Global Financial/Economic Crisis and Tourist Arrival Forecasts for Hong Kong

This paper examines the impact of the global financial/economic crisis on the demand for Hong Kong tourism by residents of 10 major source markets for the period 2009–2012. To capture the influence of this crisis, the Autoregressive Distributed Lag Model (ADLM) is used to calculate the demand elasticities, and four scenarios (ranging from the most pessimistic to the most optimistic) are created to examine the possible impacts of changes in source market income levels and the price of tourism on the demand for Hong Kong tourism in these markets. The demand elasticities reveal that the economic conditions in the source markets are the most significant determinants of demand for Hong Kong tourism. In the most pessimistic scenario, total tourist arrivals to Hong Kong are projected to reach 27.6 million in 2009 and 26.0 million in 2012, whereas in the most optimistic scenario, these numbers are 30.7 million in 2009 and 33 million in 2012. In all of the scenarios, tourist arrivals from the long-haul markets are expected to suffer more losses relative to the short-haul markets during the 2009–2012 forecasting period. The forecasts also indicate that the market shares of the source markets will change slightly over this period, with Mainland China, Taiwan and Japan constituting the dominant markets for Hong Kong tourism.     

Post-SARS tourist arrival recovery patterns: An analysis based on a catastrophe theory

In 2003, the Severe Acute Respiratory Syndrome (SARS) ravaged many Asian countries. The outbreak of SARS caused a crisis in the tourism industry in many parts of Asia in that year. The purpose of this study is to examine and to compare the post-SARS recovery patterns of inbound arrivals from Japan, Hong Kong and USA in Taiwan. Taking the cusp catastrophe model as its foundation, this study proposes a well-grounded approach to understanding the nature of the recovery processes and to explaining the difference between the recovery patterns displayed by arrivals from Japan and those from Hong Kong and USA. Implications regarding tourism promotion policies are drawn from the analysis.     

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”.     

Tourism demand forecasting with online news data mining

This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.     

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.     

The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques

The Asian financial crisis has drawn worldwide attention because of its significant economic impact on local economics, especially on the economy of a tourism‐dependent destination. Unfortunately, there have been very few articles about the relationship of the Asian financial crisis and tourism demand forecasting. This relative lack of prior studies on the Asian financial crisis and tourism demand forecasting is particularly true in the context of Hong Kong. This article reports on a study that utilized officially published data to test the accuracy of forecasts of Japanese demand for travel to Hong Kong, measured in terms of the number of Japanese tourist arrivals. Seven commonly‐used tourism forecasting techniques were used to determine the forecasting accuracy. The quality of forecasting accuracy was measured in five dimensions. Experimental results indicated mixed results in terms of forecasting accuracy. Overall, artificial neural network outperformed other techniques in three of the five dimensions.     

The influence of terrorism in tourism arrivals: A longitudinal approach in a Mediterranean country

This longitudinal study examines the impact that terrorist attacks within a representative group of European countries can have on the tourism demand of a South European country with no record of terrorism attacks. In order to analyze the connections between terrorist attacks and tourists' arrivals, occurred between 2002 and the end of 2016, an Unrestricted Vector Autoregressive model was used for multivariate time series analysis. The main results show that terrorist attacks have a strong impact on tourist arrivals and confirm the existence of terrorism spillover, namely the substitution and generalization effects phenomena.     

The relevance of business cycles in forecasting international tourist arrivals

Casual empiricism suggests that there may be a cyclical trend associated with international tourist arrivals in which variation around the linear trend can be formed by the interaction with other cyclical phenomena. This paper employs a simple model that incorporates a linear trend and sine function to capture these two characteristics in forecasting international tourist arrivals in Hong Kong. The model is extended to include a set of sine functions through the application of Fourier analysis to account for situations in which more than one phenomenon may be present in the time series. The forecasting accuracy of the model is compared with other forecasting approaches. Evaluation of the results using the mean absolute percentage error measure show that the forecasting performance of the extended model with a linear trend and two sine functions is superior in terms of accuracy when compared with other forecasting models.     

