The Intelligence Behind the Click: A Bibliometric Study of E-Commerce and Emerging Cognitive Technologies
DOI:
https://doi.org/10.48165/dbitdjr.2025.2.01.05Keywords:
E-Commerce, Artificial Intelligence, Machine Learning, Deep Learning, Bibliometric Analysis, Research Trends, UGC CAREAbstract
In the digital era, online transactions are integral to everyday life—from grocery shopping and clothing purchases to streaming services like Netflix and Amazon Prime. E-Commerce has progressed beyond simple transactions, now leveraging intelligent systems for personalized user experiences via recommendation engines analyzing user behavior, reviews, and past transactions. This transformation is largely powered by the integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Over the last decade, this convergence has garnered substantial academic attention. This study presents a bibliometric analysis of 2,464 peer-reviewed publications from 2018 to 2025, sourced from the Dimensions database and filtered through the PRISMA framework and UGC CARE List-II criteria. The analysis explores publication trends, influential authors and institutions, significant countries, high-impact papers, and emerging thematic clusters. The findings reveal an uptrend in research activity, heightened technological integration, and shifting research priorities. The study offers a roadmap for future inquiry at the intersection of E-Commerce and AI technologies.
References
Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & others. (2022). Artificial intelligence in E-Commerce: A bibliometric study
and literature review. Electronic Markets, 32, 297–338. https:// doi.org/10.1007/s12525-022-00537-z
Garg, A., & Sharma, V. (2021). Deep learning for customer sentiment analysis in e-commerce. International Journal of Data Science, 6(2), 89–105.
Hasan, I., & Rizvi, S. (2022). AI-driven fraud detection and mitigation in e-commerce transactions. In D. Gupta, Z. Polkowski, A. Khanna, S. Bhattacharyya, & O. Castillo (Eds.), Proceedings of Data Analytics and Management (Vol. 90). Springer. https://doi.org/10.1007/978-981-16-6289-8_34
Joshi, M. A. (2024). Artificial intelligence in E-commerce: A comprehensive analysis. SSRN. https://ssrn.com/ abstract=4770338 or http://dx.doi.org/10.2139/ssrn.4770338
Kapoor, R., & Singh, A. (2022). AI in online retail: Chatbots and personalized shopping. Journal of Retail Technology, 11(4), 112–128.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Policarpo, L. M., da Silveira, D. E., Righi, R. R., Stoffel, R. A., da Costa, C. A., Barbosa, J. L. V., Scorsatto, R., & Arcot, T. (2021). Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review, 41, 100414. https://doi.org/10.1016/j.
cosrev.2021.100414
Sharma, M., Sharma, V., & Kapoor, R. (2022). Study of e-commerce and impact of machine learning in e-commerce. In S. Bilgaiyan, J. Singh, & H. Das (Eds.), Empirical Research for Futuristic E-Commerce Systems: Foundations and Applications
(pp. 1–22). IGI Global. https://doi.org/10.4018/978-1-6684- 4969-1.ch001
Soegoto, E. S., Harjanto, R., & Sitompul, M. J. (2022). Bibliometric analysis using VOSviewer on artificial intelligence research. Journal of Physics: Conference Series, 2267, 012053. https://doi. org/10.1088/1742-6596/2267/1/012053
Zhang, Y., Chen, Y., & Liu, F. (2020). Interpreting black-box models: A review on explainable artificial intelligence. Journal of Information Systems, 34(3), 240–258.
Dwivedi, Y. K., Hughes, L., Bhadeshia, H. K. D. H., Ananiadou, S., Cohn, A. G., Cole, J. M., Conduit, G. J., Desarkar, M. S., & Wang, X. (2024). Artificial intelligence (AI) futures: India– UK collaborations emerging from the 4th Royal Society Yusuf Hamied workshop. International Journal of Information Management, 76, 102725. https://doi.org/10.1016/j. ijinfomgt.2023.102725
Nguyen, A., et al. (2023). Ethical implications of AI-powered recommendation systems in education and digital platforms. Educational Information Technology, 28
