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ANALYSIS OF INFLUENCING FACTORS ON SAFETY CULTURE IN THE CONSTRUCTION INDUSTRY OF SAUDI ARABIA
By
AHMED ALKHARD
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2016
© 2016 Ahmed Alkhard
To my great family
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ACKNOWLEDGMENTS
First and foremost, all thanks and glory go to Allah Almighty for his support,
mercy, and help, without which this study would have been impossible to complete.
I thank Prof. Ralph Ellis, my supervisory committee chair, for his continuous
guidance, support, and valuable comments during the dissertation journey. I appreciate
the effort and time he spent reviewing my study and providing valuable
recommendations. Also, I am grateful to the committee members (Dr. Charles Glagola,
Dr. Fazil Najafi, and Dr. Larry Muszynski) for their time and patience.
I am deeply indebted to my father (Mohammed Alkhard), peace be upon his soul,
and my mother (Zainab Alkaf) who raised me and encouraged me to seek knowledge
and to pursue education. I cannot express my gratitude for my parents in words. I also
thank my brothers (Salim, Abdullah, Ali, and Yahya) for doing their best to understand a
brother who had to leave the country and home for such a long time. Last but not least,
thanks to my wife (Hanan) and my son (Mohammed), born during the doctoral studies,
for their support, love, and sacrifices.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES ........................................................................................................ 10
ABSTRACT ................................................................................................................... 11
CHAPTER
1 INTRODUCTION .................................................................................................... 12
Background ............................................................................................................. 12
Problem Definition .................................................................................................. 13 Research Objective ................................................................................................ 14
Research Contribution ............................................................................................ 14
2 LITERATURE REVIEW .......................................................................................... 16
Definitions of Safety Culture and Related Concepts ............................................... 16
Organization Culture ........................................................................................ 16 Safety Culture ................................................................................................... 16
Safety Climate .................................................................................................. 18 Behavior Based Safety (BBS) .......................................................................... 19
Development of Safety Culture Models................................................................... 20 Cause and Effect Model ................................................................................... 20 Reciprocal Safety Culture Models .................................................................... 20
Safety Culture Dimensions ..................................................................................... 23 The Psychological Dimension .......................................................................... 23
The Behavioral Dimension ............................................................................... 24 The Situational Dimension ................................................................................ 24
Factors Affecting Safety Culture in Construction .................................................... 24
Safety Legislation and Government Acts ......................................................... 25 National Culture ................................................................................................ 25 Involvement of Stakeholders ............................................................................ 26 Role of Management ........................................................................................ 26
Organization’s safety policy ....................................................................... 26 Management support ................................................................................. 26 Management commitment.......................................................................... 27 Monitoring safety performance ................................................................... 27 Safety training ............................................................................................ 28
Reward and recognition system ................................................................. 28 Work environment ...................................................................................... 28 Status of equipment and facilities .............................................................. 29 Leadership ................................................................................................. 29
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Planning and reporting ............................................................................... 30
Risk control and assessment ..................................................................... 30 Communication .......................................................................................... 30
Involvement of people ................................................................................ 31 Construction Sector in Saudi Arabia ....................................................................... 31
Overview of Saudi Arabia ................................................................................. 31 Construction Sector in Saudi Arabia ................................................................. 32 Status of Safety Performance in Construction Projects .................................... 33
3 RESEARCH METHODOLOGY ............................................................................... 39
Compilation of Associated Literature ...................................................................... 39 Data Collection ....................................................................................................... 39
Selection of the Research Tool ........................................................................ 39 Survey Questionnaire ....................................................................................... 40 Sampling: Selection of Construction Sites ........................................................ 41
Determination of Sample Size .......................................................................... 42 Data Collection Process ................................................................................... 44
Institutional Review Board (IRB) ....................................................................... 44 Statistical Data Analysis .......................................................................................... 45
Preliminary Analysis and Data Screening......................................................... 45
Descriptive Analysis ......................................................................................... 45 Exploratory Factor Analysis (EFA) ................................................................... 46
Suitability of the sample assessment ......................................................... 46 Factor extraction ........................................................................................ 47 Factor rotation ............................................................................................ 48
Confirmatory Factor Analysis (CFA) ................................................................. 48 Parameters for CFA model ........................................................................ 48
Assessment of the hypothesized model ..................................................... 49
4 FINDINGS ............................................................................................................... 53
Overview ................................................................................................................. 53 Preliminary Analysis and Data Screening ............................................................... 54
Response Rate ................................................................................................. 54
Handling of Missing Data.................................................................................. 54 Outliers ............................................................................................................. 55 Normality Test .................................................................................................. 55
Descriptive Statistics ............................................................................................... 56
Sample Characteristics..................................................................................... 56 Multicollinearity ................................................................................................. 58
Exploratory Factor Analysis (EFA) .......................................................................... 59 Suitability of the Sample Assessment .............................................................. 59 The EFA of the First Dimension (Person) ......................................................... 59
The EFA of the Second Dimension (Behavior) ................................................. 62 The EFA of the Third Dimension (Situation) ..................................................... 63
Confirmatory Factor Analysis (CFA) ....................................................................... 63
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The CFA of the First Dimension (Person)......................................................... 65
The CFA of the Second Dimension (Behavior) ................................................. 66 The CFA of the Third Dimension (Situation) ..................................................... 67
Construction Safety Culture Model Development ................................................... 68 Background ...................................................................................................... 68 Model Validation ............................................................................................... 69 Interpretation of the Model ................................................................................ 70
5 CONCLUSIONS ..................................................................................................... 97
Analysis .................................................................................................................. 97 Limitations ............................................................................................................... 99 Future Research ................................................................................................... 100
Summary .............................................................................................................. 101 APPENDIX
A QUESTIONNAIRE SURVEY ................................................................................ 103
B PRELIMINARY ANALYSIS RESULTS.................................................................. 106
C DESCRIPTIVE STATISTICS ................................................................................ 112
LIST OF REFERENCES ............................................................................................. 117
BIOGRAPHICAL SKETCH .......................................................................................... 124
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LIST OF TABLES
Table page 2-1 Factors affecting safety culture in the construction industry. .............................. 35
2-2 Summary of previous studies on safety performance in Saudi Arabia. ............... 36
2-3 Number of injured workers in Saudi Arabia between 2004 and 2012. ................ 37
2-4 Comparative study of safety performance in eight countries in 2008. ................ 37
3-1 Scale of factor loading. ....................................................................................... 50
3-2 Indices for model validation and goodness of fit. ................................................ 50
4-1 Results of Bartlett’s test and the KMO index. ..................................................... 71
4-2 Factor loadings of the three factor model of the first dimension of safety culture “Person”. ................................................................................................. 71
4-3 Factor loadings of the two factor solution of the second dimension of safety culture “Behavior”. .............................................................................................. 72
4-4 Factor loadings of the three factor solution of the third dimension of safety culture “Situation”. .............................................................................................. 73
4-5 Parameter estimates of the initial and final measurement model of the first dimension “Person”. ........................................................................................... 74
4-6 Fit indices for the first dimension “Person”. ........................................................ 75
4-7 Parameter estimates of the initial and final measurement model of the second dimension “Behavior”. ............................................................................ 76
4-8 Fit indices for the second dimension “Behavior”. ................................................ 77
4-9 Parameter estimates of the initial and final measurement model of the third dimension “Situation”. ......................................................................................... 78
4-10 Fit indices for the third dimension “Situation”. ..................................................... 79
4-11 Parameter estimates of the final measurement model of the Construction Safety Culture Model. ......................................................................................... 80
4-12 Fit indices for the Final Safety Culture Model. .................................................... 82
B-1 Percentage of the missing values. .................................................................... 106
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B-2 The mean, 5% trimmed mean, mean difference, and standard deviation. ........ 108
B-3 The skewness and kurtosis values. .................................................................. 110
C-1 Frequency and percentage distribution of respondents .................................... 112
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LIST OF FIGURES
Figure page 2-1 Bandura’s model (1994). .................................................................................... 38
3-1 Research methodology (Graphic Summary)....................................................... 51
3-2 Conceptual safety culture model. ....................................................................... 52
4-1 Job title of the respondents. ............................................................................... 83
4-2 Years of experience of the respondents. ............................................................ 83
4-3 Level of education of the respondents. ............................................................... 84
4-4 Frequency of safety training of the respondent. ................................................. 84
4-5 Scree plot of the first dimension. ........................................................................ 85
4-6 Scree plot of the second dimension. ................................................................... 85
4-7 Scree plot of the third dimension. ....................................................................... 86
4-8 Factors correlation outputs of the first dimension. .............................................. 87
4-9 Standardized outputs of the Initial first dimension CFA model. .......................... 88
4-10 Standardized outputs of the final first dimension CFA model. ............................ 89
4-11 Factors correlation outputs of the second dimension. ........................................ 90
4-12 Standardized outputs of the initial second dimension CFA model. ..................... 91
4-13 Standardized outputs of the final second dimension CFA model........................ 92
4-14 Factors correlation outputs of the third dimension. ............................................. 93
4-15 Standardized outputs of the initial third dimension CFA model. ......................... 94
4-16 Standardized outputs of the final third dimension CFA model. ........................... 95
4-17 Standardized outputs of the final construction safety model. .............................. 96
C-1 Correlation matrix of the first dimension. .......................................................... 114
C-2 Correlation matrix of the second dimension. ................................................... 115
C-3 Correlation matrix of the third dimension. ......................................................... 116
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ANALYSIS OF INFLUENCING FACTORS ON SAFETY CULTURE IN THE
CONSTRUCTION INDUSTRY OF SAUDI ARABIA
By
Ahmed Alkhard
May 2016
Chair: Ralph Ellis Major: Civil Engineering
Safety management has been one of the most popular topics in construction
research. Construction projects are highly labor oriented, and better management can
improve safety performance. Recently, researchers have focused on the concept of
safety culture in the construction industry, and developed several safety culture models
to reduce the number of accidents and enhance safety. However, these developed
models are not effective in terms of implementation, and do not sufficiently consolidate
this concept in the construction industry, especially in developing countries.
This study aimed to explore and analyze factors that influence safety culture in
one developing country, Saudi Arabia. The study used a survey questionnaire as a tool
to collect the required data, and a factor analysis technique to analyze the responses.
Results showed that five components have a direct influence on Construction Safety
Culture: Safety Management System, Safety Resources, Social and Government Acts,
Group Effect, and Supportive Environment.
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CHAPTER 1 INTRODUCTION
Background
The construction industry is one of the most vital industries that contribute to and
enhance the overall economy of any country. This important sector is responsible for
improving a country’s’ Gross Domestic Product (GDP), and also helps to provide crucial
services to people by constructing bridges, airports, roads, commercial and residential
buildings, and dams. According to Government of Saudi Arabia (2014) , the value
added by the construction sector, as percent of GDP, was approximately 7% from 2004
to 2006.
However, the safety performance record in the construction field is still poor
(Zhang & Gao, 2012). In Saudi Arabia, the average number of major accidents in
construction in 2008 was 3,117 per 100,000 workers, while the estimated rate of fatal
injuries was 28 per 100,000 workers. Considering the present situation in Saudi Arabia,
construction has been the most dangerous industry, as 50% of the work injuries occur in
this field. Therefore, research on safety performance is of high value and significance in
improving safety conditions in this sector.
Typically, safety performance is measured by two different approaches: proactive
and reactive. Proactive tools, also known as lagging indicators, include the rate of
injuries. On the other hand, safety culture and hazard identification checklist are
considered reactive tools (leading indicators). Recently, scholars are increasingly
interested in the concept of safety culture, due to its important role in reducing the
number of accidents and deaths on construction sites (Zhang & Gao, 2012). Alasmari,
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Chrisp, and Bowles (2012) believe that any effort made to improve safety performance
will not be effective unless safety culture is considered a major factor.
Problem Definition
One of the least safe industries is the construction industry, due to its high
number of injuries and deaths on workplaces. Safety management is an important issue
for management of any construction project. Quite recently, great attention has been
paid to the concept of safety culture and its role and impact on reducing the number of
injuries and fatalities in the construction sector. This increasing interest has made the
concept of safety culture one of the most popular terms used on a daily basis in recent
years.
The focus of this research was on the definition of safety culture and safety
climate, and the relationship between them (Guldenmund, 2000). Unfortunately, safety
climate and safety culture are often confused. In the construction industry, this problem
becomes more complex as construction methods have very unique and specific
characteristics that need to be considered (Fang & Wu, 2013). Therefore, any
substantial path to assess and reinforce construction safety culture is far from
satisfactory unless construction project attributes are well addressed.
Also, a variety of studies in the literature have developed several reciprocal
safety culture models over the last two decades (Geller,1994; Cooper, 2000; Schien,
2006 Choudhry, Fang, & Mohamed, 2007a). However, these models have a couple of
major limitations. First, reciprocal models do not take into account the impact of the
national culture, that differs from one country to another, and has a significant impact on
people’s behavior (Peckitt, Glendon, & Booth, 2002; Ho & Zeta, 2004; Misnan,
Mohammed, Mahmood, Yusoff, Mahmud, & Abdullah, 2008). Therefore, safety culture
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should be understood in a specific context (Misnan et.al., 2008). Second, efforts to
improve safety culture in the construction industry will not be effective until all related
problems are solved at the most basic levels. This could be accomplished by
recognizing the factors that affect development of a safety culture in such an industry
(Misnan et.al., 2008).
Research Objective
The main objective of this research was to determine and analyze the most
influential factors on safety culture dimensions in the construction industry in Saudi
Arabia. Additional objectives included the following:
Determining the relationship between influencing factors and each dimension of safety culture.
Developing a safety culture model for the construction industry in Saudi Arabia.
Research Contribution
The research outcomes have theoretical and practical implications. An extensive
literature review on safety culture, safety climate, construction characteristics, and the
safety status in high risk environments emphasizes the crucial role safety culture plays
to improve overall safety performance. In a complex industry such as construction, this
concept becomes more difficult to understand. Additionally, there is a lack of studies to
demonstrate the concept of safety culture in the context of the construction industry.
Researchers have struggled to illustrate the relationship between safety culture and
safety climate; to date the relationship has not been identified, as researchers still use
safety climate and safety culture interchangeably. This study’s findings contribute to the
information base about how safety culture works, and in particular, safety culture in the
construction industry.
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In such a labor-intensive sector, and because most of the accidents are human-
related, determining the factors that have a direct impact on safety culture has practical
results for introducing and establishing a positive safety culture in construction projects.
Also, due to the rapid evolution in technology in the last decades, machine-related
accidents have become less common than human-related accidents. Human safety
errors can be reduced by designing a systematic safety culture to form and shape
human attitude and behavior (Shappel & Wiegmann, 2000). This study has the potential
to foster a positive safety culture in the construction industry, through an evidence-
based data analysis and a comprehensive literature review.
Running a confirmatory factor analysis (CFA), which is a subset of structural
equation modeling (SEM), is another advantage of this study. Recently, the SEM tool
has been increasingly used to validate the measurement model, especially in social and
behavioral science (Branham, 2010; Schumacker & Lomax, 2004). Safety culture is
conceptualized as a multidimensional term with three critical dimensions: person,
behavior, and management. Each dimension was validated independently. Therefore,
this methodology generates a reliable and valid construction safety culture model.
