DSpace Repository

Welcome to the NWU Repository, the open access Institutional Repository of the North-West University (NWU-IR). This is a digital archive that collects, preserves and distributes research material created by members of NWU. The aim of the NWU-IR is to increase the visibility, availability and impact of the research output of the North-West University through Open Access, search engine indexing and harvesting by several initiatives.

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An intervention framework to enhance commuter road safety within the minibus taxi industry in Sedibeng District Municipality
(North-West University(South Africa)., 2026) Moyake, Mxolisi Samuel; Mahlala, S; Rapanyane, B
Road traffic injuries resulting in deaths are a global crisis affecting vulnerable road users and commuters. The alarming statistics on road accidents, according to the World Health Organization's road safety status reports, disproportionately affect many countries' socio-economic conditions. Africa as a whole and South Africa specifically, have a high incidence of road accidents. Public transportation plays a central role and accounts for a higher proportion of usage compared to private vehicles. Among the various modes of public transport, the minibus taxi industry was identified for study in South Africa's Sedibeng District Municipality. Statistics reveal that commuter road safety has not been adequately prioritised within the taxi industry in this region. The central objective of this study is to identify and examine factors that contribute to road accidents involving the minibus taxi industry, with the focus on commuter safety in Sedibeng District Municipality (SDM). The study also intended to draw an intervention framework that will enhance commuter road safety within the minibus taxi industry. This study employed a hybrid analytics approach emanating specifically from qualitative research designs. The researcher employed a descriptive research design to gather a sample of participants using a non-probability convenience sampling strategy. This sampling strategy was utilised to identify relevant and appropriate minibus taxi industry stakeholders for semi-structured interviews. The primary outcomes of the investigation reveal shortcomings in regulations governing the minibus taxi industry. Traffic rules are frequently violated with speeding, reckless driving, drunk driving and traffic officers' bribery and corruption among the key contributing factors to road traffic accidents. Furthermore, road infrastructure was identified as a contributing factor to the high rates of road accidents in the region. The majority of participants identified lack of commitment to road safety, which is a significant contributing factor to road accidents, in essence, the failure of the SDM office to intervene. Another significant contributing element was the presence of malfunctioning traffic signals, which was widely acknowledged as a causative factor in road collisions. The results demonstrated that most participants concurred that a significant portion of the taxi drivers on the road were not suitable to share the road with other drivers, leading to the elevated occurrence of road traffic fatalities. The situation requires urgent attention. Recommendations were made in line with the enhancement of commuter road safety through the application of total quality management (TQM) principles. The researcher has offered an intervention framework that stems from TQM, which is underpinned by system theory derived from TQM scholars. However, this framework was adapted to suit the minibus taxi industry as an organisation.
A blockchain security model for personal data sharing
(North-West University(South Africa)., 2026) Mandinyenya, Godwin; Malele, V
The rapid growth of cloud computing has created significant risks of data misuse, breaches, and identity theft, as service providers have frequently acted as sole custodians of user data without adequate transparency or enforceable consent mechanisms. High-profile incidents involving organisations such as Yahoo, Adobe, and JP Morgan illustrated the limitations of centralised trust models. Although regulations such as the European Union's General Data Protection Regulation (GDPR) imposed stricter controls on personal data processing, they also exposed tensions between confidentiality through encryption and broader requirements of accountability, auditability, and user rights. The aim of this study was to design and formally validate a Blockchain-Based Security Model (BSM) that enables secure, privacy-preserving, and regulation-aligned personal data sharing in decentralised environments. The model integrated a permissioned blockchain platform (Hyperledger Fabric) with Chaincode-as-a-Service (CCaaS), Intel SGX secure enclaves, InterPlanetary File System (IPFS) off-chain storage, and optional Zero-Knowledge Proofs (ZKPs). Methodologically, the study followed a Design Science Research approach grounded in a pragmatic research paradigm. The BSM was developed and evaluated through a combination of systematic literature review, architectural design, simulation-based performance benchmarking, and formal security verification. In line with standard Design Science Research theory, the artifact was justified using relevant kernel theories from cryptography, decentralised systems Design Theory (ISDT) to clarify constructs, design principles, and evaluation criteria. Formal validation was conducted using ProVerif under the Dolev-Yao adversary model, confirming that the BSM satisfied confidentiality, integrity, authentication, authorisation, and auditability requirements. Performance evaluations demonstrated sub-second access-control enforcement, verifiable deletion, and audit accuracy of 99.98%, while maintaining scalability and modularity. The results showed that the BSM effectively reconciled privacy with transparency, providing a compliance-ready framework aligned with GDPR, HIPAA, and regional data protection regulations. The study contributed a formally verified security architecture, a hybrid on-chain/off-chain storage strategy, a consent management mechanism, and deployment blueprints applicable to healthcare, finance, and government services, establishing a robust foundation for privacy-preserving digital ecosystems.
