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Identifying changes and trends in Hong Kong outbound tourism

Author

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

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.

Suggested Citation

  • Law, Rob & Rong, Jia & Vu, Huy Quan & Li, Gang & Lee, Hee Andy, 2011. "Identifying changes and trends in Hong Kong outbound tourism," Tourism Management, Elsevier, vol. 32(5), pages 1106-1114.
    • Handle: RePEc:eee:touman:v:32:y:2011:i:5:p:1106-1114
    DOI: 10.1016/j.tourman.2010.09.011
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    References listed on IDEAS

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    1. Hokey Min, 2006. "Developing the profiles of supermarket customers through data mining," The Service Industries Journal, Taylor & Francis Journals, vol. 26(7), pages 747-763, October.
    2. Wong, Jehn-Yih & Chung, Pi-Heng, 2007. "Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques," Journal of Air Transport Management, Elsevier, vol. 13(6), pages 362-370.
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    Cited by:

    1. Rong, Jia & Vu, Huy Quan & Law, Rob & Li, Gang, 2012. "A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining," Tourism Management, Elsevier, vol. 33(4), pages 731-740.
    2. Yuan, Hua & Xu, Hualin & Qian, Yu & Li, Yan, 2016. "Make your travel smarter: Summarizing urban tourism information from massive blog data," International Journal of Information Management, Elsevier, vol. 36(6), pages 1306-1319.
    3. Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    4. Tosporn Arreeras & Mikiharu Arimura & Takumi Asada & Saharat Arreeras, 2019. "Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    5. Hussain, Muhammad Noshab, 2023. "Evaluating the impact of air transportation, railway transportation, and trade openness on inbound and outbound tourism in BRI countries," Journal of Air Transport Management, Elsevier, vol. 106(C).
    6. Versichele, Mathias & de Groote, Liesbeth & Claeys Bouuaert, Manuel & Neutens, Tijs & Moerman, Ingrid & Van de Weghe, Nico, 2014. "Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium," Tourism Management, Elsevier, vol. 44(C), pages 67-81.
    7. Monica WC Choy & Ken Kamoche, 2022. "Factors influencing recommendation of sub-Saharan Africa travel products: A Hong Kong–Kenya importance–performance analysis," Tourism Economics, , vol. 28(4), pages 1101-1128, June.
    8. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.

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