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Exploring Key Indicators of Residual Value Risks in China’s
Public–Private Partnership Projects
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Jingfeng Yuan1; Wei Xu2; Bo Xia, Ph.D.3; and Mirosław J. Skibniewski, M.ASCE4
Abstracts: Public–private partnerships (PPPs) are gaining popularity in China because of governments’ increasing budget constraints and
the urgent need to develop infrastructure since 2014. However, residual value risk (RVR) is a significant threat to the success of PPP projects
and challenges the governance capability of China’s government, where key risk indicators (KRIs) can be used as a measure to reveal the
potential presence, level, or trend of RVR. To help the public sector obtain estimated value as specified in a concession agreement when the
projects are transferred back to the government at the end of the PPP agreement or in earlier termination due to RVR, this paper proposes and
refines a KRI conceptual model composed of seven risk dimensions and 61 indicators. A structured questionnaire survey with PPP experts
investigated the relative significance of those 61 KRIs for RVR management in PPPs. Although the survey results show that all KRIs are important, seven risk dimensions contribute differently to the exposure of RVR. A confirmatory factor analysis (CFA) was used to test whether
the proposed conceptual model fit the observed set of collected data in a predictable way by goodness-of-fit indices, and to further consolidate
the KRIs. The refined KRI model uses 41 KRIs based on survey and CFA results, indicating that residual value (RV) of a PPP project is
strongly influenced by effective maintenance, long-term sustainable development, and reasonable profitability and refinanceability. The KRIs
including high-quality design work in the early stages of PPP projects, construction quality and public service quality, comparatively low operation costs during the operation period, and feasible technologies adopted by private sectors have a greater value in corresponding dimensions.
These 41 KRIs have the ability to indicate trends of losses of RV in PPPs, control weaknesses for RVR management in PPPs, and obtain longterm and sustainable development through PPP projects. DOI: 10.1061/(ASCE)ME.1943-5479.0000561. © 2017 American Society of Civil
Author keywords: Public–private partnerships (PPPs); Residual value risk (RVR); Key risk indicators (KRIs); Confirmatory factor analysis (CFA); Questionnaire survey.
Public–private partnerships (PPPs) have been adopted widely in
China and other countries as significant means to provide quality
public goods and services through skilled construction and experienced operation by involving the private sector during the concession period (Ng et al. 2010; Chan et al. 2010; Zhang et al. 2015).
However, many PPP projects in China are now facing various problems, such as early termination, inadequacy of feasible design,
unnormative operation, and fewer investments in or experiences
Associate Professor, Dept. of Construction and Real Estate, School of
Civil Engineering, Southeast Univ., Nanjing 210096, People’s Republic
of China (corresponding author). E-mail:
Lecturer, Institute of Construction Law, School of Law, Southeast
Univ., Nanjing 210096, People’s Republic of China. E-mail: stevenxu@
Senior Lecturer, School of Urban Development, Queensland Univ. of
Technology, Garden Point Campus, 2 George St., Brisbane, QLD 4000,
Australia. E-mail:
Professor, Dept. of Civil and Environmental Engineering, Univ. of
Maryland, College Park, MD 20742. E-mail:; Ordinary
Professor, Institute for Theoretical and Applied Informatics, Polish
Academy of Sciences, Gliwice 44100, Poland; T.-S. Yang Honorary
Distinguished Professor, Chaoyang Univ. of Technology, Taichung
41349, Taiwan.
Note. This manuscript was submitted on February 16, 2017; approved
on June 16, 2017; published online on October 27, 2017. Discussion period open until March 27, 2018; separate discussions must be submitted
for individual papers. This paper is part of the Journal of Management in
Engineering, © ASCE, ISSN 0742-597X.
with complex infrastructure (Chan et al. 2015; Cheng et al. 2016).
As a result, the public sector in China in many cases has limited and
ineffective control of PPP projects, which has been evidenced in
many prior researches (Zhang 2006; Yuan et al. 2010a; Wibowo
and Kochendoerfer 2011; Osei-Kyei and Chan 2015).
Of those various problems, residual value risk (RVR) should be
viewed as a significant threat to the successful implementation of
PPP projects in China (Xu et al. 2010). RVR is “the risk that on the
expiry or earlier termination of the services contract, the asset (tangible or intangible) is not in accordance with the value (originally
estimated by the government), at which the private party agreed to
transfer” (Yuan et al. 2015). When RVR occurs, many PPP projects
fail to provide quality public goods and services after the concession
period elapses or when the PPP project is transferred (Ng et al.
2010; Chan et al. 2010; Li and Zou 2011), which would consequently lead to a high loss of residual value (RV). As a result, the
government cannot obtain an estimated value as specified in the
concession agreement when the project is to be transferred back to
the government at the end of the PPP agreement or in earlier termination due to RVR.
Multiple factors, including project performance, function, profitability, maintainability, operability, sustainability, and the possibility of being refinanced, could influence the RV (Private Finance
Panel 1996; Burke and Demirag 2015; Yuan et al. 2015). RVR
could result in high maintenance cost, low-quality service, and
functional problems of facilities, which would fail to meet the prescribed requirements in PPP contracts. In this case, the accountability and credit worthiness of public sectors must be greatly challenged (Rwelamila et al. 2015; Chan et al. 2015). Moreover, RVR
may harm the benefits of the general public (Yuan et al. 2015;
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Bulsara et al. 2015). In addition, the RVR will damage the collaboration between public and private sector, forcing the private sectors
to quit future PPP market opportunities. Therefore, effective RVR
management is critical for the success of PPP projects, comprising
the identification of critical factors leading to RVR, evaluation of
RVR, and precontrol of RVR.
In particular, an accurate identification of the critical factors
leading to RVR and key risk indicators (KRIs) of RVR would
greatly improve RVR management. Furthermore, the reason for
RVR has been mainly concluded as the cumulative effects on the
change of RV in PPP projects (Algarni et al. 2007; Yuan et al.
2016). Six critical risk factors have been identified by authors’ prior
work: (1) downfall of product or service performance, (2) functional problems, (3) decrease of profitability and low possibility of
being refinanced, (4) deterioration of maintainability, (5) decline of
operability, and (6) failure of sustainability (Yuan et al. 2015).
However, KRIs for those critical risk factors that measure how risky
an activity is have not yet been thoroughly identified. KRIs can be
utilized as tools for monitoring controls, risk drivers, and exposures.
KRIs can shed light on the potential risk events. Therefore, this
study aimed to develop an effective set of KRIs for China’s PPP
projects. The findings will provide useful insights about potential
risks that may have an undesirable impact on the achievement of
appropriate and effective RVR management, which can help
China’s government improve its ability to reduce RVR and provide
better public facilities and services.
The remainder of this paper begins with a literature review of the
related fields, followed by a presentation of the research approaches
used in the study, and establishment of the proposed conceptual
model of KRIs for RVR management for PPP projects to describe
theoretical relationships between KRIs and six critical factors. After
that, a national research survey aimed at investigating KRIs in
China’s PPP projects is demonstrated. A series of statistical methods were adopted to analyze the data, and a confirmatory factor
analysis (CFA) was used to test whether the conceptual model fit
the observed set of collected data as predicted through fit indices
and improve the model, identifying KRIs and clarifying their
RVR Management and Related KRIs in PPPs
Literature Review
Recent Development of PPPs in China
In the past three decades, the demands for infrastructure projects in
China have been largely driven by urbanization (Zhang et al. 2015).
