© 2021 by the author(s). This
is an Open Access article
distributed under the terms
of the Creative Commons
Attribution 4.0 International
(CC BY 4.0) License (https://
creativecommons.org/
licenses/by/4.0/), allowing
third parties to copy and
redistribute the material in
any medium or format and to
remix, transform, and build
upon the material for any
purpose, even commercially,
provided the original work is
properly cited and states its
license.
Citation: Fehan, H. and
Aigbogun, O. 2021. Influence
of Internal Organizational
Factors and Institutional
Pressures on Construction
Firms’ Performance.
Construction Economics and
Building, 21:2, 81–99. http://
dx.doi.org/10.5130/AJCEB.
v21i2.7593
ISSN 2204-9029 | Published by
UTS ePRESS | https://epress.
lib.uts.edu.au/journals/index.
php/AJCEB
Construction
Economics and
Building
Vol. 21, No. 2
June 2021
RESEARCH ARTICLE
Influence of Internal Organizational Factors
and Institutional Pressures on Construction
Firms’ Performance
Hassan Fehan1, Osaro Aigbogun2
1 Binary University of Management and Entrepreneurship Selangor, Malaysia, hassanfehan747@
gmail.com
2 Binary University of Management and Entrepreneurship Selangor, Malaysia, osaro.aigbogun@
gmail.com
Corresponding author: Hassan Fehan, Binary University of Management and Entrepreneurship
Selangor, Malaysia, hassanfehan747@gmail.com
DOI: http://dx.doi.org/10.5130/AJCEB.v21i2.7593
Article History: Received: 08/02/2021; Revised: 26/03/2021; Accepted: 20/04/2021;
Published: 15/06/2021
Abstract
A significant number of empirical studies have reported contrasting results regarding the
effects of certain internal organizational factors (Leadership Style - Team competency
and Skills - Effective Communication) on construction performance. As a result,
generalizations remain sketchy, and a better understanding is needed. This study lends
a voice to the literature’s debate by introducing the part played by institutional pressures.
The aim is to evaluate the impact of internal organizational factors and institutional
pressures on a Syrian construction firm’s performance outcomes, with institutional
pressures playing a mediator’s role. Data were collected using a questionnaire instrument
from a sample of 197 building experts working in large public construction companies
in Syria and analysed using the partial least squares structural equation modelling
(PLS-SEM). The results reveal that leadership style and effective communication have
a significant and positive effect on construction firm performance outcomes. However,
the effect of team competency and skill was not supported; nonetheless, providing
institutional pressures as a mediator into the relationship made it significant, thus,
providing a vital theoretical contribution worth considering in future research. Practically,
this study is the first attempt at evaluating organizational factors and institutional
pressures as a critical determinant of organizational performance that should interest
management at organizational levels.
81 DECLARATION OF CONFLICTING INTEREST The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this article. FUNDING The author(s) received no
financial support for the research, authorship, and/or publication of this article.
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
http://dx.doi.org/10.5130/AJCEB.v21i2.7593
http://dx.doi.org/10.5130/AJCEB.v21i2.7593
http://dx.doi.org/10.5130/AJCEB.v21i2.7593
https://epress.lib.uts.edu.au/journals/index.php/AJCEB
https://epress.lib.uts.edu.au/journals/index.php/AJCEB
https://epress.lib.uts.edu.au/journals/index.php/AJCEB
mailto:hassanfehan747@gmail.com
mailto:hassanfehan747@gmail.com
mailto:osaro.aigbogun@gmail.com
mailto:osaro.aigbogun@gmail.com
mailto:hassanfehan747@gmail.com
http://dx.doi.org/10.5130/AJCEB.v21i2.7593
Keywords
Construction Firms; Performance Measures; Internal Organizational Factors; Institutional
Pressures; Smart-PLS
Introduction
The construction industry is a significant player in any nation’s economic growth and occupies a central
role in the region’s development plan and its ties to other sectors of the economy. For the past few years,
both professionals and scholars have stressed the challenges facing the construction industry as it has
been characterized as a complex and dynamic industry in which organizations that work meet relentless
challenges and enormous demands (Balatbat, Lin and Carmichael, 2011). Many of these challenges force
construction firms to be highly flexible, efficient, and customer-oriented to compete with increasingly
strong emerging-market players effectively and achieve high performance in future construction markets
(Accenture, 2012).
For construction companies to address these challenges, the performance measurement models as a
management improvement tool have been introduced to bring out desired improvements in performance
(Hubbard, 2009). Several studies have found that the construction firms’ performance outcomes were
undesirable due to the lack of effective and efficient measures (Luu, et al., 2008). The central dilemma of
choosing these measures is linked to their reasoning and design, regulation and operation, and adjustment
of dysfunctional effects when implemented in different countries (Luu, et al., 2008; Wang, El-Gafy and
Zha, 2010; Yang, et al., 2010). Moreover, as construction firms have faced several challenges when seeking a
suitable mechanism to deliver construction projects, it is believed that advancement could only be evaluated
by measurement (Marr, 2007).
Although performance outcomes right after measurement present benefits to those who implement
them for functions such as evaluation, control, and the advancement of-business procedures, the factors
that influence these performances are still not studied well enough on an organizational level (Dorsey and
Mueller-Hanson, 2017). These factors exert pressure on organizations and set their performance at the
medium or high stage, or adapt to dynamic business environments in a way that will reduce or eliminate
business threats (Sousa and Aspinwall, 2010). In that light, growing concern about the effect of certain
internal organizational factors (leadership style, team competency and skills, and effective communication)
on construction firm performance has reignited interest in the questions of various literature (Adeleke,
Bahaudin and Kamaruddeen, 2017; Jin, 2018; Onana, 2018). In addition to this, many management scholars
have contended that the external pressures determine heterogeneity in organizational performance outcomes
within the industry structure that an organization operates (Dubey, Gunasekaran and Samar Ali, 2015; Iliya
Nyahas, et al., 2017; Wang, et al., 2018). Such a move to the organizational level of analysis has highlighted
that complex institutional pressures generate variation in organizational factors’ impact on firms’ outcomes.
However, a potentially missing piece of the puzzle relates to the fact that construction firms are part of a
network inside the construction industry that faces institutional pressures (Li, et al., 2019). The potential
for the effect of institutional pressures as a mediator, beyond mere responses to institutional demands, has
not received sufficient attention. To better depict institutional pressures within a construction firm, we need
a more fine-grained explanation for how these pressures influence the construction firm’s performance.
Such a consideration might enhance our theoretical understanding and render empirical evidence on how
institutional pressures affect construction performance outcomes.
The World Bank has estimated the damage due to the conflict in Syria at $200bn, while the UN
Economic and Social Commission for West Asia (ESCWA) forecasts that the total cost of restoring the
country to its 2010 condition will be almost $400bn. These are huge figures, and it is hard to imagine such
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202182
resources being found quickly or easily (Asseburg, 2020). The enormous challenges extend far beyond
mine clearance and physical rebuilding of infrastructure and housing: a massive loss of (skilled) labour,
contraction of the economy, currency devaluation, and the collapse of public services head the list (Talbot
and Dacrema, 2019). As a result, this presents a colossal toll to an already weakened construction industry.
