Does market exit of a network airline affect airline prices and frequencies on tourist routes?

This paper examines the influence of network airline bankruptcy (and consequently its market exit) on prices and route frequencies. Specifically, the 2011 case of Spanair is analyzed, using Spanish route data for the period 2006–2013. The study finds that the Spanair bankruptcy led to a reduction in prices on those routes where its services were replaced by low-cost airlines. On the other hand, there was no evidence of any clear reduction in flight frequencies. Given that tourist passengers are particularly sensitive to prices, this paper provide evidence about the positive impact of low-cost airlines on tourism.     

Modeling Tourist Flows to Indonesia and Malaysia

Summary

The purpose of this study was to examine the major factors that influence the flow patterns of tourists from six important tourist-generating countries to Indonesia and Malaysia. The primary determinants included in the demand models were income, prices, and time trend. Two models that employed different indicators for the price variable were estimated; one with exchange rates in addition to relative prices, whereas the other included only an exchange rate adjusted-relative price variable. Annual time-series data covering the period 1980 to 1997 were used for estimation. The results generally indicated that the factors provide reasonably good explanations for the demand for Indonesian and Malaysian tourism. The measure of thejoint effect of the changes in exchange rates and relative prices also seems to be a better indicator for the price variable for both destination countries. The study has important marketing implications for the tourism industries in Indonesia and Malaysia.     

Dynamic heterogeneous panel data analysis of tourism demand for Singapore

The objective of this study is to investigate tourism demand for Singapore using heterogeneous dynamic panel estimation methods. A binary variable representing the operation of Integrated Resorts (IRs), as well as income and real exchange rate, are included as determinants of tourism demand growth. Using seasonally adjusted and actual/raw quarterly data of 16 origin countries, our study shows that the pooled mran group (PMG) estimator is able to provide consistent and efficient estimates of long-run relationships between tourism demand and the determinants. The PMG results show that the long-run income (price) elasticity is positively (negatively) significant, with an elasticities range between 0.915 and 3.05 (?0.275 and ?0.34). In addition, the study reveals that the IRs contribute to tourism demand growth, especially in attracting tourists from Asia. In the long term, tourism demand by the Asian markets is less income-sensitive than that of the non-Asian counterparts.     

Estimating the long-run effects of socioeconomic and meteorological factors on the domestic tourism demand for Galicia (Spain)

Tourism is one of the more important sectors for the economy of Galicia (northwest of Spain). The aim of our study is to quantify determinants of Galician domestic tourism demand and their impacts. Additionally, confidence intervals for these impacts are calculated by bootstrapping data. The results show that income, Eastern vacations and the celebration of the Holy Year of the Apostle Saint James have had a relevant positive impact on tourism. There is also a significant positive connection between a meteorological phenomenon, the North Atlantic Oscillation (NAO), and tourism demand. On the other hand, the differential rates of inflation between Galicia and the rest of Spain and the 2008 economic crisis have had a negative effect.     

Do expectations of future wealth increase outbound tourism? Evidence from Korea

As international tourism is generally considered a luxury good, models to date have shared an understanding that demand is dependent on discretionary income. However, consumption theories predict that a shift in demand can be induced without changes in actual earnings when expectations for future income are adjusted. This presumes demand for international tourism can be influenced by “wealth effects” from real estate and financial assets. This study tested for the wealth effect on Korean outbound travelers during the 20 years between 1989 and 2009. Korea is a unique place to examine in that Korean households possess housing assets and financial assets that are traded actively in markets. The results of this study favored the possibility of a significant wealth effect from housing on outbound travel demand, but not from financial assets. This may be explained by data sensitivity and the relative importance of financial assets in the Korean people's wealth portfolios. Implications and suggestions for future research are provided along with the findings of the study.     

Tourism Market Recovery in the Maldives After the 2004 Indian Ocean Tsunami

SUMMARY

This paper will use a range of tourism data and a case study to examine the rates of recovery of ten source markets for the Maldives in the wake of the 2004 Indian Ocean tsunami. The market response and recovery rates for these ten markets varied significantly and the reasons for this will be explored. It is evident that a 'one size fits all' marketing strategy will not in itself be sufficient to achieve acceptable rates of recovery following a disaster. The strategies, opportunities and rates of market recovery experienced in the Maldives will provide useful insights for the marketing and monitoring of other destinations that have experienced a crisis or disaster.     

The determinants of Chinese visitors to Australia: A dynamic demand analysis

Although China has progressively become an important inbound tourism market for Australia, its demand elasticities have been little studied to date. This study examines the determinants of Chinese visitors to Australia using a dynamic time-series estimator. Interesting findings include a high income elasticity as a source of the continuous doubledigit growth rates in Chinese arrivals that Australia has experienced over the past two decades, together with relatively high total trip price elasticities for both short run and long run. A trend of Chinese outbound to Australia is also identified. From a policy perspective, the results confirm that keeping a low cost of visiting Australia, both ground and travel costs, is a good strategy to secure greater numbers of Chinese tourists.     

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.     

Modelling Australian domestic and international inbound travel: a spatial–temporal approach

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant temporal and spatial dynamics have been uncovered for both domestic and international tourism demand. For example we find strong seasonal temporal autocorrelations, significant trends and significant spatial autocorrelations at both the origin and the destination. Moreover, the spatial patterns are found to be most significant during peak holiday seasons. Understanding these patterns in tourist behaviour has important implications for tourism operators.     

Mapping time series into networks as a tool to assess the complex dynamics of tourism systems

This paper contributes to filling two gaps: i) the presence of a limited amount of studies focused on tourism demand turning points, ii) the prevalent recourse to linear models in demand analysis, disregarding the complex structure of tourism destinations. The paper uses the Horizontal Visibility Graph Algorithm, a technique able to transform a time series of observations into a network whose topology preserves some fundamental characteristics of the system examined. The empirical work focuses on Livigno, an Italian alpine destination.Findings reveal four turning points in the last 50 years; these changes are built around shifts in the origin market segments. The network's degree distribution confirms the complex structure of the destination and reconfirms the importance of non-linear models and methods for the analysis of tourism demand.