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CHAPTER 2 LITERATURE REVIEW
Definitions of Safety Culture and Related Concepts
Organization Culture
Since the 1970s, there has been an argument about the definition of organization
culture (Zhang & Gao, 2012). Hofstede, Hofstede, and Minkov (1991) described
organization culture as a matter associated with top-level management. Ekvall (1983)
and Cooper (2000) said that organization culture is about values and beliefs shared by
people within a community regarding the organization's mission, goals and function.
Nevertheless, it has been argued that not all employees respond in a similar way.
Values, attitudes, and behaviors may change from division to division, department to
department, and individual to individual within the same organization. Consequently,
subcultures can be created inside an organization. Thus, few common attitudes and
behaviors are shared by a whole corporation. However, Pidgeon (1998) viewed these
subcultures as useful, providing a diversity of perspectives and interpretations when
problems arise.
Schein (2006) defined organization culture as “A pattern of basic assumptions
made by a given group based on lessons learnt from the problems that arise in the past,
to ensure members will respond correctly in relation to these problems.” Choudhry et
al.(2007a) said that organizational culture is “the interaction between organization and
individuals, where employees’ behavior can change through mutual interaction.”
Safety Culture
The term ‘safety culture’ was first introduced by the International Nuclear Safety
Advisory Group of the International Atomic Energy Agency (IAEA) in the investigation
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and analysis report after the Chernobyl disaster in 1986 (Zhang & Gao, 2012). Since
then, many researchers have offered definitions of safety culture. The IAEA (1991)
defines safety culture as “that assembly of characteristics and attitudes in organizations
and individuals, which establishes that, as an overriding priority, nuclear plant safety
issues receive the attention warranted by their significance.” This definition highlights
two major components:
1. Safety culture is good safety behavior, and also good safety management; 2. Good safety culture gives the highest priority to safety issues (Cooper, 2000).
Cox and Cox (1991) said safety culture “reflects the attitude, beliefs, perceptions,
and values which are shared by employees in relation to safety”. One of the most
prominent definitions was adopted by the Health and Safety Executive Commission
Advisory Committee on Safety of Nuclear Installations (HSCASNI). It gave an extended
description of the concept of safety culture: “The product of individual and group values,
attitudes, perceptions, competencies and patterns of behavior that determine the
commitment to, and the style and proficiency of an organization’s health and safety
management” (Health and Safety Executive (HSE), 2003).
However, Cooper (2000) identified several weaknesses of the HSC’s definition.
First, it reflects what safety culture has rather than what it is. Second, there is a need to
clarify the definition of the “product”; this could lead to greater understanding of the
nature of safety culture. Third, the sub-goals of safety culture must be outlined to come
up with a comprehensive definition of safety culture. The specific purposes of safety
culture include:
1. Setting behavioral norms
2. Reducing the number of accidents and injuries
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3. Ensuring the importance of safety issues
4. Ensuring that employees have common thoughts and attitudes about safety and risk
5. Increasing people’s and organizations’ commitment to safety
6. Establishing safety and health programs.
Therefore, Cooper (2000) redefined the concept of safety culture as the product
of multiple goal-directed interactions among people, job, and the organization.
Correll and Andrewartha (2000) stated that safety culture consists of two things:
something an organization is (beliefs and attitude of employees), and something an
organization has (policies and practices control to enhance safety performance).
Choudhry et al. (2007a) examined 27 studies regarding safety culture. They
concluded that the definition given by Cooper is more practical than definitions adopted
by others, because it clearly summarizes the content of safety culture. Cooper’s
definition failed to relate safety culture to individual behavior and attitudes, and to safety
performance within the organization safety system. Therefore, Choudhry et al. (2007a)
proposed another definition particularly for the construction industry:
The product of individual and group behaviors, attitudes, norms and values, perceptions and thoughts that determine the commitment to, and style and proficiency of, an organization’s system and how its personnel act and react in terms of the company’s on-going safety performance within the construction site environment.
Safety Climate
The concept of safety climate first officially appeared in 1980. Safety climate is a
set of perceptions shared by employees toward the workplace ( Zohar, 1980).
However, Zohar (2002) re-defined safety climate : “safety climate relates to shared
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perceptions with regard to safety policies, procedures and practices.” Employees’
perception is described by Misnan et.al., (2008) as follows:
management attitude towards safety
perceived level of risk
effect of workplace
management actions towards safety
status of the safety committee
importance of safety training
social status of safety
Another definition suggested that safety climate is a sign of problems in relation
to safety that might be recognized before injuries take place. Wiegmann, Zhang, Von
Thaden, Sharma, and Gibbons (2004) considered safety climate as a psychological
phenomenon or intangible issue that indicates the state of safety culture at a particular
time. Hahn and Murphy (2008) proposed that safety climate refers to employees’
awareness about safety issues within the organization and provides a background
against which daily tasks are performed. The assessment of safety climate can be a
reliable measure of the overall level of the corporate safety performance (Misnan et.al.,
2008).
Behavior Based Safety (BBS)
Behavior-Based Safety (BBS) is an analytic system that identifies and observes
unsafe actions to be changed to obtain a based-time score. Based on this score, a
regular meeting with the participation of employees is set to suggest alternative safe
actions (Choudhry, Fang, & Mohamed, 2007b).
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Development of Safety Culture Models
Cause and Effect Model
Schien (2006) proposed a safety culture model best described by a three-layer
representation. It consists of three linear levels: core, beliefs and values, and behavior.
The core layer (first layer) dictates the pattern of beliefs and values (second layer)
which, in turn, shapes behavior (third layer). However, this linear model fails to
demonstrate the dynamics among the three components. Moreover, this simple version
does not add any valuable component to improve the overall model (Cooper, 2000).
Reciprocal Safety Culture Models
Since the beginning of the concept of safety culture, several models have been
developed. Bandura’s (1977, 1986) model (Figure 2-1) was formed based on a
psychological theory called “Reciprocal Determinism.” This theory states that a person’s
behavior affects, and is affected by, personal factors and also by environmental factors.
It also presents the term “triadic reciprocal causation” referring to the mutual interaction
among three dimensions: person, behavior, and environment. In other words, any
changes in one factor directly impact the other two.
Building on Bandura’s model, Geller (1994) developed a Total Safety Culture
model and outlined ten basic principles to achieve a total safety culture. These
principles are as follows:
1. the culture should maintain the safety process, not OSHA 2. success depends heavily on behavior-based and person-based factors 3. attention must be paid to process, not outcomes 4. behavior is guided by activators and motivated by consequences 5. focuses on achieving success, not avoiding failure 6. continuous observation contributes to safe actions 7. coaching is a key factor 8. observing and coaching are vital caring processes 9. self-esteem, belonging and empowerment increase safety
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10. safety is a value, not a priority.
Cooper (2000) believed, for different reasons, that this model is a perfect basis to
analyze safety culture. First, the three dimensions of the model (person, behavior, and
environment) reflect the accident-causation relationship. Second, the model is designed
to allow human and organization to be easily measured. Finally, the triangulation
approach offers multi-level analysis.
Geller’s model has since been adjusted to reflect the definition of safety culture
provided by Cooper (2000). Cooper’s model is similar to Geller’s model, but the term
“environment” was replaced by “situation.” Therefore, Cooper’s model comprises three
aspects: internal psychological factors, external observable behavioral factors, and
situational factors. Psychological aspects describe what people feel about safety; this
involves attitudes, values, and perception of employees at different levels of the
organization. Behavioral aspects refer to peoples' actions regarding safety-related
activities. The situational aspects of safety culture are concerned with what the
organization has, such as an organization’s policies, operating procedures, safety
standards, management systems, and control systems (Cooper, 2000).
Using Cooper’s model, safety culture can be measured and quantified by
evaluating each aspect independently. A number of quantitative and qualitative
measurement tools can be used to measure the model’s aspects. In terms of
psychological aspects, a safety climate questionnaire is used to measure peoples’
beliefs, attitudes, perceptions and values. Additional methods for measuring safety
climate include interviews and discussion groups. Behavioral aspects can be assessed
through different means, such as peer observation, self-report measures, risk
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assessment, and standard operating procedures. Since situational aspects are reflected
in the organization’s policy, these aspects can be examined by audits and safety
management systems (Cooper, 2000).
Based on Cooper’s model, Choudhry et al. (2007a) offers an integrative
framework to enhance safety culture specifically designed to be implemented in
construction projects. To identify the relation between safety culture and individual
behavior and attitude in addition to the safety system, Choudhry’s model incorporated
three related concepts: safety climate, Behavior-Based Safety (BBS) programs, and
safety systems. This addition allows safety-culture components to be measured in
combination or individually. Another advantage of this model is that
“environment/situation” takes into account the project conditions as well as the
organization environment.
Even though Choudhry et al. (2007a) made an excellent effort to implement
Cooper’s model to construction projects, Alasamri et al. (2012) said that Choudhry’s
model does not take into account the role of top management, especially when
implementing safety-culture procedures in the construction workplace. This argument
suggests that Choudhry’s model omitted one of the most significant factors that can
directly improve the model: education and training in construction safety culture. The
factor of education is extremely important, especially for two levels of people:
technicians who manage the work, and workers who execute them (Pellicer &
Molenaar, 2009). Moreover, Choudhry’s model failed to address the important link
between enablers (what an organization is doing) and goals (what it aims to achieve).
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Safety Culture Dimensions
Based on the review of safety culture models in the last section, the reciprocal
safety culture model presented by Cooper (2000) and developed by Choudhry et al.
(2007b) was adapted as a conceptual framework for this research. This is because the
fact that this reciprocal model, that consists of Physiological, Behavioral, and Situational
dimensions, tolerates triangulation of perspectives and perceptions for safety culture in
organizations ( Ismail, Hashim, Ismail, & Majid, 2009). Furthermore, these three
dimensions reflect and identify the relationship with accident causations recognized by
researchers. Also, this reciprocal model shows consistency to the safety culture
definition: “The product of individual and group values, attitudes, perceptions,
competencies and patterns of behavior that determine the commitment to, and the style
and proficiency of, an organization’s health and safety management.” ( Ismail et al.,
2009)
The Psychological Dimension
The psychological dimension describes what people feel and think about safety,
frequently referred to as “safety climate”. (Health and Safety Executive HSE, 2003).
Safety climate is associated with shared values, attitude, perceptions, and beliefs of
people or groups about safety at different levels of an organization (Cooper, 2000;
Health and Safety Executive (HSE), 2003; Ismail et al., 2009).
Relating to safety climate, a variety of tools can be used to capture and measure
the psychological aspects (Cooper, 2000). The most popular approach is a “safety
climate questionnaire” ( Zohar, 1980), which encompasses of a set of questions aiming
to measure individuals’ belief, values, and perceptions about safety ( Teo & Feng,
2009). The survey results determine strengths and weakness of current organization
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safety practices (Choudhry et al., 2007a). However, other methods for measuring this
dimension include interviews, documents analysis, and actual data (Cooper, 2000).
The Behavioral Dimension
The second dimension, behavioral aspects, is related to how people act and
behave within an organization, specifically in terms of safety-based activities such as
coaching complying, recognizing, communicating and demonstrating (Health and Safety
Executive (HSE), 2003). This dimension of safety culture can be assessed through peer
observations. This method begins with conducting a safety assessment survey to
identify weaknesses. With support of the management workforce, a safe/unsafe
checklist is prepared to allow a certified observer to establish the safety score and
evaluate the safety behavior.
The Situational Dimension
The situational dimension, which describes corporate aspects, is concerned with
the organization’s role in safety issues. This can be seen in an organization’s policies,
procedures, communication system, and management system (Health and Safety
Executive HSE, 2003).
Factors Affecting Safety Culture in Construction
Identifying the factors that affect the development of a safety culture is the first
step in fostering the concept of safety culture in the construction industry. Efforts to
improve safety performance in such a labor-intensive industry will not be effective until
the safety culture is improved (Misnan et.al., 2008). Therefore, it is crucial to solve the
root of the problems effectively. Through the literature review, and considering the
characteristics of the construction industry, a set of factors that have a direct impact on
safety culture, internal and external, has been collected as detailed in Table 2-1.
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Safety Legislation and Government Acts
According to Sorenson (2002), a regulatory agency (focusing on construction
safety law and policies, safety standards, and safety inspection) is one of the key
elements to establish a positive safety program and a better safety culture in such a
labor-intensive industry. However, the role of the regulatory environment is not simple,
especially when the regulator holds the responsibility for safety rather than the operator.
The influence of regulatory activities extends to organizations, also includes the overall
industry in which organizations work, and therefore affects the organizational culture
(Sorensen, 2002). Tam, Zeng, and Deng (2004) emphasize the critical role of
governments in enforcing stricter laws and organizing safety programs.
National Culture
The impact of national culture on safety culture is given in the definition of safety
culture proposed by Waring (1992): “aspects of culture that affect safety.” People with
different cultural backgrounds observe, react, and respond to risk differently, as they
behave according to their different cultural norms obtained from a different social life
(Fetscherin, 2009). However, few empirical studies have addressed the impact of
national culture on safety behavior and attitude (Mearns & Yule, 2009). Peckitt et al.
(2002) and Mearns and Yule (2009) examined the relationship between cultural values
and safety behavior in a multinational construction setting. They concluded that
differences in national culture influence the safety process, and it is important to
consider different cultural backgrounds for safety issues. Thus, an organization’s
practices cannot be ignored when developing ways to improve safety culture within the
organization (Misnan et.al., 2008).
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Involvement of Stakeholders
Wright, Brabazon, Tipping, and Talwalkar (1999) said cultural norms should not
be defined independently by management. Instead, all key stakeholders (such as
regulators, customers, staff, and contractors) must be involved in the decision-making.
This ensures that those safety norms are acceptable, realistic, and clear for all parties.
Role of Management
Safety Management System (SMS) has a direct impact on safety culture in
construction projects ( Tam et al., 2004; Fang & Wu, 2013; Zhang & Gao, 2012). A
safety system includes all the aspects of an SMS: policies, procedures, committees, etc.
(Choudhry et al., 2007a). Fernández-Muñiz, Montes-Peón, and Vázquez-Ordás (2007)
suggested that an SMS includes the following : organization policy and rules, safety
training, incentives and disincentives, communication, planning ,and control. However,
other researchers included other managerial attributes that could have impacts on
safety culture elements, including safety audit and inspection, leadership, and
involvement of employees (Kunreuther, McNulty, & Kang, 2002; Mohamed & Chinda,
2011)
Organization’s safety policy
A positive safety culture requires a safety policy that has realistic and workable
rules and procedures in all situations ( Aksorn & Hadikusumo, 2008). Guldenmund
(2000) found that safety policy is a prominent factor in implementing safety culture. This
factor can be defined by an employees’ perception of the frequency of rules violations.
Management support
Management has a proven role in enhancing safety culture (Abudayyeh,
Fredericks, Butt, & Shaar, 2006). Several actions must be taken by management to
27
support safety culture, including a written safety policy, reacting to safety feedback and
suggestion, providing necessary resources, regular visiting to the worksite, and so on
(Abudayyeh et al., 2006). In addition, when decisions are made, management should
weigh safety as much as productivity and profitability.
