Ghorbany, S; Yousefi, S and Noorzai, E (2024) Evaluating and optimizing performance of public-private partnership projects using copula Bayesian network. Engineering, Construction and Architectural Management, 31(1), pp. 290-323. ISSN 0969-9988
Abstract
Purpose: Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures. Design/methodology/approach: The literature review was used in this study to extract the PPPs KPIs. Experts' judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network. Findings: The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework. Practical implications: Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs' critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs' performance management that can be used to develop management systems in future research. Originality/value: For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs' behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | integration; optimization; project management |
| Index terms: | integration, methodology, programming, questionnaire, machine learning, infrastructure construction, financing, feasibility study, compliance, manager, interview, sensitivity analysis, management system, literature review, risk identification, effectiveness, statistics, option, project management, performance management, causal structure, judgment, key performance indicator, project manager, private sector, partnership, bayesian network, public sector |
| Subjects: | artificial intelligence, data analysis and analytics, project delivery, economic analysis, design stages, programming, practitioner, dispute resolution, data collection methods, profession, mathematical modelling, administrative law, industry analysis, environmental hazards, partnership management, organizational analysis, probabilistic model, decision analysis, health safety and environment, financial risk, project management theory and practice, management, structural engineering, performance measurement, research methods, performance management, civil engineering |
| Topics: | Organizational Design, Quality Management, Digital Applications, Design Practice, Legal Issues, Sustainability, Stakeholder Management, Roles and Professions, Risk Management, Engineering Principles, Project Management, Research Practice, Health and Safety, Cost Management, Business Strategy |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here