About: The forecasting of International Expo tourism using quantitative and qualitative techniques

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The forecasting of International Expo tourism using quantitative and qualitative techniques
Creator	Lee, Choong-Ki Song, Hak-Jun Mjelde, James
Source	Elsevier; Medline; PMC
abstract	Abstract This study predicts the number of visitors to an international tourism Expo to be held in Korea in 2012, an unprecedented event for the host city. Forecasting demand for such a mega-event has received only limited attention in the literature. Unlike most studies forecasting international tourism demand, forecasting Expo demand involves using both quantitative forecasting models and qualitative technique because of data limitations. Combining quantitative techniques with willingness-to-visit (WTV) surveys predicts the Expo demand at 8.9 million visitors. In comparison using the Delphi method, experts predict Expo demand at 6.8 million visitors. For this study, the Delphi method provides more conservative estimates than estimates from combining quantitative techniques with WTV. Policy implications presented are directed toward Expo planners and practitioners in terms of demand and supply side, application of these results in the decision-making process, and future challenges surrounding demand forecasting.
has issue date	2008-12-31(xsd:dateTime)
bibo:doi	10.1016/j.tourman.2008.02.007
bibo:pmid	32287725
has license	els-covid
sha1sum (hex)	7a3674479e7d81e55f10cd4388d69419e52cd65c
schema:url	https://doi.org/10.1016/j.tourman.2008.02.007
resource representing a document's title	The forecasting of International Expo tourism using quantitative and qualitative techniques
has PubMed Central identifier	PMC7115425
has PubMed identifier	32287725
schema:publication	Tourism Management
resource representing a document's body	covid:7a3674479e7d81e55f10cd4388d69419e52cd65c#body_text
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