Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90567
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | en_US |
dc.creator | Hu, M | en_US |
dc.creator | Xiao, M | en_US |
dc.creator | Li, H | en_US |
dc.date.accessioned | 2021-07-22T05:35:29Z | - |
dc.date.available | 2021-07-22T05:35:29Z | - |
dc.identifier.issn | 0959-6119 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/90567 | - |
dc.language.iso | en | en_US |
dc.publisher | Emerald Group Publishing Limited | en_US |
dc.rights | © Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher. | en_US |
dc.rights | The following publication Hu, M., Xiao, M. and Li, H. (2021), "Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?", International Journal of Contemporary Hospitality Management, Vol. 33 No. 6, pp. 2022-2043 is published by Emerald and is available at https://dx.doi.org/10.1108/IJCHM-06-2020-0559 | en_US |
dc.subject | Baidu Index | en_US |
dc.subject | Mobile device | en_US |
dc.subject | PC | en_US |
dc.subject | Search query | en_US |
dc.subject | Tourism demand forecasting | en_US |
dc.title | Which search queries are more powerful in tourism demand forecasting : searches via mobile device or PC? | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2022 | en_US |
dc.identifier.epage | 2043 | en_US |
dc.identifier.volume | 33 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1108/IJCHM-06-2020-0559 | en_US |
dcterms.abstract | Purpose: While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting. | en_US |
dcterms.abstract | Design/methodology/approach: Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting. | en_US |
dcterms.abstract | Findings: Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal. | en_US |
dcterms.abstract | Practical implications: Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management. | en_US |
dcterms.abstract | Originality/value: This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | International journal of contemporary hospitality management, 9 Aug. 2021, v. 33, no. 6, p. 2022-2043 | en_US |
dcterms.isPartOf | International journal of contemporary hospitality management | en_US |
dcterms.issued | 2021-08-09 | - |
dc.identifier.scopus | 2-s2.0-85107451437 | - |
dc.identifier.eissn | 1757-1049 | en_US |
dc.description.validate | 202107 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0984-n12 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingText | 25500520 | en_US |
dc.description.fundingText | This study is supported by the National Natural Science Foundation of China (71761001), Early Career Scheme from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 25500520) and the Guangxi Key Research and Development Plan (Guike-AB20297040). | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Hu_Xiao_&_Li-IJCHM-2021.pdf | Pre-Published version | 2.15 MB | Adobe PDF | View/Open |
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