http://hdl.handle.net/11189/1918 2026-10-13T00:16:42Z 2026-10-13T00:16:42Z Pharaoh, Courtley http://hdl.handle.net/11189/10912 2026-10-09T20:45:30Z 2025-01-01T00:00:00Z

Title: Executive leadership insights: navigating crises with adaptive management competencies Authors: Pharaoh, Courtley Abstract: This study investigates the complex university management environment, specifically, universities of technology, seeking to unravel the complicated dynamics that describe changes in management competencies from regular operational periods to times of crisis. The goal is to comprehend how management competencies evolve in response to the unique challenges posed by crises, with a particular emphasis on the impact of the #FeesMustFall student protest and the COVID-19 pandemic. The exploratory research study employs a qualitative methodology that includes semi-structured online interviews conducted through videoconferencing, providing a safe and flexible platform for gathering real-time, audio-visual insights from participants, closely resembling traditional in-person interviews adhering to the strict COVID-19 pandemic requirements and Qualitative Content Analysis (QCA) enables a comprehensive exploration of management and crisis competencies. The findings of the study identify six critical changes executives make when a crisis evolves. Ultimately, this study provides a valuable contribution to understanding how university executives evolve and adapt their management competencies during crises, with the unprecedented backdrop of the COVID-19 pandemic serving as a critical focal point.

2025-01-01T00:00:00Z Rena, Ravinder Paul, Linda http://hdl.handle.net/11189/10903 2026-10-09T20:45:36Z 2025-01-01T00:00:00Z

Title: Decoding funding dynamics for AI start-ups: investor infl uence, innovation strategies, and ecosystem synergies Authors: Rena, Ravinder; Paul, Linda Abstract: In this research, we analyze the multiple relationships of funding for AI start-ups and specify investor influence, technological changes, and funding types within a start-up ecosystem. The research has identified over 500 relevant AI start-ups and splits the analysis based on regions and time into several years from 2019 to 2024. The research focuses on issues such as the involvement of venture capitalists, corporate investors, governmental support, and funding models of AI businesses. This study makes use of both descriptive statistics of financial and operational data and subjective data collection from the identified start-up founders, investors, and policymakers. Machine learning algorithms, statistical tools like R and Python, and business intelligence tools like Tableau are used to analyze patterns of funding to determine patterns in the data sets. To enhance the findings, the study also relies on secondary data from local and international venture capital databases and financial statements. Some of the findings have to do with ways in which funding ecosystems shape the technological development path of AI start-ups through, inter alia, emphasizing ethical approaches to AI, regulatory frameworks, and sustaining innovations. The study highlights the standout of investor preferences, systematic positioning of innovation centers, and socio-cultural imperative of multi-stakeholder collaboration as the drivers of sustainable growth. In addition, it recognizes challenges such as selection algorithm bias and data privacy issues and it presents policy suggestions regarding funding approaches. The present work advances the knowledge in the field by presenting an overall model of funding processes in AI start-ups, explaining the actions of investors, and providing tools for entrepreneurs. It also educates policymakers about specific areas that should be prioritized to enable a positive culture of unleashing and supporting AI, thereby filling knowledge gaps, and reinforcement AI stability and growth.

2025-01-01T00:00:00Z Paul, Linda Rena, Ravinder http://hdl.handle.net/11189/10902 2026-10-09T20:45:28Z 2025-01-01T00:00:00Z

Title: Risk management, digital innovation, and regulatory frameworks in banking and finance Authors: Paul, Linda; Rena, Ravinder Abstract: This research examines the effects of digital innovations, regulatory frameworks, and advanced risk management on banking system stability, alongside social media sentiment’s impact on electric vehicle companies’ financial choices. Employing a mixed-methods approach analyzing financial documents, licensure records, and social media data, the study uses econometric measurement and qualitative framework inspection. Sentiment analysis of electric vehicle data from 2022-2023, utilizing NLP tools, reveals an increasing positive attitude towards EVs, although security and infrastructure concerns remain. Key banking findings indicate that AI-driven risk assessment algorithms achieve an 89% measurement success rate, surpassing traditional methods at 72%. Furthermore, GPU acceleration in AI-based financial models improves execution efficiency by a factor of four. Sentiment analysis shows a strong positive correlation (0.85) between the sentiment index and market stability indicators. Digital Twin simulations demonstrate a 91% accuracy rate in forecasting financial crises, significantly higher than the 76% accuracy of historical models. The research suggests that banks implementing digital transformation and regulatory adaptability achieve stronger financial stability, with AI for risk management improving organizational performance. Policymakers should create adaptable fintech regulations and utilize robust risk control methods. Emerging tech firms and financial institutions should leverage sentiment trends for strategic planning. This research establishes novel connections between traditional finance risk management, digital transformation, and sentiment-driven analysis, offering a sustainable framework for stability and technology adoption, and proposes new methodologies for risk and consumer pattern analysis.

2025-01-01T00:00:00Z Paul, Linda Rena, Ravinder http://hdl.handle.net/11189/10901 2026-10-09T20:45:35Z 2025-01-01T00:00:00Z

Title: Generative AI in South African Healthcare: navigating challenges and harnessing opportunities for industry and research Authors: Paul, Linda; Rena, Ravinder Abstract: This research seeks to evaluate how Generative Artificial Intelligence (GAI) can be used to disrupt and revolutionise the South African healthcare sector in addressing anomalies and utilising opportunities. This study employed a qualitative approach using secondary data analysis, drawing on policies formulated from the years 2015-2024, the study combines findings from published literature, institutional reports, and reputable databases to explore the potential opportunities, applications, and challenges of integrating generative artificial intelligence (AI) in South Africa's healthcare system. Thematic analysis and trend mapping were used to investigate GAI in diagnostics, drug discovery, personal medicine, data clerking, and medical training. The results make clear the ability of GAI to improve healthcare availability, quality, and organisation. Some critical opportunities, scientific developments, and technological advances present include better diagnostic tests, individualised therapy, rationalisation of work procedures, and an enhanced drug discovery process. However, the study brings out emerging problems like ethical issues, data sources and types, privacy issues, infrastructural constraints, and policy hindrances. These challenges call for multisectoral approaches that involve policymakers, healthcare players, researchers, tech firms, and the South African government in designing and implementing AI programs suitable for the South African setting. This research provides novel knowledge because these advancements are positioned in the South African context from a global perspective. This paper gives specific solutions on how the resulting ethical, technical, and infrastructural issues of GAI implementation may be mitigated. Lastly, this research is the roadmap for stakeholders to leverage the opportunity presented by GAI and place South Africa well ahead in the usage of AI for healthcare development.

2025-01-01T00:00:00Z