Leveraging Artificial Intelligence for Enhanced Operational Efficiency in the Telecommunications Industry: A Case Study from Zimbabwe
Abstract
This study uses one telecommunications company as a case study to investigate the potential of artificial intelligence (AI) to enhance operational efficiency in Zimbabwe's telecommunications industry. Despite global advancements in AI adoption, its integration within Zimbabwe remains limited, particularly in addressing inefficiencies such as high operational costs, poor service quality, and outdated infrastructure. The research is grounded in the TOE framework, the RBV model, and the DOI theory. A quantitative approach was adopted, and data were collected from 117 respondents using structured questionnaires. Analysis was conducted using descriptive statistics, factor analysis, Pearson correlation, regression modelling, and ANOVA to assess AI adoption levels, its impact on efficiency, and the barriers to integration. Findings indicate that while AI adoption is still emerging, it has already led to improved service delivery, reduced downtime, and enhanced resource utilisation. However, several barriers persist, including financial constraints, regulatory uncertainty, infrastructure deficits, and limited technical expertise. The study proposes a five-pillar strategic framework focusing on technological readiness, supportive policy, capacity building, financial planning, and stakeholder collaboration to guide sustainable AI implementation. In conclusion, the research underlines that with targeted strategic investments and institutional support, AI can significantly transform operational efficiency in Zimbabwe's telecom sector. The findings offer practical insights for industry leaders, policymakers, and researchers seeking to drive digital transformation in emerging economies for operational efficiency.
Downloads
References
Shambira, T., "The Digital Shift: AI in Zimbabwean Enterprises," Journal of Emerging Technologies in Africa, vol. 12, no. 2, pp. 45-59, 2020.
Hokonya, K., "Artificial Intelligence for Service Optimisation in Southern Africa," ICT for Development Review, vol. 9, no. 1, pp. 21-33, 2024.
POTRAZ Report, "Sector Performance Report Q1 2024," Postal and Telecommunications Regulatory Authority of Zimbabwe, Harare, 2024.
Adebayo Olusegun Aderibigbe et al., "AI-Driven Efficiency in African Telecoms," African Journal of Information Systems, vol. 18, no. 4, pp. 112-130, 2023.
Econet Wireless, "Innovation Report: Chatbots and Service Delivery," Harare, Zimbabwe, 2024.
Dube, S., and Nhamo, G., "Hydropower and Network Reliability in Zimbabwe," Energy & Environment in Africa, vol. 15, no. 3, pp. 207-223, 2023.
Bonga, W. G., and Sithole, S., "Telecom Infrastructure Gaps in Zimbabwe," Journal of Infrastructure and Development, vol. 6, no. 1, pp. 87-99, 2020.
Masuta, T., "MTN and Safaricom AI Case Studies," Telecom Advances in Africa, vol. 10, no. 2, pp. 56-67, 2025.
Government of Zimbabwe, Vision 2030: Towards a Prosperous and Empowered Upper Middle-Income Society by 2030, Harare, 2018.
Saunders, M., Lewis, P. and Thornhill, A., Research Methods for Business Students, 8th ed., Pearson Education, 2019.
Walliman, N., Social Research Methods, 2nd ed., SAGE Publications, 2014.
Neuman, W. L., Social Research Methods: Qualitative and Quantitative Approaches, 7th ed., Pearson, 2014.
Yamane, T., Statistics: An Introductory Analysis, 2nd ed., Harper and Row, 1967.
Gerber, M. and Hall, D., "Quantitative data analysis for social researchers," Journal of Statistical Techniques in Social Sciences, vol. 6, no. 2, pp. 45–60, 2017.
Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E., Multivariate Data Analysis, 7th ed., Pearson, 2014.
Jain, R., Dubey, S. and Jain, R., "Measuring Technology Adoption: A Review of Methods," Journal of Technology in Society, vol. 8, no. 1, pp. 33–48, 2016.
Balmer, M., Levin, P., & Schmidt, F., "Leveraging AI in Telecom: Insights from Emerging Economies," Journal of Telecommunications Management, vol. 32, no. 2, pp. 110–122, 2020.
Elapanda, P., Adinarayana Rao, M., & Sravan Kumar, R., "AI-Based Predictive Models in Network Management," International Journal of Intelligent Systems and Applications, vol. 14, no. 3, pp. 89–98, 2020.
Pasunuri, P., "AI Predictive Maintenance Models for Next-Gen Networks," Telecom AI Quarterly, vol. 6, no. 1, pp. 42–57, 2025.
Salehi, A., "The Economics of AI Integration in Developing Countries," Emerging Markets Technology Review, vol. 10, no. 4, pp. 211–227, 2023.
Abanga, E.A. & Dotse, S., "AI and Digital Economies: A Comparative Analysis of Africa," Asian Journal of Research in Computer Science, vol. 17, no. 10, pp. 12–25, 2024.
Moyo, T., Watyoka, T., & Chari, C., "AI Infrastructure Challenges in Sub-Saharan Africa," African Journal of ICT Policy and Innovation, vol. 15, no. 1, pp. 55–70, 2022.
Oliveira, T., Thomas, M., & Espadanal, M., "Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors," Information & Management, vol. 51, no. 5, pp. 497–510, 2014.
Barney, J., "Firm resources and sustained competitive advantage," Journal of Management, vol. 17, no. 1, pp. 99–120, 1991.
Edith Ebele Agu, Angela Omozele Abhulimen, Anwuli Nkemchor Obiki-Osafiele, Olajide Soji Osundare, Ibrahim Adedeji Adeniran, and Christianah Pelumi Efunniyi, "Artificial intelligence in African insurance: A review of risk management and fraud prevention," Int. J. Manag. Entrep. Res., vol. 4, no. 12, pp. 768–794, 2022, doi: 10.51594/ijmer.v4i12.1473.
G. V. Musyoka, R. M. Mutua, and J. Tocho, "AI-Based Framework for Government Oversight of Personal Data Consent Compliance: A Case Study of Nairobi County," Int. J. Prof. Pract., vol. 12, no. 6, pp. 40–48, 2024, doi: 10.71274/ijpp.v12i6.498.
A. Sreerangapuri, "AI-Driven Service Transformation : Revolutionizing Operational Excellence," Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 10, no. 6, pp. 132–140, 2024.
E. Susanto and Z. D. Khaq, "Enhancing Customer Service Efficiency in Start-Ups with AI : A Focus on Personalization and Cost Reduction," J. Manag. Informatics, vol. 3, no. 2, pp. 267–281, 2024.
A. K Saxena, "AI in Governance and Policy Making," Int. J. Sci. Res., vol. 13, no. 5, pp. 1218–1223, 2024, doi: 10.21275/sr24519015426.
Abstract views: 292 times
Download PDF: 105 times
Copyright (c) 2025 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
- I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
- I certify that all authors have approved the publication of this and there is no conflict of interest.
- I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
- I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
- I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
- If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
- I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)
