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http://hdl.handle.net/10397/90557
Title: | Forecasting tourism demand with multisource big data | Authors: | Li, H Hu, M Li, G |
Issue Date: | Jul-2020 | Source: | Annals of tourism research, July 2020, v. 83, 102912 | 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. | Keywords: | Multisource big data Online review Search engine Tourism demand Tourist attraction |
Publisher: | Pergamon Press | Journal: | Annals of tourism research | ISSN: | 0160-7383 | EISSN: | 1873-7722 | DOI: | 10.1016/j.annals.2020.102912 | Rights: | © 2020 Elsevier Ltd. All rights reserved. © 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/. 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. |
Appears in Collections: | Journal/Magazine Article |
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Li_Forecasting_tourism_demand.pdf | Pre-Published version | 1.62 MB | Adobe PDF | View/Open |
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