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E-government platform attributes and revenue collection Efficiency in selected parastatals using e-citizen in Kenya
(KCA University, 2025) Kirema, Bernadette M.
The increasing reliance on technology within public administration has made the integration of digital platforms essential for efficient revenue mobilization. This study investigated on the effect of key e-Government platform attributes like user compliance, system integration, and real-time reporting on revenue collection efficiency in selected Kenyan parastatals using the e-Citizen platform, with digital infrastructure as a moderating factor. The study was grounded in the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI) Theory, Agency Theory, and the Resource-Based View (RBV) Theory to explain and understand the study. Additionally, the research adopted a descriptive-explanatory research design with primary data being collected using semi-structured questionnaires from 215 staff across seven parastatals. Analysis using Structural Equation Modelling (SEM) showed that system integration (β = 0.31, p < 0.001) emerged as the strongest predictor, followed by user compliance (β = 0.22, p = 0.006) and real-time reporting (β = 0.18, p = 0.017). The baseline model explained 55% of the variance in efficiency (R² = 0.55), while the moderated model incorporating digital infrastructure explained 59% of the variance in efficiency (R² = 0.59), indicating improved explanatory power. Moderation analysis revealed that digital infrastructure significantly strengthened the effects of user compliance (β = 0.12, p = 0.043) and system integration (β = 0.15, p = 0.004) on efficiency but had an insignificant effect on real-time reporting (β = 0.05, p = 0.327). The study concludes that strong system integration, timely reporting, and user compliance enhance accuracy and accountability, while robust digital infrastructure amplifies these effects. The findings offer actionable insights for policymakers to strengthen Kenya’s revenue mobilization through strategic investment in infrastructure, digital literacy, and system reliability.
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Fintech adoption, digital maturity and operational efficiency Among 3-star hotels in Nairobi Metropolitan area, Kenya
(KCA University, 2025) Kibe, Caroline W.
The study examined the effect of financial technology (FinTech) adoption on the operational efficiency of three-star hotels within the Nairobi Metropolitan Area, Kenya. FinTech innovations have become critical tools for enhancing service delivery, cost control, and financial management in the hospitality industry. Despite this, many mid-sized hotels continue to experience inefficiencies arising from delayed payments, manual invoicing, and limited digital integration. Guided by the Technology Acceptance Model (TAM), the Technology–Organization– Environment (TOE) framework, the Resource-Based View (RBV) theory, and the Financial Intermediation Theory, the study sought to assess how mobile money payment, card payment, and electronic invoicing influence operational efficiency, and to analyze the moderating effect of digital maturity on these relationships. The study adopted a descriptive research design and targeted all 62 registered three-star hotels in the Nairobi Metropolitan Area, where the unit of analysis was the hotel and the unit of observation was the operations manager. A census approach was employed, and data was collected using structured questionnaires administered through both physical and electronic channels. The data were analyzed using descriptive statistics, correlation, and hierarchical multiple regression analysis. Diagnostic tests were conducted to ensure normality, linearity, homoscedasticity, and the absence of multicollinearity. Results revealed that all FinTech dimensions were positively correlated with operational efficiency. Regression analysis indicated that card payment and electronic invoicing had significant positive effects on operational efficiency, while mobile money payment showed a positive but weaker relationship. After introducing digital maturity as a moderator, the strength of all relationships increased, and the model’s explanatory power improved (R² rising from 0.217 to 0.650). The moderation results confirmed that hotels with higher digital maturity derived greater efficiency gains from FinTech use. The study concludes that FinTech adoption significantly enhances operational efficiency among three-star hotels and that digital maturity amplifies this relationship by improving technological readiness, staff competence, and innovation capability. It recommends that hotel management invest in digital capacity building, continuous system upgrades, and strategic partnerships with FinTech providers to optimize service delivery and operational performance. Policymakers should also support digital transformation initiatives in the hospitality sector through incentives, training programs, and regulatory frameworks that promote innovation and competitiveness.
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A linear regression model of secure remote access to enterprise networks by employees: a case study of Kericho county
(KCA University, 2024) Keter, Edwin K.
The Covid-19 pandemic has made working from home the usual practice for many companies and organizations around the world. While remote work has provided flexibility and kept things running during unusual times, it has also given criminals new chances to take advantage of weaknesses, causing serious cyber-attacks like scams, fake emails, and hacking. Protecting remote work systems is now more important than ever. This study looks at how people working from home handle security problems, develops a special security plan for remote work, and checks if it works well. The study used a survey to gather information about corporate networks and weaknesses in remote access. Data was collected from teleworkers at different levels within organizations to find out what affects remote access security. The process of collecting and analyzing the data was well-organized, and the results were checked using a statistical method called Ordinary Least Squares (OLS) linear regression. This made sure the findings were reliable and accurate. The research found six important things that affect secure remote access: Technology, Organizational Factors, Employees, Monitoring & Evaluation, Resource Management & Controls, and Data Protection & Monitoring. These six areas together form a guide for companies to improve their information security while allowing employees to work remotely. The OLS linear regression analysis showed that the model can predict remote access security well, with an Adjusted R-squared value of 0.634. This means that the six independent variables explain 63% of the changes in remote access security. ANOVA tests also confirmed that these variables are statistically significant for predicting remote access security. This research is important for organizations that use county and enterprise networks. The study suggests that organizations should use strong security measures, such as secure devices, employee training, ongoing monitoring, and better management of resources. By following these recommendations, organizations can reduce cybersecurity risks and create safe and efficient remote work settings. This research presents a complete and proven model for teleworking security, designed to handle the special problems caused by working from home during the Covid-19 pandemic. The model provides a useful way for companies to protect their computer networks from increasing cyber dangers. By focusing on safe systems and keeping an eye on potential issues, the study helps improve remote work methods and makes sure companies can stay strong against ongoing cybersecurity problems.
