AI-augmented consumer decision-making and AI-augmented memory

Abstract

This thesis examines how consumers make decisions in the current AI-enabled retail environment, with a specific focus on how AI-augmented memory influences the consumer decision-making process. Based on in-depth qualitative interviews in Singapore, this thesis analyses how conventional consumer behaviour models, particularly the Engel-Kollat-Blackwell (EKB) model, are challenged by AI-enabled applications in retailing. AI-driven tools, such as reminders, recommendations, virtual assistants, and product review curation, are more than just decision support – they represent a collective network of memory and an active augmented memory for consumers, functioning as cognitive extensions for shoppers.
The thesis presents a revised theoretical framework for consumer decision-making processes. This revised framework is dynamic, nonlinear, and recursive, reflecting consumers’ decision-making processes in the new AI environment. It depicts how AI plays a role throughout the entire shopping experience, from sparking a need to conducting information searches, to post-purchase reflection and influencing future buying behaviour. Drawn from 30 semi-structured interviews with Singaporean consumers, the findings indicate a hybrid AI-augmented journey in which AI systems act as co-agents, providing filtering and cues, as well as reminders and recommendations, while consumers remain the ultimate decision-makers.
Furthermore, by reframing consumer memory as externally augmented, this study contributes to theoretical development by challenging the EKB model’s underlying notion that memory represents an internally generated process. Apart from academic contributions, retailers and marketers seeking to design and implement effective strategies in the retail space need to consider the implications of AI-augmented consumer journeys.

Date of Award	2026
Original language	English
Supervisor	Tom CHEN (Supervisor) & Irfan KHAN (Supervisor)