A study on consumer preference and mental modeling in generative design based on emotional affordance : Focusing on the aesthetics and preference of chair design

As generative design evolves beyond shape automation toward user-centered approaches powered by artificial intelligence, emotional value becomes a critical differentiator in product and spatial design. This study investigates how emotional affordance and mental modeling theories can inform emotionally resonant generative design strategies. Using a mixed-methods approach—including web-based data crawling, text mining, and quantitative user surveys—this research identifies and categorizes key emotional affordance factors and examines their influence on consumer preference and aesthetic judgment. The results reveal that vertically structured forms, constraint-based modeling, natural materials, and sculptural aesthetics are positively correlated with emotional appeal and perceived usability. In contrast, highly complex and topology-optimized designs may trigger cognitive dissonance or discomfort due to their unpredictability. This study contributes to the theoretical foundation for emotion-driven generative design and offers actionable insights for AI-powered UX systems seeking to incorporate affective design principles in early-stage development.