An intelligent security model for defence against routing attacks on the Internet of-Things
(North-West University(South Africa)., 2026) Sejaphala, Lanka Chris; Lugayizi, F.L; Malele, V
The Internet of Things (IoT) is fundamentally revolutionising diverse sectors such as agriculture, smart cities, and health, enabling critical applications such as environmental monitoring, military surveillance, and efficient waste management. These pervasive deployments often rely on Low-power and Lossy Networks (LLNs). The network caters for resource-constrained devices which rely on the Routing Protocol for Low-power and Lossy Networks (RPL) to facilitate efficient routing decisions. RPL is a widely used routing protocol designed for LLNs. Its operational integrity hinges on control messages like DODAG Advertisement Object (DAO), DODAG Information Object (DIO), and DODAG Information Solicitation (DIS) control messages, which collectively establish and maintain network topology. However, the limitations of IoT devices, including battery, processing capacity, and memory, as well as the complexities of RPL, make these networks particularly susceptible and vulnerable to various threats. Routing attacks pose a severe challenge to network stability and data integrity. Among these routing attacks, the DIS flooding attack stands out as the most destructive and resource-consuming threat. The attack specifically exploits RPL's DIS mechanism by overwhelming the network with an excessive volume of DIS messages. Such a disruption can lead to severe resource exhaustion, network congestion, and ultimately, a denial-of-service condition, significantly undermining the reliability of IoT network operations. The urgent need to counteract these sophisticated routing attacks is paramount to safeguarding the functionality of modern RPL-based IoT networks. Despite the proliferation of security models in the literature for general IoT environments, there remains a significant gap in the implementation of lightweight intelligent security models specifically tailored for RPL-based IoT. Existing solutions often struggle to balance detection efficacy with the stringent resource constraints of LLN devices. This research study's primary objective is to address this critical gap by implementing a novel, lightweight and intelligent security model designed to effectively detect the DIS-flooding attack with a high detection rate, low false alarm and minimum program flash memory utilisation of the IoT devices. To achieve this, the study adopted a simulation-based quantitative approach. A robust experimental setup was created within the Cooja simulation tool, utilising nodes running the Contiki 3.x operating system. This environment allowed for the precise implementation of the routing attacks that the study addresses and the generation of a comprehensive dataset under two distinct scenarios: a baseline normal operation and a DIS-flooding attack scenario. This dataset was then meticulously used to build, train, and test six(6) distinct machine learning (ML) algorithms, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Multilayer Perceptron (MLP), K-Nearest Neighbours (KNN), and Naive Bayes (NB). This study contributes three key advancements to the field: a theoretical contribution highlighting the imperative for intelligent and resource-efficient security models in RPLbased IoT; a methodological contribution presenting a robust framework for implementing and evaluating routing attacks within the Cooja simulation environment; and a significant practical contribution underscoring the real-world applicability of the proposed DT-based lightweight and intelligent security model to detect anomalies in IoT networks. The results of this study demonstrate that a tree-based algorithm, the Decision Tree model, performed significantly well as compared to other evaluated models, showcasing its higher performance with below threshold False Negatives (FN), and a remarkably small model size. Specifically, the DT model achieved an outstanding 98.21% Matthews Correlation Coefficient (MCC), 99.12% accuracy, 99.12% recall, and 98.86% precision, coupled with an exceptionally low 3.79% FN rate. Furthermore, the model required only 4.17 KB of program memory, confirming its suitability for deployment on resourceconstrained IoT device. The novelty of this study lies in the integrated implementation and evaluation of a memory-efficient intelligent detection model directly tailored to RPL-based IoT, validated within a realistic LLN simulation framework. Unlike prior approaches that prioritise detection performance without resource considerations, this work demonstrates that high detection accuracy and minimal memory footprint can be simultaneously achieved in RPL-based IoT environments. The findings provide a practical and scalable pathway toward securing LLNs against DIS-flooding attack, thereby enhancing the resilience of modern IoT networks.