However, quantitative estimates of the costs on the construction industry are not readily available. Although
there is a lack of official data, Maya (2016) argues that the Syrian construction sector’s recent performance
is weak, with a significantly reduced yearly contribution to GDP in the last decade. Devarajan and Mottaghi
(2017) attribute this challenge to significant disruptions in supply chains of raw material inputs caused
by the war. This reasoning is in line with the allegation that wars reduce GDP per capita by about 10%
to 15% permanently, with a total loss of output at around 18% (Collier, et al., 2003). Though, to a certain
level, some of these reports are based on sketchy evidence, as no insight has been offered to organizational
factors’ influence on the operating construction firm’s performance outcomes in Syria. Thus, it is crucial to
clarify the indecisive deductions on the relationship among the internal organizational factors, institutional
pressures, and construction firms’ performance outcomes. As such, a comprehensive model is needed which
will integrate these factors in the Syrian construction context.
Literature Review
CONCEPTUALIZATION OF CONSTRUCTION FIRM PERFORMANCE
Over the years, several construction firms have shown an imperative for identifying vital areas of their
business model that are crucial to their performance. These perspectives highlight indicators that have
been defined by the National Institute for Standards and Technology (NIST) and adopted for this study
as “numerical information used to quantify the input, output and performance dimensions of processes,
products, programs, projects, services and the overall outcomes of an organization” (NIST, 2019. p17).
Construction firms’ outcomes are not homogeneous due to their diverse nature, so integrating a limited
number of performance measures to fit all types of their operated projects is complex (Rathore and Elwakil,
2020). Many measurement frameworks developed emphasized measuring project performance rather
than the firm’s level performance (Ali, Al-Sulaihi and Al-Gahtani, 2013). Besides, the measurement of
firm performance was primarily based on financial measures, and however, due to its limitations, authors
have recommended the use of non-financial performance measures (Othman, et al., 2015). Consequently,
some research scholars (e.g., Yu, et al., 2007) have suggested that the original perspectives of the Balanced
Score Card (BSC) should be utilized in analyzing the construction firms’ performance. However, other
authors (e.g., Ozorhon, et al., 2011; Ali, Al-Sulaihi and Al-Gahtani, 2013; Jin and Deng, 2012; Oyewobi,
Windapo and Rotimi, 2015) have either replaced the original BSC with newer dimensions or added other
vital dimensions to the original perspectives of the BSC to appraise construction firms’ performance. This
reasoning is consistent with Lueg (2015) argument, who believes that the original BSC does not consider
specific natural, social and industry-specific contexts.
Furthermore, research scholars have made attempts to operationalize the construction performance
concept. In Vietnam, Luu, et al. (2008) developed a model that combines the original BSC with strength-
weakness-opportunities-threats (SWOT) analysis in large contractors’ performance evaluation. In China,
Jin and Deng (2012) applied a revised BSC by adding a market and stakeholder dimensions for the
construction firm performance measures. While in Iraq, Tofan and Breesam (2018) applied five dimensions
(financial, customer, social and environmental, internal business, and learning and growth) as performance
measures. Given these facts, this study adopts a holistic approach for measuring organizational performance
that reflects the current reality in Syria using revised dimensions of the original BSC (financial performance,
customer satisfaction, internal business processes, environmental performance).
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202183
ORGANIZATIONAL INTERNAL FACTORS AND CONSTRUCTION FIRM PERFORMANCE OUTCOMES
Researchers in construction management have emphasized considerable effort to understand the
organizational factors that influence construction firms’ performance. Rathore and Elwakil (2020)
demonstrated that though there is adequate awareness of performance management within the construction
industry, the internal organizational factors’ impact as an invisible and intangible resource on overall
performance remains unclear. Some scholars in construction firms have explored the causes of performance
heterogeneity based on adopted internal organizational factors (Geraldi, Lee and Kutsch, 2010; Zuhairy,
et al., 2013). Many studies conclude that the effects of internal organizational factors on firms’ performance
are heterogeneous as some of the studies present positive impacts, while the other show negative impact
(Lee, Kim and Lee, 2011; Yidizs, Basturk and Boz, 2014; Leje, Kasimu and Kolawole, 2019). These factors
vary from study to study without being clear about why some studies emphasize some of them over others,
though the findings are heterogeneous regarding whether the impact is direct or indirect (Ortega, Azorin
and Cortes, 2010).
Furthermore, it is acknowledged that specific organizational characteristics will yield better outcomes for
organizations under different environmental situations (Nandakumar, Ghobadian and Regan, 2010). How
these constructs interact to generate superior performance remains unexplored primarily in the construction
context. There were several reasons for selecting the particular variables from the range of variables
covered in the literature. Firstly, internal organizational factors have a considerable impact on organization
performance (Black, et al., 2019). A review of previous studies in the construction industry reveals that
construction firms’ performance is also influenced by certain internal organizational factors (leadership
style, team competency and skills, effective communications). This would be sufficient reason for including
them in the study, but little research has investigated these variables within developing countries such as
Syria. Secondly, these variables have been paid insufficient attention by construction firms’ performance
researchers across countries. Toor and Ofori (2008) view leadership style in construction firms as the way
project managers execute their responsibilities in line with construction activities; hence we developed the
first hypothesis, which is:
H1: Leadership Style has a positive relationship with a construction firm’s performance outcomes.
Furthermore, Lee, Kim and Lee (2011) view team competency and skills as a reflection of a firm’s vital
intangible assets, such as the additional skills an employee deploy during a construction project which this
was making us develop the second hypothesis, which is:
H2: Team Competency and Skills has a positive relationship with a construction firm’s performance
outcomes.
More so, effective communication is essential to both the employee and the organization. It enables
efficient communication during construction, leading to enhanced employee productivity and firm
performance ( Jallow, et al., 2014) and hence the third hypothesis:
H3: Effective Communication has a positive relationship with a construction firm’s performance
outcomes.
INSTITUTIONAL PRESSURES AS A MEDIATOR
Within the construction management field, the construction business environment’s dynamic nature makes
it necessary for construction organizations to identify institutional pressures that could lead to superior
performance and vigorously promote and incorporate these pressures to achieve performance excellence
within organizations (Druckman, Singer and Van Cott, 1997). Institutional pressures help explain the
source of performance heterogeneity within construction organizations’ performance (Wang, et al., 2018).
In understanding these external variables, this present study draws from the power of the ‘Institutional
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202184
Theory’ developed by DiMaggio and Powell (1983) by recognizing their effect on firm performance. There
are three kinds of institutional pressures that affect firm performance outcomes: coercive pressure to fulfill
regulatory requirements, mimetic pressure to monitor competitors’ actions, or normative pressure to invest
in developing its leadership. How firms respond to institutional pressures can vary widely, depending on
the characteristics of the isomorphic pressures, the organization itself, and their organizational environment
(Samairat, 2008). A longitudinal study by Wang, et al. (2018) affirmed that both mimetic and normative
pressures created significant impacts on megaprojects’ environmental performance, while no evidence
existed of a significant impact from coercive pressures. The study of Li, et al. (2019) provided insight into
the regulative, normative, and cognitive institutional pressures faced by Chinese construction companies and
supported their efforts in improving relevant laws, norms, and cognitions.
