Structural Equation Model for Evaluating Factors Affecting Quality of Social Infrastructure Projects

Abstract

The quality of the constructed social infrastructure project has been considered a necessary measure for the sustainability of projects. Studies on factors affecting project quality have used various techniques and methods to explain the relationships between particular variables. Unexpectedly, Structural Equation Modeling (SEM) has acquired very little concern in factors affecting project quality studies. To address this limitation in the body of knowledge, the objective of this study was to apply the SEM approach and build a model that explained and identified the critical factors affecting quality in social infrastructure projects. The authors developed a quantitative approach using smart-PLS version 3.2.7. This study shed light on the views of different experts based on their experience in public construction projects in Pakistan. Particularly, the authors aimed to find out the relationships between construction, stakeholders, materials, design, and external factors, and how these relate to project quality. The findings of this study revealed that the R2 value of the model was scored at 0.749, which meant that the five exogenous latent constructs collectively explained 74.9% of the variance in project quality. The Goodness-of-Fit of the model was 0.458. The construction related factor was the most important out of the five constructs. This study determined that better planning and monitoring and evaluation should be developed to better address and control the quality defects by decision-makers, project managers as well as contractors. These findings might support practitioners and decision makers to focus on quality related problems that might occur in their current or future projects.

Full text

sustainability
Article
Structural Equation Model for Evaluating Factors
Affecting Quality of Social Infrastructure Projects
Shahid Hussain 1, *ID , Zhu Fangwei 1, *, Ahmed Faisal Siddiqi 2, Zaigham Ali 3
and Muhammad Salman Shabbir 4
1Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China
2Institute of Business & Management, University of Engineering & Technology, Lahore 54000, Pakistan;
ahmedsiddiqi@uet.edu.pk
3
Department of Business Management, Karakoram International University, Gilgit-Baltistan 15100, Pakistan;
zaigham.ali@kiu.edu.pk
4OYA Graduate School of Business, Universiti Utara Malaysia, Sintok Kedh 06010, Malaysia;
m_salman_shabbir@oyagsb.uum.edu.my
*Correspondence: shahidkhoja@mail.dlut.edu.cn (S.H.); zhufw@dlut.edu.cn (Z.F.)
Received: 12 March 2018; Accepted: 30 April 2018; Published: 3 May 2018


Abstract:
The quality of the constructed social infrastructure project has been considered a necessary
measure for the sustainability of projects. Studies on factors affecting project quality have used various
techniques and methods to explain the relationships between particular variables. Unexpectedly,
Structural Equation Modeling (SEM) has acquired very little concern in factors affecting project
quality studies. To address this limitation in the body of knowledge, the objective of this study
was to apply the SEM approach and build a model that explained and identified the critical factors
affecting quality in social infrastructure projects. The authors developed a quantitative approach
using smart-PLS version 3.2.7. This study shed light on the views of different experts based on their
experience in public construction projects in Pakistan. Particularly, the authors aimed to find out
the relationships between construction, stakeholders, materials, design, and external factors, and
how these relate to project quality. The findings of this study revealed that the R
2
value of the model
was scored at 0.749, which meant that the five exogenous latent constructs collectively explained
74.9% of the variance in project quality. The Goodness-of-Fit of the model was 0.458. The construction
related factor was the most important out of the five constructs. This study determined that better
planning and monitoring and evaluation should be developed to better address and control the
quality defects by decision-makers, project managers as well as contractors. These findings might
support practitioners and decision makers to focus on quality related problems that might occur in
their current or future projects.
Keywords: critical quality factors; public construction industry; PLS-SEM; key constructs; Pakistan
1. Introduction
Time, cost and quality have been accepted as key factors of project success [
1
]. Among these
three generally accepted constraints, the quality dimension is studied as the least explicit feature
of project success. Quality is currently becoming as imperative a distinguishing factor as cost and
time of public infrastructure project. In order to accomplish the time and cost objectives, project
quality is mostly ignored [
2
,
3
]. Turk [
4
] indicated that quality might sometimes be overlooked by
the contracting parties in the construction industry to cut back the project costs and schedule. Better
quality in construction projects is a well-known factor in resulting success and sustainability of projects
by public construction industry globally. In the construction industry, quality is defined as the effective
Sustainability 2018,10, 1415; doi:10.3390/su10051415 www.mdpi.com/journal/sustainability

Sustainability 2018,10, 1415 2 of 25
and successful accomplishment of contracted project goals between client and the service provider or
main contractor [
5
] and conformance with requirements [
6
]. Quality requires proper supervision in
every phase of the project which is the particular accomplishment of the quality assurance system [
7
].
In general, currently, quality is given extra attention to client’ expectations of quality are increasing [
8
]
because it relates to the durability and sustainability of the project.
Social infrastructure projects (SIP) (hospitals, prisons, courts, educational institutions, government
accommodation, and others) are the combination of sub-sectors that facilitate social development
and enhance quality of life [
9
], particularly constructed to serve the community at large. The quality
of building projects generally implies satisfying the users’ expectations and meeting the quality
requirements [
10
]. Traditionally, time, cost and quality have been considered as the “triple constraint”
in the field of project management. Most importantly, in contemporary times, quality is assumed as an
additional consideration as the end users’ beliefs in quality are growing. Quality management has
gradually been implemented by public construction organizations as a mechanism to figure out quality
problems and to fulfil the requirements of the end-users [
11
,
12
]. The public construction industry
plays a vital role for the betterment of the society as the public construction industry is dynamic
and complex in its nature as it involves vast numbers of participants as clients, service providers
(contractors), consultants, communities, regulators, and others [
13
]. Furthermore, a construction
project is a multifaceted process including a number of different phases throughout which various parts
influence the quality performance of projects. A number of participants are included in the construction
process, all of them seeking to care for their particular agenda [
14
]. In spite of this complication,
the public construction industry plays an imperative role in the improvement and accomplishment
of organizational as well as societal goals. In construction work, an industry’s capability to execute
a quality outcome in a safe manner is a fundamental necessity for business success [
15
] and is a
substantial benefit to the community. The success of quality management is governed by management
practices such as the commitment of the project team towards site supervision [
16
,
17
], the contractor
commitment to quality management [
18
], and combining continuous improvement measures towards
the strategic goals throughout the complete project cycle [16].
In spite of the numerous benefits of project quality, it seems that quality implementation
techniques have not yet been effectively realized in the public construction industry, specifically
in the context of developing nations such as Pakistan. The lack of quality in public social infrastructure
projects is more critical for Pakistan. However, there is currently no finalized research in the case
of Pakistan to provide a key to the lack of quality in public social infrastructure projects. The public
construction sector in Pakistan has suffered from lack of quality projects for several years. Therefore,
it is extremely important to enhance the quality of social infrastructure project in developing nations
like Pakistan. Lack of quality in SIP have received a lot of attention recently. The most key feature in
lack of quality in SIP analysis is to find the key constructs and, consequently, the sustainability and
durability of SIP projects. Therefore, finding potential constructs of lack of quality is imperative in
determining quality problems before they actually happen. In particular, this study tried to find out
the answer to the question of how project quality is influenced by the considerations of construction,
stakeholder, design, material, and external factors. This was achieved by building a conceptual model
by using smart-PLS (v3.2.7) using construction, stakeholder, design, material, and external factors as
exogenous latent constructs and project quality as the endogenous latent construct. Based on earlier
literature, project quality is influenced by numerous factors, and different authors have suggested
different recommendations for improving and overcoming the quality glitches. To our knowledge,
very little or no research has been provided with a complete conceptual framework that justifies
how these exogenous latent constructs influence the project quality and which construct is the most
important one affecting the project quality. This study attempted to establish an empirical model that
will benefit not only researchers, but also practitioners seeking to understand how critical factors
(exogenous constructs) directly impact the project quality (endogenous constructs).

