The impacts of international tourism demand on economic growth of small economies dependent on tourism

This paper studies the impacts on economic growth of a small tourism-driven economy caused by an increase in the growth rate of international tourism demand. We present a formal model and empirical evidence. The ingredients of the dynamic model are a large population of intertemporally optimizing agents and an AK technology representing tourism production. The model shows that an increase in the growth of tourism demand leads to transitional dynamics with gradually increasing economic growth and increasing terms of trade. In our empirical application, an econometric methodology is applied to annual data of Antigua and Barbuda from 1970 to 2008. We perform a cointegration analysis to look for the existence of a long-run relationship among variables of economic growth, international tourism earnings and the real exchange rate. The exercise confirms the theoretical findings.     

The long-run impact of exchange rate regimes on international tourism flows

Notwithstanding the systematic inclusion of an exchange rate variable of some form in studies examining international tourism flows, hardly any research has been carried out to test for a possible exchange rate regime effect. Drawing from recent advances in exchange rate regime classifications, this paper begins to fill this gap by investigating the impact of exchange rate regimes on international tourism flows. The study employs a system generalized methods of moments (SYS-GMM) estimation for tourist arrivals on a panel of 27 Organization for Economic Co-operation and Development (OECD) and non OECD countries for the period 1980–2011. The results identify multiple exchange rate regime effects and support the importance of maintaining a relatively stable exchange rate to attract international tourist arrivals.     

Forecasting international tourism demand: a local spatiotemporal model

This study investigates whether tourism forecasting accuracy is improved by incorporating spatial dependence and spatial heterogeneity. One- to three-step-ahead forecasts of tourist arrivals were generated using global and local spatiotemporal autoregressive models for 37 European countries and the forecasting performance was compared with that of benchmark models including autoregressive moving average, exponential smoothing and Naïve 1 models. For all forecasting horizons, the two spatial models outperformed the non-spatial models. The superior forecasting performance of the local model suggests that the full reflection of spatial heterogeneity can improve the accuracy of tourism forecasting.     

Remodeling international tourism demand: Old theory and new evidence

The purpose of this study is to advance the tourism demand theory by excluding simultaneous effects of exchange rates and prices in empirical models, formulating an alternative pricing modus operandi consistent with recent research in the area, and demonstrating the efficacy of the use of an industrial production index (IPI) as a proxy for income. A panel fully modified ordinary least squares (FMOLS) method is employed to estimate the inbound tourism demand for Turkey. Study findings suggest that the inclusion of exchange rates and prices, as mutually exclusive components, can be misleading; the IPI is not a good proxy for income; and country-specific coefficients need to be analyzed to accurately explain determinants of tourism demand for countries in the panel.     

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.     

A meta-analysis of international tourism demand forecasting and implications for practice

Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980–2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.     

The overembeddedness impact on tourism cooperation

According to the concept of social embeddedness economic decisions are determined by actors' embeddedness in social relations. Too high level of this embeddedness leads to so-called ‘overembeddedness effect’ having negative consequences for business activity. The aim of the paper is to identify the negative consequences of social embeddedness for business cooperation among entrepreneurs in a tourist destination. Semi-structured interviews were conducted among 48 entities associated in the Wisła Tourism Organization in Wisła – a tourist destination in Poland. As a result, numerous negative consequences of actors' social embeddedness for cooperation were identified. These are: limiting the innovativeness of cooperative activities, less adaptive abilities, limiting economic rationality of partners' decisions, nepotism, interpersonal conflicts and being vulnerable to a partner's opportunist behavior.     

The impact of terrorism on European tourism

This paper estimates the influence of terrorist attacks on European tourism through the short-term post hoc response of the airline industry and passengers. We use a seasonally-adjusted ARMA-GARCH methodology on unique datasets that examine changes in tourism as measured by ASKs, seats filled and changes in both fares and revenues. Traffic flows are found to fall despite significant fare reductions; however, this response varies substantially based on the flight origin and ticket-type purchased. We found that business travel slows substantially due to duty of care legislation for corporate transport. While we found evidence indicating substantial airline fare reductions, in the majority of investigated cases this response was unable to mitigate substantial reductions in passenger demand and flows across varying ticket types.     

