The Impact of Open Competition Method Compared to Restricted/Selective Tendering Methods: Comparative Analysis in Public Procurement in Nigeria

The Impact of Open Competition Method Compared to Restricted/Selective Tendering Methods: Comparative Analysis in Public Procurement in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Impact of Open Competition Method Compared to Restricted/Selective Tendering Methods: Comparative Analysis in Public Procurement in Nigeria Michael Igara Nmecha, Adebowale Abraham Adedokun, Victor Ugochukwu Okorondu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9282939/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Public procurement methods significantly influence transparency and cost efficiency. Objective: To compare open and restricted procurement methods in different contexts. Methods: An empirical study was conducted through surveys, interviews, and machine learning clustering analysis. Results: Open bidding was rated high for fairness (mean = 4.42) and value for money (mean = 4.27), and restricted methods favored urgency and complexity. Conclusion: Context-sensitive procurement frameworks combining both methods are ideal. A decision support model was proposed for policy implementation. Plain Language Summary: This study compares two methods of public procurement— open competitive bidding and restricted tendering—to determine which works best in different situations. Using survey and statistical data from Nigeria, West Africa, and the UK, this study shows that open bidding is better for fairness and transparency, while restricted methods are useful for urgent or complex projects. A new decision model is proposed to help governments choose the right method based on context. Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing Procurement Methods Open Competitive Bidding Restricted Tendering Decision Models Policy Benchmarking E-Procurement Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Public procurement plays a significant role in government operations and economic development, with expenditures accounting for up to 22% of national GDP [1]. In Nigeria, federal procurement exceeded N60 trillion [2]. The procurement method significantly influences outcomes, such as transparency, efficiency, cost-effectiveness, and inclusivity. While open competitive bidding is globally recognized for fostering transparency and broad participation, restricted/selective tendering is often preferred for specialized or urgent projects because of its efficiency [3]. However, the comparative performances of these methods remain underexplored, particularly in Nigeria. This study fills this gap by evaluating the suitability of each method in various procurement environments. The conceptual framework guiding this study is presented in Figure 1, linking procurement method choice (open competitive bidding versus restricted/selective tendering) to contextual factors and outcome variables including transparency, cost-effectiveness, fairness, and value for money. Research Objectives Analyze the key differences between open competitive bidding and restricted/selective tendering. Identify the context in which each method demonstrates superior outcomes. Examine how urgency, complexity, and market conditions influence method choice. Explore how both methods align with modern challenges such as digital transformation and sustainability. Provide policy recommendations based on empirical findings. Develop a mathematical decision-support model. Hypotheses H₁: Open competitive bidding enhances transparency and inclusivity. H₂: Restricted/selective tendering is more efficient for urgent technical procurement. H₃: Contextual factors outweigh procedural features in method selection. 2. Literature Review Theoretical Framework Principal-Agent Theory highlights the need for transparency to reduce agency risk [4]. Transaction Cost Economics suggests that open bidding incurs higher transaction costs while restricted tendering streamlines costs but may reduce value for money [5]. Public Choice Theory indicates that open bidding limits favoritism, whereas restricted methods may encourage it without proper oversight [6]. Empirical Studies The World Bank [7] emphasizes transparency through open competitive bidding in public procurement systems. The OECD [8] supports hybrid approaches to balance efficiency and control of corruption. Essien and Daniel [9] demonstrate that restricted tendering is practical for urgent Nigerian projects. Eze and Okonkwo [10] show that digital tools improve procurement efficiency and transparency in Nigeria. Recent literature highlights the growing emphasis on AI integration and platform governance in procurement [11], [12]. These studies call for adaptive digital ecosystems, reinforcing the urgency of the digital readiness addressed in this research. As Nigeria moves toward e-procurement reforms, insights from other regions offer comparative learning and justification for the proposed hybrid decision model. Tan et al. [13] and Tang et al. [14] provided frameworks supporting blockchain and digital integration in procurement systems, aligning with the transparency benefits noted in this study. Furthermore, Gaie and Mehta [15] discuss public service transformation through hybrid structures, reinforcing the utility of mixed procurement models. Research Gap Few studies have empirically compared these methods or analyzed how contextual factors shape their effectiveness, particularly in Nigeria and West Africa. 3. Methodology 3.1 Research Design and Study Area This study adopts a mixed-methods research design, combining quantitative analysis of procurement outcomes (e.g., cost efficiency, project timelines, and bidder participation) with qualitative insights from case studies and expert interviews. This dual approach enables comprehensive evaluation of procurement methods under various conditions. The overall research design and analytical procedures are summarized in Figure 2, integrating survey statistics, qualitative themes, k-means clustering, and the mathematical decision-support model into a coherent mixed-methods approach. Study Location: The research spans multiple geopolitical zones of Nigeria, reflecting the country's ethnic, economic, and administrative diversity. Data were collected from federal (Abuja), state (Lagos, Rivers, Kano), and private-sector procurement actors across Nigeria, West Africa, and comparative data from the UK. Nigeria was selected because of the scale and complexity of its public procurement system, which accounts for 15–25% of national GDP. The geographic spread of the study across Nigeria’s six geopolitical zones is shown in Figure 3a, highlighting the national coverage and regional diversity of procurement environments. The key states are depicted in Figure 3b, emphasizing the mix of federal, state, and private-sector procurement actors. This study utilized both quantitative and qualitative data to ensure holistic understanding of procurement practices. Quantitative data were obtained from procurement databases, Qualitative data were obtained from government audit reports, structured interviews with procurement stakeholders, and assessments by independent third parties. Data Analysis: The collected data were analyzed using a multilayered approach. Descriptive statistics (mean and standard deviation) summarized perceptions and trends across respondent groups. Cost-benefit analysis evaluated the efficiency of procurement methods in terms of resource allocation and value for money. K-means clustering segmented respondents based on attitudes, roles, and regional affiliations, enhancing the interpretation of survey insights. 