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

Qian Chen, Yeming Gong (), Yaobin Lu and Jing Tang
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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

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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.

Keywords: AI; Chatbot (search for similar items in EconPapers)
Date: 2022-06-01
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Citations: View citations in EconPapers (6)

Published in Journal of Business Research, 2022, 145, 552-568 p. ⟨10.1016/j.jbusres.2022.02.088⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04325624

DOI: 10.1016/j.jbusres.2022.02.088

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