Management commitment
Effective organizational safety culture in the construction industry depends
greatly on management’s commitment (Teo, Ling, & Chong, 2005). In the literature,
management commitment is a key factor determining employees’ behavior and attitude
regarding safety (Zohar, 1980). Management commitment refers to the degree to which
top-level management recognizes safety as a guiding principle in the organization
(Misnan et.al., 2008). According to Hinze and Raboud (1988), safety culture begins at
top-level management; if it succeeds, it is adopted at all levels of the organization.
Numerous previous studies showed that organizations where top-level management
showed a high priority for safety commitment tend to have a better safety culture (Hinze
& Raboud, 1988; Lingard, Blismas, Fang, Choudhry, & Hinze, 2006).
Monitoring safety performance
Kunreuther, McNulty, and Kang (2002) said proper safety monitoring, inspection,
and audits are essential in an SMS. To achieve an effective safety culture, employers
must deliver active and sufficient supervision to protect individuals from potential
hazards and risks in the worksite (Thanet Aksorn & Hadikusumo, 2008). A successful
supervisor should have the capability to align work with the workers’ ability, and to
appreciate workers when jobs are done safely. This requires a competent persona who
communicates effectively by speaking and listening, and being a good example in
following safety rules and solving any arising problems ( Fang, Xie, Huang, & Li, 2004).
28
Safety training
To improve the safety culture, all employees should be given periodic
educational sessions and training programs (Tam et al., 2004; Cooper, 2000; Fang et
al., 2004). This establishes a positive safety behavior, and ensures that people carry out
activities effectively, safely, and with high quality (Tam et al., 2004; Teo et al., 2005).
Organizations with high safety culture ensure that their personnel are well trained and
aware of the consequences of unsafe behavior (Lardner, Fleming, & Joyner, 2000).
Worker safety training programs help increase the psychological and mental dimensions
of safety culture (safety knowledge, perception). As a result, this helps minimize the
number of injuries and accident in the workplace (Christian, Bradley, Wallace, & Burke,
2009).
Reward and recognition system
Molenaar, Brown, Caile, and Smith (2002) described the reward and recognition
system, also called incentives and disincentives, as an important indicator of safety
culture in construction organizations. This reward and recognition system aims to
encourage employees to comply with safety regulations. A fair system, on the other
hand, does not reward employees who fail to maintain safety procedures (Hsu, Lee,
Wu, & Takano, 2008). Safety incentive rewards could take different forms: social
(recognition), informational (feedback), financial (bonuses), and tangible (awards)
(Lingard et al., 2006). Gibb and Foster (1997) indicated that there is a direct positive
relationship between safety incentive and disincentive, and safety performance.
Work environment
Development of safety culture cannot be successful on an individual basis
(Langford, Rowlinson, & Sawacha, 2000). Management should be responsible for
29
fostering a safety culture and also held accountable for establishing an environment
where employees can understand and accept norms and roles in order to prevent
accidents. Hence, management must transfer safety activities from top management to
the lower levels of the organization ( Aksorn & Hadikusumo, 2008). To create a safer
work environment, cooperation between workers and the safety system coordination
process is critical (Langford et al., 2000). Sites where construction workers effectively
interact with workmates, and continually provide suggestions to each other, report fewer
accidents and less workers distress (Olcott, 1997; Siu, Phillips, & Leung, 2004)
Status of equipment and facilities
Aksorn and Hadikusumo (2008) and Fang et al. (2004) insist that the a strong
safety culture cannot be implemented when safety resources are lacking. Sufficient
safety resources must be provided by management to accomplish day-to-day activities
in accordance with the organization’s short and long-term safety strategies (Abudayyeh
et al., 2006). The required safety resources include effective staff, time and information,
methods used, tools, equipment, and machines (Sorensen, 2002).
Leadership
Leadership is a vital part of an organization’s safety and health system (Lingard
et al., 2006) , and is an integral component of the organization safety culture (Misnan
et.al., 2008). In the construction industry, various studies showed that leadership is a
major enabler in fostering an effective safety culture (Health and Safety Executive
(HSE), 2003; Lingard et al., 2006; Molenaar et al., 2002; Teo et al., 2005). Leaders
must improve and achieve the organization’s mission and vision of safety and health by
delivering values, and implementing them by appropriate behavior (Misnan et.al., 2008;
30
Mohamed & Chinda, 2011). Also, a successful leader must be able to deal with people
and influence their actions to strengthen safety culture (Misnan et.al., 2008).
Planning and reporting
Speirs and Johnson (2002) noted that organizations with a positive safety culture
produce high quality safety reports. Incident reports and near-incident reports
contribute to reducing the number of long-term injuries (Nielsen, Carstensen, &
Rasmussen, 2006). To ensure reporting quality, Nielsen et al.(2006) suggested that a
reporting procedure can be tracked using either a computer-based or paper-based
system. Employees should not only report incidents or near-misses, but they should
also have the chance to offer suggestions to prevent incidents in the future (Nielsen et
al., 2006).
Risk control and assessment
In construction safety-related issues, the existence of a learning environment is
important. Learning lessons from previous projects should include analysis of unsafe
behavior of people that lead to incidents, incidents in similar industries, and previous
accidents (Health and Safety Executive HSE, 2003). For this reason, regular risk
assessment helps organizations identify, learn, and alter unsafe conditions (Health and
Safety Executive HSE, 2003). To maintain safety performance and mitigate major
effects on construction safety performance resulting from organizational changes, it is
necessary to have a process of risk identification, analysis, and assessment.
Communication
Effective two-way communication is key to a positive safety culture, as it delivers
a message to employees in the clearest way possible (Health and Safety Executive
HSE, 2003). This helps employees maintain a good understanding of the company’s
31
direction. Communication within an organization fits into three different categories: top-
down (management to frontline), down-top (frontline to management), and horizontal
(two individuals at the same level). Signs of successful communication include written
and oral methods that consolidate the importance of safety issues, a good safety policy
statement in different locations, and safety management tours.
Involvement of people
Involvement of people in safety issues has a great impact on improving people’s
safety accountability, increasing their sensitivity to risks, and preventing major
accidents. The more people are involved in safety matters, the better the safety climate
(Mohamed, 2002). According to Hudson’s study in 2001, development of a safety
culture has three levels, one of which is workers’ involvement in the regulatory process.
Different approaches exist to achieve this involvement: safety training and motivation,
reporting unsafe practices and hazards, and involving individuals in safety decisions
and process ( Aksorn & Hadikusumo, 2004; Health and Safety Executive HSE, 2003)
Construction Sector in Saudi Arabia
Overview of Saudi Arabia
The Kingdom of Saudi Arabia is the largest country among the Gulf countries
located in the Arabian Peninsula. Saudi Arabia, located in southwest Asia, shares its
northern border with Kuwait, Iraq, and Jordan; while Yemen and Oman run along its
southern border. The Arabian Gulf, the United Arab Emirates, and Qatar make up the
country’s eastern edge. On the west, the kingdom is surrounded by the Red Sea, with a
coastline of 1,760 km (1,100 miles). The geographical location of Saudi Arabia is at the
crossroad of the three continents: Asia, Europe, and Africa (Government of Saudi
Arabia, 2014)
32
According to statistics in 2010, the total population of Saudi Arabia is 27,136,977,
and the annual incremental rate of population growth is 3.2%. The population density is
14 people per square kilometer (Government of Saudi Arabia, 2014). As of 2011, Saudi
Arabia had a total GDP of $576.8 billion ( World Bank, 2013).
Federally, Saudi Arabia has 13 provinces, each with its own capital. The
kingdom’s capital city is Riyadh, which is located in the center of the country. Saudi
Arabia has been a royal system since the foundation of the kingdom. The constitution is
basically driven from the holy book (Quran) and the traditions of the prophet
Mohammad, Peace be upon him. The official language is Arabic, while English is
commonly used in business and education, especially in science and technology
(Government of Saudi Arabia, 2014).
Construction Sector in Saudi Arabia
One of the most important sectors, that heavily contributes to improvement of the
overall GDP, and enhances the health of the economy is the construction sector. In
Saudi Arabia, the construction industry is the largest market in the gulf region; one-
fourth of the ongoing construction projects in the region, with a total cost of $1.9 billion,
are located in Saudi Arabia (U.S.-Saudi Arabian Business Council, 2009). Furthermore,
because of increasing demand for commercial, industrial, and residential projects, the
construction market in Saudi Arabia is expected to grow (Venture Middle East, 2011).
The size of the construction market increased from $79,927 million in 2008 to
$110,784 million in 2011. Half of the construction market was dedicated to buildings,
while 13% was for industrial projects, and 17% was for the oil and gas sector (Venture
Middle East, 2011). As a result of this revolution in the construction industry, the labor
33
force was estimated at 1,825,862 in 2011, compared to 1,410,517 in 2008 (General
Organization for Social Insurance, 2013).
The role of the Saudi government in the construction industry is important and
significant. The government spent $137 billion between October 2008 and April 2009,
despite the negative effects of the global economic crisis that influenced the growth of
the industry around the world. Moreover, the Saudi government plans to invest about
$400 billion for large infrastructure projects in the next 5 years (U.S.-Saudi Arabian
Business Council, 2009).
Status of Safety Performance in Construction Projects
One of the main contributions to injuries and accidents on construction sites is
the lack of a safety culture (Choudhry et al., 2007a). In Saudi Arabia, although the
construction market is considered the largest in the region, the safety performance level
continues to be labeled relatively poor (Alasamri et al., 2012).
Over the last two decades, few studies have measured the safety performance
level in Saudi Arabia (Table 2-2). Some of these studies used traditional approaches
(lagging indicators), such as a rate of injuries, that consider the number of injuries per
million working hours. However, numerous studies preferred modern approaches
(leading indicators), including the hazard identification checklist.
Jannadi and Al‐Sudairi (1995) examined 16 construction organizations of
different sizes and found the rate of injuries are 11, 19, and 43 for large, medium, and
small, respectively. Using a combination of traditional and modern approaches (injury
rate and checklist score) it was determined that the safety level is good for large firms,
and fair for medium and small firms (Al-Utaibi,1996; Baig, 2001). Implementing the
attitude score tool, Al-Amoudi (1997) found that all participants’ safety levels were poor
34
and unsatisfactory; however, Alasamri et al.(2012) said that large firms have a good
level of safety, but small firms have a poor level. Overall, these studies confirm that the
lack of safety culture is a main cause of accidents and injuries on construction sites in
Saudi Arabia (Alasamri et al., 2012).
Statistically, The General Organization for Social Insurance (GOSI) publishes the
annual number of injuries in each industry. For the construction sector over the last
decade, figures prove that the safety performance is still far from satisfactory (Table 2-
3). From 2004 to 2012, the total number of injuries was 334,970, making the annual
average rate 3,721.9 per 100,000 employees (General Organization for Social
Insurance, 2013).
To give a clear picture of the current state of safety in Saudi Arabia, Alasmari et
al., (2012) conducted a comparative study to determine where Saudi Arabia stands in
relation to other countries. As illustrated in Table 4, the eight countries involved in this
study were Australia, the United States of America, the United Kingdom, the United
Arab Emirates, Kuwait, Bahrain, Jordan, and Saudi Arabia. Alasmari’s 2012 study
concerned the total number of employees, and the rate and number of deaths and
injuries on a scale of 100,000 employees.
His key findings are:
1. Injuries in Saudi Arabia was the highest, with 3,117 per 100,000 workers in 2008 2. Injuries in the UK was the lowest, with only 254.1 per 100,000 workers 3. Fatalities in Saudi Arabia was also the highest, with 28 per 100,000 workers in
2008 4. Fatalities in the UK was the lowest, with 3.4 per 100,000 in the same year.
35
Table 2-1. Factors affecting safety culture in the construction industry. Group Factors
Government acts Safety legislation in the country. Periodical supervision of government agencies.
Social impacts Promotion of safety within the society. Impact of national culture (customs and habits)
Industry environment Monitoring safety performance (safety product) in the construction industry.
Involvement of stakeholders. Existence of migrant workers.
Internal (organization) environment
Establishment of good working environment. Status of equipment and facilities. Safety training Support from management (motivation) Effect of rewards and punishment systems Degree of management commitment Organization’s safety policies
Project condition effect of leadership in the project site Preparing a safety plan Attitude of safety supervisor in the workplace Degree of risk control and assessment on the worksite
Group effect Shared employees’ perception of safety Effective communication Involvement of employees
36
Table 2-2. Summary of previous studies on safety performance in Saudi Arabia.
Study Participants
Small firms Medium firms Large firms
Safety assessment method Safety assessment method Safety assessment method
Mean Injury rate
Attitude score %
Checklist score
Injury rate%
Attitude score %
Checklist score
Injury rate%
Attitude score%
Checklist score
1995 16 43.0 - - 19 - - 11 - -
1996 45 35.8 - 66.80% 29.8 - 68.05% 10.06 - 88.62%
1997 122 - 16.0 - - 37 - - 45. -
1998 14 sites - - 65.21% - - - - 84.5 -
2001 28 89.4 - 0.47/1 34.8 - 0.61 /1 13.79 - 0.8 /1
2010 38 - 45.4 - - - - - 75.23 -
37
Table 2-3. Number of injured workers in Saudi Arabia between 2004 and 2012.
Year Workers in construction No. of injuries %
2004 749,964 15,357 2.0
2005 833,098 39,299 4.7
2006 916,505 42,326 4.6
2007 1,055,496 37,427 3.5
2008 1,248,774 38,929 3.1
2009 1,410,517 44,430 3.1
2010 1,599,903 43,308 2.7
2011 1,825,862 37,527 2.1
2012 2,174,962 36,367 1.7
Table 2-4. Comparative study of safety performance in eight countries in 2008.
Country No. of workers
No. of Injuries
Rate of injuries
No. of deaths
Rate of deaths
(Thousands) (per 100,000 /year)
(per 100,000 /year)
United Kingdom 2,402 Major 3,286 254.1
53 3.4 Minor 6,789 524.9
Unites States of America
13,735 Major 164,900 1,200.0
975 9.7 Minor 317,800 1,500.0
Australia 926 Major 1,621 175.0
55 5.9 Minor 13,118 1,416.0
Unites Arab of Emirates
1,349 Serious 690 233.0 20 6.7
Kuwait 127 Serious 1,257 1013.0 13 10.4
Jordan 374 Serious 2,306 615.9 -- --
Bahrain 133 Serious 475 357.1 -- --
Saudi Arabia 1,248 Serious 38,929 3117.0 402 28.19
38
Figure 2-1. Bandura’s model (1994).
39
CHAPTER 3 RESEARCH METHODOLOGY
The methodology used to explore factors affecting safety culture in the
construction industry in Saudi Arabia included the activities shown in Figure 3-1. The
overall methodology for conducting this research was as follows:
Research background and compilation of relevant literature
Data collection: selection of research tools, sample size, and collection process
Data analysis: descriptive analysis, factor analysis and structural equation modelling
Compilation of Associated Literature
The sound foundation of this work was the background research. Literature on
construction safety and its related issues was reviewed. Much of the literature discusses
the concept of safety culture and its place in the construction industry. To build on this,
several safety culture models were reviewed, showing the pros and cons of each. A list
of influence factors was also extracted and categorized; and a review was carried out
on Saudi Arabia, its construction industry, and the current safety status.
