Bibliometric Analysis of Artificial Intelligence on Consumer Purchase Intention in E-Retailing
DOI:
https://doi.org/10.22399/ijcesen.1007Keywords:
Artificial Intelligence, Consumer Purchase Intention, E-retailing, Bibliometric Analysis, Personalized Recommender SystemsAbstract
This study conducts a comprehensive bibliometric analysis of the impact of Artificial Intelligence (AI) on consumer purchase intentions in the e-retailing sector. By examining data from over 500 peer-reviewed articles published between 2000 and 2023, sourced from leading academic databases such as SCOPUS and Web of Science, these study maps the intellectual and research activities in this burgeoning field. The key AI technologies analzsed include machine learning, natural language processing, and data mining, which enhance personalized shopping experiences, recommend products, and provide virtually assisted sales. The analysis revealed significant growth in research output, highlighting four primary themes: personalized recommender systems, chatbots and virtual assistants, customer sentiment analysis, and predictive analytics. These themes underscore AI’s role of AI in improving consumer satisfaction, loyalty, and conversion rates. Despite these advancements, gaps remain in areas such as the ethical implications of its AI, long-term effects on consumer behavior, and cross-cultural impacts. Addressing these gaps could pave the way for future research and more responsible AI deployment in e-retail.
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