Search Results (35)

This study analyzes domestic tourism mobility in Spain using aggregated and anonymized mobile phone data, with a particular focus on the outbound market of the municipality of Madrid and its territorial redistribution between September 2019 and September 2025. Using experimental statistics from the National Institute of Statistics (INE), a monthly series of origin–destination flows to all Spanish municipalities was constructed, harmonizing the municipal database and incorporating intensive indicators to improve inter-territorial comparability. The spatiotemporal dynamics were integrated into a Space–Time Cube (monthly resolution), and Emerging Hot Spot Analysis (EHSA) was applied to classify the persistence, intensification, or attenuation of high- and low-intensity clusters. Additionally, the grouping of time series allowed for the identification of seasonal patterns associated with coastal, urban, and nearby inland destinations. The results show: (i) a synchronous disruption in the spring of 2020 linked to COVID-19; (ii) a staggered recovery beginning in 2021, consolidating in 2023–2025; and (iii) a dual structural pattern, with a strong concentration of volumes in large urban and coastal hubs, along with high relative intensities in small municipalities in the ring surrounding Madrid. EHSA identifies intensifying hotspots in established coastal systems (Costa del Sol and Costa Blanca) and cooling or attenuated dynamics in parts of the inland region, consistent with the reconfiguration of the “tourism radius” following the pandemic. Limitations arising from statistical confidentiality and the representativeness of the source are discussed, and future research directions are proposed based on the integration of the information with expenditure and transportation data and on spatiotemporal modeling to support destination planning and management. Full article

This study examines the relationship between governance quality and outbound tourism expenditure. Annual panel data for 54 countries over the period 1996–2023 are analyzed using symmetric and asymmetric panel ARDL models. The results highlight the existence of a long-run relationship between governance quality and outbound tourism expenditure. The symmetric ARDL results indicate that governance quality exhibits a significant negative long-run effect on outbound tourism expenditure, suggesting that improvements in governance quality reduce residents’ spending on international travel. The effect remains significant only in low-governance countries when the sample is separated into low- and high-governance countries. The asymmetric ARDL results further reveal that positive governance shocks significantly reduce outbound tourism expenditure, while negative shocks increase it only in high-governance countries. Overall, the findings highlight the important role of institutional quality in shaping tourism consumption behavior and provide policy insights for managing tourism-related outbound expenditures. Full article

Emerging technologies, such as artificial intelligence (AI), including chatbots, are now transforming the hospitality and tourism industry. Chatbot technology is an excellent tool for enhancing communication, boosting service delivery efficiency, reducing costs, and improving the tourist experience. Despite their potential benefits, the adoption of AI-powered chatbots in Goa’s hospitality and tourism industry remains low, underscoring the need to identify the determinants influencing tourists’ behavioural intention to adopt this technology and use behaviour. Therefore, this study examines the key determinants influencing tourists’ behavioural intentions to adopt AI-powered chatbots in the hospitality and tourism industry. In addition, the study also examines the impact of tourists’ behavioural intentions to adopt AI-powered chatbots on use behaviour. For this purpose, a revised UTAUT2 model is assessed by leveraging a quantitative research approach. Structured questionnaires were distributed to a total of 400 inbound and outbound tourists, of which 227 respondents who were aware of AI-powered chatbots were chosen as the respondents for this study based on purposive sampling. The collected data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0. The findings revealed that attitude, performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment significantly influence tourists’ behavioural intention to adopt AI-powered chatbots, whereas automation and habit do not significantly influence their behavioural intention to adopt AI-powered chatbots. This study has implications for tourism managers and policymakers in the tourism and hospitality industry, who can gain insights into the factors that can encourage tourists to adopt AI-based facilities. Full article

Forecasting of travel demand has become increasingly important in the context of evolving mobility patterns and structural disruptions, including economic fluctuations and public health crises. Classical time series models, although well established in travel-demand analysis, are often limited in their ability to capture non-linear dependencies or adapt to abrupt regime shifts. This study develops and evaluates forecasting techniques drawn from both traditional statistical modeling and machine learning approaches. Their predictive performance and adaptability are benchmarked for U.S. outbound air travel demand across eight global destination regions, Europe, the Caribbean, Asia, South America, Central America, Oceania, the Middle East, and Africa, respectively. Using historical outbound passenger data, six forecasting models are constructed and assessed through multiple forecasting accuracy measures, including the Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Empirical results demonstrate that machine-learning-based models, particularly those incorporating adaptive learning components, consistently outperform conventional approaches in modeling structural changes in travel demand data. The study further contributes a generalizable methodological framework that enhances robustness under uncertainty and offers broad applicability to forecasting problems in transportation, tourism, and related domains. Full article

