Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/118530
DC FieldValueLanguage
dc.contributorResearch Centre for Digital Transformation of Tourismen_US
dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorZhang, Hen_US
dc.creatorLiu, Yen_US
dc.creatorLiu, Xen_US
dc.creatorLiu, Aen_US
dc.creatorLin, VSen_US
dc.date.accessioned2026-04-20T03:58:13Z-
dc.date.available2026-04-20T03:58:13Z-
dc.identifier.issn0160-7383en_US
dc.identifier.urihttp://hdl.handle.net/10397/118530-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.subjectChinese outbound Tourismen_US
dc.subjectDelphi methoden_US
dc.subjectForecast combinationen_US
dc.subjectJudgmental adjustmentsen_US
dc.subjectRecovery patternen_US
dc.titleForecasting Chinese outbound tourism recovery : a Triple-layer forecast combination frameworken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume116en_US
dc.identifier.doi10.1016/j.annals.2025.104079en_US
dcterms.abstractForecast combinations became particularly significant in the post-pandemic era due to heightened uncertainty. This study introduces a Triple-layer Forecast Combination Framework to predict Chinese outbound tourism recovery from August 2023 to July 2024 across 20 destinations. The framework integrates baseline quantitative models, expert-based model selection, and real-time judgmental adjustments to enhance forecast accuracy in post-crisis contexts. Results show Chinese visitor arrivals rebounding, on average, to 80% of July 2019 levels by mid-2024, with East and Southeast Asia—particularly Hong Kong SAR, Macao SAR, and Thailand—recovering faster than long-haul markets such as Hawaii, Canada, and the Czech Republic. By combining statistical rigor with contextual insight, the framework supports replicable, adaptive forecasting under uncertainty for tourism recovery planning.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationAnnals of tourism research, Jan. 2026, v. 116, 104079en_US
dcterms.isPartOfAnnals of tourism researchen_US
dcterms.issued2026-01-
dc.identifier.eissn1873-7722en_US
dc.identifier.artn104079en_US
dc.description.validate202604 bcchen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera4198c-
dc.identifier.SubFormID52243-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by the National Natural Science Foundation of China (Grant No. 72402194, 72572153), Start-up Fund for RAPs under the Strategic Hiring Scheme at The Hong Kong Polytechnic University (Grant No. 1-BD2W), and Project of Strategic Importance (Grant No. 1-ZE2S).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2029-01-31en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2029-01-31
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