Electronic Theses and Dissertations (Masters/MBA)

Permanent URI for this collectionhttps://hdl.handle.net/10539/37942

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    The Impact of Artificial Intelligence on the future of jobs in the South African automotive sector
    (University of the Witwatersrand, Johannesburg, 2024) Setati, Portia
    This report attempted to provide insights on the impact of Artificial Intelligence on the future of work in the automotive industry. The aim of this paper was to identify the potential benefits and challenges of AI adoption in the automotive sector and to develop strategies to maximize the benefits while mitigating the risks. South Africa, a significant player in the global automotive market, faces unique challenges and opportunities as AI integration progresses.The study employed a qualitative method approach and conducted semi-structured interviews to gather data. By examining current trends and future projections, the research interprets how AI technologies are reshaping traditional automotive manufacturing processes and workforce dynamics.Findings suggest that while AI adoption promises enhanced productivity, efficiency, and product quality, it also poses challenges in terms of workforce displacement, skill gaps,and job redefinition. The South African automotive industry, characterized by a diverse workforce and socio-economic disparities, must navigate these changes with a strategic approach to ensure inclusive growth and equitable distribution of opportunities.This research contributes to a deeper understanding of the transformative impact of AI on the future of work in the South African automotive industry, offering insights into the opportunities and challenges that lie ahead and proposing strategies for harnessing AI's potential for inclusive and sustainable growth
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    Adoption of custom artificial intelligence models in South African small and medium-sized enterprises
    (University of the Witwatersrand, Johannesburg, 2023) Mdingi, Yanga; Lee, Gregory
    This study quantitatively investigates the potential adoption of custom AI models in South African SMEs using the Technology-Organization-Environment (TOE) framework. The adoption of AI in organizations is influenced by technological, organizational, and environmental elements, which can be examined using the TOE framework to understand the complexities of AI adoption. This research is guided by a post-positivist philosophical perspective to addresses the question: What factors impact the adoption of custom AI models in South African SMEs? The study employed a quantitative research design and survey methodology to collect data from South African SMEs. Participants were selected through a snowball sampling method, and data was gathered using a self-administered online questionnaire based on TOE model constructs, with each item assessed using a five-point Likert scale to capture participant opinions and attitudes. Statistical analysis, including Pearson correlation and hierarchical regression, revealed significant positive relationships between factors such as top management support, technological competence, competitive pressure, and external support, and the adoption of custom AI models. While perceived compatibility does not have a direct significant effect on AI adoption, the study revealed that it moderates the influence of top management support and technological competence on custom AI adoption. Practical recommendations of this study include prioritizing executive education, developing leadership training programs, recruiting and retaining technologically competent individuals, investing in employee training programs, leveraging external support from technology vendors and partners, recognizing the strategic importance of AI in competitive industries, and balancing efforts on perceived compatibility and management support. The findings provide actionable recommendations for enhancing AI adoption in South African SMEs, helping them overcome adoption challenges and improve competitiveness and sustainability in the local and global markets