© 2016 loai alkhattabi - university of...

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

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

9

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



3-5 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.

18

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


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

http://www.cdsi.gov.sa/en/797

https://portal.mol.gov.sa/en/statistics

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


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


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


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


60


Figure 3-5. Data Analysis flowchart.

61

Table 3-1. Cultural factors coding

Dimension No. Label Factors


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


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

63

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)

68

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

70

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

71

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

72

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

73

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


(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


(4) = 2.653,

7.171, and 5.834, p = 0.617, 0.127, and .212 respectively.

74

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

78

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

79

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)

82

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









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



Laborers work in order to live MAS_3L 3.560 13 Laborers acquire information and knowledge from

research CT_1L 3.510 14


There is a wide range in salary PDI_5H 3.440 16 Laborers are involved in risk taking and unfamiliar


Laborers live in order to work MAS_3H 3.390 18


High stress and high anxiety UAI_1H 3.290 20 Relationship between laborers and managers is a


Power and decisions are decentralized PDI_1L 3.270 22

Managers are autocratic and paternalistic PDI_2H 3.270 23



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








84

Table 4-3. Mean ranking for cultural factors influencing safety









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


Relationship between laborers and managers is like a family link IDV_2L 3.460 12


Laborers work in order to live MAS_3L 3.350 14 Laborers are involved in risk taking and unfamiliar




Laborers live in order to work MAS_3H 3.210 18 Relationship between laborers and managers is a


High stress and high anxiety UAI_1H 3.120 20


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


There is a wide range in salary PDI_5H 3.000 26






Laborers act according to their own interests IDV_1H 2.860 32



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)


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


UAI_1L Low stress and low anxiety 0.627

CT_2L Laborers communicate directly 0.599


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


MAS_2H Laborers are rewarded based on their performance 0.452


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





88

Table 4-8. Factor analysis results for the second indicator (Safety)


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




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


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





89

Table 4-9. Kruskal-Wallis test on quality


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




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




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

90

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

91

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,

92

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


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


100

Table 5-2. Nationality and cultural factors influencing productivity

Nationality

Factors

Arab Country

Africa West

Africa East




UAI_1L X X X X

CT_2L

MAS_1L X X X X


PDI_5L

UAI_2L X X X X

PDI_3L

PDI_2L

MAS_2H X X

CT_1L


UAI_1H X X X X

UAI_3L X X X X

MAS_1H X X


IDV_1H




101

Table 5-3. Nationality and cultural factors influencing safety

Nationality

Factors

Arab Country

Africa West

Africa East



UAI_3H X X X X

CT_2L

UAI_1L X X X X


MAS_1L X X X X

CT_1L

MAS_2H X X


PDI_2L

TH1


PDI_3L


UAI_1H X X X X

MAS_1H X X

UAI_3L X X X X



IDV_1H

UAI_2L X X X X



102

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

identifying information will be obtained. Also, you have the right to withdraw consent at

any time without any consequences. Furthermore, you do not have to answer any

question that is inconvenient for you.

There is a minimal risk that security of any online data may be breached, but our survey

host Qualtrics uses strong encryption and other data security methods to protect your

information. Only the researchers will have access to your information on their server.

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

[email protected].

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

mailto:[email protected]

mailto:[email protected]

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


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


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


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


Safety O O O O O

S6. Labors are involved in decision making

1 2 3 4 5

Quality 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


Safety O O O O O

S8. Small number of supervisory personnel

1 2 3 4 5

Quality 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


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


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


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


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


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


Safety O O O O O

S15. Tasks are more important than relationships

1 2 3 4 5

Quality 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


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


Safety O O O O O

S18. Conflict is resolved by negotiation

1 2 3 4 5

Quality O O O O O

109


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


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


Safety O O O O O

S21. Laborers live in order to work

1 2 3 4 5

Quality 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


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


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


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

110


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


Safety O O O O O

S27. Laborers have security of employment

1 2 3 4 5

Quality 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


Safety O O O O O

S29. Laborers do several things at once

1 2 3 4 5

Quality 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


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


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


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


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


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)



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)



