Abstract
Public-private partnership (PPP) has increasingly been used in community infrastructure projects in recent decades. Many studies have been done in identifying critical success factors (CSFs) in PPP projects; however, few were focused specifically on the operational phase. As the success of PPP projects greatly relies on the operational performance, precise analysis of the influencing factors at an early stage of the project is an important task. In this study, the CSFs were identified in the operational PPPs through an extensive literature review. The importance weight or impact of each CSF was determined based on linguistic description of the factors in the selected literatures. The importance of each CSF was calculated through the fuzzy analytic hierarchy process (AHP) approach. The capacity and commitment of project participants were recognized as the most important factor, followed by the alignment of interest, mutual trust, and collaboration between public and private sectors. Presence of conducive and enabling legal and regulatory framework, as well as appropriate risk allocation also plays an important role in improving performance of operational PPPs.
Keywords: public-private partnership, critical success factors, operational performance, analytic hierarchy process, value for money
Introduction
Public-private partnership (PPP) has been increasingly used in community infrastructure projects in the world during recent decades. In the partnership, the private sector invests in infrastructure, provides goods and services for public sector, and gains profit from the project. The government retains responsibility for the delivery of services, meantime saves a great deal of money by Private Financing Initiatives (PFI) (Spackman, 2002). The relationship between the public sector and the private sector is governed by long-term contracts.
A number of studies have been done about the factors that lead to success in PPP projects. Elements such as financial risks, potential schedule delays, type of contract, etc. were identified as the CSFs in PPP projects (Diekmann & Girard, 1993; Gordon, 1994; Zhang, 2005). However, as the project proceeds from the initiation stage, all the way through tendering stage, construction stage, to operational stage, the impact of these CSFs will be different. For instance, potential schedule delays will be a tremendous risk in the construction phase, whereas it will not be an issue in the operational phase.
For the infrastructure projects, planning, tendering, and construction take a small portion of time within the project life span. After completion of construction, projects stay in the operational phase for a long time. Moreover, user satisfaction of the project is primarily determined by the long-term operational performance. In the UK, a database of PPP projects was maintained by Partnership UK. Within 695 PPP projects, approximately 500 are in the operational phase at present. Along with the increasing use of PPP in community projects, identifying factors and their impact on PPP projects in the operational phase has been increasingly important.
Statistical analysis of 450 operational PFI projects in the UK showed that one third of the PFI projects are regarded as “not satisfactory” in the operational performance, assessed from level of user satisfaction, value for money for the public sector, and how far the project was delivered to expectation (PUK report, 2006) In order to improve the performance of operational PPP, identifying the factors leading to project success is substantial.
In addition, the long-term nature of operational PPPs brings complexities into management. The uncertainty caused by long-term operation affects the quality of service delivered, as well as user satisfaction, namely, the performance in operational PPPs. For instance, a change of service provider is not unusual in projects such as in the water sector, which may affect the standards of service delivered.
In order to cope with the challenge in long-term operational PPPs, and improve their performance, the general factors that are critical to the success of the projects have to be identified, categorized, and assessed (Zhang, 2005). The identification of the CSFs will benefit the resource allocation to efficiently improve the performance of PPP projects (Zhang).
A number of studies have been done to conclude CSFs throughout the project life cycle, not confined to the operational phase. In this study, CSFs that do not pertain to the operational phase are filtered out, leaving the factors closely related to the operational performance. From the literature review, several CSFs in the operational phase were identified, such as legal and contractual framework, risk allocation, communication, capacity of project participants, etc. (Chua, Kog, & Loh, 1999; Van der Heijden, 2004; Southwood 2004; Zhang 2005).
In the UK, the performance of more than 450 PFI projects during their operational phase was reviewed. The PFI projects cover a range of sectors, including transport system, hospital projects, school projects, accommodation and training facilities, etc. The overall CSFs, as well as sector-specific CSFs were identified, and verified by industrial survey (PUK report, 2006). The identified CSFs included payment mechanism in the contract, flexibility of the contract, benchmarking and reviewing system, as well as communication and relationship between private and public sectors.
In this study, the critical factors from different literatures were filtered to be confined into operational phases. The CSFs were summarized and integrated in Table 1. However, in order to achieve efficient allocation of limit resources in a project, identification of CSFs is far from sufficient. For instance, effective stakeholder management and performance reviewing system are both important to the performance of operational PPPs; however, the impacts of the two factors on the project are different. With the limited resources of the project, more attention has to be drawn on the factor that has a larger impact on the project.
