Households’ Net Financial Wealth as a Determinant of Tourism Demand Cycles: Evidence from US Travel to Selected Caribbean Destinations

Ridderstaat, Jorge

doi:10.1177/0047287520925174

Cited by 10 publications

(5 citation statements)

References 78 publications

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“…With the development of computer network, the development of tourism is also changing rapidly: there are more and more tourism products and more varieties; travel costs are lower and travel is more convenient; even the way of traveling is quietly changing [ 1 ]. The position and role of tourism in relieving people's living pressure, improving people's living standard, stimulating domestic demand, and promoting residents' consumption are becoming increasingly prominent.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Tourism Demand Prediction Model Using Particle Swarm Algorithm and Neural Network in Big Data Environment

Xu¹,

Wang²

2022

Journal of Environmental and Public Health

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Since demand forecasting is the first step in managing and operating a tourism business, its accuracy is very important to tourism businesses. In order to address NN’s drawbacks, such as local optimization, slow convergence, and large sample sizes, this paper organically combines the PSO and NN models and builds a PSO-NN-based tourism demand forecasting model. The tourism demand forecasting indexes, the choice of NN forecasting models, the modelling process, and the implementation methods are first analysed and studied along with the fundamental theories and forecasting techniques of PSO and NN. In order to increase the precision of the prediction model, the PSO algorithm is also used to optimise the weights and thresholds of the NN. The final section of the paper compares the performance of the model developed in this paper with the most widely used model for forecasting tourism demand. According to the experimental findings, this model’s prediction accuracy can reach 95.81 percent, or about 10.09 percent higher than the prediction accuracy of the conventional NN model. There are some practical implications to this research. Applying the optimization model to the forecast of tourism demand is doable and practical.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Tourism Demand Prediction Model Using Particle Swarm Algorithm and Neural Network in Big Data Environment

Xu¹,

Wang²

2022

Journal of Environmental and Public Health

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“…J. Ridderstaat (2021) has applied a similar approach to predict tourism demand cycles. The formula for logistic regression can be described in the following equation:…”

Section: Methodsmentioning

confidence: 99%

“…(Kubickova, Kirimhan, & Li, 2019). Ridderstaat (2021) stated that tourist behavior is subjective, and tourism demand is likely to be influenced by personal factors (e.g., moods and attitudes) rather than economic factors (e.g., financial wealth and income) alone. The availability of financial resources may not necessarily lead to consumption.…”

Section: Shock Effects and Tourism Demand Cyclesmentioning

confidence: 99%

“…The study found that the impacts are asymmetrical, depending on the development stage. Furthermore, other determinants of tourism demand cycles, such as income (Croes et al, 2018) and household financial position of source market countries, also explain the variations in the tourism demand cycles (Ridderstaat, 2021).…”

Section: Shock Effects and Tourism Demand Cyclesmentioning

confidence: 99%

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Shock Effects on Tourism Demand Cycles to Bali

Kurniawati

2022

Asean J. Hosp. Tour.

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Various kinds of shocks can influence tourism demand cycles. However, previous studies have typically concentrated within economic frameworks in explaining variations of tourism demand cycles, ignoring the possibility of different shocks influencing tourism demand cycles. This paper analyses various shocks in explaining the variation of tourism demand cycles to Bali on different source market countries. Monthly tourist arrivals data from January 2002 to January 2020 are decomposed into cyclical and irregular components. The cyclical patterns represent tourism demand cycles, and shock events are represented by irregular component patterns of international tourist arrivals data series. This study employs an unobserved component model (UCM) procedure to decompose the data, unit root tests to examine data stationarity, and logistic regression to analyse the relationship between shock effects on tourism demand cycles. The unit root test result indicates that shock effects on tourism demand cycles are not permanent. However, the persistence and magnitude of shock effects vary between source market countries. Shock effects could be persistent and influence the negative phase of the cycles for several countries. The persistence and magnitude of shock effects are the highest for tourist arrivals from the United States, India, and Germany. The results provide theoretical and managerial implications.

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“…In this vein, Bernini et al, (2017) document that there are relevant differences in tourism consumption among Italian households with the same income levels that are attributed to heterogeneity in lifestyles and taste for tourism. Furthermore, tourism demand is not only affected by income levels but also by households' financial wealth and position perceptions (Ridderstaat, 2021).…”

Section: Background and Related Literaturementioning

confidence: 99%

Windfall money and outbound tourism: A natural experiment from lottery winnings

Boto-García,

Muñiz-Fernández,

Pérez

2023

Tourism Economics

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Income is a relevant factor for explaining outbound tourism demand. However, when working with regional- or country-level data, it is difficult to disentangle the role of income in stimulating tourism travelling from other factors that correlate with greater income levels. This paper exploits a natural experiment from Spanish Christmas Lottery to estimate the causal effect of income shocks on outbound tourism. We leverage the staggered and quasi-random assignment of lottery winnings across Spanish regions to estimate the elasticity of outbound annual trips and expenditure to windfall gains. Using difference-in-differences with the amount of lottery prizes as an indicator of treatment intensity, we show that lottery winnings per capita increase both the annual number of tourism trips and expenditure per capita. This effect operates during the first 2 years following the draw, which is likely explained by bandwagon and income multiplier effects.

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Households’ Net Financial Wealth as a Determinant of Tourism Demand Cycles: Evidence from US Travel to Selected Caribbean Destinations

Cited by 10 publications

References 78 publications

[Retracted] Tourism Demand Prediction Model Using Particle Swarm Algorithm and Neural Network in Big Data Environment

[Retracted] Tourism Demand Prediction Model Using Particle Swarm Algorithm and Neural Network in Big Data Environment

Shock Effects on Tourism Demand Cycles to Bali

Windfall money and outbound tourism: A natural experiment from lottery winnings

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