Research Publications (Management Sciences)
Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/217
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Item Application of explainable artificial intelligence technique to model the predictors of South African SMMEs resilient performance during the Covid-19 pandemic(Center for Strategic Studies in Business and Finance SSBFNET, 2024) Zhou, Helper; Chamba, Lucy T.; Zondo, Robert Walter DumisaniVarious studies have been carried out to establish the key drivers impacting small enterprise sustainable performance in developing countries. Despite many policy-oriented studies to uncover the factors influencing SME resilience in emerging markets, these firms continue to register high failure rate, which has been further exacerbated by the Covid-19 pandemic. Guided by a history of linear- and log-linear econometric model estimation that ignores potential network effects, our study extends the literature by implicating SMME resilience as a production network. Utilising data from both incubated and non-incubated SMMEs, marking a departure from traditional linear econometric models, radial basis function artificial neural network algorithm was invoked to establish the drivers of SMME resilience during Covid-19 regime. The study extends the literature by implicating eXplainable Artificial Intelligence (XAI) methods. Specifically, optimal SHapley Additive Explanations values (SHAP values) were computed to enhance the prediction output from the machine learning algorithm. The XAI analytics provide insightful findings on the key drivers which influenced the resilience of SMMEs during the Covid-19 pandemic. The importance of innovation through introduction of new products, company age and higher number of marketing mediums is confirmed however total assets, analytics, educational level and number of workers surfaced as a threat to these enterprises’ sustainable performance. The study recommends that both the government and SMEs should leverage XAI to identify their heterogeneous attributes and inform intelligent decision-making which necessities their resilient performance.Item The role of business incubation programmes on the performance of small and medium enterprises in South Africa(2023-05-15) Zhou, Helper; Zondo, Robert Walter DumisaniResearch has shown that Small and Medium Enterprise (SMEs) play a pivotal role in socio-economic development of countries across the globe. In recent years, business incubation programmes have gained popularity to address the perennial challenge of SME failure in South Africa. However, there have been very limited studies to empirically assess the impact of incubation in South Africa. To address this gap, this study utilised dataset, comprising 387 incubated and non-incubated firms to assess the impact of business incubation of performance. The originality of our study lies in valuable insights we established relating to the impact of incubators on SME performance. Utilising Generalised Least Squares technique in R, the study revealed that incubation has a positive impact on SME performance. Further to that the results revealed differing attributes between the incubated and non-incubated cohorts. It was recommended that the government should invest into incubation programmes to drive sustainable SME performance. Further to that, the heterogeneity between the two cohorts demands a shift from a “one size fits all” approach to supporting SMEs in South AfricaItem Do firms’ growth rates follow a random walk? evidence from incubated small and medium enterprises in South Africa(Durban University of Technology, 2023) Zhou, Helper; Zondo, Robert Walter DumisaniDebate on the validity of the Law of Proportionate Effect (LPE) on firm growth is ongoing decades after it was postulated by Gibrat in 1931. The theoretical model which asserts that firm growth follows a random walk has been largely tested in developed economies using data from non-incubated firms, with scanty research in developing regions like Africa. This paper, therefore, aims to address this gap by being the first to assess the validity of Gibrat's law on incubated small, medium, and micro enterprises (SMMEs) in South Africa. The study utilised four-year panel data from 300 incubated SMMEs across the country, for the period between 2018 to 2021. Utilising the Law's generalised growth rate model, the generalised least square regression modelling was harnessed, using R Software. The findings, using sales as firm size proxy, confirmed Gibrat’s Law. The results showed that firm size had no effect on the sales growth rate of incubated firms, on the other hand when employment proxied performance the LPE was rejected. The findings provide important implications for both practitioners and pertinent stakeholders in the SMME sector in South Africa.Item Key performance drivers of small enterprises in the manufacturing sector in KwaZulu Natal province, South Africa(Allied Academies, 2021-08-31) Zhou, Helper; Victor, GumboThis paper sought to establish key performance drivers for small, medium and micro enterprises (SMMEs) in the manufacturing sector in KwaZulu Natal (KZN) province, South Africa. A quantitative approach was adopted, utilising three-year panel data of manufacturing small enterprises. The results utilising Fixed Effects panel data modelling technique, revealed that productivity, permanent and temporary workers have a positive effect, whilst company age and unemployment have a negative effect on SMMEs sales performance. Based on these results, it was recommended that SMMEs should leverage their human resources to drive sustainable performance. It is also important that key interventions targeting the SMME sector should not only focus on internal but external environment drivers like unemployment which have a significant impact on the performance and thus long-term survival.Item 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, VictorThe 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.Item Rural-urban comparison of manufacturing SMMEs performance in KwaZulu Natal province, South Africa(Adonis and Abbey Publishers, 2021-03-01) Zhou, Helper; Gumbo, VictorThe 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.