| dc.description.abstract | Academic institutions are increasingly integrating Generative AI into apparel design curricula to align with industry demands. This exploratory study examines the factors influencing students’ adoption of Generative AI for apparel design by drawing on multiple theoretical perspectives, including Diffusion of Innovation Theory, Hedonic System Acceptance Model, and Motivational Theory, and incorporating multiple variables to align with the study’s exploratory objectives. This study investigates (a) the direct effects of fashion students’ personality traits (AI learning anxiety, AI anxiety in job replacement, AI fear, curiosity, tech-optimism, and tech self-efficacy) on pragmatic factors (perceived usefulness, perceived sustainability, extrinsic motivation related to the use of Generative AI in apparel design) and hedonic factors (perceived ease of use, perceived enjoyment, perceived creativity, intrinsic motivation related to the use of Generative AI in apparel design). Additionally, it examines (b) the direct effects of the pragmatic and hedonic factors on their intentions to use and willingness to learn this technology for apparel design. Lastly, this study explores (c) the mediating effects of the pragmatic and hedonic factors in the relationship between personality traits and adoption outcomes, including intention to use and willingness to learn Generative AI for apparel design. Data were collected through an online questionnaire administered to 134 fashion students, with 89 valid responses analyzed. Multivariate regression analyses revealed that tech-optimism was the most significant personality trait, positively influencing both pragmatic factors (i.e., perceived usefulness, extrinsic motivation) and hedonic factors (i.e., perceived ease of use, perceived enjoyment, perceived creativity, intrinsic motivation). AI anxiety in job replacement significantly and negatively influenced pragmatic factors (i.e., perceived sustainability, extrinsic motivation) and hedonic factors (i.e., perceived enjoyment, perceived creativity, intrinsic motivation). Tech self-efficacy significantly positively predicted only one hedonic factor, perceived ease of use. Among pragmatic and hedonic factors, extrinsic motivation, a pragmatic factor, had the most significant positive influence on both adoption outcomes: intention to use and willingness to learn Generative AI for apparel design. Perceived usefulness, a pragmatic factor, had a significant positive influence only on intention to use, while perceived enjoyment, a hedonic factor, had a significant positive influence only on willingness to learn Generative AI for apparel design. Mediation analyses through Hayes PROCESS macro Model 4 demonstrated that extrinsic motivation, a pragmatic factor, partially mediated the relationship between two personality traits, AI anxiety in job replacement and tech-optimism, and both adoption outcomes, including intention to use and willingness to learn Generative AI for apparel design. Perceived usefulness, a pragmatic factor, also partially mediated the relationship between tech-optimism and intention to use Generative AI for apparel design. A hedonic factor, perceived enjoyment, played a significant mediating role in the relationships between two personality traits, AI anxiety in job replacement (partial mediation) and tech optimism (full mediation), and willingness to learn Generative AI for apparel design. Theoretically, this study contributes by integrating multiple frameworks to provide a comprehensive understanding of students’ adoption of Generative AI in apparel design education, emphasizing the distinct roles of personality and both pragmatic and hedonic adoption pathways. Practically, the findings suggest that fashion educators should foster a supportive environment that reduces AI-related anxiety, particularly regarding job replacement, while enhancing students’ tech-optimism, extrinsic motivation, perceived usefulness, and enjoyment related to the use Generative AI in apparel design. | en_US |