4. Results Open competitive bidding scored highest for fairness (mean = 4.42) and value for money (mean = 4.27). Restricted tendering is efficient for complex projects (mean = 3.68), particularly in technical clusters. Key Findings High transparency ratings for open bidding across Nigeria, the UK, and West Africa. Restricted tendering is preferred in urgent/complex scenarios. Delays and costs are associated with open methods. Digital tools improved transparency (mean = 4.22). Machine learning clustering confirmed context-based preferences. Table 1: Procurement Likert Descriptive Statistics Procurement Belief Statement Mean SD Min Max Open bidding ensures fairness and access 4.42 0.68 1 5 Open competition provides best value for money 4.27 0.72 1 5 Selective tendering reduces corruption 2.85 1.15 1 5 Digital tools improve transparency 4.22 0.81 1 5 Training is needed to reduce inefficiencies 4.57 0.63 1 5 Open methods are most effective 4.49 0.70 1 5 Table 1: Summary of descriptive statistics for Likert-scale responses (1 = Strongly Disagree to 5 = Strongly Agree) on various procurement-related beliefs. Data represents aggregated responses from survey participants across all regions. Table 2: Regional Procurement Belief Comparison Procurement Statement Nigeria UK West Africa Others Open bidding ensures fairness and access 4.45 5.0 2.71 5.0 Open competition provides best value 4.30 5.0 2.86 5.0 Selective tendering reduces corruption 2.53 3.0 3.71 2.0 Digital tools improve transparency 4.22 5.0 3.86 4.0 Training needed to reduce inefficiencies 4.57 5.0 4.29 5.0 Open methods are still most effective 4.49 5.0 3.57 5.0 Table 2: Regional comparison of procurement beliefs. Data shows variations across geographic regions, with UK and "Others" category showing uniformly high ratings for open bidding, while West African responses show more moderate ratings. Table 3: Risk Analysis—Qualitative Themes Risk Theme Mentions Time Delays 46 Cost Overruns 38 Bias 14 Approval Bottlenecks/Complexity 10 Corruption 4 Table 3: Summary of qualitative themes extracted from open-ended responses. Participants most frequently identified time delays and cost overruns as primary risks in procurement processes. These qualitative themes reflect widespread concerns over inefficiencies in procurement systems, particularly regarding time and cost. As seen in Figure 6 below , respondent attitudes toward cost-effectiveness and value-for-money further highlight these differences between open and restricted procurement methods, with open bidding consistently scoring higher for value alignment. Table 4: Policy and Legal Analysis Summary Policy Reform Theme Mentions E-Procurement Systems 28 Transparency Measures 24 Digital Transformation 15 Training and Capacity Building 10 Approval of Threshold Reforms 1 Table 4: Policy priorities identified through stakeholder consultations. E-procurement systems and transparency measures emerge as the most frequently mentioned reform priorities. The k-means clustering analysis segmented respondents into three distinct groups— Cautious, Critical, and Progressive—based on their procurement beliefs, as illustrated in Figure 6. Table 5: Machine Learning/Data Analytics—Cluster Profiles Theme/Statement Cluster 0 (Cautious) Cluster 1 (Critical) Cluster 2 (Progressive) Open bidding ensures fairness 4.47 4.44 4.36 Transparency increased via open competition 3.56 4.61 4.55 Corruption less likely in restricted/selective tendering 2.77 1.69 3.15 Selective tendering encourages accountability 3.35 1.93 4.27 Procurement should prioritize inclusive vendor participation 3.82 4.19 4.25 Table 5: Machine learning cluster analysis results (K-Means, k=3). Three distinct respondent groups emerged: Cluster 0 (Cautious) shows moderate support for both methods; Cluster 1 (Critical) strongly favors open bidding and transparency; Cluster 2 (Progressive) supports selective tendering for specific contexts while valuing accountability. Figure 10 below shows equal respondent counts for five agreement levels on three statements: combining open and restricted tendering improves procurement, open competitive bidding remains most effective, and training procurement officials reduces biases and inefficiencies as seen in Table 5. As shown in Figure 12, responses favoring open competitive bidding are concentrated at the “Agree” and “Strongly Agree” levels, confirming broad stakeholder support for open competition. The distribution of responses favoring restricted/selective tendering across different contexts is presented in Figure 13, which shows that support for restricted methods increases with project urgency and technical complexity. Figure 14 illustrates that most respondents agree or strongly agree that sustainability and economic recovery should guide procurement decisions, highlighting the reform pressure toward greener, more resilient procurement systems. Table 6: Procurement Decision Support Model Parameters and Results Parameter Value Interpretation Transparency (T) 4.5 High transparency required Cost-Effectiveness (C) 3.8 Moderate cost consideration Digital Maturity (D) 4.0 Good digital capability Sustainability (S) 3.5 Moderate sustainability focus Urgency (U) 4.2 Time-sensitive requirement Complexity (X) 4.7 High technical complexity Market Conditions (Mₖ) 3.0 Moderate market availability Model Output (Ψ) 0.285 Open Competitive Bidding Recommended Table 6: Decision support model parameters using normalized inputs (0–5 scale) and resulting procurement method recommendation. With Ψ = 0.285 ≥ threshold (θ), the model recommends Open Competitive Bidding (M = 1). 5. Discussion To further assess which method respondents viewed as most effective overall, Figure 10 presents a consolidated view of procurement preferences across stakeholder groups. The visualization reveals a strong preference for open competition in transparency-centric environments, while restricted tendering dominates in technically complex or urgent project contexts. Objective 1 focuses on understanding differences between open competitive bidding and restricted/selective tendering. Table 1 and Figure 4 show that open competitive bidding scored highest for fairness (mean = 4.42) and value-for-money (mean = 4.27), affirming H₁ [1], [8]. These findings echo World Bank [7] and OECD [8] recommendations promoting open bidding as a transparency and corruption mitigation tool. Figure 7 reinforces this by identifying open bidding as the most effective method across sectors. Objective 2 addresses context-specific efficiency. Figure 3.1&3.2 and Table 3 show that restricted tendering is preferable for urgent, technically complex projects. These findings align with Essien and Daniel [9], affirming H₂ and the strategic relevance of restricted methods for specialized, time-sensitive processes. Objective 3 examines contextual variables affecting procurement choices. Figures 5 and 9 and Table 2 highlight regional and cluster-based variations. Restricted tendering dominates complex and urgent environments, whereas open bidding is favored when inclusiveness and oversight are priorities. Machine learning clustering analysis (Table 5) corroborates this, citing time delays and complexity as decision drivers, validating H₃ and reinforcing Transaction Cost Theory [5]. Objective 4 addresses modern challenges: sustainability and digitization. Digital tools improved transparency (mean = 4.22) as shown in Table 1. Figure 11 confirms rising expectations for procurement systems to promote sustainability and economic recovery [10]. Objectives 5 and 6 are supported by reform priorities in Table 4, where e-procurement, transparency, and capacity building were highly emphasized. Figure 8 shows support for hybrid approaches [8]. The feedback from Figures 3, 4, and 5 underscores stakeholder demand for integrated reform and oversight. Mathematical Decision-Support Model A key innovation is the mathematical decision-support model for method selection: Ψ = α₁T + α₂C + α₃D + α₄S − β₁U − β₂X − β₃Mₖ Where: T = Transparency C = Cost-Effectiveness D = Digital Maturity S = Sustainability U = Urgency X = Complexity Mₖ = Market Conditions α and β = weighted coefficients Procurement method M is chosen as: M = 1 (Open Bidding) if Ψ ≥ θ; otherwise, M = 0 (Restricted Tendering) Where θ is an institutional threshold typically set at 0.25–0.35. Using normalized inputs: T = 4.5, C = 3.8, D = 4.0, S = 3.5, U = 4.2, X = 4.7, Mₖ = 3.0 and weights: α₁ = 0.25, α₂ = 0.20, α₃ = 0.15, α₄ = 0.10, β₁ = 0.20, β₂ = 0.30, β₃ = 0.10: Ψ = 0.285 ⇒ M = 1 → Open Competitive Bidding This model: Builds on and enhances the World Bank CPAR (2017), adding dynamic scoring Extends the OECD Integrity Model (2020) with quantifiable AI-ready inputs Addresses limitations in Essien and Daniel [9] by including all key variables Bridges theory and practice with adaptable, evidence-based decision-making Supports real-time automation and hybridization Aligns with evolving global procurement norms Hypothesis Validation All three hypotheses were supported: H₁ Supported: Open bidding is widely considered transparent and inclusive (mean fairness = 4.42). H₂ Supported: Selective methods work best for technical/urgent projects (mean efficiency = 3.68). H₃ Supported: Market dynamics and complexity drive procurement choices more than method design (clustering analysis shows contextual clustering patterns). Open methods support sustainability and digital transformation better, whereas restricted methods perform better under time and technical pressure. Hybrid frameworks combining both methods' strengths are recommended. 