AI TECHNOLOGY INTEGRATION AND COMPETITIVE ADVANTAGE: EVIDENCE FROM TELECOMMUNICATIONS FIRMS IN NAIROBI CITY COUNTY, KENYA
Authors/Creators
- 1. Corresponding Author, School of Business, Economics and Tourism, Kenyatta University, Nairobi, Kenya
- 2. Lecturer, School of Business, Economics and Tourism, Kenyatta University, Nairobi, Kenya
Description
Purpose of Study: This study investigated the effect of AI technology integration on the competitive advantage of telecommunications firms in Nairobi City County, Kenya, addressing the gap between AI adoption and measurable performance outcomes in emerging market contexts where infrastructure and skills constraints moderate technology returns.
Methodology: A descriptive correlational design was employed, collecting primary data from 67 senior managers across four licensed telecommunications firms (Safaricom, Airtel, Telkom, Jamii) using structured questionnaires. The study was anchored in the Resource-Based View, Dynamic Capabilities Theory, and Technology Acceptance Model. Multiple regression analysis was conducted to estimate the independent contribution of AI technology integration to competitive advantage.
Findings: AI technology integration emerged as the strongest independent predictor of competitive advantage (β = 0.412, p < 0.001), with the overall model explaining 71.4% of variance in competitive outcomes (Adjusted R² = 0.701). Operational efficiency improvement was the highest-rated competitive gain (M = 3.94), while market share improvement rated lowest (M = 3.54). A notable integration protocol gap was identified: formalisation of documented AI integration protocols (M = 3.52) lagged behind operational integration capabilities (M = 3.89). This pattern reflects the distinction in dynamic capabilities literature between operational capabilities and higher-order strategic reconfiguring capabilities, representing a key barrier to sustainable competitive advantage.
Conclusion: AI technology integration significantly enhances competitive advantage in Kenya's telecommunications sector, affirming RBV and DCT generalisability to emerging markets. Addressing the integration protocol gap through formalised, documented processes and governance structures is essential for translating episodic AI adoption into sustained, institutionally embedded competitive differentiation.
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References
- Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
- Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. https://us.sagepub.com/en-us/nam/research-design/book255675
- Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Fascinari, M., & English, V. (2025). The Impact of Artificial Intelligence on Strategic Technology Management: A Mixed-Methods Analysis of Resources, Capabilities, and Human-AI Collaboration. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2512.08938
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. https://www.cengage.com/c/multivariate-data-analysis-8e-hair/
- IBM. (2024). AI in telecommunications: How artificial intelligence is transforming the telecom industry. https://www.ibm.com/think/topics/ai-in-telecommunications
- Jalali, M. S., & Kaiser, J. P. (2022). Dynamic capabilities for digital transformation: A systematic literature review. Journal of Strategy and Management, 15(2), 272–295. https://doi.org/10.1108/JSMA-04-2021-0089
- Jamal, A., & El Guermai, R. (2024). Unravelling the managerial impact: The influence of artificial intelligence on telecommunications in Morocco. Revista de Management Comparat Internațional, 25(4), 699–716. https://doi.org/10.24818/RMCI.2024.4.699
- Kassa, B. Y., & Worku, E. K. (2025). The Impact of Artificial Intelligence on Organizational Performance: The Mediating Role of Employee Productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100474. https://doi.org/10.1016/j.joitmc.2025.100474
- Kothari, C. R. (2014). Research methodology: Methods and techniques (3rd ed.). New Age International Publishers. https://www.newagepublishers.com/
- Mugenda, O. M., & Mugenda, A. G. (2003). Research methods: Quantitative and qualitative approaches. African Centre for Technology Studies. https://catalog.ihsn.org/index.php/catalog/4272
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. https://www.mheducation.com/
- Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press. https://www.hbs.edu/faculty/Pages/item.aspx?num=195
- Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
- Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B., & Kiron, D. (2020). Expanding AI's impact with organizational learning. MIT Sloan Management Review. https://sloanreview.mit.edu/article/expanding-ais-impact-with-organizational-learning/
- Safaricom PLC. (2024). Annual report and financial statements 2024. https://www.safaricom.co.ke/annualreport_2024/
- Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson. https://www.pearson.com/en-gb/subject-catalog/p/research-methods-for-business-students/P200000003287
- Sun, Y., Chen, L., & Mei, J. (2024). Integrating the Resource-Based View and dynamic capabilities: A framework for sustaining competitive advantage. ResearchGate. https://doi.org/10.36713/epra18157
- Tech in Africa. (2026, February 27). Safaricom innovation strategy 2026: AI, M-Pesa expansion, and Kenya's startup ecosystem growth. https://www.techinafrica.com/safaricom-innovation-strategy-2026-ai-m-pesa-kenya-startup-growth/
- Teece, D. J. (2023). The evolution of the dynamic capabilities framework. In R. Adams, D. Grichnik, A. Pundziene, & C. Volkmann (Eds.), Artificiality and sustainability in entrepreneurship. Springer. https://doi.org/10.1007/978-3-031-11371-0_6
- Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z
- Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308, 641–652. https://doi.org/10.1007/s10479-020-03918-9
- Veritis. (2024). AI in telecommunications industry: Market growth and key use cases. https://www.veritis.com/blog/impact-of-ai-in-telecommunications-industry/