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





PDI 2H Does Not Influence 40 11.7 33 9.7 49 14.4








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


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



118

Table B-6. Continued



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 82 24 94 27.6 78 22.9



PDI 5H Does Not Influence 36 10.6 27 7.9 54 15.8





PDI 5L Does Not Influence 43 12.6 41 12 52 15.2





IDV 1H Does Not Influence 69 20.2 54 15.8 74 21.7




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





119




IDV 2H Does Not Influence 34 10 25 7.3 40 11.7



Highly Influence 85 24.9 82 24 70 20.5







IDV 3H Does Not Influence 19 5.6 19 5.6 26 7.6




Very Highly Influence 88 25.8 92 27 83 24.3





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



Highly Influence 51 15 51 15 47 13.8


MAS 1L Does Not Influence 21 6.2 17 5 25 7.3





120









MAS 2L Does Not Influence 41 12 41 12 51 15



Highly Influence 58 17 56 16.4 39 11.4







MAS 3L Does Not Influence 26 7.6 18 5.3 32 9.4




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





UAI 1L Does Not Influence 27 7.9 24 7 28 8.2





121









UAI 2L Does Not Influence 31 9.1 24 7 34 10










UAI 3L Does Not Influence 64 18.8 64 18.8 72 21.1





TH 1 Does Not Influence 26 7.6 23 6.7 34 10



Highly Influence 82 24 73 21.4 89 26.1


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



122




CT 1H Does Not Influence 33 9.7 32 9.4 39 11.4





CT 1L Does Not Influence 27 7.9 29 8.5 31 9.1


Somewhat Influence 74 21.7 77 22.6 75 22



CT 2H Does Not Influence 45 13.2 42 12.3 52 15.2





CT 2L Does Not Influence 16 4.7 14 4.1 20 5.9





123

APPENDIX C MULTIVARTE ANALYSIS RESULT

Figure C-1. Correlation Matrix of the First Indicator (Quality)

124

Figure C-1. Continued

125

Figure C-2. Correlation Matrix of the Second Indicator (Productivity)

126


127

Figure C-3. Correlation Matrix of the Third Indicator (Safety)

128


129

LIST OF REFERENCES

Ali, H. A., Al-Sulaihi, I. A., and Al-Gahtani, K. S. (2013). "Indicators for measuring performance of building construction companies in Kingdom of Saudi Arabia." Journal of King Saud University-Engineering Sciences, 25(2), 125-134.

Ali, T. H. (2006). "Influence of national culture on construction safety climate in Pakistan." Doctor of Philosophy Doctoral Dissertation, Griffith University, Gold Coast.

Assaf, S. A., and Al-Hejji, S. (2006). "Causes of delay in large construction projects." International Journal of Project Management, 24(4), 349-357.

Baba, K. (1996). "Development of construction management based on regional culture." The organization and management of construction: Shaping theory and practice, 1, 119.

Barthorpe, S., Duncan, R., and Miller, C. (2000). "The pluralistic facets of culture and its impact on construction." Property Management, 18(5), 335-351.

Bik, O. P. G. (2010). The behavior of assurance professionals: A cross-cultural perspective, Eburon Uitgeverij BV.

Brown, T. A. a. (2015). Confirmatory factor analysis for applied research, The Guilford Press, New York.

Canadian Trade Commissioner Service (2014). "Construction Sector Profile - Saudi Arabia." <https://www.enterprisecanadanetwork.ca/_uploads/resources/Construction-Sector-Profile-Saudi-Arabia.pdf>. (12 Feb, 2015).

Central Department of Statistics and Information (2014). "National accounts statistics."

Central Department of Statistics and Information (2015). "Key Indicators." <http://www.cdsi.gov.sa/en>. (4 Feb, 2015).

Chan, E. H. (1997). "Amicable dispute resolution in the People's Republic of China and its implications for foreign-related construction disputes." Construction Management & Economics, 15(6), 539-548.

Chan, E. H., and Tse, R. Y. (2003). "Cultural considerations in international construction contracts." Journal of Construction Engineering and Management, 129(4), 375-381.

https://www.enterprisecanadanetwork.ca/_uploads/resources/Construction-Sector-Profile-Saudi-Arabia.pdf

https://www.enterprisecanadanetwork.ca/_uploads/resources/Construction-Sector-Profile-Saudi-Arabia.pdf

http://www.cdsi.gov.sa/en

130

Choudhury, I. (2000). "Cross-cultural training of project personnel for implementation of international construction projects by US contractors." International Proceedings of the Associated Schools of Construction, 87-94.

Chui, K. W. (2010). "Comparison of Construction Labor Productivity between US and China: Using On-Site Productivity Measurement Methods." https://kuscholarworks.ku.edu/handle/1808/6955

Comu, S., Unsal, H. I., and Taylor, J. E. (2010). "Dual impact of cultural and linguistic diversity on project network performance." Journal of Management in Engineering, 27(3), 179-187.

Construction Week Online Middle East (2015). "Construction Projects." <http://www.constructionweekonline.com/projects/?projectType=1&country=Saudi+Arabia&category=&Submit2=Submit>. (25 Feb, 2015).

