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

Author

Listed:
  • Qian Chen
  • Yeming Gong

    (EM - EMLyon Business School)

  • Yaobin Lu
  • Jing Tang

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
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    Cited by:

    1. 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).

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    Keywords

    AI; Chatbot;

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