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The reduction in rural marginalization of infrastructure in Africa has been achieved through the wide diffusion of the so-called fiscal deviation. According to New Institutional Economics, the provision of public resourcing at the local level is more economic since it meets the geographic specificity of infrastructure provisions. However, when intergovernmental fiscal transfers are towards rural road provision, it is majorly because of several structural impediments, that the corrected road will not be realized. This systematic review of literature with regards to devolution and infra-structure provision has laid emphasis on going beyond overly simplistic input-output models to explore the multiple filters regulating decentralized public works. The focus here is on institutional capacity as a crucial explanatory factor and the way deficits in technical engineering, equipment maintenance, and procurement governance conspire to undermine the potency of devolved funding. Moreover, the study discusses the moderating effects in the face of instability in the broader economy, illustrating how hyper-inflation and currency depreciation can deplete real infrastructure funds purchasing power and result in the erosion of the contractor attrition as observed. Finally, the review has identified systemic barriers, such as climate vulnerability and ministerial red-tape friction. The synthesis of empirical literature from 2010 to 2024 suggests that fiscal decentralisation without a built institutional capacity and insulated mechanisms against macroeconomic turbulence cannot deliver in the long run.

This study examines the impact of wage disparities on employee performance within Zimbabwe's telecommunication industry using advanced structural equation modelling techniques informed by recent post-pandemic organizational behaviour research. A quantitative research approach was employed, collecting data from 227 respondents across various job levels in Harare through structured questionnaires. Data analysis was conducted using structural equation modelling, incorporating recent methodological advances in measurement invariance testing and multi-group analysis. Results reveal significant wage differentials with employees perceiving substantial unfairness in wage distribution. The structural model demonstrates that wage disparities significantly impact employee performance, with regulatory frameworks and economic instability identified as primary contributing factors. Organizations should adopt AI-enhanced transparent compensation systems and implement performance-based wage structures informed by recent Environmental, Social, and Governance frameworks to enhance employee satisfaction and organizational performance. 

This study investigates the use of artificial intelligence (AI) to improve operational performance in Zimbabwean road haulage enterprises, with a focus on driver training as a moderator. As the logistics industry faces new difficulties, AI technologies have great promise for increasing efficiency and decision making. However, the usefulness of these technologies is determined by the skill levels of the drivers using them. This study demonstrated how extensive driver training improves the capacity to comprehend AI-generated insights, resulting in better route management, lower operating costs, and increased safety. This study examines how AI affects key performance variables such as cost savings, productivity, customer happiness, and environmental sustainability, using real data from road haulage companies. Key findings demonstrate how AI is transforming decision-making, improving operational effectiveness, and optimizing routes. The research highlights several noteworthy obstacles in addition to their obvious advantages, such as budgetary limitations, difficulty in obtaining pertinent data, and the need for more regionalized AI solutions. The findings, which are based on case studies and performance data from diverse enterprises, indicate that (i) organizations that invest in both AI and driver training benefit from a synergistic impact, resulting in higher operational outcomes, (ii) there is need to combine technical improvements with human experience to achieve maximum performance in Zimbabwe's competitive road-haulage market and finally (iii) this study offers helpful recommendations for successfully integrating artificial intelligence (AI) into haulage processes, along with insights into best practices and alternative approaches to overcome current obstacles. This study emphasizes the importance of context-specific solutions in emerging regions, enhancing the expanding corpus of knowledge on AI applications, particularly in logistics.

The main aim of this study was to empirically assess the main microeconomic factors that affect a bank's performance. The objectives were to ascertain if there is a relationship between the performance variables with the microeconomic variables, determine those that are significant and their impact on the performance of banks in Zimbabwe. An econometric model was built from balanced panel data and the Arellano-Bond estimation procedure was employed. The empirical analysis was carried out on a sample of 17 banks that were operational in the years 2010 to 2017 in Zimbabwe. Return on Assets (ROA), Return on Equity (ROE) and Net Interest Margin (NIM) were used as the performance indicators in the analysis. The results indicate the main microeconomic factors to be those attributed to growth, credit risk, capitalisation, managerial efficiency, liquidity and diversification in the Zimbabwean financial institutions. Performance in these institutions is generally good as measured by positive persistent profits, that is, ROA, ROE and NIM. These returns reflect the extent to which these institutions are resilient to the economic crisis.