Performance risk assessment in public–private partnership projects based on adaptive fuzzy cognitive map
Introduction
Public–private partnership (PPP) is widespread with great interest adopted by the governments from both developing and developed countries [1]. The PPP mode has become popular in the infrastructure development for various benefits, and the most important merit is that it saves the government fund inputs in many ways. The government can concentrate on core competencies [2], [3]. Compared to other procurement modes, the PPP mode enables both public and private sectors to participate at different stages in the entire project lifecycle and share the responsibility and risk. The participation of private sectors can provide professional knowledge, management skills, and technology to utilize the assets, information, and intellectual property, which would significantly improve efficiency and quality of the PPP project [4]. However, due to various participants, huge scale, and large investment involved, the resulting high complexity gives rise to the difficulty of PPP project management [5], [6]. The PPP mode demands an efficient way to assess and manage project performance and the associated risk.
With the widespread of PPP projects, various matured methods have been developed to assess and manage project performance and risk, such as the performance evaluation form, the balanced scorecard (BSC), key performance indicator (KPI), and analytic hierarchy process (AHP). These methods enable the improvement of project management. For instance, the performance evaluation form plays a fundamental role in numerous performance assessment methods. BSC quantifies the indicators and organically combines the project goal with performance evaluation systems [7]. The application of BSC enables a comprehensive evaluation based on both long-term and short-term goals and evaluates the performance of every project department from both financial and strategic aspects. Wu and Wu [8] used BSC to combine financial variables with non-financial variables for assessing PPP performance. KPI decomposes and analyzes the goal in order to identify key indicators in order to make an objective evaluation of project performance [9]. Lu and Chu [10] used KPI to evaluate the pension institution in PPP projects, where the target was decomposed to build a KPI conceptual model. AHP is especially useful for simplifying complex projects with various intangible indicators. Hossain et al. [11] used Bangladesh as a case to evaluate PPP performance risk. The AHP method has been employed to resolve a complicated project with weights and hierarchies [12]. Though the aforementioned techniques are widely used in project performance risk analysis and assessment, there are still some limitations that restrict the accuracy and efficiency in project performance risk assessment. One significant drawback lies in the fact that they concentrate on the structure and measurement of the indicators and cannot consider internal relationships among indicators [13], [14]. In addition, the assessment by using KPI or AHP is static in nature, and they had limited capability to predict and diagnosis the magnitude of performance risk over time [15]. More specifically, the application of KPI requires to assess the variables that can affect the project performance based on the current status, and its result aims to provide the performance of the project at the time that the information is collected [16], [17]. Liu et al. [18] indicated the KPI method only gave lag indicators that could not improve the project performance during the project process. A main drawback of AHP is that the structure of the model depends on the judgment of the experts, which results in the subjectivity on the judgment of casual relationships [19]. Indeed, internal causal relationships among variables related to project performance are generally ignored in those traditional methods. Wee et al. [20] claimed that the existence of causal relationships played an important role in root cause analysis in a complex system. Actually, how to define causal relationships among relevant variables is a challenging problem which is associated with uncertainty.
Soft computing is a kind of methods that can calculate and solve complex problems with a tolerance of imprecision and uncertainty [21], [22]. Fuzzy Cognitive Map (FCM) is one of the widely used soft computing methods [23], which is an extension of the traditional cognitive map. A cognitive map is able to simulate the human thinking system, and Konar [21] introduced step-by-step procedures on how to construct a cognitive map model for artificial intelligence applications, such as autopilots. FCM is a combination of the cognitive map and the fuzzy logic, where the fuzzy logic improves the reliability in the fields with a high level of uncertainty and complexity [24]. FCM is capable of using subjective and vague linguistic variables which are collected from domain experts and modeling dynamic and complex systems that have numerous indicators, vectors, and weights [22]. FCM produces the fuzzy weighted digraph models which can perform what-if scenario analysis that could be used in not only evaluation but also prediction [23]. The impact of the pending alternative strategies or policies can be then tested and analyzed for optimization purposes [25]. However, there is a high level of uncertainty in building casual structures, particularly when a large number of variables are involved. At the same time, the weights representing the strength of causal relationships among various variables are uncertain in determination, where the criteria to justify a robust casual model is always vague in nature [22], [26]. In order to address this issue, this paper attempts to propose a hybrid soft computing approach by merging FCM and Structural Equation Modeling (SEM), where SEM serves as a data-driven approach to learn internal relationships from given data in a robust manner.
