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This Collection incorporates all Research works, Thesis and Dissertations under the School of Humanities and Social Sciences
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This Collection incorporates all Research works, Thesis and Dissertations under ZCAS Professional

Recent Submissions

An Investigation of the Impact of Technological Advancements in Financial Services on Customer Experience in Selected Commercial Banks in Zambia
(ZCAS University, 2026-07) Shelly Esinam Amoah
This study investigated the impact of technological advancements in financial services on customer experience within selected commercial banks in Zambia. The research was motivated by the rapid expansion of digital banking platforms in the country, particularly internet banking, mobile banking, and Automated Teller Machines (ATMs), and the continuing concerns regarding customer satisfaction, system reliability, digital literacy, and security. Data was collected using a structured questionnaire on a 5-point Likert scale and Key-Informant Interviews. Data was analyzed using SPSS to generate descriptive statistics, correlations, and regression outputs that establish the predictive effect of each technological advancement on customer experience as from 400 bank customers drawn from Access Bank and Zambia National Commercial Bank in Luapula province and First National Bank as well as Zambia Industrial Commercial Bank in Lusaka city. The study also sampled 7 fintech experts from the four commercial banks, Bankers Association of Zambia, Ministry of Finance, and Zambia Information and Communications Authority. The results showed that correlation analysis shows strong associations between internet-banking/mobile banking challenges and overall perceived difficulty, with ATM issues secondary yet notable. Technological advancements have transformed the landscape of financial services, particularly in developing economies such as Zambia. The proliferation of mobile banking, internet banking, and ATMs has redefined customer experience, service delivery, and operational efficiency in commercial banks. However, understanding the complex interplay between technology adoption, customer familiarity, perceived service quality, and actual usage remains a challenge. To address this gap, this synthesis proposed a new theoretical framework – the Integrated Customer-Centric Technology Adoption and Service Quality (ICTASQ) Model for Financial Services in Zambia – by integrating the Technology Acceptance Model (TAM) (Davis, 1989), Familiarity Theory, Contrast Theory, SERVQUAL (Parasuraman, Zeithaml, and Berry, 1988), and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). This model is grounded in empirical findings from this study which investigated the impact of technological advancements on customer experience in selected commercial banks in Zambia, drawing on data from bank customers, customer service managers, and Fintech experts in Lusaka and Luapula provinces. In synthesis, objectives converge: challenges persist, but advancements drive positive experiences, amplified by supportive regulation. Collectively, the findings revealed a nuanced landscape in which government policies and regulatory frameworks significantly influence customer experience in digital banking, but with varying degrees of impact. Security and compliance measures are the most potent drivers, followed by the effectiveness of complaint-redress mechanisms and, to a lesser extent, regulatory awareness.
Investigating Factors Contributing to the Underperformance of State-Owned Enterprises: Evidence from Zambia Railways Limited
(ZCAS University, 2026-07) Nizah Lawrence Mutambo
State-Owned Enterprises play a strategic role in national development through the provision of critical infrastructure and public services. However, many state-owned enterprises in developing economies continue to experience operational, financial, managerial, and institutional challenges that undermine their performance. This study investigated the factors contributing to the underperformance of State-Owned Enterprises using Zambia Railways Limited as a case study. Specifically, the study examined the influence of maintenance practices, funding adequacy, leadership practices, competition from road transport, and government policies and regulatory frameworks on ZRL’s performance. The study adopted a pragmatic research philosophy and a convergent parallel mixed-methods design. Quantitative data were collected from 109 respondents using structured questionnaires and analyzed using descriptive statistics, Spearman’s correlation and multiple regression in IBM SPSS Version 26. Qualitative data were collected through interviews, document review, and stakeholder consultations and analyzed using thematic and content analyses. Findings from both strands were integrated through triangulation. The findings revealed that ZRL’s underperformance is driven by interrelated technical, managerial, market, and regulatory challenges. Maintenance, leadership, competition from road transport, and government policies significantly influenced ZRL's performance. Maintenance challenges included aging infrastructure, inadequate preventive maintenance, and frequent rolling stock breakdowns. Leadership effectiveness emerged as the strongest predictor of performance, while competition from road transport reduced ZRL’s market share and competitiveness. Policy inconsistencies and weak enforcement further constrained performance. Although funding was widely perceived as inadequate, its statistical effect was not significant, suggesting that resource utilization and financial governance are equally important. The study contributes to knowledge in three dimensions. Theoretically, it adapts and integrates Total Productive Maintenance Theory, Contingency Theory, Institutional Theory, and Porter’s Competitive Forces Model into a framework for SOEs railway reform. Methodologically, it demonstrates the value of a convergent mixed-methods approach and triangulated analysis. CSC11003 Page vii of 373 NIZAH LAWRENCE MUTAMBO Practically, the study proposes a multi-level reform strategy integrating institutional readiness, operational efficiency, market responsiveness, stakeholder engagement, and strategic transformation, supported by maintenance modernization, transformational leadership, sustainable financing, regulatory reforms, and multimodal transport partnerships to improve the performance of Zambia Railways and similar state-owned enterprises.
