Understanding behavioral intentions toward AI-generated safety clothing: The role of safety concern, perceived safety clothing attributes and color

This study examines how safety concern influences consumer behavioral intentions toward AI-generated children’s safety clothing (in terms of recommending, buying, and wearing the clothing), with an emphasis on the mediating roles of aesthetic and safety attributes. Unlike traditional approaches that rely on designers’ intuition, AI systems integrate data-driven pattern recognition and generative algorithms to produce optimized designs that balance aesthetic and safety features. Using a sample of teachers – credible evaluators of children’s safety needs – the study employs partial least squares structural equation modeling to uncover relationships among the variables. Results indicate that aesthetic attributes not only enhance consumers’ recommendation intention but also strengthen their intention to wear the clothing, underscoring the role of appealing design in shaping favorable behavioral responses. Safety attributes, in contrast, primarily reinforce recommendation intention, highlighting their role in validating product credibility. Importantly, the study investigates the moderating effect of traditional versus non-traditional safety clothing colors, providing evidence that safety clothing colors can influence how safety clothing is perceived. By integrating perspectives on aesthetics, safety, and color symbolism, this research extends the literature on AI-generated products and offers practical implications for designers, marketers, and policymakers that can enhance and promote children’s safety.