Researchers often get puzzled between moderating, mediating, and controllable variables with being
directly related to firm performance. However, adequate understanding and critical review by this present
study further facilitate the resolution of the conflicts. Because the institutional pressures are correlated with
firm performance outcomes as it was mentioned in previous literature, as well as the precondition that the
relation between the antecedent and the outcome should be significant makes it preferred for examining
the mediation effect in this study (Wu and Zumbo, 2008; Aguinis, Edwards and Bradley, 2017). Thus, this
study evaluates the latent institutional pressures that can influence construction organizations performance
and mediate the relationship between internal organizational factors and construction firm performance as
shown in Figure 1 with the developed hypothesis as follows:
Effective
Communication
Team Competency
and Skills
Leadership Style
Institutional
Pressures
Construction
Firm
Performance
Figure 1. Conceptual Model
Consistent with Figure 1, the hypotheses are stated as follows:
H4: Institutional pressures have a positive relationship with a construction firm’s performance outcomes.
H5: Institutional pressures positively mediate the relationship between leadership style and a construction
firm’s performance outcomes.
H6: Institutional pressures positively mediate the relationship between team competency and skills and a
construction firm’s performance outcomes.
H7: Institutional pressures positively mediate the relationship between effective communications and a
construction firm’s performance outcomes.
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202185
Figure 2. Power Analysis for Medium Effect
Figure 3. X-Y Plot for Medium Effect Power Analysis
Research Method
After pooling the research instrument to 8 subject matter experts in content validity, a pilot survey was
conducted among 25 construction organizations in the study area to test and improve the reliability of the
instrument, as well as ensure the clarity of the final research instrument before the primary survey (Fehan
and Aigbogun, 2020). A deductive research approach using quantitative methods, a methodology widely
adopted in social sciences, was carried out. This study is cross-sectional; Therefore, the data was collected
at a single point in time using a questionnaire survey anchored on the scale of a 5-point Likert to measure
the feedback to the questionnaires ranging from 1- Strongly Disagree; 2- Disagree; 3- Neutral; 4- Agree;
5- Strongly Agree. The target population for the study was public-sector construction firms around Syria
considered as a unit of analysis. Whereas the unit of observations were the professionals inside these firms,
which were sampled using the snowball technique. For this study’s sample size to be ascertained, a power
analysis was done using a software package named G*Power 3.1.9.4. Based on the G*Power model, this
study used four (4) predictors’ variable equations in determining the sample size (Faul, et al., 2007). Based
on Figures 2 and 3, a minimum adequate sample of 129 assumptions for PLS-SEM. Therefore, using a
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202186
Snowball sampling technique, a total number of 250 questionnaires were distributed, and 197 valid surveys
were returned with a response rate of 78.8%, which was considered acceptable.
Analysis and Results
Using SmartPLS 3 software to assess the effect of manifest variables on construction firm performance. The
PLS modelling was deemed to be a valuable technique for this study as it possesses the potential to estimate
the relationships among the indicators and their corresponding latent constructs (measurement model); the
relationships between the constructs (structural model) concurrently; and the predictive relevance of the
endogenous latent variable (Henseler, 2018). Figure 4 illustrates the steps of data analysis per Smart-PLS.
Step 1.
Measurement Model
1- Model Reliability
. Individual item
reliability
. Composite
reliability
. Average Variance
Extracted (AVE)
2- Discriminant
Validity
. Fornell-Larcker
Criterion
. HTMT
Discriminant
Criteria
Step 2.
Structural Model
1- Path Coefficient of the
Research Hypotheses
2- Coefficient of Determination
(R2)
3- Effect size (F2)
4- Predictive Relevance (Q2)
5- Goodness of Fit of the Model
(GoF)
Step 3.
Testing Mediating Effect
1- Bootstrap of the indirect
effect
2- Bootstrapped Confidence
Interval (Lower and upper
level)
Figure 4. Steps of Smart-PLS data analysis
MEASUREMENT MODEL
Model Reliability
The adopted model’s reliability in the current research was determined based on two factors. First,
individual item reliability was determined by analysing each construct’s measure’s outer loadings, which
should be above the threshold of 0.70, and the loadings less than the threshold should be omitted (Chin,
1998). Hence, for the whole model, 25 items remained as they depicted loadings between 0.705 and 0.969
(see Figure 5). Second, the composite reliability coefficient and Average Variance Extracted (AVE) were
used to ascertain the reliability of measures’ internal consistency. Hair, Ringle and Sarstedt (2011) proposed
that the composite reliability coefficient must be at least 0.70, and Average Variance Extracted (AVE) must
be at least 0.50. Table 1 depicts the composite reliability coefficients of each latent construct ranging from
0.883 to 0.962, and the AVE was ranged from 0.621 to 0.848, and it is beyond the baseline threshold of
0.70, 0.50, respectively. Therefore, the consistency reliability of measures used in the current study is viewed
as adequate.
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202187
Figure 5. Measurement Model (Outer loadings and Composite Reliability)
Table 1. Result of measurement model-convergent validity
Constructs AVE CR
Construction Firm Performance 0.716 0.962
Leadership Style 0.848 0.917
Team Competency and Skills 0.676 0.912
Effective communication 0.791 0.883
Institutional Pressures 0.621 0.907
Discriminant Validity
The discriminant validity assessment has the objective of ensuring that a reflective construct has the most
intense relationships with its indicators (e.g., in comparison with any other construct) in the PLS path
model (Hair, et al., 2017). The Fornell-Larcker criterion and the Heterotrait-monotrait ratio of correlations
(HTMT) criterion were employed.
Fornell-Larcker Criterion
Table 2 represents the results of the Fornell-Larcker criterion to assess the discriminant validity of the
measurement model.
As shown in Table 2, the off-diagonal elements’ value was smaller than AVE’s square root value.
Therefore, it proves that each latent construct measurement was completely discriminating against each
other.
HTMT Discriminant Criteria
Table 3 represents the results of HTMT discriminant criteria to assess the discriminant validity of the
measurement model.
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202188
Table 3. HTMT discriminant criteria
CFP EC IP LS TCS
CFP -
EC 0.825 -
IP 0.839 0.838 -
LS 0.435 0.514 0.845 -
TCS 0.633 0.554 0.78 0.797 -
All the HTMT values of the latent constructs were below 0.9, as seen in Table 3. Thus, it assured that
each latent construct was fully discriminating against each other.
STRUCTURAL MODEL
Path Coefficient of the Research Hypotheses
With 5000 bootstrap samples and 197 cases, this study presents the significant paths of the coefficients for
the research model as illustrated in Table 4 and Figure 6.
