The Emerging Role of Artificial Intelligence in Personalized and Ethical Green Retail Marketing: A Bibliometric Analysis
DOI:
https://doi.org/10.51325/5bphzj87Keywords:
Artificial Intelligence, Personalized Marketing, Ethical Marketing, Green Retail Marketing, Sustainability, Bibliometric Analysis.Abstract
Artificial intelligence (AI) is transforming retail marketing, reshaping how firms personalize offerings, govern consumer data responsibly, and pursue sustainability goals simultaneously. This study presents a bibliometric analysis of the intersection of AI, personalized marketing, ethical governance, and green retail marketing, a combination not previously mapped as an integrated field. Bibliographic records were retrieved from Scopus (2018 to May 2026), screened using PRISMA, yielding 140 peer-reviewed articles analysed using Bibliometrix, Biblioshiny, and VOSviewer. Findings show a marked acceleration in publication activity from 2024 onward, with India, China, and the United States as the leading contributing countries. Core research themes include artificial intelligence, e-commerce, consumer behaviour, sustainability, machine learning, data privacy, and ethical marketing, with AI and e-commerce terms remaining more central to the field's intellectual structure than sustainability-specific terms. Author co-citation and bibliographic coupling analyses reveal a small number of foundational reference clusters bridged by a few hub studies, while the collaboration network shows research teams working largely in isolated pairs and triads rather than as a connected international community. A thematic evolution map shows the field's vocabulary diversifying from two dominant early terms, artificial intelligence and sustainability, into more specific downstream themes such as responsible AI, data analytics, and digital transformation, with growing conceptual overlap between the AI and sustainability streams. The study contributes the first integrated bibliometric mapping of these four domains and offers a future-research agenda for researchers, practitioners, and policymakers advancing ethical, sustainable, and consumer-centric AI adoption in digital retail.
References
Adadi, A., & Berrada, M. (2018). Peeking inside the black box: A survey on explainable artificial intelligence. IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052 DOI: https://doi.org/10.1109/ACCESS.2018.2870052
Ai, Q., Azizi, V., Chen, X., & Zhang, Y. (2020). Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, 11(9), 137. https://doi.org/10.3390/a11090137 DOI: https://doi.org/10.3390/a11090137
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007 DOI: https://doi.org/10.1016/j.joi.2017.08.007
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019 DOI: https://doi.org/10.1162/qss_a_00019
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. National Bureau of Economic Research Working Paper Series, 31161. https://doi.org/10.3386/w31161 DOI: https://doi.org/10.3386/w31161
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013 DOI: https://doi.org/10.1016/j.indmarman.2021.07.013
Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., & Zha, H. (2022). Sequential recommendation with user memory networks. Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 108–116. https://doi.org/10.1145/3159652.3159668 DOI: https://doi.org/10.1145/3159652.3159668
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0 DOI: https://doi.org/10.1007/s11747-019-00696-0
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070 DOI: https://doi.org/10.1016/j.jbusres.2021.04.070
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1), 1–15. https://doi.org/10.1162/99608f92.8cd550d1 DOI: https://doi.org/10.1162/99608f92.8cd550d1
Fu, Z., Huang, X., Chen, X., Liu, Y., & Zhang, M. (2020). FairRec: Fairness-aware news recommendation with decomposed adversarial learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 128–136. https://doi.org/10.1609/aaai.v35i1.16129 DOI: https://doi.org/10.1609/aaai.v35i1.16129
George, G., Merrill, R. K., & Schillebeeckx, S. J. D. (2023). Digital sustainability and entrepreneurship: How digital innovations are helping tackle climate change and sustainable development. Entrepreneurship Theory and Practice, 47(4), 999–1027. https://doi.org/10.1177/1042258721993883 DOI: https://doi.org/10.1177/1042258719899425
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 1–8. https://doi.org/10.1007/s11747-019-00711-4 DOI: https://doi.org/10.1007/s11747-019-00711-4
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of artificial intelligence in retailing and e-commerce. Materials Today: Proceedings, 56, 2134–2139. https://doi.org/10.1016/j.matpr.2021.11.585 DOI: https://doi.org/10.1016/j.matpr.2021.11.585
Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, T., McAuley, J., & Zhao, W. X. (2024). Large language models are zero-shot rankers for recommender systems. Lecture Notes in Computer Science, 14609, 364–381. https://doi.org/10.1007/978-3-031-56060-6_24 DOI: https://doi.org/10.1007/978-3-031-56060-6_24
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9 DOI: https://doi.org/10.1007/s11747-020-00749-9
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2 DOI: https://doi.org/10.1038/s42256-019-0088-2
Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864–877. https://doi.org/10.1016/j.jbusres.2020.01.007 DOI: https://doi.org/10.1016/j.jbusres.2020.01.007
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420 DOI: https://doi.org/10.1509/jm.15.0420
Martin, K., & Murphy, P. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4 DOI: https://doi.org/10.1007/s11747-016-0495-4
Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J., Bull, J. S., & Waddoups, R. (2021). How technology is changing retail. Journal of Retailing, 97(1), 13–27. https://doi.org/10.1016/j.jretai.2020.10.006 DOI: https://doi.org/10.1016/j.jretai.2020.10.006
Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., Jiya, T., & Kirichenko, A. (2021). Organisational responses to the ethical issues of artificial intelligence. AI & Society, 37(1), 23–37. https://doi.org/10.1007/s00146-021-01148-6 DOI: https://doi.org/10.1007/s00146-021-01148-6
Tan, J., Xu, S., Ge, Y., Li, Y., Chen, X., & Zhang, Y. (2022). Counterfactual explainability for algorithmic recourse in recommender systems. Proceedings of the 31st ACM International Conference on Information and Knowledge Management, 1953–1962. https://doi.org/10.1145/3511808.3557291 DOI: https://doi.org/10.1145/3511808.3557291
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3 DOI: https://doi.org/10.1007/s11192-009-0146-3
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022 DOI: https://doi.org/10.1016/j.jbusres.2019.09.022
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