Please use this identifier to cite or link to this item: 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
Appears in Collections:Journal/Magazine Article

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