Tourism demand for Italy and the business cycle

This study provides a strategy for modelling the effect of the business cycle on tourism demand under the rationale that tourism cycles are heavily influenced by lagged effects of the overall business cycle. Using quarterly data on overnight stays in Italian hotels, both domestic and inbound between 1985 and 2004, we adopt a structural time series approach to evaluate two alternative models, the first with a latent cycle component (LCC) and the second based on specific economic explanatory variables (XCV). The two models are compared in terms of explanatory power, best-fit, residual diagnostics and forecasting ability. The results show similar performances. The policy implication is that the XCV model can be used for calibrating countercyclical interventions in tourism policy.     

Local tourism cycle and external business cycle

This paper uses a Markov switching model (MSM) to decompose Macao’s tourism cycle into high and low growth states (HGS, LGS) for the period of 2005Q2–2017Q2. The likelihood of the cycle maintaining HGS is 93% but the risk of staying in LGS is 80%. The Macao cycle is favorably asymmetric, with HGS (14.7 quarters) lasting much longer than LGS (5.1 quarters). Further, the paper combines structural regressions with the MSM to identify determinants of the Macao cycle, with useful policy implications derived from the regression results. We find that Macao’s tourism cycle is heavily affected by Mainland China’s business cycle and other external factors. Additionally, outward-looking marketing, albeit very costly, is found to be effective for keeping the local cycle in HGS.     

International tourism demand in Asia: before and after the economic crisis

A subprime mortgage crisis erupted in the United States in 2007 and resulted in an economic crisis spread worldwide. This study focused on seven major Asian tourist destinations and investigated the changes in international tourism demand before and after economic crisis. The data period was January 2004 to December 2014. The results demonstrate the tourist numbers in each country was affected by the income variable. The decrease in tourist numbers was most significant during and after the economic crisis, indicating an increase in the sensitivity of tourists toward their economic-crisis-based income fluctuations, causing changes in their travel behavior.     

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.     

A tourism demand function for the Okanagan,BC

This study examines the relative importance of the existing determinants of tourism demand in the Okanagan. Because national coefficients cannot be used intercnageable with regional coefficients, it is hypothesized that the values of the Okanagan demand elasticities are likely to be lower than that of the Province and the nation. Five major markets for Okanagan tourism with identified and separate regressions run to determine the relative importance of the determinants of demand for Okanagan tourism with respect to these markets. Empirical results indicate that national coefficient will tend to overestimate the regional coefficient values. Contrary to the results of other studies for the nation as a whole, the exchange rate and travel cost did not seem to be significant determinants of Okanagan tourism receipts.     

The length of stay in the demand for tourism

One of the main characteristics of current tourism is the reduction in the length of stay at a destination. Nevertheless, this variable has received little attention in literature. This paper examines the microeconomic determinants of the length of stay at one of the Mediterranean's leading sun-and-sand destinations. The estimation of a conditional demand function model highlights the explanatory power of the tourist's sociodemographic profile and of holiday characteristics, as well as the sensitivity of the length of stay to price changes.     

Causality in direct air services and tourism demand

Unlike income or relative prices, air transport attributes and tourism demand on a given route can be endogenous. Using instrumental variables, this study attempted to account for the circular causality in estimating the effect of direct air service on tourism demand. Although we found evidence of endogeneity, the nature of the circular causation is context-specific; while direct air service can be regarded as an exogenous variable in one direction, it can have an endogenous relationship on the other. Findings emphasise the need to explicate information about the network nature of transportation and its endogenous relations with tourism.     

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.     

Productivity growth and income in the tourism sector: Role of tourism demand and human capital investment

This paper proposes a model for the demand for tourism in the context of a developing country. The parameters of the model are a tourist sector characterised by monopolistic competition, where human capital is the main factor of production and hotels have market power. Additionally land use is marked by demand from both agricultural and tourism sectors. From the household side, a simplified OLG approach is developed to consider consumption, human activity and the number of children. A dynamic framework is therefore identified to investigate the long-run consequences of increasing labor productivity and lowering the fertility rate. If the supply-side policy leads to economic growth, the tourism led growth hypothesis is theoretically confirmed. It is concluded that an increase in labor productivity generates positive growth effects only if the demand for tourism is elastic, otherwise negative results arise.     

Foreign travel and tourism prices and demand

Sunday, Alexander A., “Foreign Travel and Tourism Prices and Demand,” Annals of Tourism Research, Vol. V, No. 2, April/June 1978, pp. 268–273. Using regression analysis and panel data this study estimates the parameter effect of prices on American demand for foreign travel and tourism. Findings suggest that higher air fares generate fewer tourists but greater expenditure per tourist visit.     

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.     

Progress in tourism demand research: Theory and empirics

To explore recent progress in tourism demand research, we comprehensively survey current studies in the leading tourism and hospitality journals, asking six evaluative questions about the scientific merits of the studies and three explorative questions about emerging areas in the literature. The examination identifies potential flaws and their consequences in the field of tourism demand. A theoretical foundation is recommended for future tourism demand studies with a view to reduce bias in the empirical analysis of tourism demand. Several emerging areas of analysis in the field of tourism demand are recognized and discussed. Our study provides critical insights that will enable future tourism demand research to generate more reliable, impactful information than in the past.     

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.     

