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Various 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.

This study examines the integration of digitalization, mobile applications, and vernacular languages in Small, Medium and Micro Enterprise (SMME) management within KwaZulu-Natal Province, South Africa. A quantitative descriptive analysis was conducted using systematic probability sampling of 147 SMMEs across five sectors (agriculture, hospitality, construction, healthcare, and transportation) in the KwaZulu-Natal Midlands region. Findings reveal significant gaps between policy support and the actual implementation of digital-vernacular language integration, with 45% of respondents indicating inadequate policy support and 58.9% expressing dissatisfaction with the current progress of digitalization.  Results suggest the need for targeted policy interventions to support the digital transformation of SMMEs that incorporate indigenous language capabilities. The study contributes to understanding the intersection of digital transformation and linguistic diversity in emerging economy entrepreneurship management, and the dynamic capabilities theory was undertaken as the benchmark to support the literature. The findings revealed a disconnect between the provision of support from policymakers and authorities regarding the program of action and deliverables that could help SMMEs integrate their entrepreneurial management with the demands of digitalisation, including resources and capabilities. The findings further exposed the gap between the expectations of SMMEs and the current level of digital and vernacular language integration in terms of systematic support and deliverables. Additionally, the findings characterized the slow pace of transformation supported by policymakers in digitalization, mobile applications, and the adoption of vernacular languages, which is already at an advanced stage in similar emerging economies in the global South.

This study examines the integration of digitalization, mobile applications, and vernacular languages in Small, Medium and Micro Enterprise (SMME) management within KwaZulu-Natal Province, South Africa. A quantitative descriptive analysis was conducted using systematic probability sampling of 147 SMMEs across five sectors (agriculture, hospitality, construction, healthcare, and transportation) in the KwaZulu-Natal Midlands region. Findings reveal significant gaps between policy support and the actual implementation of digital-vernacular language integration, with 45% of respondents indicating inadequate policy support and 58.9% expressing dissatisfaction with the current progress of digitalization.  Results suggest the need for targeted policy interventions to support the digital transformation of SMMEs that incorporate indigenous language capabilities. The study contributes to understanding the intersection of digital transformation and linguistic diversity in emerging economy entrepreneurship management, and the dynamic capabilities theory was undertaken as the benchmark to support the literature. The findings revealed a disconnect between the provision of support from policymakers and authorities regarding the program of action and deliverables that could help SMMEs integrate their entrepreneurial management with the demands of digitalisation, including resources and capabilities. The findings further exposed the gap between the expectations of SMMEs and the current level of digital and vernacular language integration in terms of systematic support and deliverables. Additionally, the findings characterized the slow pace of transformation supported by policymakers in digitalization, mobile applications, and the adoption of vernacular languages, which is already at an advanced stage in similar emerging economies in the global South.

Various 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.