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Governance is still a contested concept in both theory and practice.  A proliferation of the concept has sprung up in literature.  One comes across variations where reference is made to co-production, public governance, good governance, hybridity governance, hybrid governance, to name but a few. The aim of this paper is to provide a conceptual overview of the evolution of the concept governance in general and hybrid governance particulary focused on selected African countries.  It is important to clarify what hybrid governance involves, and its implications for state capacity and service delivery.  Furthermore, it is imperative to discern whether hybrid governance could be a useful conceptual tool for facilitating more organised systems of service delivery in parts of Africa and South Africa, or whether it is just “new developmental jargon”. The paper employed a qualitative research approach where secondary sources of information, constituting document analysis, premised around forms of hybrid governance in selected African countries were explored. The paper makes a conceptual contribution to the collaborative international governance discourse.

This study examines the collaboration between Lean Six Sigma principles and order fulfilment procedures, which are essential for facilitating international trade. The objective is to ascertain the extent to which sustainable and effective lean processes improve customer satisfaction and accelerate global product delivery. The study employs a dual methodology: initially, doing a comprehensive analysis of the current body of literature on the integration of Lean Six Sigma in order fulfilment inside various manufacturing companies, with the aim of finding any deficiencies or areas that require further investigation. Furthermore, we utilise a quantitative approach by administering surveys to professionals in the South African manufacturing industry. Deductive reasoning examines responses using random sampling to ensure unbiased selection. The findings emphasise that using Lean Six Sigma practices in the South African manufacturing sector can address customer concerns regarding the quality of orders and the timeliness of deliveries. This can provide a competitive advantage in the global market. The study validates that the inclusion of Lean Six Sigma measurements alleviates bottlenecks by focusing on improvements in operations, procedures, and communication. The study demonstrates the effectiveness of Lean Six Sigma methodologies in improving order fulfilment for enterprises engaged in international trade. It promotes these concepts to optimise processes, assuring timely deliveries and increased customer satisfaction. This study addresses several deficiencies in the current research on the incorporation of Lean Six Sigma in the manufacturing order fulfilment process. These gaps encompass the challenges of incorporating emerging technology, integrating collaborative supply chains, and incorporating sustainable practices. The distinctive methodology of integrating a comprehensive analysis of existing literature with empirical evidence enhances the novelty of the research outcomes. Manufacturing companies that aim to achieve global market excellence get practical advantages.

This study focused on improving the diagnosis of African swine fever (ASF) by improving accuracy, reliability and precision using various machine learning algorithms. Data from the European Union Reference Laboratory for ASF and the EU Animal Disease Information System (ADIS) containing clinical information on wild and domestic pigs were processed and converted into numerical and categorical formats for analysis. Various machine learning models were tested as part of the study, including linear regression, Bayesian regression, support vector machines (SVM), decision trees, random forest classifiers, artificial neural networks (ANNs) and logistic regression. The models were evaluated using metrics such as accuracy, precision, recall, F1-score and root mean square error (RMSE). The results showed that the SVM achieved the lowest performance with 44.4% accuracy, followed by the logistic regression model with 61.5%. The neural network model achieved 69% accuracy, closely followed by the decision tree model with 70%. The linear regression model performed slightly better with 74% accuracy. Bayesian regression achieved a higher accuracy of 80%, while the Random Forest model outperformed all others, achieving the highest accuracy of 88.9%. These results make an important contribution to economic efficiency and innovative applications in veterinary medicine and improve disease management through advanced machine learning techniques.