Sustainability 2018,10, 1415 3 of 25
The following sections describe the literature review, the conceptual model of the study,
the methodology used in this study, data analysis, discussion of the results, and conclusions of this study.
2. Literature Review
In previous studies, it has been found that the successful completion of project objectives and
success deals with the quality of the final product [
19
–
21
]. Indeed, factors affecting the quality of
construction projects have been identified in previous studies such as Arditi and Gunaydin [
22
],
who classified factors, for example, management commitment to continuous quality improvement,
personnel training, efficient and effective teamwork among and between parties involved in the project,
as common factors that affected project process quality. Likewise, the Hong Kong construction industry
is also affected by quality problems. By examining and analyzing the factors affecting the quality
of building projects, Chan and Tam [
23
] surveyed Hong Kong building projects. They determined
that the management activities of the project staff were the most dominant quality influencing factor,
followed by the client’s view on the importance of quality. Oyedele et al. [
24
] highlighted empirical
research in Nigeria that showed that the poor quality of materials delivered to the site, low level
of skill and labor experience, poor inspection and testing, poor site installation procedures, and the
lack of quality assurance were the top five most vital factors. They also suggested that there was a
need for the awareness and understanding of project quality among all construction parties through
different educational programs and courses. A study was carried out by Refaat and Abdel-Razek [
17
]
on factors affecting building construction projects in Egypt. A Delphi technique was employed by
the author and it was found that design, contract, material, labor, equipment, site staff, and execution
were the main quality influencing factors. A study done by Bubshait and Al-Atiq [
25
] showed the
contractor’s quality assurance system and verified that consistent quality was imperative in averting
quality related matters as well as the repetition of problems. The authors also found that the majority
of the contractors were unfamiliar with the quality record system. Jha and Iyer [2] managed research
in India on critical factors affecting quality performance in construction projects and revealed the
competencies of project managers and administrative support as the essential factors that influenced
the quality of the project.
Human and organizational errors are recognized as the key contributors to the need for rework
and quality defects [
26
]. Lack of experienced labor and lack of on-site monitoring during construction
are noteworthy attributes that affects project quality [
27
]. Mandal et al. [
28
] developed a quality process
model combining the human resources, technical, and quality improvement system, where the human
resources factor was found as one of the three subsystems affecting the quality. Rustom and Amer [
19
]
used a fuzzy triangle methodology and revealed that the most significant parameters influencing
the quality of projects were skilled and experienced personnel in the teams of both the owner and
contractor and the efficiency of the owner’s assessment team. Furthermore, construction quality could
be improved by increasing the skilled and experienced labor on a project site [
29
]. Tam et al. [
18
]
found that the lack of monitoring the performance quality of contractors and labor turnover were the
factors that impacted public housing construction in Hong Kong. The authors further introduced a
Performance Assessment Scoring Scheme to measure the standard and performance, and showed that
the expected continuous improvement in construction quality had not been fulfilled over a particular
period, as well as the quality of projects seemed far away on paper when compared to the actual
work done on the project site. In the Gaza Strip, Rustom and Amer [
30
] observed that the quality
of construction projects was substantially influenced by better site layout design, complete design
documents, skilled human resources on site, proper installation of equipment and materials, and
the contractor’s financial position. A framework introduced by Juran [
31
] was comprised of three
key dimensions such as quality planning, quality control, and quality improvement. The author
further stressed that statistical tools and techniques could be incorporated to eradicate the quality
deficiencies. Rowlinson and Walker [
32
] revealed that the construction industry was described by
its non-standardization and there was no universally accepted standard to measure the construction

Sustainability 2018,10, 1415 4 of 25
quality. Furthermore, the authors argued that changes that were often made in the design of a project
may be the resulting delay in the construction project and that quality was also usually at risk due
to these changes. If the design of the construction project is to improve substantially during the
pre-construction phase and there was proper monitoring during buildability, then quality management
and quality standards would be improved [
33
]. Auchterlounie [
34
] observed that 57% of 300 UK
houses that were examined moderately or completely failed to fulfil the owners’ expectations. The key
cause for the client’s dissatisfaction was associated with quality defects. Yeoh and Lee [
35
] found that
the most key factors affecting the construction quality management system are lack of management
commitment, inconclusive interpretation of standard requirements and lack of training policies.
Table 1shows the methodologies and data sources of previous studies worldwide. From Table 1,
the results suggest that most studies acquired information through questionnaire surveys and most
of them used the relative importance index (RII) and factor analysis. Some of them used multiple
regression using SPSS. Furthermore, a thorough consideration of Table 1further showed that the
sample sizes were insufficient when compared to the variables the researchers aimed to survey as
the smallest sample size must be five times larger than the sum of the total exogenous (observed)
variables [
36
]. Moreover, past researchers have endeavored to ascertain the critical factors (only
observed variables) affecting project quality in the construction industry; a model has yet to be
established for this purpose. Therefore, this study surveyed the factors affecting quality in the public
construction industry by using a larger sample size, and a multivariate analysis method of partial least
squares (PLS) path model was used to validate the structural equation modeling (SEM).
Previous research as exhibited in Table 1regarding factors affecting quality of construction
projects has primarily used the same methodology in different countries and regions. As an extension
of the previous studies, this study introduced a method for quantifying the factors affecting the
quality of social infrastructure projects. The primary purpose of this study was to examine the main
constructs affecting quality in SIP, especially in the context of the public construction industry in
Pakistan. Therefore, this study built and tested a conceptual model conducted by previous literature
related to quality and presented a complete picture of the contributory factors that influenced project
quality. Furthermore, this study aimed at identifying the most significant construct that affected the
quality of public projects from the owner’s perspective, which has been ignored in previous studies.
Nevertheless, the public construction industry in Pakistan is still faced with poor quality. Therefore,
quality defects are a crucial problem in the public construction industry in Pakistan, which needs
severe consideration in improving project quality as it affects overall stakeholder satisfaction and
sustainability of the project. In many developing nations, the quality of public infrastructure projects is
still inadequate [
37
]. It is significant to ascertain the causative factors affecting the quality to effectively
improve the quality performance of the projects. This study applied an advanced multivariate analysis
method of the PLS path model for the quality influencing factors and examined the causal relationship
based on the conceptual framework, with statistical analysis conducted using smart–PLS software
to examine and validate the conceptual model. This is a very effective method for examining the
cause-effect relationships between variables [
38
]. The findings of the conceptual model are presumed
to give insights into the factors affecting the quality in SIP. The quality was evaluated based on the
perspectives of public sector construction experts. This study addressed the main quality challenges
faced by the SIP with reference to the Pakistani public construction industry.

Sustainability 2018,10, 1415 5 of 25
Table 1. The methodologies and data sources of previous studies worldwide.
Sr No. Authors Title Data Source Sample Size Methods Country
1 Gan et al. [8]Critical Factors Affecting the Quality of Industrialized
Building System Projects in China Questionnaire survey 179 respondents t-statistics and ANOVA China
2 Jha & Iyer [2]Critical Factors Affecting Quality Performance in
Construction Projects Questionnaire survey 112 respondents Factor analysis India
3 Chan et al. [39]Assessing quality relationships in public housing:
An empirical study Questionnaire survey 54 respondents Factor analysis and multiple
regression analysis Hong Kong
4 Ling et al. [40]
Determinants of international architectural,
engineering and construction firms’ project success
in China
Questionnaire survey
and interview 27 interviews Correlation analysis and
multiple regression China
5 Ling et al. [41]Predicting Performance of Design-Build and
Design-Bid-Build Projects
Questionnaire survey
and interview 87 projects Multivariate linear regression Singapore
6 Nguyen et al. [42]
A study on project success factors in large construction
projects in Vietnam Questionnaire survey 109 respondents Factor analysis Vietnam
7 Phua and Rowlinson [43]How important is cooperation to construction project
success? A grounded empirical quantification
Interviews and
questionnaire survey
29 interviews, 398
quantitative response Factor analysis Hong Kong
8 Nguyen et al. [42]
A study on project success factors in large construction
projects in Vietnam Questionnaire survey 109 respondents Factor analysis Vietnam
9 Cooke-Davies [44] The “real” success factors on projects Questionnaire survey
and interview 70 large organizations Correlation analysis Israel
10 Chan and Tam [23]Factors affecting the quality of building projects
in Hong Kong
Interviews and
questionnaire survey
55 respondents in 110
cases
Factor analysis and multiple
regression analysis Hong Kong
11 Chua et al. [45] Critical success factor for different project objectives Questionnaire survey 20 respondents Relative importance Singapore
12 Arditi and Gunaydin [22]Factors that affect process quality in the life cycle of
building projects Questionnaire survey 137 respondents Mean scores and ranked by RII USA
13 Abdel Razek [17]Factors affecting construction quality in Egypt:
identification and relative importance Delphi technique 159 respondents Pareto analysis Egypt
14 Albanese [46] Team-Building Process: Key to Better Project Results Questionnaire survey
and interviews 41 projects Mean scores USA
15
Mohsini and Davidson [
47
]
Determinants of performance in the traditional
building process Questionnaire survey 21 respondents Multiple regression Canada