6. Conclusion This study confirms that procurement method selection must be context-sensitive. Open bidding suits transparent procurements, whereas restricted methods are efficient for specialized needs. Data-driven models support real-time adaptive decision-making. The evidence strongly supports the adoption of context-aware hybrid procurement frameworks that leverage both open and restricted methods based on organizational and environmental factors. 7. Recommendations Adopt hybrid procurement frameworks balancing transparency and efficiency. Implement flexible threshold policies responsive to contextual variables. 3. Mandate and support nationwide e-procurement system implementation. Strengthen oversight institutions (e.g., the NCPP). Provide certified training to procurement officers and stakeholders. Institutionalize supplier feedback mechanisms and continuous improvement loops. Integrate decision-support models into digital procurement platforms. Pilot the mathematical model across key government ministries, departments, and agencies (MDAs). Establish monitoring and evaluation frameworks to track procurement outcomes and model effectiveness. Abbreviations and Acronyms Acronym Full Form CPAR Country Procurement Assessment Report MDAs Ministries, Departments and Agencies NCPP National Council on Public Procurement OECD Organisation for Economic Co-operation and Development AHP Analytic Hierarchy Process K-Means K-Means Clustering Algorithm ML Machine Learning e-procurement Electronic Procurement Declarations Participant Consent Statement All participants involved in this study were duly informed about the purpose, procedures, and nature of the research. Informed consent was obtained from all participants prior to their inclusion in the study. Participation was entirely voluntary, and respondents were assured of the confidentiality and anonymity of the information provided. In cases where direct human participation was not required, or where data were obtained from publicly available or secondary sources, the requirement for participant consent was reviewed and waived by the appropriate ethics oversight in accordance with established research guidelines. Funding Declaration Funding Statement: This research received no specific grants from any funding agency in the public, commercial, or nonprofit sectors. This study was self-funded by the research team. No external financial support influenced the study design, data collection, analysis, interpretation, or publication decisions. Ethics Declaration Ethics Declaration: All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Conflict of Interest Declaration: The authors declare that they have no affiliations with or involvement in any organization or entity with commercial interests in the subject matter or materials discussed in this manuscript. Data Access Statement: Data supporting the findings of this study are available upon reasonable request. Owing to the inclusion of sensitive organizational and professional responses, anonymized datasets can be shared with researchers who obtain ethical approval or permission from the principal investigators. 8. Contribution to Knowledge This study uniquely applies machine learning and contextual scoring to the procurement decision-making process. It benchmarks procurement practices across regions, introduces a dynamic decision-support model, and advances the literature on sustainable and transparent public procurement in Nigeria and West Africa. The mathematical framework provides a replicable, scalable tool for procurement reform across developing nations. 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Modernizing public procurement: Trends and insights for inclusive economies . https://www.oecd.org/ Essien, J. A., & Daniel, C. (2022). Urgent procurement and restricted tendering: A Nigerian perspective . African Journal of Procurement Research, 14(3), 156–178. Eze, V. C., & Okonkwo, U. V. (2023). Digital tools and procurement efficiency in Nigeria. Journal of Public Sector Innovation , 8(2), 112–135. Addo, A. (2022). Orchestrating a digital platform ecosystem to address societal challenges: A robust action perspective. Journal of Information Technology , 37(4), 456– 478. https://journals.sagepub.com/doi/abs/10.1177/02683962221088333 Lungu, M. (2024). Enhancing public service delivery in government procurement: The role of artificial intelligence. In Handbook of public service delivery (pp. 234–256). Edward Elgar Publishing. https://www.elgaronline.com/abstract/book/9781035315314/chapter11.xml Tan, E., Mahula, S., & Crompvoets, J. (2022). Blockchain governance in the public sector: A conceptual framework for public management. Government Information Quarterly , 39(4), 101–119. https://www.sciencedirect.com/science/article/pii/S0740624X21000617 Tang, Z., Adjorlolo, G., Wauk, G., Sarfo, P. A., & Braimah, A. B. (2025). Evaluating corruption-prone public procurement stages for blockchain integration using AHP approach. Systems , 13(4), 267–289. https://www.mdpi.com/2079-8954/13/4/267 Gaie, C., & Mehta, M. (2024). Digital transformation of public services: Trends and directions. In Transforming public services (pp. 178–203). Springer. https://link.springer.com/chapter/10.1007/978-3-031-55575-6_1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9282939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615440576,"identity":"5182c114-cf9b-4b73-afe1-be50af3864ea","order_by":0,"name":"Michael Igara 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1","display":"","copyAsset":false,"role":"figure","size":105111,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework linking procurement methods (open competitive bidding versus restricted/selective tendering) with contextual factors (urgency, complexity, market conditions, digital maturity, and sustainability) and procurement outcomes (transparency, cost-effectiveness, fairness, timely delivery, and value for money), underpinned by the mathematical decision-support model.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/87dedc9a1a10e02586c8cbb4.jpg"},{"id":106070492,"identity":"fd00a083-8838-482f-9ffa-09e05848799d","added_by":"auto","created_at":"2026-04-03 06:28:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133014,"visible":true,"origin":"","legend":"\u003cp\u003eMixed-methods research design and analysis flow, showing the progression from problem definition and instrument design through quantitative and qualitative analyses, k-means clustering, decision-support model development, and the derivation of context-aware hybrid procurement and policy recommendations.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/390ac6101521483222bf0e43.jpg"},{"id":106070493,"identity":"2e922823-cc0d-4db6-bdc1-42dc1fc4b3b2","added_by":"auto","created_at":"2026-04-03 06:28:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115133,"visible":true,"origin":"","legend":"\u003cp\u003ea Map of the Study Area showing Nigeria's six geopolitical zones, highlighting national coverage and regional diversity influencing procurement practices.\u003c/p\u003e\n\u003cp\u003eb Map of Study States focusing on key procurement-active states (Lagos, Rivers, Enugu, Abia, Kano, Abuja), displaying federal, state, and private 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methods.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/7454487fd59ee8e435b8893c.jpg"},{"id":106095033,"identity":"f2b768ad-ab2e-46f6-b906-1f496262b06f","added_by":"auto","created_at":"2026-04-03 11:44:04","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":74920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisks Identified\u003c/strong\u003e displays the frequency distribution of risk themes mentioned in qualitative interviews.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/e8f0956302cd2f61cd53e1c4.jpg"},{"id":106094504,"identity":"ba9beb99-3b44-4b27-ac9b-00b7343af0e7","added_by":"auto","created_at":"2026-04-03 11:42:46","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":71451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePolicy Reform Bar Chart\u003c/strong\u003e illustrates stakeholder preferences for procurement reforms, with e-procurement systems, transparency measures, and digital transformation identified as top priorities.