The good,the bad and the ugly on COVID-19 tourism recovery

This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.     

Modelling and forecasting the demand for Hong Kong tourism

The main objectives of this paper are to identify the factors which contribute to the demand for Hong Kong tourism with the aid of econometric models and to generate forecasts of international tourism arrivals to Hong Kong for the period 2001–2008. The general-to-specific modelling approach is followed to model and forecast the demand for Hong Kong tourism by residents from the 16 major origin countries/regions and the empirical results reveal that the most important factors that determine the demand for Hong Kong tourism are the costs of tourism in Hong Kong, the economic condition (measured by the income level) in the origin countries/regions, the costs of tourism in the competing destinations and the ‘word of mouth’ effect. The demand elasticities and forecasts of tourism arrivals obtained from the demand models form the basis of policy formulations for the tourism industry in Hong Kong.     

Overtourism,optimisation, and destination performance indicators: a case study of activities in Fjord Norway

Abstract

Many global tourist destinations have experienced growth in arrivals. This has triggered various conflicts in destinations and sparked debates as to how to deal with what is increasingly referred to as ‘overtourism’. Most Destination Marketing Organisations (DMOs) pursue strategies to stimulate arrivals even further. Pro-growth discourses are reinforced by lead bodies such as the World Tourism Organisation (UNWTO). However, maximisation strategies based on higher numbers of tourists increasingly cause conflicts with local residents, whereas simultaneously undermining climate change mitigation pledges as negotiated in the Paris Agreement. New approaches to destination management based on optimisation are therefore warranted. Drawing on a survey of international tourists (n?=?5,249) in south-western Norway, this article discusses whether ‘activities’, i.e. the development of local, small-scale and ideally more sustainable experiences, can contribute to economic growth without necessarily increasing numbers of arrivals. Results confirm that destinations should seek to better understand their markets, including length of stay, spending, and/or activity intention, to identify profitable markets. Ultimately, such knowledge may help addressing overtourism conflicts while building tourism systems that are more economically, socially, and environmentally resilient.     

Global trends in length of stay: implications for destination management and climate change

Abstract

Length-of-stay (LOS) is a key parameter in destination management that determines the number of guest nights relative to arrival numbers, with concomitant repercussions for revenue generation and other performance indicators. This article investigates the development of LOS for 32 destinations in developed and emerging economies as well as Small Islands and Developing States (SIDS). The analysis is based on UNWTO data for 478.5 million international tourist arrivals, or about 40% of the global total in 2015, for the years 1995–2015. Results show considerable differences in LOS between destinations, with a global trend of falling LOS, by 14.8% over the study period. However, in individual destination countries, LOS was found to be increasing. Analyses of LOS trends reveal that these can neither be explained by distance–decay relationships nor business to leisure arrival ratios. Results are discussed with regard for destination management and revenue optimisation, transport infrastructure needs, as well as sector greenhouse gas emissions.     

Africa’s outbound tourism: An Almost Ideal Demand System perspective

While African outbound tourism represents 3% of international tourism, the continent is experiencing high economic growth rates, contributing to a fast-growing middle-class and a large potential market for international travel. This article analyses African outbound travel to all other continents from an Almost Ideal Demand System (AIDS) perspective. Both static and dynamic AIDS are estimated and the resulting elasticities indicate that: (i) African tourism to all continents is a normal good, although Africa and Oceania can be considered luxury destinations; (ii) Asia and North America are the most price elastic destinations, and price increases in these continents will lead to substitution to Europe and Africa; (iii) there is persistence in African arrivals to North and South America.     

Examining the link between meetings,incentive, exhibitions,and conventions (MICE) and tourism demand using generalized methods of moments (GMM): the case of Singapore

ABSTRACT

This study investigates the impact of meetings, incentive, exhibitions, and conventions (MICE) on tourism demand in Singapore over a period of 10 years (2003–2012). Past studies have shown that MICE matters a great deal to host destinations but researchers have rarely conducted any empirical research to verify the significance of this sector to tourism demand. Our study intends to fill the gap by using Difference and System generalized methods of moments (GMM) estimators for dynamic panel models. Tourism demand is measured by tourist arrivals from the top 30 origins, and the influence of real income of the tourist generating country and real exchange rate is also examined. The GMM results show a significant positive relationship between tourism demand and MICE (with international meetings as proxies). Additionally, the findings reveal that tourism demand growth is significantly positive (negative) with respect to changes in income (relative prices). The coefficient of lagged tourist arrivals indicates a high level of habit persistence and revisiting.     

