Artificial Intelligence in Shopping Applications: A Systematic Review of Consumer Behaviour, Personalization, and Ethical Implications

Abstract

The integration of artificial intelligence (AI) into shopping applications has fundamentally transformed digital commerce, reshaping how consumers discover, evaluate, and purchase products. This paper presents a systematic review of AI applications in shopping environments, synthesizing findings from Scopus-indexed literature to examine the technological foundations, consumer behavioural impacts, and emerging ethical considerations. Drawing on bibliometric trends demonstrating exponential publication growth since 2020, the review identifies machine learning, natural language processing, and recommendation systems as core enabling technologies. AI-driven personalization emerges as a primary mechanism influencing consumer attitudes, trust, and purchase intentions, while ethical concerns surrounding data privacy, algorithmic transparency, and bias persist as critical challenges The analysis further reveals significant gaps in longitudinal research and cross-cultural investigations, particularly regarding the long-term effects of AI interactions on consumer loyalty and the psychological dimensions of human-AI engagement in retail contexts This paper contributes a comprehensive framework for understanding AI's transformative role in shopping applications and proposes actionable directions for future research that balance technological innovation with responsible, consumer-centric practices.