This study examined the mediating role of financial distress on the relationship between financial leverage and financial performance of firms listed on East African Community exchange markets. The study utilized explanatory and longitudinal research designs with the guidance of a positivist research philosophy. The study targeted 112 listed firms on the East African Community exchange markets. After applying inclusion and exclusion criteria, the study focused on 48 firms for a span of 10 years from 2015 to 2024, yielding 480 observations. Financial leverage was positively related to financial distress (b = 8.390, 95% CI [4.468, 11.214], p <.001) and negatively related to financial performance directly (b = -0.226, 95% CI [-0.293, -0.175], p <.001). Financial performance was positively related to financial distress (b = 0.011, 95% CI [0.008, 0.015], p < .001). The indirect effect was positive (0.093, 95% CI [0.044, 0.146], p <.001) and in the opposite direction of the direct effect, indicating competitive mediation. The total effect was negative on financial performance (-0.133, 95% CI [-0.211, -0.073], p < .001).These findings highlight that manager should weigh the cost of additional leverage against the returns it generates, especially where liabilities already approach total assets. The results also bring attention to management on redirecting their focus to financial leverage by maintaining adequate liquidity buffers, proactive covenant and refinancing management, and continuous internal monitoring of the firm's asset-to-debt ratio, which can cushion the mechanism through which excessive financial leverage adversely affects financial performance. Keywords: Financial Performance; Financial Leverage; Financial Distress; East African Community Exchange Markets
Commercial banks in Kenya have invested heavily in artificial intelligence (AI), yet sector profitability has weakened, with average return on assets (ROA) falling from 3.3 percent in 2021 to 2.6 percent in 2024. This research investigated the effect of AI adoption on the financial performance of commercial banks in Kenya. Specifically, it examined the effects of AI-enabled customer service, machine learning credit scoring, robotic process automation and AI-driven fraud detection on financial performance, measured by ROA, as well as the mediating effect of operational efficiency and the moderating effect of bank size. A census of all 38 commercial banks licensed by the Central Bank of Kenya was undertaken, using data from audited annual reports and Central Bank of Kenya supervisory returns. Data were analysed using random effects panel regression with cluster-robust standard errors, the causal steps approach supplemented by the Sobel test, and moderated regression. AI-enabled customer service had a positive and significant effect on ROA (β = 0.927, p = 0.003). Machine learning credit scoring (β = −0.046, p = 0.841), robotic process automation (β = −0.450, p = 0.098) and AI-driven fraud detection (β = 0.137, p = 0.459) had no significant effects. AI-enabled customer service lowered the cost-to-income ratio (β = −0.194, p < 0.001), but operational efficiency did not significantly mediate its relationship with ROA (Sobel z = 1.287, p = 0.198). The interaction between AI adoption and bank size was not significant (β = 0.154, p = 0.589). The study concluded that the effect of AI adoption on bank performance is application-specific. Only customer-facing AI translated into higher profitability, while the credit scoring, process automation and fraud detection applications did not. Operational efficiency did not mediate, and bank size did not moderate, the connection between AI adoption and performance.
Taxation serves as the primary source of government revenue in both developed and developing economies, providing financial independence from external aid. However, tax avoidance and evasion pose significant threats to this revenue source, with developing economies being the most affected. Income tax compliance remains a central concern for governments globally because it is critical to financing public goods and sustaining fiscal stability. Income tax reforms have been a key component of broader tax reform packages, such as the implementation of eTIMS and the widening of the income tax base to include other goods and services. Despite these reforms, overall tax revenue mobilization, including Income tax, remains low. This research aimed to determine the effect of electronic tax services on income tax compliance among retail Small and Medium Enterprises in Nairobi Central Business District, Kenya. This study was guided by the Ability‑to‑Pay Theory and the Unified Theory of Acceptance and Use of Technology. This study will use an explanatory research design. The target population was 3805 retail small and medium enterprises in Nairobi Central Business District, Kenya, and the sample size was 318 respondents out of the 362 questionnaires issued, resulting in an 87.8% response rate. The questionnaires collected primary data, and the analysis used both descriptive and inferential statistics. The study regression analysis found that electronic tax service had a significant positive effect on income tax compliance (β = 0.096, p = 0.034). The study recommends that policymakers at the Kenya Revenue Authority and the National Treasury prioritize continuous investment in the usability and accessibility of the iTax platform, ensuring system downtime is minimized and the interface is simplified for traders with limited digital literacy. Further studies should explore the influence of peer behaviour and social networks on income tax compliance within the same population.
The telecommunications industry operates within an increasingly complex and rapidly evolving environment characterized by technological innovations, digital transformation, changing customer demands, and growing regulatory pressures. These developments expose firms to operational risks arising from internal processes, systems, people, and external events, which may adversely affect financial performance. This study examined the effect of operational risk management on Kenya Airtel's financial performance. Specifically, the study assessed the influence of process risk, system risk, people risk, and external event risk on the company's financial performance. The study drew on a previous study on operational risk management and profitability of non-withdrawable deposit-taking cooperatives in Nairobi City, Kenya. Theories underpinning the study were risk management theory, financial distress theory, and behavioral theories of profit. The previous study that anchored the present findings measured financial performance using Return on Assets (ROA), while operational risk variables were measured using various constructs. The data were analyzed using descriptive statistics, correlation analysis, diagnostic tests, and Ordinary Least Squares (OLS) regression. The findings provided empirical evidence on the relationship between operational risk management and financial performance in the telecommunications sector and will contribute to the growing body of knowledge on enterprise risk management. The study also offered practical insights for managers and policymakers on strengthening operational risk management practices to enhance organizational resilience and improve financial performance in the telecommunications industry.
Risk identification practices are increasingly important in the microfinance sector, where institutions operate amid credit, liquidity, operational, market, and emerging risks. Effective risk identification enables institutions to take corrective measures, minimize losses, and sustain financial performance. This study examined the effect of risk identification practices on the financial performance of microfinance institutions in Nairobi County, Kenya. Guided by Enterprise Risk Management Theory, the study adopted a positivist philosophy, a descriptive and correlational research design, and a quantitative approach. The target population comprised managers, risk officers, credit officers, and finance officers in licensed microfinance institutions. The study collected primary data using structured five-point Likert-scale questionnaires. Of 165 questionnaires issued, 144 were returned, representing an 87.3% response rate. We analyzed the data in SPSS using descriptive statistics, Pearson correlation, and simple linear regression. Risk identification practices were generally implemented (M = 3.95, SD = 1.15), with risk prioritization recording the highest mean (M = 4.06) and documented risk mapping the lowest (M = 3.74). Risk identification practices were strongly and positively related to financial performance (r = 0.638, p < 0.01). Regression results showed that risk identification explained 40.7% of the variation in financial performance (R² = 0.407), with a positive and significant effect (β = 0.218, t = 2.907, p = 0.004). The model was statistically significant, F (1, 142) = 97.21, p < 0.001. A pilot study of 10 respondents across two microfinance institutions in Kiambu County showed satisfactory reliability, with Cronbach’s alpha coefficients ranging from 0.812 to 0.879 and an overall alpha of 0.861. The study concluded that risk identification practices significantly influenced financial performance. It recommends strengthening risk mapping, early-warning systems, employee risk awareness, and identification and prioritization of emerging risks, while maintaining systematic documentation of identified risks across institutional functions and decision-making processes at all levels. Keywords: Risk identification practices, financial performance, microfinance institutions, risk management, Nairobi County, Kenya.
