http://localhost:8080/xmlui/handle/123456789/2019 Thu, 07 May 2026 03:17:48 GMT 2026-05-07T03:17:48Z http://localhost:8080/xmlui/handle/123456789/4631 Title: Effects of Covid-19 Pandemic on Education Management Practices in Zimbabwe: A Case of Hallingbury Primary School, Harare, Zimbabwe Authors: Honde, Hedwick; Awasthi, Yogesh Abstract: The Covid-19 pandemic undeniably disturbed socio-economic activities, not sparing the educational system across the globe at whirlwind speed. It impacted the educational systems negatively to a larger extent. By mid-April in 2020, the pandemic had disrupted the formal education of approximately 1.6 billion learners in 192 nations. It also interrupted education in Africa and the effects will be alluded to by the study in one of the area. The study revealed that there is great need for educational system 21st modernisations. The nations went through a tough time to maintain the educational activities as the immune systems of most school children among adults, and other learners was weak, thereby affecting their health. The Pandemic did not affect the educational system alone, it also had negative effects on lives of the citizenry and on the business organisations; and it influenced the supply chain due to global lockdowns. The study investigated on the effects of Covid-19 pandemic on education management, as well as end of year results, and to proffer ways of managing education in future pandemic times. For this purpose, data were gathered through primary sources; questionnaires and interviews by 246 respondents comprising of teachers, learners, parents, school administration, and ministry of education officials. Some observations were also made for the purposes of observing social distancing practices in class and some other practices of containing the spread of the pandemic such as masking up, sanitizing and temperature checks on school entrance. Secondary sources such as published journals, thesis, textbooks, reports, newspaper articles and the internet were used, that enabled the investigator to understand better the effects of the pandemic on education. Respondents were selected using both probability and non-probability sampling. Purposive sampling was employed to select respondents at the school admin and ministry of education to enable the investigator to generalise findings from people on the ground, who have the real information required, who are knowledgeable for the study’s aim. Stratified and snowball techniques were used to select parents and pupils. The investigator used the mixed method research design, where the study combined quantitative and qualitative research. Tue, 21 Nov 2023 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/4631 2023-11-21T00:00:00Z http://localhost:8080/xmlui/handle/123456789/4630 Title: Adoption of Artificial Intelligence in the Zimbabwean Manufacturing Sector: A Critical Review and Research Agenda Authors: Masese, Thomas; Sango, Chidochomoyo; Awasthi, Yogesh Abstract: Artificial intelligence (AI) is reshaping manufacturing through quality control, maintenance, and supply chain planning, yet adoption in Sub Saharan Africa is uneven. The researchers synthesize evidence on AI adoption with a focus on Zimbabwe, guided by Technology Organization Environment (TOE), the Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology (TAM/UTAUT), the Resource Based View (RBV), and Dynamic Capabilities. The researcher applies a transparent selection process and includes n = 26 studies: Zimbabwe (n = 2), South Africa (n = 2), Africa/regional (n = 0), and global/other (n = 22). Zimbabwe specific evidence is thin and concentrated in SMEs and a food manufacturing case. The review provides a consolidated synthesis of drivers and barriers, clarifies the methodology, and presents a practical research agenda tailored to Zimbabwe. The paper offers a compact framework linking organizational and ecosystem conditions to use cases and outcomes, and a roadmap for firm actions, policy levers, and measurement priorities to scale AI adoption. Wed, 01 Oct 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/4630 2025-10-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/4629 Title: AI-Driven Decision-Making for Sustainable Industrialization in Africa Authors: Awasthi, Yogesh; Garikayi, Talon; Mafu, Elizabeth Abstract: Artificial Intelligence (AI) is rapidly reshaping industrial systems worldwide, offering unprecedented opportunities for sustainable development in Africa. This study examines how AI-driven decision-making can enhance sustainable industrialization across key African economies by improving efficiency, reducing waste, and supporting environmentally responsible practices. Using a mixed-methods approach, data were collected from 40 firms across Kenya, Nigeria, South Africa, and Zimbabwe, revealing that AI adoption remains moderate but uneven across sectors and countries. Agriculture and energy sectors demonstrate relatively higher adoption levels due to targeted innovation programs and sustainability-driven imperatives such as precision farming and energy optimization, while the manufacturing sector lags because of high costs, limited infrastructure, and a shortage of skilled professionals. The study finds that organizations using AI reported measurable benefits, including a 20% reduction in material waste, a 15% increase in productivity, and forecasting accuracy improvements up to 87%. However, adoption is constrained by persistent challenges such as inadequate digital infrastructure (64%), high implementation costs (63%), limited human capital (58%), and weak policy support. The research extends Decision Theory and the Resource-Based View (RBV) by demonstrating that AI serves as both a strategic resource and a sustainability enabler within volatile African markets. It further aligns AI integration with the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, underscoring AI’s role in promoting innovation, resource efficiency, and sustainable production. The paper concludes that for Africa to leverage AI as a driver of inclusive industrial growth, it must prioritize infrastructure investment, develop AI-related human capacity, and establish coherent regulatory frameworks that foster ethical and context-relevant innovation. Wed, 01 Jan 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/4629 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/4308 Title: Deep Learning-Based Flood Forecasting Using Satellite Imagery and IoT Sensor Fusion Authors: Awasthi, Yogesh Y; Chinzvende, Joseph Tue, 01 Jul 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/4308 2025-07-01T00:00:00Z