Table 1: Summary of the Attributes Associated with Measuring Value for Money in PPPs
| Attributes | Description | Sources |
| Project characteristics | Chua et al. (1999) | |
| Presence of a conducive and enabling legal and regulatory framework | Presence of a conducive and enabling legal and regulatory framework is the prerequisite to the success of the project. The legal and regulatory framework works as the last resort of dispute, and is able to reduce opportunistic tendencies. | Charles (2006); Jefferies (2006); Edwards, Shaoul, Stafford, and Arblaster (2004); Van der Heijden (2004) |
| Training and support | Training and support is necessary to ensure the product properly delivered to the end-user, and help improve user satisfaction. | Charles (2006); Jamali (2004); GAO (2003) |
| Measuring, monitoring, and reporting system | Monitoring and performance reviewing system should be incorporated in operational PPPs to regularly check the status of the project and identify potential problems. | Asian Development Bank (2008); GAO (2003); Van der Heijden (2004) |
| Complementary skills and dependency of resources | Complementary skills and dependency of resources will be beneficial because it enhances cooperation and avoids potential competitions. They ensure the mutual involvement of each party in the project. | Jamali (2004); GAO (2003); Edwards et al. (2004); Van der Heijden (2004) |
| Contractual arrangements | Chua et al. (1999) | |
| Contract guide | A simplified contract guide is useful due to frequent reference to the contract. Contract guide is particularly useful when there is high turnover of staff. | GAO (2003) |
| Payment mechanism | Payment mechanism provides incentives to private sectors, which motivates the private sectors to achieve the success of the project. | Partnerships UK (2006); GAO (2003); Edwards et al. (2004) |
| Formal handover | A formal handover process is found to be positive on the performance of operational PPPs. At the end of the construction phase, the project structure and organization will change. A formal handover process will provide support for the operation teams. | Partnerships UK (2006) |
| Risk allocation | Effective risk allocation between private and public sectors makes better value for money for the project. The principle of risk allocation, i.e., allocating risk to the party who is best able to manage it, requires highly collaboration and mutual trust. | Jefferies (2006); Partnerships UK (2006); Edwards et al. (2004); Van der Heijden (2004) |
| Project participants | Chua et al. (1999) | |
| Participants' capacity | Each participant should bring competent skills and ability to the partnership in order to perform and manage the project in an appropriate way. | Asian Development Bank (2008); Charles (2006); Jefferies (2006); Partnerships UK (2006); Edwards et al. (2004) |
| Goal compatibility | Alignment of interest and share of partnership vision helps collaboration and ensures fit of strategy in the long run. | Charles (2006); Jamali (2004) Partnerships UK (2006); GAO (2003); Edwards et al. (2004) |
| Commitment | The commitment of project participants permits them to improve their capability and stimulates innovation. Commitment also ensures meaningful involvement of each partner in the project. | Jamali (2004); Jefferies (2006); Partnerships UK (2006); GAO (2003); Edwards et al. (2004) |
| Working culture | The existence of working culture motivates people in the project and helps establish commitment of the team. | Jamali (2004); Jefferies (2006); Van der Heijden (2004) |
| Interactive processes | Chua et al. (1999) | |
| Ability to manage variation | The long term nature of operational PPPs often leads to changing circumstances or factors. The partnership should allow the flexibility to change project scope, and have the ability to manage variation. | Asian Development Bank, 2008 Partnerships UK 2006 Edwards 2004 Van der Heijden 2004 |
| Stakeholder management | Effective public-private partnerships should incorporate proper level of stakeholder management, including identifying interests from all stakeholders and adjusting management strategy to reflect concerns of stakeholders. | Asian Development Bank (2008); Jefferies (2006); Partnerships UK (2006); Edwards et al. (2004) |
| Communication | The partnership should be grounded in open communication in order to settle problems promptly and improve the long-term relationship and mutual trust. | Jamali (2004); GAO (2003); Edwards et al. (2004) |
| Relationship and mutual trust | Relationship and mutual trust are important particularly in operational PPPs due to their long-term nature. Good relationship between public and private sectors will have a positive impact on problem solving and significantly reduce disputes. | Partnerships UK (2006); GAO (2003); Edwards et al. (2004); Van der Heijden (2004) |
With the CSFs identified either by literature review or by expert opinions, the impact of each CSF on the project performance has to be measured quantitatively (Chua et al. 1999). The report of Partnerships UK (2006) identified some of the CSFs in operational PPPs, and statistically analyzed the correlation of CSFs with project performance, but it provided only evidence of impact on performance, without quantified degree of impact.
