p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 5 Mei 2024 http://jist.publikasiindonesia.id/
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2130
Analysis of Barrier and Driver Factors to Risk Monitoring
and Control Implementation in Construction Projects
Almutahir
1*
, I Putu Artama Wiguna
2
Institut Teknologi Sepuluh Nopember, Indonesia
Email: [email protected]
*Correspondence
ABSTRACT
Keywords: Barriers
Factors, Factor Analysis,
Risk Monitoring and
Control.
Construction projects inherently involve risks. If proper risk
management practices are employed, the likelihood of cost
overruns can reach up to 80%. Risk monitoring and control
are essential processes in risk management; however, their
implementation needs to be improved in construction
projects. This research analysed the main barriers and
drivers for implementing risk monitoring and control in
construction projects. A questionnaire survey was conducted
using a Likert scale to measure respondents' perceptions.
Data were collected from 71 respondents: top management,
project managers, project risk managers, and risk officers.
This study employs descriptive analysis and factor analysis
to achieve the objectives. The results indicate that the 19
identified barriers to risk monitoring and control were
categorised into four main factors: lack of practice, lack of
risk awareness, lack of incentives and difficulty finding
methods, and misperceptions about risk monitoring and
control. Meanwhile, the 21 drivers were categorised into five
main factors: management support, tools and information
technology, organisational structure and communication,
external environment, assigning responsibility, and
contingency reserve.
Introduction
The construction industry is characterised by varying levels of complexity and
dynamic nature, making it prone to uncertainty. These uncertain events yield negative
and positive consequences for project performance, commonly called risks (PMI, 2017).
Risks may arise during the life cycle of a construction project, potentially leading to a
decrease in project performance (Obondi, 2022). Unmonitored or uncontrolled risks can
result in cost overruns, scheduling delays, diminished project performance, and failure
(Khan & Gul, 2017). If proper risk management practices are employed, the likelihood
of cost overruns can reach up to 80%. To mitigate the adverse effects of risks on project
objectives, the practice of managing risks is implemented, commonly known as risk
management (Tessema et al., 2022).
Analysis of Barrier and Driver Factors to Risk Monitoring and Control Implementation in
Construction Projects
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2131
The construction industry has implemented project risk management for over seven
decades (Senesi et al., 2015). Monitoring and controlling risks is an integral part of the
risk management process. There appeared to be a strong, positive, and significant
correlation between project success and the application of project risk monitoring and
control practices (Obondi, 2022). Nevertheless, implementing risk monitoring and control
strategies could be more effective. A recent report from the Centre for Excellence in ERM
at St. John's University highlighted that organisations rank risk monitoring among the top
five areas needing improvement. Furthermore, findings from the 2019 State of Risk
Oversight report by NC State indicate dissatisfaction with implementing risk monitoring
and control (Strategic Decision Solutions, 2019). The challenge of insufficient project
risk monitoring and control arises from a deficit in risk management capabilities and
knowledge, a failure to promptly respond, monitor, and control identified risks, and a
tendency for project managers to inadequately consider risks (Obondi, 2022).
Moreover, most research focuses solely on risk identification, assessment, and
analysis, neglecting other crucial aspects of risk management, including risk monitoring
and control (Cakmak & Tezel, 2019). The barrier and driver variables for implementing
risk monitoring and control were obtained from a small amount of literature. The barrier
variables include : lack of intermediate management support (Cakmak & Tezel, 2019)
lack of disaster planning and recovery (Edwards, Serra, & Edwards, 2020); perception
that risk monitoring increases costs and administration; lack of understanding of
perceived value or benefits (Zhao, Hwang, & Low, 2015); insufficient resources
(Chileshe & Kikwasi, 2013; Zhao et al., 2015); lack of knowledge (Hwang, Zhao, & Toh,
2014; Shibani et al., 2022); lack of expertise (Chileshe & Kikwasi, 2013); lack of
education and training (Tummala, Leung, Mok, Burchett, & Leung, 1997; Zhao et al.,
2015); lack of risk-based meetings (Obondi, 2022); lack of risk information (Chileshe &
Kikwasi, 2013); ineffective risk reporting (Tang, Qiang, Duffield, Young, & Lu, 2007);
ineffective coordination (Chileshe & Kikwasi, 2013); difficulty in interpreting the
standards used (Tummala et al., 1997); difficulty in determining appropriate risk control
tools and techniques (El-Sayegh, 2015); no incentives (Shibani et al., 2022); the
irregularity in monitoring (Edwards et al., 2020); and lack of risk audits (Obondi, 2022).