综合国家距离对中国入境旅游的影响——基于CAGE距离框架的实证研究

入境旅游在旅游发展中具有重要战略地位,而我国入境旅游发展相对滞后,甚至影响到我国服务贸易的高质量发展。旅游具有异地性特征,现有研究往往从文化距离、行政距离、地理距离或经济距离等单一距离开展研究。然而,多种距离同时影响游客决策,单一距离模型降低了距离因素的解释力,同时纳入模型又会造成共线性问题,导致现有研究结论间存在矛盾,阻碍理论发展与应用。Ghemawat提出的国家距离框架基于多种距离形成评价总体国家距离的综合国家距离,得到普遍认可。文章基于国家距离框架,整合来自世界银行数据库、霍夫斯泰德文化维度官方数据、双边地理距离数据库、世界经济论坛等相关数据,形成2006—2018年我国55个客源国入境旅游的平衡面板数据,系统分析了综合国家距离对入境游客量的主效应、非线性影响,以及客源国互联网使用率的调节作用,得到如下结论:(1)综合国家距离是影响我国入境游客量的显著变量,距离对入境游的影响是文化距离、行政距离、地理距离和经济距离的复合效应;(2)综合国家距离与入境游客量之间呈正U形关系,综合国家距离可以是入境旅游的阻碍因素,也可以是促进因素,入境游客量随着综合国家距离增加而先减少后增加;(3)客源国互联网使用率没有弱化综合国家距离的影响,反而产生极化作用,极化了综合国家距离在拐点左侧的负影响和在拐点右侧的正影响。以上发现的主要价值为:(1)增进了对距离因素在国际旅游中作用的理解,提出影响入境游客量的综合国家距离变量;(2)识别出综合国家距离与入境游客量呈现正U形关系,证实综合国家距离是细分客源国市场的新变量;(3)揭示出客源国互联网使用率对综合国家距离产生的极化效应,突出了我国采用互联网传播目的地形象的必要性和重要性。     

Forecasting tourism demand with ARMA-based methods

The forecast of tourism volume in the form of arrivals is of special importance for tourism and other hospitality industries because it is an indicator of future demand, thereby providing basic information for subsequent planning and policy making. In this paper, three univariate ARMA-based models are applied to tourism demand, as represented by the number of world-wide visitors to Hong Kong, Japan, Korea, Taiwan, Singapore, Thailand, the Philippines, Australia and New Zealand. The study employs both monthly and quarterly time series generated from nine principal tourist destinations in Asian-Pacific region in the forecasting exercise to ensure the reliability of the forecasting evaluation. Forecasting performance based on disaggregated arrival series in a particular destination is examined as well. The general impression is that the ARMA-based models perform very well and in some cases the magnitude of mean absolute percentage error is lower than 2% level.     

The Impact of Sports Events on Inbound Tourism in New Zealand

Sports events are an instrument of destination marketing for host countries. Over the past 40 years, New Zealand has held sports events such as the Commonwealth Games and the America's Cup and many international tourists have visited New Zealand during these events. While past studies have examined the economic value of such tourism at a generic level, the impact of mega sports events at more specific levels is unknown. Thus, this study examines not only the impact of eight mega sports events upon New Zealand's international tourist arrivals over the 1983–2005 period at the overall level, but also the number of tourist arrivals from participating countries for each event. Results suggest that the 1990 Commonwealth Games, the 2000 America's Cup (yachting) and the 2005 British and Irish Lions Tour (rugby) had a significant impact on tourist arrivals overall and on arrivals from each participating country.     

Sustainability Analysis of Ecotourism on Yao Noi Island,Thailand

International visitor arrivals from Malaysia's 10 major source markets are examined using Lagrange Multiplier (LM) unit root tests with one and two structural breaks to ascertain whether shocks to the time path of tourist arrivals are permanent or transitory. The LM unit root test with one break is able to reject the unit root null for between 60% of source markets where the break is specified as in the intercept and 90% of source markets where the break is specified as in the intercept and slope. The LM unit root test with two breaks is able to reject the unit root null for all source markets, irrespective of how the break is specified. This result suggests that the effects of shocks on the growth path of tourist arrivals to Malaysia from its major markets are only transitory and that Malaysia's tourist sector is sustainable in the long run. Although the effects of shocks are not permanent, we do find that following shocks the growth in tourist arrivals from Malaysia's source markets has generally slowed. This result suggests there is a need to reduce the negative effects of slower growth in the recovery phase.     

