The importance of online tourism demand

In recent decades, the combination of tourism and Information and Communication Technologies (ICT), has originated considerable changes in tourists’ behaviour. The analysis of tourism demand resulting from the Internet is of growing importance, given the increasing number of online reservations observed in recent years. However, in order to analyse the new trends caused by online bookings, the availability of data enabling the measurement and characterization of this phenomenon is essential. This has, however, been a considerable limitation, given that either no data on key variables is available or the available data is sometimes of questionable quality. For professionals and researchers in the area of tourism, the high volume of tourists who use the Internet to make hotel and travel reservations is worth of consideration, given that it may potentiate the discovery of new source markets, the identification of clients with different characteristics and may help explain the dynamics between suppliers or countries. The existence of predictive studies to support decision-making and planning, by professionals of the tourism sector, is of great importance. Panel data models are a useful and appropriate method for the analysis and modelling of tourism demand. These models consider both the time series and the cross-sectional dimensions of the data and allow for the inclusion of social variables. The results of estimation of tourism demand, through panel data models, show that the Internet and the sharp technological development have encouraged the increasing demand for tourism. The growing number of tourism companies online will naturally promote or potentiate an increase of tourism demand.

A tourism demand model for Fiji, 1970-2000

Despite the growing importance of the tourism industry, little is known about the determinants of tourism demand in Fiji. The major findings of analyses of the 1970?2000 period are that growth in income in Fiji?s main source countries for tourists leads to an increase in visitor arrivals, while relative prices and substitute prices negatively impact visitor arrivals in the long run. This implies that Fiji needs to maintain price competitiveness. Empirical evidence was also found that coups impede the short-run growth of the industry.

An econometric analysis of international tourism demand in Hong Kong : a cointegration and error correction approach

The purpose of this study is to develop econometric models to better understand the economic factors affecting inbound tourist flows from each of six origin countries that contribute to Hong Kong’s international tourism demand. To this end, we test alternative cointegration and error correction approaches to examine the economic determinants of tourist flows to Hong Kong, and to produce accurate econometric forecasts of inbound tourism demand. Our empirical findings show that permanent income is the most significant determinant of tourism demand in all models. The variables of own price, weighted substitute prices, trade volume, the share price index (as an indicator of changes in wealth in origin countries), and a dummy variable representing the Beijing incident (1989) are also found to be important determinants for some origin countries. The average long-run income and own price elasticity was measured at 2.66 and – 1.02, respectively. It was hypothesised that permanent income is a better explanatory variable of long-haul tourism demand than current income. A novel approach (grid search process) has been used to empirically derive the weights to be attached to the lagged income variable for estimating permanent income. The results indicate that permanent income, estimated with empirically determined relatively small weighting factors, was capable of producing better results than the current income variable in explaining long-haul tourism demand. This finding suggests that the use of current income in previous empirical tourism demand studies may have produced inaccurate results. The share price index, as a measure of wealth, was also found to be significant in two models. Studies of tourism demand rarely include wealth as an explanatory forecasting long-haul tourism demand. However, finding a satisfactory proxy for wealth common to different countries is problematic. This study indicates with the ECM (Error Correction Models) based on the Engle-Granger (1987) approach produce more accurate forecasts than ECM based on Pesaran and Shin (1998) and Johansen (1988, 1991, 1995) approaches for all of the long-haul markets and Japan. Overall, ECM produce better forecasts than the OLS, ARIMA and NAÏVE models, indicating the superiority of the application of a cointegration approach for tourism demand forecasting. The results show that permanent income is the most important explanatory variable for tourism demand from all countries but there are substantial variations between countries with the long-run elasticity ranging between 1.1 for the U.S. and 5.3 for U.K. Price is the next most important variable with the long-run elasticities ranging between -0.8 for Japan and -1.3 for Germany and short-run elasticities ranging between – 0.14 for Germany and -0.7 for Taiwan. The fastest growing market is Mainland China. The findings have implications for policies and strategies on investment, marketing promotion and pricing.

Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study

This study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.

Seasonal concentration decomposition across segments of tourism demand

A la hora de aplicar medidas desestacionalizadoras, a los gestores de destinos turísticos les resulta complicado identificar qué tipo de turistas contribuyen a la desestacionalización, ya que estos turistas potenciales pueden estar pasando desapercibido por no disponer de una metodología que los identifique. Teniendo en cuenta esta deficiencia, en esta tesis se ha querido conseguir un enfoque de medición que proporcione información acerca del tipo de turista objetivo para reducir la concentración estacional en los destinos analizados. Para ello, la metodología que se emplea en esta tesis, que incluye la descomposición aditiva del índice de Gini, proporciona información acerca de la contribución de cada segmento de demanda a la concentración estacional total de un destino. Mediante el empleo de dicha descomposición, el componente estacional puede ser expresado a través de unos efectos relativos marginales que permiten identificar a aquellos turistas que se manifiesten más favorables para reducir la estacionalidad. De manera complementaria, se han estimado los factores estacionales mediante el método multiplicativo que sirven para mejorar el análisis ya que proporcionan los patrones estacionales de los segmentos de demanda analizados. Además, según el destino analizado, se han utilizado clasificaciones complejas atendiendo al origen del turista, su principal motivación de viaje y la región visitada dentro de cada uno de los destinos analizados, las cuales, han permitido discernir con mayor precisión dentro de clasificaciones poco homogéneas. La metodología empleada en esta tesis se propone como una medida de control y seguimiento con la que, analizando la evolución de los efectos relativos marginales a lo largo del período de los que se dispongan datos suficientemente desagregados y, sobre todo, del último año, podrían ajustarse las políticas turísticas orientadas a reducir los efectos de la estacionalidad. Con la aplicación de la metodología propuesta en los destinos turísticos analizados, en los que se ha empleado un nivel de desagregación suficiente, se pretende aportar información adicional a los gestores del turismo en cuanto a qué turistas deben dirigir sus políticas de captación, siempre y cuando su objetivo sea reducir la concentración estacional en estos destinos. Del mismo modo, se pretende conseguir una mejora de la efectividad de las políticas contra la estacionalidad, dirigiéndolas hacia aquellos segmentos de demanda identificados como menos propensos a la estacionalidad. Esta tesis ha sido elaborada por compendio de publicaciones y se ha estructurado en siete capítulos. El primer capítulo es una introducción donde se presentan las implicaciones de esta tesis en cuanto a los aspectos relacionados con la estacionalidad, así como la metodología empleada para la medición de la misma en los destinos analizados. En los siguientes capítulos se analiza la concentración estacional de tres destinos y sus regiones: el litoral de Andalucía (segundo y tercer capítulo), Argentina (cuarto y quinto capítulo), y el Reino Unido (sexto capítulo). Los resultados se muestran con la copia incluida de las cinco publicaciones que conforman esta tesis. Por último, se proporciona las conclusiones en el séptimo capítulo, donde se muestra un análisis general y un resumen de las conclusiones de todas las contribuciones.

Determinants of European tourism demand in a demographic ageing society

This study identifies the senior European tourists determinants that explained their decisions to go on holidays. The empirical study was conducted among European tourists by applying a logit model. The model intends to explain the determinants related to the decision to go on holidays since the probability of a senior European tourist taking holidays in a country depends on a mix of motives as previous travel experience and demographic characteristics. Policy and theoretical implications are derived for contributing to the discussion between demographic variables and tourism demand choice patterns.

Determinants of European tourism demand in a demographic ageing society

This study identifies the senior European tourists determinants that explained their decisions to go on holidays. The empirical study was conducted among European tourists by applying a logit model. The model intends to explain the determinants related to the decision to go on holidays since the probability of a senior European tourist taking holidays in a country depends on a mix of motives as previous travel experience and demographic characteristics. Policy and theoretical implications are derived for contributing to the discussion between demographic variables and tourism demand choice patterns.

