This study examines how the visual complexity in character design influences consumer preference and marketability in South Korea. As character designs increasingly serve as standalone commercial products, understanding the role of visual complexity has become essential for both creators and firms. By combining machine learning–based image analysis with consumer surveys, we extract key visual attributes and corresponding psychological responses to character designs. Using linear regression models, we find an inverted U-shaped relationship between visual complexity and both consumer preference and marketability, indicating that moderate levels of complexity yield the most favorable responses and the highest commercial potential. Additionally, character designs characterized by strong contrast, clear edges, and distinctive structures are more likely to succeed in the marketplace. By integrating perspectives from aesthetics, marketing theory, and computational analysis, this research provides novel insights into character design strategies and highlights the potential of generative AI and visual feature engineering in advancing content commercialization.
Lee et al. (Sat,) studied this question.