Cox, R. F., Issa, R. R., and Ahrens, D. (2003). "Management's perception of key performance indicators for construction." Journal of construction engineering and management, 129(2), 142-151.

Dai, J. (2006). "A latent analysis and prototype system to manage jobsite factors impacting construction labor productivity." Ph.D. thesis, University of Kentucky, Lexington, Ky.

Deloitte (2015). "Deloitte GCC Powers of Construction 2014 Construction sector overview." <http://www2.deloitte.com/content/dam/Deloitte/xe/Documents/realestate/construction/gccpowersofconstruction/me_construction_gccpoc2014_sectoroverview.pdf

>. (13 Feb, 2015).

Dulaimi, M., and Hariz, A. (2011). "The impact of cultural diversity on the effectiveness of construction project teams." Engineering Project Organization Journal, 1(4), 213-221.

Durdyev, S., and Mbachu, J. (2011). "On-site labour productivity of New Zealand construction industry: Key constraints and improvement measures." Australasian Journal of Construction Economics and Building, 11(3), 18-33.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., and Strahan, E. J. (1999). "Evaluating the Use of Exploratory Factor Analysis in Psychological Research." Psychological Methods, 4(3.272-299).

Fellows, R., and Liu, A. M. (2013). "Use and misuse of the concept of culture." Construction Management and Economics, 31(5), 401-422.

Fisher, T. F., and Ranasinghe, M. (2001). "Culture and foreign companies' choice of entry mode: the case of the Singapore building and construction industry." Construction Management & Economics, 19(4), 343-353.

https://kuscholarworks.ku.edu/handle/1808/6955

http://www.constructionweekonline.com/projects/?projectType=1&country=Saudi+Arabia&category=&Submit2=Submit

http://www.constructionweekonline.com/projects/?projectType=1&country=Saudi+Arabia&category=&Submit2=Submit

http://www2.deloitte.com/content/dam/Deloitte/xe/Documents/realestate/construction/gccpowersofconstruction/me_construction_gccpoc2014_sectoroverview.pdf



131

Ghasemi, A., and Zahediasl, S. (2012). "Normality Tests for Statistical Analysis: A Guide for Non-Statisticians." Int. J. Endocrinol. Metab., 10(2), 486-489.

Gliem, R. R., and Gliem, J. A. (2003)"Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales." Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education. Columbus, Ohio

Hafez, S. M., Aziz, R. F., Morgan, E. S., Abdullah, M. M., and Ahmed, E. K. (2014). "Critical factors affecting construction labor productivity in Egypt." American Journal of Civil Engineering, 2(2), 35-40.

Hall, E. T. (1966). The hidden dimension Doubleday & Co, New York, NY, US.

Hall, E. T. (1976). Beyond culture, Anchor, Oxford, England.

Hall, E. T., and Hall, M. R. (1990). Understanding cultural differences, Intercultural press Yarmouth, ME.

Hall, M. A., and Jaggar, D. M. "Should construction enterprises, working internationally, take account of cultural differences in culture?" Proc., the Proceedings of the 13th ARCOM conference, Cambridge, 1-10.

Hampden Turner, C., and Trompenaars, A. (1993). The seven cultures of capitalism : value systems for creating wealth in the United States, Japan, Germany, France, Britain, Sweden, and the Netherlands, Doubleday Business, New York.

Hanna, A., Russell, J., Nordheim, E., and Bruggink, M. (1999). "Impact of Change Orders on Labor Efficiency for Electrical Construction." Journal of Construction Engineering and Management, 125(4), 224-232.

Herbsman, Z., and Ellis, R. (1990). "Research of Factors Influencing Construction Productivity." Construction Management and Economics, 8(1), 49.

Hofstede, G. (1984). "Cultural dimensions in management and planning." Asia Pacific Journal of Management, 1(2), 81-99.

Hofstede, G. (1984). Culture's consequences: International differences in work-related values, Sage, Beverly Hills, CA.

Hofstede, G., Hofstede, G., and Minkov, M. (2010). Cultures and organizations: software of the mind: intercultural cooperation and its importance for survival, McGraw-Hill, New York.

House, R., Javidan, M., and Dorfman, P. (2001). "Project GLOBE: an introduction." Applied Psychology, 50(4), 489-505.

132

Jang, H., Kim, K., Kim, J., and Kim, J. (2011). "Labour productivity model for reinforced concrete construction projects." Construction Innovation: Information, Process, Management, 11(1), 92-113.

Kazaz, A., and Ulubeyli, S. (2007). "Drivers of productivity among construction workers: A study in a developing country." Building and Environment, 42(5), 2132-2140.

