AUTOMATED REVENUE SYSTEMS ON THE OWN SOURCE REVENUE OF KIAMBU COUNTY GOVERNMENT, KENYA
Lydia Wanjiru Ndung’u - Postgraduate Student, Department of Accounting and Finance, School of Business Economics and Tourism, Kenyatta University, Kenya
Dr. Francis Gitagia (Ph.D), (CPA) - Lecturer, Department of Accounting and Finance, School of Business Economics and Tourism, Kenyatta University, Kenya
ABSTRACT
Own Source Revenue (OSR) in Kiambu County has consistently fallen below expected targets, with collections averaging approximately 78 percent of annual targets between the 2018/19 and 2023/24 financial years, equivalent to about KSh 4.3 billion against projections of KSh 5.5 billion. This persistent shortfall, despite significant investments in automation systems such as e-Citizen, point-of-sale devices, mobile money platforms, and IFMIS linkages, raises concerns regarding the effectiveness of automation in enhancing revenue performance. The study examined the effect of automated revenue systems on own source revenue of Kiambu County Government, Kenya, focusing on automated revenue source identification, automated revenue billing, automated revenue collection, and automated revenue reconciliation systems. The study was underpinned on the Technology Acceptance Model, Institutional Theory and Financial Sustainability Theory and adopted a descriptive research design targeting 178 officers involved in revenue administration, including county revenue officers, sub-county administrators, revenue accountants, ICT and automation officers, and internal auditors. A census approach was used, with primary data collected through structured questionnaires and secondary data obtained from Kiambu County financial and audit reports for the period 2019–2024. Data were analyzed using SPSS through descriptive statistics and inferential techniques including Pearson correlation and multiple regression analysis, with results presented in tables. Regression results showed that automated revenue source identification systems had a positive and statistically significant effect on own source revenue, automated revenue billing systems had a positive and statistically significant effect, automated revenue collection systems had a positive and statistically significant effect, and automated revenue reconciliation systems had a positive and statistically significant effect, with automated revenue collection systems exerting the strongest influence. The study concludes that automation across the revenue cycle significantly enhances revenue performance by improving coverage, accuracy, efficiency, and accountability while reducing leakages. The study recommends strengthening integration of automation systems, enhancing digital payment infrastructure, improving interoperability with IFMIS and e-Citizen platforms, and investing in capacity building for revenue and ICT personnel, while policymakers should develop standardized frameworks to guide automation across counties. Ethical principles including informed consent, confidentiality, and voluntary participation were observed throughout the study.