Authors: Dr. V. Vetrivel, Dr. K. Vinayagam, Dr. A. Gokulakrishnan, Dr. P. Sasikumar
Abstract: The rapid growth of e-commerce has changed how consumers search for products, compare alternatives and make purchasing decisions. Artificial Intelligence (AI)-driven recommendation systems have become an important component of digital commerce because they analyse consum-er preferences and behavioural patterns to present relevant products, services and content. This study examines the impact of AI-driven recommendations on consumer decision-making in e-commerce, with particular focus on recommendation relevance, perceived usefulness, conven-ience and purchase intention. This study adopts a descriptive, quantitative research design. Fol-lowing the structure of the supplied model paper, the study uses an illustrative sample of 200 online shoppers. Data will be collected through a structured questionnaire using a five-point Likert scale. Descriptive statistics, Chi-square and one-way ANOVA are employed to examine relationships and differences. The illustrative results show a significant association between fre-quency of exposure to AI recommendations and purchase intention. The results also indicate significant differences in consumer decision-making across different levels of perceived recom-mendation usefulness. The study suggests that e-commerce businesses should improve recom-mendation accuracy, provide transparent AI-based suggestions, protect consumer data, reduce irrelevant recommendations and maintain effective customer support. The study concludes that AI-driven recommendations can positively influence online consumer decision-making when they are relevant, useful, convenient and trustworthy.
DOI: https://zenodo.org/records/23008829
