The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City

Xiankai Huang, Lifeng Zhang and Yusi Ding

Tourism Management, 2017, vol. 58, issue C, 301-306

Abstract: Tourist overcrowding of sites during the ‘Golden Week’ is a not an uncommon situation in China today. Consequently the prediction of tourist numbers is important for tourist attractions management and planning. Most existing methods rely on well-structured statistical data published by the government. However, this approach is limited in two aspects: 1) there may be significant delays in the publication of such data and 2) the sample size can be small, leading to inaccurate predictions. This paper proposes a novel approach for predicting tourist flows based on the Baidu Index. The Index provides search history containing different keywords on a daily basis dating back to 2006. The approach uses co-integration theory and Granger causality analysis to find the relationship between the internet search data and the actual tourist flow. The paper compares analysis results obtained by two kinds of predictive models, with or without considering Baidu Index. The study shows that there is a long-term equilibrium relationship and Granger causal relation between the observed number of tourists and a set of related keywords in the Baidu Index. It indicated a positive correlation between the increasing Baidu keyword search index and the increasing observed tourist flow.

Keywords: Baidu Index; Tourist attractions; Co-integration; Autoregressive moving average model; Autoregressive distributed lag model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:touman:v:58:y:2017:i:c:p:301-306

DOI: 10.1016/j.tourman.2016.03.015

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