GENERATIVE ARTIFICIAL INTELLIGENCE USEFULNESS, GOVERNMENT REGULATIONS AND CUSTOMER ENGAGEMENT RATE WITHIN SMALL TO MEDIUM ENTERPRISES IN KENYA
Abstract
Purpose of the Study: The purpose of the study was to determine the usefulness of generative AI on customer engagement rate in the small to medium enterprises in Kenya.
Statement of Problem: Generative Artificial Intelligence (GAI) has been noted to be impactful through task automation, enhancing creativity and personalizing human to machine interactions. This makes GAI a powerful tool for businesses. Small to medium enterprises require scale of operations that can be enhanced through the use of GAI. This would ensure efficiencies in their operations that allows them to highly engage customers thus increasing their engagement rate. The incorporation of GAI into the operations within small to medium enterprises is determined by positive environment brought about by favourable regulations.
Research Methodology: The study was anchored on Technology Acceptance Model, Expectation Confirmation Theory and Institutional Theory. The methodology deployed by the study was a review of literature to present a directional hypothetical representation of the independent to the dependent variables. This included a review of recent study articles to explain the usefulness of generative AI in ensuring enhanced customer engagement rates within small to medium enterprises in Kenya as well as the moderating effect of government regulations on this relationship.
Results of the Study: The review of literature indicated a positive relationship between the usefulness of generative AI towards ensuring positive customer engagement rates. The study found that higher customer engagement rates can be recorded as an outcome of deploying generative artificial intelligence technologies.
Conclusion: The findings provided practical insights for small to medium enterprises in Kenya who are trying to ensure efficiencies in their operations so as to serve their customers satisfactorily.
Recommendation: The study presented that this can be achieved through the deployment of GAI. The study also provided that policy makers should ensure positive environments for GAI deployment through the enactment of favourable policies as well as interventions.
Keywords: Business Customer Relationship, Customer Retention, Innovation, Marketing
References
Abrokwah-Larbi, K., & Tinguely, P. N. (2024). Generative artificial intelligence in small and medium enterprises: Navigating its promises and challenges. Business Horizons, 67(5), 629–648. https://doi.org/10.1016/j.bushor.2024.05.008
ADB Data Library. (2026). 2025 Asia Small and Medium-Sized Enterprise Monitor. Asian Development Bank. https://data.adb.org/dataset/2025-asia-small-and-medium-sized-enterprise-monitor
Ahn, E., & Kang, H. (2018). Introduction to systematic review and meta-analysis. Korean Journal of Anesthesiology, 71(2), 103–112. https://doi.org/10.4097/kjae.2018.71.2.103
Ajibade PhD, P. (2018). Technology Acceptance Model Limitations and Criticisms: Exploring the Practical Applications and Use in Technology-related Studies, Mixed-method, and Qualitative Researches.
Arou, J. A., Deyganto, K. O., & Chancellor, V. (2025). Harnessing AI-Driven Education and Training to Empower Small Businesses in East Africa. Artificial Intelligence, 6(1).
Ayiro, L. P. (2024). Educational Research Methods: A Functional Approach (First). Kenya Literature Bureau.
Balzano, M., Marzi, G., & Turzo, T. (2025). SMEs and institutional theory: Major inroads and opportunities ahead. Management Decision, 63(13), 1–27. https://doi.org/10.1108/MD-05-2023-0734
Borah, S., Kama, C., Rakshit, S., & Vajjhala, N. R. (2022). Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs). In Cognitive Informatics and Soft Computing (Vol. 375, pp. 717–726). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-8763-1_59
Bormane, S., & Blaus, E. (2024). Artificial intelligence in the context of digital marketing communication. Frontiers in Communication, 9, 10–23. https://doi.org/10.3389/fcomm.2024.1411226
Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer Engagement. Journal of Service Research, 14(3), 252. https://doi.org/10.1177/1094670511411703
Brown, S. A., Venkatesh, V., Kuruzovich, J., & Massey, A. P. (2018). Expectation confirmation: An examination of three competing models. Organizational Behavior and Human Decision Processes, 105(1), 52–66. https://doi.org/10.1016/j.obhdp.2006.09.008
Brown, S., Venkatesh, V., & Goyal, S. (2024). Expectation Confirmation in Information Systems Research: A Test of Six Competing Models. MIS Quarterly, 38, 729–756. https://doi.org/10.25300/MISQ/2014/38.3.05
Diderich, C. (2023). Firm Performance is About Customer Willingness to Pay. Innovate-d Insights. https://docs.innovate-d.com/insights/2023/insight-216-performance.pdf
Eng’airo, P. (2024). The Impact of AI-Driven Performance Evaluation on Organizational Outcomes in Kenya: A Systematic Literature Review. Journal of Information and Technology, 8(2), 1–15. https://doi.org/10.53819/81018102t7040
Gupta, V. (2024). An Empirical Evaluation of a Generative Artificial Intelligence Technology Adoption Model from Entrepreneurs’ Perspectives. Systems, 12(3), 103. https://doi.org/10.3390/systems12030103
Hossain, M., & Quaddus, M. (2021). Expectation–Confirmation Theory in Information System Research: A Review and Analysis. In Information Systems Theory (Vol. 1, pp. 441–469). https://doi.org/10.1007/978-1-4419-6108-2_21
Kibuacha, F. (2024, April 15). Africa MSME Pulse 2024 Report. GeoPoll. https://www.geopoll.com/blog/africa-msme-pulse-2024/
Kim, J., & Choi, G. (2024). Assessing the Impact of Generative Artificial Intelligence on Customer Engagement in Business-to-Customer Scenarios. Asia-Pacific Journal of Convergent Research Interchange, 10(2), 89–104. https://doi.org/10.47116/apjcri.2024.02.09
Kiprono, M. K. (2024). Adopted Technology And The Performance Of Micro And Small Enterprises In Nairobi.
