Influence of AI Shopping Assistants on Sustainable Purchase Behaviour: The Moderating Role of Consumer Trust
DOI: https://doi.org/10.5281/zenodo.21983327
Dr. Shivali Yadav, Dr. Inderpreet Singh
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Keywords:
AI shopping assistants; sustainable purchase behaviour; consumer trust; moderation; e-commerce; PLS-SEM
Abstract:
The shopping assistants using artificial intelligence (AI), including product-recommendation engines, chatbots, and voice-based agents, are becoming commonplace in online shopping. Meanwhile, increasing numbers of consumers are saying they would like to purchase things that are good for the environment. This paper investigates the effect of AI shopping assistant usage on sustainable buying behaviour and whether this is moderated by the extent to which a consumer has trust in the AI assistant. Based on the latest research on the use of AI in retail (Silalahi & Riantama, 2026; Li, 2026; Frank et al., 2026), the paper presents a conceptual model of how the use of an AI shopping assistant relates to sustainable purchase behaviour, with consumer trust serving as a moderating variable, thereby making the relationship stronger the more trust consumers place in the shopping assistant. The model was tested among 386 online shoppers who had previously experienced AI shopping tools over the last six months using a quantitative approach with survey design that was based on Partial Least Squares Structural Equation Modelling (PLS-SEM). The analysis is reported here as a demonstration of the proposed method and is therefore not complete findings from an independent study, but rather reported with demographic, reliability and hypothesis-testing tables as a demonstration of the method. The results show that the use of AI shopping assistants is positively associated with sustainable purchase behaviour, consumer trust itself is positively associated with sustainable purchase behaviour, and that trust significantly enhances the link between AI and sustainable purchase behaviour. The paper ends with practical tips for retailers and platform designers, and clear restrictions and guidelines for future empirical research.