Optimizing Machine Learning Models for Disease Progression Prediction In TB/HIV Coinfection Using Resource-Limited EHR Data Systems in Zambia
(2026-05) Joe Phiri
The integration of artificial intelligence (AI) and machine learning (ML) into electronic health record (EHR) systems offers a transformative Fourth Industrial Revolution (4IR) opportunity for public health decision-making in resource-limited settings. The translation of algorithmic advances into equitable, reliable and actionable clinical tools in low- and middle-income countries (LMICs), however, remains constrained by data quality deficiencies, methodological gaps and governance challenges. This thesis addresses these challenges through three inter related empirical studies conducted in the context of Zambia's national HIV EHR system (SmartCare), drawing on a deduplicated, deterministically linked dataset of 246,053 people living with HIV (PLHIV) across six high-volume public health facilities in Lusaka District. Study One presents a systematic review of 64 peer-reviewed studies published between 2018 and 2025 on ML-based predictive analytics using EHR data, with LMICs as the primary focus. Conducted in accordance with PRISMA 2020 guidelines and using a PROBAST-AI-adapted risk-of-bias instrument, the review establishes that only 18.8% of studies were conducted in LMIC settings despite these settings carrying the greatest burden of preventable disease. External validation was reported in only 7.8% of studies, calibration in 6.2%, and explainability in 8.3% of LMIC-focused studies compared with 36.4% in high-income settings. Four cross-cutting evaluation, contextual, ethical and deployment gaps are identified and used to scope the empirical chapters. Study Two develops and evaluates a leakage-safe, multi-outcome, externally validated and explainable ML framework predicting three 12-month binary outcomes among PLHIV: recorded TB treatment (TB12m), programme-recorded interruption in treatment (IIT12m) and recorded unsuppressed viral load (UVL12m). A stacked ensemble of logistic regression, random forest and XGBoost with a logistic regression meta-learner achieved AUC-ROC of 0.707 for TB12m, 0.839 for IIT12m and 0.778 for UVL12m on a held-out facility test set (n = 74,506). F1 optimised decision thresholds support operational protocols ranging from high-sensitivity screening (TB12m, threshold 0.257, sensitivity 92.3%) to highly targeted intensive intervention (IIT12m and UVL12m, thresholds near 0.9). SHAP-based local interpretability shows that education level is the dominant predictor of UVL12m and a leading predictor of IIT12m, raising critical questions about whether the signal reflects genuine clinical risk or facility-level data recording patterns. Facility-holdout validation confirms cross-site generalisability under deployment-realistic conditions. Study Three conducts a systematic eight-dimension data quality assessment (DQA) of the SmartCare EHR system using the AI pipeline as a diagnostic lens. Education-level completeness ranged from 1.2% to 7.9% across all six facilities; engagement feature coverage was only 12.3%; and 54.3% of patients with engagement data had implausible appointment-lateness values, the highest being 45,459 days (approximately 124 years). The composite Data Quality Index (DQI) ranged from 53.5% to 58.3%, all below the 70% minimum threshold proposed in the thesis for iv fully trustworthy AI training. Eight minimum data quality requirements and a reproducible DQA toolkit are proposed, with quantitative remediation targets at facility and programme levels. Complementing these empirical studies, the thesis develops a 4IR health transformation framework situating AI adoption within Zambia's broader digital health ecosystem; constructs an economic value creation model demonstrating positive cumulative cost-benefit by Year 3 of deployment under base-case assumptions; and proposes an institutional governance architecture together with a five-phase AI adoption roadmap (2025-2030) aligned with PEPFAR, Global Fund and Ministry of Health programmatic cycles. A proposed ICT integration architecture linking SmartCare to an HL7 FHIR R4-compliant ML prediction engine, DHIS2, DISA and a clinical decision support dashboard provides the engineering blueprint for transitioning from research-grade pipelines to production-ready clinical deployment at national scale. Two formal dissemination meetings were convened during the research: with the Ministry of Health Provincial Health Office Monitoring and Evaluation Data Management teams (representatives from Lusaka, Southern, Western, North-Western and Eastern Provinces); with the AI Think-Tank Team of experts at the Centre for Infectious Disease Research in Zambia (CIDRZ). Feedback across these forums was uniformly positive and is integrated into the thesis recommendations. Together, these contributions show that trustworthy AI in resource-limited public health requires as much investment in data governance, economic justification, ICT architecture and institutional capacity as in algorithmic development. The thesis offers an integrated framework, evidence base and operational roadmap for responsible AI deployment in Zambia's HIV programme, with direct relevance to comparable settings across sub-Saharan Africa.
