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The impact of debt financed expenditure on economic growth: case of South Africa
(University of Fort Hare, 2025-06) Makhaba, Sixolile
This study assesses the impact of debt-financed government expenditure on economic growth in South Africa over the period 1994 to 2022. Against a backdrop of growing public debt, subdued economic growth, and structural fiscal challenges, the research utilizes the Autoregressive Distributed Lag (ARDL) model to evaluate both the short-run and long-run dynamics between public debt and economic performance. The model is particularly suited for small samples and helps address potential endogeneity among variables. Quarterly time series data and dummy variables accounting for the Global Financial Crisis and the COVID-19 pandemic are employed to capture structural breaks and shocks. Empirical findings reveal that while debt-financed expenditure can stimulate short-term growth, its long-run effects are predominantly negative when debt surpasses sustainability thresholds. Specifically, the results indicate the presence of a nonlinear relationship, with positive growth effects observed only when the debt-to-GDP ratio remains below a certain threshold (~37%). Beyond this point, rising debt levels crowd out private investment, increase debt service burdens, and constrain fiscal space. Additionally, the study underscores the importance of governance and institutional quality in enhancing the effectiveness of debt-financed expenditure. The findings emphasize the critical need for South Africa to recalibrate its fiscal policy, improve debt management, and prioritize productive investments to ensure that public debt supports rather than impedes long-term economic growth.
Development of a mobile application to autodetect medicinal plants using an artificial intelligence approach
(University of Fort Hare, 2025) Simandla, Onke; Ngwenya, S.; Sibandze, P.
A mobile application assisting traditional healers in identifying medicinal plants is presented. Python programming language was used to build the mobile application that would auto detect five medicinal plants (Mpepho, Mhlonyane, Hlaka-Hlakana, Mthoba & Cima mlilo) on a Spyder IDE version 5.4.0. The performances of PyTorch and TensorFlow lite Python libraries were tested using the Convolutional Neural Network Deep learning method. Five medicinal plants were part of the data set to be collected using a Samsung cellphone to capture images at different angles. Data sets were collected from their natural habitats at angles 30%, 60%, and 90% per plant species. The best-performing model was selected and presented to the mobile application. Offline capability was programmed into the mobile application, making it possible to operate the mobile application in areas with no network. A machine-learning database used for plant identification was created and stored on the device. On-device storage gave the mobile application the ability to perform plant identifications offline. The offline capability of mobile applications is crucial as the traditional healing community often picks up traditional medicine in jungles where telecommunications networks can be a challenge. Mobile applications achieved an identification result of 98% accuracy, with the 90% angle being the most successful. The most preferred time of the day was the afternoon.
A framework for cyber safety awareness amongst first-year students in the Eastern Cape, South Africa
(University of Fort Hare, 2025-05) Makazhe, Everjoy C; Piderit, R; Mamba, M; Chindenga, E
Technology has impacted every part of our daily lives and supports different activities ranging from communications, games, trade and commerce. Students also benefit from social networks, enabling quicker access to information and communication. The outbreak of Covid-19 resulted in a transition from traditional learning to online learning. However, the online learning environment brought a surge in cyber hazards, threats and assaults. In light of this, a cyber safety framework for first-year university students in the Eastern Cape of South Africa was required. The pragmatism research paradigm influenced the study, resulting in a quantitative research design selection. Online surveys created on the Survey Monkey platform were used to collect data. Several closed-ended questions used a Likert scale with a score of four (4). Microsoft Excel and the Statistical Package for Social Sciences (SPSS) were used for data analysis and display. Experts then reviewed the proposed framework in the field to evaluate its suitability for the higher education context. The feedback was incorporated into the final framework proposed by this study. The study's results revealed that university students' main online risks were cyber bullying, phishing scams, identity fraud, invitations to pornography and sexting. The more time the students spend online, the more online risks they face. The study's findings further showed that the level of cyber security knowledge leads to cyber safety awareness among first-year students in Eastern Cape. Research findings indicated that there are mixed cyber security attitudes, which affect cyber safety awareness. The factors required to develop the framework include attitude, risk, time, awareness and knowledge capacitation. Based on the study's findings, future research can use a sequential mixed methods approach to determine the views of key informants from universities, service providers and even parents on the different measures they provide to ensure cyber safety.
