Modelling and measuring quality of urban life : housing, neighbourhood, transport and … · the quality of my dissertation from different angles. My appreciation also goes to the

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Modelling and Measuring Quality of Urban Life

Housing, Neighborhood, Transport and Job

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus prof.dr.ir. F.P.T. Baaijens,

voor een commissie aangewezen door het College voor Promoties, in het openbaar te verdedigen op dinsdag 30 april 2019 om 11:00 uur

door

Lida Aminian

geboren te Teheran, Iran

ii

Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de

promotiecommissie is als volgt:

voorzitter: prof.dr.ir. E.S.M.Nelissen

1e promotor: prof.dr. H.J.P. Timmermans

2e promotor: prof.dr. S. Rasouli

leden: prof.dr. D.F. Ettema (Universiteit Utrecht)

prof.dr.ir. B.de Vries

dr.ir. A.D.A.M. Kemperman

Het onderzoek of ontwerp dat in dit proefschrift wordt beschreven is uitgevoerd in overeenstemming met de TU/e Gedragscode Wetenschapsbeoefening.

iii

Modelling and Measuring Quality of Urban Life

Housing, Neighbourhood, Transport and Job

iv

A catalogue record is available from the Eindhoven University of Technology Library

ISBN: 978-90-386-4739-5

NUR: 955

Cover design: Lida Aminian, Reza Saberi

photograph: Lida Aminian Printed by the Eindhoven University Press, Eindhoven, The Netherlands

Published as issue 268 in de Bouwstenen series of the faculty of Architecture, Building and Planning of the Eindhoven University of Technology

Copyright© Lida Aminian, 2019 All rights reserved. No part of this document may be photocopied, reproduced, stored, in a retrieval system, or transmitted, in any from or by any means whether, electronic, mechanical, or otherwise without the prior written permission of the author.

v

“Yesterday I was clever, so I wanted to change the

world. Today, I am wise so I am changing myself.”

Rumi

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vii

Preface

Now since this journey is coming to end, I can see it more clearly with all its ups and

downs that made me physically and emotionally more resilient compared to the past.

Taking a step back in time, I clearly remember the day I received the prompt reply from

Prof. Harry Timmermans in response to my email and my interest in doing research in

his department. The main question he asked me after looking at my CV was: ” You are

not trained as a researcher, so what are the challenges?”. I knew, doing PhD in urban

systems and planning would not be an easy route to take for someone with an

architecture background, but at that time I was not totally aware of all the dimensions

of his question. Now, at the end of this scenario, I can reply to his question differently

as I touched all those challenges and even more.

The main challenge is adopting to a new lifestyle. As an architect, I used to

work in a team, have a lot of field work, social interactions and discussions with my

team members. However, doing research needs a lot of focus, so the isolation is

inevitable. Although some scholars love the feeling of autonomy this gives, some prefer

more contacts. This is specially more challenging for those whose social circle is

relatively limited due to long distances to family and close friends. Learning the

strategies to adopt to this new life-style has probably been the biggest milestone in my

personal growth during this episode of my life.

The second challenge for someone with a background in architecture and

design is to learn the opposite way of presenting ideas. While architecture students

learn to develop their ideas and ambiguity can be part of an innovative design, in

academic research you should write your ideas in an consistent and clear way, as

accurate as possible.

The third challenge is related to the nature of the urban planning field. People

from almost all engineering fields, geography, social science, economics and applied

sciences can contribute to urban planning. However, the most confusing part is that in

the Netherlands (and probably some other countries) all of them gathered under the flag

of architecture in the faculty of built environment, while their focus is quite different. This

can easily deviate one’s initial plan and put one in a totally new world.

viii

Despite all these challenges in the beginning, at some point I realized that I

can benefit a lot from my background and the new expertise by formulating new types of

interdisciplinary research, bridging design and behavioral science. Working on the

“Quality of Urban Life” topic gave me the opportunity to improve this potential. The topic

was really interesting in a sense that it connected me to two different directions, one

was happiness and well-being and the other concerned the various domains of the built

environment. For the first one, I had to conduct an extensive literature study, which

was very informative for me and the insights were useful for my own life. It is very rare

that you could use the knowledge of your PhD research directly for your own life,

though what I learnt is beyond what is reflected in this book. For the second direction, I

had to learn quantitative modelling, survey design, data collection etc., which enable

me to understand the foundation of research in many different fields. Now, I am happy

to spend these years extending and developing my experience in this field, a field with a

bright future and so many opportunities to grow.

Although doing a PhD research is an individual activity, the inspiration,

motivation and help of others are key success factors. Hereby, I would like to express

my gratitude and appreciation to people who have supported me directly and indirectly

during this critical period.

Firstly, my greatest thank goes to Prof. Harry Timmermans for his inspirational

guidance and patience. His optimism and cooperative attitude have attracted many

students from different corners of world and made a very international department.

Thank you Harry for all these years, I learnt a lot from you, the way you commit to your

work, always having a plan for the future, and your leadership, all were incredibly

instructive for me.

I would also like to thank Prof. Soora Rasouli who joined the project in the last

stage, but her insightful comments on my thesis enriched my experience and promoted

the quality of my dissertation from different angles.

My appreciation also goes to the members of my dissertation committee, prof.

de Vries, Dr. Kemperman and prof. Ettema for their valuable time in reviewing the

manuscript. Thank you all for your motivational comments. I really enjoyed reading how

you described my project. I also like to thank other members of our department: Aloys,

Peter, Mandy, Marielle and Joran for their valuable support during this period.

One of the challenging phases of my PhD was data collection, and without help

from others it was almost impossible to collect data in the Netherlands. I am

ix

appreciative to Astrid Kemperman, Margot Jaspars, Dujuan Yang and Mark van

Glabbeek. I also like to thank the respondents of my survey whose patience and effort

in completing such a long survey provided me a very high quality data set, and I hope

they may profit from my findings.

Knowing that social aspects of life play a critical role in well-being, I am grateful to all

those who made this experience less isolated and more joyful, my friends inside and

outside the university for all the gatherings, coffee time, sport activities and chats. I

would like to thank my vibrant colleagues Bilin, Bobin, Calvin, Dujuan, Elaine, Eleni,

Feixiong, Guangde, Iphi, Marianna, Marielle, Pauline, Rainbow, Robert, Seheon,

Sunghoon, Valeria, Wen, Widi, Xiaochen, Yanan, and Zahra (Sareh). Special thanks go

to Elaine, Wen and Widi for sharing their critical live moments and experiences with me

during this journey.

At a personal level, doing a PhD is a big sacrifice, particularly when it implies

long distance from the loved ones. At some point, I have come to believe that

emotional resilience is the greatest ability I gained during this period. I had to

disappoint my parents several times when I postponed my trips to visit them to finish

this dissertation. Nevertheless, without their unconditional support, love and care it was

impossible to reach the end of this tunnel. I owe my deepest and heartfelt gratitude to

you my dearest, and this dissertation is dedicated to you. I should thank my best friend,

a person who grew up with me and all my childhood memories are full of her presence,

my sister Leila. Thanks to you and Hosein for the emotional support, listening to my

worries, sending us gifts and flowers and visiting us in NL. Thanks also go to my older

sister Farimah for her advices and being an inspiration for me. I would also like to

extend my thanks to my family in law for their frequent visits, which made our summers

more exciting and fun.

Last but not least, my special appreciation goes to my life partner Reza, whose

encouragement, support and assistance from the beginning to conclusion enabled me to

accomplish this project. We have not spent a day without sharing our thoughts, good

and bad moments. My dear companion: I feel very lucky to have you in my life.

Lida Aminian

December 2018

x

xi

Contents

Preface ..............................................................................................................vii

Contents ............................................................................................................ xi

List of Figures ....................................................................................................xv

List of Tables ................................................................................................... xvii

1 Introduction .................................................................................................... 1 1.1 Background and motivation ................................................................. 1 1.2 Research objectives ............................................................................ 3 1.3 Thesis outlines ................................................................................... 4

2 Concept and literature review ........................................................................ 7 2.1 Introduction ....................................................................................... 7 2.2 QOL (well–being) ............................................................................... 8

2.2.1 Background ............................................................................ 8 2.2.2 Theory and measurement ........................................................ 9 2.2.3 Bottom-up spillover theory ..................................................... 11 2.2.4 QOL in environmental psychology: the concept of “person-place-

activity” ........................................................................................... 11 2.3 Quality of Urban Life (QOUL) ............................................................. 13 2.4 Conceptual framework ...................................................................... 14 2.5 Conclusions ..................................................................................... 19

3 Data collection and descriptive statistics ..................................................... 21 3.1 Introduction ..................................................................................... 21

xii

3.2 Survey design .................................................................................. 22 3.2.1 Data consideration ................................................................ 22 3.2.2 Survey instrument ................................................................. 22 3.2.3 The questionnaire ................................................................. 22

3.3 Study areas...................................................................................... 28 3.4 Sample selection and characteristics ................................................... 29 3.5 Conclusions ...................................................................................... 38

4 Housing well-being ....................................................................................... 39 4.1 Introduction ..................................................................................... 39 4.2 Literature review .............................................................................. 40

4.2.1 A place called house (terminology and definitions) ................... 40 4.2.2 Theoretical concepts .............................................................. 42 4.2.3 Factors determining housing satisfaction (Indicator sets in

housing research) ............................................................................. 43 4.3 Multiple regression analysis ............................................................... 48

4.3.1 Interpretation ....................................................................... 50 4.4 Analysis and results: Path analysis ..................................................... 52

4.4.1 Conceptualization .................................................................. 52 4.4.2 Path analysis for housing satisfaction ...................................... 53 4.4.3 Interpretation and discussion ................................................. 57

4.5 Conclusions and discussion ................................................................ 59

5 Neighborhood well-being ............................................................................. 61 5.1 Introduction ..................................................................................... 61 5.2 Literature review .............................................................................. 63

5.2.1 A place called neighborhood (terminology and definitions) ........ 63 5.2.2 Research background ............................................................ 64 5.2.3 Theoretical concepts .............................................................. 65

5.3 Factors determining neighbourhood satisfaction .................................. 66 5.3.1 Individuals’ socio-demographic characteristics.......................... 66 5.3.2 Social environment characteristics .......................................... 67 5.3.3 Physical and environmental characteristics .............................. 69 5.3.4 Physical activity satisfaction ................................................... 69 5.3.5 Satisfaction with services in neighborhood ............................... 70

5.4 Analysis and results: Multiple regression analysis ................................. 70 5.4.1 Interpretation ....................................................................... 71

5.5 Analysis and results: Structural equation modelling (SEM) .................... 76 5.5.1 Conceptualization .................................................................. 77 5.5.2 SEM model for neighborhood satisfaction ................................ 79 5.5.3 Interpretation and discussion ................................................. 87

5.6 Conclusions and discussion ................................................................ 89

xiii

6 Transport well-being..................................................................................... 91 6.1 Introduction ..................................................................................... 91 6.2 Research background ....................................................................... 92 6.3 Factors determining travel satisfaction ............................................... 93

6.3.1 Individuals’ socio-demographic and household characteristics ... 93 6.3.2 The built environment ........................................................... 94 6.3.3 Travel mode choice ............................................................... 95 6.3.4 Time, the role of rush hours ................................................... 96 6.3.5 Trip attributes satisfaction ..................................................... 96

6.4 Analysis and results: Multiple regression analysis ................................ 99 6.4.1 Interpretation ..................................................................... 100

6.5 Analysis and results: Structural equation modelling (SEM) .................. 104 6.5.1 Conceptualization ................................................................ 104 6.5.2 SEM model for transport satisfaction ..................................... 105

6.6 Interpretation ................................................................................ 110 6.6.1 Effects of travel characteristics ............................................. 110 6.6.2 Effects of socio-demographic variables .................................. 112 6.6.3 Effects of built environment ................................................. 113

6.7 Conclusions and discussion.............................................................. 114

7 Work well-being .......................................................................................... 115 7.1 Introduction ................................................................................... 115 7.2 Literature review ............................................................................ 116

7.2.1 Activity called job (terminology and definitions) ..................... 116 7.2.2 Research background .......................................................... 117 7.2.3 Theoretical concepts ........................................................... 118

7.3 Factors determining work satisfaction............................................... 119 7.3.1 Individuals’ socio-demographic factors .................................. 119 7.3.2 Employment status (Part time vs full time) ............................ 121 7.3.3 Work conditions .................................................................. 122 7.3.4 Workspace ......................................................................... 124 7.3.5 Work location ..................................................................... 126

7.4 Analysis and results: Multiple regression analysis .............................. 128 7.4.1 Interpretation ..................................................................... 128

7.5 Analysis and results: Path analysis ................................................... 131 7.5.1 Conceptualization ................................................................ 132 7.5.2 Path analysis model for work satisfaction .............................. 132 7.5.3 Interpretation and discussion ............................................... 135

7.6 Conclusion and discussion ............................................................... 142

8 Quality Of Urban Life (QOUL) ..................................................................... 143 8.1 Introduction ................................................................................... 143

xiv

8.2 Literature review ............................................................................ 144 8.2.1 Domain-based QOL ............................................................. 144 8.2.2 Factors determining QOL ..................................................... 145

8.3 Analysis and results: Multiple regression analysis ............................... 149 8.3.1 Interpretation ..................................................................... 152

8.4 Analysis and results: Path analysis ................................................... 152 8.4.1 Conceptualization ................................................................ 152 8.4.2 Path analysis model for QOUL .............................................. 153

8.5 Interpretation................................................................................. 158 8.5.1 The role of socio-demographic attributes on QOUL ................. 158 8.5.2 The influence of life domains on QOUL .................................. 159

8.6 Conclusions and discussion .............................................................. 162

9 Conclusions and Future work ..................................................................... 165 9.1 Summary ....................................................................................... 165 9.2 Contribution and Managerial implications .......................................... 170 9.3 Discussion and direction of future research ....................................... 172

References ...................................................................................................... 175

Appendix: QOUL Survey ................................................................................. 199

Authors Index ................................................................................................. 215

Subject Index ................................................................................................. 221

Summary ........................................................................................................ 225

Curriculum Vitae ............................................................................................. 229

xv

List of Figures

Figure 2.1 Life domains in relation to person-place- activity concept .......................... 15

Figure 2.2 The conceptual framework ..................................................................... 20

Figure 3.1 Flyer of the survey ................................................................................ 23

Figure 3.2 An example of the final page of neighborhood module .............................. 27

Figure 3.3 Location of the four neighborhoods in Eindhoven 1: StrijpS, 2: Schoot, 3:

Villapark, 4: Doornakkers ............................................................................... 29

Figure 3.4 Local dwelling streets of 1: StrijpS, 2: Schoot, 3: Villapark, 4: Doornakkers 30

Figure 3.5 Chart of household income in study areas ............................................... 33

Figure 4.1 The Histogram of the overall housing satisfaction ..................................... 49

Figure 4.2 Normal Probability Plot (P-P) of the regression standardized residual

dependent variable for overall house satisfaction .............................................. 49

Figure 4.3 The main categories of determinants of housing satisfaction ..................... 53

Figure 4.4 The conceptual model ............................................................................ 54

Figure 4.5 Results of the path analysis for house satisfaction .................................... 55

xvi

Figure 5.1 The Histogram of the overall neighborhood satisfaction ............................ 72

Figure 5.2 Normal Probability Plot (P-P) of the regression standardized ...................... 72

Figure 5.3 The conceptual model ............................................................................ 78

Figure 5.4 Results of SEM of the modified model with standardized coefficients for

neighborhood satisfaction ............................................................................... 86

Figure 6.1 The Histogram of the overall travel satisfaction ...................................... 101


dependent variable for overall travel satisfaction ............................................ 101

Figure 6.3 The conceptual model .......................................................................... 106

Figure 6.4 Results of the SEM model with standardized coefficients for transport

satisfaction .................................................................................................. 111

Figure 7.1 The Histogram of the overall work satisfaction ....................................... 129


dependent variable for overall work satisfaction.............................................. 129

Figure 7.3 The main determinants of the work satisfaction used in this study ........... 131


Figure 7.5 Results of the path analysis for work satisfaction .................................... 137

Figure 8.1 The Histogram of the overall QOUL ....................................................... 150


dependent variable for Overall QOUL ............................................................. 150


Figure 8.4 Results of path analysis for QOUL ......................................................... 160

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xvii

List of Tables

Table 2.1 Integrative theories of QOL ..................................................................... 10

Table 2.2 Life domains in QOL studies .................................................................... 12

Table 2.3 Summary of recent QOUL studies ............................................................ 16

Table 3.1 Number of respondents per area ............................................................. 23

Table 3.2 General situation of the four neighborhoods ............................................. 31

Table 3.3 Description of social-demographic data (N=209) ....................................... 32

Table 3.4 Descriptive statistics of life domains satisfaction ........................................ 34

Table 3.5 Descriptive statistics of satisfaction with housing characteristics ................. 34

Table 3.6 Description of housing data, and categories of categorical variables ............ 35

Table 3.7 Descriptive statistics of satisfaction with neighborhood characteristics ......... 37

Table 3.8 Descriptive statistics of satisfaction with transport characteristics ................ 37

Table 3.9 Descriptive statistics of satisfaction with work characteristics ...................... 38

xviii

Table 4.1 Different literature regarding the major contributors to the house satisfaction

.................................................................................................................... 47

Table 4.2 Goodness-of-fit of the regression model of overall house satisfaction .......... 51

Table 4.3 The estimated parameters of the house satisfaction using multiple regression

analysis ........................................................................................................ 51

Table 4.4 Goodness of the fit of the model .............................................................. 56

Table 4.5 Standardized path estimates of direct, indirect and total effects .................. 58

Table 5.1 Goodness-of-fit of the regression model of overall neighborhood satisfaction

.................................................................................................................... 73

Table 5.2 The estimated parameters of the neighborhood satisfaction using multiple

regression analysis ......................................................................................... 73

Table 5.3 Goodness of the fit of the initial model ..................................................... 81

Table 5.4 Goodness of the fit of the improved model................................................ 81

Table 5.5 Standardized path estimates of direct, indirect and total effects .................. 83

Table 5.6 Relationships among the latent and observed variables (direct effects ......... 84

Table 5.7 Relationships among the latent variables .................................................. 85

Table 6.1 Goodness-of-fit of the regression model of overall travel satisfaction ......... 102

Table 6.2 The estimated parameters of the transport satisfaction using multiple

regression analysis ....................................................................................... 102

Table 6.3 Goodness of the fit of the model ............................................................ 107

Table 6.4 Standardized path estimates of direct, indirect and total effects on overall

travel satisfaction......................................................................................... 108

Table 6.5 Relationships among the latent and observed variables ............................ 109

Table 7.1 Major attributes in the selected recent literature ...................................... 120

Table 7.2 Goodness-of-fit of the regression model for overall work satisfaction ......... 130

Table 7.3 The estimated parameters of the work satisfaction using multiple regression

analysis ...................................................................................................... 130

Table 7.4 Goodness of the fit for the path analysis model ....................................... 134

Table 7.5 Standardized path estimates of direct, indirect and total effects ................ 138

Table 8.1 Goodness-of-fit of the regression model for overall QOUL ........................ 151

xix

Table 8.2 The estimated parameters of the overall QOUL using multiple regression

analysis ...................................................................................................... 151

Table 8.3 Goodness of the fit of the model ............................................................ 155

Table 8.4 Standardized path estimates of direct, indirect and total effects ................ 156

xx

Introduction

1

1

Introduction

1.1 Background and motivation

Throughout history, philosophers attempted to define notions of good life or happy life,

as every act of human being is directed toward seeking happiness and enhancing

Quality Of Life (QOL). In its philosophical definition QOL or sense of well-being is a

state of mind that is related to fulfillments of human capacities (Eudaimonia) and

pleasure attainment (Hedonia). Beyond the philosophical foundations of the concept, in

its core QOL is a universal notion that motivates human behavior and all choices in the

life stages. Therefore as Aristotle articulated, it is about doing well and living well. With

civilization flourishing, the concept evolved and outlined many new perspectives that

reflect positive and negative features of life. These features are now the focal points of

wide range of disciplines from psychology to sociology, healthcare, public policy,

environmental studies, urban studies etc.

Introduction

2

Being home to majority of world’s population, cities are key players in

providing good life for citizens. So, it is not surprising that more than ever, the QOL

debate shifted toward living situations in urban settings, and exploring whether human

in particular places such as a city feel happier or not. For many years, social scientists

have been examining the role of urban issues such as crime rate or poverty on the

sense of well-being, and thereby the role of physical aspects have been usually

underestimated or ignored. From 1970 onwards, a major attention has been paid to the

role of place characteristics in generating satisfaction. And, basically a new stream of

studies has been focused on the role of city design on preventing crime and other

issues in major cities. Investigating the capacities of a place in making people happy

and leading a desirable life, then extended to abilities of cities in hosting activities that

potentially offered to citizens. Following that, cities were ranked based on the objective

facilities and standard of living. However, questions such as “Do cities with highest

objective characteristics host happiest people?” reflected the need to explore subjective

assessments of citizens. To develop understanding regarding city life and subjective

well-being of residents, urban scientists borrowed theories and approaches such as

domain-specific (life domains) from other fields. Thus, physical domains included to

other key domains that were typically measured in subjective well-being studies, and

different aspects of cities evaluated and measured quantitatively via surveys.

In addition to that, as judgments of people are highly influenced by individuals’

needs and constraints, socio-demographic information of individuals were also included

into subjective well-being surveys. However, despite the importance of quality of urban

life (QOUL) studies, the empirical studies that have been explicitly focused on micro

level subjective QOL in cities are very limited. Also, among the available studies certain

aspects are lacked or ignored. First, QOUL surveys are usually very similar and ask

respondents some typical questions, while the concept is very context dependent and to

capture specific characteristics of place, particular attention to context is needed.

Therefore, indicators in one study may not be relevant to other studies. Second, asking

one question regarding a particular domain may not fully capture the respondents view

regarding that domain. Also, it may not provide information about the drivers of

satisfaction or dissatisfaction, which are important information for policy makers. For

instance, the typical question “how satisfied are you regarding your transportation?”

cannot reflect what kind of trip and which particular aspects of trips can trigger the

answer to this question. In addition, it may be challenging for respondents to judge one

Introduction

3

life domain based on only one question, so some pre-questions are needed to guide the

respondents. To prevent such issues it makes sense to ask respondents detail questions

regarding their activities in their daily life, and also measure suitability of places in

generating satisfaction, using sets of indicators. However, as the concept is very broad,

detailed questions in each domain may make the survey very long, which in turn cause

data collection to become challenging. That’s why most of QOUL studies undertake by

governmental organizations and the rest focus on one or couple of domains such as

housing well-being or travel well-being.

This research has been motivated by the above-mentioned gaps in QOUL

studies, and aims to study the key issues affecting micro-level subjective well-being.

Moreover, the research tends to investigate subjective well-being with explicit focus on

the role of the physical and urban environment, divided into four main domains, namely

housing well-being, neighborhood well-being, transport well-being and job well-being.

This helps first to determine the main aspects influencing each specific domain, and

then the extent to which overall satisfaction with that domain may influence overall

QOUL. The contribution of this study is to propose an integral theoretical framework for

the analysis of QOUL, using spillover theory and domain-specific approach. All the

relationships among various indicators, sub-domains and domains are examined using

different statistical methods.

1.2 Research objectives

In light of the aforementioned motivations, the main objectives of this study are

formulated as:

1. Develop our understanding of subjective well-being1 and QOUL.

2. Examine the extent to which satisfaction with urban characteristics influence the

QOUL subjectively experienced by individuals.

3. Investigate the corresponding effects of selected urban life domains (housing,

neighborhood, transport and job well-being), and other life domains (i.e. social,

leisure, health, financial and family well-being) on each other and QOUL.

4. Examine the strength of the link between objective and subjective indicators of

selected urban life domains.

1 Subjective well-being and QOL are used interchangeably throughout the thesis.

Introduction

4

1.3 Thesis outlines

This thesis is organized in nine succeeding chapters. Details of the chapter description

are discussed below.

Following the introduction chapter, Chapter 2 summarizes the contemporary

literature on QOL and subjective well-being. It gives a general overview regarding the

concept, approaches and theories of QOL. Moreover, the QOUL concept and its

relationship with urban characteristics and person-place-activity approach are also

discussed in more details. Identifying the main gaps in the literature provides a platform

for constructing the theoretical framework, which is outlined in this chapter.

The data collection procedure and sample characteristics are described in

Chapter 3. Based on our theoretical framework, a questionnaire was designed. The

structure of questionnaire is divided into six modules, starting from socio-demographic

information, and then the four main urban life domains (housing, neighborhood,

transport and job), which are followed by the last module containing other life domains

and overall QOL questions. After explaining questionnaire design, this chapter discussed

the selected areas for our empirical research. Finally, a descriptive statistics for the

collected data is reported.

Chapter 4 explores the first domain of urban life, and thus focuses on housing

well-being. This chapter begins with reviewing the housing literature and the main

theories and factors influencing housing satisfaction are discussed in detail. Based on

the insights from the literature, and the link among various house attributes, the

conceptual framework of this chapter is elaborated. A regression model is first used to

analysis data, which is followed by estimation of a path analysis model. It is then

followed by presenting the results, discussion and analyzes of the results and

conclusion.

Chapter 5 studies neighborhood well-being domain, and starts with reviewing

the main literature, concepts and approaches including the identification of main sub-

domains and factors affecting neighborhood satisfaction. A conceptual model for this

domain is designed to investigate the role of different attributes on neighborhood well-

being. After using regression analysis, a structural equation model was built to estimate

the relationships among multiple unobserved constructs (latent variables), and

observable variables. This chapter also discusses and interprets the results, and comes

with a set of conclusions regarding factors affecting neighborhood well-being.

Introduction

5

Following our domain-specific satisfaction approach, after housing and

neighborhood, transport well-being is presented in Chapter 6. To achieve the objectives

of this chapter a theoretical model is elaborated based on an extensive literature study,

and understanding the influence of various factors on travel satisfaction. In this chapter,

the SEM method is used to analyze the complex relationships between various objective

and subjective indicators. Then, the results are discussed to investigate different factors

affecting transport well-being and the important ones are presented in conclusion as the

main factors correspond to transport well-being.

Chapter 7 offers detailed information on work well-being. It first introduces the

concepts, theories and main factor influencing job satisfaction. Then, it describes the

theoretical framework, which is estimated using regression analysis and path analysis

method. It is followed by presenting and interpreting the results. It ends with a

discussion and some important conclusions.

Chapter 8 turns towards the estimation of the effects of multiple life domains on

the overall QOUL. This chapter starts with literature review of subjective well-being and

the influence of major life domains on QOUL, and on each other. Then, the theoretical

model is presented and the first estimation is done by regression analysis, which is

followed by path analysis method. The results of both methods are presented and

discussed, and important domains affecting QOUL are highlighted. This chapter

presents a good framework for QOUL estimation and the main factors affecting it.

Finally, chapter 9 concludes the thesis. It summarizes the main findings of this

research project, discusses the implications and limitations of the research and

delineates directions for future research. This chapter shortly review all of the important

conclusions derived throughout this research from domain-specific approach, and

illustrates how different aspects of different life domains can affect overall QOUL. It also

indicates to what extent these results are applicable.

Introduction

6

Concept and literature review

7

2

Concept and

literature review

2.1 Introduction

For many years, social scientists have been examining Quality of Life (QOL) using

aggregate data and focusing on aspects such as crime and economic challenges. In

parallel, psychologists have been working on QOL as a subjective phenomenon with

affective (emotions) and cognitive (knowledge) dimensions, which are deeply rooted in

personality traits of individuals (e.g. Diener, 1994). During the last decade, urban

planning scientists have also become interested in this concept, and the QOL topic has

re-emerged with a new focus on urban settings as a response to challenges that cities

are facing. Particularly, urban growth and migration have been acknowledged as the

consequences of QOL differences among places. These differences concern both

objective features of living places and people’s subjective assessments of Quality of

Urban Life (QOUL). Therefore, in order to understand QOUL, we need to investigate


8

how residents judge urban places, and use them to conduct their daily activities.

Clearly, individuals have different judgments regarding attributes of urban life, which

influence their QOUL. To address these aspects in a meaningful theoretical framework,

it is essential to provide an overview of the theoretical and methodological evolution of

the concept.

This chapter is organized as follows. First, we will discuss the notion of QOL

and the evolution of the concept and measurement methods. Next, we will provide an

overview of the QOUL concept and related theories namely, domain-based and person-

place-activity. This will be followed by presenting the conceptual framework underlying

this dissertation.

2.2 QOL (well–being)

2.2.1 Background

The history of the QOL debate is quite long and can be traced back to the beginning of

the intellectual era when Greek philosophers debated the nature of good life or happy

life (Sirgy et al., 2012). During the history, specifically the last century, many

researchers worked on happiness. However, the concept of subjective well-being

belongs to the contemporary era, and has been first captured by Ed Diener (1984),

when he conducted empirical research based on a survey. Since then, many researchers

elaborated the mainstream process of understanding, defining and examining subjective

well-being in various disciplines. Despite the popularity of the topic, there is no

consensus among researchers in terms and definitions of the concept.

When it comes to the notion of quality of life, complexity and

multidimensionality overshadow the whole concept, as it is an all-embracing concept.

Finding a precise definition for QOL is challenging as both terms “quality” and “life”

reflect uncertain perspectives and paradigms. “Quality” in general is a characteristic or

attribute regarding the degree of fineness and is perceptual and conditional. The

American society of quality defines it as: "A combination of quantitative and qualitative

perspectives for which each person has his or her own definition”. “Life” itself is also a

difficult phenomenon to define. It is basically a characteristic and process that shows

existence, growth, and functional activity and contains all sorts of senses and beliefs.

Therefore, it is not surprising that the combinational term QOL is even more ambiguous

and difficult to define. Pinto et al. (2017) argued that the roots of the QOL concept in


9

sociology, geography, economics and health reflect the broad variety of dimensions,

terminologies and definitions that are linked with the concept. Among those,

“Happiness”, “Life satisfaction”, “Livability” and “Well-being” are the main notions that

address the theme in the academic literature. “Happiness” is the state of emotions,

derived from experiences and life events, and as Kahneman et al. (1999) stated it is

associated with real-time moods and feelings. “Life satisfaction” is the outcome of

evaluation and comparison of current life with ideal life, so it is the experienced life

versus the expected life (Diener et al., 2013). Furthermore, “Livability” refers to the

objective quality of life and is usually used for city rankings and material comfort in

terms of goods and services (Okulicz-Kozaryn, 2013). “Well-being” is the most frequent

term used interchangeably with QOL and as Pinto et al. (2017) advocated it reflects

both emotions and the degree of functionality in a person’s life. All in all, considering

the QOL notion and its objective and subjective implications, both “Life satisfaction” and

“Well-being” can be used to explain the construct. Fortunately, the lack of consensus

has not prevented empirical progress in the field. In the following, we will discuss the

main theories and approaches associated with the concept of QOL.

2.2.2 Theory and measurement

Over time, researchers achieved considerable progress in developing methodological

and theoretical approaches concerning QOL, and thus the field has witnessed new

perspectives and paradigms. Such advances have made QOL a field in transition, which

motivates researchers to go beyond the accepted methods and examine new domains

(Glatzer et al., 2015). As QOL studies vary based on the scale of the study, time and

context of the research, there is no universal method to measure QOL. However, two

principal approaches, hedonic and eudaimonic, contribute to QOL theoretically and

methodologically. The hedonic approach focuses on happiness and thus associates QOL

with pleasure, while the eudaimonic approach mainly addresses meaning, self-

actualization and life purpose (Sirgy, 2012). These two views have been used in many

studies as they both contain important aspects of well-being. Some studies used mixed

methods and found that some domains of life contribute to both hedonic and

eudaimonic well-being as they enable people to experience both happiness and

meaningfulness. For instance, Delle Fave et al. (2011) found that family life and social

relations contributes to both happiness and meaningfulness. They finally concluded that

it is difficult to find clear borders in implications of life domains. Table 2.1 presents

some of the most important theories on well-being that are mentioned in “The

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10

psychology of quality of life” (Sirgy, 2012).

Table 2.1 Integrative theories of QOL

Integrative

Theories of

QOL

Main characteristics Conceptualization Representative

authors

Livability

theory

Societal resources,

Personal resources,

Individual abilities.

Life satisfaction is strongly

influenced by life

experiences and is the

consequence of pleasures

and pains of life events.

Veenhoven (1995)

Capability

Theory

A long and healthy life,

Access to knowledge,

A decent standard of

living.

Capabilities determine

what people can do to

achieve their potential

Sen (1993)

Quality of the

Person+

Environment

Personal and

psychological

attributes,

Environmental

conditions.

QOL consists of

individual’s subjective and

objective sets of

circumstances. So both

psychological

characteristics of a person

and environmental

conditions determine well-

being.

Lane (1994)

Spillover

Model

Bottom-up effect of

variety of life domains.

Life satisfaction is

determined by satisfaction

from different life

domains.

Lance et al. (1995),

Brief et al. (1993)

QOL,

happiness

Short-term happiness,

Long-term happiness,

Life Satisfaction,

Absence of Ill-Being.

QOL is viewed as involving

three constructs:

happiness (short term&

long term) as the affective

component, life

satisfaction as a cognitive

Argyle (1996)


11

component and the

absence of ill-being.

Dynamic

Well-Being

The anticipation stage,

The planning stage,

The behavior stage,

The outcome stage,

The experience stage,

The evaluation stage.

Well-being

conceptualization is

dynamic and contains

different stages. Focusing

at different stages of well-

being process give

different results.

Dolan and White

(2006)

Ontological

Well-Being

and the 3P

Model

Past well-being,

Present well-being,

Future well-being.

Unifying the affective and

cognitive dimensions

along with temporal

perspective: past, present

and future in a 2 × 3

matrix

Şimsek (2009)

2.2.3 Bottom-up spillover theory

One of the main theories that has been applied in many empirical QOL studies is

bottom-up spillover theory (e.g. Woo et al., 2016). The theory conceptualizes the

relationships between life domains and overall QOL in a hierarchy structure and focuses

on present stage of life. In other words, life satisfaction assumed as a hierarchy with

overall QOL at the top of hierarchy, domains satisfaction in the middle of hierarchy, and

life events and broad situational and socio-demographic factors at the bottom of

hierarchy. This theory is also very consistent with need hierarchy theory, which the

fulfillment of the basic human needs can lead to happiness (Sirgy, 2012). Despite the

popularity of the approach, there is no agreement among researchers in terms of life

domains, and a broad variety of life domains have been included in QOL studies. Table

2.2 summarizes the life domains that have been addressed in some empirical studies.

2.2.4 QOL in environmental psychology: the concept of “person-

place-activity”

When addressing human QOL, it is essential to consider the role of place. The tie

between humans and the built environment has become the fundamental basis for a

relatively recent interdisciplinary field called environmental psychology (Proshansky et


12

Table 2.2 Life domains in QOL studies

Author

Main life domains in QOL studies

Financial Health Family Social Leisure Job

Carlquist et al. (2017) * * * * * *

Pietersma et al.

( 2014) * * * * * *

Loewe et al. (2014) * * * * * *

Dolnicar et al. (2012) * * * * * *

Sirgy et al. (2010) * * * * * *

Costa (2008) * * *

Praag et al. (2003) * * * *

Cummins et al.

(2003) * * *

Vestling et al. (2003) * * * * * *

al., 1970). Environmental psychologists are interested in the study of human-

environment interaction and predicting the conditions under which humans may behave

well. In other words, they conceptualize QOL in the relation with the capacity of

physical environment to fulfill human needs.

For many years, environmental psychologists have emphasized the

fundamental role of place in defining the individual’s self-identity (e.g. Bernardo &

Palma-Oliveira, 2016). As identity relates to concepts such as human territory and

belonging, the link between living places and perception of identity has attracted

attention. As a result, researchers found a strong association between sense of place

and favorable assessments of place. Particularly, the key role of residential places in

identity shaping and sense of attachment has been widely discussed by epistemologists.