Table 4. Path Coefficient of the Research Hypotheses
Hypo Relationship Std. Beta Std. Error T-value P-value Decision
H1 Leadership Style ->
Construction Firm
Performance Outcomes
0.484 0.06 8.11 0 Supported**
H2 Team Competency and
Skill -> Construction
Firm Performance
Outcomes
-0.051 0.097 0.525 0.599 Not Supported
H3 Effective
Communication ->
Construction Firm
Performance Outcomes
0.205 0.055 3.695 0 Supported**
Table 2. Latent Variable Correlations-Square Root of AVE
CFP EC IP LS TCS
CFP 0.846
EC 0.694 0.829
IP 0.784 0.685 0.788
LS 0.384 0.442 0.769 0.821
TCS 0.602 0.447 0.723 0.731 0.822
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202189
Hypo Relationship Std. Beta Std. Error T-value P-value Decision
H4 Institutional Pressures
-> Construction Firm
Performance Outcomes
1.06 0.107 9.951 0 Supported**
Significant at P**= < 0.01, p* <0.05
The findings in Table 4 illustrate the relationship between leadership style and construction firm
performance outcomes with standard beta value, standard error, t-value, and p-value of 0.484, 0.06, 8.11,
0.000, respectively, which means the relationship was positive and significant. Also, the relationship between
team competency and skills and construction firm performance outcomes was not-supported with t-value
and p-value of 0.525, 0.599 respectively. Furthermore, as for effective communication, a positive and
significant relationship was revealed between effective communication and construction firm performance
outcomes with standard beta value, standard error, t-value, and p-value of 0.205, 0.055, 3.695, 0.000,
respectively. Finally, the relationship between institutional pressures and construction firm performance
outcomes was revealed to be supported with standard beta value, standard error, t-value, and p-value of 1.06,
0.107, 9.951, 0.000, respectively, which means the relationship was positive and significant.
Figure 6. Structural Model (Path coefficient and P-value)
Coefficient of Determination (R2)
The research model revealed 75.7 percent of the total variance in construction firm performance outcomes
and 87.6 percent of the total variance in institutional pressures, as depicted in Table 6. Chin (1998) suggests
that R2 values above 0.67 are considered high, whereas values between 0.33 and 0.67 are moderate, while
values between 0.19 and 0.33 are small and R2 values below 0.19 are undesirable. This study’s R2 value is
drawn that the endogenous latent variables hold the high-rate level of R2 values.
Table 1. continued
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202190
Table 5. R-Square of the Endogenous Latent Variables
Constructs R2 Result
Construction Firms Performance Outcomes 0.757 High
Institutional Pressures 0.876 High
Effect size (F2)
Effect size would indicate the relative influence of a particular exogenous latent variable on the endogenous
latent variable(s) through shifts in R2 values, as well as if the measurement of F2 value was: 0.02, or 0.15, or
0.35, respectively, the exogenous latent variable reflects small, medium, and high impacts (Chin, 1998). As
shown in Table 6, the findings verified effect sizes for each exogenous variable on the endogenous variable.
Table 6. F-Square of the Endogenous Latent Variables
Firms Performance
Outcomes
Results Institutional
Pressures
Results
Leadership Style 0.373 Large 0.161 Medium
Team Competency and Skill 0.002 No Effect 1.114 Large
Effective Communication 0.077 Small 0.735 Large
Institutional Pressures 0.572 Large
Predictive Relevance (Q2)
The present research utilizes Stone–Geisser test to determine the entire research model’s predictive
relevance by using blindfolding processes (Stone, 1974; Geisser, 1974).
Table 7. Construct Cross validated Redundancy
Total SSO SSE Q² (=1-SSE/SSO)
Firms Performance Outcomes 1,970.00 984.02 0.5
Institutional Pressures 1,182.00 584.55 0.505
As depicted in Table 7, results have verified a Q2 statistic of 0.5, 0.505 for the studied endogenous latent
variables (construction firm performance, institutional pressures), respectively, which is greater than zero,
thus proposing predictive relevance of the model ( Jain, Vyas and Chalasani, 2016).
Goodness of Fit of the Model (GoF)
The values of GoF in the structural model analysis were 0.804, 0.865 for (construction firm performance,
institutional pressures) respectively, which is greater than the high threshold of 0.36 (Wetzels, Odekerken-
Schröder and Van Oppen, 2009). Therefore, it can be concluded that the GoF model of this study is large
enough to consider sufficient global PLS model validity.
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202191
TESTING MEDIATING EFFECT
The current study employed the bootstrap approach utilizing PLS-SEM following Preacher and Hayes
(2008) to discover the mediating effect of institutional pressures on the relationship between internal
organizational factors and construction firms’ performance outcomes. Table 8 shows the bootstrap of the
indirect effect.
Table 8. Bootstrap of the indirect effect
Relationship P-value Decision
Leadership Style -> Construction Firm Performance Outcomes 0.000 Significant
Team Competency and Skill -> Construction Firm Performance
Outcomes
0.000 Significant
Effective Communication -> Construction Firm Performance Outcomes 0.000 Significant
According to Preacher and Hayes (2008), the next step is to examine the bootstrapped confidence
interval (Lower and upper level), and it must not contain a true zero value. Table 9 shows the Bootstrapped
Confidence Interval (Lower and upper level).
Table 9. Bootstrapped Confidence Interval (Lower and upper level)
Original sample =
standard beta
IV- ->
Mediator
Mediator
--> DV
Automatic
calculation
Standard
deviation
Automatic
calculation
Bootstrapped
Confidence Interval
Path a Path b Indirect
Effect
SE t-value 95% LL 95% UL
M1(LS) 0.211 1.060 0.224 0.064 3.495 0.098 0.349
M2(TCS) 0.555 1.060 0.588 0.091 6.465 0.410 0.767
M3(EC) 0.34 1.060 0.364 0.043 8.455 0.279 0.448
Table 10. Type of Mediator
P-value of the
direct effect
(C`)
Decision Original
Sample of
Indirect Effect
(a*b)
Original
Sample of
Direct Effect
(c`)
Type of Mediator
M1(LS) 0.001 Significance 0.224 -0.26 Competitive
Partial Mediation
M2(TCS) 0 Significance 0.589 0.538 Complementary
Partial Mediation
M3(EC) 0 Significance 0.364 0.569 Complementary
Partial Mediation
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202192
Figure 7. Mediation path Model
Figure 7, and Table 8, Table 9, and Table 10, depict the approximations after applying the Preacher and
Hayes (2008) mediator analysis method to determine the mediating effect of institutional pressures on the
relationship between the exogenous and endogenous latent variables.
Hypothesis 5 stated that institutional pressures significantly mediate the relationship between leadership
style and construction firms’ performance outcomes. However, the result is statistically significant for
bootstrap indirect effect as P-value = 0.000, which means that the relationship between leadership style and
construction firms’ performance outcomes through institutional pressures is significant. As anticipated, the
results presented in Table 9 showed that the bootstrapped confidence interval values should not contain a
true zero value (95%LL = 0.098, 95%UL = 0.349). Therefore, Hypothesis 5 was supported, and there is a
mediator between leadership style and construction firm’s performance outcomes. Table 10 illustrates that
the institutional pressures played as a competitive partial mediation (Nitzl, Roldan and Cepeda, 2016).
