Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/13309
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Moro, S. | - |
dc.contributor.author | Rita, P. | - |
dc.date.accessioned | 2017-05-11T14:55:47Z | - |
dc.date.available | 2017-05-11T14:55:47Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 1755-4217 | - |
dc.identifier.uri | http://hdl.handle.net/10071/13309 | - |
dc.description.abstract | Purpose: This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach: For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings: The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value: The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps. | eng |
dc.language.iso | eng | - |
dc.publisher | Emerald | - |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147442/PT | - |
dc.rights | openAccess | por |
dc.subject | Tourism forecasting | eng |
dc.subject | Tourism demand | eng |
dc.subject | Tourists’ behavior | eng |
dc.subject | Modeling | eng |
dc.subject | Tourism prediction | eng |
dc.title | Forecasting tomorrow’s tourist | eng |
dc.type | article | - |
dc.pagination | 643 - 653 | - |
dc.publicationstatus | Publicado | por |
dc.peerreviewed | yes | - |
dc.journal | Worldwide Hospitality and Tourism Themes | - |
dc.distribution | Internacional | por |
dc.volume | 8 | - |
dc.number | 6 | - |
degois.publication.firstPage | 643 | - |
degois.publication.lastPage | 653 | - |
degois.publication.issue | 6 | - |
degois.publication.title | Forecasting tomorrow’s tourist | eng |
dc.date.updated | 2019-04-23T11:07:53Z | - |
dc.description.version | info:eu-repo/semantics/submittedVersion | - |
dc.identifier.doi | 10.1108/WHATT-09-2016-0046 | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências da Terra e do Ambiente | por |
dc.subject.fos | Domínio/Área Científica::Ciências Sociais::Psicologia | por |
dc.subject.fos | Domínio/Área Científica::Ciências Sociais::Economia e Gestão | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-29845 | - |
iscte.alternateIdentifiers.wos | WOS:000394170500005 | - |
iscte.alternateIdentifiers.scopus | 2-s2.0-85002742084 | - |
Aparece nas coleções: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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AntecipatingTomorrowsTourist_Manuscript.pdf | Pré-print | 584,88 kB | Adobe PDF | Ver/Abrir |
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