Artificial Intelligence Personalized Marketing Content and Consumer Behavior in Nigerian SMEs

Artificial Intelligence Personalized Marketing Content and Consumer Behavior in Nigerian SMEs

https://doi.org/10.35912/amor.v7i3.3418
Published: Feb 6, 2026

Abstract:

Purpose: This study examined how AI-driven personalization of digital marketing content influences consumer behavior in Nigerian Small and Medium Enterprises (SMEs).

Research Methodology: A Systematic Literature Review (SLR) of 30 empirical studies published between 2015 and 2025 was performed. Data were sourced from peer-reviewed journal articles, conference papers, review studies, and industry reports that specifically examined AI-driven personalized marketing tools, particularly, product recommendation systems.

Results: The findings indicate that 60% of the reviewed studies reported moderate-to-high adoption of AI-driven personalized recommendation systems among digitally mature Nigerian SMEs in the retail and e-commerce sectors. Across studies, AI-enabled personalization produced an average 15.8% increase in consumer purchase intention, with a strong mean correlation (r = 0.60) between personalized product recommendations and purchase intentions.

Conclusions: AI-driven personalization significantly improves marketing effectiveness and positively shapes consumer purchase intentions in Nigerian SMEs.

Limitations: The exclusive use of secondary data and SLR-based synthesis limits the generalizability and real-time assessment of AI adoption and its impact on consumer behavior.

Contributions: This study consolidates empirical evidence on AI-personalized marketing in Nigerian SMEs, highlighting its impact on consumer purchase intention, while underscoring the need to address adoption barriers and ensure ethical data practices.

Keywords:
1. Artificial Intelligence (AI)
2. Consumer Purchase Intention
3. Digital Marketing
4. Small and Medium Enterprises (SMEs)
5. Personalized Marketing
Authors:
1 . Okwudiri Nnanna-Ohuonu
Abia State University, Uturu, Nigeria
2 . Nwachukwu Peter Chikwesiri
Abia State University, Uturu, Nigeria
3 . Amarachukwu Okudo
Abia State University, Uturu, Nigeria
4 . Izuka Vivian Chikwesiri
Abia State University, Uturu, Nigeria
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How to Cite
Nnanna-Ohuonu, O., Chikwesiri, N. P., Okudo, A. ., & Chikwesiri, I. . V. (2026). Artificial Intelligence Personalized Marketing Content and Consumer Behavior in Nigerian SMEs . Annals of Management and Organization Research, 7(3), 325–339. https://doi.org/10.35912/amor.v7i3.3418
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Issue & Section
Vol. 7 No. 3 (2026): February
Articles
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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  2. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749. doi:https://doi.org/10.1109/tkde.2005.99
  3. Aguirre, E., Mahr, D., Grewal, D., De Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of retailing, 91(1), 34-49. doi:https://doi.org/10.1016/j.jretai.2014.09.005
  4. Akinyemi, A. A., Okere, W., & Gbadamosi, L. O. (2023). Formal Financing Options And Small And Medium Scale Enterprises Performance. The Case Of Ota, Ogun State Nigeria. Oradea Journal of Business and Economics, 8(2), 23-32. doi:https://doi.org/10.47535/1991ojbe170
  5. Arachie, A. E., Okwudiri, N.-O., Anagwu, V. K., & Okeke, N. C. (2025). Assessing the Technological Readiness of Small Businesses for Artificial Intelligence-Powered Transformation: A West African Context. African Journal of Management and Business Research, 20(1), 137-156. doi:https://doi.org/10.62154/ajmbr.2025.020.01018
  6. Babado?an, B. (2024). Unveiling the power of AI-driven personalization: Transforming consumer behavior in the age of digital marketing. Next Frontier for Life Sciences and AI, 8(1), 61. doi:https://doi.org/10.62802/fj43xy18
  7. Babatunde, S. O., Odejide, O. A., Edunjobi, T. E., & Ogundipe, D. O. (2024). The role of AI in marketing personalization: A theoretical exploration of consumer engagement strategies. International Journal of Management & Entrepreneurship Research, 6(3), 936-949. doi:https://doi.org/10.51594/ijmer.v6i3.964
  8. Badmus, O., Rajput, S., Arogundade, J., & Williams, M. (2024). AI-driven business analytics and decision making. World Journal of Advanced Research and Reviews, 24(1), 616-633. doi:https://doi.org/10.47715/978-93-86388-87-2/ch6
  9. Bahangulu, J. K., & Owusu-Berko, L. (2025). Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications. World J Adv Res Rev, 25(2), 1746-1763. doi:https://doi.org/10.30574/wjarr.2025.25.2.0571
  10. Basri, W. (2020). Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), 142-152. doi:https://doi.org/10.2991/ijcis.d.200127.002
  11. Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of retailing, 91(3), 390-409. doi:https://doi.org/10.1016/j.jretai.2015.04.001
  12. Chang, M.-Y., & Chen, H.-S. (2022). Understanding consumers’ intentions to purchase clean label products: Evidence from Taiwan. Nutrients, 14(18), 3684. doi:https://doi.org/10.3390/nu14183684
  13. Dudziak, A., Stoma, M., & Osmólska, E. (2023). Analysis of consumer behaviour in the context of the place of purchasing food products with particular emphasis on local products. International Journal of Environmental Research and Public Health, 20(3), 2413. doi:https://doi.org/10.3390/ijerph20032413
  14. Durucasu, R. R. (2025). Digital Marketing Channels: Basic Methods and Applications. Uluslararas? Akademik Yönetim Bilimleri Dergisi, 11(17), 71-93. doi:https://doi.org/10.51947/yonbil.1636982
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  21. Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910-936. doi:https://doi.org/10.1108/jmtm-02-2018-0057
  22. Gogia, J., & Nanda, S. (2020). Digital marketing: Significance and trending strategies during COVID-19. International Journal of Research in Engineering, Science and Management, 3(11), 45-46. doi:https://doi.org/10.47607/ijresm.2020.370
  23. Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19. doi:https://doi.org/10.1145/2843948
  24. Hassan, M., Ullah, A., Siddique, M., Qureshi, N. A., & Sikandar, H. (2022). The evaluation of digital marketing's success for firms in the age of digitalization during the previous decade. Journal of Public Value and Administrative Insight, 5(2), 245-257. doi:https://doi.org/10.31580/jpvai.v5i2.2516
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  28. Lam, N. Q. (2025). Research on Consumer Buying Behavior When Watching Livestreams on Digital Technology Platforms in Vietnam. Journal of Management World, 2025(1), 728-744. doi:https://doi.org/10.53935/jomw.v2024i4.770
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  30. Lin, B., & Shen, B. (2023). Study of consumers’ purchase intentions on community E-commerce platform with the SOR model: a case study of China’s “Xiaohongshu” app. Behavioral Sciences, 13(2), 103. doi:https://doi.org/10.3390/bs13020103
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  35. Nazeer, M. Y. (2024). Algorithmic conscience: An in-depth inquiry into ethical dilemmas in artificial intelligence. International Journal of Research and Innovation in Social Science, 8(5), 725-732. doi:https://doi.org/10.47772/ijriss.2024.805052
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