In this study, after extensive literature review, linguistic assessment has been applied to determine the relative importance weight of each CSF. The importance of each factor was extracted from the literature and represented in the form of fuzzy numbers. The fuzzy numbers were used in multi-attribute decision-making (MCDA) approaches, to derive the impact of each CSF and generating the importance weight.
In previous studies (Chua et al., 1999; Chin, Pun, Xu, & Chan, 2002; Zhang, 2005), the analytic hierarchy process (AHP) was widely adopted to analyze and synthesize CSFs together for performance improvement. In AHP, an overall goal is set before analysis, and criteria in judging the alternatives are organized in a hierarchical form. Pairwise comparisons are taken between criteria/subcriteria in a ratio scale, and the comparison scores are synthesized to figure out the best option. The benefit of AHP exists in the accuracy and efficiency in finding the relative priority of the needs in the hierarchy (Hepler & Mazur, 2007). However, it was criticized for the imprecise judgment of the comparison ratios (Leung & Cao, 2000).
Fuzzy AHP approach is one of the approaches to solve the imprecise judgment problem of AHP (Kwong & Bai, 2002). Kwong and Bai (2002) combined triangular fuzzy numbers with conventional AHP to improve the imprecise ranking. The benefits of fuzzy AHP approach exist in accommodating the fuzzy nature of human judgment, as well as extending freedom of estimating the weights in AHP. In this study, their approach was extended to assess the importance and generate the weights of CSFs in the operational PPPs.
Rationales in Public-Private Partnership
After World War II, most governments in the world delivers public infrastructure and services such as transport, telecommunication, health, education, and defense projects by state owned monopolies or other public sectors (Grimsey & Lewis, 2002; Harris, 2003; Charles, 2006). The traditional mode of procuring and delivering public infrastructure and services is that government builds or purchases the physical assets using public sector employees or private contractors (Charles, 2006). In this mode, the build, procurement, and execution from public sector is proven inefficient, costly, suffering from corruption, overstaffing, mismanagement, and stagnation (Harris, 2003).
The traditional procurement method becomes increasingly unviable in terms of endemic budget deficits especially in developing countries. From a financial point of view, public financing fails to provide value for money due to waste of resources, lack of capacity, as well as improperly managed risks. Public financing also leads to inefficiency in service provision and price determination, as the monopoly nature of public provided goods excludes market mechanism from effective resource configuration (Charles, 2006).
PPP is a new concept that replaces the roles previously played by public sectors with the private sectors to ensure efficiency in project delivery and value for money (Grout, 2003). PPP has been recognized and accepted as a key mechanism of public policy, and has increasingly become the preferred procurement method in public service delivery (Charles, 2006; Osborne, 2000). PPP refers to the procurement model that private sector obtains franchising to provide public services on behalf of government, and public sector becomes the purchaser of the services (Grout, 2003; Regan, 2005).
The PPP model is preferred in infrastructure projects due to its good value for money. The involvement of private funding leads to lower construction and operating costs than comparable public sector projects. Also, the integration of design, operation, and maintenance in a single project finance package improves the performance over the life cycle of the project. The partnership between private and public sectors reduce the costs related to risks as well. The risk associated with the project can be allocated to the party that is able to manage it, therefore it improves risk management. Moreover, the deployment of private sector capital remarkably improves value for money due to the incentives of the projects. The right commercial decisions are made about the design, operation, life-cycle asset management, etc. on behalf of the interest of the private sector, and improves the long-term performance in delivering public service as well.
The preference of PPP also exists in political perspective. PPP projects operate neither nationalized nor privatized; they work at the boundary of the public and private sectors. The collaboration between the public sector and private sector can avoid some political concerns, and can provide functions that solely the public or private sector cannot.
The essence of PPPs exists in that public sector franchises private sector to finance and construct infrastructure, and purchases the services from private sector (Grout, 2003; Regan, 2005). In other words, the end-product is the services in PPP projects, rather than the infrastructure in traditional public financing projects. As a customer of service instead of a service provider in PPP projects, public sector will focus more on the quality that the service delivers, which is by and large affected by the operational performance of the project.
Criteria for Assessing Value-for-Money in Operational PPPs
Projects will provide services and create value only in the operational phase. The performance of operational PPPs affects the service quality, user satisfaction, and, ultimately, value for money. To identify the factors that affect the performance of operational PPPs, an extensive literature review has been done and criteria for assessing value for money in operational PPPs were concluded from different sources.
