Forecasting Chinese tourist volume with search engine data

Xin Yang, Bing Pan, James A. Evans, Benfu Lv

Research output: Contribution to journalArticlepeer-review

202 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)386-397
Number of pages12
JournalTourism Management
Volume46
DOIs
StatePublished - Feb 2015

All Science Journal Classification (ASJC) codes

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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