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3. Conceptual Model
In evaluating the factors that affect project quality, the Partial Least Square Structural Equation
Modeling (PLS-SEM) was used, and a conceptual model designed. PLS-SEM is used for causal
predictive analysis and both reflective and formative variables [
48
]. This method is nonparametric
in nature, which means that this method does not need any supposition concerning the distribution
of the data. The PLS-SEM is common multivariate analysis method to calculate variance-based
structural equation models, particularly in social sciences fields [
49
]. Nevertheless, PLS-SEM presents
an opportunity to resolve multifaceted procedure of associations and causal relationships that are
otherwise hard to uncover. PLS-SEM handle the data to assessment the path coefficient. The most
commonly used application for PLS-SEM in current era is more suitable for the analysis of quantitative
data. In addition, PLS-SEM handles a distribution from the data using bootstrapping technique to
find out the significance value of path coefficient. The aim of this study is to apply PLS-SEM to
better understand the public construction projects quality influencing factors. The proposed model is
analyzed in two different stages, first the models comprises of latent variables (measurement models)
that define the relationships between latent indicators and their manifest variables, and second,
a structural model comprises of the relationships between the latent variables. The conceptual model
explicated the relationships between the latent variables and their related manifest variables. By using
the SEM technique, a model was developed, and a total of 31 quality affecting factors were also
named as the observed variables, which were finalized from the literature and categories into five
groups. The five groups were called exogenous latent constructs such as the construction related factor,
stakeholder related factor, material related factor, design related factor, and external related factor.
Whereas, the endogenous latent variable (Quality) consisted of five observed variables. The conceptual
model presenting the relationship between the exogenous latent constructs and endogenous latent
constructs are exhibited in Figure 1. Thus, project quality is influenced by the five major constructs.
The study hypotheses are as follows:
Hypothesis 1 (H1). Construction factor has a significant and positive effect on project quality.
Hypothesis 2 (H2). Stakeholder factor has a significant and positive effect on project quality.
Hypothesis 3 (H3). Design factor has a significant and positive effect on project quality.
Hypothesis 4 (H4). Material factor has a significant and positive effect on project quality.
Hypothesis 5 (H5). External factor has a significant and positive effect on project quality.
Sustainability 2018, 10, x FOR PEER REVIEW 6 of 25
3. Conceptual Model
In evaluating the factors that affect project quality, the Partial Least Square Structural Equation
Modeling (PLS-SEM) was used, and a conceptual model designed. PLS-SEM is used for causal
predictive analysis and both reflective and formative variables [48]. This method is nonparametric in
nature, which means that this method does not need any supposition concerning the distribution of
the data. The PLS-SEM is common multivariate analysis method to calculate variance-based
structural equation models, particularly in social sciences fields [49]. Nevertheless, PLS-SEM presents
an opportunity to resolve multifaceted procedure of associations and causal relationships that are
otherwise hard to uncover. PLS-SEM handle the data to assessment the path coefficient. The most
commonly used application for PLS-SEM in current era is more suitable for the analysis of
quantitative data. In addition, PLS-SEM handles a distribution from the data using bootstrapping
technique to find out the significance value of path coefficient. The aim of this study is to apply PLS-
SEM to better understand the public construction projects quality influencing factors. The proposed
model is analyzed in two different stages, first the models comprises of latent variables (measurement
models) that define the relationships between latent indicators and their manifest variables, and
second, a structural model comprises of the relationships between the latent variables. The
conceptual model explicated the relationships between the latent variables and their related manifest
variables. By using the SEM technique, a model was developed, and a total of 31 quality affecting
factors were also named as the observed variables, which were finalized from the literature and
categories into five groups. The five groups were called exogenous latent constructs such as the
construction related factor, stakeholder related factor, material related factor, design related factor,
and external related factor. Whereas, the endogenous latent variable (Quality) consisted of five
observed variables. The conceptual model presenting the relationship between the exogenous latent
constructs and endogenous latent constructs are exhibited in Figure 1. Thus, project quality is
influenced by the five major constructs. The study hypotheses are as follows:
Hypothesis 1 (H1). Construction factor has a significant and positive effect on project quality.
Hypothesis 2 (H2). Stakeholder factor has a significant and positive effect on project quality.
Hypothesis 3 (H3). Design factor has a significant and positive effect on project quality.
Hypothesis 4 (H4). Material factor has a significant and positive effect on project quality.
Hypothesis 5 (H5). External factor has a significant and positive effect on project quality.
Figure 1. Conceptual model.
Figure 1. Conceptual model.

Sustainability 2018,10, 1415 7 of 25
4. Research Methods
The data collection procedures involved three important steps, as exhibited in Figure 2. In the
first step, we obtained the preliminary variables of the factors affecting construction quality in
social infrastructure projects. Then, we conducted a pilot study to gain a better understanding and
completeness as well as modify the questionnaire. Finally, we conducted a questionnaire survey,
obtaining the importance degrees of different variables and perception of the respondents of the factors
affecting quality.
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 25
4. Research Methods
The data collection procedures involved three important steps, as exhibited in Figure 2. In the
first step, we obtained the preliminary variables of the factors affecting construction quality in social
infrastructure projects. Then, we conducted a pilot study to gain a better understanding and
completeness as well as modify the questionnaire. Finally, we conducted a questionnaire survey,
obtaining the importance degrees of different variables and perception of the respondents of the
factors affecting quality.
Figure 2. Data collection process.
4.1. Preliminary List of Factors
After a comprehensive and detailed literature review was conducted, the critical factors affecting
the quality of SIP are depicted in Table 2. The questionnaire was comprised of two sections. The first
section consisted of the respondents’ personal information, while section two consisted of the main
part of the questionnaire. Section two was categorized into six groups in accordance with the nature
of the factor: construction related factor (CRF), stakeholder related factor (SRF), material related
factor (MRF), design related factor (DRF), external related factor (ERF), and quality factors (QF).
Table 2. The preliminary list of factors affecting quality of social infrastructure projects.
Code
Factors
Stakeholder Related Factor (SRF)
SRF_1
Poor relationship and partnering among project participants [2,50]
SRF_2
Lack of contractor supervision [11,22]
SRF_3
Making site decisions on cost and not the value of work [24]
SRF_4
Lack of Management commitment [51,52]
SRF_5
Poor site installation procedure [24]
SRF_6
Inadequate site supervision [2,24]
Construction Related Factor (CRF)
CRF_1
Lack of experienced project managers in the construction [8]
CRF_2
Contractors’ poor management ability [53]
CRF_3
Lack of skilled workers in the construction [8]
CRF_4
Contractors’ lack of experience in the construction [54]
CRF_5
Lack of coordination between on-site management personnel [8]
CRF_6
Limited construction time imposed by project clients [8]
CRF_7
No corrective action on the poor-quality components [8]
CRF_8
Lack of construction quality criteria [8]
CRF_9
Inadequate involvement of the owner during construction works [24]
Material Related Factor (MRF)
MRF_1
Poor quality of raw materials [8,55]
MRF_2
Unavailability of construction materials [52,56]
MRF_3
Price fluctuation [24]
Figure 2. Data collection process.
4.1. Preliminary List of Factors
After a comprehensive and detailed literature review was conducted, the critical factors affecting
the quality of SIP are depicted in Table 2. The questionnaire was comprised of two sections. The first
section consisted of the respondents’ personal information, while section two consisted of the main
part of the questionnaire. Section two was categorized into six groups in accordance with the nature of
the factor: construction related factor (CRF), stakeholder related factor (SRF), material related factor
(MRF), design related factor (DRF), external related factor (ERF), and quality factors (QF).
Table 2. The preliminary list of factors affecting quality of social infrastructure projects.
Code Factors
Stakeholder Related Factor (SRF)
SRF_1 Poor relationship and partnering among project participants [2,50]
SRF_2 Lack of contractor supervision [11,22]
SRF_3 Making site decisions on cost and not the value of work [24]
SRF_4 Lack of Management commitment [51,52]
SRF_5 Poor site installation procedure [24]
SRF_6 Inadequate site supervision [2,24]
Construction Related Factor (CRF)
CRF_1 Lack of experienced project managers in the construction [8]
CRF_2 Contractors’ poor management ability [53]
CRF_3 Lack of skilled workers in the construction [8]
CRF_4 Contractors’ lack of experience in the construction [54]
CRF_5 Lack of coordination between on-site management personnel [8]
CRF_6 Limited construction time imposed by project clients [8]
CRF_7 No corrective action on the poor-quality components [8]
CRF_8 Lack of construction quality criteria [8]
CRF_9 Inadequate involvement of the owner during construction works [24]
Material Related Factor (MRF)
MRF_1 Poor quality of raw materials [8,55]
MRF_2 Unavailability of construction materials [52,56]
MRF_3 Price fluctuation [24]