While, the driver variables to risk monitoring and control include commitment and
support from top management; leadership style (Chileshe & Kikwasi, 2014); risk
monitoring and control procedures (Edwards et al., 2020); organizational culture (Na
Ranong & Phuenngam, 2009; Zhao, Hwang, & Low, 2013); integration of risk control
and project control (PMI, 2009); monitoring schedule (Cretu, Stewart, & Berends, 2011);
organizational structure and size (Kwaik, Sweis, Allan, & Sweis, 2023); effective
resource allocation (Zhao et al., 2015); communication behavior (Zhao et al., 2015);
contingency fund (Obondi, 2022); the existence of risk management plan (PMI, 2017);
the existence of a project risk document (Larson & Gray, 2011; PMI, 2017); teamwork
(Chileshe & Kikwasi, 2014); technology and information infrastructure support (Kwaik
et al., 2023; Zhao et al., 2015); standards and guidelines (ISO31000, 2018; PMI, 2017);
better decision making (Zhao et al., 2015); risk reassessment (Obondi, 2022); effective
Almutahir, I Putu Artama Wiguna
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2132
methods and tools (Chileshe & Kikwasi, 2014); legal and regulatory compliance
requirements (Kikwasi, 2018; Zhao et al., 2015); customer needs (Chileshe & Kikwasi,
2014); and assignment of responsibilities (Cretu et al., 2011; Larson & Gray, 2011).
Research Methods
This research is exploratory. A questionnaire survey was conducted to analyse the
central barrier and driver factors to implementing risk monitoring and control in
construction projects. The survey consisted of a preliminary survey and a primary survey.
An initial survey was conducted to verify the measurement variables and ensure their
relevance to research purposes. Respondents in the initial survey were project managers
who knew risk management and had more than 12 years of experience in construction
projects (Chileshe et al., 2016). The questionnaire employed in this preliminary survey
was semi-closed. Respondents were to select relevant variables and add some variables
not listed in the questionnaire. The primary survey uses relevant variables resulting from
the preliminary survey. The questionnaire used in this central survey is a closed type,
where respondents provide only one answer selected as correct. The questionnaire used
for this research comprises two sections. The first section gathers the respondents'
background, while the second explores the central barrier and driver factors.
Respondents were given the task of rating using a five-point Likert scale (1 =
strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree). The
population comprises all construction practitioners with extensive experience in risk
management. The sample size in this study was determined using non-probability
sampling, where the exact number of respondents was uncertain. The sampling technique
is purposive sampling, wherein samples are selected based on specific criteria. The
distribution of questionnaires is conducted online and offline. The respondents are top
management, project managers, project risk managers, and risk officers.
This study employs descriptive analysis and factor analysis to achieve the objective.
The collected data undergoes descriptive analysis to facilitate straightforward
interpretation. The function of descriptive statistics is to describe an overview of the
object under study using sample or population data. Factor analysis is used to analyse a
relatively small number of factors capable of explaining many interconnected variables.
The factor analysis process includes the following stages: (1) the variable feasibility test,
which uses the KMO-MSA (Kaiser et al. of Sampling Adequacy) and Bartlett's test of
sphericity; (2) extraction of a set of variables to generate a concise number of factors, (3)
clarifies the variables associated with each formed factor, and (4) assigning a name to the
formed factor that is deemed representative of the variable.