Food and agricultural products shape tourism by linking communities and regions to leisure travel. Consumers’ perceptions of a country and its food products can shape their attitudes and behaviors toward it as a travel destination. This study compares the effects of general country image (GCI), product–country image (PCI), and product image (PI) on Chinese dairy consumers’ engagement with the country of origin as a travel destination (TDE). It also tests whether TDE mediates the effects of country images on consumer-based brand equity (BEQ) for dairy products. We analyzed 573 valid online responses from mainland China, a major market for dairy products and outbound tourism, using covariance-based structural equation modeling (CB-SEM) in AMOS 31. The results identify TDE as a key factor that fully mediates the effect of GCI on BEQ. PCI and PI show both direct effects on BEQ and indirect effects through TDE. The proposed framework links country evaluations to destination engagement and brand outcomes, highlighting opportunities for integrated cross-sector promotion. This research is among the first to examine co-marketing between the tourism sector and the dairy industry through a country-image perspective. It provides practical guidance for cross-sector strategy and contributes to ongoing debates in both fields. Full article

This study examines how Chinese tourists perceive the value of sustainable practices implemented in five-star hotels in Phuket, Thailand, through the lens of the perceived value theory and the service experience framework. While luxury hotels increasingly adopt green initiatives, research exploring how tourists evaluate these efforts across the full guest journey is limited. Addressing this gap, this study aimed to examine how attitudinally distinct tourist segments perceive sustainable practices across three service stages: pre-consumption, consumption, and post-consumption. A cross-sectional survey of 400 Chinese tourists was conducted, applying k-means clustering to segment respondents by sustainability attitudes, followed by multi-group structural equation modeling. Two segments emerged: environmentally engaged travelers and conventional comfort travelers. The results indicate that the emotional value dominates during the stay, the functional value drives pre-stay decisions, and the ethical/social value shapes post-stay reflections. Environmentally engaged tourists were more responsive to ethical and social cues. The findings highlight sustainability as a multidimensional, stage-specific construct moderated by guest attitudes. Theoretically, this research extends perceived value frameworks by mapping sustainability perceptions across the guest journey. Practically, it offers actionable insights for hotel managers seeking to design value-aligned green strategies and segmented communication. Tailoring sustainability initiatives to tourist profiles can enhance satisfaction, loyalty, and advocacy in the luxury hospitality sector. Full article

Tourism has gained enormous attention, and every country is determined to attract more visitors. Concerned stakeholders are trying to promote their country’s image and that of their tourist destinations. Travelers attain information from different sustainable sources, and these different sustainable sources might be critical in shaping the perceived image of a country. Not enough research has been conducted with respect to investigating the association between these perceived images and sustainable information sources regarding any country. Hence, this study tries to fill this research gap by integrating country and destination image, outbound travel motivation, and information sources to obtain relevant information. Considering the above-mentioned context, data were gathered from a survey completed by respondents who had visited a developing country, i.e., Pakistan. Partial least squares structural equation modeling was utilized to examine the validity of the data of 205 potential visitors. Our findings show that a country’s image has a significant favorable influence on destination image. Destination and country image favorably impact travelers’ intentions to visit a certain destination, though this is also partially mediated by outbound travel motivation. Furthermore, the critical role of sustainable information sources in shaping country and destination image is highlighted in this paper. The proposed model offers novel insights into the literature and can be used to assist in the design of appropriate marketing strategies for the tourism sector by incorporating perceived image, outbound travel motivation, and information sources. This research offers pertinent recommendations to enhance tourism. Though we feel our research makes a pertinent contribution to the literature, in the future, other researchers may test the proposed model by integrating the data from other Chinese cities to gain more insights. Full article

Tourist engagement not only yields advantages for the destination but also holds significance within the realm of group travel. In this context, if tour guides are able to leverage tourist citizenship behaviors (TCB) to augment interactivity and participation, tourists can essentially transform into “tour guide assistants”. To this end, the study introduces intimacy as a mediating variable, aiming to investigate the influence of TCB on engagement levels among group tourists. The subjects were Taiwanese tourists who had engaged in outbound group tourism for more than five days within three years. A total of 436 online questionnaires were collected by convenience sampling and analyzed in LISREL and SPSS 21.0. The research results: 1. TCB significantly positively affects intimacy and engagement; 2. Intimacy is a mediating variable between TCB and engagement. 3. Different levels of TCB have significant differences in intimacy and engagement. The research results can help tour guides and travel agency operators know the characteristics of group tourists and improve the experience of group tour tourists. Full article

This study has adopted the theory of planned behavior as a framework to examine the relationship among subjective norms, perceived behavioral control, perceived policy orientation, perceived pandemic response capabilities, attitudes, and behavioral intentions towards outbound travel in a post-pandemic society. Specifically, a total of 895 valid questionnaires were employed to test the 9 hypotheses proposed in this study. The results of the structural equation modeling (SEM) analysis show that subjective norms, perceived behavioral control, and perceived policy orientation positively impact residents’ intention to travel abroad. Furthermore, attitude partially mediates the relationship between subjective norms, perceived behavioral control, perceived policy orientation, and residents’ intention to travel abroad. However, the study indicates that the impact of perceived pandemic response capabilities on residents’ intention to travel abroad is not significant. This study’s conclusions emphasize that external policy factors can significantly influence tourists’ intention to travel abroad, contributing to the theoretical research on consumer outbound tourism behavior. Moreover, the study provides important implications for the policy formulation of outbound tourism enterprises and destination governments. Full article

