 Forecasting Chinese tourist volume with search engine dataXin Yang, Bing Pan, James A. Evans and Benfu Lv Tourism Management, 2015, vol. 46, issue C, 386-397 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: Search engine data; Google Trends; Baidu Index; Chinese tourism market; Visitor prediction; Tourist volume forecast (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:touman:v:46:y:2015:i:c:p:386-397 DOI: 10.1016/j.tourman.2014.07.019 Access Statistics for this article Tourism Management is currently edited by Chris Ryan More articles in Tourism Management from Elsevier |
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