Kien, B. T. (2012). "Factors affecting the fluctuation of labour productivity in the construction project " Master of Business, University of Economics Ho Chi Minh City.

Kingdom of Saudi Arabia Ministry of Finance (2014). "Press Release Recent Economic Developments and Highlights of Fiscal Years 1435/1436 (2014) & 1436/1437 (2015)."

Kingdom of Saudi Arabia Ministry of Labor (2005). "The Annual Statistical Book 2004."

Kingdom of Saudi Arabia Ministry of Labor (2014). "The Annual Statistical Book 2013."

Kivrak, S., Arslan, G., Tuncan, M., and Birgonul, M. T. (2014). "Impact of national culture on knowledge sharing in international construction projects." Canadian Journal of Civil Engineering, 41(7), 642-649.

Kivrak, S., Ross, A., and Arslan, G. (2008)"Effects of cultural differences in construction projects: An investigation among UK construction professionals." Proceedings of the International Conference on Multi-National Construction Projects–Securing high Performance through Cultural awareness and Dispute Avoidance, Shanghai, China 21-23.

Kivrak, S., Ross, A., and Arslan, G. (2009)"Impacts of Cultural Differences on Knowledge Management Practices in Construction." Proc., Proceedings of the Fifth International Conference on Construction in the 21st Century (CITC-V), Istanbul, Turkey, 984-991.

Koehn, E., and Brown, G. (1986). "International Labor Productivity Factors." Journal of Construction Engineering and Management, 112(2), 299-302.

Lagrosen, S. (2003). "Exploring the impact of culture on quality management." International Journal of Quality & Reliability Management, 20(4), 473-487.

Larson, M. G. (2008). "Analysis of variance." Circulation, 117(1), 115-121.

Leech, N. L., Morgan, G. A., Barrett, K. C., and NetLibrary, I. (2005). "SPSS for intermediate statistics: use and interpretation." Lawrence Erlbaum, Mahwah, N.J.

Liu, J., Meng, F., and Fellows, R. (2015). "An exploratory study of understanding project risk management from the perspective of national culture." International Journal of Project Management, 33(15), 564-575.

133

Loosemore, M., and Muslmani, H. A. (1999). "Construction project management in the Persian Gulf: inter-cultural communication." International Journal of Project Management, 17(2), 95-100.

Loosemore, M., Phua, F., Dunn, K., and Ozguc, U. (2010). "Operatives’ experiences of cultural diversity on Australian construction sites." Construction Management and Economics, 28(2), 177-188.

MacCallum, R. C., Widaman, K. F., Zhang, S., and Hong, S. (1999). "Sample size in factor analysis." Psychological Methods, 4(1), 84.

Mahalingam, A., Levitt, R. E., and Scott, W. R. (2005)."Cultural clashes in international infrastructure development projects: Which cultures matter?" Proc., CIB W92/T23/W107 International Symposium on Procurement Systems: The Impact of Cultural Differences and Systems on Construction Performance, 645-653

Mahamid, I., Al-Ghonamy, A., and Aichouni, M. (2014). "Major Factors Influencing Employee Productivity in the KSA Public Construction Projects." International Journal of Civil & Environmental Engineering, 14(1), 16.

Mohamed, S., Ali, T. H., and Tam, W. (2009). "National culture and safe work behaviour of construction workers in Pakistan." Safety Science, 47(1), 29-35.

Mojahed, S. (2005). "A project improvement system for effective management of construction projects." Doctoral Dissertation, Louisiana State University.

Morden, T. (1999). "Models of national culture-a management review." Cross Cultural Management: An International Journal, 6(1), 19-44.

Ngowi, A. (2000). "Impact of culture on the application of TQM in the construction industry in Botswana." International Journal of Quality & Reliability Management, 17(4/5), 442-452.

Ochieng, E., and Price, A. (2010). "Factors influencing effective performance of multi-cultural construction project teams." Proc., Procs 26th Annual ARCOM Conference, Leeds, UK, 6-8.

Olomolaiye, P. O., Wahab, K. A., and Price, A. D. F. (1987). "Problems influencing craftsmen's productivity in Nigeria." Building and Environment, 22(4), 317-323.

Ozorhon, B., Arditi, D., Dikmen, I., and Birgonul, M. T. (2007). "Effect of host country and project conditions in international construction joint ventures." International Journal of Project Management, 25(8), 799-806.

Ozorhon, B., Arditi, D., Dikmen, I., and Birgonul, M. T. (2008). "Implications of culture in the performance of international construction joint ventures." Journal of Construction Engineering and Management, 134(5), 361-370.

