 A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule miningJia Rong, Huy Quan Vu, Rob Law and Gang Li Tourism Management, 2012, vol. 33, issue 4, 731-740 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. Keywords: Sharers; Browsers; Electronic word-of-mouth; Association rules; Machine learning; Data mining; Hong Kong; Outbound tourism (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:touman:v:33:y:2012:i:4:p:731-740 DOI: 10.1016/j.tourman.2011.08.006 Access Statistics for this article Tourism Management is currently edited by Chris Ryan More articles in Tourism Management from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.