The factors influencing the adoption of Machine Learning for regulation by central banks in SADC

dc.contributor.authorKunene, Sibusiso
dc.contributor.supervisorTotowa, Jacques
dc.date.accessioned2025-01-27T10:45:39Z
dc.date.available2025-01-27T10:45:39Z
dc.date.issued2024
dc.descriptionA research report submitted in fulfillment of the requirements for the degree of master’s in business administration to the Faculty of Commerce, Law and Management, University of the Witwatersrand, Johannesburg, 2024
dc.description.abstractThe study investigates SADC central banks' readiness to adopt machine learning technologies with raw data collected through an online survey. Subsequently, the raw data was transformed into modellable data using principal component analysis and further fitted into the proposed logistic regression model design. The data underwent reliability and validity tests, which confirmed that the measurements of the constructs were consistent, reliable, and appropriately represented the intended constructions. Correlation analysis was employed to examine the hypotheses of the model, and multiple and stepwise regression were performed as additional tests of the model. The results show that IT infrastructure is instrumental in enabling SADC central banks to implement machine learning capabilities. Top management is crucial for implementing ML, but adequate IT infrastructure is also essential. The regulatory environment and IT infrastructure indirectly influence SADC central banks' readiness to adopt ML capabilities, despite top management's direct impact. The derivable policy implication from these results is that working groups among the sampled SADC central banks need to be formed to address the noted shortcomings within IT infrastructure and regulatory-related aspects of this adoption holistically
dc.description.submitterMM2025
dc.facultyFaculty of Commerce, Law and Management
dc.identifier.citationKunene, Sibusiso. (2024). The role of design houses [Master’s dissertation, University of the Witwatersrand, Johannesburg].WireDSpace.
dc.identifier.urihttps://hdl.handle.net/10539/43721
dc.language.isoen
dc.publisherUniversity of the Witwatersrand, Johannesburg
dc.rights© 2025 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.
dc.rights.holderUniversity of the Witwatersrand, Johannesburg
dc.schoolWITS Business School
dc.subjectMachine Learning
dc.subjectCentral banks in SADC
dc.subjectUCTD
dc.subject.otherSDG-8: Decent work and economic growth
dc.titleThe factors influencing the adoption of Machine Learning for regulation by central banks in SADC
dc.typeDissertation

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