INVESTIGATION OF THE INFLUENCE OF NATIONAL CULTURE ON CONSTRUCTION LABORER PERFORMANCE IN SAUDI ARABIA
By
LOAI ALKHATTABI
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 Loai Alkhattabi
To my beloved parents, wife and children
4
ACKNOWLEDGMENTS
Thanks to Almighty ALLAH for giving me strength and ability to complete this
dissertation.
My sincere gratitude to my father (Abdullah) and my mother (Azza) for their
continuous support. Thanks to my wife (Noor) for supporting me on this journey and my
sons (Bader and Battal).
I am deeply grateful to my committee chair (Dr. Ralph Ellis) for his guidance and
support. I would like also to thank my committee members (Dr. Charles Glagola, Dr. Fazil
Najafi, and Dr. Ravi S. Srinivasan).
5
TABLE OF CONTENTS
page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES ........................................................................................................ 10
ABSTRACT ................................................................................................................... 12
CHAPTER
1 INTRODUCTION .................................................................................................... 13
Problem Definition .................................................................................................. 14 Research Objective ................................................................................................ 17
Research Design .................................................................................................... 17 Research Contribution ............................................................................................ 18
2 LITERATURE REVIEW .......................................................................................... 20
Culture .................................................................................................................... 20 National Culture ...................................................................................................... 21
National Culture Models .......................................................................................... 22 Single Dimension Models ................................................................................. 23
Monochromic and polychromic cultures ..................................................... 23 High and low context cultures .................................................................... 23
Multiple Dimension Models ............................................................................... 24
Hofstede’s model ....................................................................................... 24 Trompenaars and Hampden-Turner’s Model ............................................. 24
Global Leadership and Organizational Behavior Effectiveness (GLOBE) .. 26
Hofstede National Culture Dimensions ................................................................... 27
Power Distance (PDI) ....................................................................................... 27 Individualism vs. Collectivism (IDV) .................................................................. 28 Masculinity vs. Femininity (MAS) ...................................................................... 29 Uncertainty Avoidance (UAI) ............................................................................ 29
National Culture and Construction Industry ............................................................ 30
Construction Management ............................................................................... 31 Risk Management ............................................................................................ 31 Total Quality Management ............................................................................... 32 Knowledge Management .................................................................................. 32 Safety Management ......................................................................................... 33
Construction Project Teams Performance ........................................................ 33 Construction Disputes ...................................................................................... 35 Construction Joint Venture ............................................................................... 35 Communication ................................................................................................ 36
6
Cultural Factors Affecting Construction Laborers Performance .............................. 36 Construction Laborers Performance Indicators ................................................ 36 Cultural Factors Affecting Laborers’ Performance ............................................ 38
Cultural factors related to power distance (PDI) ......................................... 38 Cultural factors related to individualism (IDV) ............................................ 39 Cultural factors related to masculinity (MAS) ............................................. 39 Cultural factors related to uncertainty avoidance (UAI) .............................. 40 Cultural factors related to time handling ..................................................... 40
Cultural factors related to context .............................................................. 40 The Kingdom of Saudi Arabia ................................................................................. 40
3 RESEARCH METHODOLOGY ............................................................................... 49
Research Process .................................................................................................. 49 Background Research ............................................................................................ 49 Review of Background Literature ............................................................................ 49
Research Design .................................................................................................... 50 Sampling Design .............................................................................................. 50
Population .................................................................................................. 50 Sample size ............................................................................................... 50
Questionnaire Design ....................................................................................... 51
Data Collection ....................................................................................................... 52 Data Analysis .......................................................................................................... 52
Preliminary Analysis ......................................................................................... 53 Descriptive Statistics ........................................................................................ 53
Multivariate Statistics ........................................................................................ 53 Exploratory factor analysis ......................................................................... 53 Comparing based on educational background ........................................... 56
Research Findings and Recommendations ............................................................ 56
4 RESULTS ............................................................................................................... 62
Preliminary Analysis................................................................................................ 62 Response Rate ................................................................................................. 62
Data Screening ................................................................................................. 62 Testing for Normality ........................................................................................ 63
Descriptive Analysis ................................................................................................ 64 Respondents’ Profile Information ..................................................................... 64
Background information ............................................................................. 64
Job positions information ........................................................................... 64 Experience information .............................................................................. 65
Project’s Profile Information .............................................................................. 65 Project classification .................................................................................. 65
Number of laborers .................................................................................... 66
Nationality of laborers ................................................................................ 66 Cultural Factors Frequencies and Mean Ranking ............................................ 67
Multivariate Analysis ............................................................................................... 67
7
Exploratory Factor Analysis (EFA) ................................................................... 68 Factor analysis on the first indicator (Quality) ............................................ 68 Factor analysis on the second indicator (Productivity) ............................... 70
Factor analysis on the third indicator (Safety) ............................................ 71 Comparisons Based on Educational Background ............................................ 72
5 DISCUSSION ......................................................................................................... 90
Research Findings .................................................................................................. 90 Cultural Factors Influencing Quality .................................................................. 90
Cultural Factors Influencing Productivity .......................................................... 93
Cultural Factors Influencing Safety ................................................................... 95
Limitation and Future Research .............................................................................. 97
APPENDIX
A SURVEY QUESTIONNAIRE ................................................................................ 102
B DESCRIPTIVE ANALYSIS RESULT .................................................................... 112
C MULTIVARTE ANALYSIS RESULT ..................................................................... 123
LIST OF REFERENCES ............................................................................................. 129
BIOGRAPHICAL SKETCH .......................................................................................... 136
8
LIST OF TABLES
Table page
2-1 National culture scores by nations ...................................................................... 46
2-2 National culture and construction industry .......................................................... 46
2-3 National culture factors affecting construction performance ............................... 47
2-4 Mega construction projects in Saudi Arabia ....................................................... 48
3-1 Cultural factors coding ........................................................................................ 61
4-1 Mean ranking for cultural factors influencing quality ........................................... 82
4-2 Mean ranking for cultural factors influencing productivity ................................... 83
4-3 Mean ranking for cultural factors influencing safety ............................................ 84
4-4 Results of KMO and Bartlett’s tests .................................................................... 85
4-5 Total variance explained of the initial run for the first indicator (Quality) ............. 85
4-6 Factor analysis results for the first indicator (Quality) ......................................... 86
4-7 Factor analysis results for the second indicator (Productivity) ............................ 87
4-8 Factor analysis results for the second indicator (Safety) .................................... 88
4-9 Kruskal-Wallis test on quality .............................................................................. 89
4-10 Kruskal-Wallis test on productivity ...................................................................... 89
4-11 Kruskal-Wallis test on safety............................................................................... 89
5-1 Nationality and cultural factors influencing quality. ............................................. 99
5-2 Nationality and cultural factors influencing productivity .................................... 100
5-3 Nationality and cultural factors influencing safety ............................................. 101
B-1 Test of normality for the first indicator (Quality) ................................................ 112
B-2 Test of normality for the second indicator (Productivity) ................................... 113
B-3 Test of normality for the third indicator (Safety) ................................................ 114
B-4 Frequency and percentage distribution of the respondent’s profile .................. 115
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B-5 Frequency and percentage distribution of the projects’ profile ......................... 116
B-6 Frequency and percentage distribution of the culture factors ........................... 117
10
LIST OF FIGURES
Figure page
1-1 Motivators and background ................................................................................ 19
2-1 Amount of the GDP invested in construction, construction annual growth rates, and construction input to the overall GPD. ............................................... 45
2-2 Construction (private sector) employee increase over the last decade. ............. 45
3-1 Research process flowchart. .............................................................................. 57
3-2 Cultural factors chart. ......................................................................................... 58
3-3 Cultural factors chart. ......................................................................................... 59
3-4 Cultural factors chart. ......................................................................................... 60
3-5 Data Analysis flowchart. ..................................................................................... 60
4-1 Survey completion percent ................................................................................. 74
4-2 Power distance (PDI) univariate outliers ............................................................. 74
4-3 Individualism (IDV) univariate outliers ................................................................ 75
4-4 Masculinity (MAS) univariate outliers .................................................................. 75
4-5 Uncertainty avoidance (UAI) univariate outliers .................................................. 76
4-6 Time handling and context univariate outliers .................................................... 76
4-7 Educational background of the respondents ...................................................... 77
4-8 Job positions of the respondents ........................................................................ 77
4-9 Years of experience of the respondents ............................................................. 78
4-10 Projects classification ......................................................................................... 78
4-11 Number of laborers under the supervision of the respondents ........................... 79
4-12 Frequency of laborers nationalities ..................................................................... 79
4-13 Scree plot of the first indicator (Quality) .............................................................. 80
4-14 Scree plot of the second indicator (Productivity) ................................................. 80
11
4-15 Scree plot of the third indicator (Safety) ............................................................. 81
C-1 Correlation Matrix of the First Indicator (Quality) .............................................. 123
C-2 Correlation Matrix of the Second Indicator (Productivity) .................................. 125
C-3 Correlation Matrix of the Third Indicator (Safety) .............................................. 127
12
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
INVESTIGATION OF THE INFLUENCE OF NATIONAL CULTURE ON
CONSTRUCTION LABORER PERFORMANCE IN SAUDI ARABIA
By
Loai Alkhattabi
December 2016
Chair: Ralph Ellis Major: Civil Engineering
Labor performance is one of the most discussed topics of research in the
construction industry. Much of the research focuses on factors related to work
management, technique, characteristics, and workforce in use. When researchers
examine workforce in use they tend to concentrate on factors such as labor skills,
absenteeism, training, and turnover. However, many of the existing studies evaluating
these topics do not address the cultural differences that exist between laborers working
in construction projects.
The aim of this study was to investigate the influence of cultural factors on
construction labor performance in Saudi Arabia. A questionnaire type survey was used
as a tool to collect data regarding perceptions of project managers, project coordinators,
site engineers and field superintendents on culture factors influencing labor
performance.
Analysis of the study suggested that culture matters. National culture has both
positive and negative influences on the performance of construction labors in Saudi
Arabia.
13
CHAPTER 1 INTRODUCTION
The construction industry plays an important role in both developed and
developing countries. In the Kingdom of Saudi Arabia, the construction industry has
experienced great prosperity over the past 10 years. It is no exaggeration to say that the
construction industry has a great influence on the Kingdom’s economy. The
construction industry accounted for an average of 6% of the county’s GDP, and grew by
an average of 11.12% annually over the past 10 years. Additionally, government
spending on construction over the past decade is estimated to be about $260 billion
(Central Department of Statistics and Information 2014). According to the World Bank
Group, in 2014 Saudi Arabia ranked 44th on a scale of 189 countries on the ease of
doing business, and 21st on dealing with constriction permits (World Bank Group 2015).
These encouraging environments for construction have attracted, and are continuing to
attract, many international companies’ investment in Saudi Arabia.
As is well known, the construction industry is labor-intensive. Private sector
construction in the Kingdom provided around 3 million jobs between 2004 and 2013.
Eighty-eight percent of these jobs were occupied by non-Saudi laborers with Saudis
only occupying 12%. Recent reports by the Ministry of Labor show that the construction
industry made up 48% of the Kingdom’s total private sector manpower (Kingdom of
Saudi Arabia Ministry of Labor 2014).
Construction project cost, schedule, and quality can be significantly affected by
the relative skill of the workforce being used. It is estimated that laborer cost represents
approximately 30% to 50% of the overall cost of a construction project (Hanna et al.
1999; Yates and Guhathakurta 1993). Consequently, laborer performance is a critical
14
factor to the success of any construction company, especially when doing business
internationally. Contactors working on international projects usually deal with laborers
that have cultural differences such as language, religion, and socio-cultural factors.
These differences in culture may have an influence on the overall performance of a
laborer.
Problem Definition
Laborers are considered an essential component of any construction project,
thus their performance is critical for success. As a result, it has become necessary to
understand and investigate any factors that may have an influence on laborer
performance. For many years, researchers have studied multiple factors to determine
how they influence laborer productivity on a project site. A majority of these researchers
have concluded that work-related factors such as lack of materials, labor, equipment,
poor management, and inadequate drawings are the most influential factors on laborer
productivity (Hafez et al. 2014; Mahamid et al. 2014; Olomolaiye et al. 1987). However,
other researchers have mentioned that factors more closely related to laborer culture
such as loyalty, social life, level of education, language spoken, and time perceptions
also play a role on the overall productivity of a laborer (Durdyev and Mbachu 2011;
Herbsman and Ellis 1990; Kazaz and Ulubeyli 2007; Koehn and Brown 1986).
Laborer productivity is becoming increasingly important because globalization
has opened the doors for many construction companies to work outside their traditional
borders. As these companies vie for projects they need to demonstrate efficiency over
their competitors. A major factor affecting international construction project
competitiveness is the cultural differences between the bidding company and the
country where the project will be completed. In fact, international competiveness might
15
be negatively affected by cultural differences (Hall and Jaggar 1997; Yates 1994).
Therefore, it is very important for management teams to deeply understand the cultural
differences between their own culture and that of the host country (Sui Pheng and
Yuquan 2002). Choudhury (2000) has argued that cultural factors must be considered
an additional dimension of project management that contractors working on projects
internationally need to take into account. He believed that such projects could generate
problems for construction mangers specifically when dealing with workforces that have
many cultural differences like physical environment, language, political, religion, social,
and economic.
Mega–construction or infrastructure projects in developing countries, such as
Saudi Arabia, usually require multiple international contractors. Consequently,
differences in national cultures between international contractors may impact the
performance of such a project. The greatest sources of difficulty in international
infrastructure projects are: (a) “Local institutions”; (b) “Work practices”; and (c)
“Differences in professional cultures” (Mahalingam et al. 2005). Additionally, the
nationality of laborers has been identified as one of the major causes of delays in large
construction projects in Saudi Arabia (Assaf and Al-Hejji 2006).
The attitudes and behaviors of team members working on an international
construction project are greatly influenced by their national cultures, differences that
exist between these national cultures, and the project culture (Zuo and Zillante 2008). In
the literature, several attempts have been made to link “National Culture” to different
aspects of the construction industry, such as construction management, risk
management, total quality management, knowledge management, safety management,
16
dispute resolution, and joint venture management. In the majority of these
aforementioned studies, researchers have used only one model of national culture to
study the influence of culture on the construction industry. However, despite the growing
interest in investigating these issues, there is a lack of research that examines the
influence of national culture on construction laborer performance. Additionally, there is a
need to investigate the influence of culture on laborer performance through multiple
models of national cultures.
The construction industry in Saudi Arabia depends on a primarily expatriate
workforce, and this generates a number of issues stemming from differences in national
culture. Laborers in the Kingdom of Saudi Arabia encounter differences in culture,
customs, and lifestyle that might conflict with their own values and living habits.
Moreover, laborers might face difficulty in communication with fellow workers and a
differing management styles that they are unfamiliar with. Under these circumstances,
understanding the influence of national culture on labor performance becomes critical
for construction companies who are increasingly facing competition in the international
construction project market. Figure 1-1 demonstrates the main motivators for this study.
17
Research Objective
The guiding research question for this study is: Does national culture influence
construction laborers performance in Saudi Arabia? The answer for this question
involves the following objectives:
1. Identifying the major cultural factors affecting construction labor performance in Saudi Arabia.
2. Exploring the relationship between national culture dimensions and labor performance.
Research Design
To accomplish the above objectives, the following steps were carried out:
Step 1. Literature review: the objectives of the literature review were the following:
Understand and acquire knowledge about the concept of national culture.
Identify the shortcomings of previous studies that evaluated the relationship between national culture and the construction industry.
Identify construction relevant cultural factors that influence labor performance.
Identify key labor performance indicators that will be used in the study.
Step 2. Data Collection: the objectives of this step were twofold
1. Design data collection tools in the form of questioners.
2. Collect data from construction project managers and superintendents.
Step 3. Data Analysis:
Ensure completeness and readability of responses from project managers, engineers, and superintendents.
Apply statistical techniques such as factor analysis to achieve the study objectives.
Step 4. Thesis Writing.
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Research Contribution
Although several studies have discussed the factors that affect labor
performance, very few publications have examined the influence of national culture on
labor performance. This research fills that gap in the literature by investigating important
cultural factors affecting labor performance in the construction industry. Understanding
these factors will be helpful for both construction managers and firms. Managers who
supervise multinational laborers working abroad require a deep understanding of the
differences in culture among their workforce. This study will help construction mangers
by allowing them understand the primary cultural factors affecting labor performance in
construction projects. In addition, it will help them determine the perfect composition of
teams for each task, based on the laborers’ different cultures. This in turn has an effect
on helping construction manager choose and recruit the appropriate laborers based on
their labor performance and culture.
19
Figure 1-1. Motivators and background
Factors Affecting
Labor Productivity
Factors Affecting
International Construction
Project
Motivations
Cultural Differences and Construction Issue
Construction Management
Risk Management
Total Quality Management
Safety Management
Knowledge
Management
Joint Ventur
e Management
Constructio
n Dispu
tes
Hofstede’s National Culture Dimensions
National
Culture
Causes of Delay in
Construction project Saudi
Arabia
20
CHAPTER 2 LITERATURE REVIEW
Culture
Culture is a term used often in the media, books, articles and nightly news.