A framework for evaluating the reliability of health monitoring technologies that are based on ambient intelligence
(University of Fort Hare, 2024-12) Scott, Mfundo Shakes; Jere, N.R.; Sibanda, K.
The advancement of health monitoring technologies rooted in Ambient Intelligence (AmI) has created innovative solutions for improving healthcare delivery and supporting independent living. However, a critical challenge persists because of the absence of a standardised, integrated framework to evaluate the reliability of these systems. This gap is particularly significant in resource-constrained environments, where operational uncertainties and infrastructure limitations exacerbate reliability issues. To address this problem, this study proposes a comprehensive framework for evaluating the reliability of AmI-based health monitoring technologies. The framework encompasses key dimensions, including data accuracy, system robustness, reliability and context or environment considerations. It was developed using a combination of simulation-based modelling, reliability block diagrams, and Monte Carlo Markov Chain (MCMC) techniques to systematically quantify and analyse reliability metrics. These methods were applied to case studies such as continuous glucose monitoring and heart rate monitoring in elderly care to validate the framework's practical relevance.
Results from these applications demonstrate the framework's efficacy in identifying and addressing reliability challenges. The findings highlight its ability to facilitate systematic evaluation, enhance fault tolerance, and ensure adaptability across diverse operational contexts. The presented framework contributes significantly to various stakeholders as it offers researchers a structured methodology for reliability assessment, provides healthcare practitioners with tools to ensure dependable patient monitoring, and guides policymakers in the adoption of reliable health technologies. By bridging the gap between technological innovation and practical application, this research advances the field of AmI-based health monitoring, fostering improved healthcare outcomes and greater trust in intelligent systems. Software developers, researchers, Health Monitoring devices manufacturers, Internet of Things (IOT) experts and patients on chronic medication who require regular monitoring, and the health sector are the targeted beneficiaries of the framework. Using two case studies which are Continuous Glucose Monitoring and Heart Rate Monitoring in elderly care the main limitations was the effectiveness of reliability in addressing these issues. This lays the groundwork for further refinement and application of the framework in broader contexts. Thus, future research ought to focus on addressing operational challenges, improving explainability in AI models, and discovering innovative applications of AI in healthcare monitoring systems.
Job satisfaction and turnover: The role of creativity, engagement, and decent work amongst employees
(AOSIS Publishing, 2024-11-08) Chinyamurindi, Willie T.; Mashavira, Nhamo
Orientation:
The South African public service faces the challenge of a high labour turnover among its employees. There is a need for strategies to not only keep employees happy at work but also to retain them.
Research purpose:
The study investigates the determinants of job satisfaction and turnover intention accounting for the role of employee creativity, engagement and decent work.
Motivation for the study:
There is a need for strategies to not only retain employees within the public service but also to ensure the employees are satisfied with their jobs.
Research approach/design and method:
A cross-sectional survey was conducted with a convenience sample of 304 employees working within the South African public service in the Eastern Cape province of South Africa. The Statistical Package for the Social Sciences (SPSS) (version 25) and the Linear Structural Relations (LISREL) software packages were used to analyse data.
Main findings:
It was established that higher ratings of decent work experience relate positively to employee ratings of engagement in the work and that they also influenced outcomes such as job
satisfaction and employee turnover intentions.
Practical/managerial implications:
The findings are a useful precursor in improving not just the work experience for employees but also work related outcomes such as job satisfaction and turnover intention.
Contribution/value-add:
The study findings give practical interventions to address the challenge of high turnover and the dearth of job satisfaction among public service employees.