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Standardization versus customization in artificial intelligence-based services: what fuels continuous intention to use on digital platforms?

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  • Sung Yeon Kim

    (Korea University)

  • Jin Min Kim

    (Korea University)

Abstract

This study investigates how standardization and customization influence AI service quality and their subsequent effects on user satisfaction and continuous intention to use, with a focus on AI service preference as a moderating factor. Analysis of 1032 survey responses using PLS-SEM revealed that while standardization positively affects AI service quality dimensions, customization shows a stronger positive impact. AI service satisfaction significantly influences continuous intention to use. Additionally, AI service preference demonstrates dual moderating effects: positive between AI system quality and satisfaction and negative between AI recommendation quality and satisfaction. These findings provide valuable insights for service providers seeking to enhance their market competitiveness through AI-based services.

Suggested Citation

  • Sung Yeon Kim & Jin Min Kim, 2025. "Standardization versus customization in artificial intelligence-based services: what fuels continuous intention to use on digital platforms?," Service Business, Springer;Pan-Pacific Business Association, vol. 19(1), pages 1-23, March.
  • Handle: RePEc:spr:svcbiz:v:19:y:2025:i:1:d:10.1007_s11628-025-00580-8
    DOI: 10.1007/s11628-025-00580-8
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    References listed on IDEAS

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    1. Ki-Kwang Lee & Hong-Hee Lee & Su-Ji Cho & Gyung-Su Min, 2022. "The context-based review recommendation system in e-business platform," Service Business, Springer;Pan-Pacific Business Association, vol. 16(4), pages 991-1013, December.
    2. Kar Yan Tam & Shuk Ying Ho, 2005. "Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective," Information Systems Research, INFORMS, vol. 16(3), pages 271-291, September.
    3. Barbara H. Wixom & Peter A. Todd, 2005. "A Theoretical Integration of User Satisfaction and Technology Acceptance," Information Systems Research, INFORMS, vol. 16(1), pages 85-102, March.
    4. Marc-André Kaufhold & Nicola Rupp & Christian Reuter & Matthias Habdank, 2020. "Mitigating information overload in social media during conflicts and crises: design and evaluation of a cross-platform alerting system," Behaviour and Information Technology, Taylor & Francis Journals, vol. 39(3), pages 319-342, March.
    5. Russell W. Belk & Daniel Belanche & Carlos Flavián, 2023. "Key concepts in artificial intelligence and technologies 4.0 in services," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 1-9, March.
    6. 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.
    7. 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.
    8. Sandra Maria Correia Loureiro & Ricardo Godinho Bilro & Diogo Neto, 2023. "Working with AI: can stress bring happiness?," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 233-255, March.
    9. Kasiri, Leila Agha & Guan Cheng, Kenny Teoh & Sambasivan, Murali & Sidin, Samsinar Md., 2017. "Integration of standardization and customization: Impact on service quality, customer satisfaction, and loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 35(C), pages 91-97.
    10. Mark Anthony Camilleri & Ciro Troise, 2023. "Live support by chatbots with artificial intelligence: A future research agenda," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 61-80, March.
    11. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    12. Tversky, Amos & Thaler, Richard H, 1990. "Anomalies: Preference Reversals," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 201-211, Spring.
    13. 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.
    14. Hu, Han-fen & Krishen, Anjala S., 2019. "When is enough, enough? Investigating product reviews and information overload from a consumer empowerment perspective," Journal of Business Research, Elsevier, vol. 100(C), pages 27-37.
    15. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    16. Shin, Donghee & Zhong, Bu & Biocca, Frank A., 2020. "Beyond user experience: What constitutes algorithmic experiences?," International Journal of Information Management, Elsevier, vol. 52(C).
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