Exploring Value Creation in Human-AI Collaborative Art: An Affordance Perspective
摘要
Generative AI is revolutionizing creative practices, especially in art co-creation. This study draws on task-type literature and semiotic art theory to explore how its capabilities reshape the aesthetic and commercial value of art. We develop a conceptual framework that identifies three dimensions of generative AI’s affordances: functional, creative, and content affordances. Empirically, we analyze a dataset of 1,000 AI-generated artworks from a text-to-image platform through semantic, linguistic, and large multimodal model-based visual methods. Partial Least Squares Structural Equation Modeling is employed to examine how these affordances drive value creation through content exploration and visual style arrangement. By uncovering the human-AI co-creation process, this study advances a clearer understanding of generative AI’s role in artistic production and its broader implications for creativity, valuation, and collaboration in digital art.