Generational differences in AI adoption among fashion curation platform users

초록

This study investigates consumer acceptance of artificial intelligence (AI)–enabled fashion curation platforms by integrating the Task–Technology Fit (TTF) and Technology Acceptance Model (TAM) frameworks. Building on contemporary research that emphasizes the transformative role of AI in retail and fashion, the study examines how the alignment between AI functionalities and user needs strengthens perceived usefulness and ease of use, enhancing satisfaction and behavioral intention. Using a survey-based quantitative design with adapted TTF and TAM scales, we analyzed data from fashion curation platform users via structural equation modeling (SEM), including multi-group comparisons across Generation Z, Millennials, and Generation X. Results show cohort-sensitive pathways: Generation Z responds more to technological fit and usefulness, whereas Generation X prioritizes ease of use and experiential satisfaction; intention mechanisms converge once upstream beliefs are formed, aligning with broader AI acceptance patterns and moderators noted in consumer contexts. The findings extend technology acceptance scholarship by localizing generational heterogeneity primarily in the formation of perceived usefulness and satisfaction within AI curation contexts, while offering actionable guidance for task–technology alignment and journey design in digital fashion retail. The study motivates future multi-cohort investigations that incorporate evolving AI capabilities and examine trust and governance considerations in fashion platforms.

키워드