The interest in online museum experiences and the influence of uncertainty and sentiment factors on tourist arrivals: The case of EU Mediterranean countries

This study examines the determinants of tourist arrivals at hotels and short-stay accommodations for nine EU countries from January 2010 to March 2022. We identify four driving channels of foreign and domestic tourism flows: a traditional, a sentiment, a technological and a health channel. The latter comprises two novel variables: the museum search interest and the infectious disease equity market volatility tracker. The results reveal that traditional and new drivers related to market sentiments and interest in online tourism experiences affect arrivals. Notably, there is a substitution effect between online and in-presence tourism, and the larger the uncertainty, the more substantial the reduction in tourist arrivals. COVID-19 has affected especially Spain and Italy and more foreign than domestic tourists. © 2023 The Authors

Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors' relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

Daily tourism demand forecasting before and during COVID-19: data predictivity and an improved decomposition-ensemble framework

The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To this end, we first compare the predictivity of daily tourism demand data before and during COVID-19, and observe heterogeneous impacts across different geographical scales. Then an improved multivariate & multiscale decomposition-ensemble framework is proposed to forecast daily tourism demand. The empirical study indicates the superiority and practicability of the proposed framework before and during COVID-19. Finally, we call for more research on the comparability of tourism demand forecasting.

Uneven domestic tourism demand in times of pandemic

This study investigates the short-run impact of the COVID-19 pandemic on the number of domestic overnight stays at the regional level in the summer season 2020. Official data for 65 regions in four countries are used for the analysis (Austria, the Czech Republic, Germany and Switzerland). Dynamic panel data models are employed to estimate a tourism demand equation (real GDP and price fluctuations) augmented by average temperatures. Estimation results reveal that domestic overnight stays evolve unevenly in the first summer after the outbreak of the COVID-19 pandemic. The short-run effects show that the number of domestic overnight stays in densely populated regions decreases by 27% in July as well as in August 2020, in comparison with the same months in previous years, ceteris paribus. To the contrary, there is a surge of 27 and 10%, respectively, for sparsely populated areas in the same months.JEL: Z3, R11 and R12.

Analyzing factors affecting charter flight service demand: A case of Taiwan

Global charter flight demand remined relatively stable over the last decades, and international charter flight is an integrated product of the aviation and tourism industries. To better understand charter flight tourism in Asia, this study analyses key reasons affected Taiwan's charter flight demand from Japan, South Korea, Malaysia, and Singapore during the period 2009‒2018, with a consideration of aviation- and tourism-related variables. Empirical results show that the reduction in scheduled flight aircraft size stimulated new charter flight demand to Taiwan, offsetting the negative effects caused by scheduled seat increment to a certain degree. This study contributes to literature by exploring the impact of scheduled flight service availability on charter flight demand, and further enriches our understanding of the impact of scheduled flight services on international tourist arrivals in terms of aircraft size. Importantly, it has implications for policymakers and stakeholders involved in charter business, assisting them in facilitating aviation and tourism recovery during the post-COVID-19 era.

Model of International Tourism Demand in Indonesia During the Covid-19: Gravity Model Approach

This study examines the factors influencing tourism demand during the Covid-19 pandemic for foreign tourists in Indonesia by employing a Gravity model. This study used panel data analysis of the random effects model (REM) on Indonesia's top nine source countries of foreign tourists from 2007 to 2021. The study results show that the GDP per capita of origin countries, "Wonderful Indonesia" nation branding, and the policy of developing ten priority tourism destinations (10 new Balis) variables positively and significantly impacted the number of foreign tourists arrivals in Indonesia. On the other hand, the variables of distance, relative price, and Covid-19 negatively and significantly affected the number of foreign tourist visits in Indonesia. Therefore, the government is expected to improve cooperation in expanding international flight routes to increase the number of tourists from various countries, improve tourism facilities, continuously strive to build a positive image of the country through a nation branding strategy, and have a blueprint of policy strategy for Indonesia's tourism to deal with crisis conditions © 2022, e-Review of Tourism Research.All Rights Reserved.

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach.

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

Whether and how free virtual tours can bring back visitors

Many tour providers have pinned their hopes on providing virtual tours to bring back visitors in the ongoing coronavirus disease 2019 (COVID-19) pandemic. In this paper, we develop an analytical model to examine whether free virtual tours can help attract more visitors. We consider a tour provider deciding whether to provide a free virtual tour and its grade if any is provided to maximize visitors' physical presence. Potential visitors possess heterogeneous preferences and perceived equivalence, and the tour provider knows only their respective random distributions. The model is solved to maximize tour providers' physical demand. Our analysis finds that a free virtual tour can help if potential visitors significantly underestimate the physical tour and identifies the critical threshold;we also find that the COVID-19 pandemic reduces the likelihood that a free virtual tour can help. This paper contributes to the tourism management community by accentuating the dark side of virtual tours, suggesting that tour providers should be prudent before introducing any virtual tour. We also provide guidelines for virtual tourism, helping tour providers respond to and recover from the COVID-19 pandemic and other uncertain situations.