Culture has roots from the Latin word cultura which means “the tilling of the soil”
(Hofstede 1984). According to the Webster Dictionary, in 1958, the word culture was
defined as “the raising, improvement, or development of some plant, animal or product''
(Barthorpe et al. 2000). The definition has changed over time, which makes it difficult to
have a specific or clear definition of the term. In 1952 two American anthropologists,
Kroeber and Kluchohn, gathered 164 different definitions of the term culture (Fellows
and Liu 2013). Most of the definitions available today are from the perspective of those
who defined them.
In 1870, the British anthropologist Edward Tylor offered a more modern definition
of culture as, “that complex whole which includes knowledge, belief, art, morals, law,
custom, and any other capabilities and habits acquired by man as a member of society”
(Samovar et al. 2014; Spencer-Oatey 2012). A similar definition of culture that includes
more elements than the previous one, was first communicated by Samovar, Porter, and
Jain in 1981 (Ali 2006). They define culture as
The cumulative deposit of knowledge, experience, beliefs, values, attitudes, meanings, hierarchies, religion, notions of time, roles, spatial relations, concepts of the universe, and material objects and possessions acquired by a group of people in the course of generations through individual and group striving. (Ali 2006)
Both definitions comprise a number of elements that were believed by many
researchers to illustrate the real meaning of culture. Based on these interpretations of
21
culture, and by reviewing the literature, it can be said that culture is identified by the
cumulative actions of a group which inevitably distinguishes one group from another.
A well-known Dutch social psychologist Geert Hofstede defined culture from a
management perspective as “the collective programming of the mind which
distinguishes the members of one group or category of people from others” (Hofstede
1984; Hofstede et al. 2010). He believed that the programming of an individual’s mind
starts from the family and continues to be programmed as the individual’s social sphere
increases. This means that eventually the neighborhood, school, workplace, and
community will contribute to an individual’s culture. Hofstede et al. (2010) suggested
that each individual can, in fact, carry several levels of culture. These different levels
are:
National: according to the country the person spends his/her lifetime in.
Regional: related to ethic, linguistic, or religious differences.
Gender: related to gender.
Generational: according to the differences among grandparents, parents, and children.
Social class: related to education, occupation and socioeconomic status.
Organizational or corporate: related to hierarchy in a work organization.
For this research study, the focus will be on the national culture level to achieve
the study objectives.
National Culture
National culture is defined as the “software of the mind” that is comprised of
values, attitudes, beliefs, norms, and behaviors of any particular nationality (Hofstede
1984; Hofstede et al. 2010). According to Hofstede’s definition, national culture is
22
software that can be learned through family, school, and workplace. Therefore, culture
is a reflection of the reality in which an individual lives. In 1989, Derr and Laurent
argued that the national culture of any country is comprised of patterns of experiences,
education, language, religion, and geography (Ali 2006). In 1995 Fukuyama suggested
that national culture is an “inherited ethical habit,” such as ideas, values, and
relationships (Morden 1999). According to Bik (2010) national cultures are formed by
different forces such as history, language, wars, and religions. He believed that these
forces shape the culture of any country.
From the earlier definitions one can conclude that national culture has deep roots
in every human. Many researchers have investigated the concept of national culture
(Hall 1966; Hall 1976; Hall and Hall 1990; Hampden Turner and Trompenaars 1993;
Hofstede 1984; Hofstede et al. 2010; House et al. 2001). These studies are different in
that some researchers used one single aspect to compare the national cultures of
countries, while others used multiple aspects. The next subsection will focus on these
studies.
National Culture Models
Many national cultural models have been developed in the last four decades.
These models are classified into two different groups, and under each group there are
different models. The first group consists of models that used a single dimension or
variable. The second group is one that includes models with multiple dimensions
(Morden 1999). The word dimension as Hofstede et al. (2010) defined it is “an aspect of
culture that can be measured relative to other cultures”.
23
Single Dimension Models
This group includes studies that are based on one dimension. There are two
studies that used one single aspect of culture.
Monochromic and polychromic cultures
Anthropologist Edward Hall (1966) drew attention to the ways people from
different culture handle time. He divided culture on this dimension into monochromic
and polychromic cultures. He argued that people from monochromic cultures usually
focus on doing one thing at a time and don’t involve too much else. In addition, they
prefer to divide their time into different categories. On the contrary, people from
polychromic cultures are flexible and willing to do many things simultaneously (Hall
1966). Monochromic cultures include countries such as Germany, United States, and
Japan, while countries with polychromic culture include India, Saudi Arabia, and Latin
America.
High and low context cultures
This study looked at how individuals and society obtain information and
knowledge, with an emphasis being placed on how the cultures communicate amongst
themselves. Context was used as a variable for distinguishing between countries.
People from high context culture countries such as China, France, and Saudi Arabia
obtain their knowledge and information from their personal network (e.g. friends and
family). Furthermore, they communicate through indirect communication. On the other
hand, people from countries such as Australia and United States acquired their
knowledge and information based on research. Low context people communicate
directly and prefer clear written forms of communication (Hall 1976; Hall and Hall 1990).
24
Multiple Dimension Models
This group includes studies that examined more than one variable when studying
culture. There are many studies that are available, but the most cited three studies are
summarized below.
Hofstede’s model
One of the most cited studies in national culture is the study of Geert Hofstede in
1980. He conducted one of the most comprehensive studies that covered more than 70
countries. Hofstede (1984) proposed that values in work environments could be affected
by culture. Culture characteristics found by Hofstede (1984) that distinguished countries
from each other are based upon four major variables or dimensions: (a) power distance
(PDI); (b) individualism versus collectivism (IDV); (c) masculinity versus femininity
(MAS); and (d) uncertainty avoidance (UAI). In later studies, two more dimensions were
added to the previous four. The first, added in 1991, was based on a research that was
done on Confucian thinking. The dimension was adapted from the Chinese Value
Survey of 23 countries. It was called Long-Term Orientation vs. Short-Term Orientation.
This dimension is related to whether a society is future-oriented or past and present-
oriented (Hofstede et al. 2010). The second one was called Indulgence vs. Restraint
and was added in 2010. The first four dimensions will be discussed in detail in a
different subsection.
Trompenaars and Hampden-Turner’s Model
Another comprehensive study for understanding cultural differences was
conducted in 1998 by Fons Trompenaars and Charles Hampden-Turner in Riding the
Waves of Culture. The study focused on how culture differences might influence the
process of doing business. They believe that culture differences exist as a result of how
25
people solve problems (Trompenaars and Hampden-Turner 1998). They proposed a
model based on a survey of 30,000 mangers from 55 different countries, and identified
seven dimensions under three main categories to distinguish people from one culture to
another (Trompenaars and Hampden-Turner 1998).
The first five dimensions come from the first category, which describes
relationships with other people. The first dimension under this category is Universalism
versus Particularism that describes the degree of importance a society or group of
people dedicates to either following the laws and rules or favoring relationships with
each other. The second dimension is called Individualism versus Communitarianism.
This dimension focuses on whether people in a specific culture see themselves as
individuals or as part of group. The third dimension is Neutral versus Emotional, which
is related to the degree of displaying emotions in a culture. The fourth dimension
explains the range of involvement and if it is Specific or Diffuse. The last dimension in
this category is Achievement versus Ascription. This one describes if people are judged
based on their achievements or based on who they are and whom they know
(Trompenaars and Hampden-Turner 1998)
The second category “Attitudes to Time” includes the sixth dimension of
Trompenaars and Hampden-Turner’s model. In this dimension societies were
distinguished based on their way of managing time. The third category in the model is
“Attitude to the Environment, and one dimension is associated with this category. This
seventh dimension explains the way cultures control the surrounding environment
(Trompenaars and Hampden-Turner 1998).
26
Global Leadership and Organizational Behavior Effectiveness (GLOBE)
A research project was initiated to examine the relationship between culture and
societal, organizational, and leader effectiveness. The Global Leadership and
Organizational Behavior Effectiveness (GLOBE) project was based on data collected
from 17,300 middle managers from 58 countries. According to House et al. (2001) the
main goal of this project was to understand how culture influences leadership and
organizational processes. The results of the project are used to compare cultures
through values, practices, and leadership styles. The GLOBE project identified nine
cultural dimensions. Six of these dimensions have their origins in the model Hofstede
developed in 1980.
The first three dimensions Uncertainty Avoidance, Power Distance, and
Individualism reflect the same meaning and scale that Hofstede provided in his 1980
model. The only difference was that Individualism was divided into two dimensions
labeled Collectivism I and Collectivism II. The fifth and sixth dimensions have their
origins in Hoftsede’s Masculinity dimension. These two dimensions are Gender
Egalitarianism and Assertiveness (House et al. 2001).
The seventh dimension Future Orientation is related to how societies look at
time. Performance Orientation is the eighth dimension proposed by House et al. (2001).
This dimension refers to how societies encourage and reward indivduals based on their
performance and achivment. Finally, the Human Orientation dimension refers to how
societies encourage and reward indivduals based on “being fair, altruistic, generous,
caring, and kind to others” (House et al. 2001).
27
Hofstede National Culture Dimensions
After devoting 15 years of his life on a research project to study the culture
system for many nations, Dutch social psychologist Geert Hofstede published his book
Culture’s Consequences in 1984. He argued that people carry “mental programs” which
contain national culture. Hofstede (1984) relied on data from two previous surveys from
1968 and 1972. The surveys questioned over 116,000 employees working for IBM in 40
different countries. Through extensive statistical analysis he found four primary
dimensions that distinguish a country’s culture. In later studies he added two more
dimensions and the study extended its coverage to 107 countries.
For this research study, Hofstede’s dimensions will be used as the framework to
investigate the influence of National Culture on construction project performance in the
Kingdom of Saudi Arabia. The reasons for this are:
Hofstede’s 1980 model of national culture is considered the most methodological study on culture (Swierczek 1994).
Each dimension can be measured, which allows for statistical comparisons between countries.
It is the most referred and used study that relates culture to the construction industry.
Only the first four dimensions will be used in this study since they are related to
workplaces based on Hofstede et al. (2010).
Power Distance (PDI)
Power distance is the first dimension of the national culture model created by
Hofstede in 1980. Power distance represents the degree of power distribution among
members of families, schools, communities and workplaces. In the workplace, PDI
describes the relationship that exists between individuals in managerial positions and
28
their subordinates. Geert Hofstede (1984; 2010) divided countries on this dimension into
large power distance countries and small power distance countries.
In large power distance countries, subordinates and their bosses are unequal
and hierarchy is accepted. Superiors are usually autocratic. They have the power and
make all the decisions. Subordinates prefer to be told what to do and accept being
observed by large numbers of supervisory personnel. On the other hand, in small power
distance countries, both superiors and subordinates experience equality among each
other. Superiors are considered democratic and share both power and decisions with
their subordinates. Usually, in small power distance countries, there are less
supervisory personnel.
Individualism vs. Collectivism (IDV)
The second dimension of national cultures is called Individualism. It depicts the
relationship between an individual and his or her family, school, community, and
workplace members. Individualism relates to societies where everyone is concerned
only about himself or herself. The opposite of Individualism is Collectivism, which is
reflective of societies where people are integrated into groups (Hofstede 1984; Hofstede
et al. 2010).
In Individualism dominant cultures people are task oriented and concerned about
their own goals and achievements. Workplace relationships in this culture are strictly
business relationships. On the contrary, people living in Collectivism cultures are
relationship oriented. The relationship between an employer and employee is similar to
a familial relationship.
29
Masculinity vs. Femininity (MAS)
According to Hofstede et al. (2010), the reason for labeling this dimension
masculinity vs. femininity is that the results of the data collected by the surveyor were
completely different for men and women. The countries’ cultures were divided along
masculine culture and feminine culture lines
Masculinity was characteristic of achievement, money, material success,
assertiveness, and performance in society. Usually, conflict and dispute in countries
with masculine dominant cultures were resolved through a “good fight”. Workers in
masculine dominant cultures are rewarded based on their performance. Femininity was
characteristic of relationships, caring for others, humility, and the welfare of society.
Conflict and dispute are resolved by negotiation and compromise in feminine dominant
cultures. And workers are rewarded based on their need rather than their performance
(Hofstede 1984; Hofstede et al. 2010).
Uncertainty Avoidance (UAI)
The fourth dimension of national culture is related to how the members of a
country deal with uncertainty and ambiguity in their life. In strong uncertainty avoidance
cultures people feel threatened when thinking about the future. Therefore, they avoid
situations with high risk. On the other hand, people in weak uncertainty avoidance
cultures are more relaxed and feel secure when facing uncertainty and ambiguity in
their lives. Weak uncertainty avoidance cultures encourage risk taking (Hofstede 1984;
Hofstede et al. 2010).
Table 2-1 displays the national culture scores of the nations from which most
labors came to work in the Saudi construction industry.
30
National Culture and Construction Industry
In the last 20 years, researchers in the construction industry have drawn
attention to the influence of culture on construction projects. Previous studies have
investigated different levels of culture, such as national, organizational, and professional
cultures. As mentioned earlier, the focus of this study will be on national culture. There
are many examples in the literature of researchers who have explored the influence that
national culture has on: construction management (Baba 1996; Rees-Caldwell and
Pinnington 2013), risk management (Liu et al. 2014; Zhi 1995), knowledge management
(Kivrak et al. 2014; Kivrak et al. 2009), safety management (Ali 2006; Mohamed et al.
2009), a construction project team’s performance (Comu et al. 2010; Dulaimi and Hariz
2011; Ochieng and Price 2010; Ullah Khan 2014; Waziri and Khalfan 2014),
construction disputes (Chan 1997; Chan and Tse 2003), construction joint ventures
(Fisher and Ranasinghe 2001; Ozorhon et al. 2008; Swierczek 1994), communication
(Loosemore and Muslmani 1999; Loosemore et al. 2010), and total quality management
(TQM) by Lagrosen (2003) and Ngowi (2000).
Cultural differences in international projects may be the key sources for wasted
resources, schedule delays, and decreases in productivity. Kivrak, Ross, and Arslan
(2008) interviewed 11 senior managers who work internationally to find out if cultural
diversity has influenced their management practices. They indicated that cultural
differences have an impact on management practices including:
Human resources management
Knowledge management
Communication management
Safety management
Time management
Negotiation
31
Risk management
Quality management
IT management
Construction Management
Baba (1996) found that strife and resistance was exposed when transferring and
implementing an advanced construction management strategy from western nations, for
example, the U.S.A. and United Kingdom in Asian nations. He believed that this
contention and resistance was mainly caused by three types of culture differences:
1. “Differences in traditional organizational structures; 2. Managerial differences; and 3. Differences in fundamental concepts and philosophies”
Project planning plays an important role in construction management and thus
Rees-Caldwell and Pinnington (2013) demonstrated the influence of national culture on
the planning processes. The study focused on comparing the differences that exist
between British and Arab project managers’ attitudes and perceptions of planning. They
concluded that the understanding of planning processes is impacted by the national
culture of the project managers.
Risk Management
Zhi (1995) considered the influence of national culture on risk management for
overseas construction projects. As he indicated, risk factors at the national level can be
classified into three categories:
1. “Political situations; 2. Economic and financial situations; and 3. Social environment”
Cultural differences such as language barriers, religious inconsistencies, and
informal relationships are the main causes for social environment problems. He
32
mentioned that these risk factors could be managed, regardless of the fact that they are
beyond the control of construction companies.
In a recent paper, Liu et al. (2014) conducted an exploratory study to examine
the influence of national culture on contractors’ risk management practices. The authors
argued that national culture differences impact the understanding and managing of risk.
Their result suggested that two of Hofstede’s dimensions from 1980, IDV and UAI, have
more influence on risk management than the rest.
Total Quality Management
Ngowi (2000) and Lagrsoen (2003) discussed the impact of national culture on
the execution of total quality management (TQM) in construction firms. In the first study,
there was some conflict between implementation of TQM and national culture. Ngowi
(2000) presumed that a successful implementation of TQM in a specific culture required
including the host cultural values. Similarly, Lagrsoen (2003) found correlations between
both UAI and IDV dimensions and the implementation of TQM.
Knowledge Management
Kivrak et al. (2009) reported that there is a direct relationship between culture
differences and knowledge management in construction projects. They claimed that
culture differences control knowledge transfer, knowledge sharing, knowledge capture,
learning, and training. Along similar lines, Kivrak et al. (2014) examined the impact of
national culture on knowledge sharing in international construction projects. Both
qualitative and quantitative data were collected from three international projects. Each
project had multicultural construction professionals. They found that national culture is
one of the most prevalent obstacles to knowledge sharing in these projects. Their
findings suggested the following:
33
Both high and low context cultures can impact knowledge sharing.
Individuals from collectivist cultures share knowledge with people from their group more than with those from different groups.
People with high MAS, PDI, and UAI face more problems in knowledge sharing.
