Forecasting tourism demand with multisource big data

Li, Hengyun, Hu, Mingming and Li, Gang (2020) Forecasting tourism demand with multisource big data Annals of Tourism Research, 83, 102912.

Text FORECASTING TOURISM DEMAND WITH MULTISOURCE BIG DATA authosversion.pdf - Accepted version Manuscript Restricted to Repository staff only until 25 April 2022. Download (1MB)

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.

Item Type:	Article
Divisions :	Faculty of Arts and Social Sciences > School of Hospitality and Tourism Management
Authors :	Name Email ORCID Li, Hengyun Hu, Mingming Li, Gang G.Li@surrey.ac.uk
Date :	July 2020
DOI :	10.1016/j.annals.2020.102912
Copyright Disclaimer :	© 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/
Uncontrolled Keywords :	Tourism demand; Tourist attraction; Search engine; Online review; Multisource big data
Depositing User :	Clive Harris
Date Deposited :	10 Jun 2020 17:07
Last Modified :	10 Jun 2020 17:07
URI:	http://epubs.surrey.ac.uk/id/eprint/857770

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