Data Collection
Selection of the Research Tool
To carry out fieldwork, it is necessary to choose an appropriate tool and
instrument. Several considerations included the required depth and scope of the work.
In this study, survey research was used, and a procedure in which participants answer
specific questions through a questionnaire was the tool used to collect the essential
data. Since thoughts and opinions were to be obtained in this research, a survey
method was the most appropriate choice. The survey method is also an effective
technique for prediction and description.
40
Survey Questionnaire
For this study, a survey questionnaire was developed as shown in appendix A.
The survey questionnaire consisted of two major sections: participant’s profile and
safety culture dimensions.
The first section was established to gather demographic and occupational
information, for instance, nationality, language, age, sex, education level, occupation,
years of experience, and frequency of training sessions. The study was conducted in a
multi-national work site. Thus, it required participants to specify their nationality along
with their mother language. Participant age was divided into six categories: under 26,
26-30, 31-35, 36-40, 41-45, more than 45 years old. The educational level categories of
the respondents were: No Education, High School, College, Bachelor’s Degree,
Master’s Degree, and PhD. Participants were also asked to define their position in the
current project’s hierarchy such as: Project Manager, Engineer, Safety Officer, Worker,
or Other. The years of experience in the current occupation were scaled into five
groups: Less Than 5 Years, 6-10 Years, 11-15 Years, 16-20 Years, and More Than 21
Years. The categories of the frequency of safety training were: Never, 1-4 Times, and
More Than 4 Times.
The second section aimed to measure safety culture dimensions (person,
behavior, and situation). “Person” (the first dimension) refers to people’s perception,
attitude and values toward safety. “Behavior” (the second dimension) refers to safety
performance and action. “Situation” (the third dimension) refers to the safety
management system such as safety system, regulation, policies and laws.
According to the degree of influence, a total of 21 items in this questionnaire
were scored on each dimension. The degree of influence was divided into five levels:
41
1. no influence 2. less influence 3. general influence 4. high influence 5. enormous influence.
Items for this questionnaire survey were collected from the literature, especially
from Zhang and Gao’s (2012) study and a small group of experts in the field in Saudi
Arabia. To clarify, the literature review helped define the concept of safety culture,
identify its major dimensions, and list a set of items that have an impact on safety
culture. The professionals contributed to this questionnaire by replacing factors not
applicable in the construction sector with other factors more related to the nature of the
construction industry in Saudi Arabia.
There were several considerations during the questions-selection process. Short,
clear, and concise questions were selected. To avoid redundancy, confusing items,
lengthy items, and items with negative statements and difficult language were excluded.
Also, the unique characteristics of the construction industry were considered for the
selection process.
Sampling: Selection of Construction Sites
The population of this study encompassed all the people involved in the
construction industry who were working on infrastructure projects in Saudi Arabia. This
included project-level management personnel (such as project manager, safety officer,
and engineers); and individuals from the field (including carpenters, steel workers, and
foremen).
When selecting construction sites to be surveyed in this study, several limitations
were considered. First, sites should have a multiple range of construction activities, to
reflect different opinions of different trades involved in projects. The presence of
42
subgroups was important to observe group differences. Second, there is a lack of
collaboration between researchers and professionals in the field. To overcome these
limitations, it was necessary to initiate and develop contacts with professionals in the
industry, who provided access to the required sites and facilitated the research process.
Top-level management personnel were consulted, to obtain permission and support.
Considering the above-mentioned restrictions, selection of the seven
construction sites was based on the following criteria:
Different types of on-going mega construction projects, including airport construction, high rise buildings, economic cities, and the two holy mosques in Makkah and Almadina
Adequate number and size of trades involved in the site
Classification of the construction companies operating these sites
Cooperation of related departments and ministries
As a result, it could be argued that the sample in this study is representative and
adequate because:
Selected sites excluded very small sites and sites located in rural areas
Assigned construction sites ran crucial projects in different cities in Saudi Arabia.
Field-level people exposed to hazards on their daily routine were randomly selected
Responses represent high-classified construction companies
Determination of Sample Size
Specifying the sample size required is an important factor for any study in which
interpretations and interferences can be made about the population. In the literature,
there has been a long discussion regarding the necessary sample size. It is believed
that there is a minimum fixed number required to ensure the power of the analysis of the
43
sample. Hox and Bechger (1998) require the sample size to be as large as 400 cases,
while it is claimed that 200 responses are enough to maintain the sample reliability
(Boomsma, 2001). However, smaller sample sizes have been found and used in the
literature.
On the other hand, it is also argued that the sample size depends on the number
of parameters in the survey. Kline and Santor (1999) said a ratio of 20 respondents per
parameter is adequate for power analysis; a ratio of 10 respondents per parameter is
also reasonable. Bentler and Chou (1987) and Williams, Brown, and Onsman (2012)
provided a general rule for the sample size estimation that the minimum number of the
sample size should be five times the number of parameters.
From a statistical viewpoint, the following formulas are also used to determine
sample size. It depends on a number of values: level of precision (mc), confidence level
(CI), level of variability (P) and population size (N). Once these values are determined,
the following formulas are to calculate the sample size:
𝑛 = 𝐶𝐼2∗𝑃 (1−𝑃)
𝑚𝑐2 (3-1)
𝑆 = 𝑛
1+ 𝑛
𝑁
(3-2)
When applying these formulas for this study, population size (N) represents the
number of people working in the construction Industry in Saudi Arabia, an estimated
2,174,962 according to the GOSI (2013). The default values of confidence interval (CI)
and level of variability (P), 95% and 50% respectively, were used. As a result, the base
sample size was 384 with a +/- 5% margin of error, or 196 with a +/-7% margin of error.
44
As a result and based on Kline’s rule, the minimum number of cases needed for
this study to ensure the adequacy and to have enough power analysis is 210 (10 times
the number of parameters). Statistically, this number of responses will achieve a 93%
confidence interval (alpha level of 0.07). This value will make the researcher 93%
confident in making an inference and drawing a conclusion from this study.
Data Collection Process
To achieve the main objective of this study, seven large construction sites, in
different cities in Saudi Arabia, were included. For each project, a number of trades
were surveyed, including workers exposed to unsafe conditions: carpenters, steel fixers
and masons, foremen, field engineers, and managers. Before undertaking the
questionnaire, permission was obtained from the site administration. Furthermore, to
facilitate the procedure of data collection, workers were introduced to the survey by
management staff or their representatives. To ensure that surveyed people understood
the nature of the research and its objective, a brief background was given.
For some projects, an online-based survey link was submitted to the project
administration. Then, the survey was circulated and distributed through their own
internal procedure. The time allotted for this process was 8 weeks, during which several
reminders were sent out to motivate people to complete the questionnaire.
Institutional Review Board (IRB)
Since this study contains human subjects, it is required by federal law to obtain
Institutional Review Board (IRB) approval. The purpose of this requirement was to
protect the respondents’ interest and rights. There are no risks associated with this
study procedure. Respondents participated in this study on a voluntary basis after
receiving an invitation. Before conducting the study, consent forms were collected from
45
every participant. All personal information and results collected from this study were
kept anonymous and only used for this study. The questionnaire did not involve direct
personal information; rather, it asked about demographic information and the
perceptions of people regarding safety culture issues.
Statistical Data Analysis
After collecting the data required for this study, several statistical techniques
were applied: preliminary analysis and data screening, descriptive analysis, exploratory
factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation
modelling (SEM). Below are the detailed descriptions of each approach.
Preliminary Analysis and Data Screening
The purpose of preliminary analysis was to increase reliability and confidence in
the data collected through the questionnaire survey. In preparation for further statistical
analyses, ranging from factor analysis to structural equation modelling (SEM), several
data-screening methods and preliminary analyses were conducted, involving treatment
of missing data, the outlier test, and the normality test. (see Chapter 4).
Descriptive Analysis
Frequency tables for all variables were presented to show the number and
percentage of participants. With help of the Statistical Package for Social Science
(SPSS) program, data were analyzed to examine the study sample characteristics such
as nationality, age, education, job position, experience, and frequency of safety training.
To initially check the multicollinearity problems common in social research, a
correlation matrix observing the association among variables was also created.
Multicollinearity problems usually arise when a high correlation between two variables is
identified. The Spearman matrix, a popular matrix used to detect multicollinearity for
46
ordinal continuous data, was used to determine the correlation (Schumacker & Lomax,
2004). According to Kline and Santor (1999), a correlation value of 0.85 or higher can
indicate a possibility of multicollinearity.
Exploratory Factor Analysis (EFA)
Exploratory factor analysis (EFA) is one of the most popular statistical
procedures implemented in the early phases of analysis. Used in studies having
questionnaire surveys, EFA aims to reduce the number of variables and bring correlated
variables under one homogenous group. Therefore, results of factor analysis help the
researcher to meaningfully interpret the data.
Despite the fact that statistical software makes running this analysis much easier,
different statistical topics should be considered (Pallant, 2013):
1. analysis of data sample suitability 2. factors extraction 3. factor rotation
A description of each step is detailed below.
Suitability of the sample assessment
The first step in conducting exploratory factor analysis (EFA) involved
assessment of data suitability. To do that, three different tools were used to confirm the
sample size: adequacy, reliability, and validity of the collected data.
The first check was to measure the suitability of the sample size. According to
Pallant (2013), the minimum number of responses should be at least five times the
number of items in the questionnaire i.e., the sample to variable ratio should be 5:1.
The second check, which is Cronbach’s alpha coefficient (α), aimed to examine
the internal consistency of each factor, along with the reliability of the sample.
Moreover, it helps to evaluate the total variance percentage of each factor (Leech,
47
Barrett, & Morgan, 2012). The minimum acceptable value of Cronbach’s alpha
coefficient (α) is 0.6, while 0.9 or greater is excellent (Murphy & Davidshofer, 1988).
Lastly, the factorability of the collected data was tested. The purpose of this test
is to determine whether the collected data are suitable for factor analysis and also to
measure the correlation among items. Kaiser-Meyer-Olkin (KMO) statistics as well as
Bartlett’s test are commonly applied for factorability assessment (Pallant, 2013). To
determine the appropriateness of factor analysis, the KMO value that varies from zero
to one should be greater than 0.5, while Bartlett’s test should be significant (p<0.05)
(Tabachnick, 2007).
Factor extraction
After confirming the suitability of factor analysis, the large number of items must
be reduced into small factors (Tabachnick, 2007). To complete this process, it is
important to determine the factor extraction method along with the number of factors.
There are several methods to determine the extraction. Popular extraction
methods include principle axis factoring (PAF), and principle components (PC) (Pallant,
2013). In both approaches, the maximum variance is extracted from the data. For this
study, the principle axis factor (PAF) technique was selected.
To assist in determining the number of factors for factor analysis, two
approaches are frequently used: cumulative percentage of variance eigenvalue, and
scree test. The eigenvalue means the equivalent number of variables represented by
the factor. As a rule, the number of the extracted factor should be the number of
eigenvalues of 1.00 or higher( >1.0) (Tabachnick, 2007). The second approach is to
check the scree plot that displays the eigenvalues against the number of factors. The
48
proper number of factors to extract is the number of factors shown in the plot before the
plotted line levels off.
Factor rotation
After the factor extraction, whether a variable should be related to more than a
factor should also be considered. The goal of this rotation is to maximize high-variable
loadings and minimize low-item loadings.
Typically, there are two rotation techniques: orthogonal and oblique. The first
technique assumes the factors are independent, while the other produces correlated
factors.
Confirmatory Factor Analysis (CFA)
Confirmatory Factor Analysis (CFA), a further step of EFA, helps to evaluate the
relationship between a construct and a set of its indicators (Schumacker & Lomax,
2004). This CFA is commonly used after identifying the study factors (constructs) and
variables that form each construct (Henson & Roberts, 2006). In fact, EFA and CFA are
often carried out together, as EFA tends to extract the factors (constructs) and its
variables (indicators), while CFA is used for further analysis and construct validity. In
other words, the number of factors (constructs) is determined by EFA and then
hypothesized to be used in CFA (Rencher & Christensen, 2012).
Parameters for CFA model
In CFA models, variables can be classified into observed and unobserved.
Variables (indicators) are considered as observed variables and are represented by
rectangles. Factors (constructs) are called unobserved variables and are represented
by ovals.
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In terms of arrows, the relationship between factors and its indicators is
represented by a single-headed arrow that has a value of standardized regression
weight (factor loading). The higher the factor loading the stronger the relationship. Table
3-1 shows the scale of factor loadings according to Tabachnick (2007).
A curved double-headed arrow can represent correlation among factors
(constructs). The value of factors correlation ranges from -1 to +1 in the standarized
model. Error covariance can also exist between errors and is also represented by a
curved two-headed arrow linking the errors.
Assessment of the hypothesized model
Testing CFA models determines whether the model fits with the study data.
When evaluating the goodnes of fit of the model, applying multiple criteria is advised .
However, reporting all statistics is not recommended. In the CFA lietrature, several
parameteres can be used to evaluate model fitness. They include: Chi-square (ϰ2 )
statistics, degree of freedom (df), likelihood ratio (ϰ2/df), goodness of fit index (GFI),
comparative fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and
Hoelter‘s Critical N (CN). Recommended values for the above indices are shown in
Table 3-2.
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Table 3-1. Scale of factor loading.
Factor Loading Interpretation
>0.71 Excellent >0.63 Very Good >0.55 Good >0.45 Fair >0.32 Poor <0.32 No interpretation
Table 3-2. Indices for model validation and goodness of fit.
Index Criteria
Chi-square (x2) Low
Degrees of freedom (df) ≥ 0
Likelihood ratio (x2 /df) < 4.0
Goodness of fitness index (GFI) > 0.90
Comparative fit Index (CFI) > 0.90
Root Mean Square Error of Approximation (RMSEA) ≤ 0.05
Hoelter‘s Critical N (CN) > 200
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Figure 3-1. Research methodology (Graphic Summary).
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Figure 3-2. Conceptual safety culture model.
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CHAPTER 4 FINDINGS
Overview
The methodology of this study was based on four main methods: preliminary
analysis and data screening, descriptive analysis, exploratory factor analysis (EFA), and
confirmatory factor analysis (CFA). The purpose of the preliminary analysis was to
increase confidence in the data collected, via the questionnaire survey, before
performing any further analysis. Thus, different data screening approaches were
applied, including handling of missing data, normality test, and outlier detection. The
number of responses and the response rate was identified in this step.
Then, a descriptive analysis of this study was performed to examine the key
sample characteristics (such as nationality, age, level of education, and working
experience). A correlation matrix was also created to check for potential multicollinearity
issues.
The third main method of the study methodology was Exploratory Factor Analysis
(EFA). This EFA is commonly applied by inspecting the factor loadings of each variable
in the pattern matrix. This method helps to reduce the number of variables by
eliminating any variable that does not have a significant factor loading. After reducing
the number of variables, EFA clusters the remaining variables into homogenous factors,
so results can be meaningfully interpreted.