Safety Management
Ali (2006) and Mohamed et al. (2009) examined the influence of national culture
dimensions on the safe work behavior of construction workers in Pakistan. After a series
of analyses, a strong linear correlation was found between three dimensions of national
culture and workers’ attitudes and perceptions. Collectivism and Femininity was the
primary national cultural dimension that had a strong positive correlation with the
workers’ attitudes and perceptions. Furthermore, UAI showed a strong correlation with
the attitudinal factors. On the other hand, a negative correlation existed between PDI
and workers’ attitudes and perceptions. It was concluded that laborers working in
environments with characteristics such as high Uncertainty Avoidance, low
Individualism, and low Masculinity would have more safety awareness and beliefs that
lead to safer work behavior.
Construction Project Teams Performance
Comu et al. (2010) conducted an experiment consisting of 20 simulated project
networks to examine the effect of both cultural and linguistic diversity on the
performance of construction project networks.. The first 10 project networks involved
people from the same culture, while the remaining networks were comprised of
multicultural participants. Each project network had three participants: one architect,
one engineer, and one contractor. All 20-project networks were asked to complete a
project in 90 min. The results showed that the performance of multicultural project
34
networks suffered initially, however, they learned fast and improved their performance
throughout the experiment. It can be concluded that performance on international
construction projects might suffer initially, but they will eventually achieve project
success.
A similar study observed that cultural differences and inadequate management
styles impede the success of multi-cultural project teams (Ochieng and Price 2010).
Additionally, Dulaimi and Hariz (2011) examined the influence of cultural diversity on
both project team performance and project management style. Their empirical study
showed a negative relationship between national diversity and project performance.
They found no significant relationship between national diversity and project
management style. Waziri and Khalfan (2014) found a direct relationship between
national culture dimensions and the performance of Chinese construction firms working
in Tanzania.
Ullah Khan (2014) studied the consequence of cultural assimilation on the
performance of construction management teams for two Chinese contractors working in
the United Arab Emirates. The study used Hofstede’s five dimensions’ model as its
base theory. A comparison of the two projects revealed differences between the
national culture of both Chinese contractors and the original Chinese national culture.
Differences were also observed between the United Arab Emirates’ national culture and
Chinese contractors’ national culture. He concluded that the high level of UAI and LOT
caused the success of the first project while low level of UAI and LOT caused the
termination of the second project.
35
Construction Disputes
Chan (1997) observed the effect of culture on the management of construction
disputes in China. He claimed that disputes and the methods for resolving them are
related to cultural differences for each society. In a second study, Chan and Tse (2003)
concentrated on studying how culture impacts contractual arrangements, conflict
causation, and dispute resolution. The study depended on findings from two different
surveys conducted in 1998 in Hong Kong and 1999 in London. The results obtained
from the study suggested that inappropriate contractual arrangements and cultural
clashes are the most significant factors affecting international construction project
disputes.
Construction Joint Venture
Joint venture is a widely used method for conducting international business in the
construction industry. Therefore, studying the effect of culture on joint ventures is
critical. In 1994, Swierczek studied how culture creates conflicts in an international joint
venture. He selected a project that had managers from both single culture groups such
as Malaysian, Thai, and French, and multicultural groups of Europeans and Asians.
Swierczek (1994) concluded that different cultural frameworks for joint ventures are the
main source of conflict in international joint ventures.
Fisher and Ranasinghe (2001) investigated the relationship between national
culture and venture choice in the Singapore building and construction industry. They
developed a model to examine the effect of cultural characteristics on foreign firms’
choices of entrant. Their results showed that UAI significantly impacted joint venture
partner selection when compared to socio-cultural distance. Ozorhon et al. (2008)
however, suggested that both national culture and host country culture have a minor
36
effect on the performance of international joint ventures (IJV) but they also concluded
that organizational culture had more influence on IJV performance.
Communication
Loosemore and Muslmani (1999) investigated the communication problems in
international construction projects that result from cultural diversity between UK and
Arabian Gulf nationals such as Saudi Arabia and the United Arab Emirates. For
example, language differences were recognized to be one of the most important
communication problems in international construction projects. Another cultural
difference was the perception of time, values, technology, and uncertainty. Additionally,
Loosemore et al. (2010) claimed that language barriers had impacted laborer’s safety
behavior because some laborers could not read safety notices.
Table 2-2 depicts the relationship between national culture dimensions and the
aforementioned construction issues based on previous studies.
Cultural Factors Affecting Construction Laborers Performance
Construction Laborers Performance Indicators
It is extremely important to study key performance indicators (KPIs), which
measure labor performance in Saudi Arabia. KPIs are subjective (qualitative) and
objective (quantitative) measures that are used to meet a company or industry’s
strategic goals (Cox et al. 2003; Ozorhon et al. 2007; Ozorhon et al. 2008; Swan and
Kyng 2005). Generally speaking, in the construction industry performance is measured
on project and company levels. Many studies have been conducted to develop KPIs for
both levels.
Ali, Al-Sulaihi, and Al-Gahtani (2013) identified the KPIs at the company level in
the building construction sectors of Saudi Arabia. According to them, previous studies
37
for performance indicators at project and company levels in different countries
recognized mutual indicators such as client satisfaction, cost, communication, quality,
time, safety, and productivity. In their study of Saudi Arabia, they identified 47
performance indicators such as quality of work, safety, and productivity, that were
ranked at the top according to their relative importance which were 91 .7%, 76.7%, and
67.5% respectively.
Cox et al. (2003) studied management’s perceptions of KPIs at the project level
in the construction industry. Management’s perception was measured by using
quantitative and qualitative performance indicators. Ozorhon et al. (2007; 2008) also
used subjective and objective performance indicators to evaluate the performance of
International Joint Ventures (IJV). Quantitative or objective performance indicators can
be measured by the cost, profitability, units per man-hours, on-time completion, and
percent of completion. Qualitative or subjective performance indicators measure labor
behaviors such as safety, turnover, motivation and overall satisfaction.
The construction industry is commonly described as a labor-intensive industry
because it relies heavily on the skill and hard work of its laborers. The cost, schedule,
and quality of construction projects can be considerably affected by the performance of
the workforce. Construction labor costs represent around 30% to 50% of the overall cost
of a project (Hanna et al. 1999; Yates and Guhathakurta 1993) and thus the
performance of a workforce can be measured at the project level. For the purpose of
this study, three performance indicators (quality, productivity and safety) will be used to
determine if laborers’ national cultures affect performance.
38
Cultural Factors Affecting Laborers’ Performance
The first step to investigate the influence of national culture on laborers’
performance is identifying the factors that cause that influence. After a rigorous
literature review, a set of 17 cultural factors, extracted from various studies of national
culture dimensions in the construction industry field, were selected. These factors
include (a) national culture dimensions as defined by Geert Hofstede (1984; 2010), (b)
time handling characteristics as identified by Edward Hall (1966), and (c) information,
knowledge, and communication factors as outlined by both Edward Hall and Mildred
Hall (Hall 1976; Hall and Hall 1990). By using these three models it is possible to make
more comprehensive and solid statements on whether national culture influence
construction labor performance or not. Table 2-3 lists all the dimensions and their
related factors. Fourteen factors were derived from Hofstede’s dimensions with the
remaining three factors coming from research done by Edward and Mildred Hall.
Cultural factors related to power distance (PDI)
Power Distance (PDI) is represented by five factors. The first is the degree of
equality between mangers and laborers. This factor is related to the distribution of
power among workers on construction project sites, and if the power is centralized to a
few individuals. The second factor is managerial style and whether it is a “benevolent
autocrat” or “resourceful democrat” style. A benevolent autocrat manager is one who
acts as a “good father” for laborers while a resourceful democrat manager acts as a
friend. The third factor is the level to which laborers are involved in decision-making.
The fourth factor is the degree of trust between managers and laborers, because the
number of supervisory personnel on a project might represent the degree of trust
39
between managers and labors. And the last factor is related to the range of salary on
the project.
Cultural factors related to individualism (IDV)
The next three factors are associated with dimension of Individualism (IDV). The
first factor in this dimension is whether laborers work in terms of I or We. It explains if
laborers act according to self-interest or group-interest. The second factor is the type of
relationship between mangers and laborers. This relationship could be family-like or a
business only type of relationship. When a family relationship exists the poor
performance of any laborer will not likely be a reason for dismissal. On the other hand,
poor performance is the main reason why different levels of pay are offered between
laborers in business relationships. The third factor is related to work environment. In
task-oriented environments the task is more important than any personal relationship
whereas in relationship-oriented environments the relationship between individuals is
more important than the task being carried out in the work environment.
Cultural factors related to masculinity (MAS)
Masculinity (MAS) is displayed by three cultural factors. The first factor is the
conflict and dispute resolution style. Some cultures resolve conflict through fight, “let the
best man win,” while others use negotiation and compromise. The second factor is
linked to the reasons for rewarding a laborer’s achievement. These reasons could be
based on labor performance or labor need. The third factor represents the goal of a
laborer in life. Some laborers pursue their goals with a ‘work to live’ mentality while
others ‘live to work’.
40
Cultural factors related to uncertainty avoidance (UAI)
Uncertainty Avoidance (UAI) is outlined by three factors. The first one is the level
of stress and anxiety for a laborer. The second is the degree to which the laborer deals
with risk and ambiguity. Some labors are willing to take risks and work with new tools or
technology, while others express anxiety and fear when presented with new scenarios.
The third factor is job security.
Cultural factors related to time handling
The way that a laborer handles time is extremely significant in improving his or
her overall performance. Laborers who are only willing to perform one task at a time are
identified as monochromic, while those willing to do several things at once are
polychromic. The relationship between monochromic and polychromic laborer’s
handling of time is an important factor in analyzing how national culture influences
performance.
Cultural factors related to context
As mentioned earlier, context is connected to information, knowledge, and
communication. With this in mind, two factors will be used to define the relationship that
exists between context and a laborer’s performance. First, the way a laborer acquires
information and knowledge, such as through either a personal network or research, and
secondly, the way a laborer communicates such as directly or indirectly. Context will
further allow the development of a clear understanding of how national culture affects
laborer performance.
The Kingdom of Saudi Arabia
The kingdom of Saudi Arabia is located on the Arabian Peninsula in southwest
Asia. It is bounded on the north by Kuwait, Iraq and Jordan; on the east by the Arabian
41
Gulf, United Arab Emirates and Qatar; on the south by Yemen and Oman; and on the
west by The Red Sea. The total area of Saudi Arabia is around 2.15 million square
kilometers (SAUDI National e-Government Portal 2015). According to the Central
Department of Statistics and Information website, the country’s total population in 2014
was around 30.8 million with a growth rate of 2.55%. Population density was estimated
at 15.3 persons per square kilometer. The country is divided into 13 provinces each with
a capital city, and ruled by a governor, deputy governor, and a provincial council
(Central Department of Statistics and Information 2015).
Culturally, Saudi Arabia is considered a conservative Islamic country. The official
langue is Arabic while businesses in the region use English. Based on Hofstede et al.
(2010) Saudi Arabia scores 95 in power distance, 25 in individualism, 60 in masculinity,
and 80 in uncertainty avoidance. The high Power Distance (PDI) and Uncertainty
Avoidance (UAI) scores mean that people in the country accept hierarchy, power
centralization, being told what to do, and prefer to avoid uncertainty. On the other hand,
the low score in Individualism (IDV) reflects that Saudi Arabia is a collectivistic society.
This can be seen in the prevalence of extended family relationships and community.
Furthermore, the above medium score in Masculinity (MAS) shows that the country is a
masculine society where people live to work and reward is given according to need.
Saudi Arabia’s economy is an energy based oil economy. The 2014 Gross
Domestic Product (GDP) at current prices is 90,703 Saudi Riyal (SAR) per capita
($24,187). In 2015, government expenditures are estimated to be $229.3 billion with
appropriations for new and existing projects being $ 49.3 billion. Also, 43.8% of the
42
budget will be allocated for education and healthcare (Kingdom of Saudi Arabia Ministry
of Finance 2014).
Construction industry in Saudi Arabia. The high oil prices and high population
growth rates have prompted the government to invest heavily in infrastructure,
education, and healthcare projects. This investment has continuously improved the
contribution of the construction industry to Saudi Arabia’s gross domestic product
(GDP), which accounts for 8% of the overall GDP, with a yearly value of $38.2 Billion
(Canadian Trade Commissioner Service 2014). As mentioned on the Deloitte GCC
Powers of Construction 2014 report, Saudi Arabia is leading the Gulf Cooperation
Council (GCC) countries with over $1 trillion in value of projects that are planned or
under construction (Deloitte 2015).
Under the Ninth Development Plan, between 2010 and 2014 the government
invested around $385 billion in infrastructure and construction projects. As of April 2014,
residential projects accounted for 29% of the spending, healthcare at 21%, education at
12%, mixed use at 12%, hospitality and leisure at 11%, cultural at 9%, and finally
commercial at 7% (Deloitte 2015). Figure 2-1 displays Saudi Arabia’s construction
industry GDP, annual growth rates, and the construction industry’s input as a
percentage of the total GDP for the years of 2001 to 2014.
In a press release documenting recent economic developments and highlights of
fiscal years 2014 and 2015, the Saudi Arabian Ministry of Finance announced the
Kingdom’s 2015 budget. According to this announcement, the building and construction
sectors are estimated to grow by 6.7% in 2014 (Kingdom of Saudi Arabia Ministry of
Finance 2014).
43
Education. The budget allocated for education includes 164 new projects with a
cost of $3.7 billion and existing project costs of around $1.8 billion. In addition, projects
under construction from previous years will continue with a cost of around $74.7 billion.
The 2015 budget includes the renovation of 500 schools and 11 sport centers, and the
construction of three new universities (Kingdom of Saudi Arabia Ministry of Finance
2014).
Health and social affairs. The government has allocated approximately $42.7
billion for health and social affairs. The budget includes three new hospitals, three blood
bank laboratories, 11 medical centers, and 10 care clinics. Furthermore, there are
projects under construction, such as 117 hospitals and eight medical cites. Social
service projects include the building of 16 sport clubs, five centers for individuals with
special needs, social welfare and labor offices (Kingdom of Saudi Arabia Ministry of
Finance 2014).
Municipality services, infrastructure, and transportation. The Saudi Arabian
government has allocated $10.7 for municipal projects, and $16.8 billion for
infrastructure and transportation services. Municipal services are new projects that
include inter-city roads, bridges, drainage, and control systems. In addition to the new
projects are existing ones from previous years which have contributed around $38.4
billion to the Kingdom’s economy. On the other hand, new infrastructure and
transportation projects include the building of 2,000 km of roads, development of
existing ports, the building and upgrading of regional and international airports, and
railroads projects. Table 2-4 outlines a number of mega construction projects that have
44
been completed or are still under construction throughout the Kingdom of Saudi Arabia,
along with their estimated values (Kingdom of Saudi Arabia Ministry of Finance 2014).
As a result of the Saudi Arabian construction industry’s recent boom the number
of laborers necessary to complete this construction has increased rapidly. According to
the Ministry of Labor the number of employees in the building and construction private
sector has increased from 1,671,271 in 2004 to 4,676,359 in 2013. In 2013 the
construction industry labor force accounted for 48.31% of the country’s entire labor
force. Of the 4.7 million employees in the building and construction sector 90% are non-
Saudi (Kingdom of Saudi Arabia Ministry of Labor 2005; Kingdom of Saudi Arabia
Ministry of Labor 2014). Those employees usually come from surrounding Arab
countries like Egypt, Syria, Sudan, and Yemen, Asian countries like the Philippines,
Indonesia, China, India, Pakistan, Bangladesh, Turkey, and various African countries
including Ethiopia and Somalia. Figure 2-2 shows the increase in the number of
construction employees in the private sector.
45
Figure 2-1. Amount of the GDP invested in construction, construction annual growth
rates, and construction input to the overall GPD.
* Source: (Adapted from Kingdom of Saudi Arabia - Central Department of Statistics & Information http://www.cdsi.gov.sa/en/797 Last accessed March 2015)
Figure 2-2. Construction (private sector) employee increase over the last decade.
