Who designs what matters: How design task allocation affects consumer attitude towards Human-AI collaborative design?

Generative artificial intelligence (GAI) is increasingly integrated into design practice. Consumers increasingly encounter products created through human-AI collaboration across various domains. Understanding how consumers respond to products designed collaboratively by humans and AI has become increasingly important. Drawing on design theory, this research distinguishes two types of human-AI collaborative design according to whether AI is assigned to process-oriented or outcome-oriented activities. Building on attribution theory, this research further proposes that different human-AI collaborative design types influence consumers' inferences about firms’ motives for involving AI, particularly motives related to quality improvement and cost reduction. Across five controlled experiments (N = 1163), together with additional pilot and pretest studies, this research this research provides initial evidence that different human-AI collaborative design types are associated with differences in consumer attitudes. Study 1 (N = 150) found that consumers responded more favorably to AI-process & human-outcome (vs. human-process & AI-outcome) and Study 2a (N = 199) provided evidence consistent with the mediation roles of quality improvement and cost reduction. Study 2 b (N = 243) extended these findings using a more realistic disclosure format. Study 3 (N = 278) and study 4 (N = 293) provided preliminary evidence these effects may vary depending on product information complexity and collaboration dominance. The findings offer a preliminary theoretical understanding of how and why consumers respond differently to distinct types of human-AI cooperation design. These findings may also inform future research and offer tentative implications for firms communicating AI involvement in product design.