Sustainability 2018,10, 1415 8 of 25
Table 2. Cont.
Code Factors
MRF_4 Change in specification and type of materials during construction [55]
MRF_5 Aggressive competition during tendering [2]
Design Related Factor (DRF)
DRF_1 Design complexity [24]
DRF_2 Incomplete design information provided by project clients [8,55]
DRF_3 Flaws in design specification [8]
DRF_4 Limited design time imposed by project clients [8]
DRF_5 Lack of experienced designers [8,24]
DRF_6 Too many design changes during execution [57]
External Related Factor (ERF)
ERF_1 Hostile social environment [2]
ERF_2 Political instability [2]
ERF_3 Hostile economic environment [2]
ERF_4 Harsh climatic condition at the site [2,24]
ERF_5 Changes in government regulations [55]
Quality Factors (QF)
QF_1 Lack of quality planning [22]
QF_2 Lack of understanding the potential benefits of quality implementation [14]
QF_3 Lack of quality assurance [24]
QF_4 Lack of proper quality monitoring and evaluation [2]
QF_5 Lack of quality policy [22]
4.2. Pilot Study and Questionnaire Design
The survey method was adopted to test the hypotheses proposed in this study. A preliminary list
of factors that affected the quality was revealed in the initial research period, and this list of questions
was given to professionals in the public construction industry through interviews. Interviews were
requested and organized with different project parties with experience of more than 16 years in the
public construction industry including executives, project managers, and assistant project managers;
on average the construction experts had handled more than 30 projects in the current fiscal year.
The questionnaires were self-administered to obtain instructions on the factors affecting quality from
the experts. During this stage, we obtained valuable suggestions from the experts, their suggestions
were incorporated, and the survey questionnaire was polished based on the pilot survey responses.
4.3. Respondents’ Profile
Table 3shows the demographic information of the respondents. The respondents were selected
from a wide range of professionals engaged in the Pakistani public construction industry (City
Development Authority (CDA), Public Work Department (PWD), Defense Housing Authority (DHA),
Water and Power Development Authority (WAPDA), National Logistics Cell (NLC), and the Water
and Sanitation Agency (WASA)). Table 3shows that 23% of the respondents were executives, 14% of
the respondents were managerial personnel, 19% were designers/architects, and the majority of them
were project managers (44%), and most of them were involved in the execution and monitoring phases
of the project. Furthermore, most of the respondents had more than 20 years of professional work
experience in the public construction industry, which is an indication of the good response rate, as the
respondents have related the experience of the subject area.
Table 3. Demographic information of respondents.
Respondents’ Profile Number Percent (%)
Working Experience
5–10 years 95 21%
11–20 years 165 37%

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Table 3. Cont.
Respondents’ Profile Number Percent (%)
21–30 years 105 23%
>30 86 19%
Educational background
Diploma of Associate Engineering 160 35%
BSc 105 23%
MSc 150 33%
MBA 36 8%
Role in the organization
Chief Engineer 7 2%
Superintendent Engineer 9 2%
Executive Engineer 85 19%
Assistant Executive Engineer 90 20%
Managerial Personal 65 14%
Assistant Engineer 110 24%
Designer/Architect 85 19%
Construction Industry type
DHA 56 12%
CDA 139 31%
PWD 107 24%
WAPDA 78 17%
WASA 43 10%
NLC 28 6%
4.4. Sampling and Data Collection
The data collection was conducted by practitioners who were involved in public construction
projects. This sample group was selected from a list of Pakistani public construction industries.
The participants were public construction experts. A total of 800 public construction participants were
chosen at random to answer the survey questionnaire. The emails and face-to-face interviews were the
core sources used in collecting data for this study [
58
,
59
]. A questionnaire survey was conducted with
public construction practitioners to examine their perceptions. The respondents were selected from
publicly available organization records, which were also mostly available on the company’s website.
The final questionnaire was split into two key sections. Section 1contained the respondents’
profile such as years of experience, educational qualification, role in the organization, and industry type.
Section 2consisted of the final list of the questions on a five-point Likert scale ranging from 1 (Strongly
disagree) to 5 (Strongly agree). The questionnaires were sent to the practitioners by email or meeting
with a supplementary covering letter that described the purpose of the survey and the privacy of the
responses. Respondents were asked to answer the survey questions on the basis of their experience
and the most current completed project that they were involved in and were faced with quality issues.
From 800 distributed questionnaires, 466 questionnaires were returned. Fifteen questionnaires were
incomplete or contained inaccurate information. A total of 451 completed questionnaires were obtained
for the final analysis, equating to a 56.3% response rate. The respondents had enough experience
to understand the importance of the study. To further improve and better understand the survey
results, ten selected respondents who had rich experience in construction quality were interviewed
after analyzing the survey data.
5. Data Analysis
The simulation work in calculating the effect of the observed variables and their latent constructs
on construction quality was drawn in smart-PLS version 3.2.7 [
60
]. PLS-SEM is mostly used for
theory development in exploratory research [
61
]. Major applications of SEM contain path analysis,

Sustainability 2018,10, 1415 10 of 25
confirmatory factor analysis, second-order factor analysis, regression models, covariance structure
models, and correlation structure models [
62
]. Moreover, SEM permits the analysis of the linear
relationships between the latent constructs and manifest variables. It also has the ability to create
accessible parameter estimates for the relationships between unobserved variables. In general,
SEM permits several relationships to be tested at once in a single model with various relationships
instead of examining each relationship individually. The hypothesized structural model in Figure 1
was analyzed using Smart-PLS version 3.2.7, which has advantages over regression-based methods
in evaluating several latent constructs with various manifest variables [
63
]. PLS contains a two-step
procedure as recommended by Henseler et al. [
64
], which involves the evaluation of the outer
measurement model and evaluation of the inner structural model. Moreover, PLS-SEM is currently
known and selected within social sciences studies as a technique that is the best appropriate method
for a multivariate analysis [65,66].
Appendix Aprovides a comprehensive explanation of the descriptive statistics such as mean,
standard deviation, kurtosis, and skewness. The kurtosis and skewness (values lie between
−
1 and
+1) results showed that the data were normally distributed.
5.1. Evaluation of Outer Measurement Model
The outer measurement model is aimed to calculate the reliability, internal consistency,
and validity of the observed variables (measured through the questionnaire) together with unobserved
variables [
67
]. Consistency evaluations are based on single observed and construct reliability tests
whereas convergent and discriminant validity are used for the assessment of validity [49].
A single observed variable reliability describes the variance of an individual observed
comparatively to an unobserved variable by evaluating the standardized outer loadings of the
observed variables [
68
]. Observed variables with an outer loading of 0.7 or greater are believed
to be greatly acceptable [
49
], while the outer loading with a value less than 0.7 should be discarded [
69
].
Notwithstanding this, for this study, the cut-off value accepted for the outer loading was 0.7.
From Table 4, the outer loadings ranged between 0.708 and 0.872. Cronbach’s alpha and Composite
Reliability (CR) were used for internal consistency evaluation in the construct reliability. Nevertheless,
compared to the Cronbach’s alpha, CR is believed to be a better assessment of internal consistency
as it retains the standardized loadings of the observed variables [
70
]. Although, the analysis of the
Cronbach’s alpha and CR value was the same. Table 4shows that the Cronbach’s alpha and CR for all
constructs were greater than 0.80. Thus, the Cronbach’s alpha and CR showed that the scales were
reasonably reliable and indicated that all the latent construct values exceeded the minimum threshold
level of 0.70. To verify the convergent validity of the variables, each latent construct’s Average Variance
Extracted (AVE) was calculated [
70
]. The lowest 50% of the variance from the observed variable should
be taken by the latent constructs in the model. Hence, this indicates that the AVE for all constructs
should be above 0.5 [
38
,
71
]. From Table 4, it is seen that all of the AVE values were more than 0.5,
so convergent validity was confirmed for this study model. These results confirmed the convergent
validity and good internal consistency of the measurement model.
The next attempt was the discriminant validity of the latent constructs. Discriminant validity
defines that the manifest variable in any construct is distinct from other constructs in the path model,
where its cross-loading value in the latent variable is greater than that in any other constructs [
72
].
The Fornell and Larcker criterion and cross-loadings were used to evaluate the discriminant
validity [
70
]. The suggested standard is that a construct should not show the same variance as any
other construct that is more than its AVE value [
72
]. Table 5shows the Fornell and Larcker criterion
test of the model where the squared correlations were compared with the correlations from other latent
constructs. Table 5shows that all of the correlations were smaller relative to the squared root of average
variance exerted along the diagonals, implying satisfactory discriminant validity. This proved that the
observed variables in every construct indicated the given latent variable confirming the discriminant
validity of the model, whereas, Table 6shows that the cross-loading of all observed variables was more