Finally, CFA, which is an extended factor analysis procedure, was performed to
develop a measurement model for each dimension. This CFA helps identify the
relationship of a set of variables to a common factor. To run the CFA, AMOS 22
software was used.
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Preliminary Analysis and Data Screening
After collecting the required data, several data inspection approaches were
implemented, encompassing visual inspection of data and statistical analysis. Statistical
analyses included response rate, handling of missing data, the outlier detection test,
and the normality test. Each test is detailed below.
Response Rate
As discussed in Chapter 3, the study was conducted in several construction
projects in Saudi Arabia. The survey was conveyed to 650 subjects through the internal
portal of the assigned projects, or manually via personnel. Of the 650 subjects, 353
personnel agreed to participate in the study, which represented a response rate of
54.30%.
From the received responses, 54 cases were dropped from the dataset because
of data incompleteness exceeding 30% of the questions, or response discrepancy. As a
result, the total number of questionnaires that provided data for this study was 299,
which is an acceptable number to run the factor analysis (H. Boomsma, 2001)
Handling of Missing Data
One common problem during data analysis is missing data. The impact of
missing data depends on the amount missing and the reason it is missing. However,
Tabachnick (2007) said it is more important to find the pattern of missing data.
Several methods deal with missing values: deleting cases, substituting by mean
or median, and building a correlation matrix for missing data. According to Tabachnick
(2007), any method of handling missing data can be implemented if the missing amount
is 5% or less, in a random pattern. None of the questionnaire items had more than 5%
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missing values (the highest missing value percentage was 2%) as shown in Table B-1.
Thus, treating missing values by median substitution was chosen for this study.
Outliers
A univariate outlier is defined as a case with an extreme value on a variable,
while a multivariate outlier is a combination of two unusual scores on at least two
variables that interferes with statistical data results (Tabachnick, 2007).
A number of methods can be used to test for outliers: 5% trimmed mean, the use
of z-score, and the use of boxplots (Pallant, 2013; Tabachnick, 2007). For this study,
5% trimmed mean was used to detect outliers.
The 5% trimmed mean is calculated after omitting the highest and lowest 5% of
cases (Pallant, 2013). Based on Pallant’s (2013) recommendations, an outlier can be
detected if the difference between a mean and its 5% trimmed mean is big (>0.2). Table
B-2 shows the mean, 5% trimmed mean, mean difference, and standard deviation of all
the questionnaire items. Results show that the absence of outliers, as the difference
between trimmed mean and mean for all items is not big.
Normality Test
According to (Tabachnick, 2007), testing of normality is an important test in the
multivariate analysis. Normality is usually examined through its major components:
skewness and kurtosis. Skewness concerns about the distribution symmetry, whereas
kurtosis relates to distribution peakedness. In the case of a normal distribution, the
values of skewness and kurtosis are zero (Pallant, 2013).
To test the normality, it is recommended that the division of statistical value
(Stat.) for skewness (and kurtosis) and its standard error (S.E) is less than 5.5 (Morgan
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& Griego, 1998). The values of skewness and kurtosis of all items are shown in Table
B-3. Results concluded that all items show normal distribution.
Descriptive Statistics
Sample Characteristics
Organizational and demographic characteristics of the subjects participating in
this study are discussed next. Background information of respondents includes
nationality, native language, age, gender, level of education, current occupation, years
of experience, and frequency of safety training sessions. The descriptive statistics
including percentage distribution and frequency of the sample characteristics are shown
in Table C1.
The survey was delivered to construction projects in Saudi Arabia that rely
heavily on foreign personnel. Thus, the survey was expected to include different
nationalities and native languages. Results showed that 15 nationalities participated in
this study. In the sample:
114 respondents (38.1%) were Saudis
92 respondents (30.8%) were from neighboring countries such as Egypt, Bahrain, Sudan, Syria, Yemen, Jordan, and Somalia
58 respondents (19.3%) were from Asian countries including Pakistan, India, Bangladesh, and South Korea
21 respondents (7%) were from Turkey
5 respondents (1.7%) were from the UK
9 respondents (3%) were from the USA.
Regarding native language, most of the respondents (65.2%) were native Arabic
speakers, while English was the native language for only (5%) of the respondents.
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Regarding age, 94 respondents (31.4%) were less than 30 years old, 141
respondents (47.2%) were between the ages of 30 and 40, and 64 respondents (21.4%)
were the more than 40 years old.
The survey targeted different trades in the construction industry of Saudi Arabia,
whether participants were management-level personnel or on-site workers. As shown in
Figure 4-1:
workers numbered 82 (27.4%)
Engineers numbered 109 (36.5%)
Safety officers numbered 22 (7.4%)
Project managers numbered 38 (12.7%)
Others numbered 48 (16.1%).
To determine that respondents understood the concept of safety culture in the
construction industry, participants were asked how many years of experience they had
in the construction industry. Most of people surveyed in had a good amount of work
experience. Of 229 participants shown in Figure 4-2:
88 (29.4%) had worked for 5 years or less
79(26.4%) had worked for 6-10 years
94 (31.4%) had worked for around 15 years
26 (8.7%) had worked for 20 years
12(4.0%) had worked for more than 21 years
It is important to examine education level in social research. It is well known that
the most construction workers in Saudi Arabia are uneducated. Thus, it can be seen
from Figure 4-3 that 39 respondents (13%) have no education, 33 respondents (11%)
were high school graduates, and 25 respondents (8.4%) had obtained a college degree.
One the other hand, engineers and management personnel had obtained at least a
bachelor degree. Of the total number of respondents, 143 respondents (47.8%) held a
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bachelor‘s degree, 51 respondents (17.1%) had obtained a master‘s degree, and only 8
respondents (2.7%) were PhD-level graduates.
In responses to the survey (Figure 4-4), a few people (16.7%) had never
received any safety training sessions. However, almost 83% of respondents stated that
they had received at least one safety training session that covers the basics: 46.5% had
taken safety training sessions but fewer than 4 sessions; while 36.8% had more than 4
safety training sessions.
Multicollinearity
Multicollinearity is a statistical phenomenon associated with a correlation matrix
that occurs when two or more variables are highly correlated. When conducting factor
analysis, a correlation value of 0.85 is considered high (Tabachnick, 2007). The
presence of multicollinearity might lead to unreliable results. In this study, the correlation
matrix of each dimension of safety culture (Person, Behavior, and Situation) was
examined for multicollinearity. All the correlation matrixes were generated with the help
of SPSS.
A visual inspection of the correlation matrix of the first dimension of safety culture
(People) showed no multicollinearity. The highest correlation value found in the matrix
was 0.76. Similarly, the correlation coefficients of the second dimension’s variables of
safety culture (Behavior) was checked; the maximum correlation coefficient in the matrix
was 0.73, which confirms the absence of multicollinearity problem. The correlation
matrix of the third dimension of safety culture (Situation) was also tested for
multicollinearity, and 0.734 was the highest value, showing no multicollinearity.
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Exploratory Factor Analysis (EFA)
When conducting an Exploratory Factor Analysis (EFA), the factor loadings of the
pattern matrix were verified. The aim of applying this analysis was to maximize high
loadings and minimize low loadings. Since it is problematic to run the EFA on the all
three safety culture dimensions at once, EFA was conducted on each dimension
separately.
Suitability of the Sample Assessment
Before performing EFA, it was necessary to determine whether the data set is
suitable for factor analysis. Several criterial for the factorability assessment were used.
Firstly, the adequacy of the sample size was checked. A total number of 299
usable cases was included in this study. According to Pallant (2013), this number
confirms that the sample size is appropriate for factor analysis.
The second check was related to the strength of inter-correlation among items.
Thus, the Kaiser-Meyer-Olkin (KMO) statistics and Bartlett’s test were applied for each
dimension of safety culture. The results show that all values of KMO index were in the
acceptable range (between 0.6 and 1), and the Bartlett’s test values were also
significant (p < 0.05). Table 4-1 summarizes the Bartlett’s test and the KMO index.
Finally, all the correlation matrixes were assessed for factorability through visual
inspection. It is recommended that a significant number of correlations should be at
least 0.3 (Pallant, 2013). Inspection reveals that most of the correlation coefficients in all
matrixes have values of 0.3 or above at the level of significance of 0.01 (Appendix C) .
The EFA of the First Dimension (Person)
To extract factors, (EFA) analyzed the 21 items related to the first dimension. An
absolute value (cut-off factor loading) of 0.40 was set in SPSS to eliminate the items
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that do not have a significant factor loadings. Accordingly, the pattern matrix showed
only factors of 0.4 and above.
In the initial run, several attempts were made before reaching an acceptable
solution. This solution, based on the scree plot (Figure 4-5), suggests the presence of
three different factors. The principle component approach was used for the initial run
without computing the rotated factor loadings. To provide meaningful results for the
three extracted factors, promax rotation then was then implemented. Promax rotation
helps to increase the number of high loading items on a given factor.
After performing a promax rotation using the principle axis factoring approach,
the results made it easy to associate each item with a single factor. The three factors
extracted in the solution accounted for 50.384% of the total variance. Also, the number
of items loaded on each factor was examined. Of the 21 items, the pattern matrix
revealed that 8 items had a factor loading less than 0.4 and, thus, failed to meet the cut-
off. Consequently, the final factor analysis was performed on the remaining 13 items.
Furthermore, the internal consistency (Cronbach’s alpha coefficient α) of each
factor was assessed; the alpha coefficient values ranged from 0.633 to 0.762, showing
adequate reliability. Table 4-2 enumerates the remaining 13 items in the three factor
solution of the first dimension of safety culture, and their factor loadings, explained
variance percentage, eigenvalue, and the Cronbach’s alpha (α) value.
As demonstrated in Table 4-2, three factors were extracted for the first safety
culture dimension. The factors were numbered in descending order, based on the
explained variance percentage. Each factor was named according to the items involved
in the factor. The first factor, safety management system, represented 26.465% of the
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total variance. Most of the items have moderate factor loadings, ranging between 0.437
and 0.689. This factor contains seven items related to the safety management system.
It emphasizes the importance of the safety management system and how such an
effective system might contribute positively to improve people’s perceptions toward
safety.
The second factor, safety resources, comprised of three items, accounting for
13.20% of the variance. Two items on this factor demonstrate the physical condition of
work site, facilities, tools, and equipment provided to workers. These two items are
crucial to improving the concept of safety culture and fundamentals to make a safe work
site. The third item is associated with safety training issues. This factor, collectively,
indicates the importance of assistance given by peers (through strong relationships in
work sites that establishes a good environment) and by management (through safety
training sessions), to increase the safety level. A close examination reveals that the
majority of respondents (83%) had at least one formal safety training and recognized
safety training as a safety culture contributor (mean score was around 4.0). Therefore,
they were more inclined to agree that safety training improves the safety awareness by
gaining the skills required, such as identifying the on-site hazards.
The third factor was labelled social and government acts. It had three items,
accounting for 10.719% of the variance. The factor loadings ranged between 0.439 and
0.732. The first item demonstrates the role of society to enhance the safety values. It
was found that society appreciates and promotes organizations that consider safety
issues as a top priority. The second item appears to be related to the impact of national
culture on people’s safety perception. The last item ascertains that the presence of an
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effective legislations as well as a regular supervision by government agencies could
positively enhance the safety perception, values, and attitudes in the construction
industry.
The EFA of the Second Dimension (Behavior)
By conducting the principle component approach in the first run, 21 were
analyzed. Rotated loadings were not competed. It can be seen from the scree plot
shown in Figure 4-6, the solution identified two distinct factors. In order to obtain a clear
interpretation for these two factors, the solution was then subjected to promax rotation.
The final run, that applied the principle axis factoring approach, found that this
two factor solution accounted for 51.438% of the total variance. By keeping in mind that
the cut-off factor loading is 0.4, only nine items remained; four and five items were
loaded on the first and second factor, respectively. Table 4-3 details the nine remaining
items on the two factor solution, including their factor loadings, eigenvalue, total
variance percentage, and the Cronbach’s alpha (α) value.
Each factor was labelled based on the common characteristics that links the
individual items loaded onto the factor. The first factor, which accounted for 36.010% of
the total variance, was named group effect, as its items address the effect of group and
coworkers on safety issues. This factor includes the impact of: the shared perception of
safety among employees, employees’ involvement in safety issues, the good working
atmosphere, and effective communication. The first item had a high factor loading
(0.976), while others had moderate factor loadings ranging between 0.418 and 573.
Most of the respondents confirm the high impact of safety perception and awareness on
people’s behavior (mean score =3.7). The impact of the positive work environment
(mean score=3.74), whether physical condition or processes, and employees’
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involvement in safety matters (mean score=3.75) on safety behavior, was also
acknowledged. The presence of an effective communication system helps to improve
safe behavior.
The second factor, which involved five items and represented 15.428% of the
total variance, was labelled supportive environment. The items loaded on this factor
consisted of support and commitment by management, leading and planning the safety
matters in the worksite, and the physical condition of the tools and equipment used in
project sites.
The EFA of the Third Dimension (Situation)
After applying factor analysis for the third dimension, following the same
procedure as for the first and second dimension, three common factors were extracted
for the third dimension, accounting for 56.725% of the total variance (Figure 4-7). The
first, second, and third factor include five, three, and three items, respectively.
Details of the three extracted factors along with their eleven items are presented
in Table 4-4. The first factor, which accounted for 34.5% of the variance, mainly
consisted of items related to the project site; thus, it was named project site condition.
The second factor, accounted for 12.063% of the variance, grouped three items
referring to the effect of shared perception and beliefs, and was labelled group effect.
The third factor, social and government acts, accounted for only 10.162% of the
variance and mention the role of society and government.
Confirmatory Factor Analysis (CFA)
Confirmatory Factor Analysis (CFA), successor to Exploratory Factor Analysis
(EFA), is used to confirm the factor structure obtained from EFA. This CFA helps verify
the validity of the measurement model. A valid measurement model intends to measure
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what it is supposed to measure (Trochim & Donnelly, 2001). To Apply CFA, there are
three statistical steps: specifying the measurement model, fitting model estimates, and
evaluating the goodness of fit.
The first step was measurement model specification based on the EFA results.
Each factor (latent variable) consists of several items (indicator) that embody a
construct. The relationship between each item and its factor is measured by a factor
loading. The factor loading represents how much the item is related to its factor. In this
study, CFA of the first, second, and third dimension were tested as a second order
factor. The first and third dimension were conceptualized for three factors, while the
second dimension had only two factors.
The second step was to identify the specified model. This step can be achieved
by AMOS 22 software. The Maximum Likelihood (ML) as well as standardized estimates
were used to report the outputs of this step.
The third step was to evaluate the goodness of model fit. The evaluation process
was carried out by AMOS 22 software that generated goodness of fit statistics. The
fitness evaluation of each model was based on the following indices: Chi-square (x2),
Degrees of Freedom (df), Likelihood Ratio (x2 /df), Goodness of Fit Index (GFI),
Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and
Hoelter‘s Critical N (CN).
Lastly, all modification indices for the poor fit models were considered to improve
the overall model fit. The modification process can be done by testing the critical ratio of
each item. Items with a critical ratio less than ± 1.96 are not significant and, therefore;
removed from the model. Also, the model fit can be improved by checking the
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modification indices produced by AMOS 22 in the initial run. These indices suggest to
add covariance between some errors to decrease the chi square value, and; as a result,
improve the overall model fit.
