How AI disclosure influences consumers' purchase intention toward AI-generated Chinese ethnic-minority fashion: the roles of perceived usefulness, perceived authenticity, and algorithm aversion
- 1College of Art, Cheongju University, Cheongju, Republic of Korea
- 2School of Design, Fujian University of Technology, Fuzhou, China
Abstract
Background: With the growing application of generative artificial intelligence (AI) in fashion design and cultural and creative industries, AI-generated Chinese ethnic-minority fashion has created new opportunities for the contemporary transformation of traditional culture. However, whether consumers are willing to accept such products and how AI disclosure influences their evaluations and purchase intentions remain unclear. Drawing on the Stimulus–Organism–Response (S–O–R) model, this study examines the mechanisms through which AI disclosure, design aesthetics, cultural identity, perceived usefulness, perceived authenticity, and algorithm aversion influence consumers' purchase intentions toward AI-generated Chinese ethnic-minority fashion.
Methods: A questionnaire survey combined with a scenario-based experiment was employed. Six AI-generated fashion illustrations derived from Tujia costume elements were developed and selected as stimulus materials. Participants were randomly assigned to either an AI disclosure group or a non-disclosure group. Data were collected through Tencent Questionnaire, yielding 410 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance–Performance Map Analysis (IPMA) were employed to examine the structural relationships, mediating effects, moderating effects, and practical priorities for improvement.
Results: Design aesthetics and cultural identity influenced purchase intention through perceived usefulness and perceived authenticity, with perceived usefulness emerging as the strongest predictor of purchase intention. AI disclosure significantly reduced design aesthetics, cultural identity, perceived usefulness, and perceived authenticity. Algorithm aversion did not significantly moderate the relationship between perceived usefulness and purchase intention but significantly strengthened the positive effect of perceived authenticity on purchase intention.
Conclusions: This study reveals the multiple psychological pathways through which AI disclosure shapes consumers' evaluations of AI-generated ethnic cultural fashion. The findings further indicate that algorithm aversion alters the extent to which consumers rely on authenticity judgments when forming purchase intentions. This study extends the application of the Stimulus–Organism–Response (S–O–R) model to the context of AI-generated cultural products and provides practical implications for balancing AI transparency, cultural authenticity, and the practical value of products.
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BibTeX
@article{wei2026disclosure,
title = {{How AI disclosure influences consumers' purchase intention toward AI-generated Chinese ethnic-minority fashion: the roles of perceived usefulness, perceived authenticity, and algorithm aversion}},
author = {Kuilin Wei and Jun Liu and Shuai Wang and Yue Sun},
journal = {Frontiers in Psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1905449},
url = {https://doi.org/10.3389/fpsyg.2026.1905449},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - How AI disclosure influences consumers' purchase intention toward AI-generated Chinese ethnic-minority fashion: the roles of perceived usefulness, perceived authenticity, and algorithm aversion
AU - Kuilin Wei
AU - Jun Liu
AU - Shuai Wang
AU - Yue Sun
PY - 2026
DA - 2026/10/21
JO - Frontiers in Psychology
DO - 10.3389/fpsyg.2026.1905449
UR - https://doi.org/10.3389/fpsyg.2026.1905449
AB - Background: With the growing application of generative artificial intelligence (AI) in fashion design and cultural and creative industries, AI-generated Chinese ethnic-minority fashion has created new opportunities for the contemporary transformation of traditional culture. However, whether consumers are willing to accept such products and how AI disclosure influences their evaluations and purchase intentions remain unclear. Drawing on the Stimulus–Organism–Response (S–O–R) model, this study examines the mechanisms through which AI disclosure, design aesthetics, cultural identity, perceived usefulness, perceived authenticity, and algorithm aversion influence consumers' purchase intentions toward AI-generated Chinese ethnic-minority fashion. Methods: A questionnaire survey combined with a scenario-based experiment was employed. Six AI-generated fashion illustrations derived from Tujia costume elements were developed and selected as stimulus materials. Participants were randomly assigned to either an AI disclosure group or a non-disclosure group. Data were collected through Tencent Questionnaire, yielding 410 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance–Performance Map Analysis (IPMA) were employed to examine the structural relationships, mediating effects, moderating effects, and practical priorities for improvement. Results: Design aesthetics and cultural identity influenced purchase intention through perceived usefulness and perceived authenticity, with perceived usefulness emerging as the strongest predictor of purchase intention. AI disclosure significantly reduced design aesthetics, cultural identity, perceived usefulness, and perceived authenticity. Algorithm aversion did not significantly moderate the relationship between perceived usefulness and purchase intention but significantly strengthened the positive effect of perceived authenticity on purchase intention. Conclusions: This study reveals the multiple psychological pathways through which AI disclosure shapes consumers' evaluations of AI-generated ethnic cultural fashion. The findings further indicate that algorithm aversion alters the extent to which consumers rely on authenticity judgments when forming purchase intentions. This study extends the application of the Stimulus–Organism–Response (S–O–R) model to the context of AI-generated cultural products and provides practical implications for balancing AI transparency, cultural authenticity, and the practical value of products.
SN - 1664-1078
ER -