* Source: (Adapted from Kingdom of Saudi Arabia – Ministry of Labor https://portal.mol.gov.sa/en/statistics/ Last accessed March 2015
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
$0.00
$5.00
$10.00
$15.00
$20.00
$25.00
$30.00
$35.00
$40.00
$45.00
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Billio
ns
Construction Industry in Saudi Arabia
Construction GDP (Billion of USD) Construction Annual Growth Rate % Construction Input to Overall GDP %
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
-
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Millio
ns
Private Sector Construction Employees
Construction Employees Saudi Employees Non-Saudi Employees % of the total Employees
46
Table 2-1. National culture scores by nations
Country National Culture Dimensions
PDI IDV MAS UAI
Arab country 80 38 53 68
Africa West 64 27 41 52
Africa East 77 20 46 54
Bangladesh 80 20 55 60
China 80 20 66 30
India 77 48 56 40
Indonesia 78 14 46 48
Pakistan 55 14 50 70
Philippines 94 32 64 44
Turkey 66 37 45 85
* Source: (Construction Week Online Middle East 2015)
Table 2-2. National culture and construction industry
Issue National Culture Dimensions
PDI IDV MAS UAI
Construction Management
Risk Management X X
Total Quality Management X X
Knowledge Management X X X
Safety Management X X X
Team Performance X
Construction Disputes X
Construction Joint Venture X
Communication X
47
Table 2-3. National culture factors affecting construction performance
Dimension No. Factors
Power Distance (PDI)
1 The degree of equality between managers and laborers
2 Manager style (autocrat or democrat)
3 Involvement of laborers in decisions making
4 The degree of trust between managers and laborers 5 Salary range
Individualism (IDV)
6 Laborer acts according to self-interest or group-interest
7 Relationship between managers and laborers (family or business)
8 Task-oriented environment vs. Relationship-oriented environment
Masculinity (MAS) 9 Conflict and dispute resolution styles (negotiation or fight)
10 Rewards based on performance or need 11 Laborers goals in life
Uncertainty Avoidance (UAI)
12 Stress and anxiety levels
13 The degree of dealing with risk and ambiguity (risk taking) 14 Security of employment
Time Handling 15 Monochromic vs. Polychromic
Context 16 The way laborers acquire information and knowledge (personal network or research)
17 Laborer’s communication style (direct or indirect)
48
Table 2-4. Mega construction projects in Saudi Arabia
Project Name Client
Estimated Value (USD Million)
Year of Completion
Infrastructure Projects
Jeddah Light Rail Transit/Tram System Makah municipality 6,000 2018
King Abdulaziz International Airport - Phase 1 GACA 1,500 2012
Haramain High Speed Rail Project - Phase 1 SRO 1,900 2013
Prince Abdulmajeed Airport in Al-Ola GACA 38 2010
Riyadh Metro ARD 25,000 2019
Building Projects
Construction of 1,200 Housing Units in Jeddah MODON 147 2013
King Abdulaziz Centre for Knowledge & Culture Saudi Aramco 400 2013
King Abdullah Economic City (KAEC) Emaar Properties 50,000 2020
Kingdom Tower in Jeddah JEC 15,000 2017
King Abdullah Financial District - Packages 1 to 4
Riyadh Investment Company 1,465 2012
Power and Water
Ras Al Zour IWPP SEC & SWCC 5,500 2013
Al Qurayyah Combined-Cycle Power Plant SEC 1,850 2013
Jubail IWPP MARAFIQ 3,400 2010
Thermal Power Plant - Rabigh 6 SEC 4,000 2015
Ras Al Zour IWPP MODON 300.00 2011 * Source: (Construction Week Online Middle East 2015)
49
CHAPTER 3 RESEARCH METHODOLOGY
This chapter presents the research process and methodology used to explore the
influence of national culture on the performance of construction laborers in Saudi
Arabia.
Research Process
The flowchart (Figure 3-1) illustrates the research process, which consists of six
phases. The six phases for conducting this research are the following:
4. Background research 5. Review of literature 6. Research design 7. Data collection 8. Data analysis 9. Research findings and recommendations
Background Research
The first phase of the research effort was general background research that
covered the following areas:
Factors affecting labor productivity
Factors affecting international construction projects
Causes of delay in construction projects in Saudi Arabia As a result of this step the problem definition and objectives of this research were
determined.
Review of Background Literature
The second phase was a detailed literature review, presented in Chapter 2,
regarding national culture and the construction industry. The outputs of this step were
the 17 cultural factors and the three key labor performance indicators.
50
Research Design
Based on the results from the previous phase, the research methodology was
designed. In the research design phase the researcher determined both the sampling
and questionnaire designs.
Sampling Design
Population
As mentioned in Chapter 1, the main objective of this study is to investigate the
influence of national culture on construction laborer performance in Saudi Arabia.
Therefore, the target population in this research will cover projects throughout the
Kingdom with multinational laborers. The respondents include people who work as
project managers, project coordinators, site engineers, and field superintendents.
Sample size
Sample size in Exploratory Factor Analysis (EFA) varies greatly. Several experts
recommend having samples of 300 as the minimum number needed for factor analysis,
while others have argued that samples of 50 might be acceptable (Taherdoost et al.
2014). Many studies have used the rating scale proposed in 1992 by Comrey and Lee .
They have suggested that “samples of 100 are poor, 200 are fair, 300 are good, 500 are
very good, and 1000 or more are excellent”. Another set of recommendations relied on
the sample size to variable ratio (N: p). Many specialists have suggested different ratios.
For example, in 1975 Everitt suggested a ratio of 10:1, and then in 1983 Gorsuch
recommended a minimum ratio of 5:1 (MacCallum et al. 1999; Williams et al. 2012).
This research has 34 variables and six independent factors therefore the
minimum sample size for both EFA is reproduced below:
EFA Sample Size (n) = 5 X 34 = 170
51
As a result of these calculations the minimum sample size for this study will be
170 participants.
Questionnaire Design
To achieve the research objectives, a questionnaire (Appendix A) was utilized for
this study. This questionnaire was developed to measure the perceptions of
construction practitioners on the degree of influence of cultural factors on KLPIs. The
questionnaire was designed to enable participants to assess the influence of 17 factors
on three key laborer performance indicators (quality, productivity, and safety). Each one
of the 17 factors was represented by two variables, which formed a total of 34 variables.
Table 3-1 shows all 34 variables and their coding.
Figures 3-2, 3-3, and 3-4 show all 34 variables and their relationship to the
national culture dimensions. Each variable represents either a high or low on the scale
of national culture dimensions.
The questionnaire consisted of three sections. Under the first section, the
respondent’s profile was determined. Three questions were asked to collect information
about the respondent (i.e., respondent’s background, current position(s), and their
number of years of experience). The second section also consisted of three questions
related to the project’s profile (i.e., project type, the number of laborers in the project,
and the nationalities of laborers in the project). The third section was related to the
cultural factors influencing laborer performance in the construction industry.
Respondents were asked to evaluate the influence of 34 situations on three key
labor performance indicators (KLPIs). As mentioned before, these three indicators were
quality, productivity, and safety. A five point Likert scale was used in the questionnaire
to measure the degree of influence of cultural factors on the three KLPIs: “1”
52
represented not at all influential; “2” represented slightly influential; “3” represented
somewhat influential; “4” represented very influential; and “5” represented extremely
influential.
Questionnaire format. Since the target population of the study was
construction practitioners in Saudi Arabia, the use of an online survey was believed to
be the appropriate tool to reach this population. The online survey (Appendix A) was
hosted by University of Florida Qualtrics. The survey link was sent to the Saudi Council
of Engineers and the Ministry of Islamic Affairs. The Saudi Council of Engineers sent
emails with the survey link to its members. On the other hand, the Ministry of Islamic
Affairs sent out the survey link to the administration of each assigned project. Then,
each administration circulated the survey link to all people involved in their projects
through their internal network.
Data Collection
The primary objective of this research was to investigate the influence of national
culture on the performance of construction laborers. To achieve this objective data was
collected via questionnaires administered to project managers, project coordinators, site
engineers, and field superintendents. To increase the chance of obtaining a suitable
number of respondents, communication with representatives of the Saudi Council of
Engineers and the Ministry of Islamic Affairs were initiated. Both representatives agreed
to send emails with the survey link to their members.
Data Analysis
The data collected from the questionnaires was analyzed by using the Statistical
Package for the Social Sciences (SPSS Version 23). The data analysis performed in
this study included two methods: preliminary analysis, descriptive, and multivariate
53
statistics. preliminary analysis includes response rate, data screening, and normality
test. Descriptive statistics were used to analyze questionnaire respondents’ profiles and
project profiles. Multivariate statistics included exploratory factor analysis (EFA).
Preliminary Analysis
The goal of using preliminary analysis is preparing the data for further statistical
analyses. The preliminary analysis includes checking the response rate, screening the
data, and testing for normality. In the response rate step, the number of completed
surveys was checked against the number of total started surveys. Then, the surveys
were inspected if there are any missing data, outliers, and unengaged responses.
Finally, the normality of the data was visually tested by examining the histogram and the
normal Quantile Quantile Plot (Q-Q Plot) of each variable. Additionally, Shapiro-Wilk
test was performed to check the normality of the data in SPSS (Ghasemi and Zahediasl
2012; Öztuna et al. 2006).
Descriptive Statistics
The purpose of using descriptive statistics is to provide general information about
both participants and projects. The information offered includes distribution of
responses, ranking of cultural factors, and percentiles for all of them.
Multivariate Statistics
Multivariate statistics used in this study included exploratory factor analysis and
the Kruskal-Wallis test. Further details about exploratory factor analysis and Kruskal-
Wallis test is provided in the following subsections.
Exploratory factor analysis
Exploratory factor analysis (EFA) is a multivariate statistical approach that is
commonly used in the social sciences, education, and psychology. In recent years, the
54
wide application of exploratory factor analysis in construction research has included but
is not limited to modeling labor productivity (Jang et al. 2011), finding the factors
affecting labor productivity (Dai 2006; Kien 2012), developing a project improvement
system (Mojahed 2005), and examining the effect of national culture on safety climate
(Ali 2006).
Generally, the objectives of using exploratory factor analysis (Taherdoost et al.
2014; Thompson 2004; Williams et al. 2012) include the following:
Evaluate questionnaire validity
Minimize the number of variables
Examine the relationships between variables
Prove or disprove proposed theories In this study, EFA was used to (a) identify relationships among cultural factors,
(b) reduce and summarize these factors to a smaller number of factors, and (c) identify
which of these factors influence each of the three KLPIs. To do so, the following five
steps as shown in Figure 3-5 were followed:
Test the factorability of the correlation matrix
Measure sampling adequacy and suitability of data
Factor extraction
Factor rotation
Interpretation
Step 1: Factorability of the correlation matrix
The first step in applying EFA is inspecting the factorability of the correlation
matrix. This step is used to define the relationships between variables. Generally,
correlation coefficients over 0.30 are recommended which means that “the factors
account for an approximately 30% relationship within the data” (Taherdoost et al. 2014;
Williams et al. 2012).
Step 2: Sampling adequacy and suitability of data
55
After confirming the factorability of the correlation matrix and before the
extraction of the factor two tests ought to be conducted. The first one is the Kaiser-
Mayer-Olkin (KMO) test to measure sample adequacy. The KMO value should be equal
or greater than 0.05 to be considered suitable for the EFA. The second test is called
Bartlett’s test of Sphericity to check the suitability of data for EFA. This test should be
significant (p<0.01) in order to use an exploratory factor analysis (Taherdoost et al.
2014; Thompson 2004; Williams et al. 2012).
Step 3: Factor extraction
The aim of factor extraction is to reduce the number of variables into a smaller
number of factors or components. SPSS provides several methods of factor extraction.
The default factor extraction method, Principle Components Analysis (PCA) was used in
this study (Taherdoost et al. 2014; Thompson 2004; Williams et al. 2012).
Many approaches exist in the literatures to determine the number of factors in a
data set. These approaches include: Kaiser’s criterion, the Scree plot, the cumulative
percentage of variance, and the parallel analysis. It is recommended that multiple
approaches should be used to determine the number of factors (Taherdoost et al. 2014;
Williams et al. 2012).
For this study three of the aforementioned approaches were used. The first one
was Kaiser’s approach that suggests only factors or constructs with eigenvalues greater
than one should be considered. The second approach was the Scree Test that
considers only factors above the break to be retained. And the final approach was the
cumulative percentage of variance (CPV) where the constructs or factors must explain
more than 50% of the variance.
56
Step 4: Factor rotation
The purpose of this step was to decide whether a variable should relate to more
than one factor or not. Factor rotation works on “maximizing the high item loadings and
minimizing low item loadings”. The two methods of factor rotation are oblique rotation
and orthogonal rotation. The first one allows factors to be correlated while the second
creates uncorrelated factors (Taherdoost et al. 2014; Thompson 2004; Williams et al.
2012). For the purpose of this study the default setting of SPSS, orthogonal rotation,
was used.
Step 5: Interpretation
The final step in exploratory factor analysis was the interpretation of components
or factors. Each component or factor was labeled beside the variables that attribute
most to the component.
Comparing based on educational background
Analysis of variance (ANOVA) was carried to compare the differences among
group means of respondents based on their educational background. ANOVA is a
parametric test which assumes: (1) normality of the data, (2) homogeneity of variance,
and (3) independence of the observations (Chui 2010; Larson 2008).
If for any reason the assumptions of ANOVA are violated, the nonparametric test,
Kruskal-Wallis test, can be used as alternative. Kruskal-Wallis test is usually known as
“one-way ANAOVA on ranks” with no assumption about the data normality (Chui 2010).
Research Findings and Recommendations
The final phase presents the research Findings and recommendations for future
research. This will be covered in Chapter 5.
57
Figure 3-1. Research process flowchart.
58
Figure 3-2. Cultural factors chart.
59
Figure 3-3. Cultural factors chart.
60
Figure 3-4. Cultural factors chart.
Figure 3-5. Data Analysis flowchart.
61
Table 3-1. Cultural factors coding
Dimension No. Label Factors
Power Distance (PDI)
1 PDI 1H Power and decisions are centralized in few hands
2 PDI 1L Power and decisions are decentralized
3 PDI 2H Managers are autocratic and paternalistic
4 PDI 2L Managers are democratic and consultative 5 PDI 3H Laborers are NOT involved in decision making 6 PDI 3L Laborers are involved in decision making
7 PDI 4H Large number of supervisory personnel 8 PDI 4L Small number of supervisory personnel 9 PDI 5H There is a wide range in salary 10 PDI 5L There is a narrow range in salary
Individualism (IDV)
11 IDV 1H Laborers act according to their own interests
12 IDV 1L Laborers act according to their group’s interests
13 IDV 2H
Relationship between laborers and managers is a business relationship
14 IDV 2L
Relationship between laborers and managers is like a family link
15 IDV 3H Tasks are more important than relationships
16 IDV 3L Relationships are more important than tasks
Masculinity (MAS)
17 MAS 1H Conflict is resolved by a good fight
18 MAS 1L Conflict is resolved by negotiation 19 MAS 2H Laborers are rewarded based on their performance 20 MAS 2L Laborers are rewarded based on their need 21 MAS 3H Laborers live in order to work 22 MAS 3L Laborers work in order to live
Uncertainty Avoidance (UAI)
23 UAI 1H High stress and high anxiety
24 UAI 1L Low stress and low anxiety 25 UAI 2H Laborers avoid risk taking and unfamiliar situations 26
UAI 2L Laborers are involved in risk taking and unfamiliar situations
27 UAI 3H Have security of employment 28 UAI 3L No security of employment
Time Handling 29 TH 1 Laborers do one thing at a time (Monochromic)
30 TH 2 Laborers do several things at once (Polychromic)
Context 31 CT 1H Laborers acquire information and knowledge from personal networks
32 CT 1L Laborers acquire information and knowledge from research
33 CT 2H Laborers communicate indirectly
34 CT 2L Laborers communicate directly
62
CHAPTER 4 RESULTS
The purpose of this study was to investigate the influence of National Culture on
construction laborer performance in Saudi Arabia. In order to achieve this purpose, the
methodology described in Chapter 3 was followed. This chapter will present the survey
results. First, preliminary analysis including checking the response rate and screening
the data is presented. Next, descriptive analysis was performed for both respondents
participating in the survey and the projects they were working on. Analyses were also
executed to examine the culture factors. The final step implemented was Exploratory
Factor Analysis to reduce the number of culture factors.
Preliminary Analysis
Response Rate
The official survey of this study was conducted through online survey. As
mentioned in Chapter 3, The Saudi Council of Engineers and the Ministry of Islamic
Affairs sent emails with the survey link to their members. Data from the host server for
the survey, University of Florida Qualtrics, shows that 933 surveys were started and
only 365 surveys were completed between January and March of 2016, resulting in a
nearly 39.12% response rate. Figure 4-1 displays the survey completion percent.
Data Screening
Data screening was the first step to ensure that the data was clean and ready to
be statistically analyzed. The process of data screening included dealing with missing
data, outliers, and unengaged responses.
Missing data is the most obvious problem in collecting data through
questionnaires (Tabachnick and Fidell 2001). As shown in Figure 4-1, around 60% of
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the participants had answered 70% or less of the survey questions. To avoid any
problems resulting from missing data such as biased result, and since the number of
completed surveys was above the minimum sample size, the researcher decided to
drop any cases with missing data. Therefore, only 365 completed surveys were
included in the analysis.
According to Tabachnick and Fidell (2001), a case with extreme value on
individual variable is consider an univariate outlier. To detect this kind of outliers,
boxplots in SPSS were performed for each dimension. The result of this analysis
revealed that 21 cases of univariate outliers were founded. Figures 4-2 to 4-6 show the
result of the boxplots and the ID numbers of these cases. As a result, the number of
valid completed surveys dropped to 344.
After removing the outliers from the list, unengaged responses were also
dropped. Unengaged responses usually occurred when the participant choose to only
enter one number for all the answers such as 3,3,3,3 or 1,1,1,1. Only three participants
used this method for answering the survey. The final valid surveys used in the study
were 341, which represents a response rate of 36.55%.
Testing for Normality
One of the most important primary step in analyzing data is testing for normality
(Tabachnick and Fidell 2001). In this study, three methods were used for testing the
normality of each factor on the three performance indicators. Firstly, visual inspection of
the histogram and normal Q-Q Plot were completed which summarized the distribution
of data. The inspection revealed that some of data did not fit under the curve of
normality in the histograms, and did not fall in the straight line in the Q-Q Plots.
