Unpacking generation Z consumers' resistance to sustainable luxury products: an integrated approach using PLS-SEM and machine learning

Purpose This study explores the key factors behind generation Z consumers' resistance to sustainable luxury products, providing insights for optimizing sustainable luxury strategies. Design/methodology/approach The study employs partial least squares structural equation modeling (PLS-SEM) combined with machine learning. Data were collected through a survey of Chinese generation Z consumers (N = 227) to test the path relationships between variables. Machine learning techniques were further applied to capture nonlinear mechanisms and identify the relative importance of key factors. Findings The results show that value barriers (face consciousness, need for status), usage barrier (perceived limited choices) and risk barrier (green skepticism) significantly contribute to generation Z consumers' disengagement, leading to resistance to sustainable luxury products. Additionally, future temporal focus moderates the relationship between disengagement and resistance. Among these factors, green skepticism and the need for status were the most influential drivers of disengagement and resistance. Originality/value This study integrates PLS-SEM with machine learning to address the gap in understanding consumer resistance to sustainable luxury and validates the moderating role of future temporal focus. The findings provide actionable insights for brands to design more effective interventions to promote sustainable luxury among young consumers.