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The Brand Effect: A Case Study in Taiwan Second-Hand Smartfhone Market

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  • Wang Guan-Yuan

    (National Taiwan University, Taipei, Taiwan)

Abstract

Since the smartphone market is an oligopoly market structure, consumer purchase intention is usually driven by brand preference. This research analyses the customer-to-customer market of second-hand smartphones, pointing out how the brand factor affects the consumers’ purchasing behaviour. It is found that the recovery value and life cycle of Apple smartphones are higher and longer than those of other brands. Moreover, the recovery value of other brand smartphones is significantly driven by the debut date of the Apple smartphones, implicitly forming a consumption cycle. In addition, through machine learning models, the predictability for the recovery value is able to reach 93.55%.

Suggested Citation

  • Wang Guan-Yuan, 2021. "The Brand Effect: A Case Study in Taiwan Second-Hand Smartfhone Market," Journal of Social and Economic Statistics, Sciendo, vol. 10(1-2), pages 30-42, December.
    • Handle: RePEc:vrs:jsesro:v:10:y:2021:i:1-2:p:30-42:n:1
    DOI: 10.2478/jses-2021-0003
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    References listed on IDEAS

    as
    1. Bart J. Bronnenberg & Jean-Pierre H. Dube & Matthew Gentzkow, 2012. "The Evolution of Brand Preferences: Evidence from Consumer Migration," American Economic Review, American Economic Association, vol. 102(6), pages 2472-2508, October.
    2. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    3. Kim, Junghun & Lee, Hyunjoo & Lee, Jongsu, 2020. "Smartphone preferences and brand loyalty: A discrete choice model reflecting the reference point and peer effect," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    6. Salmi Mohd Isa & Lai Kelly & Shaian Kiumarsi, 2020. "Brand switching through marketing mix: the role of brand effect on smartphone users," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 10(3), pages 419-438.
    7. Cindy Lombart & Didier Louis, 2010. "Impact of brand personality in 3 major relational consequences (trust, attachment and commitment to the brand)," Post-Print hal-00771151, HAL.
    8. Bart J. Bronnenberg & Jean-Pierre Dubé, 2017. "The Formation of Consumer Brand Preferences," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 353-382, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Smartphone; Brand Value; Recovery Value; Consumer Purchase Intention;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L89 - Industrial Organization - - Industry Studies: Services - - - Other
    • M20 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - General

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