For instance, Clark et al. (2017) asserted that place identification strongly contributes to

residential satisfaction. Later, other studies extended the perspective by defining

different geographical scales in shaping identity and focused on various spatial units in

perceived quality of living environments. This led to various studies examining the link

between neighborhood and community in obtaining sense of satisfaction. Soon after,

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13

the functionality of place and the dynamic human life in time and space broadened the

scope of the studies from static to more dynamic. Following that, researchers started

assessing places not only in terms of the identity they make, but also by their functions

and capacities of hosting different activities. In other words, daily life experiences of

individuals were assumed to be broadly influenced by activities they commit in certain

time and place. As such, work places have received a lot of attention and satisfaction

with job as an important life domain has been included in well-being studies. More

recently, mobility has broadened the perspective and some studies have focused on the

link between (dis)satisfaction one gets from mobility and overall well-being. Activities

such as social and leisure activities have also been taken into account.

This new perspective is also in line with both the hedonic and eudaimonic

approach in well-being studies. Baumeister and Forgas (2018) argued that both hedonic

and eudaimonic well-being can be achieved through engaging in activities that fulfill

human emotions and potentials. Moreover, Steger et al. (2013) used the term

“eudaimonic activities” and examined the link between eudaimonic daily activities such

as volunteer work or participating in a meaningful conversation and well-being. They

concluded that the more individuals participate in eudaimonic activities, the greater the

well-being.

2.3 Quality of Urban Life (QOUL)

Currently, most of the world population is living in cities and metropolitan areas.

Therefore, concern over the life quality in modern cities is the major reason that

scientists invented the term QOUL to discuss issues regarding contemporary life in

modern societies. A fundamental assumption underlying many QOUL studies is that

characteristics of the urban environment and physical conditions may influence the

degree of life satisfaction that residents experience. Marans and Stimson (2011),

defined UQOL as: “satisfaction that a person receives from surrounding human and

physical conditions, conditions that are scale-dependent and can affect the behavior of

individual people, groups such as households and economic units such as firms”.

Despite the methodological advances in QOL studies by social scientists, it took long

time for urban planners to shift from traditional approaches that were mainly focused

on objective measure (such as gross national income) to subjective evaluations of urban

features. Although, there is still no agreement on how best to measure QOUL, most of

the researchers have accepted the importance of subjective evaluation of different living


14

aspects (Tay et al., 2015). Considering the subjective evaluations in estimating QOUL,

urban scientists tried to adapt theories from various disciplines such as economics,

sociology and psychology. Domain–specific approaches are probably most popular in

estimating QOUL that has been borrowed from psychology (e.g. Bloch-Jorgensen et al.,

2018). The method assumes that QOUL is a function of evaluations made in different

life domains, which are the results of objective characteristics of the environment or

situational factors (e.g. socio-demographic factors). Given that the underlying

dimensions of QOUL are numerous, different studies have selected different domains,

sub-domains and attributes to investigate QOUL. Table 2.3 presents some of these

studies. However, as explained earlier, using the concept of person-place-activity from

environmental psychology can provide meaningful theoretical base for determining the

relevant domains in QOUL studies.

2.4 Conceptual framework

As stated above, the domain-specific approach based on bottom-up spillover theory can

reflect the nature of urban settings in explaining subjective well-being. Therefore, the

assumption that physical characteristic of the environment may potentially influence the

subjective assessment of the relevant domain, which in turn influences QOUL, can

perfectly be tested by this method. Moreover, overall QOUL is hypothesized to be a

function of the person-place-activity relationship, and thereby the main life domains can

be extracted from the concept. Regarding place, housing well-being and neighborhood

well-being have been included in the model, as both house and neighborhood are key

living places. In terms of main urban activities, work well-being and transport well-being

have been added to the model as both may significantly influence QOUL (Fig 2.1).

It is noteworthy to mention that these domains have been chosen due to their

direct link to the built environment, considering the fact that urban life concept is very

broad and it is almost impossible to include all possible domains within a single study.

After conceptualizing urban life, it is time to include other important life domains that

may impact QOUL. In relation to person notion, health, family and income are the three

determinant factors. Therefore, three domains of health well-being, family well-being

and income well-being have been added to the model. Furthermore, satisfaction with

social activities and leisure activities can have a huge impact on subjective well-being.

Thus, these two domains are also included in the model. In total, nine domains will


15

Figure 2.1 Life domains in relation to person-place- activity concept

explain QOUL and among those four domains are hypothesized as urban life domains

and will be discussed in separate chapters. The other five domains will be included in

the final measurement of QOUL. Moreover, all these domains may have effects on each

other as satisfaction in one life domain can spill over to other life domains. Thus, the

links among domains are also added to the theoretical framework. Socio-demographic

information is also considered in this framework, and used as a predictor of well-being

in life domains and also overall QOUL (Fig 2.2).

Accordingly, the conceptual framework is concerned with the following

concepts:

1. Detail-oriented study of QOUL by focusing on four domains of:

Housing well-being

Neighborhood well-being

Transport well-being

Job well-being

2. Overall QOUL consisting of nine major life domains.


16

Table 2.3 Summary of recent QOUL studies

Study

Author(s)/

country

Aspects of QOUL studies

(Domains/indicators)

Personal and

Neighborhood

Indicators of

Quality of Urban

Life: A Case Study

of Hong Kong

Low et al.

(2018)/ China

Subjective assessments of 11 QOL domains: Overall

standard of living, , housing, family life, social life,

financial life, health, leisure activities, employment

status, freedom /independence, amount of time for

yourself, amount of money for yourself. And,

subjective assessments of attributes in 3 levels of

living domains: Housing (both subjective and

objective indicators), neighborhood and city as a

whole.

Smart city and

quality of life:

Citizens perception

in a Brazilian case

study

Macke et al.

(2018)/ Brazil

Socio-structural relations:

Satisfaction with: safety feeling at city, safety

feeling at neighborhood, state of the streets and

buildings

Environmental well-being:

Satisfaction with: green space, cleanness, public

space, cultural facilities, air quality, presence of

foreigners

Material well-being:

Satisfaction with: life, financial situation, place I live

Community integration:

People of city are trustable, people of my

neighborhood are trustable, public administration is

trustable

Urbanism and

happiness: A test

of Wirth’s theory

of urban life

Okulicz-Kozaryn

& Mazelis

(2018)/ United

states

City characteristics: crime, housing stress, low

education, low employment, persistent poverty,

population loss, personal income (USD 1000)/cap,

and race. And, subjective satisfaction with life

From Spontaneous

to Planned Urban

Development and

Quality of Life: the

Case of Ho Chi

Minh City

Peiser (2016)/

Vietnam

Subjective Evaluation of:

Education, job, living standard, health,

accommodation, family relation, social life, financial

condition, housing quality,

pollution/noise/congestion, adequacy of park, tree,

road, school, hospital, and recreation places, quality

of basic places, and using public facilities, social

complexity, the interaction with neighbors and

overall satisfaction.

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17

Mediating Effects

Between Objective

and Subjective

Indicators of Urban

Quality of Life:

Testing Specific

Models for Safety

and Access

Von Wirth et al.

(2015)/

Switzerland

Satisfaction with: Region, city, neighborhood

Subjective assessments of safety indicators and

access indicators. Objective access and objective

threats for safety were also measured by related

attributes.

Evaluating Quality

of Life in Urban

Areas (Case Study:

Noorabad City,

Iran)

Rezvani et al.

(2013)/ Iran

Physical domain: neighborhood, house, natural

environment, garbage collection, public transport,

street condition, water quality, traffic congestion and

calmness.

Economic domain: employment opportunities, own

economic condition, distribution of wealth,

educational facilities, health care facilities and

recreational facilities.

Social domain: sense of personal security, health

conditions, happiness, sense of belonging, relations

with neighbors, reliability of inhabitants, hope for the

future, success in life.

An evaluation of

residents‟ quality

of life through

neighborhood

satisfaction in

Malaysia

Sedaghatnia et

al. (2013)/

Malaysia

Social life, good condition for children, safety,

greenery and quietness, transport and community

facilities and services such as shopping centers,

leisure facilities, schools, universities.

The Quality of Life

in Metro Detroit at

the Beginning of

the Millennium

Marans &

Kweon (2011)/

United States

Three domain of house, neighborhood and

community Indicators: Family, Health, Friends, job,

Standard of living, things want to do, Leisure time.

Measuring Quality

of Urban Life in

Istanbul

Türkoğlu et al.

(2011)/ Turkey

Looking at three levels: dwelling, the micro

neighborhood and the macro neighborhood

Other domains: family life, health, job, friends,

standard of living, leisure activities, and satisfaction

with life as a whole.

Subjective Quality

of Life in

Queensland:

Comparing

Metropolitan,

Regional and Rural

Areas

McCrea et al.

(2011)/

Australia

Four main attributes of urban environments: access

to services and facilities, noise pollution, incivilities

and social capital.


18

The Brisbane-

South East

Queensland

Region, Australia:

Subjective

Assessment of

Quality of Urban

Life and Changes

over Time

Stimson et al.

(2011)/

Australia

Overall transportation, Economic conditions, Health

services, Social conditions, Educational services,

Natural environment, General services & facilities and

Lifestyle.

The Salzburg

Quality of Urban

Life Study with GIS

Support

Keul & Prinz

(2011)/ Austria

Years of residence at the address, public transport

quality, distance to public transport, housing

satisfaction, food shops and food shop quality, use of

green space, leisure space accessibility,

neighborhood quality, assessment of safety and

threats.

A Perception

Survey for the

Evaluation of

Urban Quality of

Life in Kocaeli and

a Comparison of

the Life

Satisfaction with

the European Cities

Senlier et al.

(2009)/ Turkey

Education facilities, Quality of environment, Safety,

Public transport, Neighborhood Social and cultural

facilities, Sufficiency of health services, Quality of

health services.

The Quality of

Urban Life and

Neighborhood

Satisfaction in

Famagusta,

Northern Cyprus

Oktay

& Rustemli

(2011)/ Cyprus

QOL domains:

Satisfaction with: family life and friends, health,

job/school, standard of living, and life as a whole.

QOUL domains:

satisfaction with: the individual home or dwelling,

the immediate (microscale) neighborhood, and the

overall (macroscale) neighborhood.

Possibilities and

Limitations for the

Measurement of

the Quality of Life

in Urban Areas

Türksever &

Atalik (2001)/

Turkey

Health, Climate, Crowding, Sporting, Housing

conditions, Travel to work, Environmental pollution

Shopping facilities, Education provision, Cost of

living, Noise levels, Job opportunities, Relation with

neighbors, Parks and green areas, Leisure

opportunities, Crime rate, Accessibility to public

transportation, Traffic congestion.


19

A Method for

Assessing

Residents'

Satisfaction with

Community-Based

Services: A QOL

Perspective

Sirgy et al.

(2000)/

United States

Community, Family, Finances, Personal health,

Leisure life, Spiritual life, Job, Education, Friendship,

Neighborhood, Environment, Housing, Cultural life

and Social Status.

To briefly explain the four main urban life domains, the first is housing well-

being, which is defined by satisfaction with dwelling attributes. These subjective

evaluations are hypothesized to be influenced by various physical characteristics of the

house. The second domain is neighborhood well-being with many attributes categorized

in three sub-domains, namely satisfaction with: neighborhood characteristics,

neighborhood physical activity and neighborhood services. Moreover, transport well-

being is conceptually related to satisfaction with trip characteristics and built

environment characteristics. Trip characteristics consist of trip attributes, depend on

mode use and time frame of the trip. Built environment characteristics consist of the

attributes of neighborhood satisfaction and accessibility satisfaction.

Finally, job well-being or work satisfaction domain is defined by well-being in

three sub domains: work conditions, workspace and work location. Each sub-domain

contains relevant attributes that reflect the satisfaction with certain job characteristics.

2.5 Conclusions

Despite the considerable amount of QOL literature particularly in social science, the

concept is still surrounded with ambiguity and lack of consensus in both theory and

methodological approaches. This chapter, however, has attempted to provide an

overview of the dominant studies of QOL and QOUL, and to clarify these concepts and

theoretical background. Furthermore, the insights from this systematic literature review

provide the theoretical basis for the empirical analysis in this thesis. The summary of

the literature review has shown that the vast majority of QOUL studies have examined

well-being in different life domains using sets of indicators, and conceptualized QOUL as

the sum of satisfaction with different life domains. However, these studies have not

examined urban life domains in detail, as it requires detailed information on residents’

daily living and also separate analysis for each domain. This provides the motivation for

conducting research on QOUL to explore the extent to which urban life domains and


20

Figure 2.2 The conceptual framework

built environment factors can influence overall QOUL of individuals. To adequately

explore this question, it is essential to use the theoretical frameworks, and collect data

to operationalize the frameworks within a particular context. The following chapters

describe the data collection and analytical findings based on the concept.

Data collection and descriptive statistics

21

3

Data collection and

descriptive statistics

3.1 Introduction

The main objective of this study is to investigate the influence of built environment and

urban life factors on overall QOUL and subjective well-being of residents. As discussed

in Chapter 2, the four domains, viz. housing well-being, neighborhood well-being,

transport well-being and job well-being are defined as the key urban life domains

related to the built environment, so they will be discussed in separate chapters of this

dissertation. In order to analysis the effects of different attributes on these domains and

also overall QOUL, detailed information regarding each domain is required. The best

way to obtain this information is to collect data by designing a survey. In this chapter,

first the survey design will be systematically explained for each QOUL domain. After

discussing the selection of study areas, sample selection and statistical properties of the

samples will be reported.


22

3.2 Survey design

In this section, we will summarize all the components of survey design in a step-by-step

manner as follows:

3.2.1 Data consideration

The data requirements for this study can be segmented into six modules: Socio-

demographic information; Housing well-being module; Neighborhood well-being

module; Transport well-being module; Job well-being module and other life domains

well-being (family life, social life, leisure life, financial life and health) as well as overall

QOUL module. Before each section of survey, respondents were informed about the

objective of the questions as they were prompted to answer to detailed questions.

3.2.2 Survey instrument

The survey digitalized in a system called “Berg system” developed by DDSS unit of

TU/e. As the survey is quite long and demanding, and has aimed to focus on specific

neighborhoods in the city of Eindhoven, it was not sufficient to approach respondents

via an opt-in Panel or social media platforms (such as Buurtpreventie group). Therefore,

the door-to-door approach was used in which residents were handed a flyer (in both

Dutch and English) with an explanation of the research goals and the webpage of the

questionnaire (Fig. 3.1).

The data were collected in the summer and autumn of 2016 (from July to

November). Data were collected during different hours across multiple days of the

week, so that both part time and full time working people could be contacted. To clarify

the general purpose of the study and reduce any misunderstanding, a Dutch student

cooperated and explained the survey to residents. The questionnaire was designed into

separate parts to reduce halo effects. The response rate was 31% (222 out of 720).

3.2.3 The questionnaire

In general, the online survey was designed in a way to prevent most of the commonly

made mistakes. Therefore, except age and postal code, all other questions are closed

ended questions, ranging from multiple-choice questions to rating scale questions. Also,

in order to avoid missing data as much as possible, respondents had to answer the

questions to be able to proceed to the next page of the survey. Satisfaction was

measured using a 10-point Likert scale.


23

Figure 3.1 Flyer of the survey

As mentioned earlier, the questionnaire consists of six modules (Appendix 1 presents

the full questionnaire). On the first page, respondents were informed about the general

aim of the study. The following modules were included in the indicated order:

The first module is related to socio-demographic and general information. In this

module, respondents were asked to provide information regarding their age, gender,

education, marital status, nationality, employment status, household size, length of

Table 3.1 Number of respondents per area

in data collection approach

StrijpS Schoot Villapark Doornakkers

Total Number of

respondents

55 53 55 59

Number of

door by door respondents

27 38 55 52

Number of

social media respondents

28 15 0 7


24

residency, car ownership and also the postal code of the household. However,

household income was asked separately among the last questions of the survey and

apart from the options of income amount, the option “I don’t want to say” was also

included as income might be considered confidential for some of the respondents.

The second module concerned housing well-being. This module included questions

regarding the following attributes:

1. Ownership status: residents were asked whether they own or rent their

current property.

2. Housing cost: respondents were asked to clarify the monthly price they

pay for their property containing six options, viz. zero euro per month (in

case the households fully owned the property), 500 to 750 euro, 750 to

1000 euro, 1000 to 1250, 1250 to 1500, and 1500 and higher.

3. Dwelling characteristics: respondents were requested to clarify the

following characteristics of their dwelling:

Type of dwelling, containing four options: flat house, semi attached

house, attached house and villa house.

Age of dwelling, containing ten options: after 2010, 2001-2010,

1991-2000, 1981-1990, 1971-1980, 1960-1970, 1945-1959, 1931-

1944, 1906-1930, before 1906.

Size of dwelling, containing three options: less than 60 m2, between

60m2 and100 m2 and more than 100 m2.

Design of dwelling containing two options “It fits my lifestyle” and “It

does not fit my lifestyle”.

Exterior space of dwelling, containing seven options: balcony,

garden, roof terrace, roof terrace and balcony, roof terrace and

garden, balcony and garden, without any exterior space.

Renovation condition of dwelling, containing seven options: no need

for major repair, kitchen needs repair, roof needs repair, floor needs

repair, bathroom needs repair, piping or wiring system need repair,

different parts of house need repair.

Number of bedrooms, containing four options: No bedroom, one

bedroom, two bedrooms, three bedrooms and more.


25

Safety/security facilities of dwelling, containing three options: no

special facility for security, traditional facilities (Fence, etc.) and high-

tech facilities (CCTV, sensors, etc.).

View of the house, containing six options: green area, blocks of

houses, street with shops, highway, vacant space and other.

Parking space of house, containing two options: free parking and paid

parking.

4. Satisfaction with ownership status, housing cost and all the dwelling

characteristics. Respondents were asked to rate their degree of

satisfaction regarding all the aspects of their house on a 10-point scale,

ranging from 1 (not satisfied at all) to 10 (extremely satisfied).

5. Finally, respondent were asked to evaluate their overall satisfaction

regarding their house on a 10-point scale.

The third is related to neighborhood well-being. As subjective evaluations have been

shown to be more important in predicting neighborhood satisfaction (e.g. Parkes et al.,

2002; Oh, 2003), in this module, respondents were asked to rate their degree of

satisfaction (from 1 to 10) with various neighborhood aspects. However, these aspects

have been categorized into four main segments:

1. Satisfaction with neighborhood characteristics consists of the questions

regarding satisfaction with: safety, cleanness, greenery/landscape, noise

pollution, water quality, road/pedestrian quality, lighting, traffic

congestion, neighborhood reputation, personal attachment, livability and

housing upkeep and architecture. At the end of this part respondents

were asked to rate their overall satisfaction regarding neighborhood

characteristics.

2. Satisfaction with neighborhood physical activity (walking/biking

condition). For walking, satisfaction with traffic safety, quality of path,

safety regarding crime and air quality were asked. For biking, satisfaction

with: traffic safety, quality of path, size of road and greenery of route

were asked. At the end of this part, respondents were asked to rate their

overall satisfaction regarding neighborhood physical activity support.

3. Satisfaction with neighborhood services consists of evaluating six major

destinations at neighborhood level. These destinations include grocery


26

shopping, sport centers, cafe/restaurants, medical centers, kids'

school/daycare, park and other shopping stores which were asked to be

evaluated by travel time, time schedule, quality and crowdedness. At the

end of this part respondents were asked to rate their overall satisfaction

regarding neighborhood services.

4. Finally, respondents were asked to evaluate their overall satisfaction

regarding their neighborhood. In order to make this evaluation easier,

respondents were briefly informed how they have already evaluated

previous questions. This can be seen in Fig. 3.2.

Forth module is transport well-being. This module contains two general groups of

questions:

1. The questions regarding travel characteristics, which consist of time

frame and mode use including the travel attributes evaluation. So

basically, respondents were asked about the transport mode of their daily

trips in both peak and off-peak hours, and should have chosen among

the five options viz. “bike”, “walk”, “car use”, “public transport use”,

“both public and private modes” and “I don’t travel”. If the respondents

chose “car”, “public transport” or “both public transport and car” in the

previous question, then they were guided to the next page for evaluating

their trip attributes. Car commuters were asked to evaluate their

satisfaction with: travel time, travel cost, travel comfort, travel safety,

traffic congestion, parking availability and parking cost. While, public

transport users were asked to evaluate seven aspects, viz. satisfaction

with: travel time, travel cost, travel safety, waiting time, stop/station

distance, crowdedness, inside transport means quality.

Questions regarding built environment characteristics, which potentially

can influence travel satisfaction. In this regard, respondents were asked

to rate their satisfaction level with travel time to the major places, viz.

work place, kids school/day care, family/friends, service places and

recreational centers. More questions regarding physical environment

have already been addressed in neighborhood well-being module and

can be recalled for the analysis.


27

Figure 3.2 An example of the final page of neighborhood module

The fifth module asked about job well-being. In this module, first, respondents were

asked if they are employed or not. If they answered they were unemployed, they

automatically skipped all the questions of this module. Also, those who were employed

should clarify their employment type (full-time vs part-time). In this module three

categories were used to measure subjective work satisfaction as follow:

1. Work conditions satisfaction where respondents were requested to rate

their satisfaction with: Workload, quality of work, salary and people at

work and finally the overall satisfaction with work conditions.

2. Work location satisfaction where respondents were asked to evaluate

their satisfaction with travel time to work, availability of public transport

at work area, safety of work area, access to shops at work area, and

finally the overall satisfaction with work location.

3. Workspace satisfaction where respondents were asked to evaluate their

satisfaction with size of workspace, access to office, privacy, noise level,

and furniture/tools, and finally the overall satisfaction with workspace.

The sixth module is about other life domains and overall QOUL. In this module,

respondents were asked to evaluate the degree of their satisfaction regarding five key

life domains, viz. family life, health well-being, social life, leisure life and financial life.

Moreover, they were asked to mention if they recently had lost a person in their family.

Finally, in the last page of survey respondents were requested to evaluate their overall

QOUL as follows: “Considering everything in your life, how do you rate the overall

quality of your life?”


28

3.3 Study areas

In order to understand the role of urban living experience and built environment

characteristics on micro-level subjective well-being, sufficient variability in

neighborhoods is needed. Therefore, we collected data in four different areas in the city

of Eindhoven. Two of these areas, namely Villapark and Doornakkers are located in East

Eindhoven in the Tongelre district and the other two namely, StrijpS and Schoot in the

Western part of the city in the Oud-Strijp district (Fig. 3.3). These four areas are totally

different in terms of their socio-economic position, physical characteristics, dwellings

type and design (Fig. 3.4). StrijpS is a former industrial area, which is now becoming a

unique smart community and innovation center with strong impact on the city. About

94% of houses are rented and all residential houses are apartments, and were built

after 2000. Residents in StriipS are mostly young and educated, and combine working

and living in apartments and studios. Compared to StrijpS, Schoot has a longer

residential history, and combines old and new houses with various characteristics and

designs as is belonged to different architecture eras. Property types are also diverse,

ranging from apartments to semi attached and attached houses and villas. However, 67

% of the properties are rented. Villapark is among the most affluent areas of Eindhoven

with 78% native Dutch residents. Most houses were built before 1945 and about 68%

are owner-occupied, while just 23% are apartments. In contrast, Doornakkers is listed

among the 40 most deprived neighborhoods of the Netherlands because of its socio-

economic problems. It has the lowest average household income and highest

unemployment rate among our study areas. Most of houses in Doornakkers were built

after the second world war for working class residents, and are mostly attached or

apartment buildings. In general and according to neighborhood thermometer of

Eindhoven2, Villapark area is ranked above average, StrijpS and Schoot are average

areas and Doornakkers is below average. In addition to fundamental differences in both

the physical environment and socio-economic position, these areas have clear

geographical boundaries, which make a clear “socio-spatial schema” for residents to

evaluate their neighborhoods. Table 3.2 shows the general characteristics of the

neighborhoods and their inhabitants (based on data from Statistics Netherlands (CBS),

2018).

2 http://www.buurtthermometer.nl/


29

Figure 3.3 Location of the four neighborhoods in Eindhoven 1: StrijpS, 2: Schoot, 3: Villapark, 4: Doornakkers

3.4 Sample selection and characteristics

Of the 720 contacted households in the four areas of Eindhoven, in total 222 (31%)

completed the questionnaire. The questionnaire took between 30 minutes and one hour

to complete. To obtain a high quality dataset, a data checking and cleaning process was

carried out. This process consists of different stages: identifying invalid cases (statistical

outliers), and handling missing data. In the first step, cases with invalid postal codes

were found and eliminated (8 cases). In the second step, cross data checks were

employed to enhance data accuracy. That is, different answers were matched with each

other to detect contradictions. For instance, those respondents who chose the same

satisfaction rating for many questions were recognized as inconsistent. Therefore,

another 5 cases were removed from the original dataset. After removing 13 cases,

eventually, 209 valid cases were chosen for our analyses. Tables 3.3 and 3.4 show the

socio-demographic characteristics of the respondents and the descriptive statistics of

domains satisfaction in the four areas, respectively. The descriptive statistics shows a

larger share of female respondents (55.5%) compared to males (44.4%), and also a

larger share of young (40%) and middle-aged respondents (46%) compared to seniors

(14%). Regarding education, as seen in Table 3.3 higher education is heavily

overrepresented in the sample. In terms of household income, as Figure 3.5 shows, the

income level of respondents is quite representative of the areas’ population, since most

respondents in Villapark are among the highest income households, while the

proportion of low-income residents is the highest in Doornakkers area.


30

Figure 3.4 Local dwelling streets of 1: StrijpS, 2: Schoot, 3: Villapark, 4: Doornakkers


31

Table 3.2 General situation of the four neighborhoods

Neighborhood

1 to 4

Area

(km2)

Number

of

houses

Owner-

Occupied

houses

Rental

houses

Men/

Women

Single

/Two

adults

Family

With

kids

Average

income

(household)

StrijpS 0.30 1012 953 54 806/

632

646/

289

40 29.9 k

Schoot 0.39 1532 1208 504 1464/

1263

795/

357

258 33.2 k

Villapark 0.56 815 552 263 1089

/950

239/

242

263 58.5 k

Doornakkers

1.22 2993 1153 1890 3332/

3059

1186/

742

787 28.7

Eindhoven 88.92 108.682 51225 57424 117.602

111.522

41.787

/29.425

28.565 36.6 k

Overall, the sample is not completely representative of the population of our

study area. However, overrepresentation of women and higher educated people in the

sample is common for most on-line surveys (e.g. Van den Berg, 2012). Moreover, as

shown in Table 3.5, the majority of households concerns renters (57%), living in non-

flat (59%) and small houses (57.5%). About 55% of the houses are old (more than 20

years old), and the majority has an exterior space (88%), and more than three

bedrooms (52%).

A greater number of households (58%) pays a low price for their housing (less

than 750 euro monthly), and 53% live less than 5 years in their current property. Most

houses are in good condition and do not need any repair (77%), and a great portion of

them is equipped with safety/security facilities (65%). In addition, people are generally

the most satisfied with the type and age of their dwellings, and the least satisfied with

the housing price, although among the car-owners households (78.0% of total)

satisfaction with house parking has the lowest ratings (Table 3.6). Regarding

neighborhood characteristics, air quality for walking and safety received the minimum

average satisfaction, which could indicate some social and environmental issues in our

study area (Table 3.7).


32

Table 3.3 Description of social-demographic data (N=209)

Variable Category Number Frequency (%)

Gender Female 116 55.5

Male 93 44.5

Age Age 20-35 years 84 40

Age 36-64 years 96 46

Age>65 years 29 14

Living status Single 70 33.5

Non-single 139 66.5

Having kid (s) Household with kids 60 28.2

Household without kid(s) 149 72.8

Education Low educated 40 19

Higher educated (college degree) 168 81

Nationality Dutch 190 90.9

Non Dutch 20 9.1

Job Employed 130 62

Unemployed 79 38

Employment

status

Part time 53 25

Full time 77 37

Household size 1 person household 73 34.9

2 persons household 80 38.3

3 or more persons 56 26.9

Household

income

(11% missing)

below 2000 euro 62 29.7

2000-4000 euro 67 32

4000 euro & higher 35 27.2

Car ownership No car 44 21.1

car owner 165 79.9


33

Figure 3.5 Chart of household income in study areas

Regarding transportation, bike and motorbike are the most used transport

modes during both peak and off-peak hours with around 36% of the commuters in peak

hours, and 48% of the commuters during off peak hours. These commuters are also the

most satisfied group of travelers (with a mean satisfaction of 8.17 for peak hours and

8.22 for off-peak hours). The car is the second most preferred transport mode (27.8%),

and the number of car commuters during peak and off peak hours is quite close, as well

as their mean satisfaction (7.68). Generally, during peak hours, bikers and non-travelers

are the most satisfied commuters, but during off peak hours those who walk and bike

are the most satisfied group. Table 3.8 lists the frequency of using different travel

modes during both peak and off-peak hours and the mean satisfaction of commuters.

As most of the variables in housing section of survey are categorical variables

with several categories, for further analysis, it is necessary to combine and merge

various categories (with small frequencies) into a new category, which is presented in

Table 3.6.


34

Table 3.4 Descriptive statistics of life domains satisfaction

STRIJPS SCHOOT VILLAPARK DOORNAKKERS

Mean SD Mean SD Mean SD Mean SD

Housing 7.92 1.36 7.8 1.4 7.2 .8 7.6 1.3

Neighborhood 7.7 1.27 8 .9 7.96 .69 7.6 1.3

Job 7.6 .98 7.8 .86 7.82 .79 7.2 .92

Transport 7.6 1.0 7.1 .93 7.6 .92 7.4 1.04

Social life 7.7 1.09 7.6 1.2 8 .67 7.6 1.3

Health 7.7 1.04 7.7 1.4 8 1.2 7.48 1.84

Income 7.4 1.4 7.8 1.7 7.1 1.4 7.6 1.8

Family life 7.7 1.5 7.9 1.8 7.2 2.09 8 1.6

Leisure life 6.63 2.4 7.54 1.96 7.89 1.42 7,13 2.31

Overall QOUL 7.6 1.1 7.7 1.2 7.8 1.3 7.48 1.1

Table 3.5 Descriptive statistics of satisfaction with housing characteristics

Ow

ners

hip

type o

f house

size

of

house

Bedro

om

s num

ber

Exte

rior

space

Age o

f house

Vie

w o

f house

Renovation s

ituation

Desi

gn/

layout

Safe

ty/

secu

rity

Month

ly P

rice

Park

ing

Mean

Satisfaction

7.91 8.00 7.93 7.67 7.25 8.00 7.6

8

7.83 7.97 7.57 7.34 5.91

Std.

Deviation

1.54 1.53 1.57 1.79 2.23 1.61 1.7 1.63 1.54 1.31 1.74 3.73


35

Table 3.6 Description of housing data, and categories of categorical variables

Variables Original categories survey Number New categories after

combining & recoding/

frequency (%)

Ownership Rent 120 Rental house/ 57%

Own 89 Owner occupied house/ 43%

Type of House

Flat 86 Flat/ 41%

Attached 74 Non-flat/ 59%

Semi attached 35

Villa 14

Size of house Less than 60 48 Small house / 57.5%

60-100 72

More than 100 89 Large house/ 42.5%

Number of bedrooms Zero 30 Continuous variable

One 36

Two 34

Three and more 109

Construction year of

House ( age of

house)

After 2010 58 New house/ 45%

2001-2010 36

1991-2000 13 Old house/ 55%

1981-1990 8

1971-1980 3

1960-1970 6

1945-1959 30

1931-1944 11

1906-1930 41

Before 1906 3

Exterior Space Balcony 25 With exterior/ 88%

Garden 91

Roof terrace 30

Balcony and Roof terrace 2

Balcony and garden 18

Garden and roof terrace 18

No exterior space 25 No exterior/ 12%


36

Housing price 0 Euro 23 Low price / 58%

500-750 Euro 97

750-1000 Euro 52 High price/ 42 %

1000-1250 Euro 14

1250-1500 Euro 12

1500 Euro & higher 11

Renovation situation

No need for major repair 161 No need to repair/ 77%

Roof needs repair 7

Needs repair/ 23%

Floor needs repair or change 3

Bathroom needs repair or change 11

Kitchen needs repair 4

Piping /wiring system 1

Different parts need repair 22

Main view Green area 59 Green view/ 28%

None Green view

/ 72%

Blocks of house 108

Street with shops 10

Highway 1

Vacant space 5

Other 26

Design/ layout

It fits my lifestyle 184 Design fits/ 88%

It doesn't fit my lifestyle 25 Design not fits/ 12%

Safety/ security

no special facility 78 No security/ 36.5%

some traditional security system 107 With security facility/ 62.5%

some high-tech security system 24

Length of residency Less than 1 year 23 Continuous variable

1 to 2 years 30

3 to 5 years 58

5 to 15 years 60

15 years or longer 38

Parking Free parking 111 Free parking / 53.1%

No free parking 98 Paid parking /46.9 %


37

Table 3.7 Descriptive statistics of satisfaction with neighborhood characteristics

Mean

Std.

Deviation Mean

Std.

Deviation

Noise pollution 6.59 2.017 Air quality 6.40 7.505

Traffic congestion 6.75 1.800 Architectural appearance 7.65 1.646

Air quality when walking 5.00 7.899 Personal attachment 7.36 1.819

Image/reputation 7.79 1.676 Cleanness 7.30 1.554

Quality of path for biking 6.30 3.280 Road/ pedestrian quality 7.63 1.533

Quality of shop (grocery) 7.78 2.481 Safety 7.29 1.406

Safety regarding crime for walking 4.82 10.724 Green space 7.23 1.753

Traffic safety for walking 4.79 10.719 Lighting 7.96 1.349

Traffic safety for biking 5.73 3.058 Livability 7.28 1.467

Road size for biking 6.30 3.211 Neighborhood services 7.79 1.015

Green route for biking 6.08 3.184 Neighborhood physical activity 7.35 1.337

Time schedule (grocery) 7.94 2.555 Neighborhood characteristics 7.59 1.218

Travel time (to grocery) 7.73 2.722 Overall neighborhood 7.83 1.103

Table 3.8 Descriptive statistics of satisfaction with transport characteristics

Mode of transport

Private (car)

Public (Bus, train, tram)

Both public and private

Bike/ motor bike

walk

No trip

Peak hours

Frequency

27.8%

7.67

8.1%

7.88

4.3%

7.44

35.9%

8.17

4.3%

7.33

19.6%

8.17

Mean satisfaction

Off-peak hours

Frequency

28.7%

7.68

6.2%

7.15

8.6%

7.67

48.3%

8.22

6.7%

8.43

1.4%

6.63

Mean satisfaction


38

Table 3.9 Descriptive statistics of satisfaction with work characteristics

Employment Frequency

Overall

work

satisfaction

Work

location

satisfaction

Work

conditions

satisfaction

workspace

satisfaction

Part-time

Mean 7.55 7.58 7.55 7.36

41% Std.

Deviation .972 1.379 .992 1.272

Full-time

Mean 7.70 7.62 7.66 7.56

59% Std.

Deviation .947 1.478 1.021 1.082

Total

Mean 7.64 7.61 7.62 7.48

100% Std.

Deviation .956 1.433 1.007 1.163

3.5 Conclusions

In this chapter, based on our theoretical framework, the design and administration of

the survey was presented. This survey aimed at collecting data on residents’ satisfaction

regarding their living experience in urban neighborhoods and their overall life well-

being. To capture the link between urban life characteristics and well-being, data were

collected from four urban neighborhoods with completely different characteristics. Data

collection was done on various days of the week in two seasons (summer and autumn

2016), so the weather influence on mood and subjective assessments was reduced to

some extent. As the survey was long and demanding, the response rate was not high,

and therefore, data collection took a long time. However, the high-quality data is well

worth the effort. The high-quality data can be justified for several reasons: first, we had

informed respondents upfront that the survey had been designed for academic research

and was anonymous. Second, the survey website had a high quality surface and was

easy to use (based on click and enter). In general, online surveys are more functional

compared to interviews or paper-based surveys and needs less effort for respondents.

Third, we conducted a pilot survey and based on the feedbacks both questions and the

digital surface were improved. For instance, we created a responsive framework

website, which means that the survey tables automatically changed to fit any device

(such as mobile phone).

