Forecasting Chinese tourist volume with search engine data
Tourism ManagementPublished 20 August 2014
Xin Yang, Bing Pan, James A. Evans, Benfu Lv
Citations416
SJR quartileQ1
SJR score4.15
SNIP3.74
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TL;DR
The study demonstrated the value of search engine data, proposed a method for selecting predictive queries, and showed the locality of the data for forecasting tourism demand, as well as comparing the predictive power of the search data of two different search engines, Google and Baidu.
Abstract
The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a prediction of their future travel behavior. This study used web search query volume to predict visitor numbers for a popular tourist destination in China, and compared the predictive power of the search data of two different search engines, Google and Baidu. The study verified the co-integration relationship between search engine query data and visitor volumes to Hainan Province. Compared to the corresponding auto-regression moving average (ARMA) models, both types of search engine data helped to significantly decrease forecasting errors. However, Baidu data performed better due to its larger market share in China. The study demonstrated the value of search engine data, proposed a method for selecting predictive queries, and showed the locality of the data for forecasting tourism demand.
Keywords
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