The CFA of the First Dimension (Person)
The first dimension of safety culture, “Person”, was conceptualized for three
factors; “Safety Management System”, ”Safety Resources” and “Social and Government
Acts”. The standardized regression weights outputs are shown in Figure 4-8. As seen in
Figure 4-8, factor loadings from factors (latent variables) to items (indicators) were in
the acceptable range, as the highest factor loading was 0.74 (Resources-RES2) and the
lowest factor loading was 0.42 (SOC & GOV- SG2). Additionally, all items were
significantly (p < 0.001) loaded on the expected factor. Furthermore, the covariance
among factors was significant values at 0.05 level (p<0.001). The correlation between
“Safety Management System” and, ”Safety Resources” was relatively high (r=0.40),
while the estimated correlation between ”Safety Resources” and “Social and
Government Acts” was relatively low (r=0.27). The highest correlation in the model was
between “Safety Management System” and “Social and Government Acts” (r=0.55).
Figure 4-9 shows the initial measurement model when tested as a second order
factor. All factor loadings were statistically significant (all critical ratios were greater than
1.96) and had values ranging between 0.42 and 0.74. Assessing the overall fit of the
model revealed that model fit was still not acceptable (CFI=0.818 and RMSEA=0.083).
In order to improve the model fit, some modifications needed to be applied. Thus,
structural paths were added based on the modification indices reported by AMOS 22. At
each time, a pair of errors were correlated. This procedure was repeated until reaching
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an acceptable model fit. According to the modification indices, six error pairs were
added.
The final CFA model of the first dimension with standardized outputs is presented
in Figure 4-10. After inserting the recommended error covariance paths, the model had
improved. As seen in Figure 4-10, the highest and lowest factor loadings range between
0.45 and 0.73. The loadings of “Safety Management System”, ”Safety Resources” and
“Social and Government Acts” on the first dimension “Person” were 0.62, 0.68, and 61,
respectively. All critical ratios were significant having values greater than 1.96. Table 4-
5 provides the parameter estimates for the initial and final measurement model.
Compared with the statistics of goodness of fit for the initial measurement model,
the overall model fit for the final model had improved. The fit indices for both models are
documented in Table 4-6. The difference in the value of Chi-square (Δx2) between the
initial and final model is substantial (73.77), representing significant improvement in the
final model. Most of the fit indices were within the recommended limits. For that reason,
the final measurement model, which had a satisfactory fit of the model, is verified as an
acceptable measurement model for the first dimension of safety culture.
The CFA of the Second Dimension (BEHAVIOR)
The second dimension of safety culture, “Behavior”, has two factors; “Group
Effect” and “Supportive Environment”. The first factor contained four items, while five
items were included in the second factor. The standardized regression outputs are
presented in Figure 4-11. From Figure 4-11, the two factor measurement model was
statistically significant (all critical ratios are greater than 1.96). Also, all factor loadings
had acceptable values ranging from 0.50 to 0.86 which were considered good and
excellent. The covariance between the two factors was significant (p<0.001) at the 0.05
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level. Moreover, it is found that the correlation between the two factors was moderate
(r=0.52).
The second order measurement model for the second dimension was created,
and the initial model is shown in Figure 4-12. It is apparent that all items were significant
(critical ratios were greater than 1.96), and all factor loadings were above 0.5. However,
the fit indices for the model failed to meet the goodness of fit recommendations
(RMSEA=0.075, and CN=195). Hence, inserting structural paths was necessary to
improve the overall model fit. According to the modification indices, three pairs of errors
were added, step by step, before a good fit model was achieved.
The final CFA model of the second dimension included the standardized outputs
(Figure 4-13). After modifying the model, there was a considerable improvement. The
items were significant and factor loading ranged from 0.5 to 0.86. The loading of “Group
Effect” and “Supportive Environment” on the second dimension “Behavior” were 0.81
and 0.65, respectively. Table 4-7 compares the parameter estimates for the initial and
final measurement model.
In terms of goodness of fit, the final measurement model was within the
recommended criteria. The fit indices for the initial and final model is shown in Table 4-
8. The chi square value dropped by 21.129 showing improvement for the model fit.
The CFA of the Third Dimension (Situation)
The third dimension “Situation” consists of three factors: “Project site condition”,
“Group Effect”, and “Social and Government Acts”. Five, three, and three items were
loaded on the first, second and third factor, respectively. All factor loadings are
statistically significant (critical ratios were greater than1.96), having values between
0.40 and 0.74. Figure 4-14 shows the correlation among factors as well as the
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standardized outputs. At a 0.05 level, all covariances were statistically significant
(p<0.001). “Project site condition” had significant positive correlation with both “Group
Effect” (r=0.67) and “Social and Government Acts” (r=0.45). “Group Effect” had also
significant moderate correlation with “Social and Government Acts” (r=0.48).
The second order measurement model of the dimension was also tested, and the
standardized outputs are shown in Figure 4-15. The statistics showed that all factor
loadings were significant, and they range from 0.40 to 0.84. Even though the initial
measurement model met all the recommended criteria, there was a possibility to
improve the overall model fit as reported by AMOS 22. Based on the modification
indices, a couple of pairs of errors were inserted.
As a result of revising the model, the overall model fit had improved. The final
measurement model is shown in Figure 4-16. All factor loadings were significant and
has acceptable values (from 0.40 to 0.84). From Figure 4-16, the loading of “Project Site
Condition”, “Group Effect”, and “Social and Government Acts” on the third dimension
“Situation” were 0.83, 0.84, and 0.56, respectively. The parameter estimates of the
initial and final measurement model is illustrated in Table 4-9. A comparison of
goodness of fit between the initial and final measurement model is revealed in Table 4-
10.
Construction Safety Culture Model Development
Background
This study’s model was developed based on the general structure of the
Reciprocal Safety Culture (Cooper, 2000) and the construction safety culture model
(Choudhry et al., 2007b). The former model helped to identify the main dimensions of
safety culture (Person, Behavior, and Situation) which stand for psychological,
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behavioral and situational factors. The latter model acknowledged the presence of a
relationship among these dimensions and provided a basic framework for the
construction safety culture. In this study, the developed model has two key features: it
addresses different aspects of construction industry such as management practice,
working environment and effect of coworkers, it quantifies the relationship among the
three dimensions, and between each dimension and its own factors.
The construction industry is quite distinguished from other industries due to its
unique characteristics. Thus, the first key of the developed model is to consider the
construction industry characteristics and to emphasize their influence on every
dimension of safety culture. The characteristics included in this study can be generally
classified into six aspects: government acts, social impacts, industry environment,
organization environment, project conditions, and group effects (refer to Table 2-1).
From the practical perspective, this approach allows for development of a model that is
suitable for construction.
Also, the developed model does not only explore the influential factors on every
safety culture dimension, it also measures the degree of influence. In addition, the
developed model shows the correlation among safety culture dimensions, indicating the
interactive relationship. Moreover, the strength of relationship between each dimension
and its corresponding factors was obtained, and the regression weights were
calculated. Table 4-11 demonstrates the parameter estimates for the final construction
safety culture model.
Model Validation
Evaluating the developed construction safety culture model from a statistical
standpoint, all factor loadings are statistically significant at 0.05 level, having values
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from 0.407 to 0.857. The final model fitness indices were within the recommended limits
as shown in Table 4-12.
Interpretation of the Model
From Figure 4-17, the developed model indicates that safety culture consists of
three dimensions. They are: Person, Behavior, and Situation. The relationship among
the three dimensions indicated a significant covariance at a 0.05 level. They had a
moderated to high correlation. The correlation coefficient between “Person” and
“Behavior” is 0.64, whereas the correlation coefficient between “Person” and “Situation”
is 0.72. “Behavior” and “Situation” were also correlated at (0.51).
According to the developed model, the first dimension “Person” is positively
influenced by three factors: “Safety Management System” (β=0.66), “Safety Resources”
(β=0.64), and “Social and Government Acts” (β=0.50). The most influential factors on
the second dimension “Behavior” are “Group Effect” (β=0.70) and “Supportive
Environment” (β=0.77). The third dimension is greatly associated to “Project Condition”
(β=0.77), “Group Effect” (β=0.82), and “Social and government acts” (β=0.64).
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Table 4-1. Results of Bartlett’s test and the KMO index.
Dimension KMO (recommended > 0.6 )
Bartlett’s test (recommended <0.05)
PERSON 0.778 0.000 BEHAVIOR 0.779 0.000 SITUATION 0.825 0.000
Table 4-2. Factor loadings of the three factor model of the first dimension of safety culture “Person”.
Factors Loadings
Factor 1 : Safety Management System SMS (Variance % = 26.465%, Eigenvalue = 3.440, Cronbach’s α = 0.762 )
SMS1 Support from management (motivation) 0.689
SMS2 Rewards and punishment systems 0.645
SMS3 Degree of management commitment 0.642
SMS4 Organization’s safety policies 0.524
SMS5 Leadership in the project site 0.462
SMS6 Preparing a safety plan 0.461
SMS7 Risk control and assessment on the project site 0.437
Factor 2 : Safety Resources (Variance %= 13.20%, Eigenvalue = 1.745, Cronbach’s α = 0.633 )
RES1 Establishment of good working environment 0.727
RES2 Status of equipment, tools and facilities 0.599
RES3 Safety training 0.483
Factor3: Social and Government Acts (Variance % = 10.719%, Eigenvalue = 1.394, Cronbach’s α = 0.655 )
SG1 Promotion of safety within the society 0.732
SG2 Impact of national culture (customs and habits) 0.458
SG3 Periodical supervision of government agencies 0.439 Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization.
72
Table 4-3. Factor loadings of the two factor solution of the second dimension of safety culture “Behavior”.
Factors Loadings
Factor 1 : Group Effect (Variance % = 36.010%, Eigenvalue = 3.241, Cronbach’s α = 0.711 )
GRP1 Shared employees’ perception of safety .976
GRP2 Involvement of employees in safety issues .573
GRP3 Establishment of good working environment .506
GRP4 Effective communication .418
Factor 2 : Supportive Environment (Variance % = 15.428%, Eigenvalue = 1.389, Cronbach’s α = 0.722 )
ENV1 Support from management (motivation) .629
ENV2 Leadership in the project site .609
ENV3 Degree of management commitment .567
ENV4 Preparing a safety plan .542
ENV5 Status of equipment, tools and facilities .515
Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization.
73
Table 4-4. Factor loadings of the three factor solution of the third dimension of safety culture “Situation”.
Factors Loadings
Factor 1 : Project Site Condition (Variance % = 34.5%, Eigenvalue = 3.804, Cronbach’s α = 0.777 )
PRG1 Preparing a safety plan .781 PRG2 Leadership in the project site .644 PRG3 Attitude of safety supervisor in the workplace .619
PRG4 Involvement of stakeholder .545 PRG5 Risk control and assessment on the project site .516
Factor 2 : Group Effect (Variance %= 12.063%, Eigenvalue = 1.327, Cronbach’s α = 0.701 )
GRP1 Involvement of employees in safety issues .814 GRP2 Shared employees’ perception of safety .786
GRP3 Effective communication .740
Factor3: Social and Government Acts (Variance % = 10.162%, Eigenvalue = 1.118, Cronbach’s α = 0.551 )
SG1 Safety legislation in the country. .763
SG2 Periodical supervision of government agencies .480 SG3 Promotion of safety within the society .419 Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization.
74
Table 4-5. Parameter estimates of the initial and final measurement model of the first dimension “Person”.
Item Initial Model Final Model
SRW URW S.E. C.R P SRW URW S.E C.R. P
SMS < PERSON 1.037 0.593 0.295 3.511 *** 1.071 0.618 0.277 3.859 ***
RESOURCES < PERSON 1.037 0.598 0.295 3.511 *** 1.071 0.677 0.277 3.859 ***
SOC & GOV < PERSON 1 0.632 1 0.612
SMS1 < SMS 0.822 0.509 0.119 6.913 *** 0.812 0.511 0.113 7.16 ***
SMS2 < SMS 0.963 0.552 0.131 7.351 *** 0.955 0.555 0.125 7.649 ***
SMS3 < SMS 0.917 0.543 0.126 7.263 *** 0.999 0.601 0.129 7.722 ***
SMS4 < SMS 1.053 0.602 0.134 7.835 *** 0.962 0.561 0.125 7.691 ***
SMS5 < SMS 0.867 0.52 0.123 7.023 *** 0.737 0.449 0.115 6.383 ***
SMS6 < SMS 0.910 0.55 0.124 7.332 *** 0.978 0.6 0.127 7.709 ***
SMS7 < SMS 1 0.609 1 0.617
RES1 < RESOURCES 1.036 0.611 0.157 6.598 *** 1.062 0.587 0.164 6.465 ***
RES2 < RESOURCES 1.282 0.729 0.193 6.632 *** 1.364 0.732 0.211 6.464 ***
RES3 < RESOURCES 1 0.534 1 0.502
SG1 < SOC & GOV 0.985 0.566 0.201 4.907 *** 0.927 0.565 0.183 5.054 ***
SG2 < SOC & GOV 0.970 0.522 0.199 4.863 *** 0.884 0.503 0.179 4.943 ***
SG3 < SOC & GOV 1 0.545 1 0.576
SRW = Standardized Regression Weight URW = Unstandardized Regression Weight S.E. = Standard Error C.R. = Critical Ration P = P-Value
75
Table 4-6. Fit indices for the first dimension “Person”. Index Criteria Initial Model Final Model
Chi-square (x2) Low 192.863 119.098 Degrees Of Freedom (df) ≥ 0 63 57 Likelihood Ratio (x2 /df) < 4.00 3.061 2.089
Goodness of Fit Index GFI > 0.90 0.910 0.944 Comparative Fit Index (CFI) > 0.90 0.818 0.913 Root Mean Square Error of Approximation (RMSEA)
≤ 0.05 0.083 0.050
Hoelter‘s Critical N (CN) > 200 143 213
76
Table 4-7. Parameter estimates of the initial and final measurement model of the second dimension “Behavior”.
