DC FieldValueLanguage
dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorHu, Men_US
dc.creatorXiao, Men_US
dc.creatorLi, Hen_US
dc.date.accessioned2021-07-22T05:35:29Z-
dc.date.available2021-07-22T05:35:29Z-
dc.identifier.issn0959-6119en_US
dc.identifier.urihttp://hdl.handle.net/10397/90567-
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Limiteden_US
dc.subjectBaidu Indexen_US
dc.subjectMobile deviceen_US
dc.subjectPCen_US
dc.subjectSearch queryen_US
dc.subjectTourism demand forecastingen_US
dc.titleWhich search queries are more powerful in tourism demand forecasting : searches via mobile device or PC?en_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1108/IJCHM-06-2020-0559en_US
dcterms.abstractPurpose: While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting.en_US
dcterms.abstractDesign/methodology/approach: Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting.en_US
dcterms.abstractFindings: Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal.en_US
dcterms.abstractPractical implications: Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management.en_US
dcterms.abstractOriginality/value: This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationInternational journal of contemporary hospitality management, 2021, ahead-of-print, https://doi.org/10.1108/IJCHM-06-2020-0559en_US
dcterms.isPartOfInternational journal of contemporary hospitality managementen_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85107451437-
dc.identifier.eissn1757-1049en_US
dc.description.validate202107 bcvcen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera0984-n12-
dc.description.fundingSourceRGCen_US
dc.description.fundingText25500520en_US
dc.description.fundingTextThis study is supported by the National Natural Science Foundation of China (71761001), Early Career Scheme from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 25500520) and the Guangxi Key Research and Development Plan (Guike-AB20297040).en_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000-00-00 (to be updated)en_US
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