Similarly, Hypothesis 6 was confirmed, which stated that institutional pressures significantly mediate the
relationship between team competency and skills and construction firm’s performance outcomes, such that
result is statistically significant for bootstrap indirect effect as P-value= 0.000. However, the bootstrapped
confidence interval values (95%LL =0.410, 95%UL =0.767) mean it does not consist of a true zero value.
Consequently, Hypothesis 6 was supported, and there is a mediating effect of institutional pressures on the
relationship between team competency and skills and the construction firm’s performance outcomes. Based
on Nitzl, Roldan and Cepeda (2016) studies with reference to Table 10, the institutional pressures play as a
complementary partial mediation.
Finally, Hypothesis 7 was confirmed, which stated that institutional pressures significantly mediate
the relationship between effective communication and construction firms’ performance outcomes, such
that the result is statistically significant for bootstrap the indirect effect as P-value= 0.000. However, the
bootstrapped confidence interval values (95%LL =0.279, 95%UL =0.448) mean it does not contain a true
zero value. As a result, Hypothesis 7 was supported, and there is a mediation effect of institutional pressures
on the relationship between effective communication and construction firms’ performance outcomes. Based
on Nitzl, Roldan and Cepeda (2016) studies with reference to Table 10, institutional pressures assume the
role of a complementary partial mediation.
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202193
Discussion
In this study, we combined two streams of literature. On the one hand, we looked at the influence of certain
internal organizational factors (leadership style, team competency and skills, effective communication) and
institutional pressures (coercive, normative, mimetic) on the construction firm’s performance outcomes. On
the other hand, we examined the mediating role of institutional pressures on the strength of the relationship
between internal organizational factors and a construction firm’s performance. The PLS measurement model
assessment results were relatively well specified in terms of its reliability and validity, and the PLS structural
model assessment results indicate that the independent variables explain 75.7% of the variance in the
construction firm’s performance. In addition to this, the predictive ability of the model and model fit were
both acceptable.
The findings revealed that the relationship between team competence and skill and construction firm
performance outcomes was not significant. However, adding institutional pressures as a mediator into this
relationship has made it significant, and this proves that aligning suitable pressures on construction firms
will improve their team’s competencies and skills. Consequently, there will be increasing in the effectiveness
of the organization’s construction activities and confers a value-addition point to construction firms. This
result might be valuable in explaining and specifying the condition under which positive associations were
derived from other studies carried out in developing country context, such as Indris and Primiana (2015)
who affirmed that organizational internal and external factors affect small and medium industries (SMEs)
performance in Indonesia; Jin (2018) who found a positive relationship between internal organizational
factors, external organizational factors, and construction performance management in Nairobi, Kenya. Also,
Onana (2018) noted that finance and other organizational factors influenced contractors’ performance
delivering road projects on time in Gabon. However, from internal and external factors, the competition was
the only factor that had a significant association with SMEs’ performance in KwaZulu-Natal, South Africa
(Sitharam and Hoque, 2016). The results suggest that institutional pressures partially mediate the effects of
internal organizational factors on construction firms’ performance. However, integrated analysis of coercive,
normative, and mimetic pressures related to environmental regulation should be a priority in helping us
move toward a complete understanding of construction firm performance in a regulated business context.
Based on this study, only coercive and normative pressures significantly affect construction firm performance
outcomes while mimetic pressures do not affect; this might be due to the lack of successful international
construction firms for Syrian construction firms to mimic.
Moreover, leadership style, effective communication, and institutional pressures are revealed as significant
predictors of a construction firm’s performance outcomes. Considering the turbulent and hypercompetitive
environment in which construction firms operate in Syria, they must become adaptable, creatively crafting
measures for these factors that will ensure their survival while also meeting their clientele’ performance
expectations and recording high performance. Further, institutional pressures separately were a significant
mediator as a lens to comprehend the factor-related effects within a firm.
This paper presents notable findings for the management of construction organizations. It first speaks
to institutional theory more broadly. Prior works on construction management rooted in institutional
theory have mostly treated performance outcomes as an isomorphic process at the project level. This paper
considered performance outcomes as an inter-organizational matter, which allowed us to focus on the
institutional mechanisms at the firm level.
Conclusion
This study contributes to the literature of construction firms’ management in two main perspectives. Firstly,
from the theoretical perspective, the study established a foundation for future researchers interested in
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202194
examining the causes of heterogeneity in construction firms’ performance. Exploring these causes will also
help new construction firms’ stakeholders, and policymakers obtain a pre-knowledge of organizational and
institutional pressures that may confront them and develop and deploy their resources and strategies to
achieve superior performance in such an evolving context. Therefore, construction firms should realize that
institutional pressures have to be consistent with the performance enhancement strategy and how they can
mould the firms in this field in their quest for legitimacy.
Secondly, from the practical perspective, this study is the first attempt at evaluating the organizational
factors and institutional pressures as a critical determinant of organizational performance that should
interest management at organizational levels. The findings are likely to be of interest to chief executive
officers, project managers, and others with managerial responsibilities in construction firms who need to
understand the type of internal pressures most appropriate for different business environments if they
wish to make strategic decisions to improve their firm’s performance. However, the findings’ interpretation
should be made with caution because when a business environment is considered complex, managers need
to acquire market and environmental data and process them to reduce its uncertainty. Public agencies
tasked with developing and implementing a policy regarding the construction industry’s performance and
construction professionals may also be interested in this research outcome.
Limitation and Directions for Future Research
Though this study has revealed some understanding of internal organizational factors’ roles with institutional
pressures on construction firms’ performance outcomes, this is not without limitations. Since the present
research adopted a cross-sectional design, underlying inferences cannot be made to the study population.
Consequently, a longitudinal approach to data collection with more robust methodologies (mixed approach)
may yield better results.
Furthermore, due to sample size limitations, the findings’ generalisability may be limited, as a larger
sample could have permitted more realistic conclusions. Future researcher works should try to increase the
study samples from the 197 for better results and consider different internal pressures capable of causing
heterogeneity in construction firms’ performance to raise the total variance explained of endogenous variable
above 75.7 percent. Furthermore, researching other charismatic traits of pressures such as legitimacy could
be another field to study.
References
Accenture, 2012. Achieving High Performance in the Construction Industry. Accenture. [online] Available at: < https://
www.accenture.com/t20150523T042717__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/
Global/PDF/Industries_2/Accenture-Achieving-High-Performance-Construction-Industry.pdf> [Accessed 13
November 2020].