A piecewise linear approach to modeling and forecasting demand for Macau tourism

In this paper, we construct and use a piecewise linear method to model and forecast, on a monthly basis, the demand for Macau tourism. Data over the period January 1991–December 2005 and a seasonally adjusted series for tourism demand are used. The study examines 4 forecasting horizons ranging from 6 to 24 months in advance. Mean absolute percentage errors and root mean square errors are adopted as criteria for evaluating the accuracy of the forecasting exercises. Finally, the forecasts of piecewise linear model are compared with those of autoregressive trend model, seasonal autoregressive integrated moving average and its arch-rival fractionally integrated autoregressive moving average models. The piecewise linear model is more accurate than the three benchmark models tested and the improvement is practically significant.     

Modeling domestic holiday tourism demand in Australia: Problems and solutions

An econometric model is very useful for understanding the underlying relationship between tourism demand and economic variables such as income and travel prices. However, a long time series horizon of data is essential to run an econometric model that is consistent with economic theory. Although time series data on the number of domestic trips and visitor nights in Australia are available since 1978–79, breaks in the time series in different years have made it difficult to estimate a domestic holiday demand model. It is because the data series in different periods are not directly comparable. In this study, a simple data adjustment technique has been used to obtain comparable data series. Among several econometric demand models, a single equation multivariate time series demand model in a double log linear functional form was found to be the most appropriate and practical model to estimate and analyze the demand parameters of domestic holiday travel in Australia. However, the model with variables in level terms was observed having the “spurious regression problem” which has been corrected using the cointegration and error correction mechanisms. The estimated income and price elasticity of domestic holiday travel demand are consistent with economic theory and therefore can be used for forecasting and other purposes.     

Modelling the interdependence of tourism demand: The global vector autoregressive approach

This study develops a global vector autoregressive (global VAR or GVAR) model to quantify the cross-country co-movements of tourism demand and simulate the impulse responses of shocks to the Chinese economy. The GVAR model overcomes the endogeneity and over-parameterisation issues found in many tourism demand models. The results show the size of co-movements in tourism demand across 24 major countries in different regions. In the event of negative shocks to China’s real income and China’s tourism price variable, almost all of these countries would face fluctuations in their international tourism demand and in their tourism prices in the short run. In the long run, developing countries and China’s neighbouring countries would tend to be more negatively affected than developed countries.     

Deterrents of tourism business crowdfunding: Aversion decision and aversion behavior

Crowdfunding has been recently suggested as an effective means for tourism business founders to gain financial support. Yet, few business founders in tourism have employed the method to fund their business creation and development. Using the behavioral decision model in complex and uncertain business situations, this qualitative study explored the deterrents to crowdfunding adoption and the factors that shape the perception of the deterrents in tourism business founders. The data collected by in-depth and face-to-face semi-structured interviews with 22 informants. Our findings revealed a combination of perceived personal, social and contextual deterrents that avert crowdfunding adoption decision and actual behavior. More specifically, our analysis explored attitude towards crowdfunding, perceived control over using crowdfunding, social norms and perceived contextual feasibility of crowdfunding as the determinants of crowdfunding aversion decision and aversion behavior. This study contributes deeper insights into tourism business financial management and crowdfunding policy, theory, practice and research development.     

Climate in the region of origin and destination choice in outbound tourism demand

This paper describes the relationship between regional climate in the home area and the choice of taking holidays in the region of origin or abroad. This decision is simultaneously estimated with a bivariate probit model. The study combines the socioeconomic characteristics of European households with information on the region of residence, such as climate, which is defined according to a new annualized climate index. The estimated probabilities are analysed using GIS and nonparametric techniques. The results of modelling support the hypothesis that the climate in the region of residence is a strong determinant of holiday destination choice. They show that residents in regions with better climate indices have a higher probability of travelling domestically and a lower probability of travelling abroad.     

Modeling Airbnb demand to New York City while employing spatial panel data at the listing level

Using spatial panel data comprising a cross section of 1,461 continuously active Airbnb listings obtained from AirDNA, as well as time series data from NYC and Company and the OECD covering the time period September 2014 to June 2016, the present study quantifies own price, cross price, and income elasticities of Airbnb demand to New York City within an empirical tourism demand framework. The particular goal of the study is to establish whether the relationship between Airbnb and the traditional accommodation industry is of a substitutional or of a complementary nature. Employing a one-way fixed-effects spatial Durbin model, it can be concluded that demand is price-inelastic for Airbnb accommodation in New York City, which is a luxury good, and that the city's traditional accommodation industry as well as neighboring Airbnb listings are substitutes for the investigated Airbnb listings. The estimation results are robust against several alternative specifications of the regression equation.     

Seasonal concentration of tourism demand: Decomposition analysis and marketing implications

This paper analyzes seasonality in the United Kingdom, specifically the English regions in relation to tourists' place of origin and main travel motivation. The method used is a decomposition of the Gini index, which provides relative marginal effects that facilitate the identification of market segments open to counter-seasonal marketing efforts. This method has been combined with a graphical multivariate technique (biplot), which groups segments according to their seasonality characteristics. Seasonal patterns associated with particular segments differ significantly when studied on a disaggregated basis. Therefore, an adequate level of disaggregation is essential in the design of counter-seasonal strategies. Although this study focuses on British destinations, this methodology could be used as a control and monitoring measure in the regional analysis of any destination, facilitating regular adjustment of regional tourism marketing campaigns to minimize seasonality effects, specifically by targeting the types of tourists less prone to seasonality.