The recent COVID-19 pandemic significantly exacerbated mental health issues worldwide, with South African university staff facing unique stressors that led to the adoption of e-health tools for digital mental health services. This study explores the university staff experiences gained during COVID-19 pandemic in the provision of digital mental health services through e-health tools. We conducted a case study at a public university in Cape Town, South Africa complemented by a survey using quantitative methods. Data from 348 respondents were collected via voluntary response sampling and analysed with Statistical Software Package for Social Sciences (SPSS) version 28, employing exploratory factor analysis. High factor loadings above 0.4 confirmed significant contributions from seven items: perceived usefulness of e-health tools, perceived usefulness of social media, user experience and satisfaction, university commitment, variety of digital mental health services, post-COVID-19 delivery mode preference and ethical considerations. These seven items revealed that during COVID-19, e-health tools provided significant benefits to university staff including increased access to digital mental services. However, the study findings revealed that university staff were concerned about the lack of a legal framework during COVID-19, and the role of AI-powered mental health applications did not significantly influence their experiences during the pandemic. Our study provides theoretical insights into the effectiveness and perceived usefulness of e-health tools for digital mental health services during COVID-19, reinforcing their significance in reducing anxiety, stress, and depression while enhancing emotional resilience. Practically, our study findings suggest that universities should integrate e-health tools into the provision of digital mental health services given that these tools proved effective during COVID-19.

The South African construction sector is confronted by significant obstacles, particularly concerning cost overruns and project delays within the road construction sphere. This research sought to investigate the factors impacting cost and duration in road construction projects under the purview of a KwaZulu-Natal municipality. A qualitative research approach was adopted to explore this phenomenon. A sample size of 27 participants was selected from a population of 171 for in-depth semi-structured interviews. Critical determinants of cost overruns and delays were identified as ineffective communication, suboptimal procurement processes, deficient stakeholder management, and shortcomings in project execution and personnel capabilities. This study offers valuable insights into the relationship between cost, time, and the successful delivery of road projects within the local government context. By illuminating these factors, the research contributes to a deeper theoretical understanding, informs evidence-based policy development, and provides actionable recommendations for enhancing project management practices.

This review emphasizes the urgent need for effective diagnostic strategies for African swine fever (ASF), a serious disease affecting pig populations worldwide. The aim of the review is to analyze the existing research on ASF diagnostics through a comprehensive literature review, focusing on different diagnostic approaches, including clinical assessments, PCR tests, ELISA, rapid tests and epidemiological models. It examines their sensitivity, specificity and overall performance, addressing challenges such as the varying sensitivity of tests and cross-reactivity. The review highlights both the strengths and limitations of current methods and suggests areas for improvement and standardization. Recommendations are made for future research and the development of innovative diagnostic tools to improve ASF surveillance and control. This study makes a practical contribution by providing a detailed assessment of ASF diagnostic methods from which veterinary scientists and practitioners can benefit. Theoretical contributions include the identification of gaps in ASF diagnostics and the refinement of discussions on diagnostic accuracy and reliability. These findings are consistent with the journal’s focus on infectious diseases and veterinary research and support progress in veterinary medicine and animal welfare.

This paper explored factors for infusing change management practices in the public sector and gave recommendations for policy execution of technology. The Western Cape Department of Community Safety (DoCS) launched the Expanded Partnership Programme (EPP) in 2010, as provided for in section 206 of the Constitution to reorient social crime prevention by Community Policing Forums Educate and Share towards Monitoring Guided Actions to increase sustainability, the efficiency of Community Police Forums (CPFs), and the accountability of the police. Even with an investment of R18 million, participation levels were still low and there was no service delivery improvement by the police, thus the need for a change management was borne. The study pointed out the need of strategic methods such as Kotter’s 8-Step Process, Prosci ADKAR, and McKinsey Influence Model to implement institutionalised changes. The research pursued a practical stance and therefore merged objectivist and constructivist positions with an abductive approach. Count data were collected through the use of a Likert scale questionnaire since it was quantitative in nature while qualitative data was obtained from semi-structured focus group discussions. Data analysis was performed using qualitative and quantitative methods such as descriptive statistics, Pearson’s Chi-square, and content analysis, among others. The research findings highlighted key factors such as organizational culture, interests of stakeholders, politics, resources, and the external environment a determinants of change management. The creation of detailed guidelines that are supported by evidence to direct decision making, resource allocation and prioritisation of policies by the government was advocated for in the study. These findings closed the loop between the research academic domain and the practical field, and demonstrated clear steps towards improving accountability, transparency and adequacy of change implementation.

