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Ethnic Restaurant Selection Patterns of U.S. Tourists in Hong Kong: An Application of Association Rule Mining

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Information and Communication Technologies in Tourism 2018

Abstract

Food is widely recognized as an important attribute that attracts tourists to a particular destination. Drawing on data related to the online reviews of U.S. tourists on ethnic restaurants in Hong Kong, this study presents the first attempt to identify tourists’ restaurant selection patterns at destinations by applying the association rule mining technique. Results of this study show that most U.S. tourists have tried Cantonese food while in Hong Kong. Japanese, Asian Fusion, Shanghainese and Taiwanese cuisines are found to be substituting alternatives to Cantonese cuisines, as tourist review patterns reveal negative relationships between them and ethnic Cantonese restaurants. A portion of U.S. tourists exhibit food neophobic behaviours as they appear to be reluctant to try Cantonese cuisines and stick to their own food instead. Given these findings, practical implications are provided for destination marketing oganisations.

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Authors and Affiliations

  1. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Muhyang (Moon) Oh, Irene Cheng Chu Chan & Fuad Mehraliyev

Authors
  1. Muhyang (Moon) Oh
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  2. Irene Cheng Chu Chan
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  3. Fuad Mehraliyev
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Corresponding author

Correspondence to Irene Cheng Chu Chan .

Editor information

Editors and Affiliations

  1. School of Hospitality and Tourism Management, University of Surrey, Guildford, United Kingdom

    Brigitte Stangl

  2. Business School, Centre for Tourism Studies, University of Eastern Finland, Kuopio, Finland

    Juho Pesonen

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(Moon) Oh, M., Chan, I.C.C., Mehraliyev, F. (2018). Ethnic Restaurant Selection Patterns of U.S. Tourists in Hong Kong: An Application of Association Rule Mining. In: Stangl, B., Pesonen, J. (eds) Information and Communication Technologies in Tourism 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-72923-7_10

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