Predicting consumer acceptance of sustainable luxury using adaptive AI and ensemble machine learning

The rise of sustainability in the luxury sector presents a paradox: for decades, luxury has been seen as antithetical to sustainability, yet younger socially conscious consumers increasingly demand eco-friendly and ethical practices from luxury brands. This study investigates the factors influencing consumer adoption of sustainable luxury products, with a focus on generational differences. We developed a framework identifying five categories of drivers of sustainable purchase intent and generated - using AI techniques - a simulated survey dataset of 1000 luxury consumers spanning four generations (Boomers, Gen X, Millennials, Gen Z). Using this dataset, we trained an ensemble of machine learning models - Random Forest and Neural Network - to test the possibility to predict purchase propensity and identify key drivers of sustainable luxury adoption. Feature importance analysis revealed that ethical sourcing was the top driver of purchase intent, followed by environmental impact and product quality. According to consumer segmentation, younger generations exhibited a higher propensity to adopt sustainable luxury, confirming a generational shift toward stronger sustainability values. Consumer adoption of sustainable luxury hinges on brands delivering superior quality and prestige alongside authentic sustainability commitments. The adaptive AI-driven profiling and forecasting can help tailor strategies to consumer segments, guiding the luxury sector toward more sustainable consumption. Across the identification of luxury value constructs and dimensions, we proposed strategies tailored on specific customer segments to predict luxury consumers behaviors and, eventually, to promote acceptance of sustainable practices.