Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90557
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | en_US |
dc.creator | Li, H | en_US |
dc.creator | Hu, M | en_US |
dc.creator | Li, G | en_US |
dc.date.accessioned | 2021-07-22T05:35:25Z | - |
dc.date.available | 2021-07-22T05:35:25Z | - |
dc.identifier.issn | 0160-7383 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/90557 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Li, H., Hu, M., & Li, G. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83, 102912 is available at https://dx.doi.org/10.1016/j.annals.2020.102912. | en_US |
dc.subject | Multisource big data | en_US |
dc.subject | Online review | en_US |
dc.subject | Search engine | en_US |
dc.subject | Tourism demand | en_US |
dc.subject | Tourist attraction | en_US |
dc.title | Forecasting tourism demand with multisource big data | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 83 | en_US |
dc.identifier.doi | 10.1016/j.annals.2020.102912 | en_US |
dcterms.abstract | Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Annals of tourism research, July 2020, v. 83, 102912 | en_US |
dcterms.isPartOf | Annals of tourism research | en_US |
dcterms.issued | 2020-07 | - |
dc.identifier.scopus | 2-s2.0-85083668373 | - |
dc.identifier.eissn | 1873-7722 | en_US |
dc.identifier.artn | 102912 | en_US |
dc.description.validate | 202107 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0984-n02 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | P0013971 | en_US |
dc.description.fundingText | This paper and research project (Project Account Code: 5-ZJLT) is funded by Research Grant of Hospitality and Tourism Research Centre (HTRC Grant) of the School of Hotel and Tourism Management, The Hong Kong Polytechnic University. This paper is also supported by the National Natural Science Foundation of China (71761001) and Hong Kong Scholars Program. | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Forecasting_tourism_demand.pdf | Pre-Published version | 1.62 MB | Adobe PDF | View/Open |
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