Digital Transformation and Procurement Performance of the Kenya Revenue Authority in Kenya

Abstract

Public sector institutions in Kenya continue to experience inefficiencies in procurement processes, characterised by delays, high operational costs, limited transparency, and weak compliance with regulatory frameworks, despite the global recognition of digital transformation as a solution to these challenges. The extent to which digital transformation initiatives had improved procurement performance at the Kenya Revenue Authority (KRA) remained unclear. This study determined the effect of digital transformation on the procurement performance of KRA in Kenya. The general objective of the study was to determine the relationship between digital transformation and the procurement performance of KRA. The specific objectives were to assess the influence of automation of process, examine how data analytics influences procurement performance, analyze the role of e-procurement systems, and investigate the effect of cybersecurity measures on the procurement performance of KRA. The study was anchored on the Resource-Based View, Transaction Cost Economics, Technology Acceptance Model, and Diffusion of Innovation theories. A cross-sectional survey research design was adopted, targeting a sampling frame of 150 KRA staff comprising the technical team, auditors, and management. Using Yamane's formula, a sample of 110 respondents was selected through simple random sampling. A pilot study involving 11 respondents was conducted, and all five research constructs returned Cronbach's alpha coefficients above the 0.7 threshold. Data were collected using structured questionnaires and processed in SPSS version 28, using descriptive statistics, Pearson correlation, and multiple regression analysis. Of the 110 questionnaires distributed, 76 were completed and returned, yielding a response rate of 68.9 percent. The correlation results showed that automation of process, data analytics, e-procurement systems, and cybersecurity were each positively and significantly associated with procurement performance, with e-procurement systems recording the strongest association (r = 0.498, p<0.05). The regression results revealed that all four variables significantly predicted procurement performance, with automation of process recording the largest standardised effect (β = 0.386), followed by e-procurement systems (β = 0.357), data analytics (β = 0.189), and cybersecurity (β = 0.183); the model explained 53.9 percent of the variation in procurement performance (Adjusted R² = 0.539, F = 18.123, p<0.05). The study concluded that digital transformation significantly determines the procurement performance of the Kenya Revenue Authority in Kenya. The study recommends that KRA continue investing in integrated procurement automation and system interoperability, strengthen procurement analytics and data governance capabilities, upgrade and expand e-procurement platforms across the full procurement lifecycle, and reinforce cybersecurity through modern encryption, intrusion detection, and continuous staff awareness training.