Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94045
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | - |
dc.creator | Hu, M | - |
dc.creator | Li, H | - |
dc.creator | Song, H | - |
dc.creator | Li, X | - |
dc.creator | Law, R | - |
dc.date.accessioned | 2022-08-11T01:06:37Z | - |
dc.date.available | 2022-08-11T01:06:37Z | - |
dc.identifier.issn | 0261-5177 | - |
dc.identifier.uri | http://hdl.handle.net/10397/94045 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.subject | Hong Kong | en_US |
dc.subject | MIDAS | en_US |
dc.subject | Online review | en_US |
dc.subject | Social media data | en_US |
dc.subject | Tourism demand forecasting | en_US |
dc.title | Tourism demand forecasting using tourist-generated online review data | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 90 | - |
dc.identifier.doi | 10.1016/j.tourman.2022.104490 | - |
dcterms.abstract | This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists’ online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models. | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | Tourism management, June 2022, v. 90, 104490 | - |
dcterms.isPartOf | Tourism management | - |
dcterms.issued | 2022-06 | - |
dc.identifier.scopus | 2-s2.0-85122628099 | - |
dc.identifier.eissn | 1879-3193 | - |
dc.identifier.artn | 104490 | - |
dc.description.validate | 202208 bcch | - |
dc.identifier.FolderNumber | a1520 | en_US |
dc.identifier.SubFormID | 45321 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Others: National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2025-06-30 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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