Determinants of project control system effectiveness in Saudi Arabian construction project delivery

Abstract

Construction projects in the Kingdom of Saudi Arabia (KSA) continue to face persistent challenges, including schedule delays, cost overruns, and uncontrolled scope deviations. Despite increasing emphasis on Project Control Systems (PCSs), research has yet to offer a comprehensive, empirically tested framework for assessing the determinants that shape PCS effectiveness. This study addresses that gap by developing a structured, multi-layered framework that evaluates how various Project Control System Determinants (PCSDs) influence project performance across cost, schedule, and scope dimensions. Data were collected through a cross-sectional survey of 222 completed construction projects in KSA, capturing variations in control practices and performance outcomes. A multi-stage analytical design was applied at both strategic and tactical levels using two approaches: a structural-based model and an indicator-based predictive approach, to address the research aim and objectives. At the strategic level, Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test a structural model grounded in empirical evidence and informed by relevant theoretical perspectives, linking 13 PCSDs to project performance. Results showed that operational control determinants, including pre- and post-operational controls, as well as uncertainty controls, had significant direct and mediating effects. Organisational, human, and technological inputs influenced performance indirectly through their effects on operational controls, validating the study’s Input–Process–Output (IPO) modelling logic. As part of the structural modelling approach, tactical-level insights were derived using Importance–Performance Map Analysis (IPMA). This analysis identified priority areas for improvement, including leadership and team capacity, estimation accuracy, integrated stakeholder engagement, Project Management Office (PMO) involvement, audit frequency, knowledge management for continuous improvement, and schedule compression techniques. These elements were both high in importance but underperforming, marking them as critical leverage points for enhancing PCS effectiveness. In the indicator-based predictive approach, a combination of machine learning models, including random forest, gradient boosting, and ridge regression, was employed to rank the most influential determinants of cost, schedule, and scope outcomes. Project Planning and Scheduling (PPS), Corrective Actions (CA), and Change Control (CC) emerged as consistently strong predictors. Furthermore, Lasso and Elastic Net Regression were used to analyse 59 sub-indicators, offering granular insights into practices with the greatest impact, such as estimation precision, change control efficiency, earned value analysis (EVA), and resource optimisation. This research makes three key contributions. First, it offers theoretically significant contribution by introducing a novel, integrative PCS framework grounded in empirical evidence and structured across strategic and tactical levels. Second, it demonstrates methodological rigour through the use of multi-model triangulation, latent variable modelling, and machine learning. Third, it provides practical value by presenting a replicable, diagnostic roadmap for project managers and policymakers to evaluate and improve control systems in complex settings such as construction. These contributions have broader relevance to complex project environments and are particularly well suited to advancing the goals of Vision 2030 and strengthening project delivery in Saudi Arabia’s evolving construction sector.