The relationship between COVID-19 and the tourism industry has important lessons for the post-pandemic period. The tourism industry is undergoing major changes after the pandemic. Analyzing the impact of tourism on the spread of coronavirus around the world may help us to understand how it could be a catalyst for spreading epidemics. To investigate the impact of the tourism industry on the spread of coronavirus, tourism data, as well as cases of coronavirus in the year 2020–2021, were used for OECD countries. The quantile regression method was used to estimate the effects of different types of tourism on the spread of coronavirus. The results showed that, in the first season of 2020, all types of tourists had an impact on the spread of the coronavirus. However, until the end of 2020, only outbound tourism had a significant impact on total deaths caused by the coronavirus, and in 2021, the tourism industry did not have any significant effect on the total deaths caused by the coronavirus. The findings of this article show that prior preparedness, comprehensive guidelines and roadmaps, and the establishment of international travel monitoring agencies are required to assess global constraints in critical situations. Advanced systems for controlling domestic travel in a country at a time of contagious diseases are essential. Full article

Due to the rising appeal of travel, as well as the appearance of low-cost carriers that fly to both popular and exotic destinations, people nowadays tend to book their accommodation through the more convenient and flexible sharing economy platforms. The success of such platforms among consumers depends on various factors, such as satisfaction and platform credibility, and on sustainable performance among contributors, increasing their relevance and visibility. This research proposes a conceptual model for measuring the success of sharing economy platforms in tourism, based on added value, unique experience, and enhanced social interactions, together with credibility and sustainable performance. In this vein, the authors conduct an empirical investigation in an emerging market—Romania—where the development of such sharing economy platforms is still in its infancy. Data gathered by the authors by means of a self-developed questionnaire distributed to 500 respondents are modelled via structural equations with SmartPLS. The results indicate that the platform credibility and sustainable performance of sharing economy platforms are key elements in generating their success. The research adds value to the stimulus–organism–response approach, highlighting some insights from a relevant emerging market, which in the future could become a turning point for inbound and outbound tourism. Full article

COVID-19 opened a window of opportunity to change the green development of the hospitality industry. For many years, Chinese tourists have been the world’s largest source of outbound tourists. Therefore, this study attempted to improve built-environment strategies for green rooms at B&Bs using the empirical statistics of Chinese tourists after the end of COVID-19 control measures and different green B&B standards, combining IPA (importance-performance analysis). For the lack of a green built-environment study from a tourism perspective, this study can be used mainly for improving the green satisfaction of urban B&Bs as it attempted to fill the gaps in research on green B&B rooms. This study will significantly help improve the quality of green rooms for the B&B industry in the future, and it also provides an improved green B&B room sample for other countries and regions. Moreover, it is an optimistic attempt at hospitality and tourism recovery. Full article

Analyzing the perception differences and influencing factors of cross-cultural groups in heritage tourism can help heritage sites to formulate differentiated service and improve tourist satisfaction. This research adopted the BERT model to undertake sentiment analysis of 17,555 Chinese online reviews for nine scenic spots in Melaka. Using vocabulary filtering, co-occurrence analysis, and semantic clustering technology, the emotional characteristics of Chinese outbound tourists when they visited heritage sites in Melaka were analyzed, which revealed the factors influencing their positive and negative emotions. Results showed that: 1. The BERT-based deep learning approach can obtain improved sentiment predictive performance. 2. Chinese tourists’ general emotional perceptions of Melaka were positive and they were very interested in heritage sites. 3. The most important reason for the negative emotions of Chinese tourists was a lack of cultural experience in Melaka. This research expands the application of deep learning in the field of tourism, and it helps heritage tourism destinations to improve their marketing plans for Chinese tourists and achieve long-term sustainable development of the destination. Full article

In this study, the gravity model is applied via the dynamic generalised methods of moments estimation to assess the role of geographical distance in moderating the impact of culture on outbound tourism flows. The results show that cultural distance has a positive impact on Chinese outbound tourism flow. However, the effect of cultural distance on outbound tourism flow decreases as geographical distance increases since travellers to nearby destinations are more strongly influenced by cultural distance than travellers to more distant markets. These results therefore support the concept of the “diminishing effect of cultural distance” on the Chinese outbound tourism market. In addition, the results of this study serve as a basis for promoting the sustained contribution of Chinese outbound tourism to the development of destination management, which will help with the recovery of international tourism following the COVID-19 pandemic. Full article

This study examined whether information and communication technology (ICT), over the top (OTT) market growth, and economic freedom affect tourism at the global level. Toward this end, the present study tested regression models of inbound tourism and outbound tourism with panel data from 50 countries covering the years 2013 to 2020. The results of the panel-data analysis suggest that high levels of OTT advertising revenue and mobile broadband penetration contribute to high levels of inbound tourism. The results also reveal that high OTT advertising revenue, labor freedom, and income are associated with high levels of outbound tourism. The obtained results underscore that ICT and media play key roles in promoting international tourism. Full article