DC FieldValueLanguage
dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorLi, Hen_US
dc.creatorHu, Men_US
dc.creatorLi, Gen_US
dc.date.accessioned2021-07-22T05:35:25Z-
dc.date.available2021-07-22T05:35:25Z-
dc.identifier.issn0160-7383en_US
dc.identifier.urihttp://hdl.handle.net/10397/90557-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectMultisource big dataen_US
dc.subjectOnline reviewen_US
dc.subjectSearch engineen_US
dc.subjectTourism demanden_US
dc.subjectTourist attractionen_US
dc.titleForecasting tourism demand with multisource big dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume83en_US
dc.identifier.doi10.1016/j.annals.2020.102912en_US
dcterms.abstractBased on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationAnnals of tourism research, July 2020, v. 83, 102912en_US
dcterms.isPartOfAnnals of tourism researchen_US
dcterms.issued2020-07-
dc.identifier.scopus2-s2.0-85083668373-
dc.identifier.eissn1873-7722en_US
dc.identifier.artn102912en_US
dc.description.validate202107 bcvcen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera0984-n02-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextP0013971en_US
dc.description.fundingTextThis paper and research project (Project Account Code: 5-ZJLT) is funded by Research Grant of Hospitality and Tourism Research Centre (HTRC Grant) of the School of Hotel and Tourism Management, The Hong Kong Polytechnic University. This paper is also supported by the National Natural Science Foundation of China (71761001) and Hong Kong Scholars Program.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023.07.31en_US
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