IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i22p6708-6727.html
   My bibliography  Save this article

Mining online reviews with a Kansei-integrated Kano model for innovative product design

Author

Listed:
  • Jian Jin
  • Danping Jia
  • Kejia Chen

Abstract

Optimising affective design based on customer needs help to earn competitive advantages. However, affective design has been studied qualitatively and the value of online opinions providing affective design ideas has not been exploited deeply. To fill this gap, a framework is proposed to reveal customer affective needs from a perspective of the Kansei-integrated Kano model. Firstly, inspired by Kansei Engineering, customer affective emotions are extracted contextually from online reviews. Next, related product features are positioned based on syntactic relations and a clustering algorithm. Enlightened by the Kano model, product features are prioritised based on affective emotions to show their importance on customer satisfaction. Finally, experiments with practical data are illustrated to evaluate the effectiveness in innovative product design. Take smartphone for example and camera, material, aesthetic design, safety, service, physical interface, and price are found to be attractive features in this study. Additionally, product reviews are transferred into a structured format by analysing affective emotions and corresponding features. It provides a straightforward perspective on affective needs, which facilitate new product design by including innovative design ideas and the proposed KE-integrated Kano model helps to capture customer affective needs and give inspirable insights for affective design from the viewpoint of companies.

Suggested Citation

  • Jian Jin & Danping Jia & Kejia Chen, 2022. "Mining online reviews with a Kansei-integrated Kano model for innovative product design," International Journal of Production Research, Taylor & Francis Journals, vol. 60(22), pages 6708-6727, November.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:22:p:6708-6727
    DOI: 10.1080/00207543.2021.1949641
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2021.1949641
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2021.1949641?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Ting & Xiong, Yu, 2024. "Product positioning of low-carbon products based on blockchain-enabled product communities," Technovation, Elsevier, vol. 132(C).
    2. Jia-Li Chang & Hui Li & Jian Wu, 2023. "How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating," Group Decision and Negotiation, Springer, vol. 32(2), pages 327-358, April.

    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:tprsxx:v:60:y:2022:i:22:p:6708-6727. 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/TPRS20 .

    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.