No abstract

No abstract

A set of seventeen criteria are selected, with attention to their "independence," which are the principle measures for the determination of "touristic attractiveness." Then, through a process designed to elicit consistent judgments from an interviewee, the contributions of twenty-six tourism "experts" were combined to form a set of numerical weights which establish the relative importance of the seventeen criteria. The process of establishing a "score," as an indicator of the relative attractiveness of a touristic location, is outlined in the discussion of two illustrative applications.

No abstract

Many publications on tourism forecasting have appeared during the past twenty years. The purpose of this article is to organize and summarize that scattered literature. General conclusions are also drawn from the studies to help those wishing to develop tourism forecasts of their own. The forecasting techniques discussed include time series models, econometric causal models, the gravity model and expert-opinion techniques. The major conclusions are that time series models are the simplest and least costly (and therefore most appropriate for practitioners); the gravity model is best suited to handle international tourism flows (and will be most useful to governments and tourism agencies); and expert-opinion methods are useful when data are unavailable. Further research is needed on the use of economic indicators in tourism forecasting, on the development of attractivity and emissiveness indexes for use in gravity and econometric models and on empirical comparisons among the different methods. KEY WORDSTourism Forecasting Econometric models Gravity models Delphi Time series Tourism is one of the worlds largest industries. The tourism product, however, is perishable in nature, and its demand is vulnerable to many external factors. These two features make the forecasting of tourism flows essential.Three levels of organizations require tourism forecasts-the concerned government agencies, national and regional tourism organizations and the individual suppliers of tourist and travel facilities. This article surveys methods used for forecasting tourism, and suggests the most appropriate forecasting methods for governments, regional tourism organizations and private companies.Many taxonomies have been used to classify forecasting methods; (Archer, 1976(Archer, ,1980 Quandt, 1970; Van Doorn, 1982; Baron, 1979; Vanhove, 1980;Swart et al., 1978). This article consolidates the various approaches to forecasting tourism and aggregates them by classifying them into three categories: time series models, econometric causal models (including the gravity model) and expert-opinion methods. A discussion on each of these methods follows.