Housing well-being

39

4

Housing well-being

4.1 Introduction

Adequate housing has always been acknowledged as a basic need, and an

indispensable part of everyone’s life (e.g. Fernandez-Portero, 2016). Scholars, who

studied the effects of different physical constructs upon quality of life, illustrated the

potential insights that can be derived from housing research. Several empirical studies

found a direct link between quality of life and housing satisfaction, which resulted in the

recognition of housing as a criterion for determining quality of life (e.g. Burton et al.,

2011; Costa-Font, 2013; Fernández-Carro, et al., 2015; Fernández-Portero et al., 2017;

Zebardast & Nooraie, 2018; Zhang et al., 2018). On the other hand, whereas housing

satisfaction research has received substantial attention in marketing, urban economics

and sociology, it has only received marginal attention in the well-being research. This

https://www.sciencedirect.com/science/article/pii/S0272494416300962#bib7




Housing well-being

40

little emphasis on housing in terms of quality of life measurement highlights the gap in

this research stream and the need for more detailed studies on this interdisciplinary

field.

In formulating the conceptual model of this PhD research, the concept of place

plays a fundamental role, and helped capturing more insights on the relationship

between human well-being and the surrounding environment. Here, housing will be

considered as an important physical component of the built environment that affects

residents’ satisfaction. Therefore, housing satisfaction contributes to quality of life as a

main domain, and thus, this chapter will focus on it. Explicitly, the following research

questions will be addressed:

I. What are the main predictors of housing satisfaction?

II. To what extent do these indicators contribute to predicting housing

satisfaction?

III. What are the relationships among these indicators and to what extent do they

affect each other?

4.2 Literature review

Over the past 50 years, many studies have been carried out to examine housing

satisfaction. Comparing the current studies with the older ones shows that there have

not been so many changes in the concepts, terminology and definitions associated with

housing satisfaction. However reviewing these definitions, concepts etc. in other

researchers’ work provides insights and a good platform for our conceptualization.

4.2.1 A place called house (terminology and definitions)

Human beings understand the world through their senses and the information they get

from the environment, where their lives occur. In the epistemic relation between human

and environment, they both influence each other and the existing interactive system

among them. House in this regard is “a product of the society in which we live in”

(Massey, 1995). This enables personal and interpersonal relationships by having

fundamental impact on our perception toward life (Biswas-Diener & Diener, 2006).

“First we shape the place, then places shape us” (Winston Churchill), this

statement has proven to be true, and house in this sense is not just where we live, but

a sign that manifests who we are and how we live, and that’s why it has been an

Housing well-being

41

evidence for civilization. In fact, a place, which labeled as a house is beyond a physical

notion, but rather a source for stability, identification and recognition, which in turn

should provide us, comfort, safety and a foundation for our spiritual life (Easthope,

2004; Vera-Toscano & Ateca-Amestoy, 2008). This implies that housing contributes to

different level of needs at the hierarchy of needs (Maslow, 1954), started from a

material structure responding to physiological needs and upgraded to higher level of

motivational factors (Zavei & Jusan, 2012). In general, house, as a spatial unit in

connection with its surrounding, has different functionalities. In its micro scale, it is a

shelter that reflects living style and culture of its inhabitants. In the bigger scales, it can

structure social life and be a demonstration for the attachments to the living

environment (Jiboye, 2014; Aragonés et al., 2017). Therefore, it is an agent that

intertwines between the personal and interpersonal environments and can be affected

from both inside and outside. There is an interactive system between the way of living

and the environment and this relation is a mutual influential one. House, in this regard,

is the most substantial place that can affect perception toward life and can reflect social

realities (Easthope, 2004). Staying in a place that fulfills most needs and aspirations is

essential and since needs and aspirations change during the life cycle, housing

satisfaction should be considered as a dynamic process (Ogu, 2002). Therefore, based

on different stages of life, individual demand for housing services varies.

In summary, housing is as an inevitable demand and homeownership is a sign for

proper financial situation. Having a house is always a big investment and an important

financial decision since it is the most expensive object that someone can purchase in

one’s lifetime (Flavin & Yamashita, 2002; Dusansky & Koc, 2007). Therefore, the

conception of house is closely linked with the economy. Following utility theory,

everyone wants to derive the maximum utility out of their house, while the various

individual constraints lead to different levels of house satisfaction (Makinde, 2015).

Moving for a better accommodation at different life stages of individuals brings the topic

of mobility to housing research literature.

In general, housing research is a huge field beyond individual and household

scale, and is also connected with regional, national and international issues such as

migration, economic decline (recession) and other related topics. While there is an

extensive amount of literature on housing, the majority of these studies is dedicated to

housing displacements and mobility, and thereby, the focus has been on residential

satisfaction as a combination of neighborhood and dwelling satisfaction. However, very

Housing well-being

42

few studies have examined the link between housing satisfaction and well-being

(Dekker et al., 2011; Li & Wu, 2013). On the other hand, the topic of housing is

associated with diverse sets of disciplines ranging from architecture and engineering to

psychology, sociology etc. and thereby, is an interesting topic for designers, planners,

developers and policy makers who attempt to address the community needs by

providing better housing situations (Dekker et al., 2011).

The prominent aim of most housing studies is to examine residential situations in

order to understand housing demands and future consequences on the surrounding

urban environment. Most research on housing satisfaction has been performed in socio-

behavioral sciences and has looked at housing satisfaction as an essential explanatory

input to study migration patterns, affordable housing and residential mobility. Apart

from large governmental housing studies, most other studies narrow their research to

get more detailed findings. Among those, some have focused on specific socio-

economic groups of residents such as low income families (e.g., Addo, 2016), senior

citizens (e.g., Temelová & Dvořáková, 2012), ethnics groups (e.g., Hall &

Greenman, 2013), migrant workers (Tao et al., 2015), etc. In addition, several studies

have examined satisfaction in a certain type of houses, such as those who have focused

on public housing (e.g., Mohit & Nazyddah, 2011), informal housing settlements (e.g.

Mitchell et al., 2018) and low cost housing (e.g., Makinde, 2015). Nevertheless, in some

studies, housing satisfaction has been assessed to understand residents’ subjective well-

being (e.g. Fernández-Portero et al., 2017; Aragonés et al., 2017).

4.2.2 Theoretical concepts

Housing satisfaction is a cognitive, multi-dimensional concept, which involves a process

of complicated judgments and can be assessed through various methods. The

satisfaction process starts from a comparison between what we have and what we want

to have and several key theories have been formulated based on this fact. An example

is “gap theory” (Galster, 1987) which is centered around the gap between the actual

and desired housing situation and the theory of “person–environment fit” which has

been applied by Kahana et al. (2003) to explore residential satisfaction. Other studies

use similar theories to explore the consequences of housing dissatisfaction on the

patterns of migration and residential mobility (Jiang et al., 2017). In addition to these

theories, the concept of adaptation and housing adjustments serves a different

perspective and explains the process of matching residential needs with characteristics

https://scholar.google.nl/citations?user=zAeZQDMAAAAJ&hl=en&oi=sra

https://scholar.google.nl/citations?user=swvOzHwAAAAJ&hl=en&oi=sra

https://scholar.google.nl/citations?user=KEGlzQUAAAAJ&hl=en&oi=sra

http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9906.2011.00549.x/full#b36

Housing well-being

43

of a house during a period of time. This can result in reducing the deficits and can

improve residential satisfaction (Wang & Wang, 2016).

In general, housing satisfaction is theorized based on the degree of which actual

housing performance meets the household current needs and aspirations (Ogu, 2002).

It contains numerous factors with a complex relationship among them. However, the

extent to which these factors contribute to satisfaction is not clear, and the interrelation

among these factors needs to be explored based on the contextual differences.

Therefore, prior to discussing methodological approaches, we should define the main

contributors to housing satisfaction, which can be supported by the existing scholarly

literature.

4.2.3 Factors determining housing satisfaction (Indicator sets in housing

research)

Studies on housing satisfaction develop various indicator sets according to the purpose

and rationales of that specific research. Amongst these indicators, individuals’ socio-

demographic attributes, dwelling’s characteristics and perceptions of housing conditions

have been the most widely used factors. To study these main attributes, we classified

them based on their relevance and popularity in the scientific literature and study them

separately. Therefore, four categories of attributes starting from socio-demographic

factors and then length of residency, ownership and cost and finally dwellings’ physical

attributes are discussed in more detail.

4.2.3.1 Socio-demographic factors

Different households have different expectations and desires about their house. These

expectations come partly from household demographic characteristics such as age

composition, household size, household income etc. According to the literature of

housing, socio-demographic and economic characteristics of households play a

considerable role in housing satisfaction. Age, gender, marital status, education,

employment status, income, number of households and length of residency are the

most studied indicators. However, there is no consensus among the findings of

researchers in terms of the positive or negative effects of these factors on housing

satisfaction. Among the personal and household socio-economic variables, income has

proven to have the clearest effect on households’ satisfaction, since it has a direct

relation with obtaining desired material life and as a result enabling residents to have

Housing well-being

44

suitable housing. Therefore, higher income usually contributes to higher level of

satisfaction (e.g. Huang et al., 2015).

Education level is among the controversial factors, Lu (1999) and Oh (2003)

referred to education as an insignificant factor, while some researchers (e.g. Vera-

Toscano & Ateca-Amestoy, 2008) mentioned that higher education level has a positive

influence on housing satisfaction as it can contribute to a better employment and

economic situation. In contrast, being well educated might also increase expectations of

living standards and accordingly brings lower satisfaction. Age is another relevant

variable in the housing studies, as different generations have different needs. Age

differences also address broader differences in life course and physical decline that can

contribute to certain type of needs. However, age of the households referred as both

significant and insignificant predictor in different housing studies (e.g. Baum et al.,

2010; Mohit et al., 2010). For instance, Salleh et al. (2011) advocated that older tenants

have higher satisfaction with their housing conditions than younger tenants, which is

also confirmed by and Dekker et al., (2011). However, there is another argument that

mentions older people spend more time at their houses so they would expect to have

higher expectations of their house, which can contribute to lower residential

satisfaction. Regarding other age groups, results from several studies state that age

differences among young and middle-aged have not significantly contribute to a higher

or lower residency satisfaction.

Gender differences based on the assumptions regarding the biological aspects

has been examined in many built environment studies, and particularly in relation to

physical activity and perceived safety (e.g. Amole, 2010). Hence, most gender studies

focused on spatial issues such as way finding, crowding, density etc. rather than

preferences on housing and other physical constructs (Amole, 2010). Very few studies

found significant differences between males and females in terms of housing

preferences. For example, Foye (2017) found that there is a positive but weak

relationship between house size and men’s well-being. Also Aiello et al. (2010) argued

that males are less satisfied with their housing than females, which was later confirmed

by the study of Ibem and Amole (2013). The higher level of housing satisfaction among

females has been explained through higher coping abilities and the stronger emotional

attachment they can make with their environment. Marital status has also been proven

to be an influencing parameter, and married households have shown a higher level of

satisfaction compared with single individuals who have never got married. Employed

Housing well-being

45

people, even those with a part time job may show a higher level of housing satisfaction

as having a job can provide a more stable financial situation for them in compared with

those who are unemployed. Despite this fact, most previous studies have not found any

significant relationship between employment status and housing satisfaction, and even

some studies have observed a negative correlation among them. Family size and

number of children can tell a lot about the socio-demographic position of households

and can determine satisfaction, as many scientists believe that an increasing number of

family members can lead to decreasing housing satisfaction (Theodori, 2001).

4.2.3.2 Length of residency

Length of residency is normally studied among socio-demographic factors, but it has

received special attention in many studies, and been found as a factor with significant

influence on housing satisfaction (e.g. Mohit et al., 2010). Living duration is associated

with the development of sense of belonging to the residency, which is an important

human need and has been listed in Maslow's hierarchy of needs (1954). This has been

shown to be consistent with the theory of adaptation and previous research on housing

among various groups such as adults, ethnic groups, migrants etc. (e.g. AhnAllen et al.,

2006; den Besten, 2010). In brief, length of residency can develop a sense of

attachment to the dwelling (Adriaanse, 2007), and enhance the coping abilities of the

occupants. Therefore, it can be counted as an important indicator in housing

satisfaction measurements. On the other hand, it is inevitably related to home

ownership, as home owners generally stay longer than renters (Diaz-Serrano, 2009). In

other words, length of residency can be a sign for stable financial situation, which can

increase the likelihood of satisfaction. However, as many researchers have already

mentioned, living longer can also decrease the housing satisfaction, because of the

dropping quality of the dwelling by aging and also changing the needs of the occupiers

over time (e.g. Wang & Wang, 2016).

4.2.3.3 Ownership status and housing cost

Home ownership has been found to be an influential factor in residency evaluation since

it provides residents with sense of control and security (Jussila et al., 2015). Lu (2002)

argued that house renters have less control over their house in terms of changes, repair

and maintenance, and as a result housing satisfaction has a lower level among renters.

On the other hand, renters have fewer ties to the house as they feel it is a temporary

place, while owners can establish a strong sense of stability and higher satisfaction

Housing well-being

46

during the years. Also Elsinga and Hokstra (2005), who did a research on housing

satisfaction in eight European countries, found that owner-occupiers are happier than

tenants in general, and just in one of those countries, satisfaction was not significantly

different among owners and tenants.

Many authors tend to support a positive relation between cost of housing and

satisfaction, as higher cost houses can have a better quality (e.g. Ismail et al., 2018). At

the same time, living in a low cost rental houses should not necessary bring lower levels

of satisfaction as Ibem and Aduwo (2013) reported that residents in low cost public

houses were mostly satisfied with their houses’ conditions. However, cost of housing

and rent price of a house can be a reason for dissatisfaction as well. Accordingly, cost

should be matched with the condition of the dwelling and if a tenant feels that there is

a possibility of finding a better quality house with the same price, or even same quality

and lower price, then this would lead to dissatisfaction. Additionally, cost of housing

could cover monthly maintenance costs, which can be relevant for apartment houses

and those with shared spaces. These kinds of costs also can be seen as unfavorable

costs at some cases and can decrease housing satisfaction.

4.2.3.4 Dwelling physical characteristics

Physical attributes of a dwelling are major contributors to housing satisfaction and poor

physical house characteristics can result in certain level of dissatisfaction. These

attributes include structural attributes such as type, age, size, design, exterior space,

interior quality, number of bedrooms, safety/security facilities and parking space of

house (e.g. Baum et al., 2010; Hipp, 2010). Among those, size, type and design of the

house are reported to have profound influence on the housing overall satisfaction. For

instance, Chen et al. (2013) found that bigger size and better housing facilities can

influence housing satisfaction positively. Roberts-Hughes (2011) concluded that small

sizes house and shortage of space have resulted in dissatisfaction and consideration for

moving home for some residents. Jiboye (2014) found that type of house has a

significant impact on residential assessments. Aulia and Ismail (2013) also found that

type of residence is an important physical factor among middle-income households.

Buys and Miller (2012) concluded that satisfaction with dwelling design play a significant

role in residential satisfaction particularly for higher density housing. Table 4.1 shows

the major contributors to the house satisfaction in the selected recent literature.

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47

4.2.3.5 Subjective evaluation of all the house objective characteristics

The vast majority of housing studies have focused on the physical characteristics

(functionality and aesthetics) of dwellings including housing cost and ownership status,

while some other have addressed the perceived satisfaction of households. Recently,

some empirical studies have used both objective characteristics and subjective

evaluation of residents to assess the degree of house satisfaction (e.g. Sealy–Jefferson

et al., 2017). This approach reflects the effects of dwelling conditions on the

satisfaction, and thus can provide more information on what residents like or dislike

about their house.

Table 4.1 Different literature regarding the major contributors to the house satisfaction

Scientific literature (Author’ s name)

Housing Variables

Socio-demographic attributes Dwelling characteristics attributes

Ag

e

Gen

der

Ed

uca

tio

n

Ma

rita

l sta

tus

Ho

useh

old

siz

e

Ho

useh

old

in

co

me

Len

gth

of

resid

en

cy

Ow

ners

hip

sta

tus

Typ

e o

f H

ou

se

Siz

e o

f h

ou

se

Inte

rio

r Q

ua

lity

Ex

teri

or

Sp

ace

Nu

mb

er

of

bed

roo

ms

Pri

ce o

f h

ou

sin

g

De

sig

n

Ag

e o

f h

ou

se

Sa

fety

/se

cu

rity

fa

cil

ity

Pa

rkin

g

Fernandez-Portero

et al. (2017) * * * * * * * * * * * * *

Azimi & Esmaeilzadeh

(2017) * * * * * * * * * * * * *

Wang & Wang

(2016) * * * * * * * * * * * *

Huang et al. (2015) * * * * * * * * * * * * *

Pekkonen & Haverinen-

Shaughnessy (2015)

*

*

*

*

*

*

*

*

Zumbro (2014) * * * * * * * * * * * * * * * * *

Mohit &

Nazyddah(2011) * * * * * * * * * * * * * * *

Li &Sung (2009) * * * * * * * * * * * * * * * *

Ioannides &

Zabel(2008) * * * * * * * * * * * * *

Vera-Toscano & Ateca-

Amestoy (2008) * * * * * * * * *

* *

Housing well-being

48

4.3 Multiple regression analysis

In order to explore the implicit effect of various house dimensions on the residents’

overall assessment of their house multiple regression analysis was conducted with

overall house satisfaction as the dependent variable and 31 independent variables

(including socio-demographic variables). The reason that we first used this method is

that regression serves as a primary method to shed light on more fundamental relations

without addressing the complex relationships among variables. Also, the reason that we

chose regression from various alternative methods (ordered logit, etc.) is that the

estimated responses were checked and were positive.

We assumed that the scales, which used to measure satisfaction have interval

properties (same degree of difference between items) based on many studies that

utilized such scales (e.g. Diaz-Serrano & Stoyanova, 2010). Moreover, as the number of

respondents is relatively low compared to the number of variables, we have to reduce

the number of estimated variables. So, all multiple categories were merged into binary

categories. House size was divided into “Small house” and “Non-small house”. House

type was divided into “Flat” and “Non-flat”. Age of house includes “New house” and

“Old house”. “Exterior space” was divided into “With exterior space” and “No exterior

space”. Monthly price of housing was merged into “Low cost house” and “High-cost

house”. House view was divided into “Green view” and “Non green view”.

Design/layout refers to “Design fits” and “Design does not fit lifestyle”. Safety facility

divided into “With security facility” and “No security facility”. Renovation situation

contains “Needs repair” and “No need to repair” and parking situation was divided into

“Free parking” and “No free parking” (Table 3.6).

Then, to examine our conceptual model by multiple regression analysis, the

overall house satisfaction treated as the dependent variable and examined by 31

independent variables. Since one of our regression assumptions is that the residuals

(prediction errors) are normally distributed, therefore, checking the histogram of

residuals allows us to check the extent to which the residuals are normally distributed.

The well-behaved residuals can lead to correct P values and thus are very important for

any linear regression analysis. Our histogram (Figure 4.1) shows a fairly normal

distribution. Thus, based on these results, the normality of residuals assumption is met.

Furthermore, the normal P-P Plot of regression residuals (Figure 4.2) can also be used

to assess the assumption of normally distributed residuals. If data points (little circles)

follow the diagonal line, the residuals are normally distributed. Our P-P plot shows

https://www.spss-tutorials.com/spss-stepwise-regression-example-2/#assumptions

https://www.spss-tutorials.com/histogram-what-is-it/

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49

Figure 4.1 The Histogram of the overall housing satisfaction

Figure 4.2 Normal Probability Plot (P-P) of the regression standardized residual dependent variable for overall house satisfaction

Housing well-being

50

minor deviation and thus the normality of residuals assumption is satisfied. The results

about R-squared (R2) and adjusted R-squared are shown in Table 4.2. It shows that the

indicators accounted for 80 percent of variance of the overall house satisfaction.

4.3.1 Interpretation

Regression coefficients in this analysis represent the magnitude of the influence of the

explanatory variables on the residents’ overall house satisfaction (Table 4.3). Among all

of the indicators in this analysis, ten variables show a significant relationship with the

overall house satisfaction (P<.05). From the socio-demographic variables just “Highly

educated” variable have a strong positive correlation with the dependent variable. “New

house” variable from the objective characteristics of the house also is significantly

related to the “Overall house satisfaction”. The other significant predictors are all from

the subjective category and included “Ownership satisfaction”, “Design satisfaction”,

“House-type satisfaction”, “Safety satisfaction”, “Exterior space satisfaction”,

“Renovation situation satisfaction”, “Parking satisfaction” and “House size satisfaction”

(Table 4.3).

Among the significant predictors, the only variable that shows a negative

relation with the overall house satisfaction is “New House” variable (-.10), which

indicates the houses that were built after year 2000. Unexpectedly, “Highly educated”

variable shows a significant relationship with overall house satisfaction (.08). The

significancy is mainly due to the fact that this variable presents a large group of

samples (residents with any collage degree), which is almost 81% of our entire sample

size. Apart from this variable, the other independent variables that have strong positive

correlation with the overall housing satisfaction are “House-type satisfaction” (26),

“Design satisfaction” (.20) and “Ownership satisfaction” (.19), respectively. In contrast,

“Renovation situation satisfaction” influences the overall house satisfaction with the

lowest effect size (.10), following by “Parking satisfaction” (.09). The results clearly

indicate the importance of subjective variables compared with the objective

components.

Although multiple regression analysis provides some insights into the impact of socio-

demographic characteristics and all the mentioned house-related characteristics on

overall house satisfaction, but this method is unable to capture the indirect relationships

and the complex associations among these variables. Thereby, further analysis is

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Table 4.2 Goodness-of-fit of the regression model of overall house satisfaction

R R2 Adjusted R

2 Std. Error of the Estimate

.895 .801 .766 .632

Table 4.3 The estimated parameters of the house satisfaction using multiple regression analysis

Model Parameters

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

Socio-

demographic

indicators

(Constant) .107 .513

.207 .836

Age -.001 .003 -.017 -.402 .688

Household income .045 .032 .058 1.382 .169

Male -.047 .097 -.018 -.477 .634

Single person -.021 .129 -.008 -.163 .871

Highly Educated .270 .124 .082 2.173 .031

Household Size .045 .057 .041 .784 .434

Length of residency .049 .041 .046 1.198 .233

Objective

characteristics

Number of bedrooms -.015 .051 -.010 -.287 .774

Flat .260 .156 .098 1.665 .098

Small house -.158 .137 -.060 -1.147 .253

New house -.262 .127 -.100 -2.056 .041

No exterior space .006 .167 .002 .036 .971

Low cost house .139 .107 .053 1.297 .196

Needs repair .069 .122 .022 .569 .570

Green view house .063 .121 .022 .518 .605

No security facility .062 .101 .023 .614 .540

Design does not fit lifestyle -.127 .148 -.032 -.861 .390

Rental house -.169 .150 -.064 -1.127 .261

Free parking -.157 .140 -.060 -1.121 .264

Parking satisfaction .037 .014 .107 2.641 .009

Ownership satisfaction .161 .049 .185 3.250 .001

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52

Subjective

evaluation

House-type satisfaction .227 .051 .262 4.468 .000

House size satisfaction .118 .053 .141 2.241 .026

Number of bedrooms

satisfaction

-.017 .039 -.024 -.440 .660

Exterior space

satisfaction .082 .028 .141 2.897 .004

Age of house satisfaction -.003 .038 -.003 -.070 .944

House view satisfaction -.013 .036 -.017 -.364 .716

Renovation situation

satisfaction .076 .038 .091 2.014 .045

Monthly price satisfaction -.021 .032 -.029 -.646 .519

Safety satisfaction .138 .041 .139 3.339 .001

Design satisfaction .178 .044 .208 4.033 .000

necessary to understand the mechanism of effects among the study variables, which

will be explained in the next part.

4.4 Analysis and results: Path analysis

The key objectives of this chapter are to evaluate and understand the interrelationships

among the study variables in formulating house satisfaction. A path analysis is deemed

to be an adequate solution for the achievement of these objectives, thus it is used in

this chapter. By this method, it is possible to conduct simultaneous assessments of the

associations among multiple dependent and independent attributes, which can increase

the statistical efficiency (Hair Jr et al., 2016).

4.4.1 Conceptualization

Based on the aforementioned insights from different housing studies and well-being

research, and the relevance of these studies to investigate the link between different

house attributes (objective and subjective) and socio-demographic variables as well as

the overall subjective assessment of house, a conceptual model is elaborated and will

be discussed in this section. To develop the conceptual model, first the main

determinants of housing satisfaction categorized in three different groups (Figure 4-3).

The first category is the socio-demographic variables of the residents including the

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53

Socio demography

variables

Objective characteristics

Subjectiveevaluation

Overallsatisfaction

Figure 4.3 The main categories of determinants of housing satisfaction

length of residency. The second category is the objective characteristics of a house

including ownership, cost of housing and all the physical components of the dwelling.

And finally the third category is the satisfaction with each of the objective

characteristics. All of these variables together influence overall housing satisfaction

within our conceptual framework. So overall housing satisfaction will be studied by

exploring the relationships between the degree of housing satisfaction with a set of

personal/household characteristics, objective characteristics of the house and subjective

evaluation of these characteristics. Here, personal/household characteristics are

associated with different needs of people, while house characteristics indicate the

extent by which a house offers the opportunity to satisfy these needs. Figure 4-4

presents the conceptual model of this study in more details.

4.4.2 Path analysis for housing satisfaction

Our conceptual model was estimated using AMOS 22 software package with the

Maximum Likelihood Estimation (MLE) method. This method assumes multivariate

normality, which is a robust method. It enables estimating the direct and indirect

correlations among factors, the p-values of the paths including the standardized

regression weights of the conceptual model. In the first step, all of the attributes were

included in the model to see whether they have any direct or indirect relation with the

exogenous variables. After omitting the insignificant relationships from the path model,

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54

Figure 4.4 The conceptual model

age, length of residency and household income from the socio-demographic group were

remained. However, Age of respondents and length of residency were highly correlated

which causes less accuracy for path coefficient. Thereby, due to the multicollinearity,

one of them should be deleted. Since length of residency variable is theoretically more

meaningful for housing satisfaction, it remained and age variable deleted from

themodel. Another strong correlation among the significant variables in the same group

also found among “House size satisfaction” and “Type of house satisfaction”. It is also

theoretically meaningful, as small houses are usually flat houses and large houses are

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55

Figure 4.5 Results of the path analysis for house satisfaction

villa. Therefore, again due to the multicollinearity one of the variables should be

omitted. Considering the theoretical and statistical relevance of “Type of house

satisfaction”, it stayed and accordingly “House size satisfaction” omitted from the model

(Figure 4.5).

The results of the final path model (Table 4.4) demonstrate that the model

excellently fit to the data. Chi Square divided by the model degrees of freedom (/df or

CMIN/DF) is an essential measure, which should to be less than 2 and preferably close

to 1 (Ullman, 2006). This indicator is equal to 1.43 for our model. In addition to that,

the Root Mean Square Error of Approximation (RMSEA) should be less than .08 and

ideally less than .05 to represent a great fit (Washington et al., 2010) and in our model

RMSEA value is .045. Other indicators of a good fit as mentioned by many researchers

(e.g. Moss, 2009) are indices that compare target model with null model, such as

Comparative fit index (CFI), Incremental fit index (IFI), Tucker Lewis index (TLI),

Normed fit index (NFI) and Goodness of Fit Index (GFI). If these indexes exceed the

amount of .90 the model tends to generate an acceptable fit. Checking all of these

indicators shows that our model perfectly fits.

Moreover, R squared (R2) of the ultimate endogenous variable (called squared

multiple correlation value in AMOS) is another important criteria signaling a good model.

Housing well-being

56

Table 4.4 Goodness of the fit of the model

Fit index Estimate

Chi-square (CMIN) 1.43

Degrees of freedom (DF) 28

Chi-square / degrees of freedom 40

Root mean square error of approximation (RMSEA) .045

CFI .98

IFI .98

TLI .97

NFI .95

Our proposed theoretical model satisfactorily accounts for the 73 percent of the

variance in residents’ overall house satisfaction (R2=.73). Statistically significant

relationships are found among the main model variables as below (refer to Table 4.5):

“Type of house satisfaction” has the largest direct effect on the overall house

satisfaction (.32).

“Design satisfaction” has the second direct and strong correlation with the

effect size of .28, followed by “Ownership satisfaction” (.21), “Exterior

satisfaction” (.20) and “Safety satisfaction” (.15).

Among the indirect relationships, “Household income” has the strongest

influence on the overall house satisfaction (.17).

The other main results can be summarized as:

Overall house satisfaction is negatively and indirectly influenced by “Rental

house” (-.09), “Low cost house” (-.05) and “No security facility” (-.04).

In the objective characteristics group, “Low cost house” significantly influences

“Design satisfaction” with negative effect (-.19). As expected, “Rental house”

show significant negative effect on “Ownership satisfaction” (-.24). Likewise

the correlation between “Rental house” and “Exterior space satisfaction” is

negative (-.22). Moreover, “No security facility” strongly impacts “Safety

satisfaction” (-.30), which indicates the importance of safety facilities for

feeling safe at home.

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57

From the socio-demographic variables “Household income” and “Length of

residency” are indirectly related to the overall housing satisfaction (with the

effect size of .02 and .17, respectively). “Household income” also shows

significant effects on “House-type satisfaction” (.22) and “Ownership

satisfaction” (.20). “Length of residency” only shows strong correlation with

“Rental house” (-.27).

4.4.3 Interpretation and discussion

Generally speaking, our results support the main parts of the initial conceptual model,

and are also coherent with the previous studies. Although many of the variables from

the initial model have been insignificant and omitted, but all of the key aspects have

been remained and showed significant influence on the overall housing satisfaction.

Below are the main findings and interpretations on the results:

House-type satisfaction, design satisfaction and ownership satisfaction have

the strongest influence on the overall evaluation of house, which is in line with

the findings of other researchers (e.g. Buys & Miller, 2012; Jiboye, 2014).

Being satisfied with house-type can predominantly reflect a large portion of

housing satisfaction. It is due to the fact that house-type is more than just a

dwelling form. It is linked with lifestyle and socio-cultural factors that reflects

the ideals of occupiers including their expectation regarding certain house

qualities. House-type satisfaction is directly associated with household income

(.22), since certain income-level households look for certain type of house and

to some extent house type can reflect the income level of households. The

degree of satisfaction with dwelling design can also highly reflect the extent to

which house is adequate for performing different activities and can fulfill the

occupants’ needs. House design can basically tell whether different space

features of house are satisfying and thus implying satisfaction with house size,

number of bedrooms satisfaction, style of the house (age of house) etc.

Satisfaction with home ownership has been found to be an influential factor in

residency evaluation. It is highly influenced by household income (directly and

indirectly), as certain amount of income leads to property ownership. Negative

impact of “Rental house” on “Ownership satisfaction” again emphasizes on the

renters’ dissatisfaction with their ownership status. Moreover, our results

indicate that “Length of residency” indirectly impacts “ownership satisfaction”

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Table 4.5 Standardized path estimates of direct, indirect and total effects

Dependent

variables

Paths Direct

effect

Indirect

effect

Total

effects

Overall

house

satisfaction

(OHS)

Length of residency OHS .00 .25 .25

Household income OHS .00 .17 .17

Rental house OHS .00 - .09 - .09

No security facility OHS .00 -.05 -.05

Low-cost house OHS .00 -.05 -.05

HTS OHS .32 .00 .32

DS OHS .28 .00 .28

OS OHS .21 .00 .21

ESS OHS .20 .00 .20

SS OHS .15 .00 .15

Ownership

Satisfaction (OS)

Household income OS .20 .09 .29

Rental house OS -.24 .00 -.24

Length of residency OS .00 .06 .06

Design

Satisfaction (DS)

Low-cost house DS -.19 .00 -.19

Household income DS .00 .06 .06

House-type

Satisfaction

(HTS)

Household income HTS .20 .00 .20

Safety

satisfaction (SS)

Household income SS .00 .06 .06

No security facility SS -.30 .00 -.30

Exterior space

satisfaction

(ESS)

Rental house ESS -.22 .00 -.22

Household income ESS .00 .08 .08

Length of residency ESS .00 .06 .06

Rental house Household income Rental house -.36 .00 -.36

Length of residency Rental house -.27 .00 -.27

Low-cost house Household income Low-cost house -.33 .00 -.33

No security facility Household income No security facility -.20 .00 -.20

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59

(.06) which is obvious, as long-term residency is usually bond with house

ownership and rental contract are usually temporary.

According to our result tenants have lower house satisfaction. Other researchers

(e.g. Elsinga & Hoekstra, 2005; Jussila et al., 2015) also found the similar

results, as renters have less control over their house in terms of changes, repair

and maintenance and also have fewer ties to the house. Also, living in rental

house is negatively correlated with exterior space satisfaction, which might be

due to the fact that the majority of rental houses are apartments with little or

no exterior space.

The negative influence of “Low cost house” on “Design satisfaction” (-.19)

indicates that these houses just meets the basic needs of dwellers and provide

less design-related qualities. Actually, living in a low cost house can negatively

influence the overall house satisfaction in an indirect relationship (.05).

The indirect effect of household income on housing satisfaction has clearly been

proven, since it has a direct relation with obtaining desired material life and as a

result enabling residents to have a suitable housing.

“Length of residency” positively influences the overall assessment of the house,

which might be due to the sense of attachment and coping ability of

households.

4.5 Conclusions and discussion

This chapter has aimed to enhance the understanding of the housing satisfaction

concept and how this major life domain is affected by socio-demographic characteristics

of individuals as well as the characteristics of one’s house. Path analysis was used to

capture the links between various variables and their impacts on overall housing

satisfaction. The findings highlight the major influence of house-type satisfaction,

design satisfaction and ownership satisfaction on feeling satisfied with the house.

Owner-occupiers are happier than tenants in general which stem from the fact that

people have a natural preference for owning their territory apart from other economic

and social reasons behind the desire for ownership.

Type of house also reflects many qualities of the house including the

dependency and freedom for activities. The importance of house design in compared

with house size has been particularly reflected in small lofts of “StrijpS”, one of our

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60

study area in Eindhoven (See Chapter 3), where innovative and smart design enables

residents combining working and living in multifunctional spaces. Moreover, this study

shows that living in affordable houses does not matter as long as the house design

satisfies the occupant’s needs. Although household income shows a significant

contribution to house satisfaction and influences almost all aspects of housing, the

dominant impact of income is on ownership satisfaction. This partly reflects the fact that

people usually buy a property based on the type and design of the house. Overall, the

residential satisfaction is complex in its nature. It is bonded to economic, social and

physical structure of the community and at a higher level to the housing policies.

Furthermore, housing satisfaction is connected to its surrounding and getting influence

from the area it is located in, thereby our next chapter will address neighborhood

satisfaction.


61

5


5.1 Introduction

Neighborhood is the second immediate space after housing that urban residents’

encounter during their life course. It is the most basic spatial unit that provides

opportunities for the fulfillment of its residents’ daily needs and social interaction

(Ettema & Schekkerman, 2016). A vibrant neighborhood can shape the activity patterns

of residents and make their life more efficient, pleasant and relaxed. It can also be

considered as the most integral part of its residents’ life by offering different

infrastructures and mixture of facilities, which furthermore will act as a source for

physical, recreational and social activities. From a physical point of view, neighborhoods

provide access to all amenities, high quality roads, pedestrian and bike paths to

generate a connected and functional space and promote an active lifestyle.

Neighborhood environmental condition such as good mixture of trees and greenery


62

including clean streets, water and air with no traffic congestion and noise pollution

influence residents’ physical and mental health positively. In addition, from a social

perspective, by bringing residents together in local public spaces, providing a safe area

for all residents to participate in activities and having contact with neighbors,

neighborhoods can produce a sense of attachment and be a meaningful source for

identity. Thereby, it is said that satisfaction with various neighborhood features

associate with overall satisfaction of neighborhood, which can play a significant role in

the assessment of quality of life (e.g. Węziak-Białowolska, 2016). In other words, the

central position of the neighborhood in providing and facilitating life with different

elements explains why neighborhood satisfaction can be a significant predictor of life

satisfaction (Marans, 2015).