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/ee7fb1ccb4ff123b4fc68021.jpg"},{"id":106070504,"identity":"c37b85bd-c70c-4f6a-930c-7cd319d4ce1c","added_by":"auto","created_at":"2026-04-03 06:28:21","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":145438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProcurement Opinion Clusters (K-Means)\u003c/strong\u003e presents machine learning-based segmentation of respondents into three distinct clusters based on procurement preferences and beliefs.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/f8e0400ff3e7c2c5b5b95a0f.jpg"},{"id":106070505,"identity":"862bc3e3-42ae-4101-909f-c1d560e2e968","added_by":"auto","created_at":"2026-04-03 06:28:21","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":93921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMost Effective Procurement Method\u003c/strong\u003edisplays respondent preferences regarding overall method effectiveness.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/2f23bbfe9ee028235022f6e1.jpg"},{"id":106070499,"identity":"95cf1657-d5ab-49ce-98ee-d541a153f9e5","added_by":"auto","created_at":"2026-04-03 06:28:21","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":115414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiple Values by Combination of Open and Restricted Tendering\u003c/strong\u003e illustrates support for hybrid procurement frameworks.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/866c84226dbddb1aea91f892.jpg"},{"id":106094483,"identity":"309955d1-52f5-4792-ad69-74a0362fcdc6","added_by":"auto","created_at":"2026-04-03 11:42:42","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":65630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency for Open Competition\u003c/strong\u003e shows the frequency distribution of responses favoring open competitive bidding.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/9e0df58dd65dcbcccfa28485.jpg"},{"id":106070503,"identity":"59ef8d3a-e411-4bef-ace9-2c54bbe25a73","added_by":"auto","created_at":"2026-04-03 06:28:21","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":83199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency for Restricted Tendering\u003c/strong\u003e shows the frequency distribution of responses favoring restricted tendering methods.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/d5143d59766dd9baf0687caf.jpg"},{"id":106094875,"identity":"cfff9368-4737-43ef-87d8-a5a7ff2a97ea","added_by":"auto","created_at":"2026-04-03 11:43:35","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":75528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRespondent Count for Sustainability and Economic Recovery\u003c/strong\u003e indicates stakeholder perspectives on sustainability considerations in procurement decision-making.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/7f5071ede9ae917bd86efa94.jpg"},{"id":106414767,"identity":"4f25d9a4-8242-4fc5-af5e-888bc24ec870","added_by":"auto","created_at":"2026-04-08 10:23:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243729,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9282939/v1/1a26e678-766a-4796-b2d8-94bff9c9c1b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Open Competition Method Compared to Restricted/Selective Tendering Methods: Comparative Analysis in Public Procurement in Nigeria","fulltext":[{"header":"1. Introduction ","content":"\u003cp\u003ePublic procurement plays a significant role in government operations and economic development, with expenditures accounting for up to 22% of national GDP [1]. In Nigeria, federal procurement exceeded N60 trillion [2]. The procurement method significantly influences outcomes, such as transparency, efficiency, cost-effectiveness, and inclusivity. While open competitive bidding is globally recognized for fostering transparency and broad participation, restricted/selective tendering is often preferred for specialized or urgent projects because of its efficiency [3]. However, the comparative performances of these methods remain underexplored, particularly in Nigeria. This study fills this gap by evaluating the suitability of each method in various procurement environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe conceptual framework guiding this study is presented in Figure 1, linking procurement method choice (open competitive bidding versus restricted/selective tendering) to contextual factors and outcome variables including transparency, cost-effectiveness, fairness, and value for money.\u003c/p\u003e\n\u003cp\u003eResearch Objectives\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAnalyze the key differences between open competitive bidding and restricted/selective tendering.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIdentify the context in which each method demonstrates superior outcomes.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eExamine how urgency, complexity, and market conditions influence method choice.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eExplore how both methods align with modern challenges such as digital transformation and sustainability.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProvide policy recommendations based on empirical findings.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDevelop a mathematical decision-support model.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eHypotheses\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eH₁:\u003c/strong\u003e Open competitive bidding enhances transparency and inclusivity.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH₂:\u003c/strong\u003e Restricted/selective tendering is more efficient for urgent technical procurement.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH₃:\u003c/strong\u003e Contextual factors outweigh procedural features in method selection.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"2. Literature Review ","content":"\u003cp\u003eTheoretical Framework\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal-Agent Theory\u003c/strong\u003e highlights the need for transparency to reduce agency risk [4]. \u003cstrong\u003eTransaction Cost Economics\u003c/strong\u003e suggests that open bidding incurs higher transaction costs while restricted tendering streamlines costs but may reduce value for money [5]. \u003cstrong\u003ePublic Choice Theory\u003c/strong\u003e indicates that open bidding limits favoritism, whereas restricted methods may encourage it without proper oversight [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmpirical Studies\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe World Bank [7] emphasizes transparency through open competitive bidding in public procurement systems. The OECD [8] supports hybrid approaches to balance efficiency and control of corruption. Essien and Daniel [9] demonstrate that restricted tendering is practical for urgent Nigerian projects. Eze and Okonkwo [10] show that digital tools improve procurement efficiency and transparency in Nigeria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent literature highlights the growing emphasis on AI integration and platform governance in procurement [11], [12]. These studies call for adaptive digital ecosystems, reinforcing the urgency of the digital readiness addressed in this research. As Nigeria moves toward e-procurement reforms, insights from other regions offer comparative learning and justification for the proposed hybrid decision model. Tan et al. [13] and Tang et al. [14] provided frameworks supporting blockchain and digital integration in procurement systems, aligning with the transparency benefits noted in this study. Furthermore, Gaie and Mehta [15] discuss public service transformation through hybrid structures, reinforcing the utility of mixed procurement models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch Gap\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFew studies have empirically compared these methods or analyzed how contextual factors shape their effectiveness, particularly in Nigeria and West Africa. \u003c/p\u003e"},{"header":"3. Methodology ","content":"\u003ch2\u003e3.1 Research Design and Study Area\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study adopts a mixed-methods