64
Secondly, the Shapiro-Wilk test was performed for testing the normality of the
data. Table B1, B2, and B3 show that the p-values were less than 0.05 for all factors
which mean that the data were not normally distributed (Ghasemi and Zahediasl 2012;
Öztuna et al. 2006).
Descriptive Analysis
Respondents’ Profile Information
As mentioned previously in Chapter 3, the first part of the survey was related to
the respondent’s profile. Three questions describe the educational background, job
positions, and experience for the participants. Figures 4-7, 4-8, and 4-9 show the
characteristics of the respondents. The percentage distribution and frequencies of the
respondent’s profile are demonstrated in Appendix B.
Background information
The educational background distribution of the respondents is presented in
Figure 4-7. There were 99 (29%) respondents with an Architecture background; 129
(37.8%) with a Civil Engineering background; 48 (14.1%) with a Mechanical Engineering
background; and 33 (9.7%) with an Electrical Engineering background. Furthermore 32
(9.4%) respondents had other educational backgrounds such as Architecture
Engineering, Landscape Architecture, Survey Engineering, and Safety Engineering.
Job positions information
The four main positions are shown in Figure 4-8. Eleven (3.2%) of the
respondents were field superintendents, 149 (34.7%) were engineers, 18 (5.3%) were
project coordinators, and 99 (29%) were project managers. Additionally, 64 (18.8%) of
the respondents had other positions such as architects, civil inspectors, general
managers, safety mangers, site engineers, and site inspectors.
65
Experience information
Most of the respondents had a good experience in the Saudi construction
industry as shown in Figure 4-9. Out of the 341 respondents, 94 (27.6%) had less than
5 years of experience; 122 (35.8%) had 5 to 10 years of experience; 74 (21.7%) had 11
to 20 years of experience; and 51(15%) had more than 20 years of experience in the
construction industry.
Project’s Profile Information
The second part of the survey included three questions that were related to the
projects’ profile. These questions describe the projects’ classification, number of
laborers under the supervision of each respondent, and the nationality of theses
laborers. Figures 4-10, 4-11, and 4-12 display the projects’ profile information. The
percentage distribution and frequencies of the projects’ profile are demonstrated in
Appendix B.
Project classification
The projects were classified as bridge and highway construction, building
construction, infrastructure construction, and industrial construction. In addition,
respondents had the chance to add different types of projects. As shown in Figure 4-10,
there were 22 (6.5%) bridge and highway construction projects; 201 (58.9%) building
construction projects; 42 (12.3%) infrastructure construction projects; and 26 (7.6%)
industrial construction projects. Moreover, 50 (14.7%) of the respondents had
mentioned different types of projects such as airports, electrical power plants,
landscape projects, parks and waterfront development, and urban planning.
66
Number of laborers
Participants were asked to indicate the number of laborers working under their
supervision in the project. 80 (23.5%) respondents had less than 10 laborers under their
supervision; 48 (14.1%) had 11 - 20 laborers under their supervision; 26 (7.6%) had 21 -
30 laborers under their supervision; 28 (8.2%) had 31 - 40 laborers under their
supervision; and 159 (46.6%) had more than 40 laborers under their supervision. Figure
4-11 shows these numbers.
Nationality of laborers
In order to ensure that respondents had experience working with laborers of
different nationality, they were asked to specify the nationalities of the laborers under
their supervision. Figure 4-12 clarifies the frequencies of ten different nationalities
working in the Saudi Arabia construction industry. In the sample, 283 (83%) of the
respondents had laborers from Arab countries such as Egypt, Syria, Sudan, and
Yemen; 18 (5.3%) of the respondents had laborers from West African countries such as
Ghana, Mali, Nigeria, and Senegal. Out of the 341 respondents, only 24 (7%) of the
respondents had laborers from East African countries such as Ethiopia, Eritrea,
Somalia, and Kenya; 158 (46.3%) of the respondents had laborers from Bangladesh.
Approximately 19 (5.6%) of the respondents had laborers from China; 223 (65.4%) of
the respondents had laborers from India, 26 (7.6%) of the respondents had laborers
from Indonesia; 226 (66.3%) of the respondents had laborers from Pakistan; 194
(56.9%) of the respondents had laborers from the Philippines; and 30 (8.8%) of the
respondents had laborers from Turkey. In addition, respondents had mentioned different
nationalities such as Saudi Arabia, Italy, Latin American countries, and the USA.
67
Cultural Factors Frequencies and Mean Ranking
In order to determine the culture factors with the most significant influence on
laborer performance, the mean ranking for all the factors on the three performance
indicators quality, productivity, and safety are illustrated in Tables 4-1, 4-2, and 4-3. In
addition, the percentage distribution and frequencies of all cultural factors are
demonstrated in Appendix B. The ranking shows that cultural factors had almost the
same influence on three performance indicators.
Multivariate Analysis
Before performing Exploratory Factor Analysis, the researcher checked if the
data was suitable for factor analysis. First the sample size was checked. As mentioned
previously in Chapter 3, the rule of thumb is to have at least sample size of 100.
Another rule is to have a minimum sample to variable ratio of 5:1. The sample size of
this study was 341, with the ratio of 10 participants to each variable. According to the
rating scale proposed by Comrey and Lee (Williams et al. 2012), the 341 cases were
considered a good sample size for Exploratory Factor Analysis.
Secondly, the factorability of the correlation matrix was inspected. This visual
inspection was used to define the relationships between variables. Generally,
correlation coefficients over 0.30 are recommended (Taherdoost et al. 2014; Williams et
al. 2012). The visual inspection of the correlation matrix (Appendix C) revealed that
some of the correlation coefficients were over 0.30. Additionally, the correlation matrix
disclosed the absence of multicollinearity. Multicollinearity usually occurs when
variables have high intercorrelations with each other (correlation value of 0.8 or more)
(Leech et al. 2005)
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After confirming the factorability of the correlation matrix and before the
extraction of the factors, both Kaiser-Mayer-Olkin (KMO) test and Bartlett’s test of
Sphericity were checked for each performance indicator. As indicated in Table 4-4, both
KMO test and Bartlett’s test exhibit acceptable results. All the KMO values were greater
than 0.5, and the Bartlett’s test values were significant.
After inspection of these values, the data collected were believed to be suitable
for EFA.
Exploratory Factor Analysis (EFA)
The main purposes for using EFA in this study was to identify relationships
among cultural factors, reduce and summarize these factors to a smaller number of
factors, and identify which of these factors influence each of the three KLPIs. The
conceptual framework of this study had identified 34 independent variables influencing
the laborer performance indicators. To ensure the accuracy of the analysis, EFA was
conducted independently on each indicator.
Factor analysis on the first indicator (Quality)
As mentioned previously, the data collected was suitable for Exploratory Factor
Analysis (EFA). The KMO test and the Bartlett’s test values in Table 4-4 show
satisfactory results. After checking the suitability and factorability of the data, the factor
analysis was performed in SPSS 23.
Initially, the extraction method used was principal component analysis as a
default setting in SPSS 23. In addition, small coefficient (factors loading lees than 0.40)
were suppressed, and the factors were not rotated. The first solution suggested to retain
nine factors based on the Eigenvalues Table 4-5. However, the Scree Plot as shown in
Figure 4-13 suggested retaining only three factors. Since SPSS 23 was used, it was
69
possible to repeat the analysis process using a different number of factors (9 to 3)
(Taherdoost et al. 2014; Thompson 2004; Williams et al. 2012).
Before reaching the acceptable final solutions, several unsuccessful attempts
were made. The final solution with three factors was generated by principal axis
factoring (PAF) with promax rotation to help maximize the number of high loading items.
Additionally, (PAF) doesn’t require the data to be normally distributed (Brown 2015;
Fabrigar et al. 1999). The three factors remaining explained 30.729% of the total
variance. Out of the 34 items, 23 items had factor loadings over 0.40.
Reliability of each factor was assessed by using Cronbach’s coefficient alpha.
The first two factors had Cronbach’s alpha values of 0.835 and 0.840 respectively,
which indicate a good internal consistency. On the other hand, the third factor had a
poor Cronbach’s alpha value of 0.525 (Gliem and Gliem 2003). Table 4-6 details all the
information related to eigenvalues, total variance explained, Cronbach’s alpha, and
factor loadings for the three factors.
The first reliable factor represented cultural factors that positively influence the
quality of the work done by laborers. It accounted for 16.314% of the total variance and
comprised 11 items with a moderate loadings range (0.412 to 0.689). The items related
to this factor were mixed from Geert Hofstede (1984; 2010) national culture dimensions
(9 items) and Edward Hall (Hall 1976; Hall and Hall 1990) dimensions (2 items). Six of
the positively influential culture factors were directly related to the laborers, such as their
level of stress and anxiety, the type of the communication they typically use, and their
involvement in decision-making. The other five factors were linked to the environment
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they work in, such as their relationships with their mangers and the approaches they
use to solve conflict. (Refer to Table 4-6)
The second reliable factor included cultural factors that negatively influence
quality. It accounted for 10.890% of the total variance and was comprised of nine items
with loadings ranging between 0.433 and 0.747. These items were also diverse; six
were from Geert Hofstede national culture dimensions, and three were from Edward
Hall dimensions. Some of the items were directly related to the laborers while others
were related to the work environment, similar to the items on the first factors. (Refer to
Table 4-6)
The third factor was comprised of cultural factors that could be either positively or
negatively influence quality. The three items accounted for 3.525% of the total variance
with a boor Cronbach’s alpha value of 0.525. Thus, this factor was neglected when
discussing culture factors influencing quality in Chapter 5.
Factor analysis on the second indicator (Productivity)
By following the same process as in the first indicator, the factor analysis was
performed on the second indicator. Initially, the extraction method used was principal
component analysis without factors rotation. Factors loadings less than 0.40 were
disregarded. The Scree Plot as shown in Figure 4-14 identified only three factors.
The final solution with three factors was produced by using principal axis
factoring (PAF) with promax rotation. The three factors remaining explained 30.358% of
the total variance. Out of the 34 items, 21 items had factor loadings over 0.40. Reliability
analysis revealed that the first two factors had Cronbach’s alpha values of 0.841 and
0.835 respectively. On the other hand, the third factor had a poor Cronbach’s alpha
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value of 0.439. Table 4-7 shows all the information related to eigenvalues, total variance
explained, Cronbach’s alpha, and factor loadings for the three factors.
The first represented cultural factors that negatively influence labor productivity.
Eight items with loadings range between 0.431and 0.756 were included in this factor,
and accounted for 16.412% of the total variance. The items related to this factor were
mixed from the six national culture dimensions. (Refer to Table 4-7)
The second reliable factor comprised cultural factors that positively influence
productivity. It accounted for 10.860% of the total variance and comprised of 11 items
with loadings range between 0.424 and 0.635. These items were also mixed as in the
previous factors. (Refer to Table 4-7)
The third factor accounted for 3.083% of the total variance with a boor
Cronbach’s alpha value of 0.439. It included two items that could be either positively or
negatively influence productivity. This factor was ignored when discussing culture
factors influencing productivity in Chapter 5.
Factor analysis on the third indicator (Safety)
The factor analysis was performed on the third indicator. Initially, the extraction
method used was principal component analysis without factors rotation. Factors
loadings lees than 0.40 were ignored. The Scree Plot as shown in Figure 4-15 identified
only three factors.
The final solution with three factors was formed by using principal axis factoring
(PAF) with promax rotation. The three factors remaining explained 29.881% of the total
variance. Out of the 34 items, 22 items had factor loadings over 0.40. Reliability
analysis revealed that the first two factors had Cronbach’s alpha values of 0.857 and
0.810 respectively. In contrast, the third factor had a poor Cronbach’s alpha value of
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0.383. Table 4-8 shows all the information related to eigenvalues, total variance
explained, Cronbach’s alpha, and factor loadings for the three factors.
The first factor represented cultural factors that positively influence construction
safety. 12 items with loadings range between 0.434 and 0.736 were included in this
factor and accounted for 16.752% of the total variance. The items related to this factor
were mixed from the six national culture dimensions. (Refer to Table 4-8)
The second reliable factor comprised cultural factors that negatively influence
construction safety. It accounted for 9.831% of the total variance and comprised of eight
items with loadings range between 0.411 to 0.692. These items were also mixed as in
the previous factors. (Refer to Table 4-8)
The third factor accounted for 3.298% of the total variance with a boor
Cronbach’s alpha value of 0.383. It included two items that could be either positively or
negatively influence productivity. This factor was disregarded when discussing culture
factors influencing productivity in Chapter 5.
Comparisons Based on Educational Background
The scores of the three culture factors influencing quality, productivity, and safety
were calculated to compare the different groups of participants. Since the data was not
normally distributed, the Kruskal-Wallis H test was used as nonparametric test to
determine if there were statistically significant differences between these groups. Three
separate Kruskal-Wallis H tests were conducted.
The first test was performed on the three factors influencing quality. As reported
in Table 4-9, results indicate that there were no significant differences among
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participants with different educational backgrounds on the three factors, χ2
(4) = 1.643,
4.037, and 1.354, p = 0.801, 0.401, and .0852 correspondingly.
The second test was performed on the three factors influencing productivity. As
shown in Table 4-10, results indicate that there were no significant differences among
participants with different educational backgrounds on the three factors, χ2
(4) = 5.831,
1.080, and 2.875, p = 0.212, 0.897, and .0579 correspondingly.
The third test was performed on the three factors influencing safety. As reported
in Table 4-11, results indicate that there were no significant differences among
participants with different educational backgrounds on the three factors, χ2
(4) = 2.653,
7.171, and 5.834, p = 0.617, 0.127, and .212 respectively.
