A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining

Jia Rong, Huy Quan Vu, Rob Law, Gang Li

Research output: Contribution to journalArticleResearchpeer-review

66 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)731-740
Number of pages10
JournalTourism Management
Issue number4
Publication statusPublished - 1 Aug 2012
Externally publishedYes


  • Association rules
  • Browsers
  • Data mining
  • Electronic word-of-mouth
  • Hong Kong
  • Machine learning
  • Outbound tourism
  • Sharers

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