Assessing impacts of SARS and Avian Flu on international tourism demand to Asia

The purpose of this paper is to investigate the impacts of infectious diseases including Avian Flu and severe acute respiratory syndrome (hereafter SARS) on international tourist arrivals in Asian countries using both single datasets and panel data procedures. An autoregressive moving average model together with an exogenous variables (ARMAX) model are used to estimate the effects of these diseases in each SARS- and Avian Flu-infected country, while a dynamic panel model is adopted to estimate the overall impact on the region of these two diseases. The empirical results from both approaches are consistent and indicate that the numbers of affected cases have a significant impact on SARS-affected countries but not on Avian Flu-affected countries. However, since the potential damage arising from the Avian Flu and subsequent pandemic influenza is much greater than that resulting from the SARS, the need to take the necessary precautions in the event of an outbreak of Avian Flu and pandemic influenza warrants further attention and action. Therefore, the empirical findings of this study could add to the knowledge regarding the relationship between tourism and crisis management, especially in so far as the management of transmissible diseases is concerned.     

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.     

Density tourism demand forecasting revisited

This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

ISSUES AND OPPORTUNITIES OF INTERNET HOTEL MARKETING IN DEVELOPING COUNTRIES

Despite the continuously increasing number of published work on the use of the Internet in tourism and hospitality literature, little has been written specifically on issues faced by hoteliers in developing countries and how they could learn from other successful practices. Indeed, analyzing the well performing destinations would provide useful insights for hoteliers in less performing counterparts around the world so as to better exploit the advantages of Internet technologies within their own constraints. Using Northern Cyprus and Hong Kong as examples, this study attempts first to find out the typical issues and usage of Internet marketing in a less developed tourist destination—Northern Cyprus, and compare the Internet‐related practices carried out by hotels in another well developed tourist destination—Hong Kong. A set of self‐administered questionnaires were mailed to members of major hotel associations in both locations. Results revealed that hoteliers in Hong Kong used the services of professionals in designing their websites and launched their websites before their Cypriot counterparts. A discussion of the findings, implications, and limitations are also given.     

Do shocks have permanent or transitory effects on tourist inflow? An application of stationarity test with structural breaks: evidence reexamined for Gilgit-Baltistan,Pakistan

ABSTRACT

Do shocks affect tourist inflows permanently or temporarily? To examine this question, we consider a region in Northern Pakistan, Gilgit-Baltistan, known for its natural and scenic beauty and with a history of huge tourist inflows, both domestic and international. The tourist arrivals from significant source markets are investigated using univariate and Lagrange Multiplier (LM) unit root tests with two structural breaks to examine if shocks to the time path of visitors’ inflow are permanent or transitory. According to the results, the univariate and LM unit root test with two breaks reject the unit root null for all major source markets. The findings suggest transistory effects rather than permanent effects of shocks on the growth path of tourist arrivals to Gilgit-Baltistan. This result further predicts the sustainability of the tourism sector in the region in the long run.     

Forecasting tourist arrivals with machine learning and internet search index

Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.     

An empirical study of inbound tourism demand in China: a copula-GARCH approach

This paper proposes new models for analyzing the volatility and dependence of monthly tourist arrivals to China applying a copula-GARCH approach. A desegregation of the top six origins of China inbound tourists from the period January 1994 to December 2013 is used in this study. The empirical results show that there is a strong seasonal effect in all cases and ?????? some habit persistence on monthly tourist arrival growth rate for South Korea, Russia, the United States (US), and Malaysia. Second, the volatilities of arrival growth rates to China are impacted significantly by their own short- and long-run effects, except for Russia and South Korea. Only short-run shock affects Russian arrivals while only long-run shocks are affecting South Korea arrivals. Third, the conditional dependence among different source countries is found to be positive and significant, but the conditional dependence for all considered pairs is low. Moreover, there is extreme co-movement (tail dependence) between the six major tourism source countries, suggesting the pairwise of international tourist arrivals shows a related increasing or decreasing pattern during extreme events. Implications are discussed and recommendations provided.     

Modeling Tourism Demand

SUMMARY

Most of the existing studies on tourism demand forecasting apply economic models that use mathematical functions, which require many statistical assumptions and limitations. This paper presents a new approach that applies the rough sets theory to form a forecasting model for tourism demand. The objective of this research is to create patterns which are able to distinguish between the classes of arrivals in terms of volume, based upon differences in the characteristics in each arrival. The information about the arrivals was organized in an Information Table where the number of arrivals corresponds to condition attributes, and the classification was defined by a decision attribute that indicated the forecast categorical value of future arrivals. Utilizing Japanese arrivals data in Hong Kong, empirical results showed the induced decision rules could accurately forecast (86.5%) of the test data.