The report from the confederation of the British industry in 2003 demonstrated that competition, especially in the private sector, can play a constructive role in PPP service delivery. They found that a number of local authority markets are not constructed and the local government is incompetent to manage markets. Southwood (2004) suggested that in PPP, the private sector has to understand the nature of public service, public accountability, and the local democratic process. Van der Heijden (2004) identified key risks to PFIs, including inadequate project organization, lack of communication plan, inadequate change management, lack of culture supporting and unforeseen dependencies, poor people management, etc. Danny Ertel (2005) emphasized the collaboration and shared responsibilities between partners in PFI projects.
Wouters, Kokke, Theeuwes, and Donselaar (1999) figured out that operational performance measures are most importance for financial performance in the transportation and distribution sectors. They identified critical operational measures by the following procedures. They first applied one-factor correlation analysis between financial performance measures and operational performance measures to identify correlated operational measures. Subsequently, they compared the operational measures between financially well-performing companies and companies with poor financial performance. This enabled the impact of each operational measure to be identified. Expert opinions were incorporated to segment the operational measures further.
In the United States, Peters and Philips (2004) investigated the success of the Mectizan Donation Program (MDP) in the public health field and identified potential success factors for governance and management of PPPs. They performed factor analysis from the survey of 25 partners in MDP to identify the factors that affects partner's perception, and found that the factors affecting the positive perceptions include the involvement of senior leaders from each sector, the alignment of interest with partners, balancing long-term vision with clarifying roles, and management of coordination, as well as professionalism and accountability.
In the UK, Partnerships UK (PUK) (2006) investigated the operational performance of PFI projects from surveying 105 PFI projects in the operational stage with the fields among the Department of Health, Department for Transport, Department for Education and Skills, and the Ministry of Defence. Most of the projects are regarded with “good performance,” with respect to user satisfaction, Payment Mechanism, Change Mechanism, Benchmarking and Market Testing, Monitoring and Governance, Communications, Relationships, Dispute Resolution, as well as Training and Support.
In the UK, Edwards et al. (2004) evaluated the operation of PFI in roads and hospitals. They concluded that three major factors affects performance of PFI. For partnership and contractual issues, partnership with implications for monitoring and accountability relationships is aspired to rather than the actual working relationship; planning of the performance monitoring system is often poor; self-monitoring system require high degree of trust; subject performance measurement causes monitoring difficulties; and the contingency plan should be prepared for all major PFIs. In terms of value for money and risk transfer, the soft objectives are often immeasurable and incomparable with respect to performance evaluation; additional monitoring costs to public sectors reduces value for money; when risk is shared between partners, the unclear allocation of risks cause uncertainty. Financial reporting and accountability is often unavailable to the public, which leads to difficulties in verifying value for money. They also concluded that the risk in many PFI projects does not appear to be transferred to the party best able to manage it.
In the US, the report on long-term performance of PPP projects for the Department of Defense (DoD) from the US General Accounting Office (GAO, 2003) concluded a number of success characteristics that partnerships need to achieve success, includes long-term relationship and commitment, shared partnership vision and objectives, the right metrics and incentives, complementary skills and abilities, sound business case analysis, mutual trust and shared risk, flexibility to change partnership scope, balanced workload, independent review and oversight, enforce partnership decisions and requirement, full coordination with all stakeholders, as well as clearly documented objectives in the partnering agreement. They recommend the DoD to establish overarching goals for expected outcomes from its partnering initiative, refine current metrics for measuring partnership benefits, and require specific assessment and planning for new capability where partnerships are expected for new systems.
The PPP handbook published by the Asian Development Bank (2008) referred to key elements in operational phase PPP projects including stakeholder management, the capacity of both public and private sectors, and the ability to manage variation. Charles (2006) reviewed the CSFs in PPP projects, and proposed that the presence of a conducive and enabling legal and regulatory framework is the critical prerequisite for the success of PPP projects. In addition, other CSFs that affect operational phase performance includes goal compatibility, capacity of partners to execute their roles, and greater education and sensitization of stakeholders.
Jamali (2004) concluded the experience of PPP projects in Lebanon, and identified success and failure mechanisms of PPPs in developing countries. The necessary precursors for success in PPP projects includes the commitment of government, the capacity of public sectors, transparent and sound regulatory framework, as well as the review and monitoring framework. In addition, key formation requirements of PPP projects were identified such as: resource dependency, commitment symmetry, common goal symmetry, intensive communication, alignment of cooperation learning capability, converging working cultures (Jamali 2004; Samii et al., 2002). Chua et al. (1999) concluded the success factors of a construction project from four perspectives, namely project characteristics, contractual arrangements, project participants, and interactive processes (see also Zhang, 2005).
