UMU Institutional Repository
The Uganda Martyrs University Institutional Repository (UMU-IR) preserves research output from the Uganda Martyrs University community
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Recent Submissions
Perspectives on Uganda’s education sector services and education quality
(Springer, Cham, 2026-06-02) Namisi, Charles Patrick; Byaruhanga, Aloysius; Munuve, Veronica; Nyadoi, Priscilla; Kyamanywa, Patrick; Namisi, Patrick Charles | https://orcid.org/0000-0002-0615-7178; Byaruhanga, Aloysius | https://orcid.org/0009-0000-0258-2604; Nyadoi, Priscilla | https://orcid.org/0009-0006-0050-8113; Kyamanywa, Patrick | https://orcid.org/0000-0002-4270-4431
Quality education has ripple effects on many aspects of life, including success or not in employment. The government of Uganda has had interventions such as universal free primary and secondary education and the sector liberalization, allowing private institutions to offer educational services. Despite these interventions, the quality of education remains a challenge, as findings from secondary data review and analysis reveal, among others, limitations related to teachers’ competencies, instruction and curriculum design, cost of education, and school completion rates. Our review finding also pinpoints the need for teachers’ capacity building on transformative learning, curriculum and policy review, and research to identify innovative strategies for education financing and servicing to deliver desired standards and quality.
Perspectives on the challenges to timely progress and completion of study programmes among doctoral students
(Springer, Cham, 2026-06-02) Nyadoi, Priscilla; Ssekandi, Joseph; Ssentongo, Spire Jimmy; Esibo, Omaada Simon; Byaruhanga, Aloysius; Lokolimoi, John Bosco; Segawa, K. Edward; Otaala, Ariko Laura; Kyamanywa, Patrick; Nyadoi, Priscilla | https://orcid.org/0009-0006-0050-8113; Ssekandi, Joseph | https://orcid.org/0000-0002-0632-5506; Ssentongo, Spire Jimmy | https://orcid.org/0000-0002-8260-1568; Esibo, Omaada, Simon | https://orcid.org/0009-0005-0844-652X; Byaruhanga, Aloysius | https://orcid.org/0009-0000-0258-2604; Lokolimoi, John Bosco | https://orcid.org/0009-0007-3783-9191; Segawa, K. Edward | https://orcid.org/0009-0004-7682-5698; Otaala, Ariko Laura | https://orcid.org/0009-0008-0140-445X; Kyamanywa, Patrick | https://orcid.org/0000-0002-4270-4431
This chapter presents perceptions of students and staff on factors underpinning timely progress and completion of doctoral studies. Overall, doctoral students perceived that supportive activities such as conferences, publications in university outlets, timely feedback, and frequent interaction with supervisors/managers help them progress. Staff, however, perceived that late or non-payment of supervisors and examiners, poor quality students, and burdensome workload are problems that impede the timely completion of doctoral studies. Together, doctoral students and staff perceived that unharmonized timetabling and teaching in graduate and undergraduate programmes, insufficient supervisor–supervisee interactions, and limited ICT application in pedagogy lead to delayed progress and completion. While an in-depth investigation is still needed, these perspectives provide useful pointers to strategies that may be needed to improve progress and completion rates of doctoral candidates.
Test
(2025-10-21) DERRICK WALAKIRA; | https://orcid.org/0000-0002-6209-0561
AI-driven transformation and sustainable competitive advantage: evaluating Globaltech Solutions' marketing, customer service, and supply chain strategies
(SSRN, Elsevier Ltd., 2026-08-21) Bazigu, Rodgers
Artificial intelligence has moved from an experimental capability to a routine feature of daily commercial operations, and firms with strong internal dynamic capabilities are consistently better positioned to translate AI adoption into measurable performance gains. This paper evaluates how a composite technology-services firm, referred to here as GlobalTech Solutions, might apply AI across four interlinked functions: customer segmentation and personalisation, revenue forecasting and dynamic pricing, sentiment-driven service governance, and logistics optimisation. The analysis argues that while GlobalTech's leadership frequently reports confidence in its capacity to innovate, this self-assessment is not consistently matched by the systems needed to operationalise that innovation, a gap that is common across firms competing in markets with low product penetration and high growth potential. Reviewing evidence from recommender systems, actuarial pricing models, aspect-based sentiment analysis, and AI-enabled logistics, the paper concludes that GlobalTech's competitive position depends less on adopting any single technology than on embedding customer trust and transparent data practices into each application from the outset, since responsible design is what converts technical capability into sustained commercial advantage.
From data to decisions: a staged framework for AI, NLP, and computer vision adoption at InnovateCorp
(SSRN, Elsevier Ltd., 2026-08-17) Bazigu, Rodgers
Firms accumulating transactional, sensor, textual, and visual data frequently lack the analytical infrastructure to convert that data into decisions, a gap this paper examines through the case of a representative technology-enabled enterprise, InnovateCorp. The analysis develops a staged framework spanning four domains: predictive and prescriptive analytics for strategic decision-making; neural network architectures for predictive maintenance, inventory, and scheduling; natural language processing and conversational AI for customer engagement; and computer vision for quality inspection and retail intelligence. Rather than treating these as independent technology initiatives, the paper argues that they form concentric layers of a single organisational capability, one built on a unified data warehouse, staged deployment, and continuous human oversight rather than on the sophistication of any individual model. Reviewing evidence on ensemble methods, recurrent and convolutional architectures, transformer-based language models, and detection networks, the paper finds that the dominant constraint on value realisation is organisational rather than algorithmic: governance, data quality, and employee adoption determine outcomes considerably more than model selection. The paper concludes that mainstream, well-understood architectures, deployed conservatively and in sequence, offer InnovateCorp a more defensible path to sustained analytical advantage than pursuing technically novel but organisationally unproven alternatives.