ITEMS INITIAL MODEL FINAL MODEL
URW SRW S.E. C.R. P URW SRW S.E. C.R. P
Group Effect < BEHAVIOR 1 0.808
1 0.812
SUPPORTIVE ENV
< BEHAVIOR 1 0.648
1 0.643
GRP1 < Group Effect 1.847 0.862 0.241 7.671 *** 1.873 0.858 0.248 7.544 ***
GRP2 < Group Effect 1.440 0.655 0.196 7.363 *** 1.478 0.660 0.203 7.296 ***
GRP3 < Group Effect 1.071 0.511 0.168 6.394 *** 1.048 0.495 0.169 6.197 ***
GRP4 < Group Effect 1 0.502
1 0.494
ENV1 < SUPPORTIVE
ENV 1.181 0.711 0.151 7.834 *** 1.194 0.715 0.153 7.781 ***
ENV2 < SUPPORTIVE
ENV 0.922 0.502 0.144 6.422 *** 0.949 0.513 0.146 6.505 ***
ENV3 < SUPPORTIVE
ENV 1.017 0.555 0.148 6.881 *** 1.025 0.556 0.150 6.854 ***
ENV4 < SUPPORTIVE
ENV 1 0.572
1 0.569
ENV5 < SUPPORTIVE
ENV 1.100 0.586 0.154 7.127 *** 1.049 0.560 0.151 6.937 ***
SRW = Standardized Regression Weight URW = Unstandardized Regression Weight S.E. = Standard Error C.R. = Critical Ration P = P-Value
77
Table 4-8. Fit indices for the second dimension “Behavior”. Index Criteria Initial Model Final Model
Chi-square (x2) Low 70.070 48.941 Degrees Of Freedom (df) ≥ 0 26 23 Likelihood Ratio (x2 /df) < 4.00 2.695 2.128 Goodness of Fit Index GFI > 0.90 0.953 0.967 Comparative Fit Index (CFI) > 0.90 0.925 0.956 Root Mean Square Error of
Approximation (RMSEA) ≤ 0.05 0.075 0.052
Hoelter‘s Critical N (CN) > 200 195 254
78
Table 4-9. Parameter estimates of the initial and final measurement model of the third dimension “Situation”.
items Initial model Final Model
URW SRW S.E. C.R. P URW SRW S.E. C.R. P
P.CONDITION < SITUATION 2.223 0.795 0.578 3.844 *** 2.389 0.827 0.616 3.881 ***
GROUP EFFECT
< SITUATION 2.863 0.841 0.740 3.868 *** 2.849 0.845 0.729 3.909 ***
SOC & GOV < SITUATION 1 0.568 1 0.562
PRG1 < P.CONDITION 1.107 0.701 0.116 9.549 *** 1.032 0.673 0.112 9.198 ***
PRG2 < P.CONDITION 0.878 0.571 0.108 8.156 *** 0.763 0.509 0.105 7.240 ***
PRG3 < P.CONDITION 0.979 0.682 0.104 9.371 *** 0.958 0.684 0.101 9.499 ***
PRG4 < P.CONDITION 0.854 0.591 0.102 8.386 *** 0.843 0.598 0.099 8.550 ***
PRG5 < P.CONDITION 1 0.661 1 0.679
GRP1 < GRP EFFECT 0.662 0.558 0.083 7.967 *** 0.672 0.558 0.083 8.103 ***
GRP2 < GRP EFFECT 0.869 0.720 0.093 9.388 *** 0.914 0.742 0.095 9.588 ***
GRP3 < GRP EFFECT 1 0.738 1 0.726
SG1 < SOC & GOV 1.509 0.639 0.326 4.630 *** 1.495 0.635 0.322 4.647 ***
SG2 < SOC & GOV 1.357 0.601 0.293 4.637 *** 1.355 0.603 0.291 4.652 ***
SG3 < SOC & GOV 1 0.397 1 0.399
SRW = Standardized Regression Weight URW = Unstandardized Regression Weight S.E. = Standard Error C.R. = Critical Ration P = P-Value
79
Table 4-10. Fit indices for the third dimension “Situation”.
Index Criteria Generic Model Revised Model
Chi-square (x2) Low 77.716 55.571 Degrees Of Freedom (df) ≥ 0 41 39 Likelihood Ratio (x2
/df) < 4.0 1.896 1.425 Goodness of Fit Index GFI > 0.90 0.954 0.968
Comparative Fit Index (CFI) > 0.90 0.951 0.978 Root Mean Square Error of Approximation (RMSEA)
≤ 0.05 0.055 0.038
Hoelter‘s Critical N (CN) > 200 250 335
80
Table 4-11. Parameter estimates of the final measurement model of the Construction Safety Culture Model.
item SRW URW S.E. C.R. P
SMS <--- PERSON 0.647 1.556 0.399 3.897 ***
RESOURCES <--- PERSON 0.634 1.556 0.399 3.897 ***
SOC & GOV <--- PERSON 0.483 1
GROUP EFFECT <--- BEHAVIOR 0.7 1
SUPPORTIVE ENV <--- BEHAVIOR 0.761 1
P.CONDITION <--- SITUATION 0.786 1.954 0.453 4.311 ***
GROUP EFFECT <--- SITUATION 0.822 2.416 0.553 4.370 ***
SOC & GOV <--- SITUATION 0.642 1
SMS1 <--- SMS 0.513 0.837 0.115 7.311 ***
SMS2 <--- SMS 0.545 0.962 0.125 7.680 ***
SMS3 <--- SMS 0.605 1.037 0.127 8.151 ***
SMS4 <--- SMS 0.587 1.038 0.127 8.152 ***
SMS5 <--- SMS 0.524 0.894 0.119 7.503 ***
SMS6 <--- SMS 0.595 0.996 0.125 7.943 ***
SMS7 <--- SMS 0.602 1
RES1 <--- RESOURCES 0.595 0.991 0.141 7.015 ***
RES2 <--- RESOURCES 0.732 1.266 0.169 7.492 ***
RES3 <--- RESOURCES 0.544 1
SG1 <--- SOC & GOV 0.592 1.095 0.229 4.783 ***
SG2 <--- SOC & GOV 0.517 1.019 0.213 4.776 ***
SG3 <--- SOC & GOV 0.513 1
GRP1 <--- GROUP EFFECT 0.857 1.653 0.17 9.728 ***
GRP2 <--- GROUP EFFECT 0.668 1.303 0.149 8.731 ***
GRP3 <--- GROUP EFFECT 0.523 0.968 0.133 7.303 ***
GRP4 <--- GROUP EFFECT 0.554 1
ENV1 <--- SUPPORTIVE ENV 0.579 1.283 0.184 6.956 ***
ENV2 <--- SUPPORTIVE ENV 0.704 1.378 0.179 7.715 ***
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Table 4-11. Continued
item SRW URW S.E. C.R. P
ENV3 <--- SUPPORTIVE ENV 0.494 1.075 0.172 6.261 ***
ENV4 <--- SUPPORTIVE ENV 0.563 1.219 0.178 6.834 ***
ENV5 <--- SUPPORTIVE ENV 0.500 1
PRG1 <--- P.CONDITION 0.664 1.021 0.112 9.144 ***
PRG2 <--- P.CONDITION 0.508 0.759 0.104 7.313 ***
PRG3 <--- P.CONDITION 0.677 0.945 0.101 9.334 ***
PRG4 <--- P.CONDITION 0.611 0.859 0.1 8.633 ***
PRG5 <--- P.CONDITION 0.680 1
GRP1 <--- GROUP EFFECT 0.560 0.665 0.083 8.047 ***
GRP2 <--- GROUP EFFECT 0.721 0.872 0.091 9.560 ***
GRP3 <--- GROUP EFFECT 0.736 1
SG1 <--- SOC & GOV 0.650 1.500 0.307 4.884 ***
SG2 <--- SOC & GOV 0.585 1.289 0.266 4.842 ***
SG3 <--- SOC & GOV 0.407 1
SRW = Standardized Regression Weight URW = Unstandardized Regression Weight S.E. = Standard Error C.R. = Critical Ration P = P-Value
82
Table 4-12. Fit indices for the Final Safety Culture Model.
Index Criteria Final Model
Chi-square (x2) Low 844.783 Degrees Of Freedom (df) ≥ 0 480 Likelihood Ratio (x2 /df) < 4.00 1.758
Goodness of Fit Index GFI > 0.90 0.858 Comparative Fit Index (CFI) > 0.90 0.846 Root Mean Square Error of
Approximation (RMSEA) ≤ 0.05 0.050
Hoelter‘s Critical N (CN) > 200 360
83
Figure 4-1. Job title of the respondents.
Figure 4-2. Years of experience of the respondents.
0
20
40
60
80
100
120
Worker Engineer Safety Officer Project Manager Other
Job Title
0
10
20
30
40
50
60
70
80
90
100
Less than 5 6-10 years 11-15 years 16-20 years more than 21years
Experience
84
Figure 4-3. Level of education of the respondents.
Figure 4-4. Frequency of safety training of the respondent.
0
20
40
60
80
100
120
140
160
No Education High School College Bachelor Master PhD
Education Level
0
20
40
60
80
100
120
140
160
Never 1-4 More than 4
Safety Training Frequency
85
Figure 4-5. Scree plot of the first dimension.
Figure 4-6. Scree plot of the second dimension.
86
Figure 4-7. Scree plot of the third dimension.
87
Figure 4-8. Factors correlation outputs of the first dimension.
88
Figure 4-9. Standardized outputs of the Initial first dimension CFA model.
89
Figure 4-10. Standardized outputs of the final first dimension CFA model.
90
Figure 4-11. Factors correlation outputs of the second dimension.
91
Figure 4-12. Standardized outputs of the initial second dimension CFA model.
92
Figure 4-13. Standardized outputs of the final second dimension CFA model.
93
Figure 4-14. Factors correlation outputs of the third dimension.
94
Figure 4-15. Standardized outputs of the initial third dimension CFA model.
95
Figure 4-16. Standardized outputs of the final third dimension CFA model.
96
Figure 4-17. Standardized outputs of the final construction safety model.
97
CHAPTER 5 CONCLUSIONS
The main aim of this study was to explore the influencing factors on safety
culture in the construction industry in Saudi Arabia. A secondary aim was to determine
the relationship between the influencing factor and its associated safety culture’s
dimension. Also, this study was designed to develop a construction safety culture model
based on the explored influencing factors. This chapter discusses in details the
research findings, conclusion, limitations, and recommendations for future research.
Analysis
Based on the safety culture reciprocal theory model (Choudhry et al., 2007b;
Cooper, 2000; Geller, 1994), safety culture is conceptualized by three major
dimensions; Person, Behavior, and Situation. The analysis found that the first
dimensions’ influencing factors are Safety Management System, Safety Resources, and
Social and Government Acts. The influencing factors on the second dimension are
Group Effects and Supportive Environment. The third dimension is influenced by Project
Condition, Group Effect, and Social and Government Acts. The following paragraphs
discuss each factor.
A Safety Management System SMS involves all process and managerial actions
to control safety. The results indicated that a safety management system has a direct
and significant impact on the psychological dimension (safety climate) of safety culture.
According to this study’s results, an effective SMS includes the following aspects:
management support and commitment, reward and punishment system, safety policy
and procedures, safety leadership, safety plan, and risk control and assessment. This
finding supports the earlier studies that emphasize the importance of an effective safety
98
management system to enhance safety climate. Zohar’s (1980) study concluded that
there is a positive correlation between safety climate and an effective safety
management system. This study’s results are also similar with that of Glendon and
Litherland (2001).
Safety Resources are not only limited to physical equipment and facilities, such
as personal protective equipment PPE, it also includes safety education, and
establishing and controlling a good, safe working environment (Ismail, Doostdar, &
Harun, 2012). The results show that a significant direct influence of safety resources on
safety climate exists. These findings are consistent with previous studies. Sokas,
Jorgensen, Nickels, Gao, and Gittleman (2009) observed a measurable improvement in
knowledge and attitude three months after one training session. Also, Glendon and
Litherland (2001) highlighted the importance of providing PPE and establishing a safe
working environment.
Society and Government Acts have a positive significant impact on two
dimensions: Person and Situation. This result confirms the impact of the national culture
as reported in the literature. Understanding the influence of cultural backgrounds allows
us to identify most of the safety related attitudes ( Zohar, 1980). The impact of different
cultures is not only limited to safety climate but extends to the management aspects.
Several studies have discussed the relationship between the national culture and
management aspects such as planning and making decision.
Even though a few empirical studies recognize the importance of laws and
regulations especially in the developing countries (Zhang & Gao, 2012), the findings
emphasized the critical role of government to achieve excellence in safety culture
99
through setting rules, regular inspection, and research support. As shown in Table 2-3,
the increasing number of injuries and fatalities over the last few years in the
construction industry of Saudi Arabia indicates that the construction industry alone
cannot establish a positive safety culture. To reach this goal, employers and employees
must work together with the government’s support. This will require a strong
cooperation between the public and private sectors. Examples of proposed government
actions include: setting the safety legislation in the country, providing voluntary safety
programs, supporting safety research, and offering safety consultation.
A positive association between safety behavior and Group Effect and Supportive
Environment was found. Group Effect can be seen in group discussion, communication,
motivation, support, and leadership. A possible explanation of this can be attributed to
Hofstede's cultural dimensions theory (Hofstede, 1984) . According to Hofstede's model,
most of the cultures working in the Saudi construction sector, including Saudi Arabian,
are classified as collectivist. Members from the collectivism culture have strong and
cohesive ties and tend to affect each other. Also, people living in this culture may focus
in group unity rather than management attitude. Thus, they encourage and support
each other to establish group unity.
Limitations
There are several limitations that can be noted from this analysis of the
influencing factors on safety culture in the construction industry in Saudi Arabia. First,
the data analyzed in this study was collected through survey questions that required
participants to specify the degree of the influence of the items listed in the survey on
each safety culture dimension. Due to some privacy reasons, it is possible that
participants might have been conservative about making their own thoughts and
100
opinions public. In fact, people in the construction sites selected for this study had no
reason to reveal their thoughts and beliefs toward government, management,
colleagues, and industry. Rather than expressing what they believe, individuals might
have reported what they thought were desired responses. Thus, the answers might
have been subjected to bias. However, several approaches were implemented in this
study to reduce the impact of this limitation. To ensure identity protection, the survey did
not ask any personal questions. Therefore, the confidentiality and anonymity were
made clear for participants. Moreover, the participation in this study was voluntary and
that was declared clearly.
Another limitation related to this survey based investigation is the five-point Likert
system. In order to avoid the negative implication associated to “extremism”,
participants might have avoided using the extremes options in the scale (enormous
influence or no influence) even if an extreme option was the most accurate answer.
Lastly, the safety culture model developed in this study was based on the
questionnaire survey that targeted the Saudi construction. Therefore, the developed
model might not be appropriate in other countries.
Future Research
Despite efforts, deep investigations are still needed in some areas related to
safety culture. Based on the findings of this study, examples of future research
opportunities are discussed below.
The main focus of this study was to investigate the influencing factors on safety
culture as well as the degree of influence on safety culture in the construction industry
of Saudi Arabia, which is a developing country. In the literature, there were no attempts
to perform a comparative study with a developed country. Therefore, it is recommend
101
for future research to conduct such a comparative study to identify the differences in
construction safety culture perceptions.
Also, it is recommended that the survey designed for this study be utilized for a
comparative study with another developing country having a similar operational
environment. This could be conducted in one of the neighboring countries of Saudi
Arabia.
The findings of this study revealed the vital role of safety leadership on all safety
culture dimensions in the construction industry. Thus, future research could be
undertaken in this area. A few studies highlighted this area, but are still far from
satisfactory. Future research needs to provide a framework for the role of safety
leadership in construction to foster a strong safety culture.
Summary
Tragic construction accidents fill history pages. These accidents have several
negative consequences: loss of lives, physical injuries, excessive costs, and public
opinion damage. Recent safety culture models highlighted the major dimensions of
safety culture and how they are related rather than investigating factors influencing each
dimension. In other words, analyzing influencing factors on safety culture is more
effective to consolidate the concept of safety culture in the construction industry. It is
important to identify which factors lead to a better safety culture. This study aimed to
explore the most influencing factors on safety culture as well as the degree of influence.
