Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/4205
Title: The influence of key risk drivers on the performance of SMMEs in the manufacturing sector in KwaZulu-Natal
Authors: Zhou, Helper 
Keywords: Small Medium and Micro Enterprises (SMMEs);Socio-economic development;Modelling manufacturing;Performance drivers;KwaZulu-Natal;Machine learning;Manufacturing;Performance;SMMEs
Issue Date: Dec-2021
Abstract: 
Small Medium and Micro Enterprises (SMMEs) have been shown to be key
contributors to sustainable socio-economic development, constituting more than 90%
of private sector enterprises around the world. Inevitably, many developing countries
continue to explore means aimed at enhancing the performance of small enterprises.
However, despite the implementation of various interventions the failure rate of
SMMEs in South Africa particularly KwaZulu-Natal (KZN) is disturbing, reaching up to
80% in the first year of operation. As such, to contribute to addressing this challenge,
the study adopted a novel approach to establishing and modelling manufacturing
SMMEs performance drivers. Utilising a unique three-year panel dataset, key risk
drivers were established and modelled via R software version 3.6.3. To achieve the
study objectives, a series of independent but related papers were carried out and
these make up the main chapters of this thesis. The first chapter provided the
background to the study. The second chapter explored the characteristics of
manufacturing SMMEs based in KZN province. The findings showed the complexity
of firm performance, indicating the heterogeneity between rural and urban based
SMMEs. The next chapter, harnessing Stochastic theory aimed to establish whether
SMMEs’ growth performance followed a random walk. The theoretical model was
rejected, thus providing a basis for the claim that firm performance is a function of
certain risk drivers. Armed with findings from the previous papers, the investigation of
key drivers impacting the sales and growth performance of manufacturing SMMEs
ensued. The fourth chapter, harnessing the Penrosian and strategic management
theories established key drivers of SMMEs’ performance. The fifth chapter
concerningly, revealed that SMME owners in the manufacturing sector are largely not
aware of the impact of established drivers on their enterprises’ performance. In the
next chapter, a total of five machine learning algorithms were evaluated of which
Artificial Neural Network and Support Vector Machines were identified as the best
algorithms for SMME sales and growth predictive modelling, respectively. The two
algorithms informed the development of a dedicated machine learning application for
SMMEs that’s being commercialised through the DUT Technology Transfer and
Innovation Directorate.
Description: 
Submitted in fulfillment of the requirements for the degree Doctor of Philosophy (Business Administration), Durban University of Technology, Durban, South Africa, 2021.
URI: https://hdl.handle.net/10321/4205
DOI: https://doi.org/10.51415/10321/4205
Appears in Collections:Theses and dissertations (Management Sciences)

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