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Figure 4-1. Survey completion percent
Figure 4-2. Power distance (PDI) univariate outliers
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
0
50
100
150
200
250
300
350
400
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% Complete
Survey Completion Percent
Count % Rate
75
Figure 4-3. Individualism (IDV) univariate outliers
Figure 4-4. Masculinity (MAS) univariate outliers
76
Figure 4-5. Uncertainty avoidance (UAI) univariate outliers
Figure 4-6. Time handling and context univariate outliers
77
Figure 4-7. Educational background of the respondents
Figure 4-8. Job positions of the respondents
0
20
40
60
80
100
120
140
Architecture Civil Engineering Mechanical Engineering
Electrical Engineering
other
Educational Background
0
20
40
60
80
100
120
140
160
Field Superintendent
Engineer Project Coordinator
Project Manager other
Job Position
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Figure 4-9. Years of experience of the respondents
Figure 4-10. Projects classification
0
20
40
60
80
100
120
140
Under 5 years 5 - 10 years 11 - 20 years Over 20 years
Years Experience
0
50
100
150
200
250
Bridge and highway
construction
Building construction
Infrastructure construction
Industrial construction
other
Project Classification
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Figure 4-11. Number of laborers under the supervision of the respondents
Figure 4-12. Frequency of laborers nationalities
0
20
40
60
80
100
120
140
160
180
Less than 10 11 -- 20 21 - 30 31 - 40 More than 40
Number of Laborers
0
50
100
150
200
250
300
Laborers Nationality
80
Figure 4-13. Scree plot of the first indicator (Quality)
Figure 4-14. Scree plot of the second indicator (Productivity)
81
Figure 4-15. Scree plot of the third indicator (Safety)
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Table 4-1. Mean ranking for cultural factors influencing quality
Factors Label Mean Rank
Laborers are rewarded based on their performance MAS_2H 4.210 1
Have security of employment UAI_3H 4.040 2
Managers are democratic and consultative PDI_2L 3.950 3
Laborers act according to their group’s interests IDV_1L 3.940 4
Laborers communicate directly CT_2L 3.910 5
Conflict is resolved by negotiation MAS_1L 3.850 6
Large number of supervisory personnel PDI_4H 3.800 7
Laborers do one thing at a time (Monochromic) TH1 3.670 8
Tasks are more important than relationships IDV_3H 3.640 9 Laborers acquire information and knowledge from
research CT_1L 3.580 10 Relationship between laborers and managers is like a
family link IDV_2L 3.570 11
Low stress and low anxiety UAI_1L 3.550 12
Power and decisions are centralized in few hands PDI_1H 3.540 13
Laborers work in order to live MAS_3L 3.490 14
Laborers are involved in decision making PDI_3L 3.380 15
Laborers live in order to work MAS_3H 3.330 16 Laborers are involved in risk taking and unfamiliar
situations UAI_2L 3.310 17
There is a wide range in salary PDI_5H 3.290 18
High stress and high anxiety UAI_1H 3.270 19 Relationship between laborers and managers is a
business relationship IDV_2H 3.250 20
Laborers avoid risk taking and unfamiliar situations UAI_2H 3.250 21
Laborers do several things at once (Polychromic) TH2 3.250 22
Managers are autocratic and paternalistic PDI_2H 3.220 23
There is a narrow range in salary PDI_5L 3.190 24
Power and decisions are decentralized PDI_1L 3.170 25 Laborers acquire information and knowledge from
personal networks CT_1H 3.120 26
Small number of supervisory personnel PDI_4L 3.070 27
Laborers act according to their own interests IDV_1H 3.050 28
Relationships are more important than tasks IDV_3L 3.030 29
Laborers are NOT involved in decision making PDI_3H 3.020 30
Laborers are rewarded based on their need MAS_2L 3.020 31
No security of employment UAI_3L 3.000 32
Conflict is resolved by a good fight MAS_1H 2.910 33
Laborers communicate indirectly CT_2H 2.890 34
83
Table 4-2. Mean ranking for cultural factors influencing productivity
Factors Label Mean Rank
Laborers are rewarded based on their performance MAS_2H 4.310 1
Have security of employment UAI_3H 4.090 2
Managers are democratic and consultative PDI_2L 4.060 3
Laborers act according to their group’s interests IDV_1L 3.980 4
Laborers communicate directly CT_2L 3.900 5
Conflict is resolved by negotiation MAS_1L 3.860 6
Large number of supervisory personnel PDI_4H 3.760 7
Tasks are more important than relationships IDV_3H 3.670 8 Relationship between laborers and managers is like a
family link IDV_2L 3.640 9
Laborers do one thing at a time (Monochromic) TH1 3.610 10
Power and decisions are centralized in few hands PDI_1H 3.600 11
Low stress and low anxiety UAI_1L 3.580 12
Laborers work in order to live MAS_3L 3.560 13 Laborers acquire information and knowledge from
research CT_1L 3.510 14
Laborers are involved in decision making PDI_3L 3.480 15
There is a wide range in salary PDI_5H 3.440 16 Laborers are involved in risk taking and unfamiliar
situations UAI_2L 3.410 17
Laborers live in order to work MAS_3H 3.390 18
Laborers do several things at once (Polychromic) TH2 3.350 19
High stress and high anxiety UAI_1H 3.290 20 Relationship between laborers and managers is a
business relationship IDV_2H 3.280 21
Power and decisions are decentralized PDI_1L 3.270 22
Managers are autocratic and paternalistic PDI_2H 3.270 23
There is a narrow range in salary PDI_5L 3.260 24
Laborers avoid risk taking and unfamiliar situations UAI_2H 3.240 25
Laborers act according to their own interests IDV_1H 3.180 26 Laborers acquire information and knowledge from
personal networks CT_1H 3.090 27
Small number of supervisory personnel PDI_4L 3.070 28
Laborers are rewarded based on their need MAS_2L 3.050 29
Laborers are NOT involved in decision making PDI_3H 3.040 30
Relationships are more important than tasks IDV_3L 3.040 31
No security of employment UAI_3L 3.020 32
Conflict is resolved by a good fight MAS_1H 2.960 33
Laborers communicate indirectly CT_2H 2.840 34
84
Table 4-3. Mean ranking for cultural factors influencing safety
Factors Label Mean Rank
Laborers are rewarded based on their performance MAS_2H 4.010 1
Have security of employment UAI_3H 3.910 2
Large number of supervisory personnel PDI_4H 3.900 3
Managers are democratic and consultative PDI_2L 3.840 4
Laborers act according to their group’s interests IDV_1L 3.830 5
Laborers communicate directly CT_2L 3.810 6
Conflict is resolved by negotiation MAS_1L 3.700 7
Tasks are more important than relationships IDV_3H 3.530 8
Laborers do one thing at a time (Monochromic) TH1 3.530 9 Laborers acquire information and knowledge from
research CT_1L 3.530 10
Low stress and low anxiety UAI_1L 3.480 11
Relationship between laborers and managers is like a family link IDV_2L 3.460 12
Laborers avoid risk taking and unfamiliar situations UAI_2H 3.400 13
Laborers work in order to live MAS_3L 3.350 14 Laborers are involved in risk taking and unfamiliar
situations UAI_2L 3.330 15
Power and decisions are centralized in few hands PDI_1H 3.320 16
Laborers are involved in decision making PDI_3L 3.260 17
Laborers live in order to work MAS_3H 3.210 18 Relationship between laborers and managers is a
business relationship IDV_2H 3.130 19
High stress and high anxiety UAI_1H 3.120 20
Laborers do several things at once (Polychromic) TH2 3.120 21
Power and decisions are decentralized PDI_1L 3.090 22
Managers are autocratic and paternalistic PDI_2H 3.040 23 Laborers acquire information and knowledge from personal
networks CT_1H 3.020 24
Small number of supervisory personnel PDI_4L 3.010 25
There is a wide range in salary PDI_5H 3.000 26
Relationships are more important than tasks IDV_3L 2.990 27
There is a narrow range in salary PDI_5L 2.980 28
Laborers are NOT involved in decision making PDI_3H 2.940 29
Laborers are rewarded based on their need MAS_2L 2.890 30
Conflict is resolved by a good fight MAS_1H 2.880 31
Laborers act according to their own interests IDV_1H 2.860 32
No security of employment UAI_3L 2.820 33
Laborers communicate indirectly CT_2H 2.770 34
85
Table 4-4. Results of KMO and Bartlett’s tests
Indicators KMO (0.5 or greater)
Bartlett’s test (significant)
Quality 0.828 0.000
Productivity 0.835 0.000
Safety 0.843 0.000
Table 4-5. Total variance explained of the initial run for the first indicator (Quality)
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 6.208 18.260 18.260 6.208 18.260 18.260
2 4.265 12.545 30.805 4.265 12.545 30.805
3 1.924 5.660 36.465 1.924 5.660 36.465
4 1.478 4.347 40.811 1.478 4.347 40.811
5 1.390 4.088 44.899 1.390 4.088 44.899
6 1.265 3.721 48.621 1.265 3.721 48.621
7 1.226 3.605 52.226 1.226 3.605 52.226
8 1.143 3.363 55.589 1.143 3.363 55.589
9 1.045 3.073 58.662 1.045 3.073 58.662
10 .978 2.876 61.539
… … … …
34 .236 .693 100.000
86
Table 4-6. Factor analysis results for the first indicator (Quality)
Variable's name Factor
F1 F2 F3
UAI_1L Low stress and low anxiety 0.689
IDV_1L Laborers act according to their group’s interests 0.638
UAI_3H Have security of employment 0.622
CT_2L Laborers communicate directly 0.609
IDV_2L Relationship between laborers and managers is like a family link 0.555
MAS_1L Conflict is resolved by negotiation 0.519
MAS_2H Laborers are rewarded based on their performance 0.502
CT_1L Laborers acquire information and knowledge from research 0.499
PDI_2L Managers are democratic and consultative 0.450
PDI_3L Laborers are involved in decision making 0.440
UAI_2L Laborers are involved in risk taking and unfamiliar situations 0.412
UAI_1H High stress and high anxiety 0.747
UAI_3L No security of employment 0.741
MAS_1H Conflict is resolved by a good fight 0.713
IDV_1H Laborers act according to their own interests 0.654
CT_2H Laborers communicate indirectly 0.603
TH2 Laborers do several things at once (Polychromic) 0.561
PDI_2H Managers are autocratic and paternalistic 0.518
IDV_3L Relationships are more important than tasks 0.456
CT_1H Laborers acquire information and knowledge from personal networks 0.433
IDV_3H Relationship between laborers and managers is a business relationship 0.587
IDV_2H Tasks are more important than relationships 0.439
PDI_4H Large number of supervisory personnel 0.426
Eigenvalue 6.208 3.703 1.199
Variance Explained (%) 16.314 10.98 3.525
Cronbach’s Alpha 0.835 0.840 0.525
Total Variance Explained (%) 30.729
Keiser-Meyer-Olkin Measure 0.828
87
Table 4-7. Factor analysis results for the second indicator (Productivity)
Variable's name Factor
F1 F2 F3
UAI_1H High stress and high anxiety 0.756
UAI_3L No security of employment 0.738
MAS_1H Conflict is resolved by a good fight 0.727
CT_2H Laborers communicate indirectly 0.660
IDV_1H Laborers act according to their own interests 0.654
PDI_2H Managers are autocratic and paternalistic 0.577
PDI_5H There is a wide range in salary 0.456
TH2 Laborers do several things at once (Polychromic) 0.431
IDV_1L Laborers act according to their group’s interests 0.635
UAI_1L Low stress and low anxiety 0.627
CT_2L Laborers communicate directly 0.599
MAS_1L Conflict is resolved by negotiation 0.594
IDV_2L Relationship between laborers and managers is like a family link 0.548
PDI_5L There is a narrow range in salary 0.502
UAI_2L Laborers are involved in risk taking and unfamiliar situations 0.468
PDI_3L Laborers are involved in decision making 0.457
PDI_2L Managers are democratic and consultative 0.454
MAS_2H Laborers are rewarded based on their performance 0.452
CT_1L Laborers acquire information and knowledge from research 0.424
IDV_3H Tasks are more important than relationships 0.510
PDI_4H Large number of supervisory personnel 0.471
Eigenvalue 5.416 3.584 1.018
Variance Explained (%) 16.412 10.862 3.083
Cronbach’s Alpha 0.841 0.835 0.439
Total Variance Explained (%) 30.358
Keiser-Meyer-Olkin Measure 0.835
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Table 4-8. Factor analysis results for the second indicator (Safety)
Variable's name Factor
F1 F2 F3
UAI_3H Have security of employment 0.736 CT_2L Laborers communicate directly 0.716 UAI_1L Low stress and low anxiety 0.678
IDV_1L Laborers act according to their group’s interests 0.651
MAS_1L Conflict is resolved by negotiation 0.592
CT_1L Laborers acquire information and knowledge from research 0.563
MAS_2H
Laborers are rewarded based on their performance 0.562
IDV_2L
Relationship between laborers and managers is like a family link 0.549
PDI_2L Managers are democratic and consultative 0.494
TH1 Laborers do one thing at a time (Monochromic) 0.471
PDI_4H Large number of supervisory personnel 0.466 PDI_3L Laborers are involved in decision making 0.434 UAI_1H High stress and high anxiety
0.692
MAS_1H Conflict is resolved by a good fight
0.687 UAI_3L No security of employment
0.633
CT_2H Laborers communicate indirectly
0.621
TH2 Laborers do several things at once (Polychromic)
0.571
IDV_1H Laborers act according to their own interests
0.568
UAI_2L Laborers are involved in risk taking and unfamiliar situations
0.502
CT_1H
Laborers acquire information and knowledge from personal networks
0.426
PDI_2H Managers are autocratic and paternalistic
0.411 PDI_5H There is a wide range in salary
0.498
IDV_2H Relationship between laborers and managers is a business relationship 0.427
Eigenvalue 5.696 3.342 1.121
Variance Explained (%) 16.752 9.831 3.298
Cronbach’s Alpha 0.857 0.810 0.383
Total Variance Explained (%) 29.881
Keiser-Meyer-Olkin Measure 0.843
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Table 4-9. Kruskal-Wallis test on quality
Educational Background
Factor1 Factor2 Factor3
N Mean Rank N Mean Rank N Mean Rank
Architecture 99 164.07 99 166.89 99 176.26
Civil Eng. 129 172.44 129 183.16 129 171.11
Mechanical Eng. 48 165.52 48 164.2 48 156.85
Electrical Eng. 33 179.95 33 149.11 33 175.24
other 32 185.61 32 167.47 32 171.13
Total 341 341 341
Chi-Square 1.643 4.037 1.354
df 4 4 4
Asymp. Sig. 0.801 0.401 0.852
Table 4-10. Kruskal-Wallis test on productivity
Educational Background
Factor1 Factor2 Factor3
N Mean Rank N Mean Rank N Mean Rank
Architecture 99 167.39 99 174.7 99 175.39
Civil Eng. 129 184.07 129 172.47 129 171.22
Mechanical Eng. 48 161.31 48 158.72 48 157.39
Electrical Eng. 33 141.65 33 176.38 33 189.24
other 32 174.28 32 166.5 32 158.14
Total 341 341 341
Chi-Square 5.831 1.080 2.875
df 4 4 4
Asymp. Sig. 0.212 0.897 0.579
Table 4-11. Kruskal-Wallis test on safety
Educational Background
Factor1 Factor2 Factor3
N Mean Rank N Mean Rank N Mean Rank
Architecture 99 160.16 99 160.76 99 162.54
Civil Eng. 129 170.57 129 186.61 129 181.74
Mechanical Eng. 48 175.92 48 163.63 48 148.25
Electrical Eng. 33 187.94 33 144.35 33 171.64
other 32 181.41 32 178.3 32 187.38
Total 341 341 341
Chi-Square 2.653 7.171 5.834
df 4 4 4
Asymp. Sig. 0.617 0.127 0.212
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CHAPTER 5 DISCUSSION
The main goal of this study was to answer the question “Does national culture
influence construction laborers’ performance in Saudi Arabia?” The answer of this
question involved investigation of the influence of national culture on three laborer
performance indicators (quality, productivity, and safety). Another goal was to identify
the major cultural factors affecting construction laborer performance. This chapter
summarizes the research findings, limitations, and contains a recommendation for
future research.
Research Findings
Based on the participants’ perceptions, national cultural has some influence on
laborer performance in Saudi Arabia. The study found that national cultural factors could
positively or negatively influence quality, productivity, and safety of the construction
industry. Additionally, no significant differences among the participants’ perceptions
were detected.
Cultural Factors Influencing Quality
The cultural factors that influenced quality included items from the dimensions of
national culture, suggested by Geert Hofstede (1984; 2010), and Edward Hall (Hall
1976; Hall and Hall 1990). These dimensions are power distance (PDI), individualism
(IDV), masculinity (MAS), uncertainty avoidance (UAI), and context (CT).
As shown in Table 5-1, two of the items can be found in cultures with low power
distance such as the USA, Australia, and most of the European countries. The first item
describes the mangers as being democratic and consultative. This type of mangers is
rarely found in culture with high power distance which most of laborers in Saudi Arabia
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came from. The second item is defined as laborers are involved in decision making.
Both items are related to each other; usually if the manger is democratic, laborers will
have the chance to be involved in decision making. Laborers’ involvement could
improve the methods they employ in doing their work, which leads to improved quality of
their work. On the other hand, only one item that represents high power distance culture
was found to negatively influence quality. This item delineates managers as autocratic
and paternalistic. The result indicated that this item is found in all the countries
presented in the study except Pakistan.
The second dimension represents cultures with low individualism characteristics.
As in Geert Hofstede’s model, most of the laborers working in Saudi Araba’s
construction industry have low individualism culture (collectivism). The two items related
to this dimension are laborers act according to their group’s interests, and family
relationships among laborers and mangers. Such characteristics will encourage and
support laborers to work as a group to improve the quality of their work. In the literature,
it was not clear if there is any direct relationship between the quality of the work done by
laborers and this dimension. However, Lagrsoen (2003) found correlations between the
IDV dimension and the implementation of TQM. Additionally, two items from the
individualism dimension were found to have negative influence on quality. These
include laborers act according to their own interests, and relationships are more
important than tasks. The first item is not linked to culture of laborers working in Saudi
Arabia because it belongs to a high individualism culture. On the other hand, the second
item is found in all the countries presented in the study except the Philippines. Labor,
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acting in its own self-interests, was shifted away from cooperation and communication
which might affect quality.
The third dimension is masculinity. This dimension includes conflict resolution by
negotiation, and laborers are rewarded based on their performance. The first item,
which is considered as a low masculinity, can be found in cultures of West and East
African countries, Indonesia, and Turkey. The second item is found in countries with
high masculinity such as China and the Philippines. Only one item found in culture with
high masculinity, such as China and Philippines, negatively influenced quality. It is
solving conflict through a good fight. Fighting to solve a conflict consumes labor time
and effort and could lead to miscommunication among laborers.
Fourth, the uncertainty avoidance dimension was represented by three items.
The first two can be found in low uncertainty avoidance cultures such as China, India,
Indonesia, and Philippines. These items included having low stress and anxiety and
involved risk taking and unfamiliar situations. The third item was having security of
employment and can be found in high uncertainty avoidance cultures such as Arab
countries, Bangladesh, Pakistan, and Turkey. According to Lagrsoen (2003),
uncertainty avoidance influences quality management. Conversely negatively
influencing factors include two items. The first one, having high stress and anxiety, is
related to high uncertainty avoidance cultures such as Arab countries, Bangladesh,
Pakistan, Turkey. The second item, not having security of employment, is related to low
uncertainty avoidance cultures such as China, India, Indonesia, and the Philippines.
Fifth, the context dimension includes laborers that communicate directly and
laborers acquire information and knowledge from research. These two items represent
93
low context cultures such as the USA, and much of Western Europe. On the contrary,
laborers that communicate indirectly and laborers that acquire information and
knowledge from personal networks negatively influence quality. These two items are
found in high culture context such as those in the study.
Finally, the time handling dimension includes laborers who do several things at
once negatively influence quality. This known as Polychromic culture and can be found
in all the cultures of the laboerrs working in Saudi Arabia.
Cultural Factors Influencing Productivity
Similarly, positively and negatively influential culture factors on productivity
comprises factors from all the cultural dimensions. Table 5-2 shows the nationality of
laborers and the culture factors influencing productivity.
The results revealed that three items which related to the low power distance
dimension positively influence productivity. These items include: managers are
democratic and consultative, laborers are involved in decision making, and a narrow
range in salary. On the other hand, high power distance culture negatively influence
productivity. The two items that represent high power distance are managers are
autocratic and paternalistic and there is a wide range in salary. All the countries
presented in the study have high power distance except Pakistan. It can be noted that
salary range either positively or negatively influences productivity but not quality or
safety.