Based on the above review, it is evident that operational performance of PPP projects is an important consideration, yet an adequate instrument for the same is not quite available. In an attempt to establish a benchmark for the measurement of the operational performance of such projects, based on an extensive review of the scientific literature, this study summarized the CSFs under the four perspectives, as shown in Table 1.
Research Framework and Hypothesis
The life cycle of a typical infrastructure project can be divided into initiation, planning, tendering, construction, and operation. In the five phases of the project life cycle, the operational phase takes the longest time and is the phase that is most related to user's perception of service quality. The operation phase is also the only phase in the life cycle that generates a revenue stream. Therefore, the performance of operational PPPs is substantial from both the project owner and end-user's perspective.
In this study, emphasis was put on the operational performance of PPP projects. Unlike the performance in the tendering phase, which is primarily focused on time, cost, and quality, performance in the operational phase is measured in service quality, customer service, customer satisfaction, and affordability (Mandri-Perrott & Cledan, 2009). Key performance indicators (KPIs) are often used to quantitatively measure the performance of the operational PPPs. For instance, for the water sector projects, the operational performance can be measured in terms of the coverage of the service, the reliability, numbers of complaints received, and the prompt response to complaints and requests, as well as the affordability of the project.
In order to improve the performance of operational PPPs, CSFs have been identified from an extensive literature review. However, the relative importance of the CSFs and how they will affect the operational performance of PPPs remain unknown. The main objective of this study is to provide a reasonable ranking of the CSFs, analyze the impact of the CSFs on the operational performance, and ultimately form a code for best practice in operational PPPs.
Fuzzy Analytic Hierarchy Process
With the CSFs identified in the previous section, the objective of this study is to rank the CSFs related to the operational performance. In this study, a formal multicriteria decision making model—AHP is used for ranking CSFs.
The AHP was developed by Saaty in 1986. As a powerful and flexible decision-making process, it enables setting priorities among different attributes in a hierarchical structure (Salmeron, 2005). In the rationale of AHP, the nature of decision-making involves three essential steps: analysing, prioritizing, and synthesizing (Saaty, 1986). Analysing, as in the first letter of the acronym AHP, which means to decompose complex problems into pieces simple enough for people without formal training to understand and participate. Hierarchical structuring is a natural way of thinking complex problem in human thought, where the complex problem can be decomposed into homogeneous clusters of factors for people to understand and cope with. Prioritizing is the process of quantifying the factors in a problem, by which they can be incorporated into formal models. Synthesis is the essence of multiple objective analyses, and also is where the power of AHP exists in. The hierarchical structure of AHP not only facilitates analysis, but also, more importantly, it plays a substantial role in measuring and synthesizing the multifaceted aspect of a complex problem.
Hierarchical Model for AHP Analysis
The AHP hierarchy for optimizing the performance of operational PPPs was formulated from the literature review. The hierarchy classified the CSFs into four levels depicted in Figure 1. The first level of the hierarchy is the objective of the assessment process, which is to optimize the performance of operational PPPs. The objective can be divided into four perspectives, namely project characteristics, contractual arrangement, project participants, and interactive processes (Chua, 1999; Zhang, 2005). The project characteristics describes the factors associated with the configuration and organizational framework of the projects, including well-established legal and regulatory framework, training and support system, monitoring and reporting system, as well as the complementary skills and resources between public and private sectors. Contractual arrangements describes the factors with respect to contractual management, including providing a contract guide, the payment mechanism in the contract, formal handover process, as well as the risk allocation defined in the contract. In the criteria of project participants, the characteristics of project participants are included, such as the capacity and commitment of participants in the projects, their compatibility of goals, and respective working culture. The interactive process describes the interaction between public and private sectors, such as communication, relationship and mutual trust, stakeholder management, and the ability to manage variation.
Figure 1: Hierarchy for Optimizing Performance of Operational PPPs
Pairwise Comparison
Another key success factor of AHP is the preference determination between alternatives. In the prioritization process, pairwise comparisons are used to determine the preference of options. For each criterion, the decision-maker compares two alternatives using a ratio preference scale, which assigns numerical values to different preference level (Ozdagoglu & Ozdagoglu, 2007; Taha, 2003). The classical preference scale for AHP is the 9-point scale, which assigns 1-9 points from “equal importance” to “extreme importance”. For instance, the value 9 indicates that one factor is extremely more important than the other, and as a ratio preference scale, the value 1/9 indicates that one is extremely less important (Sarkis & Talluri, 2004).