New Developments in Tourism and Hotel Demand Modeling and Forecasting

Abstract Purpose The purpose of the study is to review recent studies published from 2007-2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field. Design/Methodology/approach Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed. Findings This review found that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area. Research limitations/implications The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Tourism and economic development in a cash-in-advance economy

This paper examines the impact of tourism on welfare in a cash-in-advance economy. As a result of the expansion in tourism, the price of the non-traded good increases. This gives rise to a terms-of-trade improvement. However, the cash-in-advance constraint causes a distortion in consumption. For tourism demand, where the gain from the terms-of-trade improvement dominates (does not dominate) the loss from the consumption distortion, tourism is welfare-improving (welfare-reducing). A similar condition for welfare improvement (deterioration) holds for a model of capital inflow and endogenised tourism.

The impact of climate change on the tourism sector in the Caribbean

The main aim of this study is to estimate the economic impact of climate change on nine countries in the Caribbean basin: Aruba, Barbados, Dominican Republic, Guyana, Jamaica, Montserrat, Netherlands Antilles, Saint Lucia and Trinidad and Tobago. A typical tourism demand function, with tourist arrivals as the dependent variable, is used in the analysis. To establish the baseline, the period under analysis is 1989-2007 and the independent variables are destination country GDP per capita and consumer price index, source country GDP, oil prices to proxy transportation costs between source and destination countries. At this preliminary stage the climate variables are used separately to augment the tourism demand function to establish a relationship, if any, among the variables. Various econometric models (single OLS models for each country, pooled regression, GMM estimation and random effects panel models) were considered in an attempt to find the best way to model the data. The best fit for the data (1989-2007) is the random effects panel data model augmented by both climate variables, i.e. temperature and precipitation. Projections of all variables in the model for the 2008-2100 period were done using forecasting techniques. Projections for the climate variables were undertaken by INSMET. The cost of climate change to the tourism sector was estimated under three scenarios: A2, B2 and BAU (the mid-point of the A2 and B2 scenarios). The estimated costs to tourism for the Caribbean subregion under the three scenarios are all very high and ranges from US$43.9 billion under the B2 scenario to US$46.3 billion under the BAU scenario.

An assessment of the economic impact of climate change on the tourism sector in the Bahamas

The Bahamas is at great risk and vulnerability given its geographical features as a low-lying, sea encircled country. If projected sea level rise is reached by 2050, between 10-12% of territory will be lost, especially in coastal zones where the main tourism assets are located. Vulnerability could also be manifested if flight carbon emission taxes are established in the main source markets, representing an economic threat to the tourism sector for the islands. The impact of climate change on main tourism demand variables will cause some losses to the country‟s income and government revenues. This would be acting conjointly with some local threats to tourism assets and trends in future global tourism demand. The second and no less important threat is tropical cyclones, which may be associated with raising sea level. Estimations posited the amount of losses in excess of 2400 million US$ for the four decades under examination. It is to be pointed out that there is still a lack of comparatively accurate data collection and analysis on this subject, a point deserving more attention in order to deepen the understanding of, and to extract better lessons from these extreme events. In the same period, total estimated impacts of progressive climate change are between 17 and 19 billions of B$ with estimated discount rates applied. The Bahamas is a Small Island Developing State with low growth on GHG emissions (second in Latin America), as well as a relative short capacity to lower emissions in the future. The country has a relative delay in the application of renewable energy systems, a solution that, provided documented studies on-site, might turn out to be fundamental in the country‟s efforts to establish mitigation related policies. The Bahamas currently has institutions and organizations that deal with climate change-related issues and an important number of measures and courses of action have been set up by the government. Nevertheless, more coordination among them is needed and should include international institutions. This coordination is essential even for the first steps, i.e. to conduct studies with a bottom-up approach in order to draw more accurate programs on adaptation and mitigation. It is fundamental for tourism to keep track of potential losses in tourist attractions (and to act accordingly), related to correspondent losses in biodiversity, water resources and coastal erosion. Also, actions to fight climate change impacts might improve the islands security standards, quality of living and protect cultural and heritage assets. These elements may definitely shape the future of the country‟s competitiveness as a tourism destination. It is possible and necessary to decide about the options with good cost-benefit ratio and reasonable payback periods, notwithstanding that cost-benefit analysis requires more refined and accurate data to provide precise and locally adapted options.

