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Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method

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  • Kim, Samuel Seongseop
  • Timothy, Dallen J.
  • Hwang, Jinsoo

Abstract

This study was designed to assess the factors affecting Japanese tourists’ shopping preference and intention to revisit Korea. The analytical method applied in this study was Decision Tree Analysis, which is under-utilized in tourism studies. A total of 300 questionnaires were collected on the basis of on-site survey method and used for data analysis. Among interesting findings, three groups including ‘respondents who were satisfied, accompanied, and spent US$50–300 on shopping’, ‘respondents who were satisfied, accompanied, and had a shopping expenditure of US$300–1000’ and ‘respondents who were satisfied, accompanied, and had a shopping expenditure of US$1000–5000,’ showed a high level of intention to return to Korea for the purpose of shopping. In addition, two groups ‘those who were interested in shopping in Korea, preferred a shopping mall as a shopping destination, and had an educational level of below high school’ and ‘those who were interested in shopping in Korea, preferred a shopping mall as a shopping destination, and had an educational level of a college graduate or above’ showed a higher level interest in merchandise than in shopping venue attractiveness.

Suggested Citation

  • Kim, Samuel Seongseop & Timothy, Dallen J. & Hwang, Jinsoo, 2011. "Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method," Tourism Management, Elsevier, vol. 32(3), pages 544-554.
    • Handle: RePEc:eee:touman:v:32:y:2011:i:3:p:544-554
    DOI: 10.1016/j.tourman.2010.04.008
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    1. Stoel, Leslie & Wickliffe, Vanessa & Lee, Kyu Hye, 2004. "Attribute beliefs and spending as antecedents to shopping value," Journal of Business Research, Elsevier, vol. 57(10), pages 1067-1073, October.
    2. Lesser, Jack A. & Hughes, Marie Adele, 1986. "Towards a typology of shoppers," Business Horizons, Elsevier, vol. 29(6), pages 56-62.
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    3. Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.
    4. Boulhosa Inês & Casais Beatriz, 2019. "Motivations for Tourism Shopping in Department Stores: An Exploratory Research about Tourists’ Profiles Visiting El Corte Inglés Gaia," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 9(1), pages 18-26, May.
    5. Yuhao Liu & Yunseon Choe, 2023. "Motivation-Based Segmentation of Tourist Shoppers to Hainan During COVID-19," SAGE Open, , vol. 13(3), pages 21582440231, September.
    6. Likhil Sukumaran & Ritanjali Majhi, 2025. "Not all who proclaim to be green are really green: analysis of intention behavior gap through a systematic review of literature," Management Review Quarterly, Springer, vol. 75(2), pages 1535-1574, June.
    7. OROIAN, Maria & RATIU, Ramona-Flavia & GHERES, Marinela, 2013. "Using The Residents’ Profile As Potential Tourists In Tourist Market Segmentation: The Case Of Mures County, Romania," Academica Science Journal, Economica Series, Dimitrie Cantemir University, Faculty of Economical Science, vol. 1(2), pages 21-34, May.
    8. Marquet, Oriol & Miralles-Guasch, Carme, 2014. "Walking short distances. The socioeconomic drivers for the use of proximity in everyday mobility in Barcelona," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 210-222.
    9. Fernanda Oliveira & Pedro Pintassilgo & Patrícia Pinto & Isabel Mendes & João Albino Silva, 2017. "Segmenting visitors based on willingness to pay for recreational benefits," Tourism Economics, , vol. 23(3), pages 680-691, May.
    10. Xiaolong Guo & Ben Li & Yan Liu & Liang Liang, 2017. "Eliminating the Inconvenience of Carrying: Optimal Pricing of Delivery Service for Retailers," Service Science, INFORMS, vol. 9(3), pages 181-191, September.
    11. Dong-sheng Liu & Shu-jiang Fan, 2013. "Tourist Behavior Pattern Mining Model Based on Context," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-12, November.
    12. Jin, Haipeng & Moscardo, Gianna & Murphy, Laurie, 2017. "Making sense of tourist shopping research: A critical review," Tourism Management, Elsevier, vol. 62(C), pages 120-134.
    13. Brida, Juan Gabriel & Tokarchuk, Oksana, 2017. "Tourists' spending and adherence to shopping plans: The case of the christmas market in Merano, Italy," Tourism Management, Elsevier, vol. 61(C), pages 55-62.
    14. Pantano, Eleonora & Dennis, Charles, 2019. "Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 304-310.
    15. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    16. Antonia Correia & Metin Kozak & Seongseop (Sam) Kim, 2018. "Luxury shopping orientations of mainland Chinese tourists in Hong Kong," Tourism Economics, , vol. 24(1), pages 92-108, February.
    17. Grant, Delvin & Yeo, Benjamin, 2018. "A global perspective on tech investment, financing, and ICT on manufacturing and service industry performance," International Journal of Information Management, Elsevier, vol. 43(C), pages 130-145.
    18. Metin Kozak & Antónia Correia & Giacomo Del Chiappa, 2017. "The propensity to bargain while on a vacation," Tourism Economics, , vol. 23(1), pages 150-167, February.
    19. do Valle, Patrícia Oom & Pintassilgo, Pedro & Matias, António & André, Filipe, 2012. "Tourist attitudes towards an accommodation tax earmarked for environmental protection: A survey in the Algarve," Tourism Management, Elsevier, vol. 33(6), pages 1408-1416.

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