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

, I Putu Artama Wiguna

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

Woman

Education

Masters

Undergraduate

Other

Position

Top Management

Project Manager

Project Risk

Manager

Risk officer

Other

Work

experience

< 5 years

5-10 Years

10-15 Years

>15 Years

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

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

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 2135

Barrier Grouping

Lack of understanding of perceived value or

benefits

0.777

Eigenvalue

10.497

1.439

1.160

1.004

Variance (%)

55.247

7.574

6.107

5.282

Cumulative variance (%)

55.247

62.821

68.928

74.211

In Tabel 2, barrier variables will be grouped into certain factors based on the most

significant factor loading. Loadings factor greater than 0.5 are considered practically

significant. In Table 2, factor loadings below 0.5 are hidden. The results indicate that

factor 1 comprised nine variables and was interpreted as a lack of practice. Factor 2

consisted of four variables and was construed as needing more risk awareness. Factor 3

involved three variables and was interpreted as a lack of incentives and difficulty finding

suitable methods. Factor 4 included two variables and was interpreted as misperceptions

about risk monitoring and control.

The first barrier factor, lack of practice, consists of nine variables: ineffective risk

reporting, lack of risk-based meetings, lack of intermediate management support,

irregularity in monitoring, lack of risk information, lack of disaster planning and recovery,

lack of risk audits, resistance to change, and ineffective coordination. Project risk

monitoring and control practices, such as risk-based meetings, risk audits, and risk

reporting, have been correlated with project success in construction projects. Although

verbal risk reporting is considered the most direct and realistic method, it is also the least

reliable and most inconsistent. Considering the various barriers to effective verbal

communication, promptly following up such reports with more formal reporting methods

is advisable. Lack of risk information stands as one of the obstacles faced by stakeholders

in the construction industry. Additionally, there exists a reluctance among individuals to

share risk information (Zhao et al., 2015). Which in turn can hinder effective risk

monitoring and control.

The second barrier factor, lack of risk awareness, comprises four variables: lack of

knowledge, lack of expertise, lack of risk awareness, and lack of education and training.

Awareness of the risk management process and lack of experience are the most significant

barriers that project stakeholders need to overcome. Implementing effective risk

management involves fostering risk awareness and facilitating risk communication across

the enterprise, providing decision-makers with comprehensive information.

The third barrier factor is the need for incentives and finding a method. This factor

comprises three variables: no incentives, difficulty determining appropriate risk control

tools and techniques, and difficulty interpreting the standards used. The need for

incentives for improved risk management remains a significant obstacle in construction

management in Lebanon (Shibani et al., 2022). Companies must provide training to staff

and managers to increase their understanding of risk monitoring and control techniques.

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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2136

The fourth barrier factor is misperceptions about risk monitoring and control. This

factor consists of two variables: the perception that risk monitoring increases costs and

administration and a lack of understanding of perceived value or benefits. Risk

monitoring increases costs, and administration is deemed a biased perception or a

misunderstanding of the implementation of risk management, including risk monitoring

and control process, due to the difficulty in demonstrating real value or benefits.

Grouping of Drivers to Risk Monitoring and Control

Various tests were administered to assess the appropriateness of the variables before

embarking on factor analysis. From the analysis results, the Bartlett Test of Sphericity

value was 980.863 with a significance of 0.000. Thus, it meets the requirements because

it is below 0.05. The Kaiser-Meyer-Olkin (KMO) value was 0.834; it meets the

requirements because it is greater than 0.5. All MSA values are more significant than 0.5,

so it meets the requirements. The number of factors was determined by examining the

eigenvalues. According to the calculation results in Table 3, five components have

eigenvalues greater than 1, resulting in five factors formed. The percentages of variation

are explained by each of the five factors (44.408%, 9.489%, 7.374%, 5.960%, and

4.879%, respectively). The five factors formed were able to explain 72.109% of the

variation. This is considered sufficient in terms of the total explained variation. The

communalities value for all variables is more significant than 0.5.

