The impact of AI-Enabled personalization on sustainable fashion consumption behavior: a theory of planned behavior approach

Abstract

This study develops and empirically tests an integrated model that embeds artificial intelligence-enabled personalization into the Theory of Planned Behavior (TPB) to explain sustainable fashion consumption intention in e-commerce in emerging economies. Artificial intelligence enabled personalization is theorized as an external technological driver of TPB’s belief set - attitude, subjective norms and perceived behavioral control, thereby shaping intention. A survey of 419 Vietnamese Gen Y and Gen Z consumers was analyzed with PLS-SEM. Results show that artificial intelligence-enabled personalization has strong positive effects on attitude, subjective norms and perceived behavioral control, with large effect sizes, and that all three beliefs significantly predict stainable fashion consumption intention; subjective norms is the strongest predictor, followed by attitude and perceived behavioral control. The model demonstrates substantial explanatory power (R square of SFCI = 0.556) and positive predictive relevance. These findings extend TPB by identifying designable, personalized artificial intelligence cues that enhance favorable evaluations, social validation, and perceived efficacy for sustainable choices, particularly salient among young consumers in Vietnam’s digitally mediated market. The study addresses a theoretical gap on integrating artificial intelligence into sustainable consumer-behavior models and offers actionable guidance for leveraging artificial intelligence-enabled personalization to promote responsible consumption. This research addresses theoretical gaps regarding the integration of artificial intelligence technology into sustainable consumer behavior models and offers practical implications for businesses aiming to leverage artificial intelligence to encourage responsible consumer behaviors. Research purpose: To investigate how artificial intelligence-enabled personalization influences attitude, subjective norms and perceived behavioral control within TPB, and how these beliefs, in turn, shape sustainable fashion consumption intention in the context of e-commerce sustainable fashion consumption among Vietnamese Gen Y and Gen Z consumers. Research motivation: While TPB is widely used to predict sustainable consumption, it under-specifies contemporary technological influences. The rapid diffusion of artificial intelligence-enabled personalization suggests a need to integrate artificial intelligence as an external antecedent to TPB beliefs to better explain sustainable choices in digital retail, especially in emerging markets such as Vietnam. Research design, approach, and method: A online survey of Gen Y and Gen Z Vietnamese consumers with sustainable-fashion experience (n = 419) was analyzed using PLS-SEM following a two-stage procedure (measurement and structural models). Main findings: Artificial intelligence-enabled personalization significantly enhances attitude, subjective norms and perceived behavioral control; all three beliefs positively predict sustainable fashion consumption intention, with SN strongest, followed by ATT and PBC. The model explains R² = 0.556 (SFCI) Practical/managerial implications: Retail platforms should configure artificial.intelligence-enabled personalization to (i) improve ATT via personalized sustainability benefits (impact dashboards, life-cycle information), (ii) amplify SN via social proof (community ratings, certifications, KOL cues), and (iii) raise PBC by reducing friction (smart filters, green defaults, transparent ecoscores, AR try-on), making the sustainable option the most convenient default.