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Lifestyle·The Trace·Dual reading·Published 2026-08-18

AIGC adoption among Chinese fashion-design practitioners and its effect on psychological need satisfaction and behavioral intention

Source article: Exploring fashion designers' acceptance of AIGC: A dual-pathway analysis from the stimulus-organism-response perspective

Abstract: Artificial Intelligence Generated Content (AIGC) is increasingly used in creative design. Understanding fashion designers' willingness to adopt these tools has therefore become important for both research and practice. Drawing on the Stimulus-Organism-Response (SOR) model, this study integrates Self-Determination Theory (SDT) with perceived risk, social influence, and facilitating conditions. It examines how these contextual stimuli shape designers' basic psychological need satisfaction and behavioral intention.…

TRV-2026-0818Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 68The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 64The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Exploring fashion designers' acceptance of AIGC: A dual-pathway analysis from the stimulus-organism-response perspective

"Chinese paper money - Mao" by kevin dooley is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

The quick read

Researchers examined why fashion designers adopt Artificial Intelligence Generated Content, which is described as increasingly used in creative design. Using the Stimulus-Organism-Response framework combined with Self-Determination Theory, they surveyed 318 Chinese fashion-design practitioners and analyzed 21 items with PLS-SEM to link perceived risk, social influence and facilitating conditions to autonomy, competence, relatedness and behavioral intention.

The pattern matters for creative work because competence emerged as the strongest predictor of intention to use AIGC, while perceived risk undermined all three psychological needs, suggesting practical levers like reducing risk perceptions and improving support and training. What remains uncertain is whether self-reported intention in this Chinese sample translates into sustained real-world use, and how copyright governance and designer-AI collaboration will evolve beyond the study context.

Main points
  • Study applied Stimulus-Organism-Response model integrating Self-Determination Theory with perceived risk, social influence and facilitating conditions.
  • Analysis based on 318 valid responses with complete data for all 21 measurement items from Chinese fashion-design practitioners using PLS-SEM.
  • Perceived risk negatively predicted autonomy, competence and relatedness, while social influence and facilitating conditions positively predicted them.
  • Autonomy, competence and relatedness each positively predicted behavioral intention, with competence showing the largest effect.
Gain

Among Chinese fashion-design practitioners, satisfaction of basic psychological needs, especially competence, increased behavioral intention to adopt AIGC tools.

Problem

Perceived risk around AIGC reduced fashion designers' feelings of autonomy, competence and relatedness, undermining psychological conditions for adoption.

The rundown

The authors surveyed 318 Chinese fashion-design practitioners and modeled responses to 21 measurement items with PLS-SEM, testing nine indirect paths from perceived risk, social influence and facilitating conditions through autonomy, competence and relatedness to behavioral intention.

Results showed social influence and facilitating conditions positively predicted the three organismic states, while perceived risk negatively predicted them, and bootstrapped analyses confirmed all nine specific indirect effects to intention, informing guidance for copyright governance and prompt training.

What this doesn’t fix

Findings are based on self-reported intentions from a single-country sample of 318 Chinese practitioners with complete data for 21 items, limiting generalizability beyond that population and to actual adoption behavior.

Sources

Reader signal

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The debate