Identifying changes and trends in Hong Kong outbound tourism

Rob Law, Jia Rong, Huy Quan Vu, Gang Li, Hee Andy Lee

Research output: Contribution to journalArticleResearchpeer-review

34 Citations (Scopus)

Abstract

Despite the numerous research endeavors aimed at investigating tourists' preferences and motivations, it remains very difficult for practitioners to utilize the results of traditional association rule mining methods in tourism management. This research presents a new approach that extends the capability of the association rules technique to contrast targeted association rules with the aim of capturing the changes and trends in outbound tourism. Using datasets collected from five large-scale domestic tourism surveys of Hong Kong residents on outbound pleasure travel, both positive and negative contrasts are identified, thus enabling practitioners and policymakers to make appropriate decisions and develop more appropriate tourism products.

Original languageEnglish
Pages (from-to)1106-1114
Number of pages9
JournalTourism Management
Volume32
Issue number5
DOIs
Publication statusPublished - 1 Oct 2011
Externally publishedYes

Keywords

  • Association rules
  • Contrast analysis
  • Data mining
  • Hong Kong
  • Machine learning
  • Outbound tourism

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