Sustainability 2018,10, 1415 11 of 25
than the inter-correlations of the construct of all the other observed variables in the model. Therefore,
these findings confirmed the cross-loadings assessment standards and provided acceptable validation
for the discriminant validity of the measurement model.
As a result, the suggested conceptual model was supposed to be acceptable, with confirmation
of adequate reliability, convergent validity, and discriminant validity and the verification of the
research model.
Table 4. Construct reliability and validity.
Main Constructs Items Loadings Cronbach’s Alpha CR AVE
Construction Related Factor
CRF_1 0.739
0.933
0.944
0.654
CRF_2 0.735
CRF_3 0.760
CRF_4 0.831
CRF_5 0.839
CRF_6 0.863
CRF_7 0.829
CRF_8 0.833
CRF_9 0.836
Design Related Factor
DRF_1 0.749
0.859
0.895
0.587
DRF_2 0.805
DRF_3 0.708
DRF_4 0.800
DRF_5 0.784
DRF_6 0.748
External Related Factor
ERF_1 0.747
0.846
0.890
0.619
ERF_2 0.794
ERF_3 0.769
ERF_4 0.778
ERF_5 0.843
Material Related Factor
MRF_1 0.837
0.859
0.899
0.641
MRF_2 0.720
MRF_3 0.810
MRF_4 0.769
MRF_5 0.859
Stakeholder Related Factor
SRF_1 0.715
0.869
0.902
0.607
SRF_2 0.872
SRF_3 0.781
SRF_4 0.709
SRF_5 0.739
SRF_6 0.845
Quality Factors
QF_1 0.859
0.873
0.907
0.662
QF_2 0.789
QF_3 0.791
QF_4 0.819
QF_5 0.807
Table 5. Fornell–Larcker criterion test.
CRF DRF ERF MRF QF SRF
Construction Related Factor
0.809
Design Related Factor 0.230 0.766
External Related Factor 0.258 0.266 0.787
Material Related Factor 0.260 0.242 0.265 0.800
Quality Factors 0.561 0.555 0.526 0.547 0.814
Stakeholder Related Factor 0.247 0.290 0.233 0.258 0.560 0.779

Sustainability 2018,10, 1415 12 of 25
Table 6. Cross-loadings.
CRF DRF ERF MRF QF SRF
CRF_1 0.739 0.151 0.203 0.244 0.419 0.183
CRF_2 0.735 0.152 0.158 0.142 0.395 0.199
CRF_3 0.760 0.154 0.226 0.165 0.418 0.179
CRF_4 0.831 0.182 0.233 0.209 0.462 0.185
CRF_5 0.839 0.179 0.227 0.182 0.465 0.237
CRF_6 0.863 0.232 0.224 0.261 0.497 0.222
CRF_7 0.829 0.244 0.200 0.264 0.464 0.191
CRF_8 0.833 0.164 0.197 0.213 0.491 0.227
CRF_9 0.836 0.206 0.202 0.205 0.459 0.169
DRF_1 0.159 0.749 0.177 0.235 0.433 0.236
DRF_2 0.119 0.805 0.163 0.187 0.431 0.242
DRF_3 0.166 0.708 0.184 0.129 0.388 0.195
DRF_4 0.193 0.800 0.215 0.214 0.454 0.203
DRF_5 0.207 0.784 0.236 0.186 0.421 0.220
DRF_6 0.215 0.748 0.249 0.155 0.419 0.237
ERF_1 0.231 0.213 0.747 0.233 0.404 0.205
ERF_2 0.181 0.189 0.794 0.196 0.417 0.151
ERF_3 0.190 0.253 0.769 0.234 0.405 0.179
ERF_4 0.203 0.186 0.778 0.142 0.387 0.173
ERF_5 0.210 0.206 0.843 0.234 0.452 0.205
MRF_1 0.214 0.214 0.268 0.837 0.500 0.237
MRF_2 0.206 0.198 0.171 0.720 0.391 0.239
MRF_3 0.226 0.233 0.218 0.810 0.417 0.202
MRF_4 0.175 0.108 0.150 0.769 0.394 0.137
MRF_5 0.221 0.210 0.237 0.859 0.472 0.211
QF_1 0.522 0.520 0.491 0.509 0.859 0.502
QF_2 0.517 0.471 0.476 0.488 0.789 0.503
QF_3 0.394 0.405 0.399 0.380 0.791 0.414
QF_4 0.417 0.419 0.391 0.418 0.819 0.410
QF_5 0.405 0.422 0.358 0.405 0.807 0.427
SRF_1 0.144 0.252 0.123 0.147 0.355 0.715
SRF_2 0.218 0.215 0.246 0.213 0.516 0.872
SRF_3 0.186 0.215 0.180 0.246 0.452 0.781
SRF_4 0.161 0.205 0.097 0.175 0.378 0.709
SRF_5 0.193 0.235 0.187 0.173 0.402 0.739
SRF_6 0.236 0.246 0.223 0.238 0.487 0.845
5.2. Evaluation of the Inner Structural Model
We confirmed that the measurement model was valid and reliable. The next step was to measure
the Inner Structural Model outcomes. This included observing the model’s predictive relevancy and the
relationships between the constructs. The coefficient of determination (R
2
), Path coefficient (
β
value)
and T-statistic value, Effect size (
ƒ2
), the Predictive relevance of the model (Q
2
), and Goodness-of-Fit
(GOF) index are the key standards for evaluating the inner structural model.
5.2.1. Measuring the Value of R2
The coefficient of determination measures the overall effect size and variance explained in the
endogenous construct for the structural model and is thus a measure of the model’s predictive accuracy.
In this study, the inner path model was 0.749 for the quality endogenous latent construct. This indicates
that the five independent constructs substantially explain 74.9% of the variance in the quality, meaning
that about 74.9% of the change in the project quality was due to five latent constructs in the model.
According to Henseler et al. [
64
], and Hair et al. [
65
], an R
2
value of 0.75 is considered substantial,
an R
2
value of 50 is regarded as moderate, and an R
2
value of 0.26 is considered as weak. Hence, the R
2
value in this study was substantial.