research design, combining quantitative analysis of procurement outcomes (e.g., cost efficiency, project timelines, and bidder participation) with qualitative insights from case studies and expert interviews. This dual approach enables comprehensive evaluation of procurement methods under various conditions. The overall research design and analytical procedures are summarized in Figure 2, integrating survey statistics, qualitative themes, k-means clustering, and the mathematical decision-support model into a coherent mixed-methods approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Location:\u003c/strong\u003e The research spans multiple geopolitical zones of Nigeria, reflecting the country's ethnic, economic, and administrative diversity. Data were collected from federal (Abuja), state (Lagos, Rivers, Kano), and private-sector procurement actors across Nigeria, West Africa, and comparative data from the UK. Nigeria was selected because of the scale and complexity of its public procurement system, which accounts for 15–25% of national GDP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe geographic spread of the study across Nigeria’s six geopolitical zones is shown in Figure 3a, highlighting the national coverage and regional diversity of procurement environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe key states are depicted in Figure 3b, emphasizing the mix of federal, state, and private-sector procurement actors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study utilized both quantitative and qualitative data to ensure holistic understanding of procurement practices. Quantitative data were obtained from procurement databases,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQualitative data were obtained from government audit reports, structured interviews with procurement stakeholders, and assessments by independent third parties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collected data were analyzed using a multilayered approach. Descriptive statistics (mean and standard deviation) summarized perceptions and trends across respondent groups. Cost-benefit analysis evaluated the efficiency of procurement methods in terms of resource allocation and value for money. K-means clustering segmented respondents based on attitudes, roles, and regional affiliations, enhancing the interpretation of survey insights.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Results ","content":"\u003cp\u003eOpen competitive bidding scored highest for fairness (mean = 4.42) and value for money (mean = 4.27). Restricted tendering is efficient for complex projects (mean = 3.68), particularly in technical clusters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKey Findings\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eHigh transparency ratings for open bidding across Nigeria, the UK, and West Africa.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRestricted tendering is preferred in urgent/complex scenarios.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDelays and costs are associated with open methods.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDigital tools improved transparency (mean = 4.22).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMachine learning clustering confirmed context-based preferences.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable 1: Procurement Likert Descriptive Statistics\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"541\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProcurement Belief Statement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMax\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpen bidding ensures fairness and access\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpen competition provides best value for money\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelective tendering reduces corruption\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDigital tools improve transparency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraining is needed to reduce inefficiencies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpen methods are most effective\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Summary of descriptive statistics for Likert-scale responses (1 = Strongly Disagree to 5 = Strongly Agree) on various procurement-related beliefs. Data represents aggregated responses from survey participants across all regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Regional Procurement Belief Comparison\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eProcurement Statement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003eNigeria\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003eUK\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003eWest Africa\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eOpen bidding ensures fairness and access\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003e4.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003e2.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eOpen competition provides best value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003e4.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003e2.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eSelective tendering reduces corruption\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003e2.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003e3.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003e3.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003e2.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eDigital tools improve transparency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003e4.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003e3.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003e4.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eTraining needed to reduce inefficiencies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003e4.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003e4.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.8865%;\"\u003e\n \u003cp\u003eOpen methods are still most effective\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.234%;\"\u003e\n \u003cp\u003e4.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.97872%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5532%;\"\u003e\n \u003cp\u003e3.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3475%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Regional comparison of procurement beliefs. Data shows variations across geographic regions, with UK and \u0026quot;Others\u0026quot; category showing uniformly high ratings for open bidding, while West African responses show more moderate ratings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Risk Analysis\u0026mdash;Qualitative Themes\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"350\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76.5714%;\"\u003e\n \u003cp\u003eRisk Theme\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4286%;\"\u003e\n \u003cp\u003eMentions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp; Time Delays\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4286%;\"\u003e\n \u003cp\u003e46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp; Cost Overruns\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4286%;\"\u003e\n \u003cp\u003e38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp; Bias\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4286%;\"\u003e\n \u003cp\u003e14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp; Approval Bottlenecks/Complexity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4286%;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp; Corruption\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4286%;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Summary of qualitative themes extracted from open-ended responses. Participants most frequently identified time delays and cost overruns as primary risks in procurement processes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;These qualitative themes reflect widespread concerns over inefficiencies in procurement systems, particularly regarding time and cost. As seen in \u003cstrong\u003eFigure 6 below\u003c/strong\u003e, respondent attitudes toward cost-effectiveness and value-for-money further highlight these differences between open and restricted procurement methods, with open bidding consistently scoring higher for value alignment.