Faculty of Commerce, Law and Management (ETDs)
Permanent URI for this communityhttps://hdl.handle.net/10539/37778
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Item A feminist ontology to data commercialisation: Evaluating women's access to information and privacy within the medico-legal sphere in South Africa(University of the Witwatersrand, Johannesburg, 2024) Neto, Ângela Pacheco; Swemmer, SheenaWith the dawn of the Fourth Industrial Revolution, rapid exchanges of data have intensified. Technologies like biometric monitoring, female-oriented technologies, and artificial intelligence bring with them a host of legal issues related to consent, access, privacy, and liability. Vulnerable populations or groups must be given particular attention as standard data practices serve to reinforce existing inequalities. For this reason, female-directed and female- generated health data is specifically considered herein. By employing a data feminism lens, it becomes apparent that the current South African regulatory framework has been legislatively misapproached with regards to the medico-legal sphere in South Africa. The methodology herein draws on critical review methods, thematic analysis, and legal discourse analysis, ultimately utilising the general principles of research inherent in the socio-legal sciences. A responsive and flexible health data law that incorporates intersectional narratives is advanced. This holistic response must account for the two-faced coin of female access to information and privacy in order to address historical structures of power inequityItem Artificial intelligence and automated decision making under the GDPR and the POPIA(University of the Witwatersrand, Johannesburg, 2024) Goldman, Gavin David; Zitzke, E.This analysis considers the concepts of AI and machine learning and examines their reliance on the processing of personal data and the challenges this poses from a data- privacy and human-rights perspective, particularly in relation to profiling. It evaluates the effectiveness of the General Data Protection Regulation (GDPR) and the Promotion of Personal Information Act 4 of 2013 (POPIA) in regulating Automated Decision Making (ADM) and considers the limitations of the right to an explanation under these provisions. The analysis proposes that the current framework of the GDPR and POPIA does not clearly address the issue of explainability and that the focus should shift to providing a data subject with a counterfactual to give practical effect to this right which would better serve data subjectsItem A practical review of the effectiveness of artificial intelligence in the automated review of legal contracts(University of the Witwatersrand, Johannesburg, 2023) Fouché, Jacques GerritThis research report compares the performance of AI-driven automated contract review platforms with that of human contract reviewers, aiming to see improved effectiveness for the elements of time, cost and quality. An empirical study is done by comparing the specific performance of an automated contract review platform provider, Lawgeex, to the human contract reviewers of a business entity, Endress+Hauser. The results of the effectiveness assessment are reported on through dashboard data and questionnaires to the users of the platform. Recommendations are made both in general and specific to the two entities of the empirical studyItem Prospects for artificial intelligence to manage load-shedding in South Africa(University of the Witwatersrand, Johannesburg, 2022) Shakoane, Nomea Lerato; Lee, GregoryEskom, a state-owned utility in South Africa, is currently facing significant challenges and experiencing severe power shortages. While there is a growing expectation of adopting renewable energy in the future, a sudden and complete transition is unlikely. Legacy power systems, characterized by poor performance, breakdowns, and unpredictability, have received limited attention in AI research. This raises the question: What actions should be taken to quickly address maintenance issues in older power plants and increase generation capacity in the short term? The objective of this study is to explore AI solutions in the electrical sector and assess the feasibility and cost-effectiveness of integrating AI into Eskom's power system. The findings of this study will provide Eskom and the South African government with valuable insights to make informed decisions regarding the incorporation of artificial intelligence. These AI solutions can include detecting power and cable theft, optimizing energy usage and distribution, and implementing predictive analytics for demand planning and power production optimization. To gather data, a survey questionnaire was distributed to participants primarily located in South Africa, following a snowball selection process. The survey collected responses from a minimum of 50 participants and covered various aspects, such as load shedding at Eskom, artificial intelligence, data-AI enablers, and AI prospects. The study revealed that inadequate maintenance within the power generation division was responsible for load shedding. As a result, the implementation of AI solutions such as predictive maintenance, fault detection, and power demand monitoring systems emerged as crucial priorities for Eskom. However, it is important to note that implementing AI requires substantial capital investment. Considering Eskom's current financial situation and South Africa's mounting debt, it is challenging for Eskom to secure the necessary funds without seeking support vi from the South African government or major corporations like the IMF or World BankItem Exploration of the adoption of Artificial Intelligence within traditional recruitment and selection processes among South African Universities(University of the Witwatersrand, Johannesburg, 2023) Murerwa, GodwinIndustries across the world are investing in and embracing