Project Scope Management Practices and Project Performance in Public Sector Automation Projects: A Case of Kenya Revenue Authority

In the public sector, automation projects are expected to support improved service delivery, compliance, and revenue mobilisation. However, despite substantial investment, the outcomes of automation projects at the Kenya Revenue Authority (KRA) have been inconsistent, with some projects experiencing delays, budget overruns, and solutions that did not fully meet user expectations. This situation indicated weaknesses in project management practices, particularly project scope management. The study therefore examined how scope management practices influenced the performance of business automation projects at KRA. Specifically, it assessed the effects of scope planning (e.g., a scope management plan, clear objectives and deliverables, and stakeholder involvement), scope definition (requirements collection, scope definition, and a work breakdown structure), scope validation (formal acceptance, stakeholder sign-off, and reduced rework), and scope control (change-request procedures and monitoring and control) on project performance, measured through stakeholder satisfaction, time, quality, and cost. The study was anchored in the Resource-Based View, Agency Theory, and Systems Theory. A descriptive and explanatory research design was applied to a target population of 132 employees involved in automation projects implemented between 2013 and June 2025. Structured questionnaires were used to collect primary data, and Likert-scale items were aggregated into composite scores for each construct. The data were analysed using Pearson's correlation and regression at a 0.05 significance level. In the separate regression models, scope planning (B = 0.226, p = 0.005; R² = 0.072), scope definition (B = 0.232, p = 0.010; R² = 0.060), scope validation (B = 0.200, p = 0.005; R² = 0.072), and scope control (B = 0.210, p ≈ 0.007; R² = 0.065) were positively associated with project performance. In the multiple regression analysis, the overall model was statistically significant (F(4, 105) = 5.867, p < 0.001) and explained 18.3% of the variance in performance (R² = 0.183; adjusted R² = 0.152). Scope planning (B = 0.17, p = 0.029) and scope validation (B = 0.15, p = 0.042) remained statistically significant, whereas scope definition and scope control did not reach the 5% significance threshold. Overall, the findings indicate that structured scope planning and validation were most consistently associated with the performance of KRA business automation projects.