Vol. 18 No. 3(J) (2026)

This study examines the relationships among big data analytics capabilities, inter-organizational coordination capabilities and humanitarian supply chain resilience. Drawing on Dynamic Capability Theory (DCT), the study explains how humanitarian organizations leverage data analytics and inter-organizational coordination to strengthen supply chain resilience in uncertain and disruption-prone environments. A quantitative cross-sectional survey was conducted among 88 humanitarian organizations operating in Uganda. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS to assess the hypothesized direct relationships and the mediating effect of IOCC. The findings reveal that BDAC has a significant positive effect on both IOCC and HSCR. IOCC also significantly enhances HSCR. Furthermore, IOCC partially mediates the relationship between BDAC and HSCR, indicating that the resilience benefits of big data analytics arise both directly and indirectly through strengthened inter-organizational coordination. The cross-sectional design limits the ability to capture how analytics capabilities, coordination mechanisms, and resilience evolve. Future research could employ longitudinal and qualitative designs to examine the dynamic processes through which humanitarian organizations develop and deploy these capabilities across different stages of disruptions. Humanitarian organizations should complement investments in big data analytics and digital infrastructure with mechanisms that strengthen information sharing, joint planning, resource coordination, and collaborative decision-making among supply chain partners. Integrating analytics with strong coordination capabilities can improve preparedness, responsiveness, and recovery from humanitarian supply chain disruptions. This study extends the application of DCT to humanitarian supply chains by demonstrating that IOCC constitutes an important mechanism through which BDAC translates into HSCR

Youth unemployment remains a major development challenge in Kenya, where rapid growth of the youth labour force has not been matched by sufficient employment creation. This study examined the determinants of youth unemployment in Kenya using the Exogenous Growth Theory framework, focusing on population growth, savings rate, technological progress, capital accumulation and labor force growth. The study employed an annual time-series research design using secondary data covering the period 1991–2024. Data were obtained from the World Development Indicators and analysed using the Autoregressive Distributed Lag (ARDL) model. Pre-estimation tests included descriptive statistics, variance inflation factor (VIF), unit root tests using Augmented Dickey-Fuller and Phillips-Perron approaches, structural break tests and ARDL bounds testing for cointegration. The findings revealed that exogenous growth factors, considered jointly, did not establish a stable long-run relationship with youth unemployment in Kenya. However, labour force growth exhibited a significant short-run effect on youth unemployment, indicating that rapid expansion of the labour supply continues to exert pressure on employment absorption capacity. Population growth, savings rate, technological progress and capital accumulation were found to have no statistically significant short-run effects within the exogenous growth model. The study concludes that factor accumulation and demographic expansion alone are insufficient to address youth unemployment unless supported by complementary mechanisms such as human capital development, productivity enhancement, institutional reforms and labor-absorbing economic transformation. The study recommends policies that align investment, technology adoption, and labour market strategies with youth employment creation to ensure inclusive economic growth.

This study examines technical efficiency (TE) and identifies determinants of inefficiency among smallholder Robusta and Arabica coffee farmers in Uganda while quantifying yield losses associated with inefficiency. The study utilized pooled cross-sectional data from 1,283 smallholder coffee farmers drawn from three survey waves (2015/2016, 2018/2019 and 2019/2020) of the Uganda National Panel Survey (UNPS). A one-step Stochastic Frontier Analysis (SFA) with a translog production frontier is applied to jointly estimate technical efficiency and its determinants. The approach further quantifies yield losses attributable to inefficiency, disaggregated by coffee farmer type. Mean technical efficiency is 76.5 percent for Robusta and 73.1 percent for Arabica, implying potential output gains of 23.5 percent and 26.9 percent, respectively, using existing inputs. Efficiency variations are associated with group membership, off-farm income, cropping systems, soil type, and plot proximity, with effects differing by coffee type. Yield losses due to inefficiency averaged 113.0 kilograms for Robusta and 138.6 kilograms for Arabica per farmer. Policies should promote location-specific agronomic practices like intercropping and soil fertility management tailored to coffee type and ecological conditions. Capacity-building initiatives including input use, pest and disease management, and sustainable farming can reinforce efficiency gains. Supporting farmer groups and cooperatives could enhance knowledge sharing, coordinated input use, and market access, thereby reducing transaction costs and improving resource-use efficiency. The study employs a one-step SFA to estimate technical efficiency separately for Robusta and Arabica farmers, incorporating farm-level, institutional, and environmental factors to examine efficiency differentials across coffee types in Uganda.

Youth unemployment remains a major socio-economic challenge in Kenya, despite sustained economic growth and various employment interventions. This study examines the determinants of youth unemployment in Kenya using institutional growth theory, focusing on the role of governance quality, particularly control of corruption and rule of law. The study employed annual time-series data covering 1996–2024 and applied the Autoregressive Distributed Lag (ARDL) model to estimate both short-run and long-run relationships. The findings reveal a long-run equilibrium relationship between institutional factors and youth unemployment, with the bounds test confirming cointegration. Control of corruption has a negative and statistically significant effect on youth unemployment, indicating that improved governance and reduced corruption enhance employment opportunities for young people. However, the rule of law had an insignificant effect, suggesting that legal reforms alone may not automatically translate into improved youth employment outcomes. The study concludes that strengthening institutional quality, promoting transparent resource allocation, and improving governance effectiveness are essential for reducing persistent youth unemployment in Kenya. The findings highlight the need for integrated employment strategies that combine institutional reforms with investments in skills development, innovation, and labor-intensive economic activities.

This study examined the relationship between investment practices and household income among small-scale women entrepreneurs in Bwera Sub-County, Kasese District, Uganda. A cross-sectional correlational design was adopted, and quantitative data were collected using structured questionnaires from 98 women traders selected through simple random sampling. Data were analyzed using descriptive statistics, Pearson’s correlation, and simple linear regression. The investment-practices scale had a content validity index of 0.710 and a Cronbach’s alpha coefficient of 0.800. Investment practices were positively and significantly correlated with household income (r = .658, p < .01). Regression analysis showed that investment practices were a statistically significant predictor of household income (B = 0.520, β = .658, p < .001), accounting for 43.3% of the variation in household income (R² = .433). Using professional advice or tools to inform investment decisions recorded the highest mean score (M = 3.85, SD = 1.213), while researching and understanding investment risks recorded the lowest (M = 3.26, SD = 1.349). These findings indicate that stronger investment practices were associated with higher household income, although the cross-sectional design and lack of control for other potential determinants preclude causal conclusions. The study recommends practical investment education, strengthened risk-assessment skills, and improved access to reliable financial advice to support informed investment decisions among women entrepreneurs.