How to mitigate fashion subscription hesitation: two-step exploration using theory-based causal modeling and machine learning predictive modeling

Abstract

Purpose Although a fashion subscription offers significant environmental benefits by transforming physical products into shared services, most customers are reluctant to adopt it. This hesitation, exacerbated by poor communication from brands that primarily emphasize its personal benefits, hinders its sustainable growth. This study aims to examine specifically which concerns increase hesitation, and the role of explicitly informing consumers about the service’s environmental benefits in mitigating the impact of consumer concerns on their hesitation. Design/methodology/approach Data were collected through an online experiment with more than a thousand U.S. adults nationwide and analyzed using a two-step analysis. First, theory-based causal modeling was conducted to examine the effects of consumer concerns on hesitation, accounting for ambivalence as a mediator and informed environmental benefits as a moderator. Second, machine learning was used to cross-validate the findings. Findings Results show that certain types of consumer concerns increase hesitation, significantly mediated by ambivalence, and confirm that informed environmental benefits mitigate the effects of some concerns on hesitation. Originality/value This study contributes to building on the hierarchy of effects theory by exploring negatively nuanced constructs – concerns, ambivalence and hesitation – beyond the traditional constructs representing the cognitive, affective and conative stages of consumer decision-making. Findings provide strategic guidance to brands on how to communicate the new service to consumers. Leveraging theory-based causal modeling with machine learning-based predictive modeling provides a novel methodological approach to explaining and predicting consumer hesitation toward new services.