Sustainability 2018,10, 1415 13 of 25
5.2.2. Estimation of Path Coefficients (β) and T-statistics
The path coefficients in the PLS and the standardized
β
coefficient in the regression analysis
were similar. Through the
β
value, the significance of the hypothesis was tested. The
β
denoted the
expected variation in the dependent construct for a unit variation in the independent construct(s).
The
β
values of every path in the hypothesized model was computed, the greater the
β
value, the more
the substantial effect on the endogenous latent construct. However, the βvalue had to be verified for
its significance level through the T-statistics test. The bootstrapping procedure was used to evaluate
the significance of the hypothesis [
69
]. To test the significance of the path coefficient and T-statistics
values, a bootstrapping procedure using 5000 subsamples with no sign changes was carried out for
this study as presented in Table 7.
Table 7. Path coefficient and T-statistics.
Hypothesized Path Standardized Beta T-Statistics pValues
Construction Related Factor -> Quality 0.296 10.996 0.000
Design Related Factor -> Quality 0.276 10.602 0.000
External Related Factor -> Quality 0.239 9.030 0.000
Material Related Factor -> Quality 0.266 10.513 0.000
Stakeholder Related Factor -> Quality 0.282 10.883 0.000
In H1, we predicted that the construction factor would significantly and positively influence
project quality. As predicted, the findings in Table 7and Figure 3confirmed that the construction related
factor significantly influenced project quality (
β
= 0.296, T = 10.996, p< 0.000). Hence, H1 was robustly
supported. Furthermore, when observing the direct and positive influence of the stakeholder related
factor on project quality (H2), the findings from Table 7and Figure 3endorsed that the stakeholder
related factor positively influenced project quality (
β
= 0.282, T = 10.883, p< 0.000), and confirmed H2.
The influence of the design related factor on project quality was positive and significant (
β
= 0.276,
T = 10.602, p< 0.000), showing that H3 was supported. The effect of the material related factor on
project quality was significant (
β
= 0.266, T = 10.513, p< 0.000), therefore supporting H4. Similarly,
the findings in Table 7provided empirical support for H5, where the influence of the external related
factor on project quality was positive and significantly affected the project quality (
β
= 0.239, T = 9.030,
p< 0.000), confirming hypothesis (H5).
The greater the beta coefficient (
β
), the stronger the effect of an exogenous latent construct on the
endogenous latent construct. Table 7and Figure 3showed that the construction related factor had the
topmost path coefficient of
β
= 0.296 when compared to other
β
values in the model, which showed
that it had a greater value of variance and high effect with regard to affecting the quality of social
infrastructure projects. Whereas, the external related factor had the least effect on project quality with
β= 0.239. Figure 4shows the graphical representation of all path coefficients of the model.

Sustainability 2018,10, 1415 14 of 25
Sustainability 2018, 10, x FOR PEER REVIEW 14 of 25
Figure 3. Assessment of the structural equation model.
Figure 4. Graphical representation of the path coefficient.
5.2.3. Measuring the Effect Size (ƒ2)
The ƒ2 is the degree of the impact of each exogenous latent construct on the endogenous latent
construct. When an independent construct is deleted from the path model, it changes the value of the
coefficient of determination (R2) and defines whether the removed latent exogenous construct has a
significant influence on the value of the latent endogenous construct. The ƒ2 values were 0.35 (strong
effect), 0.15 (moderate effect), and 0.02 (weak effect) [73]. Table 8 shows the ƒ2 from the SEM
calculations. As shown in Table 8, the effect size for construction, stakeholder, design, material, and
external related factor on project quality were 0.301, 0.268, 0.257, 0.240, and 0.194, respectively. Hence,
Figure 3. Assessment of the structural equation model.
Sustainability 2018, 10, x FOR PEER REVIEW 14 of 25
Figure 3. Assessment of the structural equation model.
Figure 4. Graphical representation of the path coefficient.
5.2.3. Measuring the Effect Size (ƒ2)
The ƒ2 is the degree of the impact of each exogenous latent construct on the endogenous latent
construct. When an independent construct is deleted from the path model, it changes the value of the
coefficient of determination (R2) and defines whether the removed latent exogenous construct has a
significant influence on the value of the latent endogenous construct. The ƒ2 values were 0.35 (strong
effect), 0.15 (moderate effect), and 0.02 (weak effect) [73]. Table 8 shows the ƒ2 from the SEM
calculations. As shown in Table 8, the effect size for construction, stakeholder, design, material, and
external related factor on project quality were 0.301, 0.268, 0.257, 0.240, and 0.194, respectively. Hence,
Figure 4. Graphical representation of the path coefficient.
5.2.3. Measuring the Effect Size (ƒ2)
The
ƒ2
is the degree of the impact of each exogenous latent construct on the endogenous latent
construct. When an independent construct is deleted from the path model, it changes the value of
the coefficient of determination (R
2
) and defines whether the removed latent exogenous construct
has a significant influence on the value of the latent endogenous construct. The
ƒ2
values were 0.35
(strong effect), 0.15 (moderate effect), and 0.02 (weak effect) [
73
]. Table 8shows the
ƒ2
from the SEM
calculations. As shown in Table 8, the effect size for construction, stakeholder, design, material,

Sustainability 2018,10, 1415 15 of 25
and external related factor on project quality were 0.301, 0.268, 0.257, 0.240, and 0.194, respectively.
Hence, according to Cohen’s [
73
] recommendation, the
ƒ2
of all five exogenous latent constructs on
project quality had a moderate effect on the value of R
2
. Furthermore, all the five independent latent
constructs in this study participated relatively to the greater R
2
value (74.9%) in the dependent variable.
Table 8. Effect size.
Exogenous Latent Variables Effect Size ƒ2Total Effect
Construction Related Factor 0.301 moderate
Design Related Factor 0.257 moderate
External Related Factor 0.194 moderate
Material Related Factor 0.240 moderate
Stakeholder Related Factor 0.268 moderate
5.2.4. Predictive Relevance of the Model (Q2)
Q
2
statistics are used to measure the quality of the PLS path model, which is calculated using
blindfolding procedures [
74
], and cross-validated redundancy was performed. The Q
2
criterion
recommends that the conceptual model can predict the endogenous latent constructs. In the SEM,
the Q
2
values measured must be greater than zero for a particular endogenous latent construct.
From Figure 5, it shows that the Q
2
values for this study model was equal to 0.458, which was higher
than the threshold limit, and supports that the path model’s predictive relevance was adequate for the
endogenous construct.
Sustainability 2018, 10, x FOR PEER REVIEW 15 of 25
according to Cohen’s [73] recommendation, the ƒ2 of all five exogenous latent constructs on project
quality had a moderate effect on the value of R2. Furthermore, all the five independent latent
constructs in this study participated relatively to the greater R2 value (74.9%) in the dependent
variable.
Table 8. Effect size.
Exogenous Latent Variables
Effect Size ƒ
2
Total Effect
Construction Related Factor
0.301
moderate
Design Related Factor
0.257
moderate
External Related Factor
0.194
moderate
Material Related Factor
0.240
moderate
Stakeholder Related Factor
0.268
moderate
5.2.4. Predictive Relevance of the Model (Q2)
Q2 statistics are used to measure the quality of the PLS path model, which is calculated using
blindfolding procedures [74], and cross-validated redundancy was performed. The Q2 criterion
recommends that the conceptual model can predict the endogenous latent constructs. In the SEM, the
Q2 values measured must be greater than zero for a particular endogenous latent construct. From
Figure 5, it shows that the Q2 values for this study model was equal to 0.458, which was higher than
the threshold limit, and supports that the path model’s predictive relevance was adequate for the
endogenous construct.
Figure 5. Predictive relevance of the model.
5.2.5. Goodness-of-Fit Index
Goodness-of-Fit (GOF) is applied as an index for the complete model fit to verify that the model
sufficiently explains the empirical data [74]. The GOF values lie between 0 and 1, where values of
0.10 (small), 0.25 (medium), and 0.36 (large) indicate the global validation of the path model. A good
model fit shows that a model is parsimonious and plausible [75]. The GOF is calculated by using the
geometric mean value of the average communality (AVE values) and the average R2 value(s), and the
GOF of the model is calculated by Equation (1) [74].
GOF =�Average 2∗Average communality
(1)
It was calculated from Table 9 that the GOF index for this study model was measured as 0.69,
which shows that empirical data fits the model satisfactory and has substantial predictive power in
comparison with baseline values.
Figure 5. Predictive relevance of the model.
5.2.5. Goodness-of-Fit Index
Goodness-of-Fit (GOF) is applied as an index for the complete model fit to verify that the model
sufficiently explains the empirical data [
74
]. The GOF values lie between 0 and 1, where values of
0.10 (small), 0.25 (medium), and 0.36 (large) indicate the global validation of the path model. A good
model fit shows that a model is parsimonious and plausible [
75
]. The GOF is calculated by using the
geometric mean value of the average communality (AVE values) and the average R
2
value(s), and the
GOF of the model is calculated by Equation (1) [74].
GOF =qAverage R2∗Average communality (1)
It was calculated from Table 9that the GOF index for this study model was measured as 0.69,
which shows that empirical data fits the model satisfactory and has substantial predictive power in
comparison with baseline values.