Two items from the low individualism dimension include acting as a team and
family relationships among laborers and mangers. These two items influence
productivity positively. All the countries in the study have low individualism, which
means their laborers work as group and their relationships are strong. On the negative
94
side, one high Individualism item, laborers act according to their own interests, had a
negative influence on quality.
The masculinity dimension positively influenced productivity through two items:
conflict is resolved by negotiation, and laborers are rewarded based on their
performance. The first item represents low masculinity cultures such as West and East
African countries, Indonesia, and Turkey. While the other item represents high
masculinity cultures such as China and the Philippines. Similar to its influence on
quality, solving conflict through a good fight can be found in cultures with high
masculinity, such as China and Philippines, and negatively influences quality.
The low uncertainty avoidance dimension includes having low stress and anxiety,
and being involved in risk taking and unfamiliar situations, positively influences
productivity. Low uncertainty avoidance characterizes countries such as China, India,
Indonesia, and the Philippines. However, one item of low uncertainty avoidance
influences productivity negatively. Moreover, high stress and anxiety also influence
productivity negatively. It relates to high uncertainty avoidance cultures such as Arab
countries, Bangladesh, Pakistan, Turkey.
The context dimension includes: laborers communicate directly, and laborers
acquire information and knowledge from research. Both have positive influence on
productivity. These two items represent low context cultures such as the USA and much
of Western Europe. On the contrary, laborers that communicate indirectly negatively
influence quality. This item is found in high culture contexts such as those in the study.
95
Finally, the time handling dimension that includes laborers who do several things
at once has a negative influence on labor productivity. This known as Polychromic
culture and is found in all cultures of the labors working in Saudi Arabia.
Cultural Factors Influencing Safety
Likewise, cultural factors can influence safety both positively and negatively.
Table 5-3 shows the nationality of laborers and the culture factors influencing safety.
The low power distance dimension, which includes managers are democratic and
consultative, and laborers are involved in decision making, has some positive influence.
Conversely, the high power distance dimension, which includes managers are
autocratic, has a negative influence on safety. Most of the laborers in Saudi Arabia
came from countries with high power distance. This results support the findings of Ali
(2006) and Mohamed et al. (2009). They found a negative correlation existed between
power distance and workers’ attitudes and perceptions toward safety. They suggested
that if power distance is large between laborers and management, the laborer’s
awareness of safety issue decreases.
The low Individualism dimension includes laborers acting as a team, and have
family relationships among themselves and mangers, has a positive influence on safety.
The findings of Ali (2006) and Mohamed et al. (2009) revealed that a collectivism
environment made labor have greater awareness of safety and beliefs that led to safer
work behavior. On the contrary, high Individualism could negatively influence safety.
The masculinity dimension positively influences safety through two items: conflict
is resolved by negotiation, and laborers are rewarded based on their performance. The
first item represents low masculinity cultures such as West and East African countries,
Indonesia, and Turkey. While the other item represents high masculinity cultures such
96
as China and the Philippines. Only one item found in cultures with high masculinity such
as China and Philippines, conflict is resolved by a good fight, negatively influences
safety. In their findings, Ali (2006) and Mohamed et al. (2009) found that laborers
working in environments with characteristics such as low masculinity would have
additional safety awareness and beliefs.
Two items from the uncertainty avoidance dimension have positive influence on
safety. The first item is having security of employment which is found in high uncertainty
avoidance cultures such as Arab countries, Bangladesh, Pakistan, and Turkey. The
second item is having low stress and anxiety, and is found in low uncertainty avoidance
cultures such as China, India, Indonesia, and the Philippines. On the other hand, three
items from this uncertainty avoidance dimension negatively influence safety. Two of
them represent low uncertainty avoidance cultures such as China, India, Indonesia, and
the Philippines. The other represents high uncertainty avoidance cultures such as Arab
countries, Bangladesh, Pakistan, and Turkey. Similar to their previous finding, Ali (2006)
and Mohamed et al. (2009) found that laborers with high uncertainty avoidance
characteristics have more safety awareness and beliefs.
The context dimension includes laborers communicate directly and laborers
acquire information and knowledge from research, and both have positive influence on
safety. These two items represent low context cultures such as the USA and much of
Western Europe. On the contrary, laborers that communicate indirectly and acquire
information and knowledge from personal networks negatively influence quality. These
items are found in high culture context such as those in the study.
97
Finally, the results revealed that when laborers do one thing at a time, safety will
be influenced positively. Such a behavior is usually found in Monochromic culture. On
the opposite side, Polychromic culture negatively influences safety.
Limitation and Future Research
There were a few limitations associated with the investigation of the influence of
national culture on laborer performance. Firstly, those who participated in the survey
were mainly working in the construction industry in Saudi Arabia, hence the result might
not directly reflect the parameters of those who are working outside Saudi Arabia.
Hopefully, future research will conduct surveys on more and different countries in order
to compare the result with this study. Additionally, laborers’ perceptions were not taken
into account. This limitation would be improved if the laborers participated in the survey.
Another limitation arises from the survey which did not specify if the factors on
the survey have a positive or a negative influence on the three labor performance
indicators. This limitation could be the reason why it was not clear how each dimension
separately influenced laborers performance. Instead the result of factor analysis
revealed factors with mix dimensions.
In addition, the results of each factor analysis did not reach the recommended
cumulative percentage of variance (50% of the variance). The nature of the survey
questions cloud be the primary reason, since each factor was represented by two
variables that are contrary to each other.
Lastly, other cultural factors related to the laborers, such as the language
spoken, degree of education, and learning skills, could be considered for future
investigations of the influence of national culture on the performance of construction
98
laborers. Additionally, construction site field observations could be done regarding the
issue of how the national culture of each laborer influences the laborer’s performance.
99
Table 5-1. Nationality and cultural factors influencing quality.
Nationality
Factors
Arab Country
Africa West
Africa East
Bangladesh China India Indonesia Pakistan Philippines Turkey
Positive Influencing Factors
UAI_1L X X X X
IDV_1L X X X X X X X X X X
UAI_3H X X X X
CT_2L
IDV_2L X X X X X X X X X X
MAS_1L X X X X
MAS_2H X X
CT_1L
PDI_2L
PDI_3L
UAI_2L X X X X
Negative Influencing Factors
UAI_1H X X X X
UAI_3L X X X X
MAS_1H X X
IDV_1H
CT_2H X X X X X X X X X X
TH2 X X X X X X X X X X
PDI_2H X X X X X X X X X
IDV_3L X X X X X X X X X
CT_1H X X X X X X X X X X
100
Table 5-2. Nationality and cultural factors influencing productivity
Nationality
Factors
Arab Country
Africa West
Africa East
Bangladesh China India Indonesia Pakistan Philippines Turkey
Positive Influencing Factors
IDV_1L X X X X X X X X X X
UAI_1L X X X X
CT_2L
MAS_1L X X X X
IDV_2L X X X X X X X X X X
PDI_5L
UAI_2L X X X X
PDI_3L
PDI_2L
MAS_2H X X
CT_1L
Negative Influencing Factors
UAI_1H X X X X
UAI_3L X X X X
MAS_1H X X
CT_2H X X X X X X X X X X
IDV_1H
PDI_2H X X X X X X X X X
PDI_5H X X X X X X X X X
TH2 X X X X X X X X X X
101
Table 5-3. Nationality and cultural factors influencing safety
Nationality
Factors
Arab Country
Africa West
Africa East
Bangladesh China India Indonesia Pakistan Philippines Turkey
Positive Influencing Factors
UAI_3H X X X X
CT_2L
UAI_1L X X X X
IDV_1L X X X X X X X X X X
MAS_1L X X X X
CT_1L
MAS_2H X X
IDV_2L X X X X X X X X X X
PDI_2L
TH1
PDI_4H X X X X X X X X X
PDI_3L
Negative Influencing Factors
UAI_1H X X X X
MAS_1H X X
UAI_3L X X X X
CT_2H X X X X X X X X X X
TH2 X X X X X X X X X X
IDV_1H
UAI_2L X X X X
CT_1H X X X X X X X X X X
PDI_2H X X X X X X X X X
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APPENDIX A SURVEY QUESTIONNAIRE
Dear Participant,
My name is Loai Alkhattabi. I am a PhD student in the Department of Civil and Coastal
Engineering at University of Florida. This survey is conducted as part of research study
at the University of Florida. The purpose of this study is to investigate the influence of
National Culture on construction labor performance in Saudi Arabia.
This survey has the approval of the Ministry of Islamic Affairs, and the researcher
obtained the contacts from the agency. The following questionnaire will approximately
require fifteen minutes to complete. There is no risk associated with this study
procedure nor is there any compensation. The Ministry of Islamic Affairs will not be
informed about who has or has not chosen to participate.
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 construction laborer performance
indicators: quality, productivity, and safety.
Your participation in this study is voluntary. The information will be anonymous, as no
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For more information about your participation rights, please contact IRB02 office,
University of Florida, Box 112250, Gainesville, FL 32611; (352) 392-0433.
103
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.
I accept
104
Part 1 - Respondent’s Profile
Please answer the following question by either ticking “✔” the appropriate box or by
filling out the given blanks.
1. Which of the following describes your background? (Check one)
Architecture
Civil and Structural Engineering
Mechanical Engineering
Electrical Engineering
Other ……………………… (e.g. Safety Engineering, Fire Engineering, etc.)
2. Which of the following describes your current position? (Check one)
Filed Superintendent
Engineer
Project Coordinator
Project Manager
Other …………………… (e.g. Safety manager, Maintenance Manager, etc.)
3. How many years of experience do you have in the Construction Industry? (Check one)
Less than 5 years
5 – 10 years
11 – 20 years
More than 20 years
Part 2 - Project’s Profile
Please answer the following question by either ticking “✔” the appropriate box or by
filling out the given blanks.
4. In which area would your project be classified? (Note: you can choose more than one)
Bridge and highway construction
Building construction
Infrastructure construction
Industrial construction
Other …………………… (e.g. Port and costal construction, etc.)
105
5. How many labors are working under your supervision on the project? (Check one)
Less than 10
11 – 20
21 – 30
31 – 40
More than 40
6. Which country or counties do the labors come from? (Note: you can choose more than
one)
Arab country (e.g. Egypt, Syria, Sudan Yemen, etc.)
Africa West (e.g. Ghana, Mali, Nigeria, Senegal etc.)
Africa East (e.g. Ethiopia, Eritrea, Somalia, Kenya etc.)
Bangladesh
China
India
Indonesia
Pakistan
Philippines
Turkey
Other …………………… (e.g. South Korean, etc.)
Part 3 – Cultural Factors Influencing Labor Performance In your opinion, please indicate the level of influence of the following 34 scenarios on three key labors performance indicators. These are general scenarios, and they are not related to any specific project. The three key labors performance indicators are quality, productivity, and safety. The 5-point measurement scale with definition as below
1 2 3 4 5
Does Not Influence
Slightly Influence
Somewhat Influence
Highly Influence
Very Highly Influence
Example: - S1. Power and decisions are centralized in few hands
1 2 3 4 5
106
Quality O O O O
Productivity O O O O
Safety O O O O
in this example, the first scenario does not influence the quality of the work that is done by a labor, somewhat influence the productivity of the labor, and has very highly influence on the safety.
S1. Power and decisions are centralized in few hands
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S2. Power and decisions are decentralized
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S3. Managers are autocratic and paternalistic (e.g. make all the decisions and don’t trust worker)
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S4. Managers are democratic and consultative (e.g. trust worker and give them chance to make decisions)
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S5. Labors are NOT involved in decision making
107
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S6. Labors are involved in decision making
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S7. Large number of supervisory personnel
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S8. Small number of supervisory personnel
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S9. There is a wide range in salary
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S10. There is a narrow range in salary
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S11. Labors act according to their own interests
1 2 3 4 5
Quality O O O O O
108
Productivity O O O O O
Safety O O O O O
S12. Labors act according to their group interests
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S13. Relationship between laborers and managers is a business relationship
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S14. Relationship between laborers and managers is like a family link
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S15. Tasks are more important than relationships
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S16. Relationships are more important than tasks
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S17. Conflict is resolved by a good fight (the strongest win)
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S18. Conflict is resolved by negotiation
1 2 3 4 5
Quality O O O O O
109
Productivity O O O O O
Safety O O O O O
S19. Laborers are rewarded based on their performance
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S20. Laborers are rewarded based on their need
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S21. Laborers live in order to work
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S22. Laborers work in order to live
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S23. Laborers are under High stress and high anxiety
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S24. Labors are under low stress and low anxiety
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S25. Laborers avoid risk taking and unfamiliar situations
1 2 3 4 5
Quality O O O O O
