Abstract
This paper conducted a critical analysis and review of literature, spanning from 2019 to 2023, on how recommendation algorithms affected consumer behavior in e-commerce. The study explored the role of AI-driven recommendation algorithms in influencing consumer behavior and examined the impacts of various recommendations on e-commerce dynamics. By screening relevant articles from the Web of Science Core Collection database, with predefined keywords, the study applied visualization techniques, using the Bibliometrix and Biblioshiny tools within R Studio. The research investigated the different recommendation algorithms, documented in prior studies and their effects on consumer behavior in e-commerce. Our analysis uncovered significant insights into how these AI-driven recommendations shape consumer behavior, highlighting its influence on purchase decisions and product engagement. This study provides actionable insights for e-commerce businesses, to leverage recommendation algorithms, to boost consumer engagement and enhance sales effectiveness. By understanding the impacts of diverse recommendations, businesses can tailor their recommendation strategies to better align with consumer preferences. This paper could contribute to broader understanding of the role of artificial intelligence in consumer decision-making processes and offer valuable perspectives for future research in this field.
Published Version
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