The impact of psychological distance on Chinese customers when selecting an international healthcare service country

This paper investigates how the factors of psychological and geographical distances in conjunction with medical competence influence potential Chinese customers when they select a destination country for international healthcare services. We also explore how the severity of the customer's illness affects his/her choice. In this study data is collected with reference to the Chinese perceptions of the United States of America (USA), Republic of Korea (Korea), Thailand, Singapore, and India. The sample comprises of 1292 potential Chinese customers, and is analyzed using McFadden's conditional logit model. The results indicate that medical competency is the most important determinant of destination choice. In addition, customers contemplating treatment for major diseases tend to choose healthcare services from economically more developed countries, such as the USA, in spite of the differences in language, economic condition, and political system. However, respondents considering treatment for relatively minor diseases are more likely to select countries, such as Korea, for which there is a small psychological distance. Along with suggestions for future research, the implications for international healthcare are discussed.     

The impact of tourism research

The exceedingly competitive climate of academia has increased the emphasis on performance-based research funding. In this paper we evaluate the UK's government assessment of research impact and critically comment upon the implications for future research conduct. The key findings are as follows; firstly we provide a summary of UK tourism research impact. Secondly, we demonstrate the effect of the resulting significance gap, and comment upon the consequences of the Research Excellence Frameworks' (REF) research impact assessment in terms of a research culture change. Lastly, we proposition that the current assessment structure can have negative long-term consequences in that key issues facing tourism fall outside ‘good’ research impact.     

The impact of embassies and consulates on tourism

This paper estimates the effect of embassies and consulates on tourist flows from the G-7 countries. The results indicate that embassies and consulates have a positive and significant effect on tourism that ranges between 15% and 30% depending on the estimation technique. Moreover, the impact is larger for the sample of developing countries. The disaggregated analysis shows that, after controlling for both origin and destination fixed effects, a significant impact is found in the seven origin countries.     

The impact of tourism on the physical environment

Cohen Erik “The Impact of Tourism on the Physical Environment”, Annals of Tourism Research, Vol. V, No. 2, April/June 1978, pp. 215–237. While moderate and well-distributed tourism may help to upkeep attractions and preserve the environment, tourism as a mass industry poses new environmental risks. This paper is a first attempt to assess systematically the environmental impact of tourism and to spell out the principal factors on which this impact depends: the intensity of tourist site-use, the resiliency of the eco-system, the time perspective of the developers and the transformational character of touristic developments. The environmental dynamics of the tourist ecological sub-system are shown to consist of a constant expansion at the margins and intensification at the mature core, leading to the creation of “contrived” attractions both at the core (as “natural” attractions decline) and the margins (to supplement meager “natural” attractions as tourism expands into less attractive regions). Two major types of measures for environmental protection are discussed: those protecting the environment for tourism and those protecting it from tourism. The need for the second type of measures is emphasized, particularly in developing countries, which face greater environmental risks from tourism than the developed ones.     

Predictivity of tourism demand data

As tourism researchers continue to search for solutions to determine the best possible forecasting performance, it is important to understand the maximum predictivity achieved by models, as well as how various data characteristics influence the maximum predictivity. Drawing on information theory, the predictivity of tourism demand data is quantitatively evaluated and beneficial for improving the performance of tourism demand forecasting. Empirical results from Hong Kong tourism demand data show that 1) the predictivity could largely help the researchers estimate the best possible forecasting performance and understand the influence of various data characteristics on the forecasting performance.; 2) the predictivity can be used to assess the short effect of external shock — such as SARS over tourism demand forecasting.     

Spatial-temporal forecasting of tourism demand

This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29 Chinese provincial regions. Eight models are estimated among a-spatial models (autoregressive integrated moving average [ARIMA] model and unobserved component model [UCM]) and spatial-temporal models (dynamic spatial panel models and space-time autoregressive moving average [STARMA] models with different specifications of spatial weighting matrices). An ex-ante forecasting exercise is conducted with these models to compare their one-/two-step-ahead predictions. The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error. Auxiliary regression finds the relative error of spatial-temporal forecasting to be lower in regions characterized by a stronger level of local spatial association. Lastly, theoretical and practical implications are provided.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.