IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04325624.html
   My bibliography  Save this paper

Classifying and measuring the service quality of AI chatbot in frontline service

Author

Listed:
  • Qian Chen

    (HZAU - Huazhong Agricultural University [Wuhan])

  • Yeming Gong

    (EM - EMLyon Business School)

  • Yaobin Lu

    (HUST - Huazhong University of Science and Technology [Wuhan])

  • Jing Tang

    (RIT - Rochester Institute of Technology)

Abstract

AI chatbots have been widely applied in the frontline to serve customers. Yet, the existing dimensions and scales of service quality can hardly fit the new AI environment. To address this gap, we define the dimensions of AI chatbot service quality (AICSQ) and develop the associated scales with a mixed-method approach. In the qualitative analysis, with the coding of the interviews from 55 global organizations in 17 countries and 47 customers, we develop new multi-level dimensions of AICSQ, including seven second-order and 18 first-order constructs. Then we follow a 10-step scale development method to establish the valid scales. The nomological test result shows that AICSQ positively influences customers' satisfaction with, perceived value of, and intention of continuous use of AI chatbots. The innovative dimensions and scales of AI chatbot service quality provide conceptual classification and measurement instruments for the future study of chatbot service in the frontline.

Suggested Citation

  • Qian Chen & Yeming Gong & Yaobin Lu & Jing Tang, 2022. "Classifying and measuring the service quality of AI chatbot in frontline service," Post-Print hal-04325624, HAL.
    • Handle: RePEc:hal:journl:hal-04325624
    DOI: 10.1016/j.jbusres.2022.02.088
    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 search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Ma, Liang & Yu, Peng & Zhang, Xin & Wang, Gaoshan & Hao, Feifei, 2024. "How AI use in organizations contributes to employee competitive advantage: The moderating role of perceived organization support," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    2. Kim Shin Young & Sang-Gun Lee & Ga Youn Hong, 2024. "User satisfaction with the service quality of ChatGPT," Service Business, Springer;Pan-Pacific Business Association, vol. 18(3), pages 417-431, December.
    3. Javed Ali, 2024. "Thematic Evolution of Service Quality (2011-2023): Insights for Future Research Directions ," GATR Journals jmmr341, Global Academy of Training and Research (GATR) Enterprise.
    4. Pinochet, Luis Hernan Contreras & de Gois, Fernanda Silva & Pardim, Vanessa Itacaramby & Onusic, Luciana Massaro, 2024. "Experimental study on the effect of adopting humanized and non-humanized chatbots on the factors measure the intensity of the user's perceived trust in the Yellow September campaign," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    5. Choi, Sunhwa & Yi, Youjae & Zhao, Xiaohong, 2024. "The human touch vs. AI efficiency: How perceived status, effort, and loyalty shape consumer satisfaction with preferential treatment," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    6. Chen, Qian & Jing, Yufan & Gong, Yeming & Tan, Jie, 2025. "Will users fall in love with ChatGPT? a perspective from the triangular theory of love," Journal of Business Research, Elsevier, vol. 186(C).

    More about this item

    Keywords

    AI; Chatbot;

    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:hal:journl:hal-04325624. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.