\u003c/p\u003e\n\u003cp\u003eTable 4: Policy and Legal Analysis Summary\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"333\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3754%;\"\u003e\n \u003cp\u003ePolicy Reform Theme\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6246%;\"\u003e\n \u003cp\u003eMentions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3754%;\"\u003e\n \u003cp\u003e\u0026nbsp; E-Procurement Systems\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.6246%;\"\u003e\n \u003cp\u003e28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3754%;\"\u003e\n \u003cp\u003e\u0026nbsp; Transparency Measures\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.6246%;\"\u003e\n \u003cp\u003e24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3754%;\"\u003e\n \u003cp\u003e\u0026nbsp; Digital Transformation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.6246%;\"\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3754%;\"\u003e\n \u003cp\u003e\u0026nbsp; Training and Capacity Building\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.6246%;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3754%;\"\u003e\n \u003cp\u003eApproval of Threshold Reforms\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6246%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Policy priorities identified through stakeholder consultations. E-procurement systems and transparency measures emerge as the most frequently mentioned reform priorities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe k-means clustering analysis segmented respondents into three distinct groups\u0026mdash; Cautious, Critical, and Progressive\u0026mdash;based on their procurement beliefs, as illustrated in Figure 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5: Machine Learning/Data Analytics\u0026mdash;Cluster Profiles\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8304%;\"\u003e\n \u003cp\u003eTheme/Statement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2042%;\"\u003e\n \u003cp\u003eCluster 0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Cautious)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6471%;\"\u003e\n \u003cp\u003eCluster 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Critical)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3183%;\"\u003e\n \u003cp\u003eCluster 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Progressive)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8304%;\"\u003e\n \u003cp\u003eOpen bidding ensures fairness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2042%;\"\u003e\n \u003cp\u003e4.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e4.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3183%;\"\u003e\n \u003cp\u003e4.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8304%;\"\u003e\n \u003cp\u003eTransparency increased via open competition\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2042%;\"\u003e\n \u003cp\u003e3.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e4.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3183%;\"\u003e\n \u003cp\u003e4.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8304%;\"\u003e\n \u003cp\u003eCorruption less likely in restricted/selective tendering\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2042%;\"\u003e\n \u003cp\u003e2.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e1.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3183%;\"\u003e\n \u003cp\u003e3.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8304%;\"\u003e\n \u003cp\u003eSelective tendering encourages accountability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2042%;\"\u003e\n \u003cp\u003e3.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e1.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3183%;\"\u003e\n \u003cp\u003e4.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8304%;\"\u003e\n \u003cp\u003eProcurement should prioritize inclusive vendor participation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2042%;\"\u003e\n \u003cp\u003e3.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e4.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3183%;\"\u003e\n \u003cp\u003e4.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Machine learning cluster analysis results (K-Means, k=3). Three distinct respondent groups emerged: Cluster 0 (Cautious) shows moderate support for both methods; Cluster 1 (Critical) strongly favors open bidding and transparency; Cluster 2 (Progressive) supports selective tendering for specific contexts while valuing accountability. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 10 below shows equal respondent counts for five agreement levels on three statements: combining open and restricted tendering improves procurement, open competitive bidding remains most effective, and training procurement officials reduces biases and inefficiencies as seen in Table 5.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 12, responses favoring open competitive bidding are concentrated at the \u0026ldquo;Agree\u0026rdquo; and \u0026ldquo;Strongly Agree\u0026rdquo; levels, confirming broad stakeholder support for open competition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of responses favoring restricted/selective tendering across different contexts is presented in Figure 13, which shows that support for restricted methods increases with project urgency and technical complexity.\u003c/p\u003e\n\u003cp\u003eFigure 14 illustrates that most respondents agree or strongly agree that sustainability and economic recovery should guide procurement decisions, highlighting the reform pressure toward greener, more resilient procurement systems.\u003c/p\u003e\n\u003cp\u003eTable 6: Procurement Decision Support Model Parameters and Results\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eParameter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003eValue\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eInterpretation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eTransparency (T)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e4.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eHigh transparency required\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eCost-Effectiveness (C)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e3.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eModerate cost consideration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eDigital Maturity (D)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e4.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eGood digital capability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eSustainability (S)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e3.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eModerate sustainability focus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eUrgency (U)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e4.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eTime-sensitive requirement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eComplexity (X)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e4.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eHigh technical complexity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003eMarket Conditions (Mₖ)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e3.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003eModerate market availability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2344%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Output (\u0026Psi;)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.989%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.285\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.7766%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOpen Competitive Bidding Recommended\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u003c/strong\u003e Decision support model parameters using normalized inputs (0\u0026ndash;5 scale) and resulting procurement method recommendation. With \u0026Psi; = 0.285 \u0026ge; threshold (\u0026theta;), the model recommends Open Competitive Bidding (M = 1).