Most researchers agree that leaving factors unrotated is insufficient. The rotation

process is undertaken to resolve ambiguities among factors. Similar variables are grouped

into a single factor, with the grouping determined by the most significant factor loading.

Each grouping is named by considering the variables with high factor loadings and

identifying commonalities among them. Factor 1 is called management support and

comprises seven variables. Factor 2 is named tools and information technology, and it

includes six variables. Factor 3, designated as organisational structure and

communication, consists of three variables. Factor 4 is identified as the external

environment and comprises two variables. Factor 5, named responsibility and

contingency reserve, involves three variables.

Table 3

Result of Driver Factor to Risk Monitoring and Control Implementation

Driver Grouping

Factor 1 Management Support

Commitment and support from top

management

0.777

Organisational culture

0.760

Monitoring Schedule

0.716

Integration of Risk Control and Project

Control

0.704

Leadership style

0.700

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 2137

Driver Grouping

Risk Monitoring and Control Procedures

0.651

Teamwork

0.523

Factor 2 Tools and information technology

Standards and guidelines

0.809

Risk reassessment

0.788

Technology and information infrastructure

0.699

Better decision making

0.663

Effective methods and tools

0.574

The existence of a project risk document

0.555

Factor 3 Organizational Structure and Communication

Organisational structure and size

0.817

Effective Resource allocation

0.747

Communication behavior

0.59

Factor 4 External Environment

Legal and regulatory compliance

requirements

0.847

Customer needs

0.847

The existence of a risk management plan

Factor 5 Responsibility and Contingency Reserve

Assignment of Responsibilities

0.693

Contingency fund

0.647

Eigenvalue

9.326

1.993

1.548

1.251

1.025

Variance (%)

44.408

9.489

7.374

5.960

4.879

Cumulative variance (%)

44.408

53.897

61.271

67.230

72.109

Management support comprises seven variables: commitment and support from top

management, organisational culture, monitoring schedule, integration of risk control and

project control, leadership style, risk monitoring and control procedures, and teamwork.

Top management support is vital in successfully implementing risk management in the

construction industry. Stakeholders in Tanzania also acknowledge the importance of an

appropriate management style that facilitates the formation of project risk management

teams in both organisational and project environments.

Tools and information technology encompasses six variables: standards and

guidelines, risk reassessment, technological and information infrastructure, better

decision-making, practical methods and tools, and project risk documents. Standards,

guidelines, and risk registers are integral inputs and tools for implementing risk

monitoring and control (PMI, 2013). Emphasising the importance of a project risk

document, particularly the risk register, this variable is the backbone of risk monitoring

and control. There were positive and significant correlations between project success and

using risk monitoring and control practice tools, such as risk reassessment. Furthermore,

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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 5, Mei 2024 2138

within this factor, the support of technological infrastructure underscores the role of

advancements in information technology in facilitating effective risk management.

Organisational structure and communication have three variables: structure and

size, reasonable resource allocation, and communication behaviour. Organisational

structure and communication influence successful risk management implementation.

Organisational size significantly impacts risk management implementation (Bohnert et

al., 2019).

External environment reserve encompasses two variables: legal and regulatory

compliance requirements, as well as customer needs. Literature across various industries

suggests that the adoption of risk management is often motivated by a series of legal

compliance and corporate governance requirements. Customer needs emerge as a critical

success factor but in low ratings.

Responsibility and contingency reserve comprises two variables: assignment of

responsibilities and contingency fund. Risk monitoring and control effectively

necessitates assigning specific individual duties and ensuring accountability. A key aspect

of controlling risk is the documentation of responsibilities. The recommended approach

is to have responsible personnel approve using budget reserve funds and monitor their

usage levels—management reserves established to cover unidentified risks.

Conclusion

The results indicate that the 19 identified barriers to risk monitoring and control

implementation in construction projects were categorised into four factors: lack of

practice, lack of risk awareness, lack of incentives and difficulty finding a method, and

misperceptions about risk monitoring and control. The 21 identified drivers were

categorised into five factors: management support, tools and information technology,

organisational structure and communication, external environment, and assigning

responsibility and contingency reserve.

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 2139

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