Because of this important role, many researchers have explored the associations

between various neighborhood characteristics, residents’ satisfaction and well-being,

(e.g. Zhang & Zhang, 2017 ). However, due to the complexity of neighborhood research

and its broad nature, the vast majority of studies have focused on a particular aspect of

neighborhood rather than the whole diverse set of characteristics such as the studies on

neighborhood safety and social attachment, which in turn created a substantial body of

research in environmental psychology and social science. Nevertheless, not all the

studies of the neighborhood satisfaction revealed aligned results, which can be due to

the contextual differences. The existing contradictions in the literature brought

uncertainty in choosing the relevant attributes for future studies. In addition,

researchers believe that even similar physical circumstances can be perceived differently

across individuals (Hipp, 2016). However, there is still a lot of room for systematic

investigation of this multifaceted concept by considering all domains and subdomains.

In formulating the conceptual framework of this thesis (see chapter 2),

neighborhood satisfaction contributes to quality of life as a main domain, and thus, this

chapter will focus on neighborhood satisfaction. Explicitly, the following research

questions will be addressed:

I. What are the main predictors of neighborhood satisfaction? Or which

neighborhood features have received empirical support in predicting

neighborhood satisfaction?

II. To what extent do these indicators contribute to predicting neighborhood

satisfaction?


63

III. What are the relationships among these indicators and to what extent do they

affect each other?


Neighborhood is a multidimensional phenomenon, which is beyond a static fixed

geographical unit, but rather a dynamic concept, comprising a network of associations

among people and places (Gardner, 2011). Many researchers attempted to define

neighborhood in different fields of study in order to provide a basis for their research.

As a result, the definition evolved over time and multiple terms and definitions

developed during the past several decades.

5.2.1 A place called neighborhood (terminology and definitions)

The classic definition by Lewis Mumford (1954) considered neighborhood as a natural

urban unit: “when many functions of cities tend to be distributed neighborhoods formed

naturally”. In its classic definition neighborhood is geographically a limited area

composed around a center with clear edges and a balanced mix of activities. Later, the

emergence of other concepts such as sliding edged neighborhoods and multi-center

neighborhoods transformed this classical definition. Through approaches such as

cognitive mapping, researchers realized that people of the same area have different

perception regarding their neighborhood size and its boundaries, which is dependent on

their residential location as well as their own profile (life course and socio-demographic

characteristics). Following this result, Lee (1968) defined a concept called “socio-spatial

schema” where important physical elements of neighborhood together with social

relationships can depict a different schema for each resident, and neighborhood is the

combination of all residents schema. Moreover, Chaskin (1995) identifies three

dimensions for neighborhood: neighborhoods as social units, neighborhoods as spatial

units, and neighborhoods as a network of associations.

As many authors argued (e.g. Foster & Hipp, 2010; Koohsari et al., 2015), there

is a mismatch between the administrative definition of a neighborhood and residents

experience of a neighborhood, which also are known as: territorial neighborhood and

ego-centered neighborhood, respectively. Territorial neighborhood refers to

administrative fixed boundaries while ego-centered neighborhood reflects resident-

oriented area that is centered on individual’s unique mobility pattern, exposures and life

style. This concept can be the extension for “activity space” concept (individual's day-to-


64

day lived experience) by Albert and Gesler (2003), where they theorized neighborhood

as “the local areas within which people move or travel in the course of their daily

activities”. Among the many definitions, it seems that Meegan and Mitchell (2001)’s

definition includes all physical, social and psychological concepts on neighborhood as:

“Neighborhood is a key living space through which people get access to material and

social resources, across which they pass to reach other opportunities and which

symbolizes aspects of the identity of those living there to themselves and to outsiders.”

Given this definition, neighborhood satisfaction can be a process consisting of individual

definitions of neighborhood, their personal characteristics and their very subjective

evaluation.

As terminology involves with concepts and definitions, consequently several

terms have been used to explain this concept, among those; community, district, area

and vicinity are the most common terms that used interchangeably. The challenge of

setting boundaries for neighborhood is also reflected in various terminologies and terms

which each of them can emphasize on different geographical or social aspects and

dimensions to identify what is called as neighborhood. For instance, the geographical

and physical boundaries such as highways, main roads, rivers etc. can define a district,

whilst the area that surrounds the house with local, familiar residents (usually in a same

social class) is called community. Even by using “neighborhood” people can mean

different things, Marans and Rodgers (1975) extended the definitions and suggested

“micro neighborhood” and “macro neighborhood” terms, where the former is the

adjacent area to the house contains around 8 blocks and the latter is the official districts

with all the facilities. In Dutch terminology micro neighborhood that covers daily

activities of residents is called as “buurt” while a larger area with more facilities is called

as “wijk” (Wassenburg, 2013).

5.2.2 Research background

Community research has a long history in planning, social science and related fields.

Precisely, the emergence of neighborhood level studies has begun a century ago where

researchers became interested in investigating the effects of place on human and

collective living. Over the past few decades, many researchers attempted to explore the

neighborhood characteristics and the mechanism of its contribution to residents’ well-

being (e.g. Amérigo & Aragones, 1997; Talen, 2000; Austin et al, 2002; Chapman &


65

Lombard, 2006; Santos et al., 2007; Sirgy et al., 2013; Cao, 2015; Hadavi, & Kaplan,

2016; Zhang & Zhang, 2017).

Substantial effort has been devoted to explore the associations of health (e.g.

Rollings et al, 2017), crime (e.g. Cozens & Love, 2015), personal attachment and

network (e.g. Dassopoulos & Monnat, 2011; Clark et al., 2017) with neighborhood

satisfaction. The fundamental shift from physical to social characteristics of

neighborhood resulted in employing methods to assess satisfaction of both physical

services and social conditions. Consequently, different concepts such as social cohesion

and sense of community emerged and created a new line of research with a focus on

evaluation regarding crime, stigma and neighborhood image.

In fact, there are two separates lines of research that recently overlapped. One

line, which was developed by urban scientists, is related to planning and design

principles and service-oriented communities. The other line mainly focuses on social

structure aspects such as cohesion, stigma and sense of community. The overlap of

these two research arrays has been reflected in interdisciplinary fields and concepts

such as “New urbanism” in which both dimensions intertwined. New urbanism shows

the ability of the built environment in creating sense of community by identifying some

principal components such as permeability, accessibility, walkability and social

interaction (Talen, 2008). However, there are different criticisms on its ability to

comprehensively capture the social layers of neighborhoods and therefore investigating

other theoretical concepts is inevitable.


There is no single theory that can explain neighborhood satisfaction, mainly because

both person and environment are fluid and affect each other simultaneously, which

makes it difficult to identify the cause and the effect (Félonneau & Causse, 2017). But,

a series of theories have dealt with human contentment with environment. Among

those, theory of place, affordance theory and theory of human motivation have

provided the foundation for numerous evaluation studies (e.g. Raymond et al., 2017).

Theory of place in environmental psychology is pioneering in defining the

experience of pleasure deriving from place and contains three main psychological

stages: cognition, affection and behaviors (Rosenberg & Hovland, 1960). Affordance

theory by psychologist James Jerome Gibson (1979) emphasized human evaluation of

environment through possibilities of operation and action. This theory can have many

http://journals.sagepub.com/author/Dassopoulos%2C+Andrea


66

implications in different fields and some authors expanded it to the built environment

with respect to functionality in person–environment relations (e.g. Raymond et al.,

2017). The theory of human motivation or Maslow’s hierarchy of needs has adopted by

a variety of disciplines. It can also be well adapted to residents’ needs at the

neighborhood setting, as the general human needs can be extended to individuals and

collective needs in a community. Thereby, different levels of residential needs can be

organized hierarchically from basic needs to higher-order needs. The consistent concept

across all of these theories is that judgment and evaluation are more salient than reality

or objective components of the environment as satisfaction is a psychological construct.

5.3 Factors determining neighbourhood satisfaction

Based on the review of existing literature and available theories, numerous factors can

influence individuals’ evaluation of neighborhoods. However, due to the complexity of

the nature of the concept and the context-dependency, as expected, there is mixed

evidence on the relevance of some of attributes such as neighborhood facilities, density,

housing type, etc. (Lovejoy et al., 2010). Nevertheless, based on the empirical evidence

some variables such as safety (e.g. Lee et al., 2017), social attachment (e.g., Di Masso

et al., 2017), upkeep (Hur & Nasar, 2014) and age (Zhang & Lu, 2016) are consistently

associated with the neighborhood satisfaction. It is also good to note that neighborhood

is not a closed system but is linked to wider aspects of the regional and national system

(Pacione, 2011). Therefore, the roots of most of the neighborhood problems must be

sought beyond the neighborhood scale. Due to the limited scope of this research, we

explore only the variables that are directly related to the neighborhood level evaluation.

5.3.1 Individuals’ socio-demographic characteristics

Age is one of the personal characteristics that is always among the influential variables

in the neighborhood satisfaction studies. Many researchers (e.g. Zhang & Lu, 2016)

found that older people have a higher level of neighborhood satisfaction compared to

younger residents, which might be due to the fact that higher purchasing power

enables them to move to their favorite neighborhood (Lovejoy et al., 2010). Being

single can also impact neighborhood satisfaction but negatively, as it might affect

feelings of safety and social interactions. As such, Galster and Hesser (1981) reported

that single women were less satisfied with their neighborhood than others. Regarding

gender, many studies found a weak impact on neighborhood satisfaction (e.g. Kweon et

al., 2010).


67

Regarding other Socio-economic variables such as household income, education level,

household size and presence of children, the results are very mixed and not consistent.

For instance, Dekker and Bolt (2005) found that presence of children is positively

associated with neighborhood interactions, which can consequently enhance

neighborhood satisfaction, while Hipp (2009) reported that divorced households with

children have lower neighborhood satisfaction than others. As a result, those socio-

demographic variables that contribute to integrating more in neighborhood activities are

likely to increase the social contacts in the neighborhood and tend to be influential on

neighborhood satisfaction.

5.3.2 Social environment characteristics

A vast amount of literature emphasizes on the social environment as a key domain in

judgments regarding neighborhoods and much empirical research has used social

determinates to measure neighborhood satisfaction. However, since these variables

should be matched with local context, different authors have chosen various sets of

variables. Nevertheless, in the international context, there are some widely recognized

variables such as safety and personal attachment that were showed to have strong

connection with neighborhood satisfaction. Also, based on Maslow’s hierarchy of needs,

safety and personal attachment are the second and third levels of needs. In the Dutch

context, livability and social status have particularly been listed as relevant influential

variables of neighborhood satisfaction (e.g. Van Assche et al., 2018). Hereafter, some

of the most important social environment variables are discussed in more details.

5.3.2.1 Safety

Safety evaluation or fear of the crime is the most common used criterion in

neighborhood satisfaction research and highly contributes to residents’ subjective

assessments of neighborhoods. Also, safety has been linked to concepts such as

“control over the environment”, and thereby neighborhood physical structure and street

layout have received a lot of attention, due their impact on the feelings of safety.

Accordingly, environmental criminologists investigated the impact of spatial factors on

fear of crime. Significant research by David Lynch (1960) and Jane Jacobs (1961),

highlighted the impact of urban design on safety and concepts such as “Defensible

space” (Newman, 1972) and “Indefensible space” (Cozens et al., 2002) provided the

intellectual foundation for in-depth crime analyses. It has been proven that the level of

safety is different across different socio-demographic groups. For instance, women feel


68

less safe than men and fear of crime is higher among older people. Also, feelings of

safety are connected with stigmatized neighborhoods (Cozens, 2011) and can impact

performing physical activities such as walking, which will be discussed later in this

chapter.

5.3.2.2 Personal attachment

Personal attachment is another widely used attribute in neighborhood satisfaction

studies. It is a two-dimensional factor comprising of place attachment and social

attachment (Raymond et al, 2017). It can also be considered as a mixture of feelings

that reflects the degree of belongingness, social trust and evaluation of cohesion

(Williams & Vaske, 2003; Dassopoulos & Monnat, 2011). Principally, interaction with the

local network or social ties bind people to specific places and can maintain higher level

of personal attachment. Moreover, it has been found that age and personal attachment

are significantly related as older residents usually have a longer residency and

therefore, the familiarity with the area and people are higher among them (Cramm, &

Nieboer, 2015).

5.3.2.3 Liveliness

Liveliness has also been listed among the variables related to social satisfaction of a

neighborhood. It is a subjective indicator with links to some physical characteristics of

area such as density, number of shops, position in city, and socio-demographic

characteristics of the area. A livable area is full of life, excitement and energy and can

offer opportunities for interaction with other residents, thereby can positively impact

neighborhood assessment (Lovejoy et al., 2010). Different research studies have shown

that people like quiet micro neighborhoods but prefer lively macro neighborhoods (a

broader census-tract context) with lots of activities and events. This also has highlighted

the connectivity factor and easy access to the city center and other major activity

centers.

5.3.2.4 Image/reputation

Neighborhood image or its reputation demonstrates how residents and outsiders

evaluate the socio-economic and cultural situation of the neighborhoods and

differentiate them from surrounding areas (e.g. Müller, 2017). Generally, neighborhoods

can be an identity for their residents, as Galster (2002) argued that socio-economic

status of a neighborhood can provide “prestige” for its residents. Conversely, a negative


69

image or poor neighborhood reputation can lead to a low level of neighborhood

satisfaction and relocation decisions (e.g. Goetze, 2017). In the Netherlands, the debate

about disadvantaged neighborhoods is often connected to unfavorable social status and

stigma. Thus, neighborhood reputation should be considered an influential attribute in

neighborhood satisfaction studies.

5.3.3 Physical and environmental characteristics

The physical characteristics of neighborhoods that potentially influence satisfaction are

numerous. Employing the human needs hierarchy can provide the basis for what is

considered by residents as the main needs in the neighborhood. These needs progress

from the most basic needs such as access to clean air to higher-order needs that

include accessibility, quality of green space and layout.

Following the essential physiological needs, access to clean air, water and land

including food supply are the most important indicators for evaluating neighborhoods.

They are followed by lower traffic volumes and good quality roads and paths that

provide access to adequate services and facilities to fulfill people’s needs (Cao, 2016).

Moreover, level of noise pollution and presence and quality of green landscape (Zhang

& Zhang, 2017) are directly connected to physical and mental health of residents and

are posited at the second level of the needs hierarchy. Architecture of housing and well-

structured layout of buildings are physical qualities, which belong to a higher level of

the needs hierarchy (Sirgy & Cornwell, 2002; Hur & Jones, 2008). It is said that if a

neighborhood can fulfill the main needs of residents, then higher level of needs might

be considered by residents (e.g. Alfonzo, 2005; Buckley et al, 2017).

5.3.4 Physical activity satisfaction

As physical activity is directly linked to health, the neighborhood characteristics that can

support an active lifestyle are increasingly become important. In addition, several

studies have emphasized the associations of environmental attributes with physical

activity behaviors (e.g. Orstad et al., 2017). As a result, increasing attention is being

paid to pedestrian-friendly urban forms and walkability (e.g. Buckley et al, 2017).

The Netherlands is well known for having compact cities and high density

residential areas. These spatial characteristics including the culture of biking made most

neighborhoods bike friendly. In contrast, countries such as the US have totally car-

dependent neighborhoods, and in some cases without even pedestrian paths. Thus,


70

physical activity related indicators are very context dependent. However, as in the

Netherlands (our study context) walking and cycling are part of the daily activities, we

should investigate them separately. In general, quality of path, traffic safety (risk of

accident), safety regarding crime and greenery are the most relevant indicators to

evaluate neighborhood physical activity support (Talen & Shah, 2007; Schepers et al.,

2015; Jensen et al., 2017).

5.3.5 Satisfaction with services in neighborhood

Neighborhoods should respond to the main needs of their residents such as the need

for access to services and amenities for grocery shopping, education, healthcare,

recreational activities etc. Among those, food related facilities are the most important

and the most frequent visited places in neighborhoods. Availability of amenities is

especially crucial for older residents with physical and economic limitations (Rioux &

Werner, 2011). Generally, residential proximity to facilities, quality of the facility, time

schedule and crowdedness are the most important factors influencing satisfaction

regarding facilities.

5.4 Analysis and results: Multiple regression analysis

In order to explore the effects of various neighborhood attributes on the residents’

overall assessment of their neighborhood, first, a multiple regression analysis was

conducted, and then, a comprehensive analysis using “Structural equation modelling”

(SEM) being applied. In general, multiple regression analysis is a popular method

among researchers to estimate neighborhood satisfaction according to the changes of a

set of independent variables. Such analysis can highlight the significance and strength

of the relationship among multiple variables and the neighborhood satisfaction as a

dependent variable. Although it can provide some invaluable insights, but is unable to

capture the indirect relationships and the complex associations among the factors due

to its limitations in having more than a single dependent variable. In addition,

regression analysis does not have the ability to estimate the unobserved variables

(which are numerous in our conceptual model). This means that the hypothetical

dimensions such as social, environmental, physical, etc., which are playing key roles in

our conceptualization, cannot be investigated by regression analysis.

The choice of regression analysis reflects the assumption that the scales used

have interval properties, akin to many studies that utilized such scales (e.g. Diaz-


71

Serrano & Stoyanova, 2010). To examine our conceptual model by multiple regression

analysis, the overall neighborhood satisfaction is treated as the dependent variable and

examined by 58 independent variables. Since one of our regression assumptions is that

the residuals (prediction errors) are normally distributed, therefore, checking the

histogram of residuals allows us to check the extent to which the residuals are normally

distributed. The well-behaved residuals can lead to correct p-values and thus are very

important for any linear regression analysis. Our histogram (Figure 5.1) shows a fairly

normal distribution. Thus, based on these results, the normality of residuals assumption

is met.

Furthermore, the normal P-P Plot of regression residuals (Figure 5.2) can also

be used to assess the assumption of normally distributed residuals. If data points (little

circles) follow the diagonal line, the residuals are normally distributed. Our P-P plot

shows minor deviation and thus the normality of residuals assumption is satisfied. The

R-squared (R2) of the multiple regression analysis is shown in Table 5.1, which reveals

that the indicators account for 73 percent of the variance in the overall neighborhood

satisfaction. This suggests that variation in neighborhood satisfaction ratings are

strongly related to the differences in neighborhoods and personal characteristics.



explanatory variables on the residents’ overall neighborhood satisfaction (Table 5.2).

Among all of the indicators in this analysis, Only seven variables show a significant

relationship with overall neighbored satisfaction (p<.05), this might be due to the large

number of variables (60) compared to the number of respondents (209). Satisfaction

with “Livability”, “safety” and “image/reputation” from social domain show positive

influence as expected. The other significant predictors of the overall neighborhood

satisfaction are “satisfaction of road/pedestrian quality” from the “neighborhood

characteristics satisfaction” category, “satisfaction of travel time to stores” from

“satisfaction of neighborhood services” category, “satisfaction of size of road for biking”

from the “physical activity satisfaction” category and “age” from the “socio-demographic

characteristics”. The variable showing the strongest significant relationship with the

overall neighborhood satisfaction is “satisfaction of safety” with the effect size of (.25).

In contrast, “Age” influences the overall neighborhood satisfaction with the lowest

effect size (.16). But, except “Age”, none of the socio-demographic characteristics




72

Figure 5.1 The Histogram of the overall neighborhood satisfaction

Figure 5.2 Normal Probability Plot (P-P) of the regression standardized

residual dependent variable for overall neighborhood satisfaction


73

Table 5.1 Goodness-of-fit of the regression model of overall neighborhood satisfaction

R R2 Adjusted R


.857 .734 .632 .670

Table 5.2 The estimated parameters of the neighborhood satisfaction using multiple regression analysis

Model Parameters

Unstandardized

Coefficients

Standardized

Coefficients

t

B Std. Error Beta Sig.

Socio-

demographic

indicators

(Constant) 1.617 .579

2.793 .006

Age .011 .005

.162 2.189 .030

Car owner .113 .159 .042

.711 .478

Income Level -.042 .048 -.043

-.877 .382

Length of living at the house -.093 .059 -.104

-1.581 .116

Household Size -.113 .070

-.121 -1.611 .109

Unemployed .152 .190

.044 .798 .426

Male .022 .117

.010 .186 .853

Single person -.161 .159

-.069 -1.013 .313

Highly educated -.279 .145

-.100 -1.926 .056

Rental house -.111 .133

-.050 -.831 .407

Satisfaction of

neighborhood

characteristic

s

Satisfaction of Air quality -.012 .007 -.081 -1.624 .107

Satisfaction of Houses upkeep/

architecture appearance

.043 .048 .064 .903 .368

Satisfaction of Personal attachment .072 .040 .119 1.787 .076

Satisfaction of Green space .068 .041 .107 1.636 .104

Satisfaction of Image/reputation .160 .045 .243 3.602 .000

Satisfaction of Noise pollution .047 .037 .086 1.279 .203

Satisfaction of Cleanness .030 .044 .043 .680 .497

Satisfaction of Lighting .014 .054 .017 .258 .797

Satisfaction of Livability .099 .050 .132 2.001 .047

Satisfaction of Traffic congestion -.017 .043 -.027 -.383 .702


74

Satisfaction of Safety .194 .054 .248 3.622 .000

Satisfaction of Road/ pedestrian quality .098 .052 .136 1.889 .061

Satisfaction

of physical

activity

Satisfaction of Traffic safety (for

walking)

.026 .047 .251 .548 .585

Satisfaction of Traffic safety (for

biking)

.021 .057 .058 .368 .713

Satisfaction of Air quality (for

walking)

-.047 .042 -.337 -1.118 .265

Satisfaction of Quality of path (for

biking)

-.05 .059 -.610 -3.504 .001

Satisfaction of Safety/security (for

walking)

-.027 .047 -.265 -.578 .564

Satisfaction of Quality of path (for

walking)

.055 .042 .399 1.308 .193

Satisfaction of greenery for biking .039 .048 .114 .815 .416

Satisfaction of Size of the road

(for biking)

.165 .070 .481 2.379 .019

Satisfaction of

Services

Satisfaction of Travel time to grocery

store

.023 .047 .056 .482 .630

Satisfaction of Travel time to medical

center

.109 .055 .414 1.970 .051

Satisfaction of Travel time to sport

centers

-.002 .076 -.006 -.023 .982

Satisfaction of Travel time to

stores

.159 .061 .568 2.607 .010

Satisfaction of Travel time to park -.003 .062 -.009 -.041 .967


educational centers

.086 .092 .269 .935 .351


café/restaurants

.043 .066 .157 .644 .520

Satisfaction of Time schedule (grocery

store)

.027 .072 .063 .381 .704


75

Satisfaction of Time schedule (medical

center)

.036 .073 .127 .493 .622

Satisfaction of Time schedule (sport

centers)

.063 .083 .230 .761 .448

Satisfaction of Time schedule (stores) -.087 .067 -.307 -1.311 .192

Satisfaction of Time schedule (park) .037 .064 .146 .580 .563

Satisfaction of Time schedule

(educational centers)

-.011 .112 -.034 -.096 .923

Satisfaction of Time schedule

(cafe /restaurant)

-.063 .076 -.229 -.830 .408

Satisfaction of Crowdedness (grocery

store)

-.013 .008 -.090 -1.665 .098

Satisfaction of Crowdedness (medical

center)

-.093 .069 -.320 -1.353 .178

Satisfaction of Crowdedness (sport

centers)

.047 .069 .155 .679 .498

Satisfaction of Crowdedness (stores) -.061 .058 -.205 -1.058 .292

Satisfaction of Crowdedness (park) -.032 .068 -.118 -.469 .640

Satisfaction of Crowdedness

(educational centers)

.089 .097 .249 .921 .359

Satisfaction of Crowdedness (cafe

/restaurant)

.004 .082 .013 .047 .963

Satisfaction of Quality of grocery store -.021 .070 -.047 -.297 .767

Satisfaction of Quality of medical

center

-.068 .078 -.253 -.881 .380

Satisfaction of Quality of sport centers -.092 .088 -.330 -1.036 .302

Satisfaction of Quality of stores -.007 .076 -.024 -.090 .928

Satisfaction of Quality of park .007 .074 .026 .095 .924

Satisfaction of Quality of educational

centers

-.139 .092 -.424 -1.513 .132

Satisfaction of Quality of

cafe /restaurants

.039 .093 .142 .424 .672


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shows a significant influential impact on overall neighborhood satisfaction, which is

different from findings of other studies.

It is also notable that none of the attributes of the environmental satisfaction

domain and physical satisfaction domain is significantly associated with overall

neighborhood satisfaction, while three variables of social satisfaction domain

(satisfaction of safety, livability and image) are significant in our analysis. This highlights

the importance of the social domain in comparison with the other two domains in

predicting overall neighborhood satisfaction. Moreover, except “satisfaction of quality of

path for biking”, the signs of all other coefficients are positive, indicating that an

increase in this attribute would lead to a higher level of neighborhood satisfaction,

which is consistent with our framework.

Interpreting the results of regression analysis is not always straight forwards

due to the internal relationships among variables. A strong relationship between an

independent variable and a dependent variable could come from many other causes

including the influence of other unmeasured variables. For instance, “satisfaction of size

of road for biking” is not expected to have a very strong influence on the overall

neighborhood satisfaction, but its high coefficient (.48) contradict it, which might be

due to the strong influence of this attribute on another variable. Despite the insights we

can get from multiple regression analysis, as in the beginning of this section already

explained, this method has some limitations. Thus, further analysis is necessary to

capture the complex mechanism of internal effects among different variables of the

hypothetical model. In the next part, the same data will be analyzed using SEM method.

5.5 Analysis and results: Structural equation modelling (SEM)

The key objectives of this chapter were to evaluate and understand the

interrelationships among the selected variables and neighborhood satisfaction. To

achieve these objectives, SEM analysis is deemed to be a suitable method. Various

research have shown that SEM is a good method in revealing the underlying structure

of neighborhood as it is a hybrid model and can estimate relationships among multiple

unobserved constructs (latent variables) and observable variables (e.g. Hur et al.,

2010). In other words, as neighborhood satisfaction relates to complex concepts such

as environmental aspects, social aspects etc. (which are difficult to be measured

directly) conducting SEM should enable us to examine, address and detect all of these

immeasurable constructs. A SEM model contains of two sub-models: a measurement


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model, measuring relationships among endogenous and exogenous variables (latent

variables and observed variables) and the structural model, with the ability of

quantifying the causal effects among the latent variables (Oud & Folmer, 2008).


Based on the aforementioned insights from the neighborhood studies and well-being

research, and the relevance of these studies to investigate the link between different

neighborhood attributes, socio-demographic and household factors as well as the

overall subjective assessment of the neighborhood, a conceptual framework for SEM

model is designed (Figure 5.3) and will be discussed in this section.

Overall neighborhood satisfaction will be studied by exploring the relationships

between the degree of neighborhood satisfaction with a set of neighborhood and

personal/household characteristics. Personal/household characteristics are associated

with different needs of people, while neighborhood characteristics indicate the extent by

which a neighborhood offers the opportunity to satisfy these needs. Figure 5.1 presents

the conceptual model of this Chapter.

Based on our survey (see chapter 3), we have three main categories of

variables. The first category is the satisfaction with neighborhood characteristics, which

consists of three pre-conceptualized domains: “Environmental satisfaction”, “Physical

satisfaction” and “Social satisfaction”. These domains are concepts that cannot be

measured directly, but linking them to other measureable variables can help quantifying

them indirectly. The “Environmental satisfaction” domain is explained by four observed

variables describing how residents evaluate their neighborhood environment. These four

variables are the satisfaction with “Neighborhood cleanness”, “Neighborhood noise

pollution”, “Neighborhood air quality” and “Neighborhood green space”. The “Physical

satisfaction” domain is also identified by other four variables named as: satisfaction with

“Houses upkeep & architecture”, “Road/pedestrian quality”, “Lighting” and “Traffic

congestion”. Finally, the “Social satisfaction” domain consists of four attributes

characterizing the grade of social satisfaction named as satisfaction with “Safety”,

“Image” “Livability” and “Personal attachment”.

The second category is physical activity satisfaction, which indicates the extent

to which people are satisfied with walking and biking conditions at their neighborhood.

Therefore, it consists of two main domains: “Biking satisfaction” and “Walking

satisfaction”. “Biking satisfaction” is evaluated by four attributes: satisfaction of “Quality


78

Sch

oo

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Ne

igh

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acti

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79

of path”, “Green route”, “Traffic safety” and “Road size”. The “Walking satisfaction” is

explained by satisfaction of “Air quality”, “Safety/security” and “Traffic safety”.

Finally, the third category is the “Satisfaction of services” and contains

seven sub-groups: satisfaction with grocery shops, stores, restaurants/cafe, schools,

medical, parks, and sport centers. All of these sub-groups are explained by four

variables: satisfaction of “Travel time”, “Quality”, “Time schedule” and “Crowdedness”.

However, it is worth acknowledging that conceptually, some of these attributes could

belong to more than one theorized domain. So different researchers place them in

different domains based on their perception and the conceptually best fit of the

variables. For example, traffic congestion has been assigned to both physical and

environmental domains. In addition to the entire neighborhood related variables, all

socio-demographic and household characteristics (e.g. age, income, homeownership)

have also been added to the model. These variables are linked to different needs of

individuals and therefore may influence their neighborhood evaluation based on the

extent by which different neighborhood features can fulfill their needs.

5.5.2 SEM model for neighborhood satisfaction

Our conceptual model was estimated using software package AMOS 22 with the

Maximum Likelihood Estimation (MLE) method. Model was modified by excluding the

relationships that were not significant at a .05 significance level. Therefore, all of the

variables in the model are related to the overall neighborhood satisfaction directly and

indirectly, and all paths show significant associations at the 95% confidence level. It is

notable that from the set of socio-demographic factor just one variable (being single)

remained in the model, and from the “satisfaction of services” category, just

“satisfaction of grocery shopping” turned out to be the most significant variable. Also,

“Crowdedness” variable was insignificant in explaining satisfaction with the grocery shop

and thus was removed.

The goodness of the fit for the model (Table 5.3) demonstrates the fit between

observed data and the hypothesized model by using several indices. One of these

indices is Chi Square divided by the model degrees of freedom (/DF or CMIN/DF),

which should be less than 3, and preferably close to 1 (Ullman, 2006). This value for

our model equals to 2.6, which is acceptable but not the best fit. In addition, the Root

Mean Square Error of Approximation (RMSEA) should be less than .08 to represent a

great fit (Golob, 2003; Washington et al., 2010), and for our model it is slightly higher


80

(RMSEA=.088). Other indicators of a good fit are indices that compare target model

with null model such as Comparative fit index (CFI), Incremental fit index (IFI), Tucker

Lewis index (TLI), Normed fit index (NFI) and Goodness of Fit Index (GFI). If these

indices exceed the amount of .90 the model tends to generate an acceptable fit. Within

our initial model TLI and NFI are less than .90. Checking all of these indicators shows

that our initial model needs modification to improve the relevant indices. Thus, the

recommended modification from the software was considered to improve the

aforementioned model quality indices.

It is worth mentioning that using the software suggested modification indices

does not change the original theoretical model substantially, but adds new paths among

the current variables of the models. They are double checked to be theoretically

plausible especially for complex models such as neighborhood satisfaction with so many

attributes. Therefore, it is decided to use supervised modification indices because of the

many errors maybe assigned with the variables in a model. For our model, modification

indices suggested adding some links among the latent variables of the model. Among

the recommendations some were theoretically meaningful and thus they were added to

the model. This resulted in creation of new paths between the latent variables of the

model (i.e. social satisfaction, physical satisfaction, environmental satisfaction, walking

satisfaction and biking satisfaction) as they have been ignored in the conceptual model

such as linking of “satisfaction of air quality” factor with “satisfaction of biking”.

Implementation of the suggested changes has led to a significant improvement in the

goodness of the fit of the model as well as the R squared (R2) of the overall

neighborhood satisfaction. Table 5.4 demonstrates the goodness of the fit for the

improved model. It shows that Chi Square divided by the model degrees of freedom

(/df or CMIN/DF) now equals to 1.76, and the RMSEA value dropped to .06, which is

excellent. Also, other indicators of a good fit (CFI, IFI, TLI and NFI) have been

improved significantly. Moreover, coefficients of determination or the squared multiple

correlation value (R2) for the overall neighborhood satisfaction has improved from .62 to

.70. So the improved model accounts for 70 percent of the total variance within the

overall neighborhood satisfaction. Also, the R2 values of the other dependent

endogenous variables (the overall satisfaction of each category) have been increased as

for physical activity support from .29 to .43, for satisfaction of services from .37 to .48,

and for characteristics satisfaction from .80 to .83. Table 5.5 presents all the direct,


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Table 5.3 Goodness of the fit of the initial model

Fit index Estimate

Chi-square (CMIN) 719


Chi-square / degrees of freedom 2.6


CFI .91

IFI .91

TLI .89

NFI .86

Table 5.4 Goodness of the fit of the improved model

Fit index Estimate





CFI .96

IFI .96

TLI .95

NFI .91

indirect and total effects of the variables on the overall neighborhood satisfaction. It

shows that statistically significant relationships were found among the main model

variables as below:

(1) Characteristics satisfaction has a large-sized direct effect on the overall

neighborhood satisfaction (.42), “Satisfaction with services” (.53) (Figure 5.4). Thereby,

the “Social satisfaction” domain is a dominant construct and a key part of our model,

(2) Satisfaction of services affects the overall neighborhood satisfaction with the

moderate effect (.29),

(3) Physical activity satisfaction influences satisfaction of services (.23), and have

both direct and indirect effects on the overall neighborhood satisfaction (.18).


82

The results of the model are displayed in Tables 5.5, 5.6 and 5.7. Table 5.6

shows the relationships among the latent and observed variables participating in the

measurement model. To show the reliability of each observed variable, this table

includes unstandardized coefficients with p and t-value alongside standardized path

coefficients and squared multiple correlations. The t-value for all the paths is bigger

than 2.33. This is consistent with what Kline (1998) argued that t-value should be

bigger than 1.96 for p<.05 and bigger than 2.33 for p<0, 01. Table 5.7 shows the

relationships among the latent variables of the structural model.

In the following the other main results will be summarized:

Although in our initial model, neighborhood “Characteristics satisfaction”

consists of “Environmental satisfaction”, “Physical satisfaction” and “Social

satisfaction” but our final model indicates that only “Social satisfaction” directly

influences “Characteristics satisfaction” and as a result the direct link of the

other two were removed from the model, although they are affecting

“Neighborhood characteristics satisfaction” indirectly (Figure 5.4).

The “Environmental satisfaction” domain influences “Physical activity

satisfaction” (.51) and “Satisfaction of services” (both directly (.49) and

indirectly (.12)) with large effects (Table 5.6). Also, this domain has the largest

influence (.24) on the “Overall neighborhood satisfaction” after the “Social

satisfaction” domain (Table 5.5).

The “Physical satisfaction” domain has indirect effects through the impact of

the satisfaction of environmental domain on the “Physical activity satisfaction”

(.16) and “Satisfaction of services” (.20). It can be seen that it has a moderate

impact (.16) on the “Overall neighborhood satisfaction” (Table 5.5)

The “Social satisfaction” domain has direct and significant impacts on the

“Environmental satisfaction” domain (.61), “Physical satisfaction” domain (.76)

and “Biking satisfaction“ domain (.26) (Table 5.7). It also has a strong and

direct impact on the “neighborhood characteristics satisfaction” (.90) (Table

5.6), and indirect impacts on the “Physical activity satisfaction” (.48) and

“Satisfaction with services” (.53). Thereby, the “Social satisfaction” domain is a

dominant construct and a key part of our model, which has a strong influence

on “Overall neighborhood satisfaction” (.74).

“Walking satisfaction” and “Biking satisfaction” domains together with

“Environmental satisfaction” domain influence “Physical activity satisfaction”


83

with the effects size of (.27), (.19) and (.51), respectively (Table 5.6).

“Walking satisfaction” and “Biking satisfaction” also have significant and

indirect effects (.07 and .04) on “Overall neighborhood satisfaction” (Table

5.5).

”Grocery shopping satisfaction” construct is the only variable that remained in

the “Satisfaction of services” category with significant associations (.17) and

indirectly (.05) impacts “Overall neighborhood satisfaction” (Table 5.5) &

(Figure 5.4).

Among variables of the socio-demographic group, just “Being single” remained

in the model and is statistically significant with a negative sign (-.10) through

mediating effects on the “Personal attachment satisfaction” (-.12),

“Satisfaction of travel time to grocery shopping” (-.09), “Neighborhood

characteristics satisfaction” (-.12) and “ Satisfaction of neighborhood services”

(-.17).