In Steven's measurement classification scheme, four levels of measurement, nominal, ordinal, interval and ratio, were identified with the accuracy from lowest to highest, respectively. The high level measurement contains all information in low level, plus additional meanings. In AHP, the pairwise comparison between attributes is based on the ratio scale priorities, which enables retaining information in the hierarchical organization.
Being widely adopted in decision-making, AHP is accurate and efficient in finding the relative priority of the needs in the hierarchy (Hepler & Mazur, 2007). The ratio scale of AHP provides mathematical basis for its accuracy, and the pairwise comparison improves the capacity of decision-making by resolving “survey fatigue” (Hepler & Mazur, 2007). In addition, comparing other multicriteria decision-making approaches, the dataset required for AHP is much smaller for the abundant information conveyed by ratio preference scale.
However, the imprecise judgment of the comparison ratios was addressed as a problem in AHP (Leung & Cao, 2000). In most decision-making cases, not all attributes can be precisely assessed. Humans tend to process qualitative information more efficiently, while relatively unsuccessful in making quantitative predictions (Kulak & Kahraman, 2005). The uncertainty in judging preferences will influence the ranking of alternatives, and raise the difficulty in determining consistency of the preference (Leung & Cao 2000; Ozdagoglu & Ozdagoglu, 2007). In order to incorporate uncertainties in preference judgment, many approaches were proposed to integrate the principle of fuzzy sets into AHP.
Rationale of Fuzzy AHP
The fuzzy AHP technique is an advanced extension to traditional AHP (Ozdagoglu & Ozdagoglu, 2007). In traditional AHP, the weight of each criterion is determined by ratio-based scoring from either expert opinion or objective measurements. The information of scoring each criterion is derived from human preferences and judgment, which is normally represented in linguistic patterns, e.g., A is important than B, or A and B are equally important. The vague linguistic patterns are then quantified into the 9-point ratio scale, and used in the traditional AHP method. However, the procedure of converting linguistic description into the 9-point ratio scale makes the AHP method imprecise, as the crisp (non-fuzzy) measurement of linguistic patterns leads to a very unbalanced scale of judgement (Ozdagoglu & Ozdagoglu, 2007). A better representation of the linguistic patterns is to use fuzzy system.
In this study, the impact of the attribute on the operational performance was summarized into five levels, from irrelevant to of extreme importance (Table 2). In traditional AHP, the five levels will be quantified into a scale of 1-5, where the impact of an attribute with number 2 is twice important as with number 1. Such quantitization method is obviously imprecise, because the linguistic description of impact is not a definitive importance measurement.
Table 2: Linguistic Description of the Impact on the Operational Performance
| Level | Keywords |
| 1 | Insignificant, trivial, slight |
| 2 | Potential, possible, probable |
| 3 | Important, influential, effective |
| 4 | Significant, prominent, remarkable |
| 5 | Crucial, critical, essential |
In the fuzzy AHP, triangular fuzzy numbers (TFN) 1-5 are used to represent the impact of the attribute on the operational performance, as shown in Figure 2. For instance, the adjective “important” is assigned with a TFN of (2,3,4), which means that the impact of the attribute is measured as a probability distribution rather than single real value. In the case of TFN (2,3,4), the impact of the attribute has a probability distribution with non-zero value within (2,4), and the maximum probability at 3.
From the literature review, the attributes identified in Table 1 can be categorized into TFN 1 to 5 in terms of the linguistic descriptions.
Figure 2: The Membership Function of Triangular Fuzzy Numbers
TFN is a special fuzzy set represented with three points: The representation is interpreted as membership functions:
Alternatively, the TFN can be characterized as a crisp interval by α-cut operation:
The main operation on TFN can be defined by α-cut operation (Kaufmann & Gupta, 1991):
Fuzzy AHP Algorithm
The major steps to implement a conventional AHP include (Cheng, Yang, & Hwang, 1999):
- Break down the complex problem into multiple attributes/criteria, and structure them in a hierarchical form.
- Make the pairwise comparisons among each criterion and alternative, and assign ratio scale scores for each comparison.
- Evaluate the relative weights of the criteria using the eigenvalue method.