Sustainability 2018,10, 1415 16 of 25
Table 9. Goodness-of-Fit index calculation.
Construct AVE R2
Construction Related Factor 0.654
Design Related Factor 0.587
External Related Factor 0.619
Material Related Factor 0.641
Stakeholder Related Factor 0.607
Quality Factors 0.662 0.749
Average Values 3.77 0.749
AVE ×R20.4706
GOF = √(AVE ×R2)0.686
5.2.6. The Standardized Root Mean Square Residual (SRMR)
The SRMR is an index of the average of standardized residuals between the observed and the
hypothesized covariance matrices [
76
]. The SRMR is a measure of estimated model fit. When SRMR
= <0.08, then the study model has a good fit [
77
], with a lower SRMR being a better fit. Table 10 shows
that this study model’s SRMR was 0.05, which revealed that this study model had a good fit, whereas the
Chi-Square was equal to 1917.082 and NFI equal to 0.811 was also measured.
Table 10. Model fit summary.
Estimated Model
SRMR 0.050
d_ULS 1.632
d_G1 0.952
d_G2 0.792
Chi-Square 1917.082
NFI 0.811
5.3. Correlation Coefficient of Latent Variables
Finally, Table 11 shows the latent variable correlation coefficient. Table 11 shows that there was a
strong correlation between the latent exogenous constructs and the latent endogenous construct.
Table 11. Latent variable correlation.
CRF DRF ERF MRF SRF Quality
Construction Related Factor
1.000
Design Related Factor 0.230 1.000
External Related Factor 0.258 0.266 1.000
Material Related Factor 0.260 0.242 0.265 1.000
Stakeholder Related Factor 0.247 0.290 0.233 0.258 1.000
Quality 0.561 0.555 0.526 0.547 0.560 1.000
In accordance with the complete analysis of the measurement models and structural model, it was
determined that both models were confirmed. All of the hypotheses were statistically significant and
hence were all accepted. The results of this study support a richer and accurate picture of the factors
affecting the quality of social infrastructure project and can support building a set of strategies to
overcome the quality barriers.
6. Discussion
The key contribution of this study was to empirically reveal the constructs that affected social
infrastructure project quality using the PLS-SEM technique and a closer examination of the fundamental

Sustainability 2018,10, 1415 17 of 25
quality affecting constructs observed by construction practitioners in Pakistan. PLS-SEM is a very
effective technique for developing and analysis of complex models, and it also validates the complex
model, and social science investigators should develop latest techniques to manage more complex
model relationship of their current and future studies. The conceptual paths were tested using
SEM based on the PLS technique. Descriptive statistics such as the mean value, standard deviation,
skewness, and kurtosis values were measured. The results of the kurtosis and skewness values of
the measurement model were between +1 and
−
1, which implied that there was no violation of the
normality assumptions of the collected data. Moreover, the results of the study revealed that the
construction related factor, stakeholder related factor, material related factor, design related factor,
and external related factor had a significantly positive effect on project quality (R
2
= 0.749, p= 0.000),
predictive relevance (Q
2
= 0.458), and a substantial GOF (GOF = 0.69). The final SEM results revealed
that the construction related factor had the highest path coefficient (
β
= 0.296) with the overall
affecting quality. Therefore, the owner and service provider (contractor) should pay more attention to
construction related factors to improve the quality of public construction projects. The stakeholder
related factor was found to be the second utmost factor (
β
= 0.282) of the overall factors affecting
quality. Among the five essential quality influencing factors, the external related factor (
β
= 0.239) was
found to have a minimum direct effect on the quality, while the design related factor (
β
= 0.276) and
material related factor (
β
= 0.266) was found to have a moderate effect on the construction quality. The
findings of this study showed that all suggested hypotheses were supported, and the quality of the
social infrastructure project was affected by all five constructs, i.e., construction, stakeholder, material,
design, and external related factors.
As shown in Table 7, the path between all five exogenous latent constructs with an endogenous
latent construct (quality) showed a positive relationship and was statistically significant, thus accepting
all the hypotheses proposed in this study. The relationship between these quality influencing factors
suggests that there was ineffective planning and a lack of monitoring and evaluation during the
construction (execution) phase that had a profound effect on the quality of the social infrastructure
project and hence diminished the quality as well as the sustainability of the project. Planning and
monitoring competency is an important technical contributor to accomplishing successful project
completion [
78
]. Consequently, project owners and service providers should focus on the construction
related factor to improve quality in public construction projects. This was in line with the findings
of the study by Pheng and Ke-Wei [
79
] performed in Singapore. The service provider, nevertheless,
concentrates on finishing the project works on schedule and the completion of a project within the
estimated budget rather than on completing it according to the quality standard. It has been shown
that a detailed understanding of the project scope, plan, proper monitoring, and implementation
substantially assists in the effective quality in construction projects and hence improves the chance of
better project quality and successful project completion. Consequently, a lack of experienced project
managers on the construction site and poor management ability of the contractors especially in public
contraction projects are not able to accomplish better quality. These results were uniform with previous
research where the lack of experienced project managers on-site and the lack of experienced contractors
were emphasized in order to influence the project quality [
8
]. This indicates that most of the on-site
project managers and contractors lacked the skills of proper planning and coordination for addressing
on-site problems and used it to improve the level of quality. It is recommended that the project
contracting parties pays special attention to the construction related factor to achieve an enhanced
project quality.
Furthermore, the results of this study recommend that the stakeholder related factor had a
significant positive influence on project quality and that the quality of the project could be enhanced by
proper stakeholder supervision and monitoring of the project from initiation to completion. This was
in line with Luo et al. [
80
] who found that there lacked of a quality-supervision mechanism for the
production activities, which showed key quality challenges that decreased the assurance among
building stakeholders as to the quality of the projects. Inadequate site supervision and improper

Sustainability 2018,10, 1415 18 of 25
site decisions were part of the key significant elements influencing project quality on-site. Equally,
the findings of this study showed a statistically significant positive relationship between the design
related factor and project quality. In order to achieve better quality projects, the design team should
prepare design on time, with accuracy, conformance to standard, and complete information in design
as this can minimize the design discrepancy during the project execution. Thus, indicating that
the errors and incomplete project design during the designing phase of a project leads to a lack of
quality construction in the project execution. This finding was consistent with the previous results,
where incomplete design information was provided by project clients, the limited design time imposed
by project clients, and too many design changes during execution were highlighted to affect the project
quality [55,57].
Additionally, our findings also revealed that the material related factor was statistically significant
with project quality. Aggressive competition during bidding or the lowest bidder making more profit
usually led to the use of poor quality materials and bad technical practices that could lead to issues
of project quality. In addition, the unavailability of materials and price fluctuations in the market
during the life cycle of a project, could also be risks for the lack of quality in construction projects.
These findings were in line with previous studies by Jha and Iyer [2].
External factor such as changes in government regulations, a hostile environment, and political
instability not only decreases productivity, but also affects the harms the quality of projects.
The findings of this study showed that the external related factor had a significant influence on
project quality, thus building on and providing support to the findings of Jha and Iyer [
2
]. External
factors are generally difficult to control and are even beyond the control of the parties involved in the
construction process. Their negative effect on the project can diminish the project quality, for example,
a lack of quality caused by changes in government regulations, political instability, hostile economic
environment, harsh climatic condition at the site, and hostile social environment.
7. Comparison of Findings with Other Countries in the World
Deficiencies in project quality is considered as one of the essential critics in project delivery
throughout the world. Number of studies has been carried out on this specific topic till to date as
shown in Table 12. Table 12 presents the findings of these earlier studies and large part of the identified
quality influencing factors are equally applicable in Pakistan public construction industry. It can be
observed from Table 12 that project quality is affected by frequently occurring project management
related issues such as lack of contractor supervision, lack of experienced project manager, lack of
technical guidelines, conflict among project participants, management commitment to continuous
quality improvement, developing and improving quality control and assurance systems and lack of
competent project managers and teams. It can be seen that most of the common quality influencing
factors were related to stakeholders which are worthy of consideration in all countries in the world
either it is developed or developing. As shown in Table 12 below, most of them were also found to be
important in the current study.

