of the ARDL model. In reference to the Granger causality test, tourist arrivals cause (affect) the GDP. Such a result confirms the conception that tourism encourages economic growth. No causality runs from tourist arrivals towards CO₂ accumulation. This result may reflect the governmental effort to reduce CO₂ accumulation and/or to perform tourism activities in a sustainable way. The results predicted that the growth rate of tourist arrivals, GDP, and CO₂ accumulation equal 0.0023, 0.048, and 0.0169, respectively, during the forecast period (2021–2030), which appeared to be increasing for tourist arrivals and GDP and decreasing for CO₂ accumulation. The study recommended that, to increase economic growth, tourist arrivals should be increased alongside performing tourism activities in a sustainable way. These findings point to the benefits of governance in ensuring effective policies to decarbonize the environment, and policy proposals are put forward accordingly. Full article