A Mixed Methods Exploration of Expertise in Aesthetic Judgment of Apparel for Different Body Shapes

Abstract

Waste from the fashion industry continues to be a major source of pollution. At the consumer use phase, this problem is compounded by inefficient purchase decision making that leads to underutilization. While advances in technology can lead to improved decision making, current decision support systems utilize methods that are not effective for the apparel industry as they rely on past purchases or user similarity metrics to make recommendations. Currently, human-assisted expert knowledge is state of the art in apparel purchase decision support. Such knowledge could prove invaluable in computer-supported decision making but has not yet been empirically assessed. This study sought to first explore predictive expert style advice to identify body and garment attributes as well as garment/body attribute-value pairs used to predict subjective aesthetic appeal of garments with regards to body shape using content analysis. Then, based on the attribute-value pairs identified, to develop a theory on dressing advice based on body shapes, and quantitatively assess the extent to which the body/garment attribute-value pairs predict aesthetic judgments of taste. Qualitative content analysis was conducted on n = 5 books containing body-shape based style advice to understand how authors use body and garment relationships to achieve desired appearance. An instrument was then developed and utilized to empirically assess the findings of the qualitative study with crowdsourced judgments of outfit aesthetics using workers from CloudResearch. The results of the study suggest that factors identified as predictive of aesthetic preference from authors could be hierarchical in nature with different levels of importance for different attributes. The findings of this research indicate that while authors’ knowledge may not be fully predictive of young adults’ preference, it could form the basis for a better understanding of which factors are most important in design of user-centered apparel recommendation. Further, as one of the first studies to empirically evaluate expert advice in clothing recommendation, it highlights the importance of a validation phase where expertise is assessed prior to integration into computational system design. Finally, the findings offer a novel opportunity for apparel experts to reflect on established (but unvalidated) “rules” or theories in aesthetics.