Overall neighborhood satisfaction

Direct effect

Indirect effect

Total effect

Unobserved variables Physical satisfaction .00 .16 .16

Social satisfaction .00 .74 .74

Environmental satisfaction .00 .24 .24

Walking satisfaction .00 07 .07

Biking satisfaction .00 .04 .04

Grocery shopping satisfaction .00 .05 .05

Observed variables

Being single .00 -.10 -.10

Physical activity satisfaction .12 .06 .18

Characteristics satisfaction .42 .00 .42

Satisfaction of services .29 .00 .29

Safety satisfaction .14 .00 .14

Road/pedestrian quality satisfaction .11 .00 .11


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Table 5.6 Relationships among the latent and observed variables (direct effects

Latent

variables

Observed

variables

Standardized

coefficient

Unstandardize

d

coefficient

t

p

Squared

multiple

correlation

s

En

vir

on

men

tal

Sa

tisfa

cti

on

Satisfaction of

Cleanness

.65 .84 7.88 *** .44

Satisfaction of Noise

pollution

.60 1.0 .39

Satisfaction of Green

space

.58 .83 6.73 *** .33

Physical activity

satisfaction

.51 .58 6.30 *** .43

Satisfaction of

services

.49 .41 5.39 *** .48

Ph

ysic

al

Sa

tisfa

cti

on

Satisfaction of

Road/pedestrian

quality

.75 1.01 9.21 *** .56

Satisfaction of Traffic

congestion

.68 1.08 8.42 *** .46

Satisfaction of

Lighting

.63 .75 7.92 *** .39

Satisfaction of Houses

upkeep/ architecture

.68 1.00 .46

So

cia

l sa

tisfa

cti

on

Satisfaction of

Personal attachment

.69 1.48 7.34 *** .49

Satisfaction of

Image/reputation

.66 1.32 7.84 *** .44

Satisfaction of

Livability

.65 1.13 7.62 *** .42

Satisfaction of Safety .60 1.00 ** .36

Characteristics

satisfaction

.90 1.31 9.38 *** .83

Bik

ing

sa

tisfa

cti

on

Satisfaction of Road

size

.98 1.09 37.04 *** .96

Satisfaction of Quality

of path

.97 1.10 34.12 *** .93


85

Satisfaction of Air

quality

.14 .37 2.80 ** .54


safety

.95 1.00 .90

Satisfaction of Green

route

.93 1.04 29.61 *** .87

Physical activity

satisfaction

.19 .09 3.42 *** .43

Wa

lkin

g

sa

tisfa

cti

on


safety

1.04 1.00 1.07

Satisfaction of

Safety/security

.96 .92 37.24 *** .92

Satisfaction of Air

quality

.72 .50 14.04 *** .54

Physical activity

satisfaction

.27 .03 4.90 *** .43

Gro

cery

sh

op

pin

g

sa

tisfa

cti

on

Satisfaction of Time

schedule

.98

1.00

1.00 .97

Satisfaction of Quality .95 .93 33.87 *** .90

Satisfaction of Travel

time

.89 .95 25.67 *** .80

Satisfaction of

services

.17 .07 3.27 *** .48

*** Significant at.001 level, ** significant at.01 level.

Table 5.7 Relationships among the latent variables

Endogenous Latent

variables

Standardized

coefficients

Unstandardized

coefficient

t

p

Social

satisfaction

Physical satisfaction .76 1.03 6.88 ***

Environmental satisfaction .61 .87 4.48 ***

Biking satisfaction .26 .91 3.44 ***

Physical

satisfaction

Environmental satisfaction .32 .34 2.67 **

Walking

satisfaction

Physical satisfaction .12 .01 2.47 **

*** Significant at.001 level, ** significant at.01 level.


86

Fig

ure

5.4

Re

su

lts o

f S

EM

of

the

mo

dif

ied

mo

de

l w

ith

sta

nd

ard

ize

d c

oe

ffic

ien

ts

for

ne

igh

bo

rho

od

sa

tisfa

cti

on


87


Generally speaking, our results support the main parts of the initial conceptual model

and are also coherent with the previous studies. Although some variables are

insignificant and have been omitted from the model, but all principal categories and key

aspects have remained and show significant influence on overall neighborhood

satisfaction. Using suggested modification indices led to significant improvements as

well as getting new insights about our data set. Below are the main finding and

interpretations from the results:

As expected, satisfaction with neighborhood characteristics plays an important

role on the overall satisfaction with neighborhood, since it consists of the most

critical factors affecting the feeling toward living in an area.

Satisfactions with social, environmental and physical conditions of the

neighborhood are the most important aspects, which have also been admitted

by several studies (e.g. Hur & Jones, 2008).

Satisfaction of social dimension directly and indirectly influences all the three

major categories of neighborhood characteristics satisfaction, physical activity

satisfaction and satisfaction with services.

The more satisfied with the environmental situation of the neighborhood, the

more likely respondents were to be satisfied with the physical activity and

services of the neighborhood.

The impact of physical dimension satisfaction on the overall neighborhood

satisfaction is indirect and is through the impact on the environmental

dimension satisfaction, which makes sense as the physical and environmental

features of neighborhoods are highly associated and to some extend are tied

together.

The dominant influence of social satisfaction domain on the overall

neighborhood satisfaction basically reflects that residents who reported

stronger levels of social satisfaction also tend to have higher level of

neighborhood satisfaction. Therefore, poor neighborhood is strongly associated

with poor social conditions and specially the lack of safety. This means that

improving physical features and conditions will enhance residents’ satisfaction

only if the social conditions are developed.

The physical activity satisfaction accounts for 19% of the variance of the

overall neighborhood satisfaction both directly and indirectly through the


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impact on satisfaction of services. The indirect influence clearly indicates that

some residents tend to walk or cycle for their daily needs such as grocery

shopping. This furthermore infers that better environment and infrastructure

for active lifestyle can clearly provide easier shopping access. Especially for

senior residents, it is proved that the ease of access and walkability within the

area influences the service use.

For our respondents, the impact of satisfaction with environmental condition is

much higher than satisfaction with walking and biking infrastructure to assess

neighborhood activity satisfaction. This can be interpreted that the satisfaction

of environmental qualities such as level of noise, greenery and cleanness are

significant predictors for doing physical activity. Clearly, lacking of a good

environment reduces motivation for doing outdoor activities even if the

physical infrastructure were sufficiently provided.

Satisfaction with the social environment also associates with biking

satisfaction. This makes sense as doing physical activity relates to the social

features such as feeling safe (Van Cauwenberg et al., 2018). It is also well

proved that fear of crime reduce desire to do physical activity in urban area.

Neighborhood characteristics satisfaction on one hand, explained 42% of the

variance in the neighborhood overall satisfaction and on the other hand, it is

mainly associated with the social condition satisfaction (.90) and has no

significant relationship with physical and environmental condition satisfaction.

This indicates that for the residents, neighborhood characteristic are almost

equal to their social conditions, which is different from our initial

conceptualization.

The direct impact of the safety satisfaction attribute on the overall

neighborhood satisfaction describes the importance of the residents’ expressed

evaluation of the safety in predicting neighborhood satisfaction. Many previous

studies had also confirmed our findings and indicated that lack of safety is

associated with social isolation, limited freedom and also deprivation in

neighborhoods (e.g. Visser et al., 2015).

Grocery shopping satisfaction is the only significant service activity remaining

in our model indicating that other facilities (medical centers, et.) should not

necessarily be located in the same neighborhood. That is because of the widely

uses of grocery shops by everyone in a neighborhood and the limited or no use

of other facilities in everyone’s daily life. Satisfaction of services is significantly


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influenced by the environmental satisfaction of the neighborhood (.61). This

can highlight the fact that the external conditions of services are dominant in

comparison with internal factors such as quality, time schedule and

crowdedness of the services for our respondents. This might be due to the

availability of chain supermarkets with the same quality and time schedule in

most of the areas that can lower the impacts of the internal factors.

Social conditions satisfaction also indirectly influences satisfaction of services

(.53) through the impacts on the physical activity satisfaction. This again refers

to the importance of external conditions such as ease of access and safety

evaluation. Especially, service use for senior residents is dependent on the

ease of access and walkability of the area.


This chapter presents a methodological and empirical contribution to the literature on

the residents’ satisfaction of neighborhood using SEM method. SEM allows analyzing a

multiple layers of information about neighborhood and the links among them

simultaneously. It also enables us to have a better understanding of the complexity of

the neighborhood concept. Our findings highlight the major and distinct influence of the

social features on the residents’ overall satisfaction regarding neighborhood. This

indicates that emphasize should be placed on improving social domain within

neighborhoods.

The context of the research is very important in neighborhood studies as

different countries may have different policies and strategies for neighborhood design

and spatial land use. Considering the Netherlands as our data collection base point, it is

worth mentioning that the Dutch approach in neighborhood design and planning is

comprehensive, and addresses all aspects of living. It is based on the mixed and

compact land use concept, which provides proper access to public transport and

necessary infrastructure in multifunctional neighborhoods. So basically, the physical

components of the surveyed neighborhoods can fulfill residents’ main needs, while

concerns over social aspects are remained. This can be mainly explained by the needs

hierarchy theory, which describes that once the individuals’ lower needs have been

fulfilled, they seek to fulfill higher levels of needs.

Although this chapter has examined all the key concepts of neighborhood

satisfaction, considering the numerous factors and domains reflecting neighborhood


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satisfaction on one hand, and the dynamic nature of neighborhood on the other hand,

there are many opportunities to expand research in this field in future studies.


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6


6.1 Introduction

Following our domain-specific satisfaction approach, including satisfaction with housing,

neighborhood and job, travel satisfaction is another potentially important domain-

specific satisfaction, which may affect well-being (Schimmack, 2008; Ettema et al.,

2010). Transport is an inevitable and inseparable part of urban life. It provides access

to all necessary locations and facilitates out of home activities, and enables people to

participate in certain experiences such as employment, education, social and leisure

activities. A good transport system can influence different components of everyday

living positively, whereas inefficient transport can be costly, time consuming, risky and

stressful for commuters, and results in a huge loss of abilities and resources. This

accordingly contributes to substantial diminution of individual and public overall life

satisfaction (e.g. Cao, 2013). However, despite the important, tangible and

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consequential implications of travel experiences for well-being, the topic has just

recently received attention, and limited number of studies focused on the link between

travel satisfaction and well-being (e.g. Sirgy et al., 2011; Ettema et al., 2011; Friman et

al., 2013; Aydin et al., 2015; Gao et al. 2017). This is partly because of the broad scope

and various facets of this link, which makes researchers to normally focus and examine

the effects of an explicit component of travel satisfaction on well-being rather than the

entire travel satisfaction.

In formulating the conceptual framework of this thesis (see Chapter 3),

transportation contributes to quality of life, and thus, this chapter will focus on the

transportation domain-specific subjective well-being. To assess the quality of life

associated with this domain, judgments of the individuals’ satisfaction with travel will be

addressed and explicitly, the following research questions will be explored:

I. What are the main predictors of travel satisfaction? Or which of the

travel features have received empirical support in predicting travel

satisfaction?

II. To what extent do these indicators contribute to predicting travel

satisfaction?

III. What are the relationships among these indicators and to what extent

do they affect each other?

6.2 Research background

Travel satisfaction is an emerging topic in travel behavior science and as a result the

literature is still limited but rapidly expanding (Gatersleben & Uzzell, 2007; Friman &

Fellesson, 2009; Ettema et al., 2012; Cao, 2013; Olsson et al., 2013; Susilo & Cats,

2014; Mokhtarian & Pendyala, 2018). Some studies have included built environment

and psychological dispositions (e.g. attitude and personality) in evaluating commuters’

satisfaction (e.g. Friman et al., 2013; St-Louis et al., 2014; De Vos et al., 2016). In

general, the concept of commute satisfaction mainly comes from customer satisfaction

research, where customers’ reaction to the offered services can be considered as

customer satisfaction Moreover, an approach called Satisfaction with Travel Scale (STS)

has been developed based on the cognitive and affective judgments of satisfaction and

has been used widely in travel satisfaction studies (e.g. Friman et al., 2013).


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6.3 Factors determining travel satisfaction

In addition to socio-demographic of individuals, their access and use of travel modes

and their trip attributes influence their overall judgments regarding the travel. A

growing number of empirical studies have examined the role of instrumental factors

such as travel time, travel cost and level of service (e.g. Friman & Fellesson, 2009;

Ettema et al., 2012; Cao, 2013; Olsson et al., 2013; Susilo & Cats, 2014) or non-

instrumental variables such as safety, comfort and social interactions (e.g. Stradling et

al., 2007) on travelers’ satisfaction. Along the same line, a vast majority of current

studies have explored the mode-related travel satisfaction (e.g. Páez & Whalen, 2010;

Friman et al., 2013; St-Louis et al., 2014). Furthermore, some recent studies also

mentioned that travel satisfaction varies among urban areas, and thereby the role of

built environment and neighborhood on the travel satisfaction has been explored (e.g.

Cao & Ettema, 2014; De Vos et al., 2016). Moreover, few studies explored the impact of

travelling in rush hours and the negative influences of unpredictability and stress on the

overall trip satisfaction (e.g. Morris & Hirsch, 2016). This implies that the determinants

of travel satisfaction are numerous, and in the following, we will explain them in more

detail.

6.3.1 Individuals’ socio-demographic and household characteristics

Socio-demographic variables are linked with the individuals’ needs, desires, time and

money constraints, and thereby potentially influence travel satisfaction (e.g. Cao et al.,

2015; De Vos et al., 2016, Higgins et al., 2017). The main socio-demographic variables

that have been discussed in the literature are age, household income, employment,

household composition, gender and car ownership. There are few studies that

investigate the role of individuals’ health on travel satisfaction, and it has been

investigated among the socio-demographic variables (e.g. Ye & Titheridge, 2016).

However, empirical studies provided mixed findings when it comes to satisfaction

variations across socio-demographic variables.

In this regards, age probably is the most discussed variables in the literature.

Many studies found that travel satisfaction increases linearly with age (e.g. Cao and

Ettema, 2014; De Vos et al., 2016), but a few other studies revealed that the

relationship is U- shaped, means younger adults and seniors experience higher

satisfactions of commuting in comparison with middle aged commuters. Adversely,




94

some authors argued that increase in age contributes to difficulty in mobility for some

people (specially the lower incomes), which leads to lower travel satisfaction. Also, car

ownership is likely to influence travel mode choice and accessibility, which consequently

influence travel satisfaction (Ding et al., 2017). Regarding gender, some researchers

observed gender differences in travel satisfaction. The results are mixed as some found

male are more satisfied (e.g. Higgins et al., 2017), while some other found evidence

that males tend to be less satisfied compared to females (e.g. Gao et al., 2017).

6.3.2 The built environment

Transport is context dependent and thus, the satisfaction people derive from their daily

trips is very much influenced by the physical environment surrounding them (Clark et

al., 2016). To date, a growing number of studies have linked built environment with

travel satisfaction (e.g. Friman et al., 2013; Cao & Ettema, 2014; De Vos et al., 2016;

Ye & Titheridge, 2017). However, since the topic is very broad, to achieve a sound

understanding, different researchers have focused on one or few aspects of the physical

space. Based on the research findings, residential location, access to different transport

modes, density and land use are the most important characteristics of the built

environment that can highly contribute to travel satisfaction. As most trips start from

home, residential location affects many aspects of commuting. House location within

the neighborhood and neighborhood location within the city, all together defines

residential location, which is distinctly linked to the accessibility notion. As Lynch (1984)

described, accessibility is the main functional character of urban spaces, contributes to

the ability of residents in participating in different activities and using different

resources and information. At a micro-scale, a good neighborhood can provide access to

main facilities, such as supermarkets, kids’ playgrounds and public transport means. A

neighborhood that is easily accessible on foot is preferred by everyone especially elderly

residents and other vulnerable groups. At a wider scale, the whole area that consists of

different blocks and small neighborhoods should provide easy access to health care,

education, employment, recreation and other key services.

Commuting mode choice is another key player in travel satisfaction, which is

directly influenced by the physical built environment (e.g. Cervero, 2002; Chen et al.,

2008; Munshi, 2016). Mode choices generally impact the perception of an area and

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95

connectivity feeling. As a result, residents of suburban area are likely to have less

transport satisfaction in compared with inner city residents, mainly because of the mode

choice limitations (e.g. De Vos et al., 2016). Dense urban areas and mixed land use

(mix of residential and services) which are “New urbanism” planning paradigms

contribute to shorter distances and travel time, which consequently can enhance travel

satisfaction. In addition to that, walking and biking, which are proven to be the most

satisfying travel modes, are promoted in dense urban areas. Friman et al. (2011, 2013)

found that living close to work and school contribute to higher level of satisfaction since

the likelihood of walking or cycling increase. Furthermore, those who looks at travel

time as a buffer between multiple activities believes that active travel modes use is the

most relaxing activity, reducing stress and increasing the travel enjoyment (e.g. Urry,

2016).

Aside from short distance that dense urban areas can provide, urban

infrastructures such as availability of good pedestrian passes and separated bike routes

are important facilitators for active traveling. There are also some other factors that

indirectly encourage active travel mode use such as narrow streets, traffic congestion,

and lack of parking spaces which are physical barriers, and can reduce car commuters

satisfaction (e.g. Maat & Timmermans, 2009). In general, neighborhood satisfaction

directly influence travel satisfaction as people who live in unfavorable areas experience

lower travel satisfaction due to restrictions on their travel mode choice (De Vos et al.,

2016).

6.3.3 Travel mode choice

Previous research have linked travel mode choice to travel satisfaction and tried to find

out which mode users are the most satisfied commuters. The results, however, show

some agreements and disagreements when discussing satisfaction of commute by

different modes. In terms of active travel modes, most of studies have found that

cyclists and walking people tend to be the most satisfied (e.g. Gatersleben & Uzzell,

2007; Páez & Whalen, 2010; De Vos et al., 2016), and particularly cyclists have the

highest level of satisfaction (e.g. Avila-Palencia et al., 2018). In contrast, public

transport commuters display the lowest satisfaction compared to the users of other

modes (Páez & Whalen, 2010; Friman et al., 2013). Furthermore, some researchers

found that car commuters are more satisfied with their travel than public transit users






96

(e.g. Turcotte, 2005), while the others found higher satisfaction among public transit

than that of private transportation (e.g. St-Louise et al., 2014; Olsen et al., 2017). In

general, people choose different transportation mode based on their needs, desires and

their time-space constrains which is related to socio-demographic characteristics and

their personality.

6.3.4 Time, the role of rush hours

Time in general, is a main notion in the dynamic world and performing activities are

bonded to space, time or both. The fixed scheduled activities are bonded to both space

and time, while the flexible activities can be rescheduled and shifted in time and space.

Normally, people tend to shift their flexible activities to avoid rush hours and improve

their commute experience (Schwanen et al., 2008; Oakil et al., 2016). Traveling in rush

hours can significantly influence travel satisfactory as it may affect all the main trip

attributes (i.e. Travel time, travel cost, travel safety and travel comfort). It is likely that

trips in rush hours prolonged due to traffic congestion and crowded roads, and thereby

the overall travel satisfaction can negatively be influenced during rush hours (Morris &

Hirsch, 2016). Also cost of trips could increase significantly during rush hours. In many

countries, rush hours fees for public transportation, especially for the rail means are

higher. Moreover, because of traffic congestion, vehicles consume more fuel and the

possibility of finding free parking spaces is less. This makes rush hours trips more

expensive for both public and private transportation commuters. Travel safety in rush

hours is likely to be reduced, as the probability of accidents is getting higher in busy

roads. Additionally, delays and extreme on-board crowding in public means, and stress

exposed by traffic congestion to car commuters can reduce travel comfort significantly

during rush hours.

6.3.5 Trip attributes satisfaction

Travel satisfaction has been commonly conceptualized as a function of trip attributes

(Gao et al., 2016). The previous studies consider numerous travel-related attributes in

order to estimate travel satisfaction. Travel time, travel cost, travel safety and travel

comfort are the most examined variables (e.g. Beira & Cabral, 2007; Aydin et al., 2015).

In other words, the extent to which commuters feel happy with the trip attributes

strongly contributes to the overall evaluation of their trips, so it is logical to study them

distinctly in the following.


97

6.3.5.1 Travel time (trip duration) satisfaction

Time is very valuable and time saving is always a major benefit of a great

transportation system. Generally, travel time is the entire time devoted to travelling.

This includes delays, transfer time, waiting in traffic congestion, etc., and can be

influenced by many aspects such as vehicle’s speed and land use mix (St-Louis et al.,

2014). Shorter travel time is usually preferred, especially for frequent journeys such as

daily trips to work, school etc. (e.g. Morris & Hirsch, 2016). Therefore, clearly the

frequencies of trips together with the length of trips are the main determinants of travel

time satisfaction. Zhang et al. (2016) examined travel time and frequency of public

transport as the predictors of travel satisfaction and found that they significantly

influence travel satisfaction. Several other studies have also found negative associations

between longer trips and commuter’s satisfaction, and related it to reasons such as

stress level, exhaustion and traffic congestion (e.g. Morris & Guerra, 2015). For

instance, Higgins et al. (2017) found that longer travel time reduces travel satisfaction

among the Canadian commuters, specifically when considering the travelers’ perception

of congestion.

6.3.5.2 Travel cost satisfaction

Satisfaction with travel cost is another key attributes of travel satisfaction. Clearly, travel

needs allocation of proper budget and travel cost is defined as all of the expenses

associated with a trip. As costs and benefits are always related, satisfaction with travel

cost is substantially linked with other trip attributes (i.e. travel time, travel comfort and

travel safety),as well as the purpose of trip and activity at the destination. According to

“Microeconomic theory”, individuals tend to minimize their travel cost unless they

receive compensation for it (Stutzer & Frey, 2008). This implies that affordability or

satisfaction with travel cost is an important aspect not just for travel satisfaction, but

also at a higher level for individuals to participate in society and non-paid activities.

Especially, for low-income people high-cost transportation is a burden and leads to

isolation and lower well-being (e.g. Bocarejo & Oviedo, 2012).

6.3.5.3 Travel safety satisfaction

Safety is an important human need and thus the extent to which commuters feel safe

during their trips is a key initiative in travel satisfaction. Two major domains usually

https://www.sciencedirect.com/science/article/pii/S016604621400101X#bb0155



98

explain travel safety: safety from accidents and safety from crime (Joewono & Kubota,

2006). Whilst, the former refers to components such as roads safety, vehicles safety,

driving qualifications, etc., the latter is a reflection of safety in community and rates of

criminal incidents. Typically, private transportation is assumed to impose less risk

regarding crime, whereas public transit is seen safer in terms of accidents (especially for

road trips). Iseki and Smart (2012) found satisfaction levels for safety to be more

associated with the overall travel satisfaction than other attributes (e.g. reliability,

amenities etc.) among transit users. Also, Spears et al. (2013) argued that personal

safety concerns significantly associated with commuters' satisfaction. For active

commuters (cyclists and walking people) both crime and accident could potentially be a

threat based on the context. Many authors argue that improving safety features could

motivate people to switch their travel modes to active modes, which in turn increase

travel satisfaction (e.g. Ettema et al., 2016). The safety assessment is associated with

both direct and indirect experiences of individuals, and therefore, varies among

commuters. It is also reported that safety feeling varies among gender and different

age groups (Susilo & Cats, 2014).

6.3.5.4 Travel comfort (convenience) satisfaction

The last travel attribute that influence travel satisfaction is travel comfort satisfaction. In

most of travel satisfaction studies, travel comfort has been considered as a determinant

component of a trip (e.g. Stradling et al., 2007; Aydin et al., 2015). For instance,

Fellesson and Friman (2008) examined the perceived satisfaction with public transport

in nine European cities, and found that the most important factors influencing travel

satisfaction are comfort together with safety, staff and system dimensions.

Convenience and comfort are the two concepts that cover a wide range of

aspects and feelings and usually used interchangeably to explain the ease of use and

the amount of services provided to make a pleasant trip. In general, travel comfort

satisfaction is related to both physiological and psychological conditions. In terms of

physiological comfort ability, vehicle’s standards and thermal situation are among the

key factors to have a pleasant feeling. For public means, in addition to the above-

mentioned aspects, distance to station and number of passengers (vehicle’s crowding),

and cleanness are among the key determinant factors (e.g. Dell’olio et al., 2011;

Redman et al., 2013). Feeling stressed, freedom, social interactions and possibility of


99

doing various activities in long journey are among the factors that impact psychological

comfort feeling. Stress is the most psychological threat that is discussed for comfort

feeling and travel satisfaction. It is said that drivers tend to experience stress more

often, while journeys by public transport are more stress free (Novaco & Gonzales,

2009; Legrain et al., 2015; Mattisson et al., 2016). However, public transport’s

unpredictable delays can also trigger feeling stressed during journeys (e.g. Cats &

Gkioulou, 2017). Independence and freedom feeling are the other two main aspects

that influence psychological comfort (Steg, 2005). Private modes can makes journeys

more convenient, but on the other hand, can impose isolation in longer single

occupancy journeys (Mattisson et al., 2015). However, during the trips by public

transport modes, the possibility for interactions and social contacts with passengers

and/or driver can change the trip feeling (Bissell, 2010). Stradling et al. (2007) found

that variables such as comfort, stress, social interaction and scenery have a great

influence on trip satisfaction. In addition to all of the aforementioned factors, the

possibility of committing various types of activities (such as reading, watching movie,

etc.) in journeys by public means is likely to increase the comfort feeling for many

commuters (Ettema et al., 2012).


In order to explore the implicit effect of various travel components on the commuters’

overall assessment of their travel, a multiple regression analysis was conducted with

overall travel satisfaction as the dependent variable and 45 independent variables

(including socio-demographic variables). We assumed that the scales, which used to

measure satisfaction have interval properties (same degree of difference between

items) based on many studies that utilized such scales (e.g. Diaz-Serrano & Stoyanova,

2010). It is notable that those who walk, bike and not travel at all have not evaluated

anything regarding their trip characteristics. Therefore, a binary variable called mode

choice has defined which contains “Traveler” (including public or private transport mode

user), and “Non-traveler” (including walk, bike or not travel at all) to represent this

group in our model. Apart from the socio- demographic variables that are binary (refer

to Table 3.3), age, household income and household size were treated as continuous

variables. Also, “Health” included as a socio-demographic variable and is a self-reported

health of individuals (rating).


100

Since one of our regression assumptions is that the residuals (prediction

errors) are normally distributed, therefore, checking the histogram of residuals allows us

to check the extent to which the residuals are normally distributed. The well-behaved

residuals can lead to correct P values and thus are very important for any linear

regression analysis. Our histogram (Figure 6.1) shows a fairly normal distribution. Thus,

based on these results, the normality of residuals assumption is met.

Furthermore, the normal P-P Plot of regression residuals (Figure 6.2) can also

be used to assess the assumption of normally distributed residuals. If data points (little

circles) follow the diagonal line, the residuals are normally distributed. Our P-P plot

shows minor deviation and thus the normality of residuals assumption is satisfied. The

results about R-squared (R2) and adjusted R-squared are shown in Table 6.1. It shows

that the indicators accounted for almost 46 percent of variance of the overall travel

satisfaction.



explanatory variables on the residents’ overall travel satisfaction (Table 6.2). Among all

of the indicators in this analysis, only two variables show a significant relationship with

the overall travel satisfaction (p<.05). This might be due to relatively small sample size

compared to the number of variables in the model. “Overall neighborhood satisfaction”

(.34) and "Travel time to family /friends” (.17) from accessibility satisfaction category

both significantly influence “Overall travel satisfaction”. Clearly and according to

literature (e.g. Ewing & Cervero, 2010) neighborhood satisfaction highly influences

travel satisfaction as it determines distances to different places and access to different

modes of transport. Satisfaction with "Travel time to family/friends” has been also

mentioned in other travel satisfaction studies as an influential variable (e.g. St-Louise et

al., 2014; Ye & Titheridge, 2016; Higgins et al., 2017).

Although multiple regression analysis provides some insights into the impact of

socio-demographic characteristics and all the mentioned built environment and travel-

related characteristics on overall travel satisfaction, but this method is unable to capture

the indirect relationships and the complex associations among these variables. Thereby,

further analysis is necessary to understand the mechanism of effects among the study

variables, which will be explained in the next part.




101

Figure 6.2 Normal Probability Plot (P-P) of the regression standardized

residual dependent variable for overall travel satisfaction

Figure 6.1 The Histogram of the overall travel satisfaction


102

Table 6.1 Goodness-of-fit of the regression model of overall travel satisfaction

R R2 Adjusted R


.677 .458 .307 .952

Table 6.2 The estimated parameters of the transport satisfaction using multiple regression analysis

Model Parameters

Unstandardized

Coefficients

Standardized

Coefficients


Socio-

demographic

indicators

(Constant) 3.103 .820

3.783 .000

Age -.001 .005 -.008 -.102 .919

Household size -.052 .120 -.054 -.436 .663

Unemployed -.334 .263 -.094 -1.273 .205

Household with kids .160 .236 .065 .680 .497

Highly educated .147 .196 .051 .753 .453

Male .245 .157 .107 1.553 .122

Single -.301 .228 -.124 -1.322 .188

Car owner .021 .244 .008 .087 .931

Health .087 .048 .125 1.800 .074

Household income .006 .051 .008 .113 .911

Peak hours

satisfaction

Travel cost (private transport) -.044 .100 -.124 -.434 .665

Travel cost (public transport) .144 .184 .266 .782 .435

Crowdedness (public transport) .068 .199 .123 .340 .734

Travel time (private transport) .053 .136 .146 .388 .698

Travel time (public transport) .195 .243 .385 .802 .424

Parking availability .102 .090 .305 1.124 .263

Parking cost -.033 .063 -.084 -.518 .605

Safety (public transport) -.316 .392 -.675 -.806 .422

Traffic congestion

(private transport) .091 .136 .233 .673 .502


103

Waiting time (public transport) .106 .319 .231 .333 .740

Safety (private transport) .090 .119 .289 .760 .448

Distance to station-stop -.206 .318 -.484 -.647 .518

Inside public means quality .139 .296 .281 .469 .640

Off-peak

hours

satisfaction

Travel cost (public transport) .344 .219 .760 1.565 .119

Travel cost (private transport) -.114 .114 -.372 -1.004 .317

Crowdedness (public transport) .344 .219 .760 1.565 .119

Travel time (public transport) .331 .292 .760 1.135 .258

Travel time (private transport) -.160 .133 -.537 -1.202 .231

Parking availability .017 .078 .055 .223 .824

Parking cost .106 .062 .287 1.728 .086

Safety (public transport) -.210 .247 -.507 -.849 .397

Traffic congestion (private

transport) .067 .121 .209 .551 .582

Waiting time (public transport) -.333 .212 -.756 -1.570 .118

Safety (private transport) .090 .119 .289 .760 .448

Distance to station/stop .064 .201 .167 .316 .752

Inside public means quality .049 .228 .114 .216 .829

Accessibility

satisfaction

Travel time to work -.033 .022 -.114 -1.503 .135

Travel time to kids school/day

care -.002 .027 -.007 -.077 .939

Travel time to service places .028 .020 .104 1.390 .166

Travel time to family/friends .064 .032 .166 2.043 .043

Travel time to recreational places -.003 .024 -.010 -.127 .899

Built

environment

satisfaction

Overall neighborhood .348 .097 .335 3.583 .000

Neighborhood infrastructure for

walking/biking .052 .064 .061 .813 .417

Neighborhood safety .013 .063 .016 .203 .839

Travel Non-traveler .805 .484 .345 1.664 .098


104

6.5 Analysis and results: Structural equation modelling (SEM)

The key objectives of this chapter are to evaluate the proposed theoretical model and to

understand the interrelationships among the study variables in formulating travel

satisfaction. Structural equation modeling (SEM) is deemed to be an adequate solution

for the achievement of these objectives and has been increasingly used in studying the

travel satisfaction (e.g. de Oña et al., 2016; Wan et al., 2016; Friman et al, 2017; Gao

et al., 2017). The recently published studies on travel satisfaction have shown that SEM

is a good method in estimating the relationships among multiple unobserved constructs

(latent variables) and observable variables, and increases the statistical efficiency.


Based on the aforementioned insights from different travel studies and the relevance of

these studies to investigate the link between different travel attributes, built

environment variables and socio-demographic variables as well as the overall travel

satisfaction, a conceptual model is elaborated. Figure 6-3 depicts the hypothetical

model structure that we estimated to examine the influence of different attributes on

overall travel satisfaction. As the figure shows, overall travel satisfaction is assumed to

be influenced by travel mode choice in peak and off-peak hours and the satisfaction

with the associated trip attributes, which predicts public and private transport

satisfaction in peak and off-peak hours. In addition to the trip related factors,

satisfaction with the built environment factors may influence overall travel satisfaction.

These variables include neighborhood satisfaction and its related variables, and also

feeling regarding the accessibility to important places in daily life. Depending on socio-

demographic variables, people may choose particular transport means and may

differently rate their satisfaction with travel. Therefore, the socio-demographic variables

may directly and/or indirectly influence overall travel satisfaction.

In our conceptual model, five variables are incorporated as latent variables,

these variables are complex concepts, and thereby linking them to measurable variables

can help quantifying them. “Peak hours private transport satisfaction”, “Peak hours

public transport satisfaction”, “Off-Peak hours private transport satisfaction”, “Off-peak

hours public transport satisfaction” and “Accessibility” are the five pre-conceptualized

constructs. Both “Peak hours private transport satisfaction” and “Off-peak hours private

transport satisfaction” are measured by five main attributes. They consist of satisfaction

with: “Travel time”, “Travel cost”, "Travel safety”, "Parking availability”, "Parking cost”


105

and “Traffic congestion”. Also, both “Peak hours public transport satisfaction” and “Off-

Peak hours public transport satisfaction” are measured by seven variables. They which

consist of satisfaction with: “Travel time”, “Travel cost”, Travel safety”, “Inside means

quality”, “Crowdedness”, “Waiting time” and “distance to station/stop”. “Accessibility” is

explained by 5 attributes describing the extent to which residents feel that different

places are accessible and rating their satisfaction with: “Travel time to work”, “Travel

time to family/friends”, “Travel time to recreational places”, “Travel time to services

places”, and “Travel time to kids’ school/daycare”.

6.5.2 SEM model for transport satisfaction



normality, which enables estimating the direct and indirect correlations among factors,

and the p values of the paths including the standardized regression weights of the

conceptual model. In the first step, all of the attributes were included in the model to

see whether they have any direct or indirect relation with the observed and unobserved

endogenous variables. After omitting the insignificant relationships from the model,

most of the socio-demographic variables are remaining in the model (except “Household

size”, “Unemployed” and “Highly educated”). Also, both “Off-Peak hours Public transport

satisfaction” and “Off-Peak hours private transport satisfaction” were found

insignificant, and thus, are excluded from the model. The final model consists of 30

observed variables and 3 unobserved endogenous variables (“Peak hours public

transport satisfaction”, “Peak hours private transport satisfaction” and satisfaction with

“Accessibility”).

The results of the final SEM model (Table 6.3) demonstrate that the model

excellently fit to the data. Chi Square divided by the model degrees of freedom (/df or

CMIN/DF) is an essential measure and is equal to 2.2 for our model. In addition to that,

the Root Mean Square Error of Approximation (RMSEA) should be less than .08 to

represent a great fit (Golob, 2003; Washington et al., 2010) and in our model RMSEA

value is .07. Other indicators of a good fit as mentioned by many researchers (e.g.

Moss, 2009) are indices that compare target model with null model, such as

Comparative fit index (CFI), Incremental fit index (IFI), Tucker Lewis index (TLI),

Normed fit index (NFI). If these indexes exceed the amount of .90 the model tends to


106

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107


generate an acceptable fit. Checking all of these indicators shows that our model

perfectly fits.


multiple correlation value in AMOS) equals to .45, which means our proposed

theoretical model satisfactorily accounts for 45 percent of the variance in residents’

overall travel satisfaction. Table 6.4 presents all the direct, indirect and total effects of

the variables on the overall travel satisfaction. It shows that statistically significant

relationships were found among the main model variables as below:

“Non-traveler” in peak hours has the largest effect on “overall travel

satisfaction” (.42) followed by “Overall neighborhood satisfaction” (.39).