SEM is a multivariate statistical analysis method that is able to analyze structural relationships [27]. It includes confirmatory factor analysis, confirmatory composite analysis, path analysis, partial least squares path modeling, and latent growth modeling [28]. It can estimate the coefficients of the regression relationships in a complex model, which compares the importance of all the variables and causal relationships [29]. SEM has been widely used in analyzing performance risks in construction projects [30], [31], [32]. For instance, Wu et al. [33] used SEM to perform the prospective safety performance evaluation on construction sites, where the results provides insights into cause–effect relationships among safety performance factors and goals. Durdyev et al. [34] used SEM to analyze the influence of the factors in project management. de Carvalho and Rabechini Junior [35] employed SEM to conduct a study to test the impact of the project management effectiveness on project success. SEM displays strength in modeling multiple relationships among indicators and measuring the error of the relationships [36], which can meet the requirements of the construct of an FCM model. SEM contributes to identifying causal relationships between the factors and evaluate the importance of the relationship based on the given data. The combination of FCM and SEM can simplify the complexity of the modeling process and improve the accuracy of the finalized model.
The main research questions are (1) How to learn internal structures that illustrate casual relationships between the performance risk and relevant variables? (2) How to ensure the reliability of the estimated weights in the built FCM model? (3) How to perform predictive and diagnostic analysis under the observed information? The main objective of this research is to develop a robust model that merges the advantages of SEM and FCM to assess performance risk in PPP projects under various conditions. This research contributes to the state of knowledge by proposing an adaptive FCM model that can learn the structure and casual relationships from the given data and perceive performance risks that are subjected to uncertainty and subjectivity through what-if scenario analysis. Based on the well-verified model, the predictive, diagnostic, and hybrid analysis can be performed to discover the tendency of the performance risk and the associated sensitive variables. The developed model can also be used as a decision tool to monitor and control performance risk, where the optimal strategy can be proposed to mitigate performance risk in case an unsatisfactory performance is observed.
The rest of the paper is organized as follows. In Section 2, a hybrid soft computing approach that integrates SEM and FCM is proposed with detailed step-by-step procedures. In Section 3, a model that represents complex casual relationships for performance risk in PPP projects is established. In Section 4, based on the established model, the results are analyzed from multiple perspectives, including predictive, diagnosis, and hybrid perspectives. In Section 5, the developed adaptive FCM model is applied in a specific case study for further validation. In Section 6, the conclusions and future works are drawn up.
Section snippets
Methodology
There is no generally recognized ontological modeling method in performance risk assessment [37], particularly in PPP projects due to underlying great complexities, such as large investment, long periods, and multiple participants [5], [38]. In order to consider causal relationships and evolutional dynamics in the assessment of the performance risk in complex PPP projects, a hybrid soft computing approach that integrates SEM and FCM is proposed. Fig. 1 presents the workflow of the proposed
Data collection
Success has been always the ultimate goal of every activity for a construction project. A number of variables to the success of PPP projects have been explored by various researchers. For instance, Zhang [63] reported five important variables that are relative to the success of the PPP project, namely economic viability, risk allocation and contract, financial package, and organizations with strong technology and environment. Ng et al. [64] summarized a total of 36 critical variables impacting
Analysis of results
The condition of the PPP project changes frequently and the prediction of future performance is important for project management. Therefore, it is necessary to study the cause–effect mechanism of the PPP project and analyze how the project performance evolves according to the change of project conditions. Based on the developed FCM model, three sorts of analyses, including predictive, diagnostic and hybrid analysis, are performed to support PPP project performance risk analysis.
Discussions
In order to testify the applicability of the developed adaptive FCM model, an independent PPP project, which is the 7th Jianghan bridge located in Wuhan, China, is used for demonstration purposes. This project is outside of the list of the surveyed projects, and it is expected that the well-verified FCM model in the last section could help provide insights into a better understanding of performance risk assessment and control in this independent project. This project has a large investment of
Conclusions and future works
Due to great complexities in PPP projects, including large investment, long periods, and multiple participants, it is a challenging problem to model the performance risk and propose responsive strategies in case of undesired project performance. A hybrid soft computing approach that integrates SEM and FCM is proposed to assess the performance risk in PPP projects. Based on the internal relationship structure of the learned SEM model, an adaptive FCM model is developed to model the PPP project
CRediT authorship contribution statement
Hongyu Chen: Writing - original draft, Methodology, Visualization, Investigation, Validation, Formal analysis. Limao Zhang: Conceptualization, Supervision, Methodology, Writing - review & editing, Funding acquisition. Xianguo Wu: Supervision, Methodology, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
The Start-Up Grant at Nanyang Technological University, Singapore (No. M4082160.030) and the Ministry of Education Tier 1 Grant, Singapore (No. M4011971.030) are acknowledged for their financial support of this research.
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