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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
Sung Yeon Kim
Sung Yeon Kim
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Jin Min Kim
Jin Min Kim
Jin Min Kim
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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.
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Vol.:(0123456789)
Service Business (2025) 19:7
https://doi.org/10.1007/s11628-025-00580-8
EMPIRICAL ARTICLE
Standardization versuscustomization inartificial
intelligence‑based services: what fuels continuous
intention touse ondigital platforms?
SungYeonKim1· JinMinKim2
Received: 31 October 2024 / Accepted: 22 January 2025 / Published online: 4 February 2025
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025
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 posi-
tively affects AI service quality dimensions, customization shows a stronger posi-
tive 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 rec-
ommendation quality and satisfaction. These findings provide valuable insights for
service providers seeking to enhance their market competitiveness through AI-based
services.
Keywords AI preference· AI service quality· Continuous intention to use·
Customization· Standardization
1 Introduction
The advancement of digital technology and the emergence of the big data era are
primarily attributed to artificial intelligence (AI), which plays a central role in mod-
ern transformations (Belk et al. 2023; Lim and Zhang 2022). AI represents com-
putational architectures designed to mirror human intellectual processes, employing
sophisticated algorithms, including machine learning and deep learning, to enable
functionalities comparable to human cognition, inference, decision-making, and
* Jin Min Kim
tristan1031@korea.ac.kr
1 Program inConverging Technology Systems andStandardization, Korea University, Sejong,
RepublicofKorea
2 The Department ofStandards andIntelligence, Korea University, Sejong, RepublicofKorea
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