One of the major barriers to implementing artificial intelligence (AI) in fashion design is possibly higher consumer reluctance to accept AI-designed (vs. human-designed) products. How can brands alleviate the negative responses to AI-designed products? To answer this question, this research tests the role of product innovativeness in determining the levels of consumer resistance to AI designs.
The hypotheses were developed based on the literature on algorithm aversion and appreciation, fashion design evaluation, and mind perception theory. To test the hypotheses, we conducted three online experiments using Amazon Mturk through CloudResearch platform.
While a general preference for human designs over AI designs was found, the negative attitudes toward AI designs were stronger for low-innovative products but weaker for high-innovative products. This is because, according to the conditional process analysis, participants perceived AI-designed products as less original compared to human-designed products when innovativeness level was low. However, this pattern was not shown when innovativeness level was high.
The findings show the potential for overcoming aversion to AI-designed fashion products. Brands utilizing AI in design are recommended to aim for highly innovative designs, characterized by deconstruction fashion and avant-garde approaches and emphasize innovativeness values when promoting AI-designed products.
This research sheds light on how and why consumers' negative responses to AI designs vary depending on the final product design, contributing to the discourse on fashion creativity in the era of generative AI from consumers' perspectives.
