THE ROLE OF ORGANIZATIONAL DRIVERS IN ARTIFICIAL INTELLIGENCE ADOPTION IN MONITORING TAX COMPLIANCE AT THE KRA
Judith Wangai Kivuti - Student, Master of Arts in Public Policy and Administration, Kenyatta University, Kenya
David Minja - Lecturer, Department of Public Policy and Administration, Kenyatta University, Kenya
ABSTRACT
Kenya Revenue Authority has embarked on adopting AI technologies in order to make its tax compliance monitoring procedures more modern and minimize cases of tax evasion. Nevertheless, the continued tax evasion, the underreporting, and non-filing makes one ask whether these technologies are adopted and were implemented effectively. This research examined the role of organizational drivers in artificial intelligence adoption in monitoring tax compliance at the KRA. The survey was directed by Technology-Organization-Environment (TOE) framework Theory. The target population comprised 852 individuals, consisting of 732 compliance staff and 120 Business Systems and Technology, Enterprise Management (BSTEM) staff in the Nairobi region that were at the heart of the tax compliance operations. Stratified sampling based on Yamane formula was used to sample 272 participants because of representativeness and data saturation. So as to analyze quantitative data, descriptive and inferential statistics were applied, whereas qualitative data of interviews and open-ended answers were analyzed on a thematic basis. A descriptive type of research design was used. Quantitative and qualitative information was gathered via structured and semi-structured questionnaires as well as interview schedules. The results demonstrate that an R-value of 0.729 establishes a solid positive association between the independent factors (technological drivers, organizational drivers, and environmental drivers) and the dependent variable (artificial intelligence adoption in monitoring tax compliance at the Kenya Revenue Authority). The research shows that technological drivers which include system efficiency, data processing capabilities, and analytics tools have the greatest impact on AI adoption. Staff capacity and internal policies and management support also serve as essential factors that determine successful execution of organizational objectives. The study recommends that KRA should enhance its technological infrastructure through continuous staff training and capacity development and better internal coordination while developing AI initiatives that match changing regulatory and policy requirements to achieve complete AI adoption success in tax compliance monitoring.