Tourism Management, August 2019, Vol.73, pp.105-114
Search engines have become the main sources for tourists to obtain trip- and destination-related information, and valuable data sources for marketers to better understand the spatial-temporal patterns of tourist online search behaviours across cities. This paper utilises co-integration and Granger causality tests, taking Tianmu Lake as an example, to examine the relationship between daily tourist arrivals and search indexes from 13 cites. It further tests their spatial and temporal correlations using the impulse function in a vector auto-regressive model. Our findings are as follows. First, a long-term equilibrium relationship exists between the daily number of tourists and search volume index (SVI), with the SVI Granger causing tourist arrivals. Second, SVI is a useful predictor of tourist flows 1–2 days prior to visits for cities within 100 km driving distance, and the lag period of response increases as distance increases. Finally, SVI and tourist volume are inversely proportional to distance, while travel information demand is directly proportional.
Tourism Information Search ; Search Engine ; Tourist Flow ; Tourist Marketing ; Var ; Big Data ; Geography ; Business
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