Forecasting tourism: a combined approach

In this article, we employ a combined seasonal nonseasonal ARIMA and sine wave nonlinear regression forecast model to predict international tourism arrivals, as represented by the number of world-wide visitors to Singapore. Compared with a similar study of the accuracy of international tourist arrivals forecasts by Chan (Journal of Travel Research, 1993, 31, 58–60)¹ and Chu (Journal of Travel Research, 1998, 36, 79–84)² using other univariate time series models, our proposed model has the smallest mean absolute percentage error.     

Terrorism,Seasonality, and International Air Tourist Arrivals in Central Florida

The Orlando International Airport (OIA) is growing in its importance in the regional tourism of Central Florida. This study provides some explanation for the fast growth of the OIA international passenger traffic by developing a multiple regression model. Five variables are identified as significantly related to the passenger arrivals at the OIA. The positive relationship between the economic performance of other industrialized countries and the OIA international arrivals is consistence with the hypothesis that income is positive determinant in travel decision. The increasing hijacking incidents in Europe and the Middle East is found to have a destination substitution effect between Orlando and European/Middle East. Hijacking may have encouraged Canadian and European tourist to switch from European/Middle East destinations to the United States in general, and to Orlando in particular. The composite tourism supply variable, represented by the number of Orlando hotel/model rooms, is found to be positively related to the OIA international arrivals. The two dummy variables of seasonality represent the natural component of the tourism supply of Orlando, its pleasant winter and early spring sunshine. The model indicates that this natural factor contributes significantly to the international passenger arrivals.     

Destination characteristics that drive hotel performance: A state-of-the-art global analysis

The increased market saturation and competition in both domestic and international tourism destinations have renewed interest among hotel operators in identifying the key drivers of hotel performance. This paper presents a comprehensive analysis of the determinants of hotel performance and their relative importance across multiple tourist destinations. We employ a two-step estimation method to identify key determinants of hotel performance, using a rich sample of international hotels. Our empirical analyses show that the main drivers of hotel performance are the quality of the educational system, government support, disposable income, and number of international arrivals within a tourism destination. Results indicate that the most important barriers to hotel performance are the competition among accommodation providers, tax rate and fuel price. We argue for the need for hotel providers to develop strategies that take cognisance of the key drivers and barriers to enhancing hotel performance in an ever-changing global tourism sector.     

Safety,security and peace tourism: The case of the DMZ area

It comes as no surprise that peace and tourism is an important topic today in tourism literature. Despite the strength of global tourist demand, many destinations, especially in the developing world, are facing fluctuations in tourist arrivals, due to unsafe political conditions. This study discusses the symbiosis between tourism and peace and its opposite, war, and the likely impacts of each condition on several tourist destinations. A turbulent security environment, caused by international and civil wars, coup d'etat and terrorist attacks has already demonstrated its negative impact on tourism development in many countries around the world (Taylor & Quayle, 1994). The aim of this study is to examine the relationship between safety, tranquility, peace, and successful tourism, using surveys completed by both international and domestic tourists. More specifically, it is about the effects of the absence of safety, security and peace on domestic and international tourism in the Korean demilitarized zone (DMZ) area. The general findings demonstrate that the subjects of the study view the implication of the existence of a peaceful environment on tourism favourably.     

Forecasting tourism demand: Developing a general nesting spatiotemporal model

This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specification of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance.     

Modelling and forecasting daily international mass tourism to Peru

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO's World Heritage List. For the potentially negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability in the growth rate) in daily international tourist arrivals to Peru from 1997 to 2007. The empirical results show that international tourist arrivals and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. Moreover, the estimates resemble those arising from financial time series data, with both short and long run persistence of shocks to the growth rate in international tourist arrivals.     

Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team

In a context in which the tourism industry is jeopardised by the COVID-19 pandemic, and potentially by other pandemics in the future, the capacity to produce accurate forecasts is crucial to stakeholders and policy-makers. This paper attempts to forecast the recovery of tourism demand for 2021 in 20 destinations worldwide. An original scenario-based judgemental forecast based on the definition of a Covid-19 Risk Exposure index is proposed to overcome the limitations of traditional forecasting methods. Three scenarios are proposed, and ex ante forecasts are generated for each destination using a baseline forecast, the developed index and a judgemental approach. The limitations and potential developments of this new forecasting model are then discussed.     

The Determinants of the Travel Demand on International Tourist Arrivals to Thailand

The purpose of this study is to develop a travel demand model of international tourist arrivals to Thailand and to assess the impact of crisis incidents on Thailand's tourism industry. A 20-year (1987–2006) annual time series data of “number of international tourist arrivals”, “exchange rate”, “promotion budget”, and dummy variables of “Asia financial crisis”, “special promotional campaigns”, “SARS” and “tsunami” were used to develop the travel demand model by performing a multiple regression analysis. The results showed that travel demand of international tourist arrivals to Thailand could be explained by “exchange rate”, “promotion budget”, “Asia financial crisis” and “SARS”.     

Forecasting tourism demand with multisource big data

Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better.     

A meta-analysis of econometrics studies of tourism and low-carbon development

Inconclusive findings across different empirical studies have been found regarding the effects of tourism on low-carbon development. Corresponding to these conflicting results, this study employed the meta-analytic method to examine the effects of tourist arrivals and tourism receipts on carbon emissions and energy use, respectively, based on 260 effect sizes derived from 47 selected high-quality econometrics studies. In addition, this study further tested the moderators of these effects. The results show that tourist arrivals and tourism receipts have significant positive impacts on carbon emissions and energy use. Moreover, the destination type, research method, number of instrumental variables, the midpoint of the research period, and the research period significantly moderate these effects. This study finally discussed these results and highlighted the theoretical implications for future research and practical implications for the sustainable development of tourist destinations where decision-makers seek both low-carbon transition and tourism growth.     

The classification of heritage tourists: a case of Hue City,Vietnam

Heritage, especially with World Heritage status, is increasingly becoming the main attraction of many tourist destinations. Heritage tourism is also the major tourism product in Hue city, Vietnam. Hitherto, there are almost no official statistics and research pertaining to heritage tourism as well as heritage tourists in Hue. This study aims at providing a preliminary profile of heritage tourists to Hue city and identifying different categories of heritage tourists, with a special focus on package tourists. The international heritage tourists' profile seems to be similar to official statistics of international arrivals, indicating almost no difference in socio-demographic profile between heritage tourists and general tourists in the context of Hue. Various significant differences were found between international and domestic tourists in terms of tourist characteristics, trip profile and the perception of Hue. Adopting McKercher's [(2002) Towards a classification of cultural tourists. International Journal of Tourism Research, 4, 29–38] cultural tourist classification, five categories of heritage tourists were identified, including purposeful heritage tourists, sightseeing heritage tourists, casual heritage tourists, incidental heritage tourists and serendipitous heritage tourists. Among these, sightseeing heritage tourists and purposeful heritage tourists were dominant.     

Hawaii tourism to the year 2000 : A Delphi forecast

The Delphi torecasting technique is used to forecast tourism to Hawaii, particularly Oahu, by the year 2000. Local experts and travel agents were questioned on visitor arrivals and percentage of domestic arrivals to Hawaii, market share, visitor-to-resident ratio, maximum visitor accommodation and desirable growth rates, and probable scenarios for Oahu tourism. The results show few significant differences in responses among the groups, and confirmed expectations about convergence and consistency of managerial responses with statistical projections and existing trends. As such, this study demonstrates the value of combining qualitative with quantitative techniques in making long-term forecasts.     

Forecasting China's Monthly Inbound Travel Demand

Summary

China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonally and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naive “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.