Competition for domestic tourism in the COVID-19 pandemic: A characterization using a contest model

The COVID-19 has caused a dramatic fall in international tourism demand. Destinations within countries have revised their promotion strategies, intensifying the competition for the domestic market, less affected by mobility restrictions. This paper proposes a contest theory model for characterizing this new context. Two types of destinations, coastal (sun and sand) and rural, compete for the existing demand in terms of promotion spending. The competition is driven by two main factors: the relative strategic advantage of each destination in the international and domestic markets and the strategic value given to each market. The pandemic has likely modified these factors, reducing the traditional advantage of coastal destinations and shifting the valuation towards the domestic market. According to the model, these changes may increase competition for the domestic market, with destinations rising promotion spending even in a context of reduced demand, which is consistent with the empirical evidence.

A comparison of the early impact of government restriction and risk perception on tourist attraction demand during the COVID-19 pandemic

By collecting the daily visit data of each 5A scenic spot in China from January 1 to March 31, 2020, this paper adopted a two-way fixed-effects model to calibrate the effects of government restriction and risk perception during the pandemic. Results show that a 1% increase in government restriction level led to a 0.806% decrease in daily tourist attraction demand, while a 1% rise in individuals' risk perception resulted in a 0.084% decline. The extent of these declines moderated by factors such as GDP, population density, urbanization rate, and attraction type. The implications of these findings are discussed. © 2023 Asia Pacific Tourism Association.

An exploratory and comparative assessment of the tourist circulation at the level of some countries of the South-Eastern European union in the current pandemic context

The coronavirus epidemic (COVID19) has affected the global economy and the services sector. Quarantine measures related to travel restrictions have led to an unprecedented decline in the tourism industry with repercussions on tourism service providers, transport companies and state budgets. Travel is necessary for tourism, therefore, any factor that prevents travel can have a profound impact on the tourism industry. In the current pandemic context, the forecast in the field of tourist travel has played an important role in supporting the revival of this sector. In this study, econometric and interpretive methods were combined to predict the demand. In this study we approached a prediction model that is based on the seasonal stationary and adjustment of observed and FFT data. Experimental results show that the proposed prediction model has demonstrated a good medium-term forecast and can be used successfully in short and medium periods of time. For a certification of the exploratory evaluation of tourism forecasts there were comparatively analyzed the results obtained for three countries in south-eastern Central Europe, countries with similar natural and anthropic tourist resources (Bulgaria, Croatia and Romania). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Tourism demand for Malaysia: Further evidence from panel approaches

This study investigates the influence of price factors on tourist visits to Malaysia from 21 countries. The study samples chosen in this study are between 2000 and 2019, before the COVID-19 outbreak. Panel approaches are utilized on five regions. The results show that the tourism demand from Asian countries has a positive impact on tour prices and income, but these variables negatively affect the tourism demand from ASEAN and western countries. Most tourists in the regions choose Singapore and Indonesia as substitute destinations, while Thailand is a complementary destination for Malaysia's tourism industry. The findings are invariant among the regions. However, traveling costs do not reduce the tourism demand;hence, this factor is negligible for Malaysia. In addition, the tourism demand from ASEAN countries increases with a depreciation in Ringgit Malaysia, but the effect is the opposite for China, Asia, and western countries. The overall findings show that different regions react differently to price factors. © 2022 The Authors

Estimating Tourist Arrivals by User Generated Content Volume in Periods of Extraordinary Demand Fluctuations

In extraordinary situations, like the Covid-19 pandemic, irregular demand fluctuations can hardly be predicted by traditional forecasting approaches. Even the current extent of decline of demand is typically unknown since tourism statistics are only available with a time delay. This study presents an approach to benefit from user generated content (UGC) in form of online reviews from TripAdvisor as input to estimate current tourism demand in near real-time. The approach builds on an additive time series component model and linear regression to estimate tourist arrivals. Results indicate that the proposed approach outperforms a traditional seasonal naïve forecasting approach when applied to a period of extraordinary demand fluctuations caused by a crisis, like Covid-19. The approach further enables a real-time monitoring of tourism demand and the benchmarking of tourism business in times of extraordinary demand fluctuations. © 2023, The Author(s).

Does Distance Still Matter? Moderating Effects of Distance Measures on the Relationship Between Pandemic Severity and Bilateral Tourism Demand.

This study aims to investigate the moderating effects of various distance measures on the relationship between relative pandemic severity and bilateral tourism demand. After confirming its validity using actual hotel and air demand measures, we leveraged data from Google Destination Insights to understand daily bilateral tourism demand between 148 origin countries and 109 destination countries. Specifically, we estimated a series of fixed-effects panel data gravity models based on the year-over-year change in daily demand. Results show that a 10% increase in seven-day smoothed COVID-19 cases led to a 0.0658% decline in year-over-year demand change. The moderating distance measures include geographic, cultural, economic, social, and political distance. Results show that long-haul tourism demand was less affected by a destination's pandemic severity relative to tourists' place of origin. The moderating effect of national cultural dimensions indulgence versus constraints was also confirmed. Lastly, a discussion and implications for international destination marketing are provided.

