Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)


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The development, structure, and public policy of tourism in Scotland.


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Citations Publications citing this paper. References Publications referenced by this paper. Medlik Eds , Tourism and hospitality in the 21st century.

Sustainable Tourism Development-Guiding Principles for Planning and Management

Oxford: Butterworth-Heinemann. The Impact of Scottish devolution on the U. De Grazia. Analysing Public Policy Peter John.

Annals of Tourism Research

The sick budget. The Kathmandu. Articles published in this Curated Collection are fully free to access for a limited time. The main objective of tourism demand forecasting is to help destinations and tourism businesses maintain continuous supplies of tourism products and services to satisfy the increasing demand for international travel experiences. The ability to predict such factors is crucial in accurately forecasting future demand for tourism at the destination and product levels.

A large number of tourism forecasting studies have thus centred on the specifications of tourism demand models by taking all possible influencing factors into consideration when constructing demand models. An added advantage of this line of research is that the findings are useful for policy and decision makers in designing appropriate policies and strategies to encourage and manage future demand fluctuations.

Time series and artificial intelligence AI models have also appeared frequently in the tourism forecasting literature. Their primary objectives are to improve forecasting accuracy and minimise the cost of searching for comprehensive causal models.


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They can also be used as benchmarking models to compare forecasting performance with causal models. The first tourism demand study was published in see Menges, Since then, more than studies on tourism demand modelling and forecasting have appeared in English language academic journals. During the s and s, the static regression approach was dominant and tourism demand studies were mainly concerned with investigating the determinants of demand.

More forecasting models were applied in the s as researchers considered the time-series structure of tourism demand data. In that decade, some scholars still used static regression models, and they paid considerable attention to improving time-series models e. In the s, the number of applications of time-series models continued to increase, and models based on dynamic specifications also grew in popularity e.

Curated Collection: Tourism demand forecasting - Curated Collections - Elsevier

This trend continued into the s, with considerable new developments in modern econometric models, AI-based models and combined and hybrid methods. This curated collection of Annals of Tourism Research reviews advances in tourism demand forecasting methodologies over the past 50 years and presents studies by a number of established researchers with a view towards directing future research in the area. The issue focuses on important topics identified in the review article by Song, Qiu and Park ; this issue related to methodological advancements in density forecasting, forecasting tourism flows and spill-overs across regions using the spatial econometric model, Bayesian forecasting technique, deep-learning approach, advanced time series methods and forecasting combinations.

The review article and the seven research articles included in this VSI are invited submissions written by recognised academics who have published extensively in tourism demand modelling and forecasting. Song, Qiu and Park review more than studies published from to This is the most comprehensive review of tourism demand forecasting to date in terms of the period covered and the forecasting methods used in the published studies.

Annals of Tourism Research

This review identifies inter-decadal trends and points out new directions for research on tourism demand forecasting. Li, Wu, Zhou and Liu address an emerging issue in the tourism forecasting literature: interval forecasting to reduce the risk associated with forecasting failure in tourism decision making. They introduce methods of combining interval forecasts to improve forecasting accuracy. Song, Wen and Liu also deal with interval forecasts but go a step further by introducing density forecasts to examine the probability distributions of future tourism demand forecasts, which is particularly relevant for decision makers seeking to determine the probability of future tourism demand fluctuations.

Yang and Zhang examine a neglected research topic in tourism demand forecasting using spatial models. They propose a dynamic spatial panel model for forecasting regional tourism demand that not only generates superior forecasts for different regions but also measures the spatial associations of tourism demand among neighbouring regions. Kourentzes and Athanasopoulos address how to obtain accurate forecasts across geographical or organisational demarcations of tourism destinations and propose an innovative reconciliation method for generating coherent forecasts across sections and planning horizons.

Law, Li, Fong and Han introduce the deep learning method to forecasting tourism demand and compare its performance with a number of artificial intelligence AI forecasting techniques, with positive results. Rice, Park, Pan and Newman and Assaf and Tsionas focus on industry-level forecasting methods suitable for tourism businesses. The former consider the performance of classical and advanced time series models in forecasting the demand for campgrounds in national parks.

The latter forecast hotel occupancy rates using a Bayesian compressed vector-autoregressive approach.

Annals of Tourism Research

Some important research areas such as advanced demand system models and forecasting tourism demand using mixed frequency data or big data are omitted in this Curated Collection. However, this can serve as a platform for stimulating continuous interest in advancing tourism demand forecasting methodologies and to generate important implications for both research and practice. Assaf, A.

Forecasting hotel occupancy: Bayesian compressed methods. Annals of Tourism Research, Vol. Kourentzes, N. Cross-temporal coherent forecasts for Australian tourism. Li, G. The combination of interval forecasts in tourism.


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Menges, G Die touristische konsumfunktion der Schweiz Rice, W. Forecasting campground demand in US national parks. Law, R. Tourism demand forecasting: a deep learning approach.

Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research) Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)
Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research) Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)
Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research) Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)
Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research) Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)
Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research) Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)
Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research) Tourism Public Policy, and the Strategic Management of Failure (Advances in Tourism Research)

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