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 Ltd
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
DOI: 10.1177/0047287513500391
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

11
Last Week
6
Last month
0
Citations as of Jan 11, 2017

WEB OF SCIENCETM
Citations

7
Last Week
0
Last month
0
Citations as of Jan 13, 2017

Page view(s)

22
Last Week
0
Last month
Checked on Jan 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.