- Aggregate and synthesize the relative weights together to obtain the overall weights of criteria for the decision-making among the alternatives.
Data Gathering and Analysis
A number of studies have been done on performance of PPP project, and multiple factors have been emphasized to improve the performance of PPP projects based on the data from wide survey and expert opinion of industry. The rich information conveyed by the literature provides not only the CSFs of operational PPPs, but also a basis for ranking the factors according to their relative importance.
From the extensive literature review, the factors affecting operational PPP performance has been summarized in Table 1. In order to measure the degree of impact on the performance, the importance has to be set up for each criterion quantitatively. The importance of the criterion can be determined from the attitude of the author to the criterion, according to the linguistic description in the literature
Among the articles in the literature review, eight papers that are closely related to the operational performance of PPPs were selected, denoted as S1-S8. According to the contents of the paper, the author's attitude to each criterion was categorized into one of the five levels in Table 2, and assigned with a TFN accordingly (Table 3).
Table 3: List of Author's Attitude and Corresponding TFNs
| Impact of factors on operational performance | TFNs |
| Not mentioned | [0,0,1] |
| Level 1 | [0,1,2] |
| Level 2 | [1,2,3] |
| Level 3 | [2,3,4] |
| Level 4 | [3,4,5] |
| Level 5 | [4,5,5] |
The overall importance weight of each criterion was calculated by summing up the TFNs of the criterion from different sources. For instance, the importance weight for Cr1.1 was obtained by summing up the TFNs of Cr1.1 from sources S1-S8. The weights for the top-class criteria were calculated by adding the weights of the subcriteria together; i.e., the weight for Cr1 is the sum of weights for Cr1.1, Cr1.2, Cr1.3, and Cr1.4.
Table 4: Impact of Each Criterion on the Operational Performance of PPPs
The importance of each criterion in Table 4 is based on interval scale, while AHP requires a ratio-based preference index. The ratio-based preference index is derived by dividing the respective TFNs using α-cut operation.
To synthesize the weight of top-level criteria (i.e., Cr1 – Cr4), primary eigenvector of the judgment matrix is to be calculated. To perform eigenvector calculation, index of optimism μ is introduced to convert the crisp interval into a fixed value. Index of optimism indicates the degree of optimism in dealing with fuzzy numbers. It is a linear convex combination of the lower limit and upper limit of the crisp interval (Lee, 1999), defined as:
Where αμα is the upper limit of the crisp interval, and αια is the lower limit.
By setting the index of optimism μ = 0.5, the fuzzy judgment matrix is converted to a fixed-value matrix:
Comparison Matrix C1
Comparison Matrix C2
Comparison Matrix C3
Comparison Matrix C4
Comparison Matrix C5
The primary eigenvectors of the comparison matrices are calculated by MATLAB package, from which the relative weights of each criterion can be obtained. For instance, the eigenvalues of comparison matrix C1 is calculated by solving the characteristic equation det(C1-λI)=0.
Λ1 = 4, Λ2 = -2.6E-7, Λ3 = -3.3E-7, Λ4 = -3.3E-7
The maximum eigenvalue of the matrix is Λ1, and corresponding eigenvectors can be calculated with the equation AX= ΛX.
A = [0.800846374, 0.584549592, 1, 0.868526027]T
Normalizing the eigenvectors will give the relative weight of each criterion.
Weight of [Cr1, Cr2, Cr3, Cr4] = [0.246117263, 0.179644624, 0.307321442, 0.266916671]
Overall weight of each subcriterion is obtained by multiplying the relative weight of the specific subcriterion with the weight of its parent criterion. For instance, the overall weight of Cr1.1 is the relative weight of Cr1.1 times the relative weight of Cr1.
The importance weight of CSFs in operational PPPs is listed in Table 5.
