Tourism Demand Forecasting - Judgment Aided Models
Faulkner, B. and P. Valerio. 1995. "An integrative approach
to tourism demand forecasting." Tourism Management 16(1):29-37. This paper situates tourism forecasting within the context of the tourism
management activities it is supposed to serve. Too often, tourism forecasting
and the building of econometric models have become an end in itself. Instead,
techniques that rely on both quantitative analysis and expert involvement are
touted as more useful as they integrate forecasting into the strategic
planning exercise. This allows decision makers to understand the forecasting
process and may lead to decision-making which more reflective of the tourism
forecasts. Likewise, while citing chaos theory the authors suggest that expert
opinion can make very valuable contributions to the forecasting exercise. The
process that the Australian Tourist Commission goes through to set tourism
targets, which are essentially forecasts, is cited as an example of a valuable
integrated approach. Uysal, M. and J. L. Crompton. 1985. "An Overview of Approaches Used to
Forecast Tourism Demand." Journal of Travel Research 23(Spring):7-15. This paper presents a brief review of the tourism forecasting literature as
of 1985. Three qualitative techniques are examined: simple survey techniques,
Delphi models and judegement-aided models. Three quantitative techniques are
also reviewed: Time-series, gravity and trip generation models and
multivariate regression models. Archer, B. H. 1980. "Forecasting Demand, Quantitative and Intuitive
Techniques." International Journal of Tourism Management 1(1):5-12. This paper reviews the art of demand forecasting in tourism as of 1980.
Time-series analysis, the causal methods of multivariate regression and
gravity and trip generation models, and the qualitative techniques of surveys,
expert group processes and Delphi modeling are all discussed. The author
concludes that integrated techniques, which combine quantitative methods with
expert judgment may be the most accurate.
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