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Research Publications (Management Sciences)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/217

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Author: search.filters.author.Gumbo, VictorHas files: Yes

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    Supervised machine learning for predicting SMME sales : an evaluation of three algorithms
    (Wits School of Literature, Language and Media (SLLM), 2021-05-31) Zhou, Helper; Gumbo, Victor
    The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.
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    Rural-urban comparison of manufacturing SMMEs performance in KwaZulu Natal province, South Africa
    (Adonis and Abbey Publishers, 2021-03-01) Zhou, Helper; Gumbo, Victor
    The paper investigated the role of location on the performance attributes of manufacturing Small, Micro and Medium Enterprises (SMMEs) in South Africa’s second-largest province of KwaZulu Natal (KZN). Panel data from 191 SMMEs covering three years between 2015 and 2017 were analysed using R software. The results utilising the Random Effects Within-Between (REWB) technique show that SMMEs in KZN have related characteristics but the extent to which they influence performance is moderated by location. The findings also indicate that the use of digital media and liability registration negatively affects the performance of urban-based, with no effect on rural-based enterprises. Based on the findings, it was recommended that SMMEs in KZN should focus on productivity, permanent employees, temporary employees and total assets to drive performance despite their locations. Based on this study, the government has an informed basis for the development of effective interventions for SMMEs in the province.