Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30781
Title: A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining
Authors: Rong, J
Vu, HQ
Law, R 
Li, G
Keywords: Sharers
Browsers
Electronic word-of-mouth
Association rules
Machine learning
Data mining
Hong Kong
Outbound tourism
Issue Date: 2012
Publisher: Pergamon Press
Source: Tourism management, 2012, v. 33, no. 4, p. 731-740 How to cite?
Journal: Tourism management 
Abstract: With the widespread use of Internet technology, electronic word-of-mouth [eWOM] communication through online reviews of products and services has a strong influence on consumer behavior and preferences. Although prior research efforts have attempted to investigate the behavior of users regarding the sharing of personal experiences and browsing the experiences of others online, it remains a challenge for business managers to incorporate eWOM effects into their business planning and decision-making processes effectively. Applying a newly proposed association rule mining technique, this study investigates eWOM in the context of the tourism industry using an outbound domestic tourism data set that was recently collected in Hong Kong. The complete profiles and the relations of online experience sharers and travel website browsers are explored. The empirical results are useful in helping tourism managers to define new target customers and to plan more effective marketing strategies.
URI: http://hdl.handle.net/10397/30781
ISSN: 0261-5177
EISSN: 1879-3193
DOI: 10.1016/j.tourman.2011.08.006
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