Sustainability 2018,10, 1415 19 of 25
Table 12. Comparison of quality influencing factors with previous studies.
Sr. No This Study Pakistan Chan et al. [39] Hong Kong Gan et al. [8] China Jha & Iyer [2] India
1Lack of contractor
supervision
Project manager’s experience in
running public housing projects Lack of design norms and standards Conflict among project participants
2Aggressive competition
during tendering
Proactive quality culture; the extent
of using direct skilled labor Lack of quality criteria Hostile socio-economic environment
3Hostile economic
environment
Comprehensive subcontract
inspection system Lack of production norms and standards Harsh climatic condition
4
Lack of experienced
project managers in the
construction
Competency of site labor
Lack of quality management system in production process
PM’s ignorance & lack of knowledge
5Flaws in design
specification
Client’s emphasis on quality, safety
and environment Lack of technical guidelines for the construction Aggressive competition during tendering
6Arditi and Gunaydin
[22] USA Abdel Razek [17] Eygpt Nguyen et al. [42] Vietnam Chan and Tam [23] Hong Kong
7
Management
commitment to
continuous quality
improvement
Improving design and planning
during the pre- construction phase Competent project manager Project management action by the project team
8
Management leadership
in promoting high
process quality
Developing and improving quality
control and assurance systems Adequate funding throughout the project Effectiveness of the construction team leader
9Quality training of all
personnel
Improving the financial level and
standard of living of employees Competent project team Client’s emphasis on quality
10
Efficient teamwork to
promote quality issues
at the corporate level
Improving the accuracy of
estimating and tendering Commitment to project Client’s emphasis on time
11 Effective cooperation
between parties
Proper classification of contractors,
consultants and
construction projects
Availability of resources Project management actions

Sustainability 2018,10, 1415 20 of 25
8. Conclusions
The imperfect quality problems in the Pakistani public construction industry are both complex
and risky problems, influenced by many factors during the entire project life cycle, which not only
diminish the project quality, but also reduce the sustainability of the project as a whole. As quality will
not improve by chance, we need strategies and policies to plan for quality improvement. Similar to
sustainable development projects, this process can be initiated by identifying quality problems. Quality
is a cornerstone of public infrastructure projects to improving project sustainability performance across
its life cycle. This study concluded that better quality and sustainability will be enhanced when project
contracting parties better understand the importance of the quality management system.
The previous literature discussing the factors affecting the quality of construction projects used
the same kind of methodologies as exhibited in Table 1. The aim of this study was to present
and introduce a new methodology to analyze and examine the influence of key constructs on
quality in SIP. An advanced multivariate analysis technique, PLS-SEM has been employed to
perform the analysis. This comprehensive multivariate statistical assessment technique can test all
of the relationships between latent and manifest variables in a model simultaneously, containing
measurement and structural model assessment. The technique was chosen also because it has the
ability for the for the assessment of the psychometric properties of individual latent constructs.
The most important constructs that affect the project quality with their fundamental causes (observed
variables) were identified from the literature. A questionnaire survey was conducted with construction
experts regarding factors affecting quality where the authors collected 451 valid questionnaires from
construction experts in the public construction industry and used the latest statistical methods such
as SEM. This study was performed to explore factors affecting quality in SIP in Pakistan. Based on
the responses of the survey, it was considered that the construction related factor was the topmost
factor that led to deteriorating project quality as the paperwork is implemented on the actual work at
this stage. This implies that public construction industries and contractors need to make particular
plans and methods to concentrate on issues of the construction (execution) phase that affect the project
quality. The lack of construction techniques will increase the rate of deterioration of project quality
during the construction phase as well the lack of proper quality monitoring and evaluation and the
lack of quality policy and planning, which have caused problems in the quality of projects. Mostly,
the findings from this study indicated that most of the glitches occurring from poor quality projects
were due to a human lack of importance for quality and lack of applicable criteria and quality assurance
practices. Moreover, these problems influenced the efficiency of the construction process. Confusion
and misunderstanding of the project scope and lack of detailed knowledge of quality assurance issues
led to a poor-quality management plan, which possibly diminished the project quality. Furthermore,
the lack of experienced project managers and poor management ability of the contractors were a key
determinant in accomplishing better quality in projects as the service provider’s inability to examine
the underlying challenges in the construction phase is extremely damaging to the effective delivery of
quality projects.
This study model validated that factors affecting the quality of public construction projects were
based on various constructs that are necessary to understand to improve the quality of the project
and its outcomes. The construction- and stakeholder-related factors were observed to be the most
dominant constructs. Therefore, the owners and contractors should pay more attention to these two
constructs with a high beta coefficient as they were imperative constructs of the public projects that
should be focused on. Moreover, the material related, design related, and external related factors were
also found to have a strong and positive effect on project quality. In conclusion, the factors affecting
quality in a social infrastructure project in Pakistan were influenced by all five constructs, and these
constructs explained 74.9% of the variance in project quality with a significant relationship explained
by beta values and the GOF of the model was 0.69. A significant effect of the different quality factors
on the quality of SIP was also evaluated. The current study enhanced the expansion of research in the

Sustainability 2018,10, 1415 21 of 25
area of quality improvement and helped gain a better understanding of the lack of quality factors in
social infrastructure projects.
The findings from this study can provide decision making support for project contracting parties
and policy-makers (government) by amplification their understandings of the severe factors that affect
the construction quality, confirming that appropriate plans and quality management methods as well
as proper monitoring can be designed and implemented to assure a better quality of SIP. Through the
suggested solutions, the public construction industries need to keep an eye on flaws and control the
extent of quality defects so that similar flaws may perhaps be avoided in upcoming issues in SIP.
Author Contributions:
Shahid Hussain and Zhu Fangwei formulated the study design. Shahid Hussain and
Ahmed Faisal Siddiqi conceived and designed the research methodology. Shahid Hussain, Ahmed Faisal Siddiqi,
Zaigham Ali and Muhammad Salman Shabbir collected the data from different public construction industries
in Pakistan; Shahid Hussain, Zaigham Ali and Ahmed Faisal Siddiqi analyzed the data; Shahid Hussain wrote
the paper.
Funding: This research was funded by Zhu Fangwei grant number 71372085.
Acknowledgments:
This research work was supported by the National Natural Science Foundation of China
under Grant No. 71372085. The useful comments and suggestions from the editor and all the reviewers are
extremely appreciated. Furthermore, the authors would like to thank Adnan Haider Khoja and Karamat Ali for
their great help and support for the accomplishment of the data collection and the completion of the questionnaires.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Mean, Standard Deviation, Excess Kurtosis and Skewness Calculation.
Sr. No. Mean Standard Deviation Excess Kurtosis Skewness
MRF_1 3.361 0.977 −0.412 −0.102
MRF_2 3.224 1.101 −0.523 −0.192
MRF_3 3.051 1.061 −0.449 −0.046
MRF_4 3.483 1.062 −0.324 −0.447
MRF_5 3.670 0.953 −0.469 −0.330
CRF_1 3.639 0.999 −0.596 −0.272
CRF_2 3.184 1.082 −0.543 −0.224
CRF_3 2.900 1.068 −0.549 −0.063
CRF_4 3.188 1.004 −0.589 −0.135
CRF_5 3.102 1.003 −0.514 −0.020
CRF_6 2.761 1.006 −0.387 0.113
CRF_7 3.348 1.001 −0.382 −0.236
CRF_8 3.410 1.002 −0.476 −0.203
CRF_9 3.188 0.987 −0.245 −0.135
SRF_1 3.457 1.140 −0.439 −0.484
SRF_2 3.765 0.996 −0.439 −0.448
SRF_3 3.073 1.115 −0.628 −0.193
SRF_4 3.568 1.117 −0.426 −0.486
SRF_5 3.397 1.049 −0.450 −0.281
SRF_6 2.705 0.985 −0.513 0.032
DRF_1 2.965 1.094 −0.633 −0.103
DRF_2 2.468 1.043 −0.311 0.398
DRF_3 3.592 1.087 −0.141 −0.622
DRF_4 2.685 0.996 −0.367 0.312
DRF_5 2.967 1.105 −0.758 −0.053
DRF_6 3.242 1.085 −0.611 −0.199
ERF_1 2.652 1.064 −0.563 0.164
ERF_2 2.745 1.042 −0.578 0.112
ERF_3 3.639 1.042 −0.526 −0.401
ERF_4 2.898 1.050 −0.595 0.020
ERF_5 2.953 1.059 −0.578 0.071

Sustainability 2018,10, 1415 22 of 25
Table A1. Cont.
Sr. No. Mean Standard Deviation Excess Kurtosis Skewness
QF_1 3.563 1.015 −0.712 −0.204
QF_2 2.838 1.026 −0.440 0.082
QF_3 2.851 1.000 −0.575 0.021
QF_4 3.450 1.012 −0.380 −0.244
QF_5 3.610 1.011 −0.253 −0.488
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