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
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Citations: View citations in EconPapers (29)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300566
DOI: 10.1016/j.annals.2020.102912
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