MSc. Projects

Strategic Management Practices and Performance of Savings and Credit Cooperatives in Nairobi City County, Kenya

Tue, 29 Sep 2026 00:00:00 GMT

Strategic Management Practices and Performance of Savings and Credit Cooperatives in Nairobi City County, Kenya Mburu, Esther Wangui This paper has discussed the effects of strategic management practice on the performance of the Savings and Credit Cooperative Societies (SACCOs) that are based in Nairobi City County, Kenya. The particular dimensions of strategic management explored included strategic intent, strategy formulation, strategy implementation, and strategic control. The analysis was pegged on the Resource-Based View, Strategic Management Theory, Contingency Theory, and the Balanced Scorecard model. The research design used was descriptive and 30 mature SACCOs that were at least three years old were identified out of a population of 176 licensed SACCOs in Nairobi City County. The methodology used was census and structured questionnaires were used to collect the data by administering them to 210 senior and operational managers, where 164 valid responses were obtained indicating a response rate of 78.1. Pilot testing was done to determine the reliability of the instruments and Cronbach alpha of the individual constructs were found to be 0.69-0.73 and the reliability of the questionnaire as a whole was found to be 0.86. Descriptive statistics, Pearson correlation, and multiple regression were used to analyse quantitative data, and qualitative data based on open-ended questions were analysed by thematic analysis. The results showed that the strategic intent, strategy formulation, strategy implementation, and strategic control played a positive and significant role in SACCO performance. Correlation analysis showed that all strategic practices had strong positive correlation with performance (r = 0.68 to 0.76, p < 0.01). The regression analysis of individual practices revealed that each strategic practice is a predictor of performance, and strategy implementation has the greatest variance (R 2 = 0.578). The four strategic practices were found to explain 67.6 percent of the variance of performance (R 2 = 0.676, F (4, 159) = 83.52, p < 0.001). Strategy implementation (= 0.40), strategic intent (= 0.28), strategy formulation (= 0.13), and strategic controls (= 0.11) were the strongest predictors, which validated that strategic management practices are important determinants of the performance of SACCOs in Nairobi City County. The research findings conclude that strategic management practice is an important determinant of SACCO performance. It advises SACCO management to improve strategy implementation mechanisms, improve strategic control systems, improve stakeholder involvement in strategy formulation, and frequently communicate strategic intent to all members and staff. Master of Science in Strategic Management

Loan Portfolio Structure and Financial Performance of Microfinance Institutions in Kenya

Tue, 29 Sep 2026 00:00:00 GMT

Loan Portfolio Structure and Financial Performance of Microfinance Institutions in Kenya Chege, Emmah Wanjiru The financial performance of microfinance institutions is vital to their sustainability and to their ability to promote financial inclusion among low-income groups. Despite their important role in Kenya, many microfinance institutions still face financial challenges that threaten their long-term stability. This study examined how the structure of loan portfolio structure affects the financial performance of microfinance institutions in Kenya. Specifically, it focused on four components: loan portfolio size, diversification, quality, and maturity structure. Guided by theories of financial intermediation and portfolio management, which highlight the importance of efficient resource allocation and management for improving institutional performance, the study employed a quantitative research design using panel data from Kenyan microfinance institutions. Secondary data were collected from the published financial statements of 14 institutions over a 10-year period, yielding 140 observations. Data analysis involved panel-data regression techniques, including pooled ordinary least squares, random-effects, and fixed-effects models. Diagnostic tests, such as the Breusch-Pagan Lagrangean Multiplier test and the Hausman test, were used to select the best-fit model, with results indicating that the fixed-effects model was most appropriate for analyzing the relationships among the variables. The findings indicated that the structure of loan portfolio significantly impacts the financial performance of microfinance institutions in Kenya. All four components, loan portfolio size, diversification, quality, and maturity structure, had positive and statistically significant effects on Return on Assets. Among these, loan portfolio quality had the strongest impact, underscoring that maintaining a high-quality loan portfolio with low non-performing loans is key to increasing profitability. Portfolio diversification also contributed positively by reducing credit risk and stabilizing income, while larger loan portfolios increased interest income and profitability. Proper loan maturity structures improved repayment performance and liquidity management within these institutions. Based on these results, the study concludes that an effective loan portfolio structure is crucial for enhancing the financial performance and sustainability of microfinance institutions in Kenya. It recommends that these institutions strategically expand and diversify their loan portfolios across sectors, strengthen credit risk management to maintain high portfolio quality, and adopt suitable loan maturity structures aligned with borrowers’ repayment capacities. These actions are expected to boost profitability, reduce credit risk, and strengthen the financial stability of microfinance institutions in Kenya. Master of Science in Finance and Accounting