Both “Peak hours private transport satisfaction” (.27) and “Peak hours public

transport satisfaction” (.15) positively influence “overall travel satisfaction”.

“Accessibility” also has a direct significant relationship with “overall travel

satisfaction” (.13).

Satisfaction with neighborhood safety and neighborhood physical activity both

are associated with neighborhood satisfaction and through that contribute to

overall travel satisfaction.

“Health” is the only variable from the socio-demographic category that directly

correlates with travel satisfaction (.10). It is also the most influential variable

among the socio-demographic variables with total effect size (.26).

Fit index Estimate





CFI .95

IFI .95

TLI .94

NFI .91


108

In addition to the direct effects on travel satisfaction, neighborhood

satisfaction also influence travel satisfaction indirectly by affecting travel

attributes such as satisfaction with "Travel time" and "Parking cost" (Figure

6.4).

“Male“ and “Income level” both have negative effect on overall travel

satisfaction.

Table 6.5 shows the relationships among the latent and observed variables participating

in the measurement model. To show the reliability of each observed variable, this table

includes unstandardized coefficients with p and t-value alongside standardized path

coefficients and squared multiple correlations. The t-value for all the paths is bigger

than 2.33. This is consistent with what Kline (1998) argued that t-value should be

Table 6.4 Standardized path estimates of direct, indirect and total effects on overall travel satisfaction

Overall transport

Direct effect

Indirect effect

Total effect

Unobserved variables

Accessibility satisfaction .13 .0 .13

Peak hours public transport satisfaction .15 .0 .15

Peak hours private transport satisfaction .27 .0 .27

Observed variables

Car owners .00 .03 .03

Health .10 .16 .26

Neighborhood safety satisfaction .00 .19 .19

Satisfaction with neighborhood infrastructure for walking/biking

.00 .16 .16

Age .00 .07 .07

Single .00 .01 .01

Male .00 -.005 -.005

Household income .00 -.04 -.04

Households with kids .00 .05 .05

Non-traveler .42 .00 .42

Overall neighborhood satisfaction .39 .00 .39


109

Table 6.5 Relationships among the latent and observed variables

Endogenous

Latent

variables

Observed

variables

Standardized

Coefficients

Unstandardized

coefficient

t

p

Squared

multiple

correlations

Public

transport

satisfaction

Travel cost .97 1.00 .93

Travel time .99 1.10 48.62 *** .97

Waiting time .99 1.20 45.83 *** .97

Crowdedness .97 .99 39.81 *** .94

Distance to

station/stop

.99 1.3 47.66 *** .98

Safety 1.00 1.19 50.51 *** .99

Inside means

quality

.99 1.12 44.23 *** .98

Private

transport

satisfaction

Parking

availability

.99 1.10 36.50 *** .97

Traffic

congestion

.96 .91 31.02 *** .92

Travel time .95 .97 33.85 *** .90

Safety .96 1.05 40.41 *** .92

Parking cost .86 .84 21.69 *** .76

Travel cost .95 1.00 .89

Accessibility

satisfaction

Travel time to

work

.22 .32 2.54 *** .12

Travel time to

service places

.58 .91 6.15 *** .35

Travel time to

kids

school/day

care

.22 .30 2.91 *** .29

Travel time to

family/friends

.67 .74 6.50 *** .46

Travel time to

recreational

places

.70 1.0 .49


110

bigger than 1.96 for p<.05 and bigger than 2.33 for p<.01. The other main results are

listed below:

All the trip attributes (e.g. travel time, travel safety, etc.) for both public and

private means significantly explain the associated latent variables, and thereby

link to travel satisfaction.

Satisfaction with “Parking availability” is the most dominant attribute among

the predictors of “Peak hours private transport satisfaction”.

Satisfaction with “Safety” is the most dominant attribute among the predictors

of “Peak hours public transport satisfaction”.

Satisfaction with “Travel time to recreational places” and “Travel time to family

and friends” are the most dominant attributes of “Accessibility satisfaction”.

6.6 Interpretation

Our results show that in general all the variables in our model are significantly

correlated with travel satisfaction. Below these correlations will be discussed in more

details.

6.6.1 Effects of travel characteristics

Our model results suggest that rush hour commuting and mode of transport

significantly contribute to travel satisfaction. Non-traveler in peak hours are in average

happier with their travel than car commuters and public means commuters. The high

amount of people who does not commute in peak hours is also related to the fact that

working from home is popular in the Netherlands. The lower level of travel satisfaction

among public transport commuters in compared with car commuters also admitted by

several other researchers (e.g. St-Louis et al., 2014; De Vos et al., 2016). For instance,

Susilo and Cats (2014) who examined the key determinant of travel satisfaction for

multi-stages trip found that commuters have low satisfaction levels with public transport

trip stages while they are happy with the trip stages by other means of transport.

Regarding the trip attributes, car commute satisfaction in peak hours is strongly

explained by satisfaction with parking availability and traffic congestion, while

satisfaction with parking cost is the least important attribute. This might be due to the

availability of free parking for most of working people, who are a big proportion of car

commuters during peak hours. Public transport satisfaction in peak hours is strongly


111

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explained by seven attributes of which satisfaction with safety has the highest level of

importance while satisfaction with trip cost has the lowest importance.

6.6.2 Effects of socio-demographic variables

Regarding the impact of socio-demographic variables, similar to many other studies, we

found many significant associations. Age of commuters positively impacts travel

satisfaction ratings which is similar to findings of many other researchers (e.g. Ory &

Mokhtarian, 2005; Gao et al., 2016). However older people are less likely to be happy

with their travel time to work and car commuting in peak hours. Furthermore, people

with higher self-reported health are more satisfied with their travel, which is also

admitted by Ye and Titheridge (2016) who examined health as one of the socio-

demographic variables, and found it as an influential attribute on travel satisfaction.

Beside the direct effect, our results show that health does affect travel satisfaction

indirectly through accessibility satisfaction, neighborhood satisfaction, safety feeling and

satisfaction with physical activity, which in turn influence travel satisfaction.

In terms of gender, the relationships are more complicated as female commuters

are slightly happier with their overall transport, and they reported higher satisfaction

with accessibility feeling while male commuters have higher satisfaction levels with

safety assessment in public transport commute (e.g. Yavuz & Welch, 2010), and also

are happier with car commuting in peak hours. But eventually our results show that

female respondents are happier with overall travel satisfaction than male respondents,

which could be due to the fact that female respondents travel less in peak hours. Gao et

al., (2017) who conducted a research on the effects of mood and personality on

satisfaction ratings with daily trips also found that male travelers are more likely to feel

anger and be critical, and thereby have lower level of travel satisfaction.

Single people have higher satisfaction level with the accessibility and car

commute in peak hours, and thereby have higher level of travel satisfaction. Car

ownership has been shown to have direct influence on mode choice and positively

impacts travel satisfaction. It also influences neighborhood satisfaction, satisfaction with

travel time to family/friends and recreational centers. Our results also indicate that

presence of kids at households leads to higher level of travel satisfaction. In general,

our results have shown that female, older people, single people, car owners and

household with kids are more likely to be happy with their overall travel than others.

Further, as we already discussed the effects of travel mode choice and rush hour


113

commuting on travel satisfaction, it is good to note that socio-demographic

characteristics of respondents are associated with commuting choices which in turn

influence travel satisfaction. For instance, younger people and males are more likely to

travel during peak hours. These associations, to some extent explain the indirect effects

of socio-demographic variables on travel satisfaction.

However, although socio-demographic variables may significantly influence

travel satisfaction as their effects reflects differences in underlying needs, desires and

space-time constraints, but psychological factors associated with certain socio-

demographic characteristics may play a more critical role. For instance, older people are

generally more relaxed and thereby have higher level of travel satisfaction. In this

regard, personality has a more direct and clear link with travel satisfaction than socio-

demographic variables (Gao et al., 2017).

6.6.3 Effects of built environment

It has been shown that most of built environment characteristics are potentially

associated with travel mode choice and trip attributes (e.g. Cao & Ettema, 2014; De Vos

et al., 2016; Ye & Titheridge, 2017), which in turn influence travel satisfaction. In our

model, beside the direct effects of neighborhood satisfaction and accessibility on travel

satisfaction, built environment characteristics indirectly influence travel satisfaction

through satisfaction with car commute travel time and parking cost. Accessibility

explains the extent to which people are happy with travel time to important places such

as work, recreational places, etc. And neighborhood characteristic such as safety and

infrastructure for physical activity influence satisfaction with neighborhood, and in turn

affect travel satisfaction. Car ownership is significantly correlated with neighborhood

satisfaction and accessibility variables, particularly accessibility to family/friends and

recreational centers, while it has no significant relationship with travel to work, kids

school and service places. We can conclude that the decision of car ownership is related

to residential locations and residential choices are likely to be based on distance to work

places, etc. Therefore, satisfaction with residential location and accessibility meditate

the effect of car ownership on travel satisfaction.




114


Despite extensive research on travel behavior, the study of travel satisfaction is

relatively new. These studies mostly focus on the assessments of travel characteristics.

In this research project, satisfaction with the built environment and access to important

places in daily life has also been included. Our empirical results provide evidence that

the proposed conceptual model is acceptable. Nevertheless, data limitations and small

sample size for some modes of transport is a big challenge in interpreting the results as

travel satisfaction is just one domain of our research.

Although context differences may lead to differences in travel characteristics

(travel mode choice, level of service, etc.) and built environment characteristics among

different places, however, several findings of this study are in line with previous studies

conducted in other countries. These findings include: (1) travel mode choice is

significantly associated with travel satisfaction, (2) rush hours trips have a significant

effect on travel satisfaction, and (3) healthier commuters are happier with built

environment characteristics, and health of commuters directly and indirectly impacts

travel satisfaction. However, to the best of our knowledge, there are few findings that

are unique to this study. These findings include: (1) satisfaction with the built

environment affects travel satisfaction directly and indirectly through the path of travel

characteristics (e.g. travel time), (2) a high level of rush hours avoidance, which directly

influences travel satisfaction is related to work flexibility in the Netherlands. Working

from home is a popular trend, and thereby, part of non-travelers during peak hours

concern employed people. This indicates that work flexibility directly influence travel

satisfaction among Dutch commuters.

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7

Work well-being

7.1 Introduction

Work is a dominant sphere of life. It is the main activity that individuals across the

globe perform to generate income and pay for life’s necessities and living standards. It

is also closely associated with non-material dimensions of life such as social life and

integrating individuals into society (Fryers, 2006). At a higher level, work is related to

one’s goals, desires and achievements and from this perspective, it is more than just a

regular activity, but can be a source to shape identity and personal growth (Hulin,

2002). Thus, it is a rewarding activity and pleasure of doing a desirable work is a

significant part of life satisfaction. In that sense, many empirical studies have shown

that work satisfaction is strongly associated with overall life well-being (e.g. Sirgy.,

2012; Lee et al., 2015; Senasu & Singhapakdi, 2017). Along the same line, results from

many studies have demonstrated that the lack of work or being unhappy with one’s

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career can have many negative impacts on life and deteriorate well-being (e.g.

Gerdtham & Johannesson, 2001; Abdallah et al., 2012).

Despite the considerable role that job satisfaction plays in one’s life, the vast

majority of well-being and happiness studies have typically focused on income and

financial aspects of the job. These studies ignore important features of job satisfaction

such as social and psychological aspects, which contribute to fulfillments of higher level

of needs (Diener & Seligman, 2004). On the other hand, most of the literature on work

satisfaction is a part of economic and organizational studies with the ultimate target of

optimizing work performance and effectiveness in business organizations rather than

enhancing employees’ well-being. This little emphasis on work satisfaction as a

multifaceted concept implies a gap in the literature and the need for more detailed and

comprehensive research.

In formulating the conceptual model of this PhD research (see Chapter 2),

work as a main human activity plays a key role in quality of life, and thus, this chapter

extends the knowledge derived from prior research by investigating the role of different

attributes on work satisfaction. More specifically, in this chapter we will address the

following research questions:

I. What are the main predictors of work satisfaction?

II. To what extent do these indicators contribute to predicting work

satisfaction?

III. What are the relationships among these indicators and to what extent

do they affect each other?


7.2.1 Activity called job (terminology and definitions)

There are numerous terms to explain the regular activity that generates income. Among

those, job, career, profession, work and employment are the most common terms that

used interchangeably in the organizational literature. Duran (2015) argued that “job”

refers to work as a paid position and thereby highlights the earning aspects, while

“work” covers unpaid activities as well. Also, based on the main definition of work as

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the “Activity involving mental or physical effort done in order to achieve a purpose or

result” (Oxford dictionary), it is reasonable to say that “work” reflects more aspects of

the notion as it refer to “productivity” and higher demands of people.

However, as there is no consensus among researchers on terminology, work and

job satisfaction normally use and refer to similar concepts and various researchers

provided their own specific definition. More recently, some authors have used the term

“quality of work life (QWL)” to explain the concept with relation to well-being research.

For example, Varghese and Jayan (2013) indicated that QWL is the best term to explain

the concept of work satisfaction related to subjective well-being. They provided the

following definition for QWL: “Quality of Work Life is that part of overall quality of life

that is influenced by work. It is more than just job satisfaction or work happiness, but

the widest context in which an employee would evaluate their work environment”. We

can summarize that the current attempts at defining QWL consider it as a broader

ranging concept that covers the overall experience of work, which consists of

satisfaction from different work-related components including three main categories of

work space, work conditions and work location.

7.2.2 Research background

The emergence of work satisfaction studies can be traced back to early work of

Hoppock (1935), where he described job satisfaction as the employees’ subjective

reflections to working scenarios (Zhu, 2012). During the twentieth century, investigating

the link between employees’ attitudes and efficiency became a fascinating research

topic in social psychology and management field. But, the first study that addressed job

satisfaction measurement appeared in the work of Churchill et al. (1974), who explained

the job satisfaction concept via features of job and features of job-related environment.

However, during the years, the concept evolved gradually from single affective

perspective to multiple perspectives containing both affective and cognitive dimensions.

The current focus of most job satisfaction studies are on turnover intention (e.g. Saeed

et al., 2014; Sukriket, 2014), and the importance of work-life balance (e.g. Haar et al.,

2014; Sirgy & Lee, 2016; Mas-Machuca et al., 2016) in different occupational contexts.

In this regards, many studies found that the lack of work-life balance tends to increase

the turnover intentions among employees (e.g. Suifan et al., 2016).

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Theories that explain work satisfaction have strong roots in theories on human

motivation. One of the main theories is Maslow’s needs hierarchy theory. Based on this

theory, the concept of Quality of Life (QOL) has been defined as “the degree to which

the experience of an individual’s life satisfies that individual’s wants and needs” (Rice,

1984). In this definition, one major domain of life experience is considered as the

experience of work. Having a satisfying work life can respond to basic needs as well as

higher level of human development needs such as “self-actualization,” which means the

full realization of one’s potential, which is posited at the top of the Maslow’s hierarchy of

needs. In other words, work can contribute to all layers of human needs by providing

salary, secure work environment, friendly environment, job title, and challenging job

(Sirgy, 2012). Thus, some authors apply Maslow’s theory of hierarchical needs to

explain work life satisfaction. Other theories such as Locke’s Range of Affect Theory

(1968), Herzberg’s two-factor theory (Alshmemri et al., 2017), and Job characteristics

Model (JCM) (Hackman et al., 2005) have also been used in many empirical studies.

Range of Affect Theory basically determines satisfaction by the difference

between what an employee wants from job and what employment offers in return. It

also contends employees’ prioritization of one job facet over others. Herzberg’s two-

factor theory or motivation-hygiene theory is a two dimensional theory that explains the

difference between two groups of factors: one group are motivation factors (such as

advancement, recognition etc.) that can enhance job satisfaction, and the other group

are hygiene factors (related to the workplace and environment) with the ability of

reducing job dissatisfaction (Alshmemri et al., 2017). Moreover, job characteristics

model (JCM) listed five core job characteristics that can influence critical psychological

states. These psychological states (experienced meaningfulness, experienced

responsibility, and knowledge of the results activities), in turn, impact motivation, job

performance and job satisfaction. However, all of these theories have drawn criticisms

and none can fully explain work satisfaction, since some focus on the impact of the

surrounding environment (the motivational theories), while others focus on personality

(such as JCM). Furnham et al. (2009) proved that personality and demographic

variables of individuals together with the surrounding environment can account for a

significant portion of work satisfaction.

https://en.wikipedia.org/wiki/Edwin_A._Locke

https://en.wikipedia.org/wiki/Frederick_Herzberg


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7.3 Factors determining work satisfaction

There are numerous factors that highly impact work satisfaction. Based on an extensive

available literature, socio-demographic factors including several work-related aspects

influence judgments regarding employment (Table 7.1). Individual characteristics such

as age, gender, marriage status, education, and job characteristics such as satisfaction

with pay level, workload and work quality including the physical characteristics of

workplace are the most discussed variables (Steijn, 2004). Here, we explore these

variables separately, and in more details.

7.3.1 Individuals’ socio-demographic factors

Age is one of the mentioned factors that might influence work satisfaction. As age and

career stage are related, it is relevant to say that age tends to be positively correlated

with job satisfaction. Moreover, some researchers (e.g. Zacher & Griffin, 2015) revealed

that age influences career adoptability and thereby increases job satisfaction. However,

in contrast to this result, some studies found no significant relation between job

satisfaction and age (e.g. Chaudhuri et al., 2015) whilst others found job satisfaction is

U-shaped in relation to age (e.g. Clark et al., 1996; Gazioglu & Tansel, 2006).

Regarding gender differences, a number of studies show that females are more

satisfied with their jobs in general than males (e.g. Clark, 2005; Selvarajan et al.,

2015). However, the results are mixed, particularly when it comes to various aspects of

job evaluation (e.g. Gazioglu & Tansel, 2006). For instance, some studies found that

women are more likely to be satisfied with their salaries (Crossman & Abou-Zaki, 2003;

Hauret & Williams, 2017), but less satisfied with the opportunities for work

advancement (Carvajal et al., 2018). There are also many studies that found no

significant effect of gender differences on job satisfaction when all job conditions are

similar (e.g. Spencer et al., 2016).

The relationship between marital status and job satisfaction show that career and

family life are strongly tied to each other. In fact, both family life happiness and job

satisfaction impact each other. Similar to other socio-demographic factors, the findings

regarding the impact of marital status on job satisfaction are in conflict. For instance,

Gazioglu and Tansel (2006) found married employees are less satisfied than single

employees. In contrast, Hagedorn (2000) revealed that married employees reported

higher level of work satisfaction. Moreover, other socio-demographic factors such as

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Table 7.1 Major attributes in the selected recent literature

Study Indicators

Carvajal et al. (2018)

Salary

Stress

Workload

Opportunity for advancement

Job security

Fairness

Job atmosphere

Relations with co-workers

Supervision

Scheduling flexibility

Autonomy

Importance of job

Nanjundeswaraswamy

& Swamy (2013)

Work environment,

Organization culture and climate,

Relation and co-operation,

Training and development,

Compensation and Rewards,

Facilities,

Job security,

Autonomy of work,

Adequacy of resources.

Hosseini (2010)

Adequate And Fair Compensation,

Safe And Healthy Working Conditions,

Immediate Opportunity To Use and develop human capacities,

Opportunity For Continued Growth And Security,

Social Integration In The Work Organization,

Constitutionalism In The Work Organization,

Work And Total Life Space,

Social Relevance Of Work Life.

Saraji & Dargahi (2006)

Fair Pay and Autonomy,

Job security,

Fair environment,

Interesting and satisfying work,

Trust in management,

Support from coworkers,

Recognition of efforts,

Career prospect,

Control over work,

Health and safety standards,

Work-life balance,

Amount of work,

Level of stress,

Occupational safety.

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Steijn (2004)

Individual characteristics (age, gender, ethnicity, and education level),

Job characteristics (income, supervisory position, working full time, permanent job, skill utilization and sector of work),

Work environment (task autonomy),

Satisfaction with management, pay and workload), personnel management practices and overall satisfaction.

Ellis & Pompli (2002)

Poor working environments,

Resident aggression,

Workload, inability to deliver quality of care preferred,

Balance of work and family,

Shift work,

Lack of involvement in decision making,

Professional isolation,

Lack of recognition,

Poor relationships with supervisor/peers,

Role conflict,

Lack of opportunity to learn new skills.

Wyatt & Wah (2001)

Favorable work environment,

Personal growth and autonomy,

Nature of job,

Stimulating opportunities,

Co-workers.

education, years of work experience and income level of the family have also been

investigated in studies of job satisfaction, but no significant associations have been

reported.

7.3.2 Employment status (Part time vs full time)

The difference between part-time and full-time workers is the amount of time they

spend on work, which can lead to more flexibility on the one hand but a lower salary on

the other hand. Research findings regarding the impacts of work status (part-time vs

full-time) on job satisfaction are mixed. Some researchers argue that having a part-time

job can lead to a lower job satisfaction due to a lower salary, less stability and

promotion opportunities (e.g. Kauhanen & Nätti, 2015; Montero & Rau, 2015). In

contrast, some results display higher satisfaction among part-time employees (e.g.

Wittmer & Martin, 2011). Karatuna and Basol (2017) acknowledged that there are many

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factors that influence the relation between employment status and job satisfaction, and

among those, job characteristics and different career expectations play critical roles.

Moreover, Fagan (2001) argued that country-level policies in terms of employment

conditions can influence the standards, which are the reference for employment

judgments, and thereby work status satisfaction tends to be influenced by the country

context.

However, in general it is well documented that part-time employees who actually

choose to work part-time have equal or higher job satisfaction compared with of full-

time workers because less workload reduce problems with work-life balance, and

enables part-time workers to act better in other life domains (Russo & Buonocore,

2012). Thereby, the extent to which employment status influences workers’ satisfaction

is dependent on whether the employees want to work part-time (voluntary) or other

obligations force them choosing their employment status. Accordingly, research findings

show that women who work part-time have higher levels of job satisfaction in

comparison with part-time men workers, which is due to a higher preference (because

of family responsibilities) for part-time work among women (Booth & van Ours, 2008;

Montero & Rau, 2015).

7.3.3 Work conditions

Work conditions category contains the main determinants of job satisfaction and mainly

reflects the general nature of work or even the primary reasons of choosing a job.

Quality of work and pay level is the two key factors for most people choosing their job.

In addition, factors such as social environment, relations with colleagues and workload

can only be determined in later stages when individuals commence a job. However,

these indicators might even play a higher role in one’s assessment of job satisfaction.

7.3.3.1 Pay level

Pay level has a motivational role for any job and it is a primary reason for most

individuals to devote themselves to work. Because of this significant role, many

researchers since the 1960’s investigated the role of pay level satisfaction on job

satisfaction (e.g. Locke, 1968; Judge et al., 2010). Despite many reasons that

theoretically support the strong impact of pay level satisfaction on job satisfaction, there

are conflicts among the findings in empirical research. Some researchers found a

significant positive link between payroll satisfaction and job satisfaction (e.g. Darma &

Supriyanto, 2017), whilst others suggest that having a well-paying job is only marginally

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related to job satisfaction. However, the conflicting part is the actual size of the

relationship of pay level satisfaction to job satisfaction rather than the relation itself.

Cognitive evaluation of pay level has a complicated mechanism according to

one’s expectations. On the one hand, there should be a balance between job

characteristics such as working time, job difficulty, etc. and the pay level to make

individuals happy about their salary. On the other hand, there are other psychological

factors that influence pay level satisfaction, such as distributive justice perception. Levy

and Norris-Watts (2004) argued that employees have a certain perception regarding the

fairness of their salary. Following that, Judge et al. (2010), indicated that employees

compare their earnings with other co-workers, so they are likely to feel satisfied of their

salary when they get paid more in comparison with others. These findings highlight the

fact that earning satisfaction is not only related to the amount of pay, but the

judgments can be relative to other reference points and external factors.

7.3.3.2 Quality of work

Perceived quality of job is a significant facet of employees’ career and reflects how

employees feel about job tasks. It deals with the nature of work, its features and the

personal characteristics of employees. According to Clark (2015), quality of work is

under the influence of a series of factors such as autonomy, opportunities for

advancement and interest in the job. It is said that, when the education and experience

of employees are matched with the job level and position, the performance is more

effective. Thereby, during the recent decades, many organizations tend to measure and

enhance work quality satisfaction of their employees. In this context, scholarly findings

(e.g. Clark, 2005) advocate that quality of work is more important than wages. On the

one hand, job is a goal-directed activity, depending on whether it provides employees

with sense of progress and efficiency; it can be a source for happiness. Available

evidence demonstrates that less favorable job quality is often associated with lower

work related well-being (Van Aerden et al., 2015).

7.3.3.3 Workload and job stress

Existing research also points to workload satisfaction as a significant predictor of job

satisfaction. Workload in general can be defined as “the amount of work that should be

done in a certain period of time and with a certain quality” (Sahin & Sahingoz, 2013).

Whilst the amount of work a person can handle differs among people based on

physiological and personal characteristics, researchers agreed on the association of

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longer working hours with higher levels of job dissatisfaction and risk of turnover

(Brannon et al., 2007; Qureshi et al., 2013). In fact, feeling overworked can cause

higher health risks and job stress (e.g. Haggan, 2017). In addition to that, heavy

workload can jeopardize the work-life balance and family life, as it is more difficult for

employees to devote enough time for their personal interests (Pocock, 2005).

Consistent with this, workload is not just a predictor of job satisfaction but in a wider

context, it has strong correlations with personal well-being (Bowling et al., 2010).

7.3.3.4 Social environment (people at work)

The relationship with colleagues is an important aspect of work satisfaction. Adams &

Bond (2000) and Jain & Kaur (2014) indicated that there is a strong link between

interpersonal relationships at work and job satisfaction. Furthermore, in the quest for

greater productivity, many organizational researchers (e.g. Harris & Kacmar, 2006)

argued that good social relations at work can reduce job-related stress and can allow

employees to deal with their jobs more efficiently. There is a strand of literature in job

psychology that refers to social relations as a function of exchanging information and

knowledge sharing. The concept of “knowledge sharing” is basically defined as a

helping attitude of employees that increases the intellectual capabilities and can

encourage a positive feeling regarding the job (e.g. Jiang & Hu, 2016). At a higher

level, getting support and help from collogues at work can facilitate the fulfillment of

human need for relatedness. Erdogan et al. (2012) stated that there is a link between

the social environment at work place and the overall life well-being. Wasko and Faraj

(2005) also argued that most people spend a huge portion of their life time at their

work place and therefore, the impacts of work-related social life is beyond job

satisfaction and can play a significant role in overall life well-being.

7.3.4 Workspace

Unlike work location, workspace satisfaction has received extensive attention from

organizational and occupational scientists as well as environmental psychologists.

Especially in recent years, the emergence of economic concepts such as open-plan

office has brought new arguments to the field and thereby, a large body of research has

investigated the contribution of the physical environment to the performance of the

employees and their job satisfaction (e.g. Fornara et al., 2006; Aries et al., 2010; Veitch

et al., 2011; Lottrup et al., 2015). It is well documented that functionally uncomfortable

and poorly designed workspaces decrease both productivity (e.g. Dole & Schroeder,

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2001) and job satisfaction (e.g. Djukic,et al., 2014). In general, workspace satisfaction

is the extent to which an employee feels the space-related needs for performing the job

tasks are fulfilled. In fact, workspace characteristics can motivate employees to handle

their job better. Furthermore, Vischer (2007) argued that a good workplace should

achieve environmental comfort at three level of physical, functional and psychological.

Accordingly, other researchers (e.g. Fornara et al., 2006; Rioux, 2017) noted that the

perceived evaluation of workspace is an interaction of physical and functional

characteristics of the environment with psychological and affective attributes. Moreover,

how employees evaluate their workspace is very much dependent on their personal

characteristics, culture and the comparison between their current workplace and ideal

workspace (Marcouyeux & Fleury-Bahi, 2011). In brief, workspace has some important

components, such as size, noise, privacy including the availability of necessary facilities

(to adjust light and temperature) and tools (proper desk, chair, computer, etc.), which

have been considered frequently in empirical research.

Size of workspace refers to the necessary space every employee is entitled to

have to perform tasks and move easily. Close proximity to other co-workers produces

occupational hazards and thus should be avoided. Workplace density and size are

thereby intertwined. Space allocation standards and regulations clearly determine the

minimum space that employees need for work (especially for office work), and the

standards of the total net area that is shared by staff. Apart from the necessary space

that every employee needs, the acoustic environment and low noise levels are other

factors that certainly contribute to the employees’ performance and workspace

satisfaction (e.g. Dawal & Taha, 2006). Noise, in general, is one of the main recognized

factors of the environmental stress, which is associated with a range of negative health

effects. Loudness and intensive sound decrease concentration and contribute to

distraction, which may highly reduce motivation among employees (Rioux, 2017).

Respecting current standards and recommendations (e.g. ISO standard), the level of

noise exposure at workplaces should be limited, and employers should take preventive

actions (such as using sound proofed walls or other equipment) to control the noise

level of the workspaces.

Privacy is another significant factor associated with employees’ workplace

satisfaction. Privacy at the workspace has become a very controversial topic as the open

office plan has become popular and many believe that the absence of walls and

partitions may threat feelings of privacy and workspace satisfaction (e.g. de Croon et

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al., 2005; Danielsson & Bodin, 2009). Although boundaries and content of what is

private at workplace are not well defined but the concept of privacy at workplace

usually refers to the ability of performing tasks without unwanted distraction by others.

Moreover, Birnholtz et al. (2007) noted that privacy could be seen from two different

perspectives: optimized confidentiality and solitude. Whilst the former refers to control

over accessibility of others to employees’ information, the latter refers to the limitation

of distraction (visual, acoustic and territorial) by others.

Existing research also points to workplace facilities, tools and furniture as

important factors influencing employees’ performance and satisfaction (e.g. Vischer &

Fischer, 2005; Nguyen et al., 2015). In addition, many researchers (e.g. Lee & Brand,

2010) found that ambient workplace conditions, such as temperature and lighting can

have significant impacts on the employees’ attitudes at work. Furniture should be

flexible, adjustable and suitable for performing tasks. Also, furniture should properly

respond to ergonomic needs of employees, which in the long term affect their personal

health (Vischer, 2007). Accordingly, ambient conditions such as lighting, ventilation and

the thermal system should have the possibility of adjustment based on employees’

requirements and comfort level.

7.3.5 Work location

Just like the house location, the working place location has important implications for

employees’ well-being. In fact, residential choice and workplace location are intimately

interconnected, as job location decisions are usually based on residential location and

vice versa (Clark et al., 1999; Hincks & Wong, 2010; Head & Lloyd-Ellis, 2012). Work

location dissatisfaction can cause housing mobility and otherwise decrease overall job

satisfaction, which can lead to employees’ turnover. Living close to the job place or

having good accessibility can benefit employees by saving time, energy and cost of

commuting. Moreover, in recent decades, behavioral scientists have addressed the

impacts of work commutes on daily life experience. It has been found that having good

access to the work place can significantly affect daily life experiences. Kahneman et al.

(2004), in their well-known research, using the day reconstruction method, found that

commuting to the work episode of daily life is associated with the most negative

feelings among employees. Despite the well-established link between the work

commute and well-being in travel behavior research and urban science (e.g. Olsson et

al., 2013), organizational research displays a certain lack of establishing the link

http://journals.sagepub.com/author/Hincks%2C+Stephen

Work well-being

127

between work commute and work satisfaction. Only few studies have investigated work

commute as a key generator of stress and a reason of turnover (high level of job

dissatisfaction) among employees (Amponsah-Tawiah, 2013). Also, from a travel

behavior perspective, Gao et al. (2017) also found that travel satisfaction was not

significantly related to life satisfaction. However, there is much room left for further

research in this subject.

7.3.5.1 Travel time

Commute time to work constitutes a large part of daily routine activities and

significantly impacts employees’ well-being. Several studies have provided empirical

evidence that long travel times make employees feel exhausted and unhappy and

contribute to lower productivity (e.g. Olsson et al., 2013; Handy & Thigpen, 2018). In

addition, commute stress in long trips can affect physical and mental health, which

consequently diminishes well-being and work life satisfaction (Novaco & Gonzalez,

2009). To reduce commuting time, telecommuting became a popular trend in recent

years. Telecommuting enables employees to work from an alternative work place

(usually house) and proactively manage their schedule (Allen et al., 2015). It is argued

by many researchers that telecommuting has a positive influence on job satisfaction by

minimizing work-life conflict and job stress (e.g. Fonner & Roloff, 2010).

7.3.5.2 Other work location attributes

It is accepted that travel time to work is the most commonly mentioned attribute of

perception regarding job location (with relation to house location), and has the largest

influence on job satisfaction. However, it is not proper to neglect the role of other

characteristics of the work location on perceived satisfaction of the work location. These

characteristics comprise safety of the surrounding area, access to alternative transport

modes and availability of facilities such as shops, restaurants, parks etc. These factors

help increasing job attractiveness by providing conditions to combine activities with

work such as going out for fresh air to reenergize the workday, small grocery shopping

or performing leisure activities with colleagues (such as a short walk). Despite the fact

that these factors can play an important role in facilitating the work-life balance and job

satisfaction, it is hard to find any research that combines these factors to predict job

satisfaction. This might be due to the great emphasize on salary, workload, etc. or the

primary assumption that employees should spend most of their time inside the office

rather than outside and thereby do not care about outside qualities.

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In order to explore the implicit effect of various work dimensions on the employees’

overall assessment of their work, first, a multiple regression analysis was conducted,

and then, a comprehensive analysis using path analysis is being applied. Multiple

regression analysis is done using overall work satisfaction as the dependent variable

and 17 factors as independent factors (including socio-demographic variables).

We assumed that the scales used to measure satisfaction have interval

properties (same degree of difference between items) based on many studies that

utilized such scales (Molin & Timmermans, 2003). Since one of our regression

assumptions is that the residuals (prediction errors) are normally distributed, checking

the histogram of residuals allows us to check the extent to which the residuals are

normally distributed. The well-behaved residuals can lead to correct p values, which are

very important for any linear regression analysis. Our histogram (Figure 7.1) shows a

fairly normal distribution. Thus, based on these results, the normality of residuals

assumption is met. Furthermore, the Normal P-P Plot of the regression residuals (Figure

7.2) is a good tool to assess the assumption of normally distributed residuals. If data

points (little circles) follow the diagonal line, then the residuals are normally distributed.

Our P-P plot shows minor deviation and thus the normality of residuals assumption is

confirmed. The results about R-squared (R2) and adjusted R-squared which are shown

in Table 7.2 reveals that the indicators accounted for almost 70 percent of variance of

overall work satisfaction.


Regression coefficients in this analysis represent the magnitude of different factors that

influence employees’ overall work satisfaction, and are presented in Table 7.3. For

example, the factor that shows the strongest relationship with overall work satisfaction

is satisfaction with quality of work (.34) following by privacy satisfaction (.26). Although

multiple regression analysis provides some insights into the impact of socio-

demographic characteristics and all of the mentioned work-related characteristics on

overall work satisfaction, this method is unable to capture the indirect and complex

relationships among work location, work conditions and workspace satisfaction factors.

Thereby, further analysis is necessary to understand the mechanism of the effects

among these main categories as well as their internal dependencies, which will be

explained in the next part.




