IDEAS home Printed from https://ideas.repec.org/a/taf/rgfmxx/v14y2023i2p226-242.html
   My bibliography  Save this article

Optimize the online shopping title of men’s plain-color shirts in e-commerce based on Kansei Engineering

Author

Listed:
  • Baoru Ge
  • Nazlina Shaari

Abstract

Online shopping sales of men’s plain-color shirts have fallen in China. Improving the quality of shirts’ online titles can effectively increase click-through rates and transaction rates. The subjective evaluative adjectives (Kansei Words) in the online titles for styling and color design are the most attractive to consumers. This study uses Exploratory Sequential Mixed Method combined with Kansei Engineering. First, in the phase of qualitative data collection, through interviews and documents, researchers collect 90 subjective evaluative adjectives (Kansei Words). Second, in the phase of quantitative survey research, through questionnaires, card sorting, hierarchical cluster and quick cluster, researchers got Kansei semantical space and established four evaluation dimensions: utility evaluation, symbolic evaluation, design evaluation, and occasion evaluation. Finally, in Kansei semantical space and evaluation dimensions, the final 15 Kansei Words that consumers are most interested in are gotten to optimize the online title. That is, this study finds evaluative adjectives consumers are most interested in through Kansei Engineering. Based on this, consumers’ preferences can be found, and product titles can be optimized to increase sales. Besides, through the research process of Kansei Engineering and Exploratory Sequential Mixed Method, this study provided methodological references for other clothing research categories about consumers’ preferences.

Suggested Citation

  • Baoru Ge & Nazlina Shaari, 2023. "Optimize the online shopping title of men’s plain-color shirts in e-commerce based on Kansei Engineering," Journal of Global Fashion Marketing, Taylor & Francis Journals, vol. 14(2), pages 226-242, April.
  • Handle: RePEc:taf:rgfmxx:v:14:y:2023:i:2:p:226-242
    DOI: 10.1080/20932685.2022.2085598
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/20932685.2022.2085598
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/20932685.2022.2085598?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:rgfmxx:v:14:y:2023:i:2:p:226-242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rgfm .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.