134

Öztuna, D., Atilla Halil, E., and Tüccar, E. (2006). "Investigation of Four Different Normality Tests in Terms of Type 1 Error Rate and Power under Different Distributions." Turkish Journal of Medical Sciences, 36(3), 171-176.

Rees-Caldwell, K., and Pinnington, A. H. (2013). "National culture differences in project management: Comparing British and Arab project managers' perceptions of different planning areas." International Journal of Project Management, 31(2), 212-227.

Samovar, L., Porter, R., McDaniel, E., and Roy, C. (2014). Intercultural communication: A reader, Cengage Learning, Boston.

SAUDI National e-Government Portal (2015). "Facts About Kingdom." <http://www.saudi.gov.sa/wps/portal/yesserRoot/aboutKingdom/factsKingdom/!ut/p/b1/04_Sj9CPykssy0xPLMnMz0vMAfGjzOId3Z2dgj1NjAz8zUMMDTxNzZ2NHU0NDd2NTIEKIvEocDanTL-3ESH9XvpR6Tn5SUCn-nnk56bqF-SGRlQ6KioCAO2sz-c!/dl4/d5/L0lDUmlTUSEhL3dHa0FKRnNBLzRKVXFDQSEhL2Vu/>. (15 Feb, 2015).

Spencer-Oatey, H. (2012). "What is culture? A compilation of quotations." Global PAD, University of Warwick.

Sui Pheng, L., and Yuquan, S. (2002). "An exploratory study of Hofstede's cross-cultural dimensions in construction projects." Management Decision, 40(1), 7-16.

Swan, W., and Kyng, E. (2005). "An introduction to key performance indicators." Center for Construction Innovation.–2004.

Swierczek, F. W. (1994). "Culture and conflict in joint ventures in Asia." International Journal of Project Management, 12(1), 39-47.

Tabachnick, B. G., and Fidell, L. S. (2001). Using multivariate statistics, Allyn and Bacon, Boston,MA.

Taherdoost, H., Sahibuddin, S., and Jalaliyoon, N. (2014). "Exploratory Factor Analysis; Concepts and Theory." Proc., International Conference on Mathematical-Computational and Statistical-Sciences, Geneva.

Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications, American Psychological Association, Washington, DC, US.

Trompenaars, F., and Hampden-Turner, C. (1998). Riding the waves of culture: Understanding diversity in global business, Nicholas Brealey, London

http://www.saudi.gov.sa/wps/portal/yesserRoot/aboutKingdom/factsKingdom/!ut/p/b1/04_Sj9CPykssy0xPLMnMz0vMAfGjzOId3Z2dgj1NjAz8zUMMDTxNzZ2NHU0NDd2NTIEKIvEocDanTL-3ESH9XvpR6Tn5SUCn-nnk56bqF-SGRlQ6KioCAO2sz-c!/dl4/d5/L0lDUmlTUSEhL3dHa0FKRnNBLzRKVXFDQSEhL2Vu/





135

Ullah Khan, A. (2014). "Effects of cultural assimilation on the performance of a construction project–evidence from UAE." Benchmarking: An International Journal, 21(3), 430-449.

Waziri, F., and Khalfan, S. (2014). "Cross-Cultural Communication in Construction Industry: How do Chinese Firms Cross the Barriers in Tanzania?" European Journal of Business and Management, 6(13), 118-122.

Williams, B., Brown, T., and Onsman, A. (2012). "Exploratory factor analysis: A five-step guide for novices." Australasian Journal of Paramedicine, 8(3), 1.

World Bank Group (2015). "Doing Business in Saudi Arabia." <http://www.doingbusiness.org/data/exploreeconomies/saudi-arabia/>. (26 Feb, 2015).

Yates, J. K. (1994). "Construction competition and competitive strategies." Journal of Management in Engineering, 10(1), 58-69.

Yates, J. K., and Guhathakurta, S. (1993). "International labor productivity." Cost Engineering, 35(1), 15.

Zhi, H. (1995). "Risk management for overseas construction projects." International Journal of Project Management, 13(4), 231-237.

Zuo, J., and Zillante, G. (2008). "Construction project culture vs. national culture." CIB W112 INTERNATIONAL CONFERENCE ON MULTI-NATIONAL CONSTRUCTION PROJECTS: Securing High Performance Through Cultural Awareness and Dispute Avoidance, in-house publishing, Rotterdam (Netherlands), approx. 9 p.

http://www.doingbusiness.org/data/exploreeconomies/saudi-arabia/

136

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.