A deep literature review of safety culture and its related concepts in construction
area was conducted. Then, a quantitative research design was established based on
the safety culture model developed by Geller (1994). The data set was collected
through a survey of selected construction projects personnel in different cities of Saudi
102
Arabia. Studying the construction safety culture of ongoing projects has the potential to
provide guidelines for improving safety culture in such a high risk industry.
Exploratory factor analysis along with confirmatory factor analysis was applied to
achieve the research objectives. Based on the study findings, managerial factors,
effective safety resources, and government enforcement as well as social acts play a
significant role to improve safety knowledge and enhance people’s safety attitude.
Safety behavior at the workplace is highly affected by group norms and surrounding
environment.
Improving project site conditions, people effects and norms, and the role of
society and government are keys to developing the situational dimension of safety
culture.
According to the final construction safety model developed in this study, safety
leadership has a positive impact on all safety culture dimensions. The effectiveness of
safety leadership can be assessed through future study. This research took a snapshot
of the safety culture of construction sector in Saudi Arabia in 2015.
103
APPENDIX A QUESTIONNAIRE SURVEY
Dear Participant,
My name is Ahmed Alkhard. I am A PhD student at University of Florida. For my
dissertation, I am carrying out a research survey on the concept of safety culture in the
construction industry of Saudi Arabia. The primary objective of this study is to explore the
most influential factors on safety culture.
The following questionnaire will approximately require ten minutes to complete. There
is no risk associated with this study procedure nor is there any compensation.
If you choose to participate in this survey, you will be asked to evaluate the degree of
influence of the given variables on the following dimension: people, behavior and
organization.
Your participation in this study is voluntary. The information will be kept confidential
and will be only used for this study. Also, you have the right to withdraw consent at any
time without any consequences. Furthermore, you do not have to answer any question that
is inconvenient for you. For more information about your participation rights, please
contact IRB02 office, University of Florida, Box 112250, Gainesville, FL 32611; (352)
392-0433.
If you have any further questions, concerns, inquiries, or require additional information,
please contact me at [email protected] , or contact my supervisor Prof. Ralph Ellis at
It would be appreciated to express your thoughts and views by filling out the
questionnaire below.
Thank you for your valuable time.
I have read the information described above. I voluntarily agree to participate in the
survey.
____________________________ ___________
Signature of participant Date
104
Basic information: Please select the categories that describe yourself.
o Nationality: ___________________
o Language : [ ] Arabic [ ] English [ ] Other : ________
o Age : [ ] under 26 [ ] 26-30 [ ] 31-35 [ ] 36-40 [ ] 41-45 [ ] more than 45
o Sex : [ ] Male [ ] Female
o Education : [ ] No Education [ ] High School [ ] College [ ] Bachelor [ ]
Master [ ] PhD
o Position : [ ] Project Manager [ ] Engineer [ ] Safety Officer [ ] Worker [ ]
Other _________
o Experience : [ ] Less than 5 years [ ] 6-10 [ ] 11-15 [ ] 16-20 [ ] More than 21
years
o Frequency of safety training : [ ] Never [ ] 1-4 times [ ] More than 4 times
The Questionnaire Survey: There is a total of 21 items in the survey to be scored in 3
dimension of safety culture. The degree of influence is divided into five levels: (1) no
influence, (2) less influence, (3) general influence, (4) high influence, and (5)
enormous influence.
o The three dimensions of safety culture are: Person, behavior and management.
o PERSON refers to people’s understanding and perception of safety, while
BEHAVIOR is safety behavior performance within an organization.
MANAGEMENT is concerned to the safety system, procedures, regulations, and
policy.
o For each item identified below, circle the number to the right that best fits your
judgment.
105
106
APPENDIX B PRELIMINARY ANALYSIS RESULTS
Table B-1. Percentage of the missing values.
Dimension
Univariate Statistics
Item N
Missing
Count Percent
PE
RS
ON
1 299 0 0.0
2 299 0 0.0
3 299 0 0.0
4 299 0 0.0
5 299 0 0.0
6 299 0 0.0
7 299 0 0.0
8 299 0 0.0
9 298 1 .3
10 299 0 0.0
11 297 2 .7
12 299 0 0.0
13 299 0 0.0
14 299 0 0.0
15 299 0 0.0
16 299 0 0.0
17 299 0 0.0
18 298 1 .3
19 299 0 0.0
20 299 0 0.0
21 299 0 0.0
BE
HA
VIO
R
1 298 1 .3
2 298 1 .3
3 297 2 .7
4 298 1 .3
5 298 1 .3
6 298 1 .3
7 298 1 .3
8 298 1 .3
9 298 1 .3
10 297 2 .7
107
Table B-1. Continued
Dimension
Univariate Statistics
Missing
Item N Count Percent
11 298 1 .3
12 298 1 .3
13 298 1 .3
14 298 1 .3
15 298 1 .3
16 297 2 .7
17 298 1 .3
18 298 1 .3
19 298 1 .3
20 298 1 .3
21 298 1 .3
SIT
UA
TIO
N
1 294 5 1.7
2 296 3 1.0
3 296 3 1.0
4 296 3 1.0
5 296 3 1.0
6 296 3 1.0
7 296 3 1.0
8 296 3 1.0
9 296 3 1.0
10 296 3 1.0
11 296 3 1.0
12 296 3 1.0
13 296 3 1.0
14 296 3 1.0
15 296 3 1.0
16 293 6 2.0
17 296 3 1.0
18 296 3 1.0
19 296 3 1.0
20 296 3 1.0
21 296 3 1.0
Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR).
108
Table B-2. The mean, 5% trimmed mean, mean difference, and standard deviation.
dimension Item Mean 5%
trimmed difference S.D
PE
RS
ON
1 3.1037 3.1080 0.004 .05877
2 3.1538 3.1563 0.002 .06039
3 3.5920 3.6245 0.033 .05727
4 3.6355 3.6914 0.056 .06116
5 3.0870 3.0894 0.002 .05896
6 2.9833 2.9814 -0.002 .06204
7 3.1672 3.1858 0.019 .06064
8 3.6187 3.6691 0.050 .06091
9 3.7023 3.7620 0.060 .06308
10 3.7391 3.8029 0.064 .06647
11 3.1906 3.2046 0.014 .05881
12 3.1672 3.1858 0.019 .06371
13 3.0268 3.0297 0.003 .06157
14 3.0297 3.0037 -0.026 .06383
15 3.2107 3.2341 0.023 .06083
16 3.1003 3.1115 0.011 .06039
17 3.6890 3.7434 0.054 .06105
18 3.2040 3.2232 0.019 .06054
19 3.1104 3.1226 0.012 .06098
20 3.2809 3.2900 0.009 .05729
21 3.6656 3.7100 0.044 .06035
BE
HA
VIO
R
1 3.1706 3.1895 0.019 .05901
2 3.7124 3.7657 0.053 .06005
3 3.2642 3.2900 0.026 .05938
4 3.7860 3.8437 0.058 .05938
5 3.6923 3.7657 0.073 .05938
6 3.0836 3.0929 0.009 .05938
7 3.2040 3.2267 0.023 .05938
8 3.7358 3.7917 0.056 .05938
9 3.2575 3.2861 0.029 .05938
10 3.7559 3.8029 0.047 .05938
11 3.2207 3.2453 0.025 .05938
12 3.6689 3.7360 0.067 .05938
109
Table B-2. Continued
dimension Item Mean 5%
trimmed difference S.D
13 3.0903 3.1003 0.010 .05938
14 3.6856 0.054 .05938
15 3.1806 3.2007 0.020 .05938
16 3.1706 3.1895 0.019 .05938
17 3.3445 3.3644 0.020 .05938
18 3.1940 3.2155 0.022 .05938
19 3.7525 3.7954 0.043 .05938
20 3.6756 3.7360 0.060 .05938
21 3.7893 3.8289 0.040 .05938
SIT
UA
TIO
N
1 3.6388 3.6914 0.053 .06095
2 3.6722 3.7062 0.034 .05821
3 3.7090 3.7806 0.072 .06493
4 3.6421 3.7062 0.064 .06202
5 3.6990 3.7657 0.067 .06452
6 3.3378 3.3753 0.038 .06469
7 3.8495 3.9106 0.061 .05727
8 3.1271 3.1412 0.014 .05929
9 3.1739 3.1932 0.019 .05945
10 3.2776 3.2938 0.016 .06011
11 3.2542 3.2824 0.028 .06345
12 3.2174 3.2415 0.024 .06657
13 2.9900 2.9814 -0.009 .06539
14 3.2542 3.2824 0.028 .06655
15 3.3077 3.3419 0.034 .06300
16 3.1572 3.1747 0.017 .06475
17 3.1940 3.1897 -0.004 .05887
18 3.2508 3.2715 0.021 .06197
19 3.2375 3.2529 0.015 .05919
20 3.1672 3.1858 0.019 .06027
21 3.2107 3.2341 0.023 .06765
110
Table B-3.The skewness and kurtosis values.
Dimension item Skewness Kurtosis
Statistic SE Statistics/SE Statistic SE Statistics/SE P
ERSO
N
1 .100 .141 .706 -.520 .281 -1.849
2 .169 .141 1.199 -.615 .281 -2.189
3 -.320 .141 -2.270 -.425 .281 -1.513
4 -.514 .141 -3.649 -.283 .281 -1.008
5 .150 .141 1.063 -.503 .281 -1.789
6 .001 .141 .004 -.594 .281 -2.112
7 -.040 .141 -.286 -.476 .281 -1.692
8 -.559 .141 -3.967 -.324 .281 -1.153
9 -.496 .141 -3.520 -.477 .281 -1.698
10 -.584 .141 -4.146 -.649 .281 -2.309
11 -.043 .141 -.303 -.502 .281 -1.786
12 -.002 .141 -.015 -.692 .281 -2.464
13 -.054 .141 -.380 -.529 .281 -1.883
14 .039 .141 .274 -.670 .281 -2.386
15 -.099 .141 -.705 -.459 .281 -1.634
16 .047 .141 .334 -.503 .281 -1.790
17 -.523 .141 -3.711 -.354 .281 -1.258
18 .025 .141 .180 -.504 .281 -1.795
19 .020 .141 .141 -.384 .281 -1.366
20 -.066 .141 -.470 -.494 .281 -1.760
21 -.473 .141 -3.356 -.480 .281 -1.709
BEH
AV
IOR
1 -.023 .141 -.164 -.398 .281 -1.417
2 -.524 .141 -3.719 -.318 .281 -1.132
3 -.211 .141 -1.495 -.487 .281 -1.734
4 -.571 .141 -4.050 .039 .281 .137
5 -.671 .141 -4.758 .093 .281 .329
6 -.054 .141 -.386 -.408 .281 -1.452
7 -.210 .141 -1.493 -.157 .281 -.558
8 -.564 .141 -4.004 -.061 .281 -.217
9 -.056 .141 -.394 -.749 .281 -2.667
10 -.386 .141 -2.736 -.636 .281 -2.264
11 -.119 .141 -.848 -.158 .281 -.563
12 -.590 .141 -4.183 -.245 .281 -.870
13 -.133 .141 -.947 -.568 .281 -2.021
14 -.437 .141 -3.100 -.175 .281 -.621
111
Table B-3. Continued
Dimension Item Skewness Kurtosis
Statistic SE Statistics/SE Statistic SE Statistics/SE
15 .024 .141 .169 -.641 .281 -2.281
16 -.058 .141 -.414 -.502 .281 -1.787
17 -.039 .141 -.274 -.752 .281 -2.677
18 -.049 .141 -.344 -.683 .281 -2.432
19 -.370 .141 -2.622 -.778 .281 -2.768
20 -.511 .141 -3.623 -.044 .281 -.156
21 -.415 .141 -2.942 -.517 .281 -1.839
SIT
UA
TIO
N
1 -.465 .141 -3.301 -.353 .281 -1.255
2 -.400 .141 -2.836 -.582 .281 -2.072
3 -.596 .141 -4.228 -.391 .281 -1.390
4 -.579 .141 -4.108 -.198 .281 -.704
5 -.523 .141 -3.709 -.362 .281 -1.290
6 -.160 .141 -1.137 -.595 .281 -2.119
7 -.612 .141 -4.345 -.092 .281 -.329
8 -.031 .141 -.223 -.412 .281 -1.465
9 -.056 .141 -.395 -.483 .281 -1.720
10 .074 .141 .523 -.358 .281 -1.273
11 -.119 .141 -.843 -.712 .281 -2.534
12 -.035 .141 -.245 -.788 .281 -2.804
13 .118 .141 .837 -.494 .281 -1.758
14 -.270 .141 -1.916 -.595 .281 -2.118
15 -.165 .141 -1.171 -.483 .281 -1.718
16 .134 .141 .950 -.751 .281 -2.673
17 .045 .141 .321 -.776 .281 -2.760
18 -.069 .141 -.491 -.714 .281 -2.542
19 -.017 .141 -.120 -.485 .281 -1.727
20 .019 .141 .132 -.426 .281 -1.515
21 -.037 .141 -.262 -.721 .281 -2.566
112
APPENDIX C DESCRIPTIVE STATISTICS
Table C-1. Frequency and percentage distribution of respondents.
Variable Frequency Percent
Native Language Arabic 195 65.2
English 15 5.0
Other 89 29.8
Nationality Bangladeshi 11 3.7
Saudi 114 38.1
Indian 21 7.0
Turkish 21 7.0
Pakistani 18 6.0
Yemeni 11 3.7
Egyptian 37 12.4
Bahraini 3 1.0
Sudanese 1 .3
Somalian 11 3.7
Syrian 15 5.0
Jordanian 14 4.7
S.Korean 8 2.7
British 5 1.7
USA 9 3.0
Age Under 26 18 6.0
26-30 76 25.4
31-35 73 24.4
36-40 68 22.7
41-45 42 14.0
More than 45 22 7.4
Education No Education 39 13.0
High School 33 11.0
College 25 8.4
Bachelor 143 47.8
Master 51 17.1
PhD 8 2.7
113
Table C-1. Continued
Variable Frequency Percent
Job Title Worker 82 27.4
Engineer 109 36.5
Safety Officer 22 7.4
Project Manager 38 12.7
Other 48 16.1
Experience Less than 5 88 29.4
6-10 years 79 26.4
11-15 years 94 31.4
16-20 years 26 8.7
more than 21 years 12 4.0
Frequency of Safety Training Never 50 16.7
1-4 139 46.5
More than 4 110 36.8
114
Figure C-1. Correlation matrix of the first dimension.
115
Figure C-2. Correlation matrix of the second dimension.
116
Figure C-3. Correlation matrix of the third dimension.
117
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BIOGRAPHICAL SKETCH
Ahmed Alkhard was born in Jeddah, Saudi Arabia. Upon the completion of his
high school education, he joined the Civil Engineering Department at King Abdul Aziz
University in Jeddah, Saudi Arabia. After getting his bachelor’s degree in 2009, he
continued his education journey when he was admitted to University of Florida in 2011
for graduate studies. He successfully completed his master’s degree in civil engineering
in December 2012, and immediately enrolled into the PhD program. His journey of
education came to the end when he earned his PhD degree in civil engineering at
University of Florida in 2016.