Risk Assessment in Peer-to-Peer Lending Platforms: The Case of Lusaka, Zambia
(ZCAS University, 2026-03) Lombe Musonda
The rapid expansion of digital financial technologies has transformed credit access through Peer-to-Peer (P2P) lending platforms, which directly connect borrowers and investors. In Zambia, where 30.6% of adults remain financially excluded, platforms such as PremierCredit and Lupiya are expanding credit access for MSMEs and informal workers; however, this growth is associated with rising credit risk, operational challenges, information asymmetry, and regulatory uncertainty. This study addresses the lack of empirical evidence on risk assessment practices, credit scoring effectiveness, regulatory pressures, and investor protection in Lusaka’s P2P lending sector, and examines concerns surrounding AI-driven credit scoring models, particularly their limited transparency and contextual relevance. Using an explanatory sequential mixed-methods design, data were collected from 102 respondents and supplemented with 20 semi-structured interviews and three focus group discussions; quantitative analysis was conducted using SPSS, while qualitative insights were analysed thematically. Findings show that credit risk is the most significant concern (composite mean M = 3.957), driven by limited credit bureau coverage (M = 4.22) and the informal economy (M = 3.92); current risk assessment practices were rated below the scale midpoint (M = 2.943), with context-sensitive approaches scoring particularly low (M = 2.48); regulatory ambiguity (M = 3.75) was identified as a major barrier to platform scalability; and investor protection was perceived as inadequate, with borrowers reporting lower protection levels (M = 2.560) compared to investors (M = 3.297) and platform operators (M = 3.054), with statistically significant differences (F(2, 99) = 19.549, p < 0.001). Regression analysis revealed that credit risk assessment (β = 0.583) and methodological rigour (β = 0.446) significantly predict investor protection outcomes, explaining 76.8% of variance (R² = 0.768, F(3, 98) = 108.42, p < 0.001). The study concludes that stronger, context-specific risk assessment is essential and recommends regulatory harmonisation, expanded credit bureau coverage, improved disclosure standards, and locally adapted credit scoring models.
An Analytical Review of the Effects of Business Re-engineering Implementation Processes on Firm Performance: A Case of TAMAZA Pipeline Limited
(ZCAS University, 2023) Peggy kaponda Banda
Key Findings - IT improvements: Information technology significantly enhanced internal communication within the organization. - Capacity building gaps: Training initiatives were insufficient, failing to reach all staff members. - Positive impact of BRP: Implementation of BRP improved work processes through technology use. - Change management challenges: Resistance to change and limited stakeholder engagement were major obstacles during BRP implementation. Recommendations - Enhance work processes: TPL should continue leveraging modern technology and involve all stakeholders. - Lean structure: The organisation should streamline its structure for efficiency. - Continuous training: Regular capacity-building activities—like workshops and brainstorming sessions—should be conducted to strengthen staff engagement and adaptability. In essence, the study emphasizes that while technology and BRP have improved operations, success depends on inclusive training and proactive stakeholder involvement.