Predictors of Quality of Care among People with Epilepsy Receiving Treatment at Selected Hospitals in Nairobi, Kiambu and Machakos counties, Kenya

Fri, 07 Aug 2026 00:00:00 GMT

Predictors of Quality of Care among People with Epilepsy Receiving Treatment at Selected Hospitals in Nairobi, Kiambu and Machakos counties, Kenya Nyakwana, Tiberry D. O. Epilepsy is a common neurological disorder affecting over 70 million people globally and accounting for approximately 1% of the global disease burden, with nearly 90% of cases occurring in low- and middle-income countries. In sub-Saharan Africa, an estimated 10 million people live with epilepsy, while Kenya has a prevalence of about 18 per 1,000 population, corresponding to nearly one million people with epilepsy (PWE), of whom approximately 80% do not receive adequate treatment. Beyond its clinical burden, epilepsy is associated with stigma, discrimination, reduced productivity, and increased healthcare utilization. Despite increasing awareness and improved health-seeking behaviour, evidence on the quality of epilepsy care and its determinants in Kenya remains limited. The main objective of this study was to determine predictors of quality of care among PWE receiving treatment at selected Level Five hospitals in Nairobi, Kiambu, and Machakos counties. A cross-sectional mixed-methods study was conducted between May and September 2021. Quantitative data were collected from 373 PWE using semi-structured questionnaires, while qualitative data were obtained through focus group discussions and key informant interviews. The Donabedian Structure–Process–Outcome Framework and the Aday and Andersen Healthcare Utilization Model guided the assessment of quality domains and determinants. Quality of care was measured using a semantic differential scale, and Exploratory Factor Analysis generated normalized factor weights that were used to construct a composite weighted quality index. A median score of 47 was used to categorize quality of care into high and low levels. Although Fisher’s finite population correction yielded a minimum sample size of 276 from a sampling frame of 969 patients, the study retained the original Fisher sample size of 385 to enhance statistical power, reduce sampling error, and improve precision. Consecutive sampling recruited 373 participants, representing a 96.9% response rate. Quantitative data were analyzed using descriptive statistics, chi-square tests, logistic regression, and multiple linear regression in SPSS version 26, while qualitative data were analyzed thematically in NVivo and triangulated with quantitative findings. Majority participants were 29–49 years (37.5%), and 52.8% reported receiving high-quality care. Respect, communication, and tolerability of medication side effects were the most important contributors to quality ratings. At bivariate analysis, occupation (χ²=19.13, p<0.001), duration before treatment initiation (χ²=6.07, p=0.048), anti-seizure medication use (χ²=4.024, p=0.045), seizure frequency after treatment initiation (χ²=7.337, p=0.026), stigma experience (χ²=5.022, p=0.025), treatment regimen (χ²=10.464, p=0.015), waiting time (χ²=6.49, p=0.031), service availability (χ²=7.137, p=0.029), and affordability of care (χ²=4.034, p=0.043) were significantly associated with quality of care. Multivariate logistic regression identified stigma as the strongest independent predictor of low-quality care (OR=2.123, 95% CI: 1.119–4.026; p=0.021). Qualitative findings showed that respectful provider interactions, effective communication, and treatment effectiveness enhanced patient experiences, whereas inadequate education regarding seizure management and medication side effects reduced perceived quality. The study concludes that quality of epilepsy care is shaped by a complex interplay of sociodemographic, clinical, psychosocial, and health system factors, with stigma emerging as the most pervasive determinant. Strengthening community sensitization, healthcare worker training, patient education, diagnostic capacity, medication supply systems, and structured counselling and peer-support programmes is essential for xxi improving quality of care, narrowing the treatment gap, and informing county and national epilepsy policies. PhD in Public Health