\u0026nbsp;\u003c/p\u003e"},{"header":"5. Discussion ","content":"\u003cp\u003eTo further assess which method respondents viewed as most effective overall, Figure 10 presents a consolidated view of procurement preferences across stakeholder groups. The visualization reveals a strong preference for open competition in transparency-centric environments, while restricted tendering dominates in technically complex or urgent project contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective 1\u003c/strong\u003e focuses on understanding differences between open competitive bidding and restricted/selective tendering. Table 1 and Figure 4 show that open competitive bidding scored highest for fairness (mean = 4.42) and value-for-money (mean = 4.27), affirming H₁ [1], [8]. These findings echo World Bank [7] and OECD [8] recommendations promoting open bidding as a transparency and corruption mitigation tool. Figure 7 reinforces this by identifying open bidding as the most effective method across sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective 2\u003c/strong\u003e addresses context-specific efficiency. Figure 3.1\u0026amp;3.2 and Table 3 show that restricted tendering is preferable for urgent, technically complex projects. These findings align with Essien and Daniel [9], affirming H₂ and the strategic relevance of restricted methods for specialized, time-sensitive processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective 3\u003c/strong\u003e examines contextual variables affecting procurement choices. Figures 5 and 9 and Table 2 highlight regional and cluster-based variations. Restricted tendering dominates complex and urgent environments, whereas open bidding is favored when inclusiveness and oversight are priorities. Machine learning clustering analysis (Table 5) corroborates this, citing time delays and complexity as decision drivers, validating H₃ and reinforcing Transaction Cost Theory [5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective 4\u003c/strong\u003e addresses modern challenges: sustainability and digitization. Digital tools improved transparency (mean = 4.22) as shown in Table 1. Figure 11 confirms rising expectations for procurement systems to promote sustainability and economic recovery [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives 5 and 6\u003c/strong\u003e are supported by reform priorities in Table 4, where e-procurement, transparency, and capacity building were highly emphasized. Figure 8 shows support for hybrid approaches [8]. The feedback from Figures 3, 4, and 5 underscores stakeholder demand for integrated reform and oversight.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMathematical Decision-Support Model\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key innovation is the mathematical decision-support model for method selection:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Psi; = \u0026alpha;₁T + \u0026alpha;₂C + \u0026alpha;₃D + \u0026alpha;₄S \u0026minus; \u0026beta;₁U \u0026minus; \u0026beta;₂X \u0026minus; \u0026beta;₃Mₖ\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhere:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eT\u003c/strong\u003e = Transparency\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eC\u003c/strong\u003e = Cost-Effectiveness\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eD\u003c/strong\u003e = Digital Maturity\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eS\u003c/strong\u003e = Sustainability\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eU\u003c/strong\u003e = Urgency\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eX\u003c/strong\u003e = Complexity\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMₖ\u003c/strong\u003e = Market Conditions\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026alpha; and \u0026beta;\u003c/strong\u003e = weighted coefficients Procurement method M is chosen as:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eM = 1 (Open Bidding) if \u0026Psi; \u0026ge; \u0026theta;; otherwise, M = 0 (Restricted Tendering)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhere \u0026theta; is an institutional threshold typically set at 0.25\u0026ndash;0.35.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing normalized inputs: T = 4.5, C = 3.8, D = 4.0, S = 3.5, U = 4.2, X = 4.7, Mₖ = 3.0 and weights: \u0026alpha;₁ = 0.25, \u0026alpha;₂ = 0.20, \u0026alpha;₃ = 0.15, \u0026alpha;₄ = 0.10, \u0026beta;₁ = 0.20, \u0026beta;₂ = 0.30, \u0026beta;₃ = 0.10:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Psi; = 0.285\u0026nbsp;\u003c/strong\u003e\u0026rArr;\u003cstrong\u003e\u0026nbsp;M = 1 \u0026rarr; Open Competitive Bidding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis model:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBuilds on and enhances the World Bank CPAR (2017), adding dynamic scoring\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eExtends the OECD Integrity Model (2020) with quantifiable AI-ready inputs\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAddresses limitations in Essien and Daniel [9] by including all key variables\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBridges theory and practice with adaptable, evidence-based decision-making\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSupports real-time automation and hybridization\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAligns with evolving global procurement norms\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHypothesis Validation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll three hypotheses were supported:\u0026nbsp;\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003e\u003cstrong\u003eH₁ Supported:\u003c/strong\u003e Open bidding is widely considered transparent and inclusive (mean fairness = 4.42).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH₂ Supported:\u003c/strong\u003e Selective methods work best for technical/urgent projects (mean efficiency = 3.68).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH₃ Supported:\u003c/strong\u003e Market dynamics and complexity drive procurement choices more than method design (clustering analysis shows contextual clustering patterns).\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOpen methods support sustainability and digital transformation better, whereas restricted methods perform better under time and technical pressure. Hybrid frameworks combining both methods\u0026apos; strengths are recommended.\u003c/p\u003e"},{"header":"6. Conclusion ","content":"\u003cp\u003eThis study confirms that procurement method selection must be context-sensitive. Open bidding suits transparent procurements, whereas restricted methods are efficient for specialized needs. Data-driven models support real-time adaptive decision-making. The evidence strongly supports the adoption of context-aware hybrid procurement frameworks that leverage both open and restricted methods based on organizational and environmental factors.\u0026nbsp;\u003c/p\u003e"},{"header":"7. Recommendations ","content":"\u003col\u003e\n \u003cli\u003eAdopt hybrid procurement frameworks balancing transparency and efficiency.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eImplement flexible threshold policies responsive to contextual variables. 3.\u0026nbsp;Mandate and support nationwide e-procurement system implementation.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStrengthen oversight institutions (e.g., the NCPP).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProvide certified training to procurement officers and stakeholders.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eInstitutionalize supplier feedback mechanisms and continuous improvement loops.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIntegrate decision-support models into digital procurement platforms.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePilot the mathematical model across key government ministries, departments, and agencies (MDAs).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEstablish monitoring and evaluation frameworks to track procurement outcomes and model effectiveness.