Adeleke, A.Q., Bahaudin, A.Y. and Kamaruddeen, A.M., 2017. Organizational Internal Factors and Construction Risk
Management among Nigerian Construction Companies. Global Business Review, [e-journal] 19(4), pp.921–38. https://
doi.org/10.1177/0972150916677460
Aguinis, H., Edwards, J.R. and Bradley, K.J., 2017. Improving Our Understanding of Moderation and Mediation
in Strategic Management Research. Organizational Research Methods, [e-journal] 20(4), pp.665–85. https://doi.
org/10.1177/1094428115627498
Ali, H.A.E.M., Al-Sulaihi, I.A. and Al-Gahtani, K.S., 2013. Indicators for measuring performance of building
construction companies in Kingdom of Saudi Arabia. Journal of King Saud University - Engineering Sciences, [e-journal]
25(2) , pp.125–34. https://doi.org/10.1016/j.jksues.2012.03.002
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202195
https://www.accenture.com/t20150523T042717__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Industries_2/Accenture-Achieving-High-Performance-Construction-Industry.pdf
https://www.accenture.com/t20150523T042717__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Industries_2/Accenture-Achieving-High-Performance-Construction-Industry.pdf
https://www.accenture.com/t20150523T042717__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Industries_2/Accenture-Achieving-High-Performance-Construction-Industry.pdf
https://doi.org/10.1177/0972150916677460
https://doi.org/10.1177/0972150916677460
https://doi.org/10.1177/1094428115627498
https://doi.org/10.1177/1094428115627498
https://doi.org/10.1016/j.jksues.2012.03.002
Asseburg, M., 2020. Reconstruction in Syria: Challenges and Policy Options for the EU and its Member States.
German Institute for International and Security Affairs, SWP Research Paper 2020/RP11. [online] Available at: [Accessed 11 January 2021].
Balatbat, M.C.A., Lin, C.Y. and Carmichael, D.G., 2011. Management efficiency performance of construction
businesses: Australian data. Engineering, Construction and Architectural Management, [e-journal] 18(2), pp.140–58.
https://doi.org/10.1108/09699981111111120
Black, S., Gardner, D.G. and Bright, D.S., 2019. Organizational Behavior. [e-Book] OpenStax: Rice University.
Available at: < https://openstax.org/books/organizational-behavior/pages/1-introduction > [Accessed 17 October 2020].
Chin, W.W., 1998. The partial least squares approach for structural equation modeling. Modern methods for business
research, ( January 1998), pp.295–336. [online] Available at: [Accessed 10 March 2020].
Collier, P., Elliott, V.L., Hegre, H., Hoeffler, A., Reynal-Querol, M. and Sambanis, N., 2003. Breaking the Conflict Trap.
Washington DC: World Bank and Oxford University Press. [online] Available at: [Accessed 11 January 2020].
Devarajan, S. and Mottaghi, L., 2017. The Economics of Post-Conflict Reconstruction in MENA. Middle East
and North Africa Economic Monitor, April 2017. [online] Available at: [Accessed 7 September 2019]. https://doi.org/10.1596/26305
DiMaggio, P.J. and Powell, W.W., 1983. The iron cage revisited institutional isomorphism and collective rationality in
organizational fields. Advances in Strategic Management, [e-journal] 48(2), pp.143–66. https://doi.org/10.1016/S0742-
3322(00)17011-1
Dorsey, D. and Mueller-Hanson, R., 2017. Performance Management That Makes a Difference: An evidence-based approach.
Alexandria, VA: Society for human resource management. [online] Available at: [Accessed
12 December 2019].
Druckman, D., Singer, J.E. and Van Cott, H., 1997. Enhancing Organizational Performance. National Academy of
Sciences. Washington, DC: National Academy Press. https://doi.org/10.17226/5128
Dubey, R., Gunasekaran, A. and Samar Ali, S., 2015. Exploring the relationship between leadership, operational
practices, institutional pressures and environmental performance: A framework for green supply chain. International
Journal of Production Economics, [e-journal] 160, pp.120–32. https://doi.org/10.1016/j.ijpe.2014.10.001
Faul, F., Erdfelder, E., Lang, A.-G. and Bunchner, A., 2007. G*Power 3: A flexible statistical power analysis program
for the social, behavioral, and biomedical sciences. Journal of Behavior Research Methods, [e-journal] 39(2), pp.175–91.
https://doi.org/10.3758/BF03193146
Fehan, H. and Aigbogun, O., 2020. Analysis of the Factors Affecting Syrian Construction Companies ’ Performance.
International Journal of Innovation, Creativity and Change, 11(3), pp.243–58.
Geisser, S., 1974. A predictive approach effect to the random model. Biometrika, 61(1), pp.101–07. [online] [Accessed 10 October 2020]. https://doi.org/10.1093/
biomet/61.1.101
Geraldi, J.G., Lee-Kelley, L., and Kutsch, E., 2010. The Titanic sunk, so what? Project manager response to unexpected
events. International Journal of Project Management, [e-journal] 28(6), pp.547–58. https://doi.org/10.1016/j.
ijproman.2009.10.008
Hair, J., Hult, T., Ringle, C. and Sarstedt, M., 2017. A Primer on Partial Least Squares Structural Equation Modeling
(PLS-SEM). [online] Available at: http://hdl.handle.net/11420/4083
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202196
https://www.swp-berlin.org/10.18449/2020RP11/
https://www.swp-berlin.org/10.18449/2020RP11/
https://doi.org/10.1108/09699981111111120
https://openstax.org/books/organizational-behavior/pages/1-introduction
https://www.researchgate.net/publication/311766005_The_Partial_Least_Squares_Approach_to_Structural_Equation_Modeling
https://www.researchgate.net/publication/311766005_The_Partial_Least_Squares_Approach_to_Structural_Equation_Modeling
https://elibrary.worldbank.org/doi/abs/10.1596/978-0-8213-5481-0
https://elibrary.worldbank.org/doi/abs/10.1596/978-0-8213-5481-0
https://openknowledge.worldbank.org/handle/10986/26305
https://openknowledge.worldbank.org/handle/10986/26305
https://doi.org/10.1596/26305
https://doi.org/10.1016/S0742-3322(00)17011-1
https://doi.org/10.1016/S0742-3322(00)17011-1
https://www.shrm.org/hr-today/trends-and-forecasting/special-reports-and-expert-views/documents/performance%20management.pdf
https://www.shrm.org/hr-today/trends-and-forecasting/special-reports-and-expert-views/documents/performance%20management.pdf
https://doi.org/10.17226/5128
https://doi.org/10.1016/j.ijpe.2014.10.001
https://doi.org/10.3758/BF03193146
https://academic.oup.com/biomet/article-abstract/61/1/101/264348
https://academic.oup.com/biomet/article-abstract/61/1/101/264348
https://doi.org/10.1093/biomet/61.1.101
https://doi.org/10.1093/biomet/61.1.101
https://doi.org/10.1016/j.ijproman.2009.10.008
https://doi.org/10.1016/j.ijproman.2009.10.008
http://hdl.handle.net/11420/4083
Hair, J.F., Ringle, C.M. and Sarstedt, M., 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and
Practice, [e-journal] 19(2), pp.139–52. https://doi.org/10.2753/MTP1069-6679190202
Henseler, J., 2018. Partial least squares path modeling: Quo vadis? Quality and Quantity, [e-journal] 52(1), pp.1–8.
https://doi.org/10.1007/s11135-018-0689-6
Hubbard, G., 2009. Measuring organizational performance: Beyond the triple bottom line. Business Strategy and the
Environment, [e-journal] 18(3), pp.177–91. https://doi.org/10.1002/bse.564
Iliya Nyahas, S., Munene, J.C., Orobia, L. and Kigongo Kaawaase, T., 2017. Isomorphic influences and voluntary
disclosure: The mediating role of organizational culture. Cogent Business and Management, [e-journal] 4(1), pp. 1–18.
https://doi.org/10.1080/23311975.2017.1351144
Indris, S. and Primiana, I., 2015. Internal And External Environment Analysis On The Performance Of Small And
Medium Industries Smes In Indonesia. International Journal of Scientific and Technology Research, 4(4), pp. 188–96.
