IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-032-08483-5_9.html

“Was This Answer Helpful?”—A Taxonomy for Feedback Mechanisms in Customer Service Chatbots

In: Digital Innovation and Organizational Transformation

Author

Listed:
  • Daniel Schloss

    (Karlsruhe Institute of Technology, Human-Centered Systems Lab)

  • Saskia Haug

    (Karlsruhe Institute of Technology, Human-Centered Systems Lab)

  • Alexander Maedche

    (Karlsruhe Institute of Technology, Human-Centered Systems Lab)

Abstract

Chatbot technology has rapidly spread, especially in digital customer service. However, the automation potential of chatbots can only be realized if customers are satisfied with their service. Collecting explicit feedback is a promising technique for assessing customer satisfaction and identifying issues with the chatbot. It enables chatbot managers and developers to enhance performance and design of operational chatbots on an informed basis. The evident significance of explicit customer feedback comes with a multitude of design options available. However, there is a lack of research on chatbot feedback mechanisms and practical as well as theoretical clarity. In this paper, we address this gap by introducing a chatbot feedback taxonomy derived from existing research and a sample of N = 72 real world customer service chatbots. Furthermore, based on a cluster analysis, we identify four archetypes of feedback mechanisms and provide strategic guidelines for the informed use of each of those feedback design variants.

Suggested Citation

  • Daniel Schloss & Saskia Haug & Alexander Maedche, 2026. "“Was This Answer Helpful?”—A Taxonomy for Feedback Mechanisms in Customer Service Chatbots," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Digital Innovation and Organizational Transformation, pages 119-135, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08483-5_9
    DOI: 10.1007/978-3-032-08483-5_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:spr:lnichp:978-3-032-08483-5_9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.