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129

Figure 7.1 The Histogram of the overall work satisfaction

Figure 7.2 Normal Probability Plot (P-P) of the regression standardized residual dependent variable for overall work satisfaction

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130

Table 7.2 Goodness-of-fit of the regression model for overall work satisfaction

R R2 Adjusted R


.859 .737 .697 .526

Table 7.3 The estimated parameters of the work satisfaction using multiple regression analysis

Model Parameters

Unstandardized

Coefficients

Standardized

Coefficients


Socio-

demographic

indicators


1.955 .053

Age -.005 .004 -.070

-1.317 .191

Single person -.176 .113 -.087

-1.563 .121

Part-time employee -.046 .109 -.023

-.418 .677

Household income -.025 .033 -.040

-.748 .456

Work conditions

satisfaction

Quality of work satisfaction .266 .055 .340 4.821 .000

social environment

satisfaction .091 .045 .131 2.012 .047

Salary satisfaction .028 .038 .044 .744 .459

Workload satisfaction .088 .039 .135 2.291 .024

Workspace

satisfaction

Access to work space

satisfaction .009 .061 .011 .144 .886

Privacy satisfaction .137 .042 .263 3.227 .002

Size satisfaction .049 .050 .070 .982 .328

Noise level satisfaction .035 .036 .068 .981 .329

furniture/tools satisfaction .065 .040 .100 1.604 .112

Work location

satisfaction

Availability of shops satisfaction .042 .031 .088 1.384 .169

Travel time satisfaction .055 .026 .125 2.104 .038

Safety of area satisfaction .005 .045 .007 .116 .908

Access to public transport

satisfaction .050 .027 .121 1.885 .062

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131

Figure 7.3 The main determinants of the work satisfaction used in this study


The key objectives of this chapter are to evaluate and understand the interrelationships

among study variables affecting work satisfaction. A path analysis is deemed to be an

adequate solution for the achievement of these objectives, thus is used for a detail

analysis on work satisfaction. By this method, it is possible to conduct simultaneous

assessments of the associations among multiple dependent and independent attributes

and thus increase the statistical efficiency (Ringle & Sarstedt, 2014).

It is good to mention that some of the previous job studies performed factor

analysis prior to path analysis because it is required to reduce extensive number of

variables (that are used to quantify the immeasurable psychological concepts).

However, in our study based on the extensive literature review, three major categories

of job satisfaction have defined and within each category certain measurable variables

have been extracted. Therefore, there is no need to conduct factor analysis prior to

path analysis.

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132


To develop our conceptual model, first the main determinants of work satisfaction are

categorized in three different groups (Figure 7.3). The first category is work conditions

satisfaction, which consists of four main elements of workload satisfaction, work quality

satisfaction, salary satisfaction and social environment satisfaction. The second group is

the workspace category with five determinates of accessibility of workspace, size,

privacy, noise and furniture and tools satisfaction. And finally the third category, which

is the work location satisfaction, combines satisfaction of travel time, access to public

transport, safety of area and availability of shops and other facilities in the area.

Based on the insights from the work satisfaction literature in organizational

behavior research, as well as environmental psychology and well-being research, and

the relevance of these studies to investigate the link between different work-related

attributes, socio-demographic factors and the overall subjective assessment of work,

the conceptual model elaborated (Figure 7.4). The dependent variable of overall work

satisfaction thereby, will be studied by exploring the relationships among different

attributes and different categories including the effects of independent factors of socio-

demographic characteristics of individuals. Figure 7.4 presents the conceptual model of

this study.

7.5.2 Path analysis model for work satisfaction

Similar to other chapters, to estimate the conceptual model, software package AMOS 22

is employed. Path analysis with Maximum likelihood approach of parameter estimation

is performed to assess: the direct and indirect correlations among factors, examine P-

values of the paths, and the standardized regression weights. Maximum likelihood is a

robust method, which assumes multivariate normality. In the first step, all of the socio-

demographic factors were included in the model to see whether they have any direct or

indirect relationship with other endogenous variables. However, after removing the

insignificant relationships from the path model, just four variables of socio-demographic

group (age, marital status, income and employment status) are remained in the model.

This is in line with the findings from literature that mentioning most of the socio-

demographic factors have little or no impact on work satisfaction of individuals.

Since the aim of path analysis is to figure out how exogenous variables work

together and which paths are important in affecting the endogenous variables, for big

models checking the modification indices is one way to improve the initial hypothesized

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133

model fit. This means that by adding these suggested paths or covariance among the

variables or the error terms, several models can be developed, tested and compared to

each other. Here, the challenging part is selecting the final model, while all of the

options display a good fit despite their distinctions. Looking for an answer among

empirical studies did not provide a clear answer as most of research just presents the

final and optimal model without explaining the selection process or their criteria.

However, some authors mentioned that the only criteria that can explain adding a path,

variances or covariance is whether they have theoretical bases or not (Moss, 2009). In

line with this, just meaningful paths have added to our model and others are ignored,

so the final model is consistent with the available theories.


excellently fit the data. Chi Square divided by the model degrees of freedom (/DF or

CMIN/DF), which is an essential measure, and should to be less than 2 and preferably

close to 1 (Ullman, 2006), equals 1.29 for our model. In addition, the Root Mean Square

Error of Approximation (RMSEA) should be less than .08 and ideally less than .05 to


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134

Table 7.4 Goodness of the fit for the path analysis model

represent a great fit (Washington et al., 2010), and in our model RMSEA value is .048.

Other indicators of a good fit as mentioned by many researchers (e.g. Moss, 2016) are

indices that compare target model with the null model such as: Comparative fit index

(CFI), Incremental fit index (IFI), Tucker Lewis index (TLI), Normed fit index (NFI) and

Goodness of Fit Index (GFI). The model tends to generate an acceptable fit, if these

indexes exceed.90. However, as many researchers mentioned NFI and GFI indices are

explicitly sensitive to small sample size below 200, while RMSEA and CFI seem to be

less sensitive to sample size (Hooper et al., 2008). Since our sample size in this chapter

is 130, to reduce overestimation we considered indexes that are less sensitive to sample

size.

Moreover, R squared (R2) value of the endogenous variables (called squared

multiple correlation value in AMOS), specifically for the ultimate endogenous variables is

an additional important criteria for confirming a good model. Our proposed theoretical

model satisfactorily accounts for 73 percent of the variance in workers’ overall job

satisfaction (R2= .73). Also, R2 of the other dependent endogenous variables (the

overall satisfaction of each category) reflects that the determinants for each category

are good predictors, as for work conditions satisfaction R2= .72, for workplace

satisfaction R2= .68 and for work location satisfaction R2= .71.

Furthermore, statistically significant relationships are found among the following

model variables: (1) Satisfaction with the overall work conditions significantly influences

the overall work satisfaction (.49), (2) work location satisfaction affects the overall work

satisfaction positively (.27), (3) Workspace satisfaction influences work conditions

Fit index Estimate





CFI .96

IFI .97

TLI .95

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135

satisfaction (.22), work location satisfaction (.19), and the overall work satisfaction

(.12).

In the following the other main results will be summarized:

In the work location category, travel time satisfaction is the most

influential variable (.41) on work location satisfaction, followed by

satisfaction with access to public means of transport (.30).

In predicting work conditions satisfaction, work quality satisfaction (.50)

is the dominant variable. Considering the workspace category, privacy

satisfaction (.34) and furniture and tools satisfaction (.31) are the most

critical factors in predicting workspace satisfaction.

Exogenous variables of socio-demographic group influence variables of

the work conditions category and work location category, but not

workspace variables.

Also, age is the only variable that directly impacts the satisfaction with

work location. However, age and marital status show very small and

indirect effects on the overall work satisfaction (.04 and .05,

respectively) and only household income has a significant total effect

(.10) through mediating variables (i.e. salary satisfaction and work

quality).

Obviously, having a part time job status means less work, and thus it

has positive influence (.16) on the perception of workload. It has also

small effect on overall work satisfaction (.02).

Among the variables situated in one individual category, only social

environment satisfaction and work quality satisfaction have strong

correlations with each other, which mean that social environment

satisfaction has both direct and indirect effects on work conditions

satisfaction. It also has a direct effect (.14) on the overall work

satisfaction. Privacy satisfaction is another variable with both direct

(.22) and indirect effects on the overall satisfaction of work (Figure

7.5).


Generally speaking, our results are consistent with previous studies emphasizing on the

impacts of work conditions components such as work quality and social environment

Work well-being

136

(relations with colleagues) on feeling satisfied with work (e.g. Clark, 2005; Judge et al.,

2010; Jain & Kaur, 2014). In terms of the direct effects on overall work satisfaction,

work location satisfaction has the second place after work conditions satisfaction.

However, workspace satisfaction has both direct and indirect effects on the overall work

satisfaction. The indirect effects are also significant with positive signs, whose

magnitude is greater than the direct effect. This is a very interesting result as it

highlights the fact that space factors are means to reach a goal (such as improve the

work quality) but not the goals, and through the mediating variables they can influence

the overall work satisfaction. The opposite side is also relevant, as space related issues

become more important to those who experience the lack of proper space. One

explanation for these findings can be found in Herzberg’s two-factor theory or

motivation-hygiene theory, where he basically mentioned that hygiene variables (i.e.

environment and place related variables) influence dissatisfaction more than

satisfaction. Here, we discuss the impacts of all the components in different categories.

7.5.3.1 The impact of work conditions factors

Among the work conditions factors, satisfaction with quality of work accounts for 51%

of the variance of the work conditions satisfaction, followed by workload satisfaction

(.27) and salary satisfaction (.15). This clearly indicates the importance of work quality

satisfaction over satisfaction with salary. However, the lower impact of salary

satisfaction does not imply that it is not a motivational factor ( e.g. Gerhart & Rynes,

2003; Nyberg et al., 2018). The strong correlation among work quality satisfaction and

social environment satisfaction emphasizes on the importance of maintaining good

social relations at work. In general, social environment at work can mean different

things to different people based on their personality and social structure. It may be

described by the amount and quality of relationships with colleagues and managers,

teamwork, knowledge sharing etc. and as it is expected it has a huge impact on feeling

satisfied with work quality. However, considering the total effects of all the variables, it

is a surprising result that satisfaction with social environment have such a substantial

effect (.039) on overall work satisfaction. This might be the consequence of the wide

definition of social environment for the respondents of the survey and our limitations in

asking more detailed questions on this psychological construct.

Apart from the aforementioned results, there is a novel finding about the

negative correlation among satisfaction with travel time and satisfaction with social


Work well-being

137

Fig

ure

7.5

Re

su

lts o

f th

e p

ath

an

aly

sis

fo

r w

ork

sa

tisfa

cti

on

Work well-being

138

Environment. This negative relationship basically means that the more satisfied people

get about their travel time, the less satisfied they tend to be with their social

environment. To discover what can be the reason, the original data sets and the

comments of the respondents were reconsidered. Many of the respondents especially

residents of StrijpS (a smart neighborhood) mentioned that they are self-employed (the

Dutch term ZZPERS, zelfstandige zonder personeel means independent with no

staff) and combine working with living in studios designed for this type of living.

Although telecommuting or working from home is a popular trend in the Netherlands, it

can bring isolation to individuals. Therefore, basically for freelancers, work commute is

reduced or totally omitted, but social life can be negatively affected in return. This

interesting finding is very context dependent, as in many countries telecommuting is

less common.


Dependent

variables

Paths Direct

effect

Indirect

effect

Total

effects

Overall work satisfaction

(OWS)

WCS OWS .49 .49

WSS OWS .12 .16 .28

WLS OWS .27 .27

Privacy satisfaction OWS .22 .16 .37

SES OWS .13 .26 .39

QWS OWS .25 .25

Access to workspace

satisfaction OWS

.23 .23

Income OWS .10 .10

Age OWS .05 .05

Part-time employee OWS .02 .02

Single person OWS .06 .06

Furniture/tools satisfaction

OWS

.13 .13


satisfaction OWS

.18 .18

WLoS OWS .13 .13

QWS WCS .50 .50

Work well-being

139

Work conditions

satisfaction (WCS)

WLoS WCS .26 .26

SS WCS .15 .15

SES WCS .13 .39 .52

WSS WCS .22 .22

Privacy satisfaction WCS .20 .20

Access to workspace

satisfaction WCS

.26 .26


WCS

.15 .15


satisfaction WCS

.14 .14

Income WCS .20 .20

Work space satisfaction

(WSS)

Size satisfaction WSS .12 .12

Access to workspace

satisfaction WSS

.17 .17

Privacy satisfaction WSS .34 .34

Noise level satisfaction

WSS

.15 .15


WSS

.31 .31

Work location

satisfaction (WLS)

TTS WLS .41 .41


satisfaction WLS

.30 .30

SAS WLS .18 .18

ASS WLS .27 .27

WSS WLS .19 .19

Age WLS .12 .05 .17

Quality of work

satisfaction (QWS)

Privacy satisfaction QWS .24

SES QWS .78 .78

Access to workspace

satisfaction QWS

.33 .33

Income QWS .25 .25


satisfaction QWS

.15 .15

Work load satisfaction

(WLoS)

Part-time employee WLoS .15 .15

Income WLoS .14 .14

TTS WLoS .17 .17

Work well-being

140

7.5.3.2 The impact of workspace factors

In the workspace category, privacy satisfaction and furniture and tools satisfaction are

the two most influential factors, while workspace size is the least important component.

This is also expected, as the concepts such as shared office are currently popular

(mainly for economic reasons), so employees are adapted to working in smaller places.

Nevertheless, with the help of technology, good design and proper furniture they can

still own their semi-private spaces. Interestingly, privacy satisfaction contributes to the

overall work satisfaction both directly and indirectly through the moderator variable. It

impacts the satisfaction with quality of work, which can be described through the

relationship between control and independency with performance at work. But in

general, it highlights the fact that privacy is an essential prerequisite for employees’

working experience and the perceived value in work satisfaction.

Moreover, the other interesting results related to this category are the impacts of

noise level satisfaction and furniture and tools satisfaction on the workload satisfaction

variable. This has been theoretically and empirically proved that noise and insufficient


WLoS

.33 .33

Noise level satisfaction

WLoS

.11 .11

Salary satisfaction

(SS)

Income SS .25 .25

Single person SS .13 .13

Access to workspace

satisfaction SS

.27 .27

Social

environment satisfaction

(SES)


SES

.19 .19

TTS SES -.16 -.16

Access to workspace

satisfaction SES

.42 .42

Travel time

satisfaction (TTS)

Single person TTS

.22

.22

Safety of area

satisfaction (SAS)

Access to work space

satisfaction SAS

.38 .38

Availability of shops etc.

Satisfaction (ASS)

Age ASS .18 .18

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141

facilities lead to distraction, failure in time management and handling job demands.

Finally, the positive relationship between access to workspace satisfaction and social

environment satisfaction indicates that the office location within the building can

influence social interactions among employees. Furthermore, access to the workspace is

significantly associated with safety feeling, which is also a very interesting finding.

7.5.3.3 The impact of work status

As expected having a part time job can reduce the workload and thereby enhance the

perception of workload, while it may impact the pay level and consequently bring lower

satisfaction with salary. However, surprisingly having a part time job has no effect on

salary satisfaction. This might be due to the fact that people evaluate their salary based

on the amount of tasks they handle and the time they allocate to their jobs. Since

having a part time job does not negatively influence salary and job quality satisfaction,

it is also not expected to influence the overall job satisfaction significantly.

7.5.3.4 The impacts of socio-demographic factors

In terms of the employees’ socio-demographic factors, only household income has a

significant indirect effect on overall work satisfaction through mediating variables (i.e.

salary satisfaction and work quality). The impact of income on salary satisfaction is clear

and well documented in the literature (e.g. Judge et al., 2010). In terms of the relation

between income and satisfaction with quality of work, we can say that having a higher

household income gives the opportunity to work in a favorable job (in case of dual

earners households for instance). If the household income is equal to the individual

salary (in case of single earner families), also the relation makes sense as pay level has

a motivational role on job performance and work quality. Yet, the adverse relation is

arguable as enjoying the job, can lead to higher performance and possible pay rise,

which consequently increase the household income.

7.5.3.5 Other associations among factors

Some other results can be extracted from our model, although there are beyond the

scope of this chapter but it is good to mention them briefly:

Age and having a higher income household have a positive relationship, means

that in average people reach to the better financial position by getting aged.

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142

Being single shows a negative relationship with part time job variable. This

indicates single people are less likely to have a part time job. Also as assumed,

household with higher income may not work in part time jobs.

Single people are more satisfied with the travel time to work, which might be

due to the fact that they have less time constraints in compare of married

couples and households with kids.

7.6 Conclusion and discussion

This chapter has aimed to enhance the limited understanding of work satisfaction

concept and how this major life domain is affected by socio-demographic characteristics

of individuals as well as the characteristics of one’s job. Based on the bottom up

approach, path analysis method was used to capture the links between various

variables and their impacts on overall work satisfaction. The findings highlight the major

influence of perception regarding the social environment, work quality and privacy on

feeling satisfied with work. The comparative impact of work quality versus salary should

be a reminder that engaging in meaningful work dominates over salary. Moreover, the

study shows that bad working space restricts individuals to show their abilities and

thereby reduce the overall work satisfaction from different sides.

This study has some limitations in going into more details. First, unlike other

chapters (housing, neighborhood and transport) in this chapter we could not use the

whole sample due to the fact that some of the samples were unemployed, retired or

student. Therefore, the data on which the analysis is based provides a small sample of

people’s work satisfaction. Second, because of the psychological nature of the work

satisfaction topic more psychological measures are required, which is beyond the scope

of this project. And third, most of the studies on work satisfaction just explore some

dimensions and usually ignore factors such as commute to work. Therefore, finding the

supporting literature with comprehensive assessments was challenging.

Although the findings presented in this chapter are interesting, it is based on the

bottom-up approach, while a reverse direction (top-down approach) is also conceivable,

reflecting how overall happiness with work can spill over to other bottom factors. For

instance, as Olsson et al. (2013) argued, the happiness of having a job after being

unemployed for a long time can spill over to satisfaction with work commute. Clearly,

this study and these samples were not designed to address these issues in the first

place, but these insights may provide many opportunities to expand future research.

Quality of Urban Life (QOUL)

143

8

Quality Of Urban Life

(QOUL)

8.1 Introduction

As the objective of this research project is investigating Quality Of Urban Life (QOUL)

with an explicit focus on the role of the physical and built environment, in the last four

chapters, four main domains (housing well-being, neighborhood well-being, transport

well-being and job well-being) were examined in detail. For this chapter, the initial idea

was to include all the attributes that significantly contribute to the estimations in the

last four chapter, however, complexity of the model and the large number of variables

(around 100 significant variables) including the small size of data made software unable

to estimate the model. Muthén & Muthén (2002), mentioned that 5 to 10 cases is

needed per parameter, so to estimate such a model we needed minimum 500

respondents.


144

Due to the computational issues, we decided to include just the overall

satisfaction of domains to the final model that is presented in this chapter. So, following

our domain-based satisfaction approach, all other main life domains viz. income well-

being, health well-being, family life well-being, social well-being and leisure well-being

will be introduced and included in the final assessment of well-being. A bottom-up

approach, which is common in quality of urban life studies (e.g. Marans & Rodgers,

1975; Campbell et al., 1976; Sirgy & Cornwell, 2002) is used in our analysis, where

well-being in different life domains is used to predict overall QOUL.

In formulating the conceptual framework of this thesis (see chapter 2), apart

from the nine domains contributing to overall QOUL, socio-demographic variables may

also influence it. Thus, this chapter examines overall QOUL by using the well-being of

the selected life domains, and also controlling for socio-demographic variables.

Explicitly, the following research questions will be explored:

IV. What are the main predictors of QOUL?

V. To what extent do these domains contribute to predicting QOUL?

VI. What are the relationships among these domains and to what extent do

they affect each other?


As discussed earlier in chapter 2, the conceptualization of this research project is

motivated by the life domain approach and particularly the bottom-up spillover theory.

Thus, in this chapter we focus on the QOUL studies that have applied this approach.

8.2.1 Domain-based QOL

Researchers have achieved considerable advances in methodological and theoretical

approaches of QOL, and thus the field has witnessed new perspectives and paradigms.

Such advances have made QOL a field in transition, which motivates researchers to go

beyond the accepted methods and examine new domains (Ryan & Deci, 2001). Domain-

based or life domain approach in QOL studies indicates that the relationship between a

person’s subjective well-being in different areas of life contributes to the explanation of

his/her overall life satisfaction (Delhey, 2014).


145

The life domain approach in QOL research is not a new approach, as Sirgy

(2011) stated. Andrews and Withey (1976) and Campbell et al. (1976) are the pioneers

in using life domain approach for QOL research. They examined satisfaction with

various life domains such as satisfaction with family life and work to explain life

satisfaction as a whole. Later, Schalock (1989) identified seven main domains that

significantly influence QOL. Moreover, Cummins (1996) reviewed 1500 articles related

to QOL with special focus on the life domain approach and found 351 different domain

names. More recently, Van Praag et al. (2004) investigated the effects of seven most

important life domains on well-being, and they listed work well-being, economic well-

being, housing well-being, health well-being, leisure well-being, marital well-being and

social well-being as the main life domains. However, a big portion of domain-specific

studies focuses on satisfaction with particular life domain in relation to subjective well-

being. For instance, job satisfaction studies or health well-being studies. Also, many

studies just focus on individuals’ conditions in one domain and its relation with well-

being, such as studies on unemployment and well-being or physical disabilities and well-

being. Despite the general acceptance of such relationships between satisfaction in life

domains and overall life satisfaction, the nature of casualty has raised questions and still

is a subject of debate. Some researchers argue the existence of reversed causality,

which advocates the influence of overall life satisfaction on satisfaction in other life

domains (e.g. Argyle, 2013). This is called the “Top-down approach” and clearly points

to the dominant influence of ones’ personality on evaluating life domains and life as a

whole.

8.2.2 Factors determining QOL

Research on subjective well-being have shown that when people evaluate their life,

they review their feelings regarding various life domains and based on the relative

importance of those domains sum these evaluations to make a final judgment. This

whole process is influenced by individuals’ socio-demographic characteristics, which

reflect one’s life conditions. Hereafter, some of the most important socio-demographic

variables and life domains are discussed in more detail.

8.2.2.1 Individuals’ socio-demographic characteristics

Among the socio-demographic variables, age has been always considered as one of the

important characteristics of individuals, which influence well-being. As age is related to

maturity that provides stability of emotions and to some extent positiveness, it is said


146

that life satisfaction my increase with age. Conversely, health issues of elderly can have

negative effects on their well-being. Thereby, in general age has many physiological

and psychological implications for QOL, which should be considered in any QOL

research (Orth et al., 2010). The evidence of the influence of gender on QOL is

conflicting. Dolan et al. (2008) has argued that some authors found that females have

higher levels of QOL, but the evidence at large has not indicated any gender differences

in average subjective well- being. McCrea et al. (2005) also asserted that the

importance of different life domains in predicting QOL does not differ among males and

females.

The findings of empirical research suggest that marriage has a positive

influence on feeling positive (e.g. Blanchflower & Oswald, 2004). The benefits of

marriage are listed as feelings of attachment, stress buffering and social support.

However, these benefits are related to happy marriage and those who are not happy

with their partner reported lower level of QOL. In general, as Diener (2009) argued, the

trends of marriage, divorce and their impact on QOL are consistent around the world. In

terms of the influence of education on QOL, the findings are conflicting (Dolan et al.,

2008). On one hand, some researchers believe that there exists a positive relationship,

as education may help reaching life goals (e.g. Blanchflower & Oswald, 2004). On the

other hand, higher education can rises one’s expectations, which in turn can negatively

influence subjective well-being (Chevalier & Feinstein, 2006). Regarding employment

status, most research findings reveal that unemployment has a negative effect on

subjective well-being (e.g. Van der Meer, 2014; Clark et al., 2015). Research has also

shown that loss of job is not just about loss of income, but it affects other source of

well-being such as social approval (Van der Meer, 2014).

It is a common belief that money can buy happiness. However, the

relationship between income and life satisfaction has been one of the most controversial

topics in the literature on well-being (e.g. Ferrer-i-Carbonell, 2005; Boyce et al., 2010).

Some studies show a weak correlation among income and subjective well-being (e.g.

Boyce et al., 2010) while few studies provide evidence that higher-income people

experience much higher average well-being. Therefore, it seems that the debate is

more on the size of the effect rather than the existence of effect. Regarding the

influence of ethnicity status on QOL, as expected, the findings are also conflicting. For

instance, Safi (2009) revealed that being an immigrant negatively influence subjective

well-being. In contrast, Bartram (2010) has shown that immigrants tend to earn more

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income, which in turn increases their life satisfaction. Although, people immigrate to

enhance their QOL, however, there are many other factors that influence satisfaction

feeling such as integration in society.

8.2.2.2 Satisfaction with key life domains (life domains well-being)

Income well-being is one of the three most discussed topics (along with health and

happiness) in QOL research. Having adequate amount of income is necessary for well-

being. So, the common belief is that, the primary condition for income well-being is

whether income can fulfill the basic needs. However, how much income can satisfy

people has remained a question, as income well-being is dependent on many aspects,

such as personality of individuals and material aspirations, as well as the context of

living and reference points for comparison (Easterlin et al., 2011). Family life well-being

has proven to have a strong correlation with overall life satisfaction. Psychologists

emphasize on the role of sense of belonging to family in having positive feeling about

life (Dolan et al., 2008). Satisfaction with marriage and relationships within the family

are important predictors of family life well-being. Evidence also suggests that family life

well-being may influence feelings in other life domains (Sirgy, 2012).

Social well-being is another important life domain with strong influence on QOL

(e.g. Diener & Seligman, 2002). It refers to quality and quantity of social interactions

and participating in community. Moreover, the support and care ones can receive from

others play a significant role in feeling satisfied with social life (Hahn et al., 2010).

Satisfaction with health or health well-being is found to be an essential factor

contributing to an individual’s subjective well-being, and actually some authors found it

as the most important determinants of well-being (e.g. Campbell et al., 1976). It is

defined as the perceived personal health and may influence all life domains

substantially. Diener (2009) asserted that the impact of objective health on well-being is

weak compared with subjective health as perceived health may cover more factors

related to mental health.

There is a lot of evidence that suggests leisure well-being plays a significant

role in life satisfaction (e.g. Chen et al., 2010). Leisure well-being refers to the extent

that individuals feel alive and sets goals for their free time (Wang et al., 2011). Leisure

activities have many forms, apart from the home-based and routine leisure activities,

going on vacation is one of the main forms of leisure activities with strong influence on

well-being. Few studies have explicitly examined the link between travel trip satisfaction

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and well-being and found a significant correlation among them (e.g. Dolnicar et al.,

2012).

Job satisfaction or career well-being has also been listed as one of the main

life domains, and many empirical studies have shown a strong association among work

well-being and overall life well-being (e.g. Lee et al., 2015; Senasu & Singhapakdi,

2017). In most cases, job is a source of income and provides necessary facilities for

living, however, at a higher level, career is related to one’s goals, desires and

achievements. From this perspective, job is more than just an income source, but can

shape identity and influence personal growth (Hulin, 2002). Thus, it is a rewarding

activity and pleasure of doing desirable work is a significant part of life satisfaction.

Several empirical studies found a direct link between quality of life and housing

well-being, which resulted in the recognition of housing well-being as a criterion for

determining QOL (e.g. Fernández-Carro, et al., 2015; Fernández-Portero et al., 2017;

Zebardast & Nooraie, 2018; Zhang et al., 2018). Housing is a basic need and for most

people house is the most important place life experience happens. Moreover, house

well-being may influence family well-being as well as social interactions and home

based leisure activities. Thus, the influence of housing well-being on other life domains

is also profound.

The central position of neighborhood in providing and facilitating life with

different elements explains why neighborhood well-being can be a significant predictor

for QOL (Austin et al., 2002). Because of this important role, many researchers have

explored the associations between various neighborhood characteristics, residents’

satisfaction and well-being (e.g. Cao, 2016; Zhang & Zhang, 2017). Furthermore,

neighborhood well-being may influence other life domains such as housing well-being

and social well-being.

In recent years, a limited number of studies focused on the link between travel

satisfaction and well-being (e.g. Sirgy et al., 2011; Ettema et al., 2011; Friman et al.,

2013; Aydin et al., 2015; Gao et al. 2017). Clearly, a good transport system can

influence different components of everyday living positively, whereas inefficient

transport can be costly, time consuming, risky and stressful for commuters, and results

in a huge loss of abilities and resources. Most of them found a significant but indirect

link between travel well-being and subjective assessments of life. However, based on

including the personality trait the direction of effect may be changed. Moreover,



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transport well-being is directly associates with neighborhood assessments and the link

has extensively examined in many empirical studies (e.g. De Vos et al., 2016).


In order to explore the implicit associations between well-being in various life domains

and residents’ overall assessment of their well-being, a multiple regression analysis was

conducted with overall QOUL as the dependent variable and 18 independent variables.

From these independent variables, nine of them are socio-demographic variables and

the rest are well-being ratings in various life domains. We assumed that the scales,

which used to measure well-being have interval properties (same degree of

difference between items) based on many studies that utilized such scales (e.g. Diaz-

Serrano & Stoyanova, 2010).

Since one of our regression assumptions is that the residuals (prediction

errors) are normally distributed, therefore, checking the histogram of residuals allows us

to check the extent to which the residuals are normally distributed. The well-behaved

residuals can lead to correct p values and thus are very important for any linear

regression analysis. Our histogram (Figure 8.1) shows a fairly normal distribution. Thus,

based on these results, the normality of residuals assumption is met. The other

important issue to check is multicollinearity as it can reduce the reliability of the model,

and also causes large standard errors and incorrect coefficient values. Choosing this

option will add two new columns to the coefficient table (Tolerance and VIF). The

tolerance value is between 0 and 1, and a low tolerance value for a variable means a

strong relationship between this variable and the other variables. VIF is just the

reciprocal of tolerance, so the smaller value of it, indicates the less risk for

multicollinearity (Brace et al., 2006). A commonly used rule of thumb is that a VIF

higher than 10 indicates multicollinearity (Cohen et al., 2014). Therefore, based on

tolerance and VIF values in the collinearity column of coefficient Table 8.2, none of the

predictor variables have strong relationship with each other.

Furthermore, the normal P-P Plot of regression residuals (Figure 8.2) can also be used

to assess the assumption of normally distributed residuals. If data points (little circles)

follow the diagonal line, the residuals are normally distributed. Our P-P plot shows

minor deviation and thus the normality of residuals assumption is satisfied. The results

about R-squared (R2) and adjusted R-squared are shown in Table 8.1. It shows that the

indicators accounted for almost 61 percent of variance of QOUL.



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Figure 8.1 The Histogram of the overall QOUL

Figure 8.2 Normal Probability Plot (P-P) of the regression standardized residual dependent variable for Overall QOUL


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Table 8.1 Goodness-of-fit of the regression model for overall QOUL

Table 8.2 The estimated parameters of the overall QOUL using multiple regression analysis

Unstandardized

Coefficients

Standardized

Coefficients

Collinearity

Statistics

B Std. Error Beta t Sig. Tolerance VIF


1.018 .310

Age -.002 .004 -.032 -.570 .569 .664 1.506

Education .102 .139 .035 .738 .461 .898 1.114

Male .020 .114 .008 .172 .863 .896 1.116

Single -.020 .152 -.008 -.131 .896 .559 1.788

Household size .022 .060 .022 .372 .710 .575 1.738

Unemployed -.234 .211 -.063 -1.109 .269 .636 1.572

Family loss -.144 .130 -.053 -1.113 .267 .918 1.089

Household income -.041 .033 -.061 -1.244 .215 .864 1.157

Dutch .403 .213 .098 1.889 .060 .767 1.303

Family life well-being .045 .035 .078 1.280 .202 .555 1.802

Transport well-being .004 .058 .004 .072 .943 .676 1.479

House well-being .129 .055 .145 2.328 .021 .533 1.875

Job well-being .032 .017 .102 1.834 .068 .664 1.507

Neighborhood well-

being .152 .068 .141 2.219 .028 .508 1.968

Social well-being .236 .054 .231 4.386 .000 .747 1.339

Health well-being .064 .042 .088 1.505 .134 .604 1.655

Income well-being .134 .036 .242 3.677 .000 .475 2.105

Leisure well-being .069 .039 .123 1.775 .078 .432 2.315

R R2 Adjusted R2 Std. Error of the Estimate

.780 .608 .571 .776


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explanatory variables on the residents’ overall well-being (Table 8.2). Among all of the

indicators in this analysis, four variables show a significant relationship with the overall

QOUL (P<.05). None of the socio-demographic variables is among the significant

predictors in explaining Overall QOUL. “Neighborhood well-being”, “House well-being”,

“Social well-being” and “Income well-being” significantly and positively influences

“Overall QOUL”. The greatest coefficients in the regression model belong to “Social well-

being” (.23). This mainly indicates that overall QOUL is highly influenced by satisfaction

with social life. The second large impact belongs to “Neighborhood well-being” (.14)

followed by “Income well-being” (.24) and “House well-being” (.15).

Although multiple regression analysis provides some insights into the impact of

socio-demographic characteristics and well-being in all the mentioned life domains on

overall QOUL, but this method is unable to capture the indirect relationships and the

complex associations among these variables. Thereby, further analysis is necessary to

understand the mechanism of effects among the study variables, which will be

explained in the next part.


The key objectives of this chapter were to evaluate the proposed theoretical model

(Chapter 2) and to understand the interrelationships among the study variables in

formulating QOUL. A path analysis is deemed to be an adequate solution for the

achievement of these objectives, thus is used in this chapter. By this method, it is

possible to conduct simultaneous assessments of the associations among multiple

dependent and independent attributes, which can increase the statistical efficiency (Hair

Jr et al., 2016).


Building on the aforementioned logic from the domain-based studies of QOL and the

relevance of these studies to investigate the link among different life domains, and

socio-demographic variables in predicting overall QOUL, a conceptual model is

elaborated (Figure 8.3). In essence the model consists of a series of link among

satisfaction measures of important life domains, vis. Family life well-being, Health well-

being, Social well-being, Income well-being, Job well-being, Leisure well-being,


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Neighborhood well-being, Housing well-being, and Travel well-being and QOUL, which

can be influenced by a range of individual and household characteristics. The directions

of all main paths are from life domains to QOUL, as we use a bottom-up approach. Also,

it is assumed that the level of well-being in one domain may affect well-being in other

domains, especially the neighboring domains (based on horizontal spill over theory). As

some major life events can have long-term impacts on well-being, a variable called

“Family loss” added to the model indicting whether a respondent has recently lost

someone close in family.

8.4.2 Path analysis model for QOUL



normality, which enables estimating the direct and indirect correlations among factors,

and the p values of the paths including the standardized regression weights of the

conceptual model. In the first step, all of the attributes were included in the model to

see whether they have any direct or indirect relation with the other endogenous

variables. From the socio-demographic variables “Household size”, “Education”, “Male”



154

and “Dutch” were insignificant, and thereby, excluded from the model. However, all the

variables regarding life domains well-being are remaining in the model.


excellently fit to the data. Chi Square divided by the model degrees of freedom (/Df or

CMIN/DF) is an essential measure, and is equal to 1.32 for our model. In addition to

that, the Root Mean Square Error of Approximation (RMSEA) should be less than .08

and ideally less than .05 to represent a great fit (Golob, 2003; Washington et al., 2010),

and in our model RMSEA value is .039. Other indicators of a good fit as mentioned by

many researchers (e.g. Moss, 2009) are indices that compare target model with null

model, such as Comparative fit index (CFI), Incremental fit index (IFI), Tucker Lewis

index (TLI), Normed fit index (NFI) and (GFI) Goodness of Fit Index. If these indexes

exceed the amount of .95, then the model tends to generate a great fit. Checking all of

these indicators shows that our model perfectly fits.


multiple correlation value in AMOS) equals to .58, which means our proposed

theoretical model satisfactorily accounts for 58 percent of the variance in residents’

overall QOUL. This moderate level of explained variance in QOUL indicates that other

attributes not included in the model (such as personality) also contribute to overall

QOUL.

Table 8.4 presents all the direct, indirect and total effects of the paths in the

model including P and T value for each path. Our results show that statistically

significant relationships were found among the main model variables as below:

All the variables in the model directly and indirectly influence QOUL.

“Income well-being” has the largest total effect on QOUL (.55), and half

of its impact is indirect via its influence on other life domains.

The second largest positive effect on QOUL is the “Health well-being”

effect (.27), following by “Social well-being” effect (.25).

From the socio-demographic variables “Unemployed” is the only

variable, which directly correlates with QOUL.

Three variables influence overall QOUL negatively, viz. “Unemployed” (-

.31), “Family loss” (-.04), and “Single” (-.02).

From the domain well-being variables, only “Transport well-being“ and

“Leisure well-being” do not show a direct influence on overall QOUL.


155


Fit index Estimate





CFI .97

IFI .98

TLI .96

GFI .95

NFI .91

From the socio-demographic variables, “Age” has the largest influence

(.11). Its effect is slightly more than “Household income” effect (.10).

