Assessing procurement irregularities in the supply-chain of Ghanaian construction projects: a soft-computing approach - Owusu, Emmanuel Kingsford; Chan, Albert P. C.; Hosseini, M. Reza; Nikmehr, Bahareh (Journal of Civil Engineering and Management, 2020)
This study examines procurement irregularities, as one of the most unexplored threats in the procurement process of construction projects. It also tests the suppositions associated with the contributions of irregularities to corruption in construction procurement. An expert survey is conducted with 62 construction-related practitioners selected via non-probabilistic sampling in the context of a Ghana, to assess the criticalities of the irregularities. Eighteen irregularities were identified within the context selected for this study. A soft computing technique known as the Fuzzy Synthetic Evaluation (FSE) method is employed to examine the identified irregularities. Other relevant techniques including factor analyses, normalization, and descriptive tools are employed to factorize the identified irregularities and test the hypotheses. Out of the 18 irregularities, 11 were revealed to be critical. The findings reveal that the top three irregularities were: payments for uncompleted works, sourcing of proforma invoices from the same supplier and the lack of proper coordination among key departments. Moreover, four constructs were developed using the identified measurement items. They are administrative-specific, procedural, compliance and contract monitoring irregularities. Out of the four, the topmost critical construct turns out to be compliance irregularities. Theoretically, this study advances the scholarship of construction by shedding lights on the irregularities associated with the procurement processes of construction projects. It also contributes to an in-depth understanding of the noted irregularities. In practical terms, this study contributes to the procurement planning and policy-making process, it assists decision makers in putting in place measures to prevent or extirpate the likelihood of any of the irregularities’ occurrences.