Owing to the limited financial budget and inefficient experiences in
providing infrastructure products and services involving solely the
public sector, Chinese policymakers must find new ways to improve
the performance of public projects and services to meet the public’s
demands (Osei-Kyei and Chan 2015). Because of PPPs’ attractive
characteristics of transferring risks to private partners, reducing
public sector administration costs, solving the problem of public
sector budget restraint, providing higher-quality public products
and services, and saving time in delivering the projects, PPPs have
been gaining popularity in recent years in China to involve greater
private sector participation in infrastructure (Zhang et al. 2015).
Meanwhile, a variety of PPP studies have been conducted in the
fields of procurement (Lam and Chow 1999; Liu et al. 2017), risk
management (Akintoye and Chinyio 2005; Cheung and Chan
2011), critical success factors (Chou et al. 2012; Zhao et al. 2013;
Osei-Kyei and Chan 2015), driving factors (Chan et al. 2009; Yuan
et al. 2010a), decision making (Jin 2010; Yuan et al. 2010b;
Wibowo et al. 2012; Xu et al. 2012; Juan et al. 2016), and
stakeholder management (Appuhami et al. 2011; Brinkerhoff and
Brinkerhoff 2011).
In PPPs, both public and private sectors should make efforts in
financing, project planning and design, construction management,
maintenance, facility management, staff training, and technology
progress to achieve value for money (Grimsey and Lewis 2005;
Sobhiyah et al. 2009). PPP projects with a typical concession period
from 15 to 30 years are usually under the operation of the private
sector. In China, new issues and lessons emerge every day. Many
prior works have indicated that government decision making, government creditworthiness, law and policy, inflation, and price
change would greatly impact the success of PPP projects in China
(Song et al. 2013; Zhao et al. 2013; Chan et al. 2015). Hence, the
private sectors in China’s PPP projects ultimately have to face huge
Meanwhile, the governance of projects has been reported as
weak and ineffective in many cases due to limited control and monitoring by China’s public sector (Yuan et al. 2010a; Wibowo and
Kochendoerfer 2011; Zhang 2006). Therefore, RVR could arise
when the project is to be transferred back to the government at the end
of the PPP agreement or in earlier termination because the estimated
value cannot be achieved (Yuan et al. 2015). Since 2014, the central
government of China has facilitated the process of PPP projects,
attempting to make a transition from user-pay PPP to governmentpay PPP systems with the aim of facilitating infrastructure investment
and government transformation (State Council of China 2014; Chang
and Chen 2016). In this case, the governments in China would be
more concerned about the effectiveness and efficiency of PPP project
transfer (Liu et al. 2016), which means the RVRs in China are becoming more and more significant. Information from prior researches or
case studies demonstrates that many disputes and losses related to residual values and the transferring of PPP projects can be attributed to
policy and law change, design problems, poor maintenance of the
assets, completion delay, high costs and prices, and public opposition
(Infrastructure Australia 2008a; Xu et al. 2010; Burke and Demirag
2017). The reason can be concluded that the public sector cannot
obtain the return in the desired condition (Chan et al. 2011; Yuan et
al. 2015, 2016).
RVR has been identified as a critical risk that will have a strong
influence on the value of initial bids and the incentives facing contractors (Private Finance Panel 1996; Hall 1998; Froud 2003;
Algarni et al. 2007; Jin and Zhang 2011). However, many public
sectors may neglect the impacts of RVR because most PPP projects
have a long concession period before they are transferred to public
sectors. Hall (1998) demonstrates that risks associated with how
much the assets will be worth when a PPP project is transferred to
public sectors are very important. Many problems, such as high
maintenance cost, low-quality service, and functional problems of
the facilities, will occur if RVR cannot be identified, evaluated, or
managed. Demirag et al. (2011) believed that RVRs were the most
important in the context of PPP because they were most likely to
affect the goal of off-balance sheet project treatment. Therefore,
suitable management of RVR would be of tremendous value for
better maintenance, easier transfer of PPP projects, improvement of
ongoing management, and sustainable development of the infrastructure (Hall 1998).
Yuan et al. (2015) indicated that RVR in PPPs can be
described as the uncertainty that the RV could be lower than the
estimated or anticipated value determined in the PPP contract
when being transferred. From the perspective of uncertainty, a
traditional risk-management framework can provide useful
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lessons for RVR management. Usually, a risk-management framework includes risk identification, risk evaluation, risk allocation,
and risk control or treatment. Many previous studies have focused
on the RVR management in PPPs. Algarni et al. (2007) indicated
that RVR could occur when facility assets have been suffering
from years of neglect, overuse, deferred maintenance, and delayed
repair, which would harm the sustainable development of the infrastructure; this is agreed by Xu et al. (2010) and Chan et al. (2011).
Many official documents have identified related risk factors of
RVR, such as construction costs, total service demand, total operating costs, and technological change (Partnerships Victoria 2001;
Infrastructure Australia 2008a, b; Comptroller and Auditor
General of India 2009). How to allocate RVR between public and
private sectors has also been discussed (Arndt 1998; Li et al. 2005;
Ke et al. 2010; Marques and Berg 2011; Krüger 2012). Froud
(2003) proposed a RVR treatment method, which deliberated
whether RVR can be dealt with by simply writing off the asset
completely over the life of the contract.
Risk identification is the first step for risk management (PMI
2013). The principal work of risk identification is to detect the factors that can potentially jeopardize the successful conclusion of a
PPP project by causing cost overruns, time delay, and low level of
performance compared to the output specification (Carbonara et al.
2015; Liu et al. 2016). Many prior studies have identified risk factors in PPP projects in China, India, the United States, the United
Kingdom, etc., and risk factors of different PPP project types, such
as transportation, water treatment, power plant, and education (Lam
and Chow 1999; Wang et al. 1999; Thomas et al. 2006; Chan et al.
2011; Zhang et al. 2015; Ameyaw and Chan 2015a). Generally,
government-related risks and assets-related risks are the most important risks for PPP projects. These studies strengthened the perceptions of PPP risks and explored appropriate ways to fully understand significant risks in PPPs.
However, KRIs, which can be used as measures to reveal the
potential presence, level, or trend of a risk, have not been developed
for RVR management. KRIs can indicate whether a risk has
occurred or is emerging, the level of risk exposure, and the trend of
changes in risk exposure. Therefore, KRIs can provide information
about a risk situation that may or may not exist and serve as a signal
for further action (Beasley et al. 2010). When implemented properly, KRIs can provide significant insight into changes in the risk
profile and bring additional strategic and operational value (Fraser
and Simkins 2011; Zhang 2011; Huang et al. 2012). KRI has been
adopted widely in the fields of financing, enterprise management,
banking, and e-business (Heckmann et al. 2015; Juan et al. 2016).
In the context of RVR, KRIs can be used as a tool to predict the
change of RVR and allow for proactive intervention before the project is transferred (Yuan et al. 2015, 2016). Therefore, a significant
work of this paper is to develop KRIs for RVR in China’s PPP projects by analyzing the events that affected the RV of PPP projects in
the past or the present and go back to the roots of RV to identify and
fully understand the underlying cause of the RV.
Knowledge Gap
According to the review of previous studies, the following knowledge gaps can be identified. First, the lack of effective RVR management leads to many problems during the process to reduce the
RV of PPP projects. Considering that long-term vision on PPP projects is essential for pursuing public benefits, the RV of PPP projects
should be kept at a high level, and RVR management should be constantly improved to realize public benefits. Second, precisely knowing which indicators are to be used to measure and indicate risk factors that lead to RVR is another knowledge gap. The usage of KRIs
can contribute to improving not only RV of PPP projects, but also
project performance through a proactive management position
instead of a reactive one (Beasley et al. 2010). At the same time, the
interrelationships between KRIs are crucial to RVR management
and should be further clarified. This paper attempts to fill these
knowledge gaps, with particular emphasis on the second knowledge
gap. Identification of KRIs would be very useful for establishment
of RVR management framework, partially addressing the first
knowledge gap.