[online] Available at: [Accessed 2 October 2019].
Jain, P., Vyas, V. and Chalasani, D.P.S., 2016. Corporate social responsibility and financial performance in SMEs:
A structural equation modelling approach. Global Business Review, [e-journal] 17(3), pp.630–53. https://doi.
org/10.1177/0972150916630827
Jallow, A.K., Demian, P., Baldwin, A.N. and Anumba, C., 2014. An empirical study of the complexity of requirements
management in construction projects. Engineering, Construction and Architectural Management, [e-journal] 21(5),
pp.505–31. https://doi.org/10.1108/ECAM-09-2013-0084
Jin, M., 2018. Factors Affecting Growth of Construction Organizations in Nairobi. MBA. United States International
University - Africa. Available at: [Accessed 21 October 2019].
Jin, Z. and Deng, F., 2012. A Proposed Framework for Evaluating the International Construction Performance of
AEC Enterprises. In: Javernick-Will, A., ed. Proceedings of the Engineering Project Organization Conference, Rheden, The
Netherlands, 10-12 July. pp.1-25. [online] Available at: [Accessed 2
January 2020].
Lee, T.S., Kim, D.H. and Lee, D.W., 2011. A competency model for project construction team and project control team.
KSCE Journal of Civil Engineering, [e-journal] 15(5), pp.781–92. https://doi.org/10.1007/s12205-011-1291-9
Leje, M.I., Kasimu, M.A. and Kolawole, A.F., 2019. Impacts of Effective Communication towards Performance of
Construction Organization. Path of Science, [e-journal] 5(8), pp.3001–08. https://doi.org/10.22178/pos.49-4
Li, X., Gao-Zeller, X., Rizzuto, T.E. and Yang, F., 2019. Institutional pressures on corporate social responsibility
strategy in construction corporations: The role of internal motivations. Corporate Social Responsibility and Environmental
Management, [e-journal] 26(4), pp.721–40. https://doi.org/10.1002/csr.1713
Lueg, R., 2015. Strategy maps: The essential link between the balanced scorecard and action. Journal of Business Strategy,
[e-journal] 36(2), pp.34–40. https://doi.org/10.1108/JBS-10-2013-0101
Luu, T-V., Kim, S-Y., Cao, H-L. and Park, Y-M., 2008. Performance measurement of construction firms
in developing countries. Construction Management and Economics, [e-journal] 26(4), pp.373–86. https://doi.
org/10.1080/01446190801918706
Marr, W.A., 2007. Why Monitor Performance?. Seventh International Symposium on Field Measurements in Geomechanics.
Boston, Massachusetts, United States, September 24-27, 2007. https://doi.org/10.1061/40940(307)4
Maya, R.A., 2016. Performance Management for Syrian Construction Projects. International Journal of
Construction Engineering and Management, [e-journal] 5(3), pp.65–78. [online] Available at: < http://article.sapub.
org/10.5923.j.ijcem.20160503.01.html> [Accessed 3 September 2019].
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202197
https://doi.org/10.2753/MTP1069-6679190202
https://doi.org/10.1007/s11135-018-0689-6
https://doi.org/10.1002/bse.564
https://doi.org/10.1080/23311975.2017.1351144
http://paper.researchbib.com/view/paper/44559
https://doi.org/10.1177/0972150916630827
https://doi.org/10.1177/0972150916630827
https://doi.org/10.1108/ECAM-09-2013-0084
http://erepo.usiu.ac.ke/11732/3912
https://discovery.ucl.ac.uk/id/eprint/1359814
https://doi.org/10.1007/s12205-011-1291-9
https://doi.org/10.22178/pos.49-4
https://doi.org/10.1002/csr.1713
https://doi.org/10.1108/JBS-10-2013-0101
https://doi.org/10.1080/01446190801918706
https://doi.org/10.1080/01446190801918706
https://doi.org/10.1061/40940(307)4
http://article.sapub.org/10.5923.j.ijcem.20160503.01.html
http://article.sapub.org/10.5923.j.ijcem.20160503.01.html
Nandakumar, M.K., Ghobadian, A. and Regan, N., 2010. Business-level strategy and performance: The
moderating effects of environment and structure. Management Decision, [e-journal] 48(6), pp.907–39. https://doi.
org/10.1108/00251741011053460
NIST, 2019. 2019-2020 Baldrige Excellence Builder. Gaithersburg, MD: NIST. [online] Available at: [Accessed 25 June 2020].
Nitzl, C., Roldan, J.L. and Cepeda, G., 2016. Mediation analysis in partial least squares path modelling, Helping
researchers discuss more sophisticated models. Industrial Management and Data Systems, [e-journal] 116(9), pp.1849–64.
https://doi.org/10.1108/IMDS-07-2015-0302
Onana, J.-C., 2018. Factors Affecting the Performance of Contractors on Road Projects Supervised by the National Agency
of Public Works in Gabon. M.Sc, University of the Witwatersrand. [online] Available at: [Accessed 2
January 2020].
Ortega, E.M., Azorin, J.F. and Cortes, E., 2010. Competitive strategy, structure and firm performance: A comparison of
the resource-based view and the contingency approach. Management Decision, [e-journal] 48(8), pp.1282–1303. https://
doi.org/10.1108/00251741011076799
Othman, A.A., Rahman, S.A., Sundram, V.P.K. and Bhatti, M.A., 2015. Modelling marketing resources, procurement
process coordination and firm performance in the Malaysian building construction industry. Engineering, Construction
and Architectural Management, [e-journal] 22(6), pp.644–68. https://doi.org/10.1108/ECAM-02-2014-0030
Oyewobi, L.O., Windapo, A.O. and Rotimi, J.O.B., 2015. Measuring strategic performance in construction companies:
a proposed integrated model. Journal of Facilities Management, [e-journal] 13(2), pp.109–32. http://dx.doi.org/10.1108/
JFM-08-2013-0042
Ozorhon, B., Arditi, D., Dickmen, I. and Birgonul, M.T., 2011. Toward a Multidimensional Performance Measure for
International Joint Ventures in Construction. American Society of Civil Engineers, [e-journal] 137(6), pp.403-11. https://
doi.org/10.1061/(ASCE)CO.1943-7862.0000314
Preacher, K.J. and Hayes, A.F., 2008. Asymptotic and resampling strategies for assessing and comparing indirect
effects in multiple mediator models. Behavior Research Methods, [e-journal] 40(3), pp.879–91. https://doi.org/10.3758/
BRM.40.3.879
Rathore, Z. and Elwakil, E., 2020. Hierarchical fuzzy expert system for organizational performance assessment in the
construction industry. Algorithms, [e-journal] 13 (9):205. https://doi.org/10.3390/a13090205
Samairat, M., 2008. Organizational Response to Institutional Pressures: Example from Latin America and the
Caribbean.B.B.A. Umea Universitet. [online] Available at: [Accessed 12 May 2020].
