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User satisfaction with the service quality of ChatGPT

Author

Listed:
  • Kim Shin Young

    (Sogang University)

  • Sang-Gun Lee

    (Sogang University)

  • Ga Youn Hong

    (Sogang University)

Abstract

The present study identified the attributes of generative AI services provided by enterprises that affect satisfaction and dissatisfaction. The KANO model was validated on the data collected from two-hundred respondents participated in the present work. The results from an KANO model analysis indicate that (1) ChatGPT’s service quality factors were categorized into the sector of must-be quality, attractive quality, and one-dimensional quality, presenting a positive image to users; and (2) service improvement efforts in attractive quality factors had strong influence on satisfaction, whereas service improvement efforts in must-be quality factors had weak influence on satisfaction. The findings from the study should be applied by companies using ChatGPT to establish effective ChatGPT utilization strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:svcbiz:v:18:y:2024:i:3:d:10.1007_s11628-024-00566-y
    DOI: 10.1007/s11628-024-00566-y
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    References listed on IDEAS

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    1. Chen, Qian & Gong, Yeming & Lu, Yaobin & Tang, Jing, 2022. "Classifying and measuring the service quality of AI chatbot in frontline service," Journal of Business Research, Elsevier, vol. 145(C), pages 552-568.
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    5. 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.
    6. Ma, Xiaoyue & Huo, Yudi, 2023. "Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework," Technology in Society, Elsevier, vol. 75(C).
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    Cited by:

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