Research Method
In this study, a series of qualitative and quantitative research methods were used to explore the conceptual KRI system of RVR to
measure and manage RVR in PPPs effectively, as shown in Fig. 1.
First of all, a conceptual model of KRIs in PPPs was proposed
according to KRIs’ theoretical relationships. Based on the identified
factors that led to RVR from authors’ prior work, the authors developed an indicator system to measure RVR and identified KRIs in
PPP projects. A survey of 138 PPP experts in China using a stratified random sampling method with 5-point Likert-style rating questions was conducted to investigate the KRIs of a PPP project during
its lifecycle in China. Statistical analyses were performed, using the
SPSS 17.0 software package, by multiple methods, including
Cronbach’s alphas, mean value, and grouping discussion to validate
the survey, as well as to evaluate the perceptions of risk indicators.
A CFA was performed using the LISREL 8.70 software to test
whether the theoretical model fit the survey data, and to clarify the
relationships among KRIs. In addition, the insignificant indicators
were removed from the initial indicator system based on significance level and factor loadings. Then, the KRIs were identified for
RVR in PPPs. The CFA aims not only at detecting and explaining
but also improving the present state of RVR management in real
PPP projects and developing helpful risk management in PPPs for
future use.
Fig. 1. Methodology adopted in this study
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Conceptual Model of KRI System for Measuring RVR
in PPPs
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Proposed Conceptual Model
In prior studies, six dimensions of internal risk factors have been
identified according to their impacts on the RV change of PPP projects (Yuan et al. 2015). Internal risk factors, called local risk factors
(LRFs), come from the internal environment, and are relatively
more controllable within PPP projects and will vary between projects. In addition, RV would inevitably be influenced by the relatively uncontrollable external environment and systematic impacts
(Frilet 1997; Wang et al. 2000), which can be called global risk factors (GRFs). All GRFs would affect PPP projects by combining
with the influence of LRFs. Therefore, the RVR factors can be divided into LRFs and GRFs. According to related researches
(Beasley et al. 2010; Fraser and Simkins 2011), each risk factor can
be measured through a series of KRIs. Thus, KRIs can be identified
with the help of a proposed conceptual model comprising all risk
factors of RVR in PPPs, as shown in Fig. 2. KRIs may represent key
ratios that management tracks as indicators of evolving RVR, which
points out the need for actions to be taken. Local key risk indicators
(LKRIs) are identified from six aspects: (1) downfall of product or
service performance, (2) functional problems, (3) deterioration of
maintainability, (4) decline of operability, (5) failure of sustainability, and (6) decrease of profitability and low possibility of being refinanced. In addition, global key risk indicators (GKRIs) are identified from the external environment and systematic impacts on RV
and other internal KRIs. A conceptual model of the KRI system for
measuring RVR in PPPs is shown in Fig. 2.
Identification of Potential KRIs
During the process of PPP projects, many internal and external factors could influence the change of RV in PPPs during the concession
period; these factors are LRFs and GRFs.
• PPP projects usually suffer great and continuous changes from
the stage of preimplementation to the stage of posttransfer due
to internal factors within PPP projects. Hence, the RV of PPPs
would be influenced in different stages by many different internal risk factors, which are LRFs. Therefore, the LKRIs for
RVR in PPPs should be selected to consider and assess the full
range of LRFs that could potentially harm RV or lead to
missed performance objectives in any stages of the concession
period. To identify LKRIs for RVR in PPPs, the interaction of
process viewpoint and residual value change should be carefully considered. Usually, the process of a PPP project before
being transferred to public sectors can be divided into financing, design, construction, and operation. Meanwhile, the six
dimensions of risk factors (D1–D6) could impact on the RV in
different stages. Thus, the LKRIs for RVR in different stages
in PPPs can be further identified according to the effects of different internal risk dimensions, as shown in Table 1.
• The GRFs contain the changes in political, social, legal, and
economic conditions of a specific PPP project as well as any
changes in contract, force majeure, and interface. GRFs are
exacerbated by interdependencies among the different units often because of strong links between PPP systems and external
environments or between different units within the PPP system. These risks can be triggered by sudden events or events
built up over time, with the impact often being large and possibly catastrophic. The GKRIs can be used to measure the influence of GRFs with potential losses or damages to an entire
system as contrasted with losses to a single unit of that system.
GKRIs for RVR in PPPs can be identified according to prior
researches on the risk management of PPP projects concerning
the impacts from macro and external environment as well as
government-related influences, which are different perspectives within the dimension of GRFs (D7) as shown in Table 2.
The RVR could be fully understood and thoroughly analyzed
when GKRIs and LKRIs are integrated and then used together. Thus,
the potential KRI lists can be obtained according to the proposed
model in Fig. 2, a literature review on PPPs and KRIs, and the process
viewpoint, which are shown in Table 1. The detailed description of
different dimensions of KRIs (D1–D7) are shown as follows.
D1: Indicators Related to Performance
In this dimension, the identified LKRIs should be used to measure
the downfall of product or service performance delivered by PPPs.
From the perspective of public goods or services provided by PPP
projects, the requirements for public facilities mean high quality as
well as timely and convenient service (Li et al. 2005; Yuan et al.
2009), which strongly influences the tangible assets of RV. The
decline of performance would greatly influence the RV of PPPs,
Fig. 2. Conceptual model for KRIs for residual value of PPP projects
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Table 1. Identification of Potential LKRIs Based on Process-Based Viewpoint
Local risk factors (D1–D6)
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Process-based possible
influences on the change of RV
Reasonability of project
financing structure
Project self-liquidation
Availability of financing
Financing costs
Design reasonability or major
Delay of construction
Overrun of construction costs
Construction quality
Supply of goods and materials
Level of advanced technology
Public service quality
Operation costs
Financial capability
Market stability
Reasonability of pricing
Maintenance safety
Reasonability of ownership
Maintenance costs
Environmental protection
Reasonability of organizational
Horizontal competition
D6 Profitability
= potential LKRIs are from the impacts from Dx (x = 1,2, …, 6) on the change of RV in different stages.
Table 2. Identification of Potential GKRIs Based on External and
Systematic Impacts
Global risk factor (D7)
Political impacts
Social impacts
Legal impacts
Economic impacts
Systematic impacts
Potential risk indicator
Effectiveness of policies (e.g., ineffective
supervision and corruption)
Change of government officials
Creditworthiness of different
Public opposition
Legal risks (e.g., improper laws and rules,
regular changes of laws and rules)
Change of interest
Change of exchange rates
Imperfect financing market
Decision-making capability
Land acquisition
Project approval
Contract management
Interface management
Force majeure
which could be indicated by the design reasonability and quality,
construction quality, construction duration, construction cost, technology progressiveness, operation safety, and maintenance cost.
D2: Indicators Related to Function
In this dimension, the identified LKRIs should be used to measure
whether a series of facilities, equipment, instruments, and technical
or management documents used in a PPP project can meet the function requirements and can ensure that high performance will be
achieved. Ravindran (2010) demonstrated that necessary function
traditionally was the basis for providing quality goods and services
for a public facility. The indicators of functional problems can be
proposed as design reasonability and quality, construction quality,
construction duration, public service quality, organization change,
maintenance cost, operation cost, price adjustment, and supply efficiency and effectiveness.