Interplay of prophetic voice and pastoral care within a complex socio-political context: The case of the Church in Zimbabwe
(North-West University(South Africa)., 2026) Tafirei, Priviledge; Magezi, V
The Church in Zimbabwe has historically served as both a moral compass and an agent of societal change. However, its role in the public sphere has become increasingly contested within a complex socio-political environment characterised by authoritarian governance, economic decline, and social division. Although the Church is biblically and theologically called to embody both prophetic witness and pastoral care, its contemporary engagement has become fragmented. Some leaders have aligned themselves with political power, thereby damaging their credibility, while others have endeavoured to resist oppressive systems at great personal sacrifice. This has led to a lack of clarity and consistency in how the Church upholds its dual mandate of prophetic boldness and pastoral care. This study aims to critically examine the relationship between these two roles and to develop a framework for public pastoral care that faithfully integrates prophetic and pastoral responsibilities to enable effective church engagement in Zimbabwe. Using a literature-based approach, the research employs thematic analysis of published work, peer-reviewed, theological texts, and historical case studies on Church-State relations. It is situated within the field of public practical theology, employing Osmer's four tasks approach to describe the current situation, interpret its underlying causes, provide biblical-theological insights, and suggest practical responses. The research contends that prophetic witness and pastoral care are not opposing forces but complementary and mutually enriching roles that collectively form the Church's public pastoral mandate. By reclaiming this integrated approach, the Church can restore credibility, promote holistic well-being, and make a meaningful contribution to community healing and national transformation. The proposed framework offers both theological and practical insights by equipping church leaders to navigate Zimbabwe's socio-political challenges with courage, compassion, and contextual awareness. Ultimately, the study underscores the urgent need for the Church to embody its dual mission in ways that mediate God's shalom and promote justice, peace, and restoration within society.
Machine learning for retail credit risk scoring: a systematic literature review with insights for South African banks
(North-West University (South Africa)., 2026) Sithebe, Vumile; van den Berg, T.A.P; Goede, J.F
Credit scorecards remain central to bank lending, yet modern credit datasets increasingly demand models that capture non-linear, high-dimensional patterns. Traditional models such as logistic regression are increasingly constrained by their linear assumptions, particularly in emerging markets like South Africa where many borrowers have thin or fragmented credit histories. This study systematically reviews 32 peer-reviewed papers to examine how machine learning (ML) can be applied to retail credit scoring while maintaining the transparency requirements mandated by regulators and required by auditors. The study also conducts a performance comparison between ML approaches and barriers to adoption. Findings show that tree-based ensemble methods (Random Forest, XGBoost, LightGBM and CatBoost) consistently outperform traditional approaches in accuracy and stability while neural networks and support vector machines also perform well but raise transparency challenges. Explainable artificial intelligence (AI) techniques, especially SHapley Additive exPlanations (SHAP), emerge as practical tools to bridge predictive power with auditability. The review concludes that South African banks can adopt a staged, hybrid approach: using ML in data preparation and segmentation while retaining interpretable decision layers, thereby enhancing predictive accuracy and financial inclusion without undermining regulatory transparency.