Identification of corruption risk factors in public procurement of works at the pre-contract phase: the case of Ghana

Purpose Considering the crucial role construction plays in a country's overall development, it is essential to ensure that the processes leading to successful projects are free from corrupt practices. This study identified corruption risk factors across all stages of the pre-contract phase in public procurement of works. Design/methodology/approach A quantitative research approach was employed using a structured questionnaire administered to 104 construction professionals with procurement backgrounds. Pilot testing was conducted with 8 construction professionals familiar with public procurement, who affirmed the grouping of the risk indicators into project initiation, project planning, project design and project tendering and with reference to the statutory sequence under the procurement act. Mean-score ranking and fuzzy synthetic evaluation (FSE), using normalised mean-derived weights and empirical frequency-based membership functions, were the principal analytical tools. Within-stage Spearman correlations were used to assess indicator redundancy. Alternative equal-weight and triangular fuzzy-number FSE specifications were conducted separately as robustness analyses and are reported in Appendix. Findings The original mean-weight empirical FSE model ranked project tendering as the most critical pre-contract stage (index = 4.343), followed by project planning (4.210), project initiation (4.200) and project design (4.129). No within-stage Spearman coefficient reached the |rho| >= 0.80 redundancy threshold, so all indicators were retained. The same stage order was obtained under the alternative equal-weight empirical FSE and triangular fuzzy-number FSE specifications reported in Appendix, indicating that the substantive ranking was not materially affected by the alternative analytical assumptions. Practical implications The findings support controls targeted at the dominant risk in each stage: independent needs and cost verification during project initiation; restricted and auditable access to sensitive procurement information during project planning; central project registration and independent technical review during project design; and conflict-of-interest declarations, evaluator rotation and complete electronic audit trails during project tendering. Originality/value The study applies an empirical FSE framework to develop a stage-contingent assessment of pre-contract corruption risks in Ghana. It also demonstrates, through separate robustness specifications reported in Appendix, that the ordering of the four procurement stages remains stable when the weighting rule and fuzzy-number representation are changed.

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Publication Details

Journal
Engineering Construction & Architectural Management
Published
2026-10-30
DOI
https://doi.org/10.1108/ecam-12-2025-1993
Primary Topic
Public Procurement and Policy
Type
article
Field-Weighted Citation Impact
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article

Identification of corruption risk factors in public procurement of works at the pre-contract phase: the case of Ghana

Ernest Kissi, Victor Karikari Acheamfour, Kenneth Eluerkeh, Evans Kwesi Mireku et al.
1 links
Engineering Construction & Architectural Management
Public Procurement and Policy
article

Identification of corruption risk factors in public procurement of works at the pre-contract phase: the case of Ghana

Ernest Kissi, Victor Karikari Acheamfour, Kenneth Eluerkeh, Evans Kwesi Mireku, Josephat Bii Jimah
article en

Abstract

Purpose Considering the crucial role construction plays in a country's overall development, it is essential to ensure that the processes leading to successful projects are free from corrupt practices. This study identified corruption risk factors across all stages of the pre-contract phase in public procurement of works. Design/methodology/approach A quantitative research approach was employed using a structured questionnaire administered to 104 construction professionals with procurement backgrounds. Pilot testing was conducted with 8 construction professionals familiar with public procurement, who affirmed the grouping of the risk indicators into project initiation, project planning, project design and project tendering and with reference to the statutory sequence under the procurement act. Mean-score ranking and fuzzy synthetic evaluation (FSE), using normalised mean-derived weights and empirical frequency-based membership functions, were the principal analytical tools. Within-stage Spearman correlations were used to assess indicator redundancy. Alternative equal-weight and triangular fuzzy-number FSE specifications were conducted separately as robustness analyses and are reported in Appendix. Findings The original mean-weight empirical FSE model ranked project tendering as the most critical pre-contract stage (index = 4.343), followed by project planning (4.210), project initiation (4.200) and project design (4.129). No within-stage Spearman coefficient reached the |rho| >= 0.80 redundancy threshold, so all indicators were retained. The same stage order was obtained under the alternative equal-weight empirical FSE and triangular fuzzy-number FSE specifications reported in Appendix, indicating that the substantive ranking was not materially affected by the alternative analytical assumptions. Practical implications The findings support controls targeted at the dominant risk in each stage: independent needs and cost verification during project initiation; restricted and auditable access to sensitive procurement information during project planning; central project registration and independent technical review during project design; and conflict-of-interest declarations, evaluator rotation and complete electronic audit trails during project tendering. Originality/value The study applies an empirical FSE framework to develop a stage-contingent assessment of pre-contract corruption risks in Ghana. It also demonstrates, through separate robustness specifications reported in Appendix, that the ordering of the four procurement stages remains stable when the weighting rule and fuzzy-number representation are changed.

Engineering Construction & Architectural Management
University of Johannesburg (ZA), Kumasi Technical University (GH), Kwame Nkrumah University of Science and Technology (GH), Virginia Tech (US)
Peace, justice, and strong institutions
Openalex Percentile: Top 9%
Public Procurement and Policy
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