KNBS. (2024). Kenya -Finaccess MSE Track Survey March 2023. https://statistics.knbs.or.ke/nada/index.php/catalog/129
Lucy, I., Melhorn, S. F., & Hoover, M. (2024, May 20). See the data behind America’s small businesses. https://www.uschamber.com/small-business/small-business-data-center
Magableh, I. K., Mahrouq, M. H., Ta’Amnha, M. A., & Riyadh, H. A. (2024). The Role of Marketing Artificial Intelligence in Enhancing Sustainable Financial Performance of Medium-Sized Enterprises Through Customer Engagement and Data-Driven Decision-Making. Sustainability, 16(24), 11279. https://doi.org/10.3390/su162411279
Malatji, M., van Eck, R., & Zuva, T. (2020). Understanding the usage, Modifications, Limitations and Criticisms of Technology Acceptance Model (TAM). Advances in Science Technology and Engineering Systems Journal, 5(6), 113–117. http://dx.doi.org/10.25046/aj050612
Mao, N., Wei, J., & Zhu, X. (2025). Generative Artificial Intelligence and SMEs Innovation: A Driver of Economic Growth. International Journal of Economics, Business and Management Research, 9(4). https://doi.org/doi.org/10.51505/IJEBMR.2025.9404
Meyer, J., & Rowan, B. (1977). Institutionalized Organizations: Formal Structure as Myth and Ceremony. 83, 340–363. http://dx.doi.org/10.1086/226550
Misango, D., Yatundu, F., & Mulwa, D. (2025). AI Customer Engagement Strategies on Enterprise Growth in Kenya: Adaptation and Navigation. International Journal of Research and Innovation in Social Science, IX, 6969–6981. https://doi.org/10.47772/IJRISS.2025.90400513
Mugenda, O., & Mugenda, A. (2019). Research Methods, Quantitative, Qualitative & Mixed Methods Approaches (Third). ACT.
Mwololo, C. F. (2025). Comparative Analysis of Machine Learning Algorithms for Enhancing Social Media Marketing and Decision-Making in Kenyan SMEs. Africa Journal of Commercial Studies, 6(1), 39–52. https://doi.org/10.30574/wjarr.2024.23.1.2177
Njeri, L. K., Wekesa, M. O., & Hassan, A. S. (2026). Artificial Intelligence Adoption And Performance Of Small And Medium Enterprises In Nairobi County, Kenya: The Moderating Role Of Digital Skills. Int Journal of Social Sciences Management and Entrepreneurship, 10(1), 39–50.
Ochieng, N. J., Maina, S., & MurigiE, E. (2025). Data-Driven Marketing Strategy and Loyalty of Online Food and Grocery Customers in Nairobi City County, Kenya. Journal of Applied Humanities and Social Sciences, 4(1), 43–54.
Omoga, C. O. (2023). Perceived Benefits of Artificial Intelligence Integration within Management Information System for Decision-Making Processes in the context of County Governments in Kenya. IARJSET, 10(11). https://doi.org/10.17148/IARJSET.2023.101101
Oyugi, G. O. (2025). Digitalization And Total Factor Productivity Of Selected Firms Listed At The Nairobi Securities Exchange In Kenya.
Özkan, A. M. (2025). Sustainable Development and Institutional Theory. In Integrating Organizational Theories With Sustainable Development. 10.4018/979-8-3693-8337-7.ch004
Schulze, B. P., Katsinis, A., Lagüera, G. J., Di, B. L., Odenthal, L., Hell, M., Lozar, B., & Secades, C. B. (2025). Annual Report on European SMEs 2024/2025, SME performance review. JRC Publications Repository. https://doi.org/10.2760/7714438
Sulaiman, S., & Olen, D. (2024, June 13). AI regulation and policy in Africa. Dentons. https://www.dentons.com/en/insights/articles/2024/june/13/ai-regulation-and-policy-in-africa
Thormundsson, B. (2024, December 5). AI adoption among organizations worldwide 2017-2024. Statista. https://www.statista.com/statistics/1545783/ai-adoption-among-organizations-worldwide/
Wanyaga, N. S. (2025). Innovation Strategies And Performance Of Software Start-Ups In Nairobi City County, Kenya.
Witness, S., Ruigu, R., & Ihaji, E. (2024). Transforming The Msme Industry In Kenya. FSD Kenya.
Xin, R. U. Z. H. L. (2026). Integrating AI Usability and Transformational Leadership for Green Innovation and Environmental Sustainability in Chinese SMEs: A Hybrid SEM-ANN Analysis. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-026-03216-y
