The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Artificial intelligence in E-Commerce: a bibliometric study and literature review
Open accessAbstract
This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
bibliometrix: An R-tool for comprehensive science mapping analysis
Aria, M., & Cuccurullo, C. (2017) · Journal of Informetrics · doi:10.1016/j.joi.2017.08.007 · Cited by 16,064
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102168 · Cited by 4,608
Mining association rules between sets of items in large databases
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Artificial intelligence in service
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Cited by
Weber, P., Carl, K. V., & Hinz, O. (2024) · Management Review Quarterly · doi:10.1007/s11301-023-00320-0 · Cited by 292
“Recent calls for, e.g., trustworthy (Thiebes et al. 2021) or sustainable AI (Bawack et al. 2022) underline the need for an exaggerated concept of AI.”
He, X., & Liu, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121801 · Cited by 53
Adawiyah, S. R., Purwandari, B., Eitiveni, I., & Purwaningsih, E. H. (2024) · Applied Sciences · doi:10.3390/app14135786 · Cited by 48
Stamkou, C., Saprikis, V., Fragulis, G. F., & Antoniadis, I. (2025) · Data · doi:10.3390/data10060089 · Cited by 13
Metadata
- Authors
- Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S.
- Venue
- Electronic Markets
- Published
- 2022-03
- Volume
- 32
- Issue
- 1
- Pages
- 297-338
- ISSN
- 1019-6781 (ISSN-L)
- 1422-8890
- OpenAlex
- W4220923293
- Cited by
- 372
- References
- 28
- FWCI
- 24.15
- Type
- journal article
- Language
- en
- Open access
- bronze
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