Sitharam, S. and Hoque, M., 2016. Factors affecting the performance of small and medium enterprises in KwaZulu-
Natal, South Africa. Problems and Perspectives in Management, [e-journal] 14 (2-2), pp. 277–288. https://doi.
org/10.21511/ppm.14(2-2).2016.03
Sousa, S. and Aspinwall, E., 2010. Development of a performance measurement framework for SMEs. Total Quality
Management, [e-journal] 21 (5), pp. 475–501. https://doi.org/10.1080/14783363.2010.481510
Stone, M., 1974. Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical
Society: Series B (Methodological), [e-journal] 36 (2), pp. 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Talbot, V. and Dacrema, E., 2019. Rebuilding Syria: The Middle East Next Power Game?. Milano: ISPI (Istituto Per Gli
Studi Di Poliyica Internazionale). [online] Available at: [Accessed 14 July 2020].
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202198
https://doi.org/10.1108/00251741011053460
https://doi.org/10.1108/00251741011053460
https://www.nist.gov/system/files/documents/2017/05/09/Baldrige_Excellence_Builder.pdf
https://www.nist.gov/system/files/documents/2017/05/09/Baldrige_Excellence_Builder.pdf
https://doi.org/10.1108/IMDS-07-2015-0302
http://wiredspace.wits.ac.za/bitstream/handle/10539/25999/OJCONANA Msc Research Report Rev 4.pdf?sequence=2&isAllowed=y
http://wiredspace.wits.ac.za/bitstream/handle/10539/25999/OJCONANA Msc Research Report Rev 4.pdf?sequence=2&isAllowed=y
https://doi.org/10.1108/00251741011076799
https://doi.org/10.1108/00251741011076799
https://doi.org/10.1108/ECAM-02-2014-0030
http://dx.doi.org/10.1108/JFM-08-2013-0042
http://dx.doi.org/10.1108/JFM-08-2013-0042
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000314
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000314
https://doi.org/10.3758/BRM.40.3.879
https://doi.org/10.3758/BRM.40.3.879
https://doi.org/10.3390/a13090205
https://www.diva-portal.org/smash/get/diva2:142345/Fulltext01.pdf
https://www.diva-portal.org/smash/get/diva2:142345/Fulltext01.pdf
https://doi.org/10.21511/ppm.14(2-2).2016.03
https://doi.org/10.21511/ppm.14(2-2).2016.03
https://doi.org/10.1080/14783363.2010.481510
https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
https://www.ispionline.it/it/pubblicazione/rebuilding-syria-middle-easts-next-power-game-23863
https://www.ispionline.it/it/pubblicazione/rebuilding-syria-middle-easts-next-power-game-23863
Tofan, A.S. and Breesam, H.K., 2018. Using the Fuzzy-AHP technique for determining the key performance indicators
of public construction companies in Iraq. International Journal of Civil Engineering and Technology, [e-journal] 9 (13),
pp. 1431–1445. Available at: [Accessed 19 February 2020].
Toor, S. ur R. and Ofori, G., 2008. Leadership for future construction industry: Agenda for authentic leadership.
International Journal of Project Management, [e-journal] 26 (6), pp. 620–630. https://doi.org/10.1016/j.
ijproman.2007.09.010
Wang, G., He, Q., Xia, B., Meng, X., and Wu, P., 2018. Impact of Institutional Pressures on Organizational Citizenship
Behaviors for the Environment: Evidence from Megaprojects. Journal of Management in Engineering, [e-journal] 34 (5).
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000628
Wang, Q., El-Gafy, M. and Zha, J., 2010. Bi–level Framework for Measuring Performance to Improve Productivity of
Construction Enterprises. Construction Research Congress 2010: Innovation for Reshaping Construction Practice. pp. 970–
79. [online] Available at: https://ascelibrary.org/doi/abs/10.1061/41109%28373%2997
Wetzels, M., Odekerken-Schröder, G., and Van Oppen, C., 2009. Using PLS path modeling for assessing hierarchical
construct models: Guidelines and empirical illustration. MIS Quarterly: Management Information Systems, [e-journal] 33
(1), pp. 177–196. https://doi.org/10.2307/20650284
Wu, A.D. and Zumbo, B.D., 2008. Understanding and Using Mediators and Moderators. Social Indicators Research,
[e-journal] 87 (3), pp. 367–392. https://doi.org/10.1007/s11205-007-9143-1
Yang, H., Yeung, J.F.Y., Chan, A.P.C., Chiang, Y.H., and Chan, D.W.M., 2010. A critical review of performance
measurement in construction. Journal of Facilities Management, [e-journal] 8 (4), pp. 269–284. https://doi.
org/10.1108/14725961011078981
Yidizs, S., Basturk, F., and Boz, I.T., 2014. The Effect of Leadership and Innovativeness on Business Performance.
in: Procedia - Social and Behavioral Sciences, 10th International Strategic Management Conference , Rome, Italy. 15
September 2014, pp. 785–793. https://doi.org/10.1016/j.sbspro.2014.09.064
Yu, I., Kim, K., Jung, Y. and Chin, S., 2007. Comparable Performance Measurement System for Construction
Companies. Journal Of Management In Engineering, [e-journal] 23 (3), pp. 131–139. https://doi.org/10.1061/
(ASCE)0742-597X(2007)23:3(131)
Zuhairy, M., Tajuddin, M., Iberahim, H. and Ismail, N., 2013. Leadership Styles and Organizational Performance
in Construction Industry in Malaysia. Malaysia-Japan Joint International Conference 2015 (MJJIC2015) Yamaguchi
University, Ube, Japan. November 2015. Available at: [Accessed
21 March 2020].
Fehan and Aigbogun
Construction Economics and Building, Vol. 21, No. 2 June 202199
https://iaeme.com/Home/article_id/IJCIET_09_13_145
https://doi.org/10.1016/j.ijproman.2007.09.010
https://doi.org/10.1016/j.ijproman.2007.09.010
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000628
https://ascelibrary.org/doi/abs/10.1061/41109%28373%2997
https://doi.org/10.2307/20650284
https://doi.org/10.1007/s11205-007-9143-1
https://doi.org/10.1108/14725961011078981
https://doi.org/10.1108/14725961011078981
https://doi.org/10.1016/j.sbspro.2014.09.064
https://doi.org/10.1061/(ASCE)0742-597X(2007)23:3(131
https://doi.org/10.1061/(ASCE)0742-597X(2007)23:3(131
https://www.researchgate.net/publication/319751836