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
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.
Artificial intelligence as a competitive equaliser for mid-sized retailers
(SSRN, Elsevier Ltd., 2026-08-28) Bazigu, Rodgers
Mid-sized retailers increasingly compete against e-commerce platforms that offer large-scale personalisation, predictive supply chains, and adaptive marketing at a cost smaller firms cannot easily match. This paper develops an artificial intelligence (AI) adoption framework for a representative midsized retailer, referred to here as StyleHub, addressing four linked questions: which AI applications can meaningfully address a mid-sized retailer's core operational challenges; which design factors govern the construction of an effective personalisation model; how personalisation can be pursued without eroding customer trust; and what a realistic, phased adoption pathway looks like. Drawing on the retail AI, marketing ethics, and AI governance literatures, the paper argues that competitive advantage at StyleHub's scale depends less on acquiring the most advanced tools available and more on sequencing investment around organisational readiness, data quality, and safeguards built in from the outset rather than retrofitted later. Two contrasting adoption trajectories are outlined, one where technical investment outpaces governance and stalls, and one where phased, ethically grounded adoption compounds gradually, to argue that responsible implementation is itself a source of durable differentiation for resource-constrained retailers.
AI-driven transformation and sustainable competitive advantage
(Eliva Press, 2026-10-05) Bazigu, Rodgers
Every competitor can now buy the same algorithms. So where does lasting advantage come from? AI-Driven Transformation and Sustainable Competitive Advantage follows GlobalTech Solutions, a technology-services firm whose leaders believe they are ready for artificial intelligence while their systems tell a different story. Through this case, the book examines four areas where AI now shapes commercial success: customer segmentation and personalisation, revenue forecasting and dynamic pricing, sentiment-driven customer service, and intelligent logistics. Drawing on strategic management theory and current research in machine learning, the book reaches a clear conclusion. Advantage does not come from the technology itself. It comes from integrated data, cross-functional routines and, above all, customer trust. The same pricing engine or recommender that lifts revenue can quietly erode the relationships it depends on, unless it is built responsibly from the start. Readers will find a conceptual framework linking AI capability to sustainable advantage, a lifecycle model for responsible AI governance, a strategic prioritisation matrix and a five-phase implementation roadmap. The book is written for postgraduate students, researchers and managers in emerging markets who need to turn AI ambition into AI capability.
Antimicrobial resistance profiles and detection of mecA, blaCTX-M-1, and blaSHV genes in bacteria among diabetic foot ulcer patients from selected referral hospitals in Uganda
(Springer Nature Link, 2025-11-04) Namatovu, Alice; Vahwere, Mumbere Beinfait; Ntulume, Ibrahim; Ssebuufu, Robinson; Nalubega, Rebecca; Pius, Theophilus; Sikakulya, K Franck; Agaba, Gidio; Seremba, Emmanuel; Kyamanywa, Patrick; Nasinyama, George William
Effective management of Diabetic foot ulcers (DFU) requires timely treatment of infections with appropriate antibiotics based on culture and susceptibility results. However, challenges in diagnostic testing persist and consequently, data on prevalent bacteria, their antimicrobial resistance profiles and genes remains scanty. This study profiled the prevalent bacteria in Diabetic foot infection (DFI), their antimicrobial susceptibility patterns, and associated resistance genes. A multi-center cross sectional study was conducted from November 2021-January 2022, involving 117 patients with DFU, attending selected referral hospitals (Kiruddu, Jinja, Kampala International Teaching Hospital, Kitagata, Mbarara, Fort Portal and Hoima). Wound swabs were aseptically collected and placed in Stuart transport medium, then pre-enriched prior to inoculation onto Blood, MacConkey and Chocolate Agar to isolate aerobic microorganisms. Bacteria identification was based on colony morphology, Gram stain, conventional biochemical tests and Antibiotic susceptibility testing using Kirby-Bauer disk diffusion method were performed. Methicillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum beta-lactamase-producing Enterobacterales (ESBL-PE) were identified phenotypically. mecA gene in selected MRSA, and blaCTX-M-1 and blaSHV-1 in ESBL-PE were detected using Uniplex PCR and electrophoresed using 1.5% agarose gel. Data was analyzed using MS Excel version 15 and Stata version 15 (Stata Corp®). Microbiologically confirmed infection was observed in 89.7% (105/117) of DFU cases, yielding 144 bacterial isolates. Poly-microbial infections were detected in 23.8% of patients. Gram-positive bacteria accounted for 55.6% (80/144) of isolates. Staphylococcus aureus (36.1% (52/144)) and Proteus spp (13.9% (20/144)) were the most prevalent Gram-positive and Gram-negative bacteria, respectively. Notably, 98.6% (142/144) of the isolates were multidrug
resistant organisms (MDR). Staphylococcus aureus sensitivity was highest for Gentamycin and Ciprofloxacin (48% each) and lowest for Cefoxitin (2%). Proteus spp sensitivity was highest for Imipenem (87%) and lowest for Tetracycline, Ampicillin and Amoxiclav (0%). mecA gene was identified in 56% (5/9) of MRSA. Among the selected MDR Gram negative isolates, 60% had blaCTX-M-1, 20% had blaSHV and both blaCTX-M-1 and blaSHV in one Klebsiella pneumoniae. The high prevalence of MDR bacteria in DFI underscores the need for culture and sensitivity testing guided treatment. Detection of resistance genes calls for sustained surveillance efforts, stringent infection prevention and control, and alternative therapy exploration.