An assessment of the economic impact of climate change on the tourism sector in Montserrat

This report provides an analysis and evaluation of the likely effects of climate change on the tourism sector in Montserrat. Clayton (2009) identifies three reasons why the Caribbean should be concerned about the potential effects of climate change on tourism: (a) the relatively high dependence on tourism as a source of foreign exchange and employment; (b) the intrinsic vulnerability of small islands and their infrastructure (e.g. hotels and resorts) to sea level rise and extreme climatic events (e.g. hurricanes and floods); and, (c) the high dependence of the regional tourist industry on carbon-based fuels (both to bring tourist to the region as well as to provide support services in the region). The effects of climate change are already being felt on the island. Between 1970 and 2009, there was a rise in the number of relatively hot days experienced on the island. Added to this, there was also a decline in mean precipitation over the period. Besides temperature, there is also the threat of wind speeds. Since the early 20th century, the number of hurricanes passing through the Caribbean has risen from about 5-6 per year to more than 25 in some years of the twenty-first century. In Montserrat, the estimated damage from four windstorms (including hurricanes) affecting the island was US$260 million or almost five times 2009 gross domestic product (GDP). Climate change is also likely to significantly affect coral reefs. Hoegh-Guldberg (2007) estimates that should current concentrations of carbon dioxide in the Earth’s atmosphere rise from 380ppm to 560ppm, decreases in coral calcification and growth by 40% are likely. The report attempted to quantify the likely effects of the changes in the climatic factors mentioned above. As it relates to temperature and other climatic variables, a tourism climatic index that captures the elements of climate that impact on a destination’s experience was constructed. The index was calculated using historical observations as well as those under two likely climate scenarios: A2 and B2. The results suggest that under both scenarios, the island’s key tourism climatic features will likely decline and therefore negatively impact on the destination experience of visitors. Including this tourism climatic index in a tourism demand model suggests that this would translate into losses of around 145% of GDP. As it relates to coral reefs, the value of the damage due to the loss of coral reefs was estimated at 7.6 times GDP, while the damage due to land loss for the tourism industry was 45% of GDP. The total cost of climate change for the tourism industry was therefore projected to be 9.6 times 2009 GDP over a 40-year horizon. Given the potential for significant damage to the industry, a large number of potential adaptation measures were considered. Out of these, a short-list of 9 potential options was selected using 10 evaluation criteria. These included: (a) Increasing recommended design wind speeds for new tourism-related structures; (b) Construction of water storage tanks; (c) Irrigation network that allows for the recycling of waste water; (d) Enhanced reef monitoring systems to provide early warning alerts of bleaching events; (e) Deployment of artificial reefs and fish-aggregating devices; (f) Developing national evacuation and rescue plans; (g) Introduction of alternative attractions; (h) Providing re-training for displaced tourism workers, and; (i) Revised policies related to financing national tourism offices to accommodate the new climatic realities Using cost-benefit analysis, three options were put forward as being financially viable and ready for immediate implementation: (a) Increase recommended design speeds for new tourism-related structures; (b) Enhance reef monitoring systems to provide early warning alerts of bleaching events, and; (c) Deploy artificial reefs or fish-aggregating devices. While these options had positive benefit cost ratios, other options were also recommended based on their non-tangible benefits: an irrigation network that allows for the recycling of waste water, development of national evacuation and rescue plans, providing retraining for displaced tourism workers and the revision of policies related to financing national tourism offices to accommodate the new climatic realities.