The Mediator Effect: Exploring Privacy Concern, Perceived Benefit, Personalization and the Repurchase Intentions of AI-fashion retailing Applications

Abstract

This research looks at how customers behave regarding Artificial Intelligence (AI) in fashion retail. We focus on privacy concerns and the perceived advantages of sharing data, as these relate to customers' intentions to buy again. We also explore if personalization helps lessen negative feelings and strengthens this connection. To meet our research goals, we used stratified random sampling to create three different groups based on their experience with apps, income level, and education. We followed set rules for sample size and gathered responses from 252 participants. For data analysis, we used Factor Analysis and Structural Equation Modeling.  The results were surprising. Our analysis shows that privacy concerns do not negatively affect customers’ intentions to repurchase. In fact, both perceived benefits and personalization have strong positive effects on these intentions. However, we cannot clearly determine if privacy concerns influence customers positively, which suggests the need for more research. This study offers a new way to understand customer behavior toward AI in fashion retail by using Privacy Calculus Theory. We included personalization as a key motivating factor, recognizing its importance for customers when they use apps. This adds a valuable perspective to our research. Additionally, we examine how privacy concerns and perceived benefits affect personalization in the context of AI in fashion retailing..