This study focused on improving the diagnosis of African swine fever (ASF) by improving accuracy, reliability and precision using various machine learning algorithms. Data from the European Union Reference Laboratory for ASF and the EU Animal Disease Information System (ADIS) containing clinical information on wild and domestic pigs were processed and converted into numerical and categorical formats for analysis. Various machine learning models were tested as part of the study, including linear regression, Bayesian regression, support vector machines (SVM), decision trees, random forest classifiers, artificial neural networks (ANNs) and logistic regression. The models were evaluated using metrics such as accuracy, precision, recall, F1-score and root mean square error (RMSE). The results showed that the SVM achieved the lowest performance with 44.4% accuracy, followed by the logistic regression model with 61.5%. The neural network model achieved 69% accuracy, closely followed by the decision tree model with 70%. The linear regression model performed slightly better with 74% accuracy. Bayesian regression achieved a higher accuracy of 80%, while the Random Forest model outperformed all others, achieving the highest accuracy of 88.9%. These results make an important contribution to economic efficiency and innovative applications in veterinary medicine and improve disease management through advanced machine learning techniques.

The paper explored the digital capabilities required by the private utility environment to participate and remain competitive in Society 5.0. The development of a digital infrastructure for power utilities within a smart society, utilizing renewable energy and integrating traditional and renewable sources, presents challenges in identifying the specific skills required for effective operation within Society 5.0. While technologies like virtual power plants, smart grids, and microgrids offer potential for enhanced energy management, there is a significant gap in understanding the workforce capabilities needed to support, maintain, and sustain these advanced systems. Therefore, a thorough assessment of the proposed technological structures and requisite skills is essential to ensure successful implementation and operation in this evolving energy landscape. It investigated the type of skills would the workforce require for a private utility to support a super-smart society. A non-probability sampling strategy was utilised to access the entire interest set. Fifteen semi-structured interviews were performed, with the participants featuring a private utility company’s management, administrative, and technical staff. All interviews conducted were recorded after obtaining consent from the participants. Probing questions were asked until the participants supplied the information needed to address the study questions. The findings and the implications are that management and technical staff responsible for managing and coordinating the activities of utilities (water and electricity) and the technical staff responsible for the implementation and maintenance of energy solutions, in a private utility company were interviewed to establish and ascertain their digital capabilities and preparedness for the fourth and fifth industrial revolutions (Society 5.0).

The recent COVID-19 pandemic significantly exacerbated mental health issues worldwide, with South African university staff facing unique stressors that led to the adoption of e-health tools for digital mental health services. This study explores the university staff experiences gained during COVID-19 pandemic in the provision of digital mental health services through e-health tools. We conducted a case study at a public university in Cape Town, South Africa complemented by a survey using quantitative methods. Data from 348 respondents were collected via voluntary response sampling and analysed with Statistical Software Package for Social Sciences (SPSS) version 28, employing exploratory factor analysis. High factor loadings above 0.4 confirmed significant contributions from seven items: perceived usefulness of e-health tools, perceived usefulness of social media, user experience and satisfaction, university commitment, variety of digital mental health services, post-COVID-19 delivery mode preference and ethical considerations. These seven items revealed that during COVID-19, e-health tools provided significant benefits to university staff including increased access to digital mental services. However, the study findings revealed that university staff were concerned about the lack of a legal framework during COVID-19, and the role of AI-powered mental health applications did not significantly influence their experiences during the pandemic. Our study provides theoretical insights into the effectiveness and perceived usefulness of e-health tools for digital mental health services during COVID-19, reinforcing their significance in reducing anxiety, stress, and depression while enhancing emotional resilience. Practically, our study findings suggest that universities should integrate e-health tools into the provision of digital mental health services given that these tools proved effective during COVID-19.