D3: Indicators Related to Maintainability
In this dimension, the identified LKRIs should be used to measure
whether the PPP project can be carefully maintained to isolate
defects or their causes, correct defects or their causes, meet new
requirements, make future maintenance easier, or cope with a
changed environment (Li et al. 2005; Chan et al. 2009).
Furthermore, the reliability and serviceability of infrastructure
projects are both of importance for RVR management in a PPP
project (Yuan et al. 2015). Therefore, the indicators to measure
the deterioration of maintainability should include reasonable
planning and design, effective quality and cost control, and technology improvement.
D4: Indicators Related to Operability
In this dimension, the identified LKRIs should be used to measure
the ability to keep a system or a whole PPP project in a safe and reliable functioning condition, according to predefined operational
requirements. Meanwhile, LKRIs should also reflect the decline of
operability of RV in PPPs due to changes of tangible and intangible
assets, which would include satisfaction of stakeholders, change of
subsystems, interrelationships among different project phases or
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contracts, advanced technologies, and efficient project organization
(Sharma 2007). The changes could be derived from low public service quality, operation cost overrun, market inflation, horizontal
competition, pricing adjustment, maintenance accidents, technology improvement, and unclear ownership.
D5: Indicators Related to Sustainability
In this dimension, the identified LKRIs should be used to measure
social, environmental, and financial impacts on the change of RV of
PPPs (Koppenjan and Enserink 2009). The LKRIs for sustainable
RV management of PPPs should indicate whether the process of
social development, utilizing resources, directing investment, and
institutional change can enhance both current and future potential to
meet present and future needs after a PPP project is transferred to
public sectors (Mirza 2006). Social, environmental, and financial
sustainability in PPPs can provide a good basis for long-term realization, maintenance, and operation of public infrastructures (De
Los Ríos-Carmenado 2016). The uncertainty of sustainability could
be indicated by design reasonability and quality, construction quality, technology progressiveness, maintenance cost, public service
quality, and environment protection.
D6: Indicators Related to Profitability and Refinanceability
In this dimension, the identified LKRIs should be used to measure
financial conditions. The LKRIs for profitability and refinanceability for RVR in PPPs can indicate the potential of tangible and intangible assets in PPP projects to be invested after being transferred to
public sectors. Related indicators include financing structure, the
project’s self-liquidating ratio, availability of financing, and capital
costs, which are important for profitability of PPP projects and have
a strong relationship with financing issues. Furthermore, the
impacts on profitability will influence the process of construction
and operation, which would lead to a lower possibility of being refinanced when a PPP project is transferred to public sectors (Xenidis
and Angelides 2005; Wibowo et al. 2012).
D7: Indicators Related to Global Risks
Obviously, a PPP project is usually widely influenced by many
macro and external risk factors. According to prior researches
(Wang et al. 2000; Akintoye and Chinyio 2005; Ameyaw and Chan
2015b; Carbonara et al. 2015), the influences have a strong relationship with political, social, legal, economic, systematic, and other
impacts. Political risks in PPPs could result from change of sovereignty and policies, unstable regime system, weak supervision system, and corruption. As presented by Wang et al. (1999) and Yuan
et al. (2010a), the commitments between public and private sectors
are critical to the success of PPP projects, where the degree of trust
between public and private sectors is significant. For example, the
possibility of local authorities living up to the financial obligations
can attract more investments in PPP projects in the long run (Witz
et al. 2015). Meanwhile, support from the general public would
reduce the social risks in PPPs (Rouhani et al. 2016). Economic
influences would inevitably play key roles in PPP projects and
make the RV change, where the inflations of interest, exchange rate,
and currency are closely related (Wang et al. 2000). Moreover, the
imperfect financing market in China also has negative impacts on
the RV change of PPP projects (Wang et al. 2000; Chan et al. 2011;
Yuan et al. 2015). In fact, many systematic risks can be included
into this dimension because they can affect the PPP projects during
their whole lifecycle. These indicators can be classified into two
types. One is the work in the early stage of PPP projects that is very
important, such as decision making, land acquisition, and project
approval (Xu et al. 2012; Juan et al. 2016). Another is the work that
should be well implemented during the process, such as contract
management and interface management for different stages and different stakeholders in PPPs (Klijn and Koppenjan 2016; Liu et al.
2017). Furthermore, force majeure is usually ignored in PPP projects, which could greatly harm or reduce the RV of PPPs (e.g.,
earthquake, flood, volcanic eruption) (Carbonara et al. 2015; Xu
et al. 2015).
Hypothesized Relationships in the Conceptual Model
A conceptual framework (Fig. 2) is a representation for the RVR in
PPP projects, and serves as the foundation for exploring KRIs.
PPPs are focused on delivering the value of public products and
services, including the satisfaction of the general public, efficiency
and effectiveness, future potential of projects, and performance
improvement. Therefore, any changes of PPP projects, which influence the value of public products and services, would inevitably
reduce the RV of PPP projects. Based on these relationships, the following hypothesis can be concluded. The conceptual model can be
used to measure the RVR of PPP projects, where all risk dimensions
can contribute to the change of RV in PPP projects. As shown in
Fig. 2, all risk dimensions contribute to the RV in PPP projects from
different perspectives. Although their contributions and pathways
are different, they all influence the RV of projects. Furthermore, the
classification of these dimensions within the proposed model specifically reflects the characteristics of RV in PPPs. Additionally, the
survey data should provide an empirical evaluation of the arrangement of indicators into the different dimensions.
Research Survey on KRIs for RVR
Introduction of Survey
To obtain the opinions of professionals and researchers on KRIs for
RVR and their differences, a structured questionnaire survey was
conducted at the annual conference of The Architectural Society of
China–Construction Management Research Sector (ASC-CMRS)
in August 2015. The survey targeted individuals and organizations
with experience in PPPs.
The questionnaire comprises three parts. The first part seeks
background information about the respondents and general issues
about PPPs (Part A). The second part deals with KRIs for RVR in
PPP projects (Part B). In this part, the investigation of different
KRIs for RVR within PPPs was conducted by using Likert-style
rating questions with a 5-point scale to identify the relative significance of KRIs for RVR, by which the respondents’ opinions
about the importance of each indicator can be elicited. In the
questionnaire, an initial conceptual model of KRIs for RVR in
PPPs based on a literature review was presented. The respondents
were asked to provide their full opinions on the importance of
KRIs base on the conceptual model, through which the level of
agreement or disagreement generally can be measured. The scale
intervals are interpreted as follows: (1) can be ignored or not important, (2) maybe important, (3) important, (4) very important,
and (5) most important.
A total of 315 questionnaires were sent out at the conference
site, of which 138 were completed and returned. The effective
return rate was 44% compared to acceptable rates in prior studies of
PPPs, including 36% in the Chan et al. (2011) study and 11.4% in
the Jin and Zhang (2011) study. The response was therefore deemed
adequate for the purposes of data analysis. The respondents were
from construction enterprises, research organizations, and consulting companies. All participants met the following requirements: (1)
extensive work experience within the construction industry, and (2)
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involved in the management of PPP projects or gained in-depth
knowledge of the PPP model through research. The survey data
show that 52.7% of respondents were from academia, and 47.3% of
respondents were from industry. The experiences of respondents
can be found in Tables 3 and 4. Table 3 describes the experiences of
respondents in the construction industry and PPPs. Table 4 shows
the PPP project types reflected in respondents’ experience.
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Consistency of Survey Data
A reliability analysis was conducted to test the internal consistency
of the survey variable data. Cronbach’s alphas were 0.846 for all
LKRIs and 0.922 for all GKRIs. The values are much higher than
0.70 as recommended in the guideline by Nunnally (1978), which
suggests that in the early stages of research on predictive tests or
hypothesized measures of a construct, reliability of 0.70 or higher
should suffice. Cronbach’s value was derived for each indicator.