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Abbreviations and Acronyms ","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"517\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eAcronym\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eFull Form\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eCPAR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eCountry Procurement Assessment Report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eMDAs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eMinistries, Departments and Agencies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eNCPP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eNational Council on Public Procurement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eOECD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eOrganisation for Economic Co-operation and Development\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eAHP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eAnalytic Hierarchy Process\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eK-Means\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eK-Means Clustering Algorithm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003eML\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eMachine Learning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2437%;\"\u003e\n \u003cp\u003ee-procurement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.7563%;\"\u003e\n \u003cp\u003eElectronic Procurement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eParticipant Consent Statement All participants involved in this study were duly informed about the purpose, procedures, and nature of the research. Informed consent was obtained from all participants prior to their inclusion in the study. Participation was entirely voluntary, and respondents were assured of the confidentiality and anonymity of the information provided. In cases where direct human participation was not required, or where data were obtained from publicly available or secondary sources, the requirement for participant consent was reviewed and waived by the appropriate ethics oversight in accordance with established research guidelines.\u003c/p\u003e\u003cp\u003eFunding Declaration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e This research received no specific grants from any funding agency in the public, commercial, or nonprofit sectors. This study was self-funded by the research team. No external financial support influenced the study design, data collection, analysis, interpretation, or publication decisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Declaration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration:\u003c/strong\u003e All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Declaration:\u003c/strong\u003e The authors declare that they have no affiliations with or involvement in any organization or entity with commercial interests in the subject matter or materials discussed in this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access Statement:\u003c/strong\u003e Data supporting the findings of this study are available upon reasonable request. Owing to the inclusion of sensitive organizational and professional responses, anonymized datasets can be shared with researchers who obtain ethical approval or permission from the principal investigators. \u003c/p\u003e\n\u003cp\u003e8. Contribution to Knowledge\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study uniquely applies machine learning and contextual scoring to the procurement decision-making process. It benchmarks procurement practices across regions, introduces a dynamic decision-support model, and advances the literature on sustainable and transparent public procurement in Nigeria and West Africa. The mathematical framework provides a replicable, scalable tool for procurement reform across developing nations.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Bank. (2020). \u003cem\u003eProcurement in investment projects: Guidelines\u003c/em\u003e. https://www.worldbank.org/ \u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics. (2023). \u003cem\u003eAnnual procurement report Nigeria\u003c/em\u003e. https://www.nigerianstat.gov.ng/ \u003c/li\u003e\n\u003cli\u003eEssien, J. A., \u0026amp; Daniel, C. (2022). Urgent procurement and restricted tendering: A Nigerian perspective. \u003cem\u003eAfrican Journal of Procurement Research\u003c/em\u003e, 14(3), 156\u0026ndash;178. \u003c/li\u003e\n\u003cli\u003eJensen, M. C., \u0026amp; Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs, and ownership structure. \u003cem\u003eJournal of Financial Economics\u003c/em\u003e, 3(4), 305\u0026ndash;360. \u003c/li\u003e\n\u003cli\u003eWilliamson, O. E. (1985). \u003cem\u003eThe economic institutions of capitalism: Firms, markets, relational contracting\u003c/em\u003e. Free Press. \u003c/li\u003e\n\u003cli\u003eBuchanan, J. M., \u0026amp; Tullock, G. (1962). \u003cem\u003eThe calculus of consent: Logical foundations of constitutional democracy\u003c/em\u003e. University of Michigan Press. \u003c/li\u003e\n\u003cli\u003eWorld Bank. (2020). \u003cem\u003ePublic procurement excellence at a glance\u003c/em\u003e. https://www.worldbank.org/ \u003c/li\u003e\n\u003cli\u003eOECD. (2023). \u003cem\u003eModernizing public procurement: Trends and insights for inclusive economies\u003c/em\u003e. https://www.oecd.org/ \u003c/li\u003e\n\u003cli\u003eEssien, J. A., \u0026amp; Daniel, C. (2022). \u003cem\u003eUrgent procurement and restricted tendering: A Nigerian perspective\u003c/em\u003e. African Journal of Procurement Research, 14(3), 156\u0026ndash;178. \u003c/li\u003e\n\u003cli\u003eEze, V. C., \u0026amp; Okonkwo, U. V. (2023). Digital tools and procurement efficiency in Nigeria. \u003cem\u003eJournal of Public Sector Innovation\u003c/em\u003e, 8(2), 112\u0026ndash;135. \u003c/li\u003e\n\u003cli\u003eAddo, A. (2022). Orchestrating a digital platform ecosystem to address societal challenges: A robust action perspective. \u003cem\u003eJournal of Information Technology\u003c/em\u003e, 37(4), 456\u0026ndash; 478. https://journals.sagepub.com/doi/abs/10.1177/02683962221088333 \u003c/li\u003e\n\u003cli\u003eLungu, M. (2024). Enhancing public service delivery in government procurement: The role of artificial intelligence. In \u003cem\u003eHandbook of public service delivery\u003c/em\u003e (pp. 234\u0026ndash;256). Edward Elgar Publishing. https://www.elgaronline.com/abstract/book/9781035315314/chapter11.xml \u003c/li\u003e\n\u003cli\u003eTan, E., Mahula, S., \u0026amp; Crompvoets, J. (2022). Blockchain governance in the public sector: A conceptual framework for public management. \u003cem\u003eGovernment Information Quarterly\u003c/em\u003e, 39(4), 101\u0026ndash;119. https://www.sciencedirect.com/science/article/pii/S0740624X21000617 \u003c/li\u003e\n\u003cli\u003eTang, Z., Adjorlolo, G., Wauk, G., Sarfo, P. A., \u0026amp; Braimah, A. B. (2025). Evaluating corruption-prone public procurement stages for blockchain integration using AHP approach. \u003cem\u003eSystems\u003c/em\u003e, 13(4), 267\u0026ndash;289. https://www.mdpi.com/2079-8954/13/4/267 \u003c/li\u003e\n\u003cli\u003eGaie, C., \u0026amp; Mehta, M. (2024). Digital transformation of public services: Trends and directions. In \u003cem\u003eTransforming public services\u003c/em\u003e (pp. 178\u0026ndash;203). Springer. https://link.springer.com/chapter/10.1007/978-3-031-55575-6_1 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Procurement Methods, Open Competitive Bidding, Restricted Tendering, Decision Models, Policy Benchmarking, E-Procurement, Sustainability","lastPublishedDoi":"10.21203/rs.3.rs-9282939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9282939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Public procurement methods significantly influence transparency and cost efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To compare open and restricted procurement methods in different contexts. \u003cstrong\u003eMethods:\u003c/strong\u003e An empirical study was conducted through surveys, interviews, and machine learning clustering analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Open bidding was rated high for fairness (mean = 4.42) and value for money (mean = 4.27), and restricted methods favored urgency and complexity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Context-sensitive procurement frameworks combining both methods are ideal. A decision support model was proposed for policy implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlain Language Summary:\u003c/strong\u003e This study compares two methods of public procurement— open competitive bidding and restricted tendering—to determine which works best in different situations. Using survey and statistical data from Nigeria, West Africa, and the UK, this study shows that open bidding is better for fairness and transparency, while restricted methods are useful for urgent or complex projects. 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