Statistical analysis of the impacts of COVID-19 pandemic on the small and large-scale tourism sectors in developing countries.

The worldwide COVID-19 pandemic has affected the tourism sector by closing borders, reducing both the transportation of tourists and tourist demand. Due to the country-wide lockdown, most activities in the hotel, motel, restaurant, and transportation sectors have been postponed. Consequently, the article investigates four research issues by examining the consequences of global tourism in the private sector before and after COVID-19. As an analytical method, the article suggested qualitative research methodologies to collect information from tourism employees. The opinions of the respondents were gathered through online emails in the questionnaire survey. Further, the article considers people's future desire for specific tourism destinations based on visitor arrivals. Forecasting tourist demand is an essential component of good and efficient tourism management. Consequently, the article proposes an attention-based long short-term memory model for exact demand forecasting. The experimental findings reveal that the model's minimal prediction error accuracy is 0.45%, which indicates that it has a more robust prediction effect, a faster convergence rate, and a greater prediction accuracy. Seasonality has emerged as one of the most distinguishing and defining characteristics of the global tourist business. Accordingly, the article mandated to compare the seasonal and non-seasonal effects of the tourist sector throughout the years 2020-2021. Moreover, Governments must analyse the crises' long-term consequences and, as a result, define the components that constitute government advantages supplied to the tourist sector during the pandemic era. As a result, many governmental policies, especially those about social welfare, may perceive a fresh start during the post-pandemic period, respectively.

Economic Policy Uncertainty, COVID-19, and Tourist Stays in Croatia: Evidence from a Fourier Toda-Yamamoto Modeling Approach

This study extends the literature with respect to economic policy uncertainty measures and tourism flows to Croatia through the use of the Toda and Yamamoto modeling approach with a Fourier approximation to capture structural breaks. The results show that domestic economic policy uncertainty does not have a significant impact on tourist overnight stays. However, an increase in European economic policy uncertainty reduces total and domestic tourist overnight stays. An increase in COVID-19 cases has a negative and significant impact on total, domestic, and foreign tourist overnight stays, and contributes to increases in both Croatian and European economic policy uncertainty. © 2022 Taylor & Francis Group, LLC.

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19.

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Determinants of Tourism Demand in Selected EU Med Countries: Empirical Panel Analysis

Due to its significant contribution to the prosperity and growth of economies, the tourism industry has always been the one that attracted the attention of many practitioners and researchers who have tried in different ways and from different aspects to identify the key variables that determine tourism demand. The importance of tourism is especially evident in the group of countries included in the EUrope Mediterranean (EU Med) alliance. Considering the importance and role of tourism, the main objective of this research is to examine the influence of different factors on tourism demand for selected eight countries from EU Med alliance during the period 2010-2020 with the application of a dynamic panel data model. The variables encompassed in the model, i.e., income and trade, show a statistically significant positive influence on tourist arrivals in eight countries from EU Med alliance. The results of the empirical research confirmed the positive impact of previous demand on current demand as well as its statistical significance. On the other hand, we also found that terrorism and Covid-19 negatively impact tourist demand. These results imply that for any country in the eight countries from EU Med alliance to attract more arrivals of tourists, it should invest significantly in the tourism sector in terms of upgrading tourism infrastructure, increasing trade openness and promoting a peaceful reputation and safe country.

Halal tourism demand and firm performance forecasting: new evidence from machine learning

This study forecasts both Halal tourism demand (HTD) and the financial performance of Halal tourism industry of Malaysia using machine learning. Based on the data over the period from 2009 to 2020, this study considered 338,233 tweets sentiments, and 11 Google trend keywords, firm-specific variables, and macroeconomic variables for HTD and financial performance forecasting. Out of 14 machine learning algorithms, this study found Bagged classification and regression trees method outperforms other forecasting models. The forecasting accuracy scores of HTD and firm financial performance models are 93.71% and 80.12%, respectively. The results reveal that internet data variables (Twitter & Google Trend) significantly contribute to the forecasting models. Evidently, our models functioned optimally during the COVID-19 pandemic. This study offers valuable insights for policymakers to devise sustainable Halal tourism.

Economic Policy Uncertainty, COVID-19, and Tourist Stays in Croatia: Evidence from a Fourier Toda-Yamamoto Modeling Approach

This study extends the literature with respect to economic policy uncertainty measures and tourism flows to Croatia through the use of the Toda and Yamamoto modeling approach with a Fourier approximation to capture structural breaks. The results show that domestic economic policy uncertainty does not have a significant impact on tourist overnight stays. However, an increase in European economic policy uncertainty reduces total and domestic tourist overnight stays. An increase in COVID-19 cases has a negative and significant impact on total, domestic, and foreign tourist overnight stays, and contributes to increases in both Croatian and European economic policy uncertainty. © 2022 Taylor & Francis Group, LLC.