Finance, incubation, managerial support initiatives, and technological innovation have all been identified as major drivers of SMEs’ business location. Despite the importance of SMEs, little attention has been paid to business research regarding the impact of government support, business style, and entrepreneurial sustainability on SME activities in rural, semi-urban, and urban areas. Identifying the necessary support for SMEs in rural, semi-urban, and urban areas is critical for the government as well as stakeholders and SME owners in assessing their survival status and other goal-setting achievements. The article’s central question is whether government support, business style, and entrepreneurship sustainability affect SME operations differently depending on location (rural, semi-urban, or urban). The MANOVA technique was used for the analysis to determine whether there is a significant difference between groups on a composite dependent variable as well as the univariate results for each dependent variable separately. Because conducting a series of studies (ANOVA) reveals the possibility of an inflated Type 1 error, MANOVA is preferred. The test re-test reliability method (trustworthiness assessment of the questionnaire) and the Cronbach Alpha test (internal consistency of instrument sections) yielded satisfactory results of 0.70 and 0.875, respectively. Government support (GS), business style (BS), and entrepreneurial sustainability were used as dependent variables (SE). The independent variable was the business location. On the combined dependent variables, there was a statistically significant difference between SME location: F (3, 902) = 20.388, p = 0.001, Wilks’ Lambda = 0.88, partial eta squared = 0.06. When the results for the dependent variables were considered separately, they all reached statistical significance, using a Bonferroni adjusted alpha level of 0.017. BS: F (1, 904) = 13.29, p ≤ 001, partial eta squared = 0.03. GS: F (1, 904) = 30.28, p ≤ 0.001, partial eta squared = 0.06. SE: F (1, 904) = 8.08, p ≤ 0.001, partial eta squared = 0.02. The findings show that locational effects on government support have a knock-on effect on the business plan and long-term entrepreneurship. As a result, the government must reconsider its rural activities to ensure that support is distributed equitably across levels of location.

Although there is a wealth of evidence on the relationship between economic growth and poverty or poverty and information and communication technology (ICT), there are few studies on the interaction between the three factors. The triangle relationship between poverty, information and communication technology, and economic growth in SADC were investigated in this study from 2005 to 2019. The research looked at how ICT and economic growth impact poverty in SADC countries, using the instruments of the Mean-Group FMOLS, Mean-Group DOLS, and Robustness Mean-Group Estimators in achieving its major objective. The principal component analysis was employed in generating a single index value for ICT and the data were subjected to relevant econometric tests to achieve robust results. Findings showed that all the variables exhibited poverty-reducing effects in SADC except inflation. Results confirm the existence of the “leapfrogging” hypothesis for the region. It is necessary to strengthen existing bilateral links among member nations of the area to maintain the benefits of ICT’s poverty-reducing impacts, economic growth, financial development, and trade openness. As applicable in other advanced and some emerging economies, the digital competence of the region needs to be synchronized for effective ICT service delivery.

Agriculture is a key sector for economic growth, food security, and rural livelihoods within the member nations of the Southern African Development Community (SADC). However, the agricultural systems throughout the SADC regions face serious threats from climate change, which is seen through temperature rises, irregular rainfall patterns, and the rising frequency of droughts. The study examines the impacts of climate change on agricultural productivity in four SADC countries: South Africa, Zambia, Zimbabwe, and Malawi. It also assesses the impact of institutional structures, policy initiatives, and technological advancements in enhancing agricultural resilience to climate change. The Panel Autoregressive Distributed Lag (PARDL) model was employed to assess short and long run impact of climate change on agricultural productivity. The findings reveal that precipitation significantly increases agricultural productivity in the long run, but not in the short run. In addition, governance inefficiencies, which are measured by control of corruption index have negative long run impacts on agricultural productivity. The estimated speed of adjustment (ECT: −0.9557) demonstrated a strong long run equilibrium relationship, indicating that agricultural productivity converges to its long run trend regardless of short run fluctuations. In conclusion, the findings of this study provide essential knowledge to assist policymakers, researchers, and development agencies in the creation of evidence-based policies aimed at improving agricultural resilience to climate change across SADC member countries.

Climate conditions affect the macroeconomic and financial environment, which in turn affects sustainable agricultural development in South Africa. In this study, the short- and long-run relationships between agricultural performance, temperature, economic activity, trade openness, Consumer Price Index and financial-sector development are investigated using annual South African time-series data for the period 1972 to 2022 in an ARDL (Autoregressive Distributed Lag) model. The ARDL bounds test confirms a long-run equilibrium relationship among the variables included in the final specification. Economic activity exhibits a negative and statistically significant long-run association with agricultural performance (β = −0.7985, p = 0.0009). Temperature also has a negative long-run coefficient (β = –0.1119), but this is not statistically significant at conventional levels (p = 0.1212). In addition, financial-sector development has a negative, but marginally significant association with agricultural performance (β = −0.0032, p = 0.0589), whereas the natural logarithm of the Consumer Price Index (β = 0.3994, p = 0.0537) and trade openness (β = 0.0080, p = 0.0873) have positive, but marginally significant associations. The sign of the error correction coefficient is negative and highly significant (−0.4742, p < 0.001), suggesting that around 47.4% of short-run disequilibria will be resolved over a period of a year. Overall, the results indicate that agricultural performance is linked with climatic, macroeconomic, trade, and financial conditions in South Africa. However, the strength and statistical significance of these relationships vary across variables. The results therefore provide evidence relevant to sustainable agricultural development while supporting careful consideration of climate adaptation, macroeconomic stability, productive finance, and market integration.

Climate change is increasingly seen as a risk to the economy and financial systems. This is because weather patterns can affect how much people produce, inflation, trade, investment and how well the financial sector performs. These effects are especially important in developing countries that are sensitive to climate change. Even though there is growing research linking climate and finance, there is still no solid evidence on how climate, economic factors, and financial development are connected in South Africa. This study looks at these links using data from 1972 to 2023. It focuses on financial-sector development, which is measured by the amount of credit given to the private sector as a share of GDP. The study also includes other factors: total annual rainfall, temperature, trade openness, inflation and human development. To analyse the data, the researchers use the ARDL model developed by Pesaran, Shin and Smith in 2001. This model helps to find both long-run relationships and short-run movements between the variables. The results show a long-run relationship among the variables in the model. In this term, trade openness has a negative and statistically significant effect on financial-sector development (coefficient = −1.7548, p = 0.0260). Inflation also has a significant effect (coefficient = −0.0207, p = 0.0128). Temperature is positively and significantly associated with financial-sector development in the model (coefficient = 11.0507, p = 0.0098), but this result should be interpreted with caution. This is because temperature was transformed using a logarithm and measured in degrees Celsius, and there could be shared long-term trends affecting the results. Rainfall and human development do not show significant long-term effects. In the run, improvements in human development are positively and significantly tied to changes in financial-sector development (coefficient = 5.0723, p = 0.0005). Inflation changes also have a significant short-run effect (coefficient = −0.0152, p = 0.0005). The short-run effect of trade openness is not statistically significant. The error-correction term is negative and significant (−0.3513, p < 0.001), meaning that 35.1% of any imbalance from the long-run equilibrium is corrected within one year. Overall, the findings suggest that climate, macroeconomic and financial factors are linked in South Africa but not through strong structural causes. Instead, the relationships appear to be conditional and influenced by a mix of factors.