Results and Discussion
A total of 71 completed questionnaires were received from the primary survey. The
profile of the respondents is listed in Table 1. In terms of gender, there were 60 men
(85%) and 11 women (15%). Respondents had a master's educational background of 19
people (27%), 45 bachelor's degrees (63%), and seven others (10%). Respondents based
Analysis of Barrier and Driver Factors to Risk Monitoring and Control Implementation in
Construction Projects
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2133
on position, 9 (13%), 7 (10%), 24 (34%), 15 (21%), and 16 (22%), of the respondents
held positions in top management, project manager, project engineering/risk manager,
risk officer, others, respectively. Meanwhile, 28 respondents (39%) have experience for
less than five years, 24 respondents (34%) have 5-10 years of work experience, six
respondents (8%) have 10-15 years of work experience, and 13 respondents (18%) have
work experience for more than 15 years.
Table 1
Profile of Respondents
Category
Classification
Number
Per
cent
Gender
Man
60
85
Woman
11
15
Education
Masters
19
27
Undergraduate
45
63
Other
7
10
Position
Top Management
9
13
Project Manager
7
10
Project Risk
Manager
24
34
Risk officer
15
21
Other
16
22
Work
experience
< 5 years
28
39
5-10 Years
24
34
10-15 Years
6
8
>15 Years
13
18
The research sample has fulfilled the minimum requirements for the factor analysis.
Furthermore, the selected respondents represent construction practitioners with extensive
knowledge and experience in construction project risk management.
Grouping of Barriers to Risk Monitoring and Control
Several assumption tests were conducted to evaluate the suitability of the variables
before proceeding with factor analysis. The feasibility of a variable is assessed through
the Kaiser Meyer Olkin (KMO) and the Measures of Sampling Adequacy (MSA) value.
This research obtained a KMO value of 0.855, while all variables exhibited an MSA value
exceeding 0.5. The KMO-MSA value should exceed 0.5 for a good result. If a variable
has an MSA value below 0.5, it is advisable to exclude and reanalyse the data to achieve
MSA values for all variables greater than 0.5 (Hair et al., 2010). The Bartlett test of
sphericity resulted in a value of 1272.952 with a significance level of 0.000. The Bartlett
test of sphericity (Sig.) value should be less than 0.05 for better analysis, indicating that
the matrix was not an identity matrix. Consequently, the collected data were deemed
suitable for factor analysis (Hair, Black, Babin, & Anderson, 2010).
Following the feasibility of a variable, factor extraction was continued. This process
groups several variables that are similar into concise factors. The extraction method
employed is Principal Component Analysis (PCA). The number of factors formed can be
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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2134
seen from the components with an eigenvalue greater than 1. The extraction results show
that the number of elements that have a total eigenvalue greater than 1 is four components.
Thus, the 19 identified barriers were categorised into four factors (Table 2)—the four
factors contributed to 74.211% of the total variance. Factor 1 accounted for 55.247% of
the total variance, factor 2 accounted for 7.574%, factor 3 accounted for 6.107%, and
factor 4 accounted for 5.282%. In addition, considering the commonalities value indicates
the extent to which the formed factors can explain variance in a variable. All barriers
exhibit commonalities values exceeding 0.5. Practical considerations suggest that a
minimum communalities value of 0.5 is advisable. Furthermore, it clarifies the factor
formed with the rotation factor. In this study, factor rotation uses the varimax, an
orthogonal rotation technique, to minimise the number of indicators with high factor
loadings on each factor.
Table 2
Result of Barrier Factors to Risk Monitoring and Control Implementation
Barrier Grouping
1
2
3
4
Factor 1: Lack of practice
Ineffective risk reporting
0.792
Lack of risk-based meetings
0.784
Lack of intermediate management support
0.724
The irregularity in monitoring
0.707
Lack of risk information
0.705
Lack of disaster planning and recovery.
0.682
Lack of risk audits
0.665
Resistance to change
0.597
Ineffective coordination
0.593
Factor 2: Lack of risk awareness
Lack of knowledge
0.811
Lack of expertise
0.810
Lack of risk awareness
0.786
Lack of education and training
0.712
Factor 3: No incentives and Having difficulty finding
a method
No incentives
0.835
Difficulty in determining appropriate risk
control tools and techniques
0.705
Difficulty in interpreting the standards used
0.600
Insufficient resources
Factor 4 Misperceptions
The perception that risk monitoring increases
costs and administration
0.788