The other important results that can be extracted from the model can be

summarized as below:

Life domains’ well-being variables are not only influenced by socio-

demographic variables, but they also have correlation with each other.

“Unemployed” has the largest influence on “Income well-being” (-.35),

and also has negative influence on well-being in other life domains.

“Leisure well-being” and “Transport well-being” both are highly

influenced by “Income well-being” and “Health well-being”.

“Neighborhood well-being” is highly influenced by “Health well-being”

and “Transport well-being”.

“Single” only correlates with “Family life well-being”, but with negative

coefficient.

“Household income”, “Age” and “Unemployed” have significant effect on

“Income well-being”.

“Health well-being” is negatively influenced by “Unemployed”, “Family

loss” and “Age”.

“Family loss” negatively impacts well-being of almost all life domains

(except “Job well-being” and “Income well-being”).

“Age” and “Job well-being” have significant relationship with a negative

coefficient.


156


Dependent

variable

Path

Direct

effect

Indirect

effect

Total

effect

T

P

Quality of

Urban life

(QOUL)

Household income QOUL .00 .10 .10 -- --

Unemployed QOUL -.09 -.22 -.31 -1.84 *

Age QOUL .00 .11 .11 -- --

IW QOUL .27 .27 .54 4.62 ***

Family loss QOUL .00 -.04 -.04 -- --

Health Well-being QOUL .12 .15 .27 2.15 **

TW QOUL .00 .11 .11 -- --

NW QOUL .13 .10 .23 2.41 **

LW QOUL .00 .14 .14 -- --

HW QOUL .14 .08 .22 2.48 ***

Single QOUL .00 -.02 -.02 -- --

JW QOUL .10 .00 .10 2.21 **

FLW QOUL .14 .00 .14 2.87 ***

SW QOUL .25 .00 .25 4.99 ***

Social Well-

being

(SW)

Household income SW .00 .05 .05 -- --

Unemployed SW .00 -.13 -.13 -- --

Age SW .00 .13 .13 -- --

IW SW .00 .26 .26 -- --

Family loss SW .00 -.03 -.03 -- --

Health Well-being SW .09 .11 .21 1.32 *

TW SW .00 .08 .08 -- --

NW SW .00 .14 .14 -- --

LW SW .24 .00 .24 3.38 ***

HW SW .30 .00 .30 4.78 ***

Family Life

Well-being

(FLW)

Household income FLW .00 .06 .06 -- --

Unemployed FLW .00 -.12 -.12 -- --

Age FLW .00 .09 .09 -- --

IW FLW .00 .35 .35 -- --

Family loss FLW .00 -.04 -.04 -- --

Health Well-being FLW .12 .16 .28 2.00 **

TW FLW .00 .05 .05 -- --


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LW FLW .53 .00 .53 8.52 ***

Single FLW -.14 .00 -.14 -2.62 ***

Job Well-

being (JW)

Household income JW .00 .05 .05 -- --

Unemployed JW .00 -.09 -.09 -- --

Age JW -.38 .04 -.34 -6.03 ***

IW JW .25 .00 .25 3.95 ***

House Well-

being (HW)

Household income HW .00 .06 .06 -- --

Unemployed HW -.11 -.10 -.21 -1.88 **

Age HW .21 .09 .30 3.78 ***

IW HW .18 .12 .30 2.97 ***

Family loss HW .00 -.02 -.02 -- --

Health Well-being HW .00 .13 .13 -- --

TW HW .00 .19 .19 -- --

NW HW .45 .00 .45 8.01 ***

Neighborhood

Well-being

(NW)

Household income NW .00 .05 .05 -- --

Unemployed NW .00 -.09 -.09 -- --

Age NW .14 .00 .14 2.44 **

IW NW .00 .26 .26 -- --

Family loss NW .00 -.041 -.04 -- --

Health Well-being NW .23 .07 .30 3.71 ***

TW NW .42 .00 .42 6.90 ***

Transport

Well-being

(TW)

Household income TW .00 .07 .07 -- --

Unemployed TW .00 -.12 -.12 -- --

Age TW .00 .03 .03 -- --

IW TW .27 .08 .35 3.66 ***

Family loss TW .00 -.02 -.02 -- --

Health Well-being TW .16 .00 .16 2.22 **

Health

Well-being

Household income

Health Well-being

.00 .09 .09 -2.13 **

Unemployed Health Well-

being HW

.00 -.18 -.18 -- --

Age Health Well-being -.13 .08 -.05 -- --

IW Health Well-being .50 .00 .50 8.33 ***

Family loss Health Well-

being

-.14 .00 -.14 -2.31 **

Income Household income IW .19 .00 .19 3.02 ***


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Well-being

(IW)

Unemployed IW -.35 .00 -.35 -5.65 ***

Age IW .15 .00 .15 2.47 ***

Leisure

Well-being

(LW)

Household income LW .00 .10 .10 -- --

Unemployed LW .00 -.19 -.19 -- --

Age LW .14 .04 .18 2.44 ***

IW LW .35 .18 .53 5.46 ***

Family loss LW .00 -.04 -.04 -- --

Health Well-being LW .29 .02 .31 4.63 ***

TW LW .10 .00 .10 1.68 *

Note: ***,**,* significance at 1%, 5%,10% level.

“Neighborhood well-being” shows a significant effect on “House well-

being”.

“Health well-being” influences all life domains except “Income well-

being”.

“Transport well-being” positively impacts “Neighborhood well-being”

and “House well-being”.

“Leisure well-being” shows a significant effect on “Family life well-

being” and “Social well-being”.

“Family life well-being”, “Social well-being” and “Job well-being” are the

only domains with just direct influence on overall QOUL.

8.5 Interpretation

Our results show that the level of well-being in various life domains significantly

contributes to the overall QOUL (Figure 8.4). Moreover, most of the socio-demographic

attributes indirectly affect the QOUL assessments. To have a better overview regarding

the contribution of all the variables in our estimated model, below they are discussed in

more details.

8.5.1 The role of socio-demographic attributes on QOUL

As mentioned, most of socio-demographic variables indirectly affect QOUL.

Unemployment, however, is an exception and has a substantial negative and direct

influence on QOUL. This direct effect implies the importance of having a job on one’s

life.


159

The other interesting result is related to the impact of unemployment on income well-

being and house well-being, which reflects the financial side of unemployment rather

than the other possible negative effects (such as social effects). Therefore, based on

our findings the main negative influence of unemployment is the financial burdens that

it imposes to individuals. Age is another significant variable with positive influence on

QOUL. Regarding other associations of age, as expected, older people have lower

satisfaction with their health situation, while income well-being, house well-being and

neighborhood well-being increase with aging (e.g. Parkes et al., 2002; Chapman &

Lombard, 2006). However, the negative correlation between age and job well-being

contradicts some previous findings of studies including our job well-being section (see

chapter 8), which reveals positive influence of age on job well-being.

Our findings also indicate that being single is significantly associated with

reduced family life well-being, which in turn influence QOUL negatively. McCrea et al.

(2005) also found that single people have a significantly lower overall life satisfaction

than couples and families with children. As expected, family loss is negatively correlates

with health well-being, and via that, it negatively influence well-being in almost all the

life domains (except job well-being and income well-being). This indicates that the grief

of losing someone in family can affect all aspects of life including health (mind and

body), relationships and in general the whole feeling towards well-being. Regarding

household income, the link between household income and QOUL is indirect and

through income well-being. Similarly, well-being in all other life domains is influenced by

household income indirectly. However, many authors (e.g. Kahneman & Deaton, 2010)

have argued that low income reduces life satisfaction, but as long as basic needs have

been met. This implies that further rises in income are not associated with further

increases in well-being.

8.5.2 The influence of life domains on QOUL

The findings of this study assert that the most important predictor of QOUL is income

well-being followed by health well-being and social well-being, respectively. Satisfaction

ratings regarding physical domains such as neighborhood well-being and house well-

being have less impact compared with income well-being, health well-being and social

well-being domains. Furthermore, job well-being and transport well-being have the least

total effect on QOUL. The dominant role of income well-being on QOUL clearly reflects


160

Fig

ure

8.4

Re

su

lts o

f p

ath

an

aly

sis

fo

r Q

OU

L


161

the importance of the financial aspects in maintaining a good life. Positive assessment

of income is associated with all life domains directly and indirectly, and thereby has a

huge impact on QOUL.

It is good to note that most researchers believe that expectations and income

aspirations determine income well-being rather than the absolute income, while the

basic needs fulfilled (Frey & Stutzer, 2005). Furthermore, the influence of income well-

being on other life domains is remarkable, particularly on non-physical domains such as

health well-being. Health well-being influences almost all the domains directly and

indirectly. Different research reveals that there is a robust link between physical health,

mobility and activity satisfaction, therefore, quality and quantity of commuting, leisure

activities, and social activities are influenced by health well-being (Chang et al., 2016).

However, perceived health deteriorates with age and family loss. Our findings also

uncovered an indirect and negative correlation between unemployment and health well-

being, which has also been reported in other studies. As the financial burden imposes

by unemployment to one’s life can significantly reduce health well-being.

Individuals’ sense of social well-being plays a significant role in QOUL, and our

findings also show a robust link among them. We can also observe that better health

can promote social well-being. Moreover, the correlation that we obtained among

leisure well-being and social well-being has been also admitted by other research

findings. For instance, Chen et al., (2010) advocated that many forms of leisure

activities are linked with social interactions, which can fulfill variety of social needs.

Other research findings (e.g. Cornwell, 2002; Bjornskov, 2003; Dolan et al., 2008; Sirgy

et al., 2010) also corroborate the relationship between house well-being and social well-

being, as certain housing amenities associates with the ability of hosting, entertaining

and socializing with friends and relatives. We can see from the path coefficients that

neighborhood well-being and house well-being significantly contributes to QOUL, which

is also in line with the findings of other empirical research (e.g. Bjornskov, 2003; Dolan

et al., 2008; Marans, 2012). Moreover, regarding the link among neighborhood well-

being and house well-being, it has been shown that positive assessment of

neighborhood directly influence housing well-being, as location of housing is attached to

the overall feeling regarding house (e.g. Teck-Hong, 2012). Based on our results, age

and income well-being positively contributes to housing well-being, while

unemployment affects it negatively. The fact that no direct relationship is observed

between transport well-being and QOUL is also in line with some previous research


162

findings (e.g. Currie et al., 2010; Stanley et al., 2011). Travel well-being, however,

influences neighborhood well-being and leisure well-being and via them indirectly

impacts QOUL. But as expected, its contribution to QOUL is less than house and

neighborhood well-being.

Regarding the effect of family life well-being on QOUL, our results show a

direct link among them. Generally, family life is a strong predictor of well-being and

negatively influenced by family loss and unemployment, which is also reflected in our

findings. Also, as expected, married people are more satisfied with their family life than

single people. Finally, our results also indicate that higher levels of job well-being

promote QOUL. Based on our findings in job well-being chapter (see Chapter 7)

satisfaction with quality of work has a major effect on job well-being. This reflects that

the need for doing a meaningful activity plays a key role in personal growth and well-

being, and job well-being can fulfill higher levels of needs.


This chapter presents a methodological and empirical contribution to the literature on

the residents’ QOUL using path analysis. Path analysis allows analyzing multiple

domains of QOUL and the links among them simultaneously. It also enables us to have

a better understanding of the complexity of QOUL concept. We used the most important

domains including the socio-demographic characteristics of individuals to conceptualize

QOUL. Our findings highlight the major and distinct influence of the income well-being,

health well-being and social life well-being on overall QOUL. Moreover, from the socio-

demographic attributes, age, income, marital status and employment status significantly

contribute to QOUL. However, no significant difference is observed in the QOUL of

males and females, highly educated and low educated as well as Dutch and minorities.

The other noteworthy finding of this research is the associations among various life

domains, which is called “Horizontal spillover” (Sirgy, 2012), and explains why extreme

dissatisfaction or lack of satisfaction in one life domain may influence (spill over in)

other life domains.

In general, quality of life and well-being are complex notions. On one hand,

life domains evaluations are grounded on objective realities, and on the other hand,

these judgments are influenced by personality and psychological moods of individuals.

In this regard, optimism and positiveness can play a key role in subjective well-being of

individuals. Also, there is much overlap among the various domains of well-being and in


163

some cases the relationships are two-fold, particularly between the neighboring

domains, such as house well-being and neighborhood well-being or neighborhood well-

being and transport well-being. These inter domains interactions cause the

interpretation more challenging. Moreover, QOUL studies can address microlevel or

macrolevel well-being. While, this study has focused on microlevel subjective well-

being, macrolevel studies should focus at country-level political and economic

circumstances, as well as community’s stability, social cohesion and culture.

Although this chapter has examined all the key domains affecting microlevel

QOUL, but considering the numerous factors and broadness of the concept, there are

many opportunities to expand research in this area. The applications of such studies are

also extensive and can potentially address policy makers and urban planners as well the

residents who are curious about themselves and their living environment.

Conclusions and future work

164


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9

Conclusions and Future

work

9.1 Summary

Despite extensive research on quality of life over the past half century and the long

history of urbanization in human civilization, the study of quality of life in urban places

has only recently received attention. This recent attention is a response to concerns

about various issues in cities, and the increasing threats over human life. Moreover, it

reflects a shift in urban planning away from basic needs of citizens to a larger spectrum

of policy goals. In this regard, quality of urban life (QOUL) studies can provide practical

solutions to some of these challenges and provide guidelines and strategies for policy

makers and urban planners to enhance subjective well-being of residents.


166

Previous studies mostly focused on either specific issues in a specific urban life

domain such as housing, or just depicted a general picture of correlates of QOUL.

However, these studies did not consider the corresponding effects among various life

domains, while these effects may be significant and cannot be ignored. Also, the

strength of the link between various attributes of life domains cannot be fully captured

just by considering the objective characteristics of the environment, but the subjective

assessments that stem from individuals conditions and constraints should be addressed

as well. This PhD research tries to address these issues and fill these gaps through an

integrative approach considering the corresponding effects among various life domains

and urban life characteristics. Here, the assumption is that physical characteristic of the

environment can potentially affect the subjective assessment of residents in various

concrete areas of life, which can be classified into few main domains. In formulating the

conceptual model of this study, overall QOUL was assumed to be a function of the

person-place-activity relationship, and thereby, the main urban life domains emerged

from this concept. Regarding the place concept, housing well-being and neighborhood

well-being were included in the model, as both house and neighborhood are key living

places. In terms of main urban activities, work well-being and transport well-being

domains were added to the model, as both may significantly influence QOUL. So the

main four domains of housing well-being, neighborhood well-being, transport well-

being, and job well-being were selected as main urban life domains and investigated in

more details. The neighborhood domain includes the opportunity the environment offer

to conduct daily activities. In the following, the main results of each chapter will be

presented.

Chapter 4 explored the effects of both objective and subjective attributes of

housing on well-being, using respectively a regression model and a path model. The

empirical results of the path analysis provided evidence that subjective attributes such

as satisfaction with house ownership, house type, house design, house exterior space

and safety have significant effect on housing well-being. Among them, house type

satisfaction shows the highest influence on house satisfaction followed by house design

satisfaction. Among the objective variables, housing cost, ownership and safety facility

are the dominant attributes in influencing housing well-being, as it is observed that low-

cost house, rental house and lack of security facilities indirectly and negatively influence

housing well-being. It is also good to note that socio-demographic variables, household

income and length of residency show indirect and positive effect on housing well-being.


167

Chapter 5 explicitly focused on neighborhood satisfaction as the neighborhood is

the second immediate space after housing that urban residents encounter during their

life course, playing a key role in fulfillments of residents' daily needs. To understand the

complex relationship among the various attribute of neighborhood well-being domain,

the conceptual model of this chapter categorized the main attributes affecting

neighborhood well-being into three main categories, namely neighborhood

characteristics (physical, environmental and social condition), neighborhood physical

activity (biking and walking), and neighborhood services (e.g. grocery shopping, sport

center, etc.). To measure these constructs (latent variables) a structural equation model

was developed and applied. The results highlight the major and distinct influences of

neighborhood characteristics followed by neighborhood services and neighborhood

physical activity. Moreover, satisfaction with social conditions has the most prominent

effect on neighborhood well-being followed by satisfaction with environmental condition

and physical sub-domains. In the neighborhood services category, only grocery

shopping was shown to be influential compared with the other six major services in

neighborhood (e.g. restaurant, school, etc.). In the neighborhood physical activity

category, both walking and biking conditions were found to be significant predictors,

however, walking condition shows a stronger correlation. Among the socio-demographic

factors, only marital status has a significant effect on neighborhood well-being, as being

single negatively correlates with neighborhood well-being.

Chapter 6 investigated transport well-being (travel satisfaction), which is a multi-

dimensional and dynamic concept. To identify the main drivers of commuters’ travel

satisfaction, travel behavior of residents should take into account. Thereby, information

regarding respondents’ trips during peak and off-peak hours, and their travel mode

choice were obtained. Moreover, based on the specific mode use, satisfaction with

various trip attributes were rated to help identifying the main drivers of satisfaction, and

the extent to which these attributes influence travel satisfaction. Using private transport

mode during peak hours was explained by six attributes such as satisfaction with

parking availability, safety of roads, etc., and public transport mode use was explained

by seven variables such as travel time, safety, etc. The empirical results of structural

equation analysis provided evidence that only travel during peak hours influences travel

well-being, and consequently avoiding rush hours plays a positive and significant effect

on travel satisfaction. Also, all chosen trip attributes could significantly explain the travel

satisfaction assigned to that particular mode. In addition to all the travel characteristics,


168

our model also illustrated a significant effect of built environment characteristics and

satisfaction with accessibility and neighborhood as influential factors. In this regard,

feeling satisfied with travel time to major places (e.g. family/friends, recreational

centers) plays an in important role in travel satisfaction. Accessibility clearly reflects the

relationship that exists among one’s desires and obligations to do various activities, and

the opportunities of the environment that can fulfill these needs. Moreover, results

show that females' single people experience higher levels of travel satisfaction.

Chapter 7 investigated the role of socio-demographic variables, employment

status (part-time vs full-time), and various work-related attributes in a multi-attribute

structure, where satisfaction in three domains of work conditions (salary, workload,

etc.), work location (travel time, safety of area, etc.), and workspace (privacy, size,

etc.) were used to explain job satisfaction. The path analysis results indicated a strong

influence of work conditions satisfaction, followed by workspace and work location,

respectively. In the work conditions category, satisfaction with quality of work has a

major effect on work conditions satisfaction. Here, the comparative impact of work

quality satisfaction versus salary satisfaction can be an indication that performing

meaningful work dominates over salary. In the work location category, satisfaction with

travel time to work is the most important attribute, while in the workspace category,

satisfaction with privacy plays a key role.

There are also some intra domains relationships that can be extracted from our

model. For instance, the major effects of the attributes in the workspace category on

satisfaction with quality of work may indicate that bad working space restricts

individuals to show their abilities, and thereby, reduce overall work satisfaction. The

findings also highlight the major and direct influence of perception of the social

environment and privacy on feeling satisfied with work.

Finally, Chapter 8 investigated the role of all urban life domains discussed in

previous chapters including the other major life domains (which were not the focus of

this research study) on overall QOUL based on domain-specific approach. The proposed

theoretical model, thereby, consisted of series of links among nine major life domains,

socio-demographic variables and overall QOUL. The directions of all main paths were

from life domains to QOUL, as we used a bottom-up approach. Also, it is assumed that

the level of well-being in one domain may affect well-being in other domains, especially

the neighboring domains (based on horizontal spill over theory). Therefore, the findings

of this chapter were two-fold, showing both the influence of life domains on QOUL and


169

the inter-relationship among various life domains. In order to investigate these links, a

path analysis method was developed and used. Our results indicate that the proportions

of explained variance (predictive power) in overall QOUL are different for the various

domains. The effects of these domains can be sorted from high to low effect as: income

satisfaction, health well-being, social well-being, neighborhood well-being, house well-

being, transport well-being and job well-being, respectively. Although job well-being has

shown to be a less influential factor compared to the other main domains,

unemployment substantially decreases the degree of well-being. Our model results also

indicate that all urban life domains, except transport well-being, have a direct influence

on QOUL. Although it was not assumed in our initial hypothetical model, but this result

is in line with the findings of other research (e.g. Gao et al. 2017 & 2018). This indirect

effect runs through the neighborhood well-being domain, which emphasizes the role of

neighborhood accessibility on QOUL.

Moreover, five personal characteristics (age, marital status, household income,

employment status and family loss) were found to significantly affect overall QOUL.

Results indicate that younger people, single and unemployed individuals including lower

income households experience lower level of satisfaction regarding their life. However,

unemployment is the only variable with a direct effect on QOUL. In addition, as

hypothesized, family loss has shown to have a negative influence on health well-being

and in turn QOUL.

The findings on the interrelationships among the main domains are also

interesting. First, the major influence of income well-being on well-being in other

domains is surprising, but logically explainable, as income well-being determines access

to sources and facilitates life in many ways. Moreover, it can also be seen that many

neighboring domains influence each other, such as the impact of transport well-being

on neighborhood well-being, or the effect of neighborhood well-being on house well-

being. Clearly, positive assessment of neighborhood influence housing well-being, as

location of housing is attached to the overall feeling regarding house. In addition to

that, housing well-being influence social well-being as certain housing amenities

associates with the ability of hosting, entertaining and socializing with friends and

relatives. All in all, as the four major urban domains that we investigated have

significant influence on QOUL, we can conclude that our results confirm the hypothetical

model of this thesis. Also, all the major attributes that we found in Chapter 4 to 8

indirectly influence QOUL.


170

9.2 Contribution and Managerial implications

The findings of QOL studies in general and QOUL in particular have diverse and

profound implications for policy, planning and design, and can serve as a basis for

optimizing the well-being of urban residents. Indeed, if the actions of decision makers

are founded on the results of academic research, they are more likely to be responsive

to people’s needs and preferences. Similarly, many non-governmental organizations

who deal with consumer behaviour can apply the indicators and methods of QOUL

studies. As the interest on QOL research within urban planning grows, and the efforts to

assess and measure well-being increase, the topic attracts more attention from policy

makers. Thereby, the opportunities for collaboration between scholars and the public

and private sector might be increased in the future. However, irrespective of the

context of QOUL research, replication of studies over time can provide many useful

results in terms of changes in perception of residents regarding various dimensions of

the environment. In fact, monitoring QOL in longitudinal studies is the best way to

broaden our perspective regarding both subjective and objective QOL.

This study provides insightful understanding in terms of various life domains,

the interconnection among these domains and also their associations with QOL. Living,

working and commuting in urban environment have been studied and the results

discussed respectively. Regarding housing well-being, ownership found to have a

significant role. In this regard, governments tendencies and efforts can assist and

enable low-income families to become homeowners. In a broader sense, the global

financial crises have always had a tremendous impact on the housing market. Also,

housing issues has been part of political debates and always bounded to financial and

also ideological matters. However, the lack of quality of life in such debates is evident.

Moreover, the other interesting result in the housing chapter is related to type of house

satisfaction. The positive experience of residents in StrijpS lofts reveals that combining

living and working in a studio is a popular trend for young generations who cannot

afford renting separate places for living and working. This basically stems from the

flexible design of these studios that provide opportunities for doing various activities.

Increasing the number of these type of flats, particularly in expensive cities should be

considered.

In the neighbourhood well-being chapter, our results emphasize the role of the

social domain and therefore fostering residents from different background can improve

collective living in an area. If municipalities and related sectors develop community-


171

based organizations and encourage them to organize activities in neighbourhoods

frequently, neighbourhood activities could act as a platform to engage citizens for social

work and can benefit both individuals and communities.

In the transportation well-being chapter, the dominant result was the higher

satisfaction of those who avoid commuting in peak hours. Consequently, encouraging to

work from home, postponing trips from peak to off-peak hours, and using active modes

of transport, can be some of the solutions to promote travel well-being. Additionally,

peak avoidance policies for some companies such as delivering companies can reduce

traffic congestion and promote the drivers well-being.

In the job-well-being chapter, one result was related to characteristics of work

space and highlighted the importance of space privacy feeling in satisfaction with work

place, and consequently overall job satisfaction. As open-office layout concept grows

rapidly, it is important to consider the employees’ need for concentration, and it would

be good that office manager could provide more private rooms. Also, using furniture to

separate spaces among employees could be another option to decrease distractions and

improve privacy feeling.

In the quality of urban life chapter, the results clearly show the dominant role

of income well-being and the negative influence of unemployment on overall well-being

of individuals. Reducing unemployment and optimizing labour market have always been

challenging tasks for governments. However, it is possible to reduce some negative

effects of unemployment by employing relevant strategies. For instance, reducing social

isolation of unemployed citizens by organizing free events, engaging them in volunteer

jobs and also assisting them in finding jobs. There are many more results that can be

driven from this study regarding the social and family well-being, however it is beyond

the scope of this study to recommend particular solutions for this broad range of issues.

Although these results have been extracted from a small sample, in many ways

they are likely representative of the preferences of a larger population of residents. The

most important function of this research and similar research for public policy could be

a shift on the focus by addressing different questions. For instance, instead of asking

“How work place impact productivity?” we should ask “How work place impact work

well-being?” or instead of asking “How unemployment impact economic growth?” asking

“How unemployment impact QOL?”. In other words, shifting from concepts such as job

performance to job well-being can provide policy makers with different guidelines for

their future performances.


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9.3 Discussion and direction of future research

The aforementioned conclusions are based on the hypothetical framework that we

designed in the beginning of this study. This framework was estimated based on the

data collected from four different neighborhoods in Eindhoven. The subjective measures

described in this research, and in general in most of QOUL studies are based on several

assumptions that can generate bias. The main assumption is that individuals can derive

their overall QOUL or satisfaction in one life domain from their daily experiences. Also,

retrospective evaluations of life can be biased as they mostly reflect major and recent

experiences of respondents. To reduce this bias, we asked respondents to mention if

they have recently lost someone in their family. Moreover, situational factors (such as

environmental conditions) and moods of respondents can bias the responses toward

higher or lower ratings of subjective well-being. In order to reduce environmental

condition biases, our data were collected in two seasons. There is also the limitation

regarding measurement methods, and the choice of attributes and domains. Our study

focuses on four urban life domains, and also includes overall satisfaction of five other

main domains (financial, personal, health, social and leisure). More life domains such as

religious life, political environment, etc. may be relevant to include in future research

and/or in other areas. Moreover, this study is only concerned with four areas in a single

city in the Netherlands, and therefore, these data only reflect particular differences in

the built environment. To better understand the role of urban and built environment

characteristics, further investigation of QOUL in different cities and countries is

recommended.

In brief, any future research needs to address a series of challenges, in order to

achieve a comprehensive understanding of the role of urban living experience on QOUL.

These challenges cover collection of data, modelling and measuring urban life well-

being. The first challenge is collecting larger scale data, without compromising the

quality and depth of the presented method. For instance, in our study the number of

working people were (130) and among them (53) were part-time employee. Therefore,

in order to fully understand the impact of part-time working on overall work satisfaction

a larger sample would be desirable. This also applies to the transportation domain as

travel well-being is highly related to mode use, and dividing the sample based on mode

use needs larger samples to presents all the modes.

The second challenge concerns the nature of the relationship between QOUL and

satisfaction in the different domains of life. The issue stems from the general


173

assumption that an additive relationship exists between QOUL and satisfaction in

domains of life. It means that dissatisfaction in one aspect of a domain can be

compensated by other aspects. For instance, by improving interior design of a house,

dissatisfaction regarding house size can be decreased. However, it is shown that the

same relationships are not applicable to QOUL, and increase in satisfaction with one

domain may not compensate dissatisfaction or decline in another domain. For instance,

an increase in job satisfaction may not be enough to compensate decline in family life

satisfaction. Thus, the additive specification (used in this research) is good in predicting

QOUL, but to better understand the complex mechanism of QOUL, non-linear and non-

compensatory model specifications should be tested.

The third challenge concerns the direction of causality in the QOUL model. The

argument regarding bottom-up vs top-down approach should also be considered to

understand the impact of overall QOUL on different domains satisfaction. This may

show that QOUL impact may not be uniform across domains. Moreover, our approach in

this study is a cognitive representation of QOUL concept, and examined the relative

importance of urban life attributes. However, future studies can also consider affective

approaches.


174

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References

198

Appendix: QOUL Survey

199



200


201


202

Nu rekening houdend met alles, hoe tevreden bent u over uw huis? (van 1 zeer ontevreden tot 10 zeer

tevreden).

1 2 3 4 5 6 7 8 9 10

Kies welke kenmerken van toepassing zijn op uw huis en waardeer deze

vervolgens van 1 (zeer ontevreden) tot 10 (zeer tevreden).

Huiseigendom

Soort woonhuis

Woonoppervlakte (m2)

Aantal slaapkamers

Buitenruimte

Bouwjaar

Optie

Huur

Koop

Appartement

Rijtjeswoning

Twee-onder-

één- kapwoning

Vrijstaande

woning

Minder dan 60

60-100

100 en groter

Geen

Eén

Twee

Drie of meer

Balkon Tuin

Dakterras

Balkon en tuin

Balkon en

dakterras

Tuin en dakterras

Geen

buitenruimte

Na 2010

2001-2010

1991-2000

1981-1990

1971-1980

1960-1970

1945-1959

1931-1944

1906-1930

Voor 1906

Tevredenheid (1-10)

Hoofdweergave van het huis

Binnen/buiten

kwaliteit (reparatie

situatie)

Maandelijkse prijs van de

woning (exclusief energie)

Veiligheid van

het huis

Lay-out

(ontwerp en de plattegrond)

Energiebron

Optie

Groenrijke

omgeving

Blokken met

huizen

Straat met

winkels

Snelweg

Braakliggend

terrain

Anders

Grote

reparaties of

renovaties zijn niet

nodig

Het dak

De vloer

De badkamer

De keuken

Leidingen of

bekabeling

Verschillende

onderdelen van het

huis

0 Euro

500-750

750-1000

1000-1250

1250-1500

1500 en hoger

Mijn huis heeft

geen speciale

beveiliging

Mijn huis heeft

traditionele

beveiliging zoals

speciale sloten op

deuren en ramen..

/hond

Mijn huis heeft

high-tech beveiliging

zoals camera's,

alarm,...

Het past bij

mijn leefstijl

Het past niet

bij mijn leefstijl

Elektriciteit

Gas en

elektriciteit

Elektriciteit en

warm water

Tevredenheid (1-10)


203


204


205


206


207


208


209


210


211


212


213

Authors Index

214

Authors Index

215

Authors Index

A

Abdallah, S. 116

Adams, A. 124

Addo, I. A. 42

Adriaanse, C. C. M. 45

AhnAllen, J. M. 45

Aiello, A. 44

Albert, D. P. 64

Alfonzo, M. A. 69

Allen, T. D. 127

Alshmemri, M. 118

Amérigo, M. 64

Amole, D. 44

Amponsah-Tawiah, K., 127

Andrews, F.M. 145

Aragonés, J. I. 41, 42

Argyle, M. 10, 145

Aries, M. B., 124

Aulia, D. N. 46

Austin, D. M. 64, 148

Avila-Palencia, I. 95

Aydin, N. 92, 96, 98, 148

B

Baum, S. 44, 46

Baumeister, R. F. 13

Bernardo, F. 12

Birnholtz, J. P. 126

Bissell, D. 99

Biswas-Diener, R. 40

Bjornskov, C. 161

Blanchflower, D. G. 146

Bloch-Jorgensen, Z. T. 14

Bocarejo, S. J. P. 97

Booth, A. L. 122

Bowling, N. A. 124

Boyce, C. J. 146

Brace, N. 149

Brannon, D. 124

Brief, A. P. 10

Buckley, P. 69

Burton, E. 39

Buys, L. 46, 57

C

Campbell, A. 144, 145, 147

Cao, X. J. 65, 69, 91, 92, 93, 94, 113, 148

Carlquist, E. 12

Carvajal, M. J. 119, 120

Cats, O. 92, 93, 98, 99, 110

Cervero, R. 94, 100

Chang, P. J. 161

Authors Index

216

Chapman, D. W. 64, 159

Chaskin, R. J. 63

Chaudhuri, K. 119

Chen, C. F. 94

Chen, L. 46

Chen, L. H. 147, 161

Chevalier, A. 146

Clark, A. E. 65, 119, 123, 126, 136,

146

Clark, W. A. 12, 94

Cohen, J. 149

Costa, G. 12

Costa-Font, J. 39

Cozens, P. 65, 67, 68

Cramm, J. M. 68

Crossman, A. 119

Cummins, R. A. 12, 145

Currie, G. 162

D

Danielsson, C. 126

Darma, P. S. 122

Dassopoulos, A. 65, 68

Dawal, S. Z. M. 125

de Oña, J. 104

De Vos, J. 92, 93, 94, 95, 110, 113, 149

Dekker, K. 42, 44, 67

Delhey, J. 144

Delle Fave, A. 9

Di Masso, A. 66

Diaz-Serrano, L. 45, 48, 71, 99, 149

Diener, E. 7, 8, 9, 40, 116, 146, 147

Ding, C. 94

Djukic, M. 125

Dolan, P. 11, 146, 147, 161

Dole, C. 124

Dolnicar, S. 12, 148

Duran, M. A. 116

Dusansky, R. 41

E

Easterlin, R. A. 147

Easthope, H. 41

Ellis, N. 121

Elsinga, M. 46, 59

Erdogan, B. 124

Ettema, D. F. 61, 91, 92, 93, 94, 98, 99, 113,

148

Ewing, R. 100

F

Fagan, C. 122

Fellesson, M. 92, 93, 98

Félonneau, M. L. 65

Fernández-Carro, C. 39, 148

Fernández-Portero, C. 39, 42, 148

Ferrer-i-Carbonell, A. 146

Flavin, M. 41

Fonner, K. L. 127

Fornara, F. 124

Foster, K. A. 63

Foye, C. 44

Frey, B. S. 97, 161

Friman, M. 92, 93, 94, 95, 98, 104, 148

Fryers, T. 115

Furnham, A. 118

Authors Index

217

G

Galster, G. 42, 66, 68

Gao, Y. 92, 94, 96, 104, 112, 113, 127, 148,

169

Gardner, P. G. 63

Gatersleben, B. 92, 95

Gazioglu, S. 119

Gerdtham, U. G. 116

Gerhart, B. 136

Gibson, J. J. 65

Glatzer, W. 9

Goetze, R. 69

H

Haar, J. M. 117

Hackman, R. 118

Hadavi, S. 65

Hagedorn, L. S. 119

Haggan, M. 124

Hahn, E. A. 147

Hair Jr, J. F., 52, 152

Hall, M. 42

Handy, S. 127

Harris, K. J. 124

Hauret, L. 119

Head, A. 126

Higgins, C. D., 93, 94, 97, 100

Hincks, S. 126

Hipp, J. R. 46, 62, 63, 67

Hooper, D. 134

Hoppock, R. 117

Hosseini, D. 120

Huang, Z. 44, 47

Hulin, C. L. 115, 148

Hur, M. 66, 69, 76, 87

I

Ibem, E. O. 44, 46

Iseki, H. 98

Ismail, A. 46

J

Jacobs, J. 67

Jain, R. 124, 136

Jensen, W. A. 70

Jiang, W. 42

Jiang, Z. 124

Jiboye, A. D. 41, 46, 57

Joewono, T. B. 98

Judge, T. A. 122, 123, 136, 141

Jussila, I. 45, 59

K

Kahneman, D. 9, 126, 159

Karatuna, I. 121

Kauhanen, M. 121

Keul, A. G. 18

Kline, R. B. 82, 108

Koohsari, M. J. 63

Kweon, B. S. 17, 66

L

Lance, C. E. 10

Lane, R. E. 10

Lee, J. S. 115, 148

Authors Index

218

Lee, S. M. 66

Lee, S. Y. 126

Lee, T. 63

Legrain, A. 99

Levy, P. E. 123

Li, S. 47

Li, Z. 42

Locke, E. A. 118, 122

Loewe, N. 12

Lottrup, L. 124

Lovejoy, K. 66, 68

Low, C. T. 16, 32, 36, 48, 51, 56, 58, 59

Lu, M. 44, 45, 66

Lynch, K. 67, 94

M

Maat, K. 95

Macