Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/118530
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Research Centre for Digital Transformation of Tourism | en_US |
| dc.contributor | School of Hotel and Tourism Management | en_US |
| dc.creator | Zhang, H | en_US |
| dc.creator | Liu, Y | en_US |
| dc.creator | Liu, X | en_US |
| dc.creator | Liu, A | en_US |
| dc.creator | Lin, VS | en_US |
| dc.date.accessioned | 2026-04-20T03:58:13Z | - |
| dc.date.available | 2026-04-20T03:58:13Z | - |
| dc.identifier.issn | 0160-7383 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/118530 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Ltd | en_US |
| dc.subject | Chinese outbound Tourism | en_US |
| dc.subject | Delphi method | en_US |
| dc.subject | Forecast combination | en_US |
| dc.subject | Judgmental adjustments | en_US |
| dc.subject | Recovery pattern | en_US |
| dc.title | Forecasting Chinese outbound tourism recovery : a Triple-layer forecast combination framework | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 116 | en_US |
| dc.identifier.doi | 10.1016/j.annals.2025.104079 | en_US |
| dcterms.abstract | Forecast 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.accessRights | embargoed access | en_US |
| dcterms.bibliographicCitation | Annals of tourism research, Jan. 2026, v. 116, 104079 | en_US |
| dcterms.isPartOf | Annals of tourism research | en_US |
| dcterms.issued | 2026-01 | - |
| dc.identifier.eissn | 1873-7722 | en_US |
| dc.identifier.artn | 104079 | en_US |
| dc.description.validate | 202604 bcch | en_US |
| dc.description.oa | Not applicable | en_US |
| dc.identifier.FolderNumber | a4198c | - |
| dc.identifier.SubFormID | 52243 | - |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | This 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.pubStatus | Published | en_US |
| dc.date.embargo | 2029-01-31 | en_US |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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