Use of Geo-Information Technologies in Predicting Urban Growth Trends; an Integrated Simulation Approach:

Thu, 28 May 2026 00:00:00 GMT

Use of Geo-Information Technologies in Predicting Urban Growth Trends; an Integrated Simulation Approach: Gichuki, Ivy Njeri Many urban areas exhibit different growth patterns spanning from linear development, transit-oriented development, concentric zonal development to multi-nuclei development patterns. In today’s world, main urban areas present themselves as Central Business Districts (CBDs), that double up as mixed use commercial and residential areas, which serve majority of the population who live in and around them. Ideally, the CBD sites – for most cities around the world, were identified before any development took place, making it easier for the local authorities, urban planners and surveyors to demarcate and plan for sustainable development. Most, if not all jobs, are located in these urban areas, making these employment areas, urban growth hotspots. Decentralization of the traditional city was not only paramount for the survival of the CBD, but also necessary for creating new urban areas, which were relatively smaller than the CBD and played a significant role in shaping the urban spatial structure. The presence of multiple urban areas in a region contributes to, strengthening national competitiveness, social cohesion, service delivery, socio-economic integration and balanced regional development. When there is a shift from functional specialization of the CBD to economic specialization of the surrounding urban areas, this brings about changes in the economic processes and evolution of transport networks which are the foundation of urban growth and expansion, as in the case of Rhine Main Region in Germany. In Kenya, most of the known urban areas, like Limuru Town, emerged as traditional markets in the 1900’s and grew to modern urban areas and municipalities that we see today. However, urban growth has been accompanied by rapid land use changes and sporicidal growth of informal settlements. As a result, urban areas growing in Limuru Central Ward, are deprived of basic infrastructure, land use harmonization and spatial synergies. This study therefore attempts to explore the use of GIS and Remote sensing technologies in observing past and present urban growth trends, which should be done prior to predicting sustainable urban planning. The findings from this study are expected to contribute to the knowledge of simulating how urban centres can be planned in the present to cater for the future spatial and infrastructural needs of the growing urban population. Predicting urban growth trends introduces more practical ways of spatial planning and policy development in developing countries, through spatial analysis and modelling using GIS and Remote Sensing technologies. As a result, the study has uncovered that some of the factors that have affected the rate of urbanization in Limuru Central Ward include, slope and elevation, existing clustered urban development in specific areas especially near Limuru Town or along the existing transport network, and availability of infrastructure especially the road network which plays a key role in determining how accessible urban areas are in the study area. The impact of urbanization in Limuru Central Ward between 1999 and 2019 has been identified as; significant decrease in land occupied by bare soil and vegetation, rapid land use changes to accommodate more built-up spaces, sporadically growth of clustered development or dense built-up areas, and the need to expand infrastructure in newly developed areas to promote accessibility. Prediction of urban development in the study area between 2020 and 2050, shows that in the future, the urban footprint is likely to increase near existing urban areas, especially along the transport networks and near existing built-up areas such as schools. The urban footprint may likely adopt a mix of urban growth models – such us the multi nuclei and sector models, however, majority of the urban areas may most likely be located within proximity to the transport network – transport-oriented development. Master of Science in Geospatial Information Systems and Remote Sensing