Values exceeding 0.9, between 0.9 and 0.7, between 0.7 and 0.6,
between 0.6 and 0.5, and lower than 0.5 indicate excellent, good, acceptable, poor, and unacceptable reliability, respectively. The opinions of different participants on all KRIs were similar based on the
value of Cronbach’s alphas, which provides a basis for development
of further studies. The values of Cronbach’s alphas as mentioned
earlier are the average values of Cronbach’s alpha over all LKRIs
and GKRIs, which are higher than 0.9 or close to 0.9. Therefore, it
is not necessary to display all alpha values derived for each KRI
(Bonett and Wright 2015). Actually, many prior studies also only
used the average values of Cronbach’s alpha to indicate the consistency of survey data (Chou et al. 2012; Tang and Shen 2013;
Seshadhri and Topkar 2016).
Description of Survey Results Related to LKRIs
The survey results of LKRIs are shown in Table 5. The mean
response rating values for the 46 LKRIs range from a maximum of
Table 3. Survey Respondents’ Related Experiences in Industry
≤5 years
6–10 years
11–15 years
16–20 years
≥21 years
In construction industry Percentage In PPPs Percentage
Table 4. PPP Project Types Reflected in Survey Respondents’
Project type
Water and sanitation
Power and energy
Environmental protection
IT and communication
School and education
Urban development
4.545 (D3.1, design reasonability and change in the maintenance
dimension) to a minimum of 2.727 (D2.3, construction duration in
the function dimension). No indicator mean value scores fell into
the not important (<1.5) category, which indicates that 45 LKRIs
are considered important and can be used to measure the RVR in
PPPs. The mean values of all LKRIs from six dimensions also
indicate that the identified KRIs can be used to analyze the RVR
from six perspectives. Meanwhile, some indicators can measure
different risk factors at the same time, such as design reasonability
and change, which simultaneously serves as the dimension of performance, function, maintainability, and sustainability, and can be
described as the impacts of different risk dimensions. These indicators actually contribute differently in different dimensions, which
moreover illustrates that the occurrence of RVR in PPPs is led by
interactions between different risk factors (dimensions).
In the survey, factors from each of the dimensions were investigated. Thus, a group discussion of the different dimensions is necessary. The rankings of risk factors in the corresponding dimensions
are shown in Table 5. Detailed analysis within different dimensions
can be provided as follows.
Dimension of Performance
Based on the survey results, the KRIs in this dimension should
include indicators that reflect the impact of designing work (D1.2),
quality and technologies (D1.2, D1.5), traditional performance indicators (D1.3, D1.4), and maintenance (D1.6, D1.7). Because performance for RV management places great emphasis on goods and
services delivered by PPPs (Clifton and Duffield 2006), the indicators related to quality are more significant than other traditional performance indicators (e.g., schedule and costs). The reasonability of
design work is the most important in this dimension (D1.1, 4.500),
which would also influence the quality and technologies to be
selected by the concessionaire. In truth, designing work involves
planning for quality, which can provide excellent support for construction and operation (Yuan et al. 2010b). In recent years, performance-based design, which is an engineering approach to design
elements of infrastructure based on agreed upon performance goals
and objectives, engineering analysis, and quantitative assessment of
alternatives against the design goals and objectives using accepted
engineering tools, methodologies, and performance criteria, has
become more and more important in the field of PPP. As presented
by Love et al. (2015), performance-based design can lead to innovation and new methods. Thus, more performance-based design for
infrastructure in PPPs would notably improve the RV of PPP
Dimension of Function
Ravindran (2010) indicated that necessary function traditionally
was the basis for providing quality goods and services for a public
facility. To meet the function requirements to ensure that high performance can be achieved, the indicators related to design (D2.1,
4.409) and quality (D2.2, 4.091) are more significant compared
with other indicators based on the survey data, because the improvement of design quality and construction quality would reduce functional problems (Dikmen et al. 2005). In addition, the operation
stage is important for improving function of facilities, which is
reflected by the score of public service quality (D2.4, 3.455), maintenance costs (D2.6, 3.227), operation costs (D2.7, 3.591), and project pricing (D2.8, 3.409). Obviously, notwithstanding the early
stage of a PPP project can determine the function of the whole project, heavy work actually should be done during the operation period to ensure the basic and important functions are all kept at a high
level to deliver quality goods and services.
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Table 5. LKRIs for RVR in PPP Projects and Their Scores and Rankings in Different Dimensions
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Distribution shape
Local key risk indicator
D1 Performance
D1.1 Impacts of design on performance
D1.2 Impacts of construction quality on performance
D1.3 Impacts of construction delay on performance
D1.4 Impacts of construction costs overrun on performance
D1.5 Impacts of level of advanced technology on performance
D1.6 Impacts of maintenance safety on performance
D1.7 Impacts of maintenance costs overrun on performance
D2 Function
D2.1 Impacts of design on function
D2.2 Impacts of construction quality on function
D2.3 Impacts of construction delay on function
D2.4 Impacts of service quality on function
D2.5 Impacts of organizational structure on function
D2.6 Impacts of maintenance costs overrun on function
D2.7 Impacts of operation costs overrun on function
D2.8 Impacts of pricing overrun on function
D2.9 Impacts of goods and materials supply on function
D3 Maintainability
D3.1 Impacts of design on maintainability
D3.2 Impacts of construction quality on maintainability
D3.3 Impacts of level of advanced technology on maintainability
D3.4 Impacts of maintenance costs overrun on maintainability
D4 Operability
D4.1 Impacts of service quality on operability
D4.2 Impacts of operation costs overrun on operability
D4.3 Impacts of horizontal competition on operability
D4.4 Impacts of market stability on operability
D4.5 Impacts of pricing on operability
D4.6 Impacts of maintenance safety on operability
D4.7 Impacts of level of advanced technology on operability
D4.8 Impacts of ownership on operability
D5 Sustainability
D5.1 Impacts of design on sustainability
D5.2 Impacts of construction quality on sustainability
D5.3 Impacts of level of advanced technology on sustainability
D5.4 Impacts of maintenance costs overrun on sustainability
D5.5 Impacts of service quality on sustainability
D5.6 Impacts of environmental protection on sustainability
D6 Profitability and refinanceability
D6.1 Impacts of financing structure on profitability and refinanceability
D6.2 Impacts of project self-liquidation on profitability and refinanceability
D6.3 Impacts of availability of financing on profitability and refinanceability
D6.4 Impacts of financing costs on profitability and refinanceability
D6.5 Impacts of construction delay on profitability and refinanceability
D6.6 Impacts of construction costs overrun on profitability and refinanceability
D6.7 Impacts of operation costs overrun on profitability and refinanceability
D6.8 Impacts of horizontal competition on profitability and refinanceability
D6.9 Impacts of market stability on profitability and refinanceability
D6.10 Impacts of pricing on profitability and refinanceability
D6.11 Impacts of maintenance costs overrun on profitability and refinanceability
D6.12 Impacts of level of advanced technology on profitability and refinanceability
Dimension of Maintainability
Effective maintainability aims at substantially improving reliability
and serviceability for facilities in PPP projects, eventually reducing
RVR. As presented before, performance-based design focuses not
only on performance improvement, but also on hindering deterioration of maintainability in advance through careful and innovative
design (Love et al. 2015). Thus, design-related indicators obtain the
highest score (D3.1, 4.545) in this dimension, which is also the
highest score in all KRIs. Other indicators, including construction
quality (D3.2, 4.273), technologies adopted in projects (D3.3,
3.909), and maintainability costs (D3.4, 3.818), all obtained high
scores, which could indicate that maintainability received attention
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from respondents because maintainability can help PPP projects
improve the value and life span of facilities (Mirza 2006; Sobhiyah
et al. 2009).