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Productivity O O O O O
Safety O O O O O
S26. Laborers involve in risk taking and unfamiliar situations
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S27. Laborers have security of employment
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S28. Laborers do NOT have security of employment
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S29. Laborers do several things at once
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S30. Laborers do one thing at a time
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S31. Laborers acquire information and knowledge from personal network (e.g. family members, friends, and teachers)
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S32. Laborers acquire information and knowledge from research (e.g. books and internet)
111
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S33. Laborers communicate Indirectly (ambiguous, indirect, and emotional)
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
S34. Laborers communicate directly (clear, direct, and to the point)
1 2 3 4 5
Quality O O O O O
Productivity O O O O O
Safety O O O O O
112
APPENDIX B DESCRIPTIVE ANALYSIS RESULT
Table B-1. Test of normality for the first indicator (Quality)
Factors Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
PDI 1H 0.190 341 0.000 0.896 341 0.000 PDI 1L 0.167 341 0.000 0.909 341 0.000 PDI 2H 0.165 341 0.000 0.902 341 0.000 PDI 2L 0.246 341 0.000 0.808 341 0.000 PDI 3H 0.150 341 0.000 0.913 341 0.000 PDI 3L 0.199 341 0.000 0.895 341 0.000 PDI 4H 0.242 341 0.000 0.846 341 0.000 PDI 4L 0.176 341 0.000 0.904 341 0.000 PDI 5H 0.158 341 0.000 0.898 341 0.000 PDI 5L 0.200 341 0.000 0.903 341 0.000 IDV 1H 0.177 341 0.000 0.864 341 0.000 IDV 1L 0.238 341 0.000 0.808 341 0.000 IDV 2H 0.173 341 0.000 0.908 341 0.000 IDV 2L 0.222 341 0.000 0.859 341 0.000 IDV 3H 0.228 341 0.000 0.880 341 0.000 IDV 3L 0.154 341 0.000 0.912 341 0.000 MAS 1H 0.185 341 0.000 0.874 341 0.000 MAS 1L 0.266 341 0.000 0.828 341 0.000 MAS 2H 0.319 341 0.000 0.711 341 0.000 MAS 2L 0.182 341 0.000 0.910 341 0.000 MAS 3H 0.175 341 0.000 0.897 341 0.000 MAS 3L 0.202 341 0.000 0.889 341 0.000 UAI 1H 0.192 341 0.000 0.865 341 0.000 UAI 1L 0.230 341 0.000 0.880 341 0.000 UAI 2H 0.184 341 0.000 0.912 341 0.000 UAI 2L 0.198 341 0.000 0.904 341 0.000 UAI 3H 0.291 341 0.000 0.765 341 0.000 UAI 3L 0.185 341 0.000 0.871 341 0.000 TH 1 0.206 341 0.000 0.854 341 0.000 TH 2 0.155 341 0.000 0.899 341 0.000 CT 1H 0.182 341 0.000 0.913 341 0.000 CT 1L 0.206 341 0.000 0.876 341 0.000 CT 2H 0.190 341 0.000 0.906 341 0.000 CT 2L 0.242 341 0.000 0.830 341 0.000
113
Table B-2. Test of normality for the second indicator (Productivity)
Factors Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
PDI 1H 0.223 341 0.000 0.892 341 0.000
PDI 1L 0.178 341 0.000 0.905 341 0.000
PDI 2H 0.159 341 0.000 0.896 341 0.000
PDI 2L 0.265 341 0.000 0.783 341 0.000
PDI 3H 0.178 341 0.000 0.916 341 0.000
PDI 3L 0.219 341 0.000 0.882 341 0.000
PDI 4H 0.231 341 0.000 0.859 341 0.000
PDI 4L 0.167 341 0.000 0.906 341 0.000
PDI 5H 0.167 341 0.000 0.890 341 0.000
PDI 5L 0.202 341 0.000 0.900 341 0.000
IDV 1H 0.176 341 0.000 0.871 341 0.000
IDV 1L 0.257 341 0.000 0.799 341 0.000
IDV 2H 0.190 341 0.000 0.908 341 0.000
IDV 2L 0.210 341 0.000 0.847 341 0.000
IDV 3H 0.224 341 0.000 0.876 341 0.000
IDV 3L 0.167 341 0.000 0.909 341 0.000
MAS 1H 0.183 341 0.000 0.875 341 0.000
MAS 1L 0.243 341 0.000 0.837 341 0.000
MAS 2H 0.362 341 0.000 0.669 341 0.000
MAS 2L 0.189 341 0.000 0.908 341 0.000
MAS 3H 0.171 341 0.000 0.889 341 0.000
MAS 3L 0.195 341 0.000 0.89 341 0.000
UAI 1H 0.188 341 0.000 0.869 341 0.000
UAI 1L 0.193 341 0.000 0.882 341 0.000
UAI 2H 0.226 341 0.000 0.903 341 0.000
UAI 2L 0.205 341 0.000 0.902 341 0.000
UAI 3H 0.301 341 0.000 0.754 341 0.000
UAI 3L 0.173 341 0.000 0.875 341 0.000
TH 1 0.201 341 0.000 0.868 341 0.000
TH 2 0.172 341 0.000 0.882 341 0.000
CT 1H 0.209 341 0.000 0.908 341 0.000
CT 1L 0.196 341 0.000 0.883 341 0.000
CT 2H 0.201 341 0.000 0.904 341 0.000
CT 2L 0.232 341 0.000 0.835 341 0.000
114
Table B-3. Test of normality for the third indicator (Safety)
Factors Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
PDI 1H 0.166 341 0.000 0.905 341 0.000
PDI 1L 0.155 341 0.000 0.910 341 0.000
PDI 2H 0.156 341 0.000 0.905 341 0.000
PDI 2L 0.220 341 0.000 0.832 341 0.000
PDI 3H 0.152 341 0.000 0.913 341 0.000
PDI 3L 0.189 341 0.000 0.893 341 0.000
PDI 4H 0.223 341 0.000 0.833 341 0.000
PDI 4L 0.183 341 0.000 0.901 341 0.000
PDI 5H 0.152 341 0.000 0.904 341 0.000
PDI 5L 0.164 341 0.000 0.911 341 0.000
IDV 1H 0.195 341 0.000 0.877 341 0.000
IDV 1L 0.227 341 0.000 0.823 341 0.000
IDV 2H 0.178 341 0.000 0.909 341 0.000
IDV 2L 0.195 341 0.000 0.861 341 0.000
IDV 3H 0.202 341 0.000 0.888 341 0.000
IDV 3L 0.157 341 0.000 0.908 341 0.000
MAS 1H 0.192 341 0.000 0.875 341 0.000
MAS 1L 0.227 341 0.000 0.858 341 0.000
MAS 2H 0.281 341 0.000 0.784 341 0.000
MAS 2L 0.207 341 0.000 0.899 341 0.000
MAS 3H 0.170 341 0.000 0.900 341 0.000
MAS 3L 0.166 341 0.000 0.898 341 0.000
UAI 1H 0.182 341 0.000 0.860 341 0.000
UAI 1L 0.197 341 0.000 0.888 341 0.000
UAI 2H 0.155 341 0.000 0.894 341 0.000
UAI 2L 0.192 341 0.000 0.897 341 0.000
UAI 3H 0.275 341 0.000 0.79 341 0.000
UAI 3L 0.188 341 0.000 0.879 341 0.000
TH 1 0.201 341 0.000 0.873 341 0.000
TH 2 0.181 341 0.000 0.892 341 0.000
CT 1H 0.190 341 0.000 0.912 341 0.000
CT 1L 0.201 341 0.000 0.879 341 0.000
CT 2H 0.203 341 0.000 0.898 341 0.000
CT 2L 0.221 341 0.000 0.846 341 0.000
115
Table B-4. Frequency and percentage distribution of the respondent’s profile
Variable Frequency Percent
Educational Background Architecture 99 29.0
Civil Engineering 129 37.8
Mechanical Engineering 48 14.1
Electrical Engineering 33 9.7
other 32 9.4
Job Position Filed Superintendent 11 3.2
Engineer 149 43.7
Project Coordinator 18 5.3
Project Manger 99 29.0
Other 46 18.8
Years of Experience Under 5 Years 94 27.6
5 – 10 Years 122 35.8
11 – 20 Years 74 21.7
Over 20 Years 51 15.0
116
Table B-5. Frequency and percentage distribution of the projects’ profile
Variable Frequency Percent
Project Classification Bridge and highway construction 22 6.5
Building construction 201 58.9
Infrastructure construction 42 12.3
Industrial construction 26 7.6
other 50 14.7
Number of Labors Less than 10 80 23.5
11 - 20 48 14.1
21 - 30 26 7.6
31 - 40 28 8.2
More than 40 159 46.6
Labors Nationality Arab Country 283 83.0
Africa West 18 5.3
Africa East 24 7.0
Bangladesh 158 46.3
China 19 5.6
India 223 65.4
Indonesia 26 7.6
Pakistan 226 66.3
Philippines 194 56.9
Turkey 30 8.8
Other 33 9.7
117
Table B-6. Frequency and percentage distribution of the culture factors
Label Quality Productivity Safety
Freq. % Freq. % Freq. %
PDI 1H Does Not Influence 17 5 11 3.2 25 7.3
Slightly Influence 39 11.4 45 13.2 67 19.6
Somewhat Influence 105 30.8 89 26.1 94 27.6
Highly Influence 104 30.5 122 35.8 83 24.3
Very Highly Influence 76 22.3 74 21.7 72 21.1
PDI 1L Does Not Influence 39 11.4 33 9.7 43 12.6
Slightly Influence 67 19.6 67 19.6 73 21.4
Somewhat Influence 91 26.7 85 24.9 91 26.7
Highly Influence 84 24.6 88 25.8 77 22.6
Very Highly Influence 60 17.6 68 19.9 57 16.7
PDI 2H Does Not Influence 40 11.7 33 9.7 49 14.4
Slightly Influence 67 19.6 76 22.3 72 21.1
Somewhat Influence 84 24.6 80 23.5 98 28.7
Highly Influence 78 22.9 71 20.8 59 17.3
Very Highly Influence 72 21.1 81 23.8 63 18.5
PDI 2L Does Not Influence 19 5.6 16 4.7 23 6.7
Slightly Influence 27 7.9 23 6.7 26 7.6
Somewhat Influence 54 15.8 46 13.5 67 19.6
Highly Influence 93 27.3 96 28.2 92 27
Very Highly Influence 148 43.4 160 46.9 133 39
PDI 3H Does Not Influence 44 12.9 36 10.6 51 15
Slightly Influence 76 22.3 69 20.2 75 22
Somewhat Influence 99 29 121 35.5 103 30.2
Highly Influence 72 21.1 74 21.7 68 19.9
Very Highly Influence 50 14.7 41 12 44 12.9
PDI 3L Does Not Influence 35 10.3 34 10 49 14.4
Slightly Influence 53 15.5 41 12 46 13.5
Somewhat Influence 79 23.2 75 22 83 24.3
Highly Influence 97 28.4 108 31.7 92 27
Very Highly Influence 77 22.6 83 24.3 71 20.8
118
Table B-6. Continued
Label Quality Productivity Safety
Freq. % Freq. % Freq. %
PDI 4H Does Not Influence 17 5 18 5.3 15 4.4
Slightly Influence 41 12 37 10.9 30 8.8
Somewhat Influence 53 15.5 65 19.1 62 18.2
Highly Influence 112 32.8 111 32.6 100 29.3
Very Highly Influence 118 34.6 110 32.3 134 39.3
PDI 4L Does Not Influence 39 11.4 36 10.6 45 13.2
Slightly Influence 90 26.4 88 25.8 92 27
Somewhat Influence 82 24 94 27.6 78 22.9
Highly Influence 69 20.2 62 18.2 65 19.1
Very Highly Influence 61 17.9 61 17.9 61 17.9
PDI 5H Does Not Influence 36 10.6 27 7.9 54 15.8
Slightly Influence 63 18.5 59 17.3 71 20.8
Somewhat Influence 88 25.8 86 25.2 97 28.4
Highly Influence 73 21.4 76 22.3 58 17
Very Highly Influence 81 23.8 93 27.3 61 17.9
PDI 5L Does Not Influence 43 12.6 41 12 52 15.2
Slightly Influence 62 18.2 56 16.4 73 21.4
Somewhat Influence 79 23.2 80 23.5 89 26.1
Highly Influence 102 29.9 102 29.9 84 24.6
Very Highly Influence 55 16.1 62 18.2 43 12.6
IDV 1H Does Not Influence 69 20.2 54 15.8 74 21.7
Slightly Influence 74 21.7 77 22.6 86 25.2
Somewhat Influence 62 18.2 59 17.3 64 18.8
Highly Influence 44 12.9 56 16.4 48 14.1
Very Highly Influence 92 27 95 27.9 69 20.2
IDV 1L Does Not Influence 20 5.9 20 5.9 25 7.3
Slightly Influence 26 7.6 21 6.2 33 9.7
Somewhat Influence 52 15.2 59 17.3 53 15.5
Highly Influence 98 28.7 87 25.5 93 27.3
Very Highly Influence 145 42.5 154 45.2 137 40.2
119
Table B-6. Continued
Label Quality Productivity Safety
Freq. % Freq. % Freq. %
IDV 2H Does Not Influence 34 10 25 7.3 40 11.7
Slightly Influence 49 14.4 51 15 55 16.1
Somewhat Influence 114 33.4 126 37 121 35.5
Highly Influence 85 24.9 82 24 70 20.5
Very Highly Influence 59 17.3 57 16.7 55 16.1
IDV 2L Does Not Influence 36 10.6 36 10.6 48 14.1
Slightly Influence 45 13.2 37 10.9 39 11.4
Somewhat Influence 57 16.7 62 18.2 68 19.9
Highly Influence 95 27.9 85 24.9 79 23.2
Very Highly Influence 108 31.7 121 35.5 107 31.4
IDV 3H Does Not Influence 19 5.6 19 5.6 26 7.6
Slightly Influence 38 11.1 34 10 38 11.1
Somewhat Influence 78 22.9 80 23.5 90 26.4
Highly Influence 118 34.6 116 34 104 30.5
Very Highly Influence 88 25.8 92 27 83 24.3
IDV 3L Does Not Influence 48 14.1 43 12.6 52 15.2
Slightly Influence 67 19.6 75 22 70 20.5
Somewhat Influence 105 30.8 105 30.8 103 30.2
Highly Influence 70 20.5 61 17.9 61 17.9
Very Highly Influence 51 15 57 16.7 55 16.1
MAS 1H Does Not Influence 76 22.3 70 20.5 74 21.7
Slightly Influence 78 22.9 79 23.2 84 24.6
Somewhat Influence 62 18.2 63 18.5 64 18.8
Highly Influence 51 15 51 15 47 13.8
Very Highly Influence 74 21.7 78 22.9 72 21.1
MAS 1L Does Not Influence 21 6.2 17 5 25 7.3
Slightly Influence 26 7.6 32 9.4 37 10.9
Somewhat Influence 50 14.7 55 16.1 64 18.8
Highly Influence 129 37.8 114 33.4 105 30.8
Very Highly Influence 115 33.7 123 36.1 110 32.3
120
Table B-6. Continued
Label Quality Productivity Safety
Freq. % Freq. % Freq. %
MAS 2H Does Not Influence 20 5.9 16 4.7 19 5.6
Slightly Influence 13 3.8 14 4.1 26 7.6
Somewhat Influence 36 10.6 32 9.4 53 15.5
Highly Influence 80 23.5 65 19.1 77 22.6
Very Highly Influence 192 56.3 214 62.8 166 48.7
MAS 2L Does Not Influence 41 12 41 12 51 15
Slightly Influence 74 21.7 68 19.9 71 20.8
Somewhat Influence 115 33.7 120 35.2 131 38.4
Highly Influence 58 17 56 16.4 39 11.4
Very Highly Influence 53 15.5 56 16.4 49 14.4
MAS 3H Does Not Influence 37 10.9 38 11.1 47 13.8
Slightly Influence 54 15.8 47 13.8 55 16.1
Somewhat Influence 87 25.5 88 25.8 88 25.8
Highly Influence 84 24.6 79 23.2 82 24
Very Highly Influence 79 23.2 89 26.1 69 20.2
MAS 3L Does Not Influence 26 7.6 18 5.3 32 9.4
Slightly Influence 52 15.2 49 14.4 58 17
Somewhat Influence 78 22.9 87 25.5 90 26.4
Highly Influence 98 28.7 98 28.7 79 23.2
Very Highly Influence 87 25.5 89 26.1 82 24
UAI 1H Does Not Influence 46 13.5 47 13.8 65 19.1
Slightly Influence 78 22.9 69 20.2 75 22
Somewhat Influence 60 17.6 66 19.4 52 15.2
Highly Influence 52 15.2 55 16.1 52 15.2
Very Highly Influence 105 30.8 104 30.5 97 28.4
UAI 1L Does Not Influence 27 7.9 24 7 28 8.2
Slightly Influence 44 12.9 42 12.3 51 15
Somewhat Influence 70 20.5 85 24.9 79 23.2
Highly Influence 114 33.4 93 27.3 94 27.6
Very Highly Influence 86 25.2 97 28.4 89 26.1
121
Table B-6. Continued
Label Quality Productivity Safety
Freq. % Freq. % Freq. %
UAI 2H Does Not Influence 23 6.7 19 5.6 31 9.1
Slightly Influence 57 16.7 53 15.5 46 13.5
Somewhat Influence 123 36.1 145 42.5 103 30.2
Highly Influence 88 25.8 75 22 76 22.3
Very Highly Influence 50 14.7 49 14.4 85 24.9
UAI 2L Does Not Influence 31 9.1 24 7 34 10
Slightly Influence 48 14.1 48 14.1 63 18.5
Somewhat Influence 100 29.3 96 28.2 76 22.3
Highly Influence 108 31.7 111 32.6 92 27
Very Highly Influence 54 15.8 62 18.2 76 22.3
UAI 3H Does Not Influence 21 6.2 19 5.6 28 8.2
Slightly Influence 27 7.9 25 7.3 27 7.9
Somewhat Influence 43 12.6 42 12.3 56 16.4
Highly Influence 77 22.6 76 22.3 68 19.9
Very Highly Influence 173 50.7 179 52.5 162 47.5
UAI 3L Does Not Influence 64 18.8 64 18.8 72 21.1
Slightly Influence 83 24.3 77 22.6 87 25.5
Somewhat Influence 68 19.9 73 21.4 78 22.9
Highly Influence 40 11.7 42 12.3 37 10.9
Very Highly Influence 86 25.2 85 24.9 67 19.6
TH 1 Does Not Influence 26 7.6 23 6.7 34 10
Slightly Influence 45 13.2 48 14.1 45 13.2
Somewhat Influence 66 19.4 82 24 71 20.8
Highly Influence 82 24 73 21.4 89 26.1
Very Highly Influence 122 35.8 115 33.7 102 29.9
TH 2 Does Not Influence 33 9.7 32 9.4 40 11.7
Slightly Influence 75 22 74 21.7 90 26.4
Somewhat Influence 84 24.6 72 21.1 75 22
Highly Influence 71 20.8 67 19.6 60 17.6
Very Highly Influence 78 22.9 96 28.2 76 22.3
122
Table B-6. Continued
Label Quality Productivity Safety
Freq. % Freq. % Freq. %
CT 1H Does Not Influence 33 9.7 32 9.4 39 11.4
Slightly Influence 68 19.9 61 17.9 69 20.2
Somewhat Influence 118 34.6 138 40.5 125 36.7
Highly Influence 70 20.5 64 18.8 62 18.2
Very Highly Influence 52 15.2 46 13.5 46 13.5
CT 1L Does Not Influence 27 7.9 29 8.5 31 9.1
Slightly Influence 44 12.9 49 14.4 45 13.2
Somewhat Influence 74 21.7 77 22.6 75 22
Highly Influence 96 28.2 91 26.7 93 27.3
Very Highly Influence 100 29.3 95 27.9 97 28.4
CT 2H Does Not Influence 45 13.2 42 12.3 52 15.2
Slightly Influence 100 29.3 109 32 108 31.7
Somewhat Influence 89 26.1 92 27 91 26.7
Highly Influence 60 17.6 56 16.4 46 13.5
Very Highly Influence 47 13.8 42 12.3 44 12.9
CT 2L Does Not Influence 16 4.7 14 4.1 20 5.9
Slightly Influence 28 8.2 31 9.1 32 9.4
Somewhat Influence 55 16.1 58 17 65 19.1
Highly Influence 114 33.4 109 32 101 29.6
Very Highly Influence 128 37.5 129 37.8 123 36.1
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APPENDIX C MULTIVARTE ANALYSIS RESULT
Figure C-1. Correlation Matrix of the First Indicator (Quality)
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Figure C-1. Continued
125
Figure C-2. Correlation Matrix of the Second Indicator (Productivity)
126
Figure C-2. Continued
127
Figure C-3. Correlation Matrix of the Third Indicator (Safety)
128
Figure C-3. Continued
129
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BIOGRAPHICAL SKETCH
Loai Abdullah Alkhattabi was born in Jeddah, Saudi Arabia in 1981. He earned a
Bachelor of Architecture from the College of Environmental Design, King Abdul Aziz
University, Jeddah, Saudi Arabia in 2004. Loai’s first career started on September 4th,
2004. He began his career as an Architect at the Engineering Consultant Zaki M. A.
Farsi, Jeddah, Saudi Arabia. In 2011, he received his Master of Building Construction
from M.E. Rinker, Sr. School of Construction Management at University of Florida. He
completed his graduate education by getting his PhD in civil engineering in 2016.