Table 5: The Importance Weight of CSFs in Operational PPPs
| Cr1. Project characteristics | Importance Weight |
| Cr1.1 The presence of a conducive and enabling legal and regulatory framework | 0.073178 |
| Cr1.2 Training and support | 0.056646 |
| Cr1.3 Measuring, monitoring, and reporting system | 0.047651 |
| Cr1.4 Complementary skills and dependency of resources | 0.068642 |
| Cr2. Contractual arrangements | |
| Cr2.1 Contract guide | 0.027485 |
| Cr2.2 Payment mechanism | 0.061341 |
| Cr2.3 Formal handover | 0.018335 |
| Cr2.4 Risk allocation | 0.072484 |
| Cr3. Project participants | |
| Cr3.1 Capacity | 0.089515 |
| Cr3.2 Goal compatibility | 0.076188 |
| Cr3.3 Commitment | 0.085068 |
| Cr3.4 Working culture | 0.056551 |
| Cr4. Interactive processes | |
| Cr4.1 Ability to management variation | 0.073131 |
| Cr4.2 Stakeholder management | 0.073131 |
| Cr4.3 Communication | 0.047615 |
| Cr4.4 Relationship and mutual trust | 0.073039 |
Findings and Discussions
In this study, various success factors have been identified from an extensive literature review, and their impacts on successful delivery of the projects in the operational phase have been prioritized. The most CSFs in operational PPPs are the project participants' capacity and their commitment to the project. In addition, the alignment of interest, stakeholder management, the relationship, mutual trust, and collaboration between public and private sectors play important roles in improving the performance of operational PPPs. The presence of a conducive and enabling legal and regulatory framework, appropriate risk allocation, and the dependency between public and private sectors will affect the operational performance by and large.
From this study, project participants' capacity is found to be the most important factor that determines the success of the PPP project in the operational phase. Competency of each party in the project has to be ensured both in professional and management skills. Supported by many other studies (Charles, 2006; Rondinelli, 2004), lack of capacity for the partners to execute their roles was identified as the key factor that affects success of the project.
Hardcastle, Edwards, Akintoye, and Li (2006) stated that project implementability constitutes 17.7% of the total variances of CSFs. In order to achieve project implementability, it requires both project participants to be competent in terms of management skills, financial capacity, and technical feasibility. Competency in participants' capacity ensures the project is well-managed, adequately funded, and all the engineering uncertainties are resolved.
The commitment of project participants permits them to improve their capability and stimulates innovation. Commitment also ensures meaningful involvement of each partner in the project. Moreover, equal commitment from partners indicates balanced workload through allocation of time and resources (Samii, 2002). Due to the long-term nature of operational PPPs, commitment is one of the key factors lead to the success in the long run.
As stated in the word public-private partnership, partnership or collaboration between private and public sectors are important in PPP projects. In long-term operational PPPs, good a relationship between public and private sectors will help establishing mutual trust, improving collaboration, and settle problems more easily. As identified by PUK, the main issues with respect to relationship includes different interpretation of the contract from public and private sectors, delays in resolving snagging issues, as well as high turnover of staff (Partnerships UK, 2006). Frequent and regular communication is recommended to settle issues arisen from operational PPPs and avoid potential misunderstandings. Continuity of contract management staff on both sides is considered to be valuable in improving the performance of operational PPPs.
Capacity, commitment of project participants, as well as the relationship and mutual trust between each other impose affection on operational PPPs interactively. In other words, these factors require paying exclusive attention and effort all the way through operational phases. Other than of the interactive factors, there are a number of CSFs that have substantial impact on operational PPPs, which have been predetermined before the project steps into operational phase. Conducive and enabling legal and regulatory framework is one of the predetermined factors in operational PPPs.
In general, legal and regulatory framework is established in tendering phase, where the public and private sectors reach an agreement of project scope, risk allocation, accountability, dispute process, etc. The framework is the prerequisite to the success of the project and is able to reduce opportunistic tendencies (Kuttner, 1997).
PPP projects requires the participation of both public and private sectors, while a transparent and sound regulatory framework provides assurance to private partners, such as protection from expropriation, arbitration of commercial dispute, enforcement of contract agreements, etc. (Pongsiri, 2002). For the public sector, regulatory and legal framework ensures the operation of the partnership, and therefore leads to resource optimization and value for money (Zouggari, 2003).
As more and more PPP projects will proceed into the operational phase in the future, this research provides assessment framework for improving the performance of operational PPPs. The assessment will help optimal resource allocation within the projects, and create value for money.
Conclusion
The operational performance is important in the successful delivery of the PPP projects in terms of service quality, user satisfaction, and value for money. The objective of this paper was to identify the CSFs affecting the performance in the operational phase, and figure out the relative importance of the CSFs. This allows improvement of operational PPPs by providing a systematic process to examine and assess the performance of the project from multiple aspects. In this study, AHP was applied to quantitatively measure the importance at each level, and to calculate the composite relative weight for each CSF. From the relative weights of CSFs, the capacity and commitment of project participants were recognized as the most important factor, followed by the alignment of interest, mutual trust, and collaboration between public and private sectors. Legal and regulatory framework and appropriate risk allocation plays an important role in improving performance of operational PPPs as well.