Dimension of Operability
As concluded from prior work, operability stressed both tangible
and intangible assets (Yuan et al. 2015). According to the survey
results, the indicators related to intangible assets are more important
compared to the indicators related to tangible assets. The operation
costs (D4.2, 4.091) and public service quality (D4.1, 3.955) are the
most important indicators in this dimension, followed by project
pricing (D4.5, 3.636), market share (D4.4, 3.591), and horizontal
competition (D4.3, 3.591). The aforementioned indicators focus on
measuring the operation management in PPPs to deliver highquality public service and meet the requirements of the general public. The incidents in maintenance (D4.6, 3.500) and technology
improvement (D4.7, 3.409) are comparatively not important in this
dimension, which indicates that operation depends more on the
healthy running of a management system, as illustrated by Zhao
et al. (2013).
Dimension of Sustainability
Li et al. (2005) indicated that sustainability had strong relationships
with the success of PPP projects. The measures that help the public
sector to achieve sustainability include the use of long-term contracts, output-based specifications, and financial innovation. The
survey results show that the reasonability of design (D5.1, 4.409)
and construction quality (D5.2, 4.136) are the two most important
indicators in this dimension, which can provide strong support to
quality delivery of the PPP project to ensure that the drawings are
available for maintenance referencing and the project is durable
(Zhang 2006). Technology improvement (D5.3, 3.773) and environment influence (D5.6, 3.773) are both very important for sustainability of PPPs, which can facilitate guaranteed continued sufficiency (adequacy) of the project within its projected life span.
Meanwhile, the comparatively high scores of maintenance costs
(D5.4, 3.682) and public service quality (D5.5, 3.636) demonstrate
that adequate financial provision for maintenance and long-term
services is critical.
Dimension of Profitability and Refinanceability
The indicators in this dimension can be classified into financingrelated indicators and revenue-related indicators. For financial indicators, the project’s self-liquidating ratio (D6.2, 4.182) and the
availability of refinancing (D6.3, 4.045) are the most important
indicators and, concurrently, the most concerning indicators for private sectors (Xenidis and Angelides 2005; Appuhami et al. 2011).
Financing structure (D6.1, 3.955) and costs (D6.4, 3.864) are determined by project planning, directly influencing availability of financing and refinancing of a PPP project (Wang et al. 2000;
Wibowo et al. 2012), where the refinanceability could be strongly
influenced by the flexibility of the initial financing structure.
Refinancing would redesign the project’s initial financial structure,
utilizing an optimization potential by the contractor. With it, the
concessionaire attempts to reduce the costs of the procurement of financial services. For revenue-related indicators, constant or abundant cash flow can impact the profitability, which has strong relationships with operation costs (D6.7, 4.091), competition with a
similar project directly invested by public sectors, market share
(D6.8, 3.909), the price of the goods or services being offered
(D6.10, 3.773), and maintenance costs (D6.11, 3.727). Obviously,
operation costs, which reflect the management skills and experiences, are the most important. Additionally, the construction cost
overrun (D6.6, 3.773) can also strongly influence the profits of
private sectors, which could lead to penalties by the Chinese government in some PPP projects (Chan et al. 2009; Zhang et al. 2015).
Construction delay (D6.5, 3.318) and technology improvements
(D6.12, 3.318) are relatively not important in this dimension
because cash flow and earned money may be more significant.
Description of Survey Results Related to GKRIs
The survey results of GKRIs are shown in Table 6. The findings
revealed that political KRIs, including policy change (D7.1, 4.273)
and local authority change (D7.2, 3.818), are the most significant
risks for PPP project success and RV improvement. The social
KRIs cannot be ignored. The commitment and responsibility of
both the public and private sectors are viewed as a significant critical success factors (CSF) in PPPs (Li et al. 2005), which could also
influence the payment of government and repayment from private
sectors to loaners, reducing the RV of PPPs. Public opposition has
occurred in many prior PPP projects in China, which heavily
affected the success of PPP projects due to negative environment
and social influence (Rouhani et al. 2016; Liu et al. 2017). In this
case, the RV of PPPs would be inevitably reduced because the project could be terminated by public opposition. A legal risk comes
about when new legislation and regulations are introduced with
adverse consequences on existing transactions (Ke et al. 2010). The
consequence of legal risks could particularly be a big problem for
some transactions because the parties affected may not be able to
perform their obligations (Iyer and Sagheer 2009). Therefore, the
RV of PPPs obviously cannot be in accordance with the value prespecified in the PPP agreement. The economic KRIs were not important compared to political, social, legal, and systematic indicators. These indicators reflect the overall influences of external
economic environment, and more obvious effects resulted from the
internal indicators related to LKRIs in the dimension of profitability
and refinanceability. However, the robustness of the financial market is especially important in economic KRIs, which actually would
influence the availability of financing, financing costs, financing
structure, and financial viability (Salman et al. 2007). The systematic KRIs can be divided into indicators for an early stage and indicators for the whole process. For the indicators related to an early
stage, the reasonability of decision making was the most important,
which had close relationships with project planning, determination
of design and financing, concession period and pricing, and the
issues that affect how the project is to be built. For the indicators
related to the whole process, contract management is more important compared to force majeure and interface management.
CFA-Based Refinement of KRIs for RVR in PPPs
CFA on KRIs of RVR in PPPs
Although the research survey indicated that the proposed KRIs
were important, the large number of KRIs reduced the measurement
efficiency for RVR evaluation. Therefore, refinement of KRIs for
RVR in PPPs is critical to improve the effectiveness and efficiency.
The relationship between RVR and different KRI packages can be
helpful to further refine the KRIs for RVR in PPPs. Meanwhile, the
mean values of different KRIs are also significant for the refinement
A CFA was conducted by using the LISREL 8.70 software package to test whether the proposed conceptual model fit the empirical
data and further identify the important relationships between RVR
and different KRI packages as well as between different KRI packages and KRIs. In CFA, the most common method used to estimate
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Table 6. GKRIs for RVR in PPP Projects and Their Scores and Rankings
Distribution shape
Global risk dimension
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D7.1 Impacts of policy changes on RV
D7.2 Impacts of changes of government officials on RV
D7.3 Impacts of creditworthiness for government on RV
D7.4 Impacts of public opposition on RV
D7.5 Impacts of legal changes on RV
D7.6 Impacts of interest changes on RV
D7.7 Impacts of exchange rate changes on RV
D7.8 Impacts of inflation on RV
D7.9 Impacts of imperfect financing market on RV
D7.10 Impacts of improper decision making on RV
D7.11 Impacts of difficulty of land acquisition on RV
D7.12 Impacts of difficulty of project approval on RV
D7.13 Impacts of force majeure on RV
D7.14 Impacts of contract management on RV
D7.15 Impacts of interface management on RV
parameters is maximum likelihood (ML) estimation, which requires
the assumption of multivariate normality of the observed data
(Flora and Curran 2004; Li 2016). Researchers often use a Likerttype scale as a response format. However, the Likert scale data as a
set of ordinal scales may violate the assumption. It would not be reasonable to expect that observed data would follow the normal distributional assumption, even in some important areas of research
(Curran et al. 1996). In addition, the distribution shape of variables
in Tables 5 and 6 indicates that the observed data basically accord
with normal distribution, and the value of Cronbach’s alphas also
verifies reliability of the data. So the survey data in this study are
suitable for performing CFA.
