Mapping The Intellectual Landscape Of Ai-Based Personalisation In Online Retailing: A Bibliometric Analysis

Abstract

Artificial intelligence (AI)-based personalisation has emerged as a transformative force in online retailing, 
profoundly influencing customer satisfaction and repurchase intention. This study employs a bibliometric 
approach to systematically map the intellectual landscape of this research domain from 2016 to 2026, drawing 
on data retrieved from the Scopus and Web of Science databases. A total of 1,197 documents   comprising journal 
articles, conference papers, and review articles   were analysed using VOSviewer³ and Biblioshiny software. The 
analysis encompasses publication trends, prolific authors, influential journals, leading countries, subject area 
distribution, keyword co-occurrence, and co-citation networks. Findings indicate a compound annual growth rate 
(CAGR) of 31.6% in publications, with the United States, China, and India as the most productive nations. The 
Journal of Retailing and Consumer Services and Computers in Human Behavior emerged as the most impactful 
outlets. Keyword clustering revealed six thematic clusters: (1) recommendation systems, (2) trust and privacy, (3) 
personalisation algorithms, (4) user experience, (5) purchase intention, and (6) machine learning. The study 
provides scholars, practitioners, and policymakers with a comprehensive understanding of the evolutionary 
trajectory and future research frontiers in AI-driven personalised retailing.