ScholarWorks@숙명여자대학교

초록

This study aims to provide foundational data necessary for enhancing user satisfaction with the image-generating AI model DALL-E. As such, clothing images were generated using DALL-E, followed by a two-phase satisfaction survey to examine various aspects of generative AI’s application in the clothing industry. The survey targeted women in their 20–40s residing in Seoul and Gyeonggi-do Province, with the following findings obtained: First, no statistically significant differences were found in women’s satisfaction with their body shapes across age groups. Second, design inputs regarding color and pattern were predominantly specified for tops, while for bottoms and outerwear, color and material were predominantly specified. Third, participants were grouped by purchase intention, with satisfaction levels analyzed for tops, bottoms, and outerwear. All three categories showed statistically significant differences at the p < .01 level in relation to alignment with preferred styles and most unsatisfactory item. Fourth, images of tops were rated the most satisfactory, while those of bottoms, the most unsatisfactory. Satisfaction was primarily attributed to style alignment, whereas dissatisfaction represented any discrepancy between the generated image and participant expectations. Fifth, no statistically significant differences were found for any measured item in relation to body satisfaction and self-esteem.

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