Based on prior analytic research, CFA, as a special form of factor analysis and a confirmatory technique, can be used to test
whether the survey data fit a hypothesized measurement model as
presented in Fig. 3, including endogenous observed variables
(RVR), exogenous latent variables (dimensions, i.e., D1–D7), exogenous observed variables (risk indicators, i.e., D1.1, D1.2,…,
D7.15), errors in the variables, and pathway coefficients (factor
loadings). The latent variables that cannot be directly observed are
measured with corresponding exogenous observed variables (indicators). The straight line from the latent variables (dimensions) to
the corresponding observed variables indicates the causal effect on
the observed variables (indicators). The factor loadings on the
straight lines represent the relationship of indicators with their associated latent variables.
The results of CFA demonstrated that the adequacy of the initial
CFA model was good enough according to the model’s fit to the
data. A number of goodness-of-fit indices of the initial model were
used to evaluate how well the hypothesized model fit the actual survey data. The results of commonly used fit indices were as follows:
normal theory weighted least-squares chi-square ( x 2) = 2,478.97,
x 2/Df = 2.27 (Df, degrees of freedom = 1,091), comparative fit
index (CFI) = 0.95, root mean square error of approximation
(RMSEA) = 0.022. Compared to the recommended values of these
fit indices as listed in Table 7 (Hu and Bentler 1999; Weston and
Gore 2006), measured values of goodness-of-fit indices indicated
that the initial model of KRIs of RVR perfectly fit the observed
data. The errors in the variables and pathway coefficients are shown
in Fig. 3.
According to the component of CFA framework, different KRI
dimensions were all significant (t > 1.96) and all contributed
differently to the change of RV in PPPs. The difference can be
reflected based on the factor loadings and mean value of all KRIs in
different dimensions. The dimension of maintainability obtained
the highest loading (0.99), which indicates that maintenance is very
important to the long-term running of PPP projects and could
improve the value of PPP projects. As presented by Zhang (2006),
maintainability is a necessary factor in the operation stage of PPP
projects, which can improve the internal value of the facility and
prolong the facility’s operation period. Meanwhile, the scores of
sustainability (0.98) as well as profitability and refinanceability
(0.95) are also high, which further demonstrates that these experienced respondents tended to focus on the future development and
sustainable methods to explore a PPP project. The relatively low
scores are from the dimensions of performance (0.77), function
(0.44), operability (0.81), and GKRIs (0.76). Some KRIs in these
four dimensions had comparatively higher loadings within corresponding dimensions for LKRIs. For the dimensions of performance and function, technical indicators were more important (e.g.,
design and quality). For the dimension of operability, management
indicators are more important (e.g., costs and public services). In
the dimension for GKRIs, there were higher loadings for the KRIs
related to political changes, public opposition, legal change, and
contract management in PPP projects.
Refinement of KRIs for RVR in PPPs
To further refine the KRIs for RVR of PPP projects in China, the
mean values obtained from survey analysis and factor loadings
from CFA were both considered simultaneously. The KRIs, whose
mean values were lower than 3.50 or factor loadings were lower
than 0.400, are excluded from the initial model. Only the KRIs
whose mean values were larger than 3.80 or factor loadings were
larger than 0.600 are kept. For example, the mean value of D1.3,
D1.4, and D1.6 is <3.50; in addition, the factor loadings of these
three indicators are <0.400, therefore D1.3, D1.4, and D1.6 were
excluded. The factor loading of D1.7 is also <0.400, but the mean
value is >3.8, so D1.7 was kept. In this way, the excluded KRIs and
kept KRIs are shown in Table 8, where 19 KRIs were excluded.
Finally, 41 KRIs for RVR of PPPs were identified.
In final KRIs, many LKRIs should receive more attention. Fig. 4
shows the top-three indicators in each dimension. The designrelated indicators are the most important in the dimensions of performance, function, maintenance, and sustainability. In a PPP
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Fig. 3. Loading estimates in CFA for proposed conceptual model
project, design is usually the responsibility of private sectors, which
would help private sectors reduce the lifecycle costs, improve the
reliability of facilities, and greatly improve the residual value of
PPP projects (Li et al. 2005; Clifton and Duffield 2006).
Construction quality is the second most important for the RV of
PPPs in the dimensions of performance, function, maintenance,
and sustainability. High quality of infrastructure is delivered by the
construction process, which will not only meet the stakeholders’
requirements, but also effectively improve the life span and durability of the project (Tang et al. 2013). Operation cost is the indicator for the operation stage, which is also critical for RV change in
PPPs. Operation cost is the most important in the dimension of
operability, the second most important in the dimension of profitability and refinanceability, and ranks third in the dimension of
function. The private sector has the responsibility for both the service and management of a facility to minimize performance deduction, proactively manage risks, and meet the operational requirements, in which operation cost control is especially important
because the advantage of PPPs mainly depends on the lifecycle
cost reduction and could be strongly influenced by operation costs
(Love et al. 2015). In the operation stage, another important indicator is public service quality, which ranks second in the dimension
of operability, fourth in the dimension of function, and fifth in the
dimension of sustainability. As innovative ways to deliver high-
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Table 7. Common CFA Model Fit Indices and Their Acceptable Value
Fit index
x 2/Df (chi-square difference
with 1 degree of freedom)
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Acceptable value range
Acceptable values range
from 1 to 5; <3 indicates
good fit
A value of 0.90 or larger
indicates a good model fit;
>0.95 indicates excellent fit
A value of 0.06 or less indicates an acceptable model fit;
below 0.05 indicates excellent fit
quality public services, PPPs usually concentrate on value for
money issues to change traditional public service delivery, allowing the private sector to introduce step change and cultural change,
maximize synergies, generate internal investment, play a major
role in meeting strategic and service objectives, and adopt output/
income-based measures for success (Sobhiyah et al. 2009).
Another important finding from the research survey is that technology improvement and upgrade are viewed as key indicators for
RVR of PPPs. In the dimensions of performance, maintenance, and
sustainability, technology improvement and upgrade rank third in
each dimension. Technical feasibility is the key to providing an
imaginative technical solution for PPPs (Zhang 2006). Moreover,
technology used in a PPP project is actually more significant than
in a traditional project. The technical details, advanced degree,
update time of related equipment and devices, and time of technology upgrade should be considered carefully to improve the quality
of resources and expertise available, design capability, management capability, commissioning ability, and adaptability of technology (Zhao et al. 2013; Osei-Kyei and Chan 2015).
In final KRIs, 12 GKRIs were kept, comprising political, social,
legal, economic, and systematic indicators. The KRIs related to
exchange rate, force majeure, and interface management were
excluded, indicating that the investments from abroad are rare now
in China, and internal management of special purpose vehicle
(SPV) for PPP projects should strengthen the understanding of
uncertainty of future changes. For political KRIs, the stability of
local government (D7.2) was of great importance, indicating the
creditworthiness of government is critical in improving RV
(Abednego and Ogunlana 2006; Wibowo and Kochendoerfer
2011). Meanwhile, social KRIs (D7.3) were also very significant as
presented in prior researches, focusing on public satisfaction and
social welfare of PPPs (Zhang 2011; Rouhani et al. 2016).
Confirmed by prior researches (Wang et al. 1999; Xu et al. 2010),
legal changes greatly impact the RV changes. Furthermore, the
degree of improvement in financing market (D7.9) in China would
also heavily influence the RV (Zhao et al. 2013; Chang and Chen
2016), which was the most significant KRI from an economic perspective. Moreover, the perfect degree of contract management
(D7.14) was always viewed as a crucial indicator for the success of
PPP projects (Appuhami et al. 2011), which was the same in RVR
management in PPPs from the viewpoint of systematic impacts.
