The paper develops and validates a structural model to support the selection of procurement methods in Ghana’s construction industry, where procurement inefficiencies continue to affect timely and cost-effective project delivery. Although Ghana operates under the Public Procurement Act 2003 (Act 663) amended Act 914, existing studies are mainly descriptive and fail to provide a predictive model for procurement decision-making under local conditions. The study addresses this gap by linking factors that affect the choice, five rights and root causes of procurement inefficiencies to procurement method selection. The paper examines the interaction of key procurement variables to improve decision-making and enhance value for money in construction projects.
Quantitative research design was taken. The close-ended questionnaires were used to collect data on 360 construction professionals in Ghana. Factor analysis has been used to validate the constructs of measurements, and Partial Least Squares Structural Equation Modeling has been used to test the relationship between factors affecting the choice of procurement methods, root causes of procurement inefficiency, the five rights of procurement and procurement method selection.
The study indicates that factors affecting the choice of procurement method significantly influence procurement method selection (ß = 0.432, p < 0.001). This effect is partially mediated by adherence to the five rights of procurement (ß = 0.096, p < 0.001). Additionally, inefficiency root causes negatively impact method selection (ß = −0.260, p < 0.001), often leading to suboptimal choices such as sole sourcing. The model explains 62.8% of the variance (R2 = 0.628) in procurement method selection, demonstrating strong explanatory power and the importance of understanding these influencing factors.
The research provides a context-specific theory-based structural model which combines the procurement factors, the root causes of inefficiency and the five rights of procurement to establish a basis on future predictive procurement decision-support systems.