artificial intelligence in their recruitment and selection strategies. Artificial Intelligence (AI) in talent acquisition, fuelled through technological innovation has disrupted traditional recruitment and selection processes. Within South Africa, adoption of AI has been reported to be slow with most organisations still at a pilot stage. An organisation competitive advantage is generated from the people it employs. Due to increased globalisation and interlinked economies, companies rely on their highly talented employees for sustainable competitiveness. It is critical in a world where there is scarcity of skills, talent and resources to use an intelligent system in staffing. Traditional techniques of managing entities are being contested globally. Traditionally, recruitment and selection was a laboriously administrative, mundane and manual process susceptible to prejudices and biases of the practitioner. This research explored the factors influencing Artificial Intelligence adoption in the traditional recruitment and selection practices among South African universities. The main objectives of the study were to establish factors influencing the adoption of Artificial Intelligence in recruitment and selection processes among South African universities and consequently recommend feasible strategies of adopting such technology within the talent acquisition practices of the universities. The study utilised a quantitative research approach through a survey design. For data collection, the research instrument was an online self-completion questionnaire. The study employed non-probability sampling, purposive sampling in particular. This enabled the research to focus on the characteristics and attributes consistent with the study objectives. A total of ninety-five participants from nine South African universities completed the online questionnaire. The findings demonstrated the potential benefits of AI adoption to the hiring processes of South African universities. The benefits of AI adoption seem to be mostly in perceived accuracy and speed in which it curtails tedious tasks in recruitment and selection. There are however significant challenges that should be addressed. Issues to do with ethicsItem Factors Influencing the Adoption of Artificial Intelligence Technologies in the South African Construction Industry(University of the Witwatersrand, Johannesburg, 2023) Mgolombane, Pumza Portia; Matshabaphala, ManamelaThe study aimed to evaluate factors influencing the adoption of artificial intelligence technologies within the South African Construction Industry. The focus was to employ AI technology adoption strategies to enhance the construction industry’s’ performance in ensuring effectiveness and efficiency on productivity through organisational development. Since the construction industry is notorious for its resistance to integrating new technology; for example, it is more likely to continue working manually than to apply digitalisation. As a result, the slow adoption of technology is likely to have an impact on the effectiveness and efficiency of the construction industry building practices. Therefore the studies objectives were to assess, the adoption of AI technology strategies and its impact on effectiveness and efficiency within the construction industry. Moreover, opportunities and constraints of AI adoption by the construction industry to help improve its productivity. Also, the organisational development phenomenon within the construction industry and its effect on AI Technology adoption. This study employed a case study research method to obtain a comprehensive understanding and empirical questions were established in the literature based on the study’s’ aims and objectives. The quantitative data was administered through the Qualtrics tool whereby a survey link was obtained and then emailed to the participants. The data was analysed using Statistical Package for the Social Sciences tool (SPSS) version 27. 51 Respondents participated in the survey however, 9 were excluded because of incomplete surveys. According to the results of the data analysis, it was concluded that the employment of AI technology techniques may have an impact on the effectiveness and efficiency of the building sector, making this objective relevant to this study. Moreover, AI adoption by the construction industry may increase productivity by understanding the opportunities and limitations of adopting AI. Also, the phenomena of organizational development within the construction industry may impact on the adoption of AI Technology was found to be significantItem Factors facilitating conversational artificial intelligence at an insurance organisation in Johannesburg(2022) Tshifularo, Setebatebe BGlobally the insurance industry is changing significantly due to new technological advances, data growth asymmetry and more recently the Covid-19 related disruptions (Balasubramanian, Libarikian, & McElhaney, 2021). Further to these changes, are consumer tastes and preferences fuelled by availability of information and accessibility of platforms such as social media. Consumers have become more price and product sensitive due to the many options available from competing products and companies. To keep up with these changes which present both risks and opportunities, insurers have been long exploring with technologies such as Artificial Intelligence, Internet of Things, Cloud computing to name a few (Santenac, Majkowski, Manchester, & Peters, 2019). Artificial Intelligence is a buzzword in the insurance industry as most insurers are looking at ways to drive effective distribution channels, be cost effective and exceed customer expectations (Accenture, 2018). Conversational AI is a subset of AI which this study seeks to explore in depth. The aim is to consider global, continental, regional and local insurers’ implementations of conversational AI and derive insights on