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Article Dans Une Revue Journal of Business Research Année : 2022

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

Qian Chen
  • Fonction : Auteur
Yaobin Lu
  • Fonction : Auteur
Jing Tang
  • Fonction : Auteur

Résumé

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.

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Dates et versions

hal-04325624 , version 1 (06-12-2023)

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  • HAL Id : hal-04325624 , version 1

Citer

Qian Chen, Yeming Gong, Yaobin Lu, Jing Tang. Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 2022, 145, 552-568 p. ⟨hal-04325624⟩

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