ARTIFICIAL INTELLIGENCE APPLICATIONS IN E-COMMERCE: A BIBLIOMETRIC STUDY FROM 1995 TO 2023 USING MERGED DATA SOURCES

Abstract

Purpose: The aim of this study is to conduct a comprehensive review of scientific articles concerning artificial intelligence (AI) applications in electronic commerce through bibliometric analysis. Theoretical Framework: The current study utilized both the SCOPUS and Web of Science (WoS) databases to enrich the analysis with a wider selection of papers in the field, incorporating an examination of the most cited documents. Design/Methodology/Approach: The dataset for analysis was selected according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, integrating data from Scopus and WoS through R software, specifically using the biblioshiny library. It includes 8372 papers published from 1995 to 2023. This study's data analysis used two approaches: descriptive analysis to examine the data quantitatively and scientific mapping to explore the intellectual and social structures within the dataset. Findings: The results reveal significant trends in the application of artificial intelligence in e-commerce, highlighting the rapid growth of interest in this area over the last decade. China emerges as the country with the highest number of citations, with ZHANG Y identified as the most relevant author and HU M as the most cited author. Furthermore, the study identifies prevalent keywords used by the authors, including sentiment analysis and recommendation systems. Research, Practical & Social Implications: This study underscores the transformative potential of AI in enhancing e-commerce practices, offering insights for both academic researchers and industry professionals by providing valuable perspectives on current trends and contributions. Originality/Value: The value of the study lies in its comprehensive bibliometric approach, which integrates two major databases to explore AI's applications in e-commerce. This deviation from previous reviews, which often rely on a single database, provides a deeper understanding of the current landscape and future directions in this field.