factors that led to successful CAIs implementations.Item The perceived impact of artificial intelligence on jobs at a financial services organization in Johannesburg(2022) Zhou, NyashaThis research study investigated the impact of Artificial Intelligence (AI) on jobs at a financial services (FS) organization in Johannesburg. AI has the ability beyond human comprehension for certain human tasks to be done and its advent introduces the potential for job reconfigurations. The research study’s purpose was to explore the perceived impact of AI technologies on jobs at a FS organization in Johannesburg. It aimed to ascertain if AI is impacting jobs negatively at a FS organization in Johannesburg. The research study was conducted using the Task-Technology Fit conceptual framework. The research study was underpinned by a theoretical framework comprised of four pillars where literature was reviewed in the context of the research problem. The qualitative study was conducted using Saunders research onion’s (Mahesh, 2020) interpretivist research philosophy of an inductive nature. Through the use of a case study as a research strategy over a crosssectional time horizon, sixteen (16) semi-structured interviews were conducted online at a FS organization. This data was coded through thematic analysis and was analysed with reference to literature reviewed. From the research analysis, the perceived notion of AI impacting jobs negatively in a FS organization in Johannesburg was disproved as AI was found to impact jobs positively. The research study’s outcome provides recommendations classified into three perspectives that emanated from the study. These recommendations are to aid the FS organization in Johannesburg in mitigating the impact of AI on jobs. The research also recommends job impact mitigating factors to other FS organizations based in Johannesburg and the world at large.Item The adoption of artificial intelligence in financial services in South Africa(2022) Qwabaza, AneleArtificial intelligence (AI) is one of the driving forces behind disruptive innovations and has transformed how organisations interact and deliver services to their customers. While factors that enable the successful implementation of AI in organisations were previously studied, these studies are still in the early stages. Therefore, the objective of this study was to investigate the success factors for AI adoption by South African financial services companies, using an integration of the diffusion of innovation (DOI) theory and the technology-organisation-environment (TOE) framework. This study also aimed to understand the relative effect of factors affecting AI adoption in financial services in South Africa. The study was administered using an online survey targeting employees of South African financial services organisations. Structural equation modelling (SEM) was used to analyse the data. The results show that only complexity and technical capabilities significantly influenced AI adoption, with managerial capabilities indirectly influencing the adoption of AI in South African financial services. Therefore, when adopting AI in their organisations, the leadership of financial services organisations should consider the costs associated with AI applications, the time taken to innovate using AI, and the application of AI. External environmental factors, government involvement, competitive pressure, and vendor partnerships all had statistically significant results for AI adoption. In addition, this study also aimed to understand the assimilation of AI by customers after adoption by organisations, using the technology acceptance model (TAM). The data was collected using an online survey targeting external customers of financial services organisations, and it was analysed using SEM. The results show that vi perceived ease of use and perceived usefulness are important indicators of how customers experience AI applications of financial services organisations. Therefore, financial services organisations should ensure an optimal level of ease of use and prioritise utilitarian benefits when designing and adopting AI applications.Item The perceived impact of artificial intelligence on operations performance in the South African life insurance industry(University of the Witwatersrand, Johannesburg, 2022) Kunene, Thamsanqa Mxolisi; Appiah, Erasmus KofiOrganisations are increasingly implementing AI applications in their operations in order to stay competitive. The Covid-19 pandemic has further fuelled the implementations of AI technologies. However, do these applications improve the operation’s performance of an organisation? The study investigates how managers and employees that have implemented AI technologies within life insurance organisations perceive AI to have impacted their operations performance. Operations performance measures used by the study are cost, quality, speed, flexibility, and dependability. A qualitative methodology was undertaken by the study, using in-depth interviews that were made up of open-ended questions. The participants that contributed to the study were selected based on their profile and experience with AI technologies and in the life insurance industry. The findings of the study show that AI technologies generally improved operations performance. However, it must be noted that AI implementations come at a very high cost. Therefore, using cost savings as a sole use case driver is discouraged. Also, AI must not be implemented to improve inefficient business processes. Lastly, the quality of data to be used by the AI application is essential to the success of the project. In conclusion, managers and employees that have implemented and/or used AI technologies in the life insurance industry perceive AI to have improved operations performance of their organisations. An improved operations performance helps the organisation to stay competitive among its peers.