AI ‐Augmented Memory and Consumer Sustainability: Understanding Dual Consumption Pathways in Digital Retail

In digital retail, algorithmic personalisation shapes consumer outcomes, yet the cognitive mechanisms involved in this process remain theoretically underspecified. This paper introduces AI‐augmented memory (AI‐AM), a mechanism that algorithms use to record, retrieve and resurface prior behavioural traces during consumer decision‐making. The study draws on 30 semi‐structured interviews with Singapore‐based e‐commerce consumers and a complementary practitioner‐reflexive account by the researcher, based on over 10 years of multi‐platform retail experience. It examines how AI‐AM both enables and undermines SDG12 (responsible consumption and production). The thematic analysis identifies a dual pathway: a sustainable pathway, where AI‐AM reduces trial‐and‐error purchasing and supports deliberative restraint and an unsustainable pathway, where the same mechanism amplifies impulse purchase loops and erodes autonomous restraint. Four contextual conditions were associated with pathway activation: a pragmatic need‐based norm, platform‐mediated social proof, age‐associated variation in pathway responses and reliance on AI recommendations. The study extends the Engel–Kollat–Blackwell (EKB) model by theorising AI‐AM as a recursive dual‐pathway sustainability gate and develops a sustainability‐aligned design typology for platform designers and policymakers.