EconPapers    
Economics at your fingertips  
 

Forecasting tourism demand with multisource big data

Hengyun Li, Mingming Hu and Gang Li

Annals of Tourism Research, 2020, vol. 83, issue C

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: Tourism demand; Tourist attraction; Search engine; Online review; Multisource big data (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (29)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160738320300566
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300566

DOI: 10.1016/j.annals.2020.102912

Access Statistics for this article

Annals of Tourism Research is currently edited by John Tribe

More articles in Annals of Tourism Research from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300566