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Antecedents of integrated reporting in enhancing financial sustainability of tier I and II commercial banks in Kenya
(KCA University, 2025) Onsare, Sheila K.
This study investigated the antecedents of integrated reporting (IR) and their role in enhancing financial sustainability among Tier I and II commercial banks in Kenya. Integrated reporting had gained prominence globally as a strategic approach that combined financial and non-financial disclosures to provide a holistic view of organizational performance. Despite its growing importance, the adoption of IR remained limited and uneven across the Kenyan banking sector. The research focused on four key antecedents, firm size, leverage, firm age, and board size, and examined their influence on the adoption and quality of integrated reporting practices, ultimately assessing their contribution to financial sustainability, measured by the Altman Z score. The study anchored the independent variables, firm size, leverage, firm age, and board size, within relevant theoretical frameworks to explain their influence on integrated reporting and financial sustainability in Tier I and II commercial banks in Kenya. By applying legitimacy theory, agency theory, institutional theory, and resource dependence theory, the study built a strong conceptual foundation to examine how internal firm dynamics drove corporate transparency and sustainability reporting. The study adopted an explanatory research design using panel data collected from 17 Tier I and II banks between 2020 and 2024. Descriptive and inferential statistics, including panel regression models, were employed to test the hypothesized relationships. The findings revealed significant relationships between integrated reporting antecedents and financial sustainability among commercial banks in Kenya. Firm size demonstrated the strongest positive correlation with financial sustainability (r = 0.523, p < 0.01), indicating that larger banks exhibit superior financial health. Firm age showed a moderate positive correlation (r = 0.346, p < 0.05), suggesting established banks achieve better sustainability outcomes. Board size displayed a weak but significant positive correlation (r = 0.298, p < 0.05), while leverage showed a moderate negative correlation (r = -0.412, p < 0.01), indicating higher debt-to-equity ratios compromised financial sustainability. The fixed effects panel regression confirmed these predictors collectively explain 47.5% of financial sustainability variation. The study concluded that firm size, firm age, and board size significantly enhance financial sustainability, with firm size providing the strongest predictive power. Established banks benefit from institutional maturity and accumulated experience, while larger board sizes contribute through enhanced governance oversight. Conversely, excessive leverage substantially compromises financial sustainability by increasing financial risk and reducing long-term viability. The study recommended that bank management should pursue strategic growth initiatives to achieve optimal operational scale, maintain conservative debt-to-equity ratios within regulatory guidelines, and leverage institutional knowledge for competitive advantage. Regulatory authorities should consider these findings when developing prudential regulations, emphasizing leverage management and corporate governance guidelines. Investors should utilize firm size, leverage levels, and governance quality as key evaluation criteria for banking sector investments. Future research should incorporate additional variables such as management quality, technological innovation, and macroeconomic factors to explain the remaining 52.5% of financial sustainability variation.
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Relationship between macroeconomic factors and the stock market development in Kenya
(KCA University, 2025) Karanja, Grace N.
Stock markets constitute an essential component of modern economic systems, serving as mechanisms for capital allocation, savings mobilization, and the promotion of economic development. They offer corporations a structured avenue to raise capital for expansion, enhance market liquidity, facilitate price discovery, and contribute to both the creation and distribution of wealth. In Kenya, the interplay between macroeconomic indicators and stock market development warrants comprehensive investigation, particularly in light of the growing significance of financial markets in the country’s economic architecture. This study sought to examine the relationship between selected macroeconomic variables specifically Gross Domestic Product (GDP) growth, inflation, interest rates, and exchange rates and their influence on the development of the Kenyan stock market. The investigation was anchored in prominent theoretical models such as the Efficient Market Hypothesis (EMH), Arbitrage Pricing Theory (APT), and the Three-Factor Model. These frameworks offered foundational perspectives for interpreting the interactions between economic fundamentals and financial market performance. Using a mixed-methods approach, the study combined quantitative analysis with time series data from 2000 to 2023 on a quarterly basis. The statistical analysis was conducted using STATA 12, employing a multiple linear regression model under the ordinary least squares (OLS) methodology to identify significant relationships among the variables of interest. Primary data sources included financial disclosures from listed firms on the Nairobi Securities Exchange (NSE), macroeconomic data from the Central Bank of Kenya and the Kenya National Bureau of Statistics, as well as exchange rate information obtained from the International Monetary Fund (IMF) and the World Bank. The analytical process commenced with a descriptive statistical assessment, encompassing measures such as the mean, variance, skewness, and kurtosis. Thereafter, diagnostic tests were conducted to ensure the fulfillment of OLS assumptions, including tests for stationarity, multicollinearity, and serial correlation. To capture both short-term dynamics and long-term equilibrium relationships, the study applied advanced econometric techniques, including Vector Error Correction Model (VECM), the Johansen Cointegration Test, and Granger Causality analysis. Furthermore, the Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) were utilized to evaluate the dynamic temporal responses of the variables. The findings of the study yielded valuable insights for policymakers, investors, and financial analysts by elucidating the macroeconomic determinants of stock market performance in Kenya. The results also served to inform strategic financial decision-making, enhance risk management frameworks, and support the formulation of sound economic policies. Additionally, this study aimed to contribute to the broader academic discourse on financial market development within the context of emerging economies.