Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107074
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | en_US |
dc.creator | Wu, DC | en_US |
dc.creator | Zhong, S | en_US |
dc.creator | Wu, J | en_US |
dc.creator | Song, H | en_US |
dc.date.accessioned | 2024-06-12T05:52:47Z | - |
dc.date.available | 2024-06-12T05:52:47Z | - |
dc.identifier.issn | 1096-3480 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107074 | - |
dc.language.iso | en | en_US |
dc.publisher | Sage Publications, Inc. | en_US |
dc.rights | © The Author(s) 2024 | en_US |
dc.rights | This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). | en_US |
dc.rights | The following publication Wu, D. C., Zhong, S., Wu, J., & Song, H. (2024). Tourism and Hospitality Forecasting With Big Data: A Systematic Review of the Literature. Journal of Hospitality & Tourism Research, 0(0) is available at https://doi.org/10.1177/10963480231223151. | en_US |
dc.subject | Big data | en_US |
dc.subject | Systematic review | en_US |
dc.subject | Theoretical foundation | en_US |
dc.subject | Tourism and hospitality forecasting | en_US |
dc.subject | Unstructured data | en_US |
dc.title | Tourism and hospitality forecasting with big data : systematic review of the literature | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.doi | 10.1177/10963480231223151 | en_US |
dcterms.abstract | Empirical research has shown that incorporating big data into tourism and hospitality forecasting significantly improves prediction accuracy. This study presents a comprehensive review of big data forecasting in the tourism and hospitality industry, critically evaluating existing research and identifying five key research questions and trends that require further attention. These include the lack of theoretical foundation, the rise of high-frequency forecasting research, less attention to unstructured data, the necessity of dynamic data analysis in forecasting, and the construction of a tourism and hospitality demand information system based on cloud computing. Importantly, this study constructs a theoretical framework by combining relevant theories from psychology, communication, information processing, and other fields. Five types of big data used for tourism and hospitality forecasting are identified: web-based volume data, social media statistics, textual data, photo data, and video data. Additionally, more recent tactics such as mixed data sampling and machine learning methods are discussed. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of hospitality and tourism research, First published online January 28, 2024, OnlineFirst, https://doi.org/10.1177/10963480231223151 | en_US |
dcterms.isPartOf | Journal of hospitality and tourism research | en_US |
dcterms.issued | 2024 | - |
dc.identifier.scopus | 2-s2.0-85183855944 | - |
dc.identifier.eissn | 1557-7554 | en_US |
dc.description.validate | 202406 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a2802 | - |
dc.identifier.SubFormID | 48406 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China (72374226, 72322020, 72071218); Guangdong Basic and Applied Basic Research Foundation (2020B1515020031, 2023B1515020073); The Hong Kong Polytechnic University (1-ZE2S) | en_US |
dc.description.pubStatus | Early release | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Wu_Tourism_Hospitality_Forecasting.pdf | 777.52 kB | Adobe PDF | View/Open |
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