Potential Use of Identified KRIs
In Fig. 3, the relationships between RVR, RVR dimension, and
KRIs are clearly presented. To consolidate the number of KRIs, 21
KRIs were removed according to the statistical analysis results,
including factor loadings and statistical significance. RVR management in PPP projects strongly depends on improvement of product
or service performance, facility function, profitability and possibility
of refinancing, maintainability, operability, and sustainability.
Furthermore, political, social, legal, economic, and systematic
impacts also strongly influence the outcome of PPP projects. The
identified KRIs are useful tools for effective RVR management in
PPPs. Potential use of these KRIs can be summarized as follows:
• KRIs can be used to detect the weakness of RVR management
during different stages of PPP projects. The identified six local
risk factors and one global risk factor concurrently affect the
RV during the whole concession period. The identified 41
KRIs can indicate how the risk factors influence the RV and
detect the weakness of RVR management from seven dimensions. In the period of preconstruction, RVR management
should pay high attention to the issues related to design and financing. More emphasis of project design in PPP projects
should be put on performance, function, maintainability, and
sustainability because of the close relationship between RV
and the impacts of design. Simultaneously, reasonable financing analysis, structure, arrangement, and costs should be carefully implemented at an early stage of PPP projects. In the
period of construction, time, cost, and quality control should
be more important in influencing the RV, in which construction quality is the most important KRI that could impact the
performance, function, maintainability, and sustainability. In
the period of operation, maintainability and operation costs,
market change, pricing change, technology progress, and horizontal competition are critical for the final RV, in which maintainability and operation costs should be more significant
compared to other KRIs influencing all six local risk dimensions of RV. In the global risk dimension, policy change, government creditworthiness, legal change, decision-making ability,
and contract management greatly influence the RV during
whole concession period.
• KRIs can be used to evaluate the RVR in PPPs. Six local risk
factors and one global risk factor can be measured by different
KRIs in different stages, and impact the RV of PPP projects
during their life cycles. Possibility and degree of the loss of
RV changes can be indicated by different KRIs and their criteria in different stages. Risk evaluations typically need corresponding data to calculate the possibility of RVR events and
degree of RVR consequences according to different KRIs.
Therefore, the appropriate KRIs must be determined to evaluate the RVR to estimate possible effects of any given change
on the RV of PPP projects. Actually, the criteria of KRIs in
various types of PPP projects could be different. When the
KRIs are used in practical projects, more detailed criteria
should be customized to the application. Furthermore, more
data related to possibility of RVR events and degree of RVR
consequences should also be collected through expert experiences and case studies to improve the accuracy of RVR evaluation. In contrast, the use frequency of KRIs to evaluate the
RVR should be carefully considered. KRIs for RVR can be
used throughout the concession period of a PPP project.
However, flexible arrangement of RVR management, including RVR evaluation frequency, should take the results of the
risk evaluation and the requirements of government into
account. If the results indicate low RVR level, frequent measurement may not be necessary because RVR evaluation demonstrates that the government would not suffer huge losses of
RV when transferring the PPP projects. PPP projects would
J. Manage. Eng., 2018, 34(1): 04017046
J. Manage. Eng.
Table 8. Refinement of KRIs of RVR in PPPs
Mean value <3.50
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D1.3, D1.4, D1.6
D2.3, D2.4, D2.5, D2.6, D2.8, D2.9
D4.6, D4.7, D4.8
D5.4, D5.5
D6.5, D6.12
D7.6, D7.7, D7.8, D7.11, D7.13, D7.15
Factor loading <0.400
Kept KRIs
Excluded KRIs
D1.3, D1.4, D1.6, D1.7
D2.1, D2.2, D2.7, D2.8, D2.9
D1.7 (mean value >3.80)
D2.1 (mean value >3.80)
D2.2 (mean value >3.80)
D2.3 (factor loading >0.600)
D2.7 (mean value >3.80)
D7.6 (factor loading >0.600)
D7.8 (mean value >3.80)
D7.11 ( actor loading >0.600)
D1.3, D1.4, D1.6
D2.4, D2.5, D2.6, D2.8, D2.9
D4.6, D4.7
D4.6, D4.7, D4.8
D5.4, D5.5
D6.5, D6.10, D6.12
D7.7, D7.12, D7.13, D7.15
Fig. 4. Top-three KRIs in different risk dimensions based on survey results
suffer some loss of RV due to uncertainty from seven risk
dimensions, and government may pay more attention to the
RVR of projects during the concession period, suggesting a
rationale for more frequent risk evaluation using KRIs. In
general, RVR evaluation should be normalized work for government when monitoring the SPV of PPP projects.
This paper not only identifies KRIs of RVR for PPP projects, but
also tests the relationships between KRIs, risk dimensions, and
RVR of PPP projects. A conceptual model of KRIs was proposed
and further developed by analysis of the hypothesized relationships
among indicators measuring PPP project RVR. A questionnaire survey was conducted to investigate opinions on seven risk dimensions
and 61 KRIs that influence RVR of a PPP project before being
transferred. Reliability analysis was conducted to test the internal
consistency of the survey variable data, confirming that opinions on
KRIs are consistent, which indicate that the survey results can be
further used to analyze the relationships among KRIs, risk dimensions, and RVR. The survey results demonstrated that most of the
61 KRIs are important (>3.00) and can be used to evaluate the
RVR from the seven risk dimensions. The CFA method was also
used to test whether the hypothesized model correlates with data
collected from the survey. The results of the CFA on the initial
model showed a good model fit, which indicated that all risk dimensions contribute to the change of RV in PPP projects, and that the
classification of KRIs within the risk dimensions is accurate (hypothesis in this paper). Based on the results of the survey and CFA,
61 KRIs were refined to 41 KRIs, and the most important criteria
for reducing RVR in PPP projects are effective maintenance, longterm sustainable development, and reasonable profitability and refinanceability. Additionally, active political supports, small social
impacts, positive legal issues, stable financing market, and proper
decision making are also crucial to reduce RVR in PPPs. According
to the statistical analysis and CFA, the greater value was assigned to
high-quality design work in the early stages of PPP development,
the lifecycle quality including construction quality and public service quality, comparatively low operation costs during the operation
period, and feasible technologies adopted by private sectors.
The 41 KRIs offer a useful tool for distinguishing strengths and
weaknesses for effective RVR management and measurement in
PPPs. Moreover, the CFA results provide a basis for reduced RVR
and more effectively meet the requirement of improving the value
of PPP projects to obtain long-term and sustainable development
J. Manage. Eng., 2018, 34(1): 04017046
J. Manage. Eng.
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through PPP projects. KRIs can indicate trends of losses and control
weaknesses for RVR management in PPPs. A potential use of identified KRIs is to identify the weakness of RVR management and to
measure the RVR in PPPs, where several different measurement
methods can be adopted to monitor and calculate any specific RV
changes in the conduct of PPP projects. Although this research on
KRIs promotes improved understanding of RVR management in
PPP projects, there are some limitations of the study. The causeand-effect relationships between the different risk dimensions
should be clarified through future research. The clarification of relationships between different KRIs will promote enhanced industry
understanding of how to more effectively measure and reduce RVR
of PPP projects, suggesting that further studies should explore how
these KRIs can be most effectively applied.
The authors’ special thanks go to all reviewers of the paper and to
the National Natural Science Foundation of China (NSFC71472037, 71671042); the Social Foundation of Jiangsu Province,
China (13GLB005); the Program for Outstanding Young Teachers
of Southeast University (2242015R30009); and the Fundamental
Research Funds for the Central Universities for financially
supporting this research.
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