Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8062
Title: Predicting hotel demand using destination marketing organization's Web traffic data
Authors: Yang, Y
Pan, B
Song, H 
Keywords: Big data
Hotel occupancy
Online data
Time series
Tourism demand forecasting
Website traffic
Issue Date: 2014
Publisher: SAGE Publications
Source: Journal of travel research, 2014, v. 53, no. 4, p. 433-447 How to cite?
Journal: Journal of travel research 
Abstract: This study uses the web traffic volume data of a destination marketing organization (DMO) to predict hotel demand for the destination. The results show a significant improvement in the error reduction of ARMAX models, compared with their ARMA counterparts, for short-run forecasts of room nights sold by incorporating web traffic data as an explanatory variable.These empirical results demonstrate the significant value of website traffic data in predicting demand for hotel rooms at a destination, and potentially even local businesses' future revenue and performance. The implications for future research on using big data for forecasting hotel demand is also discussed.
URI: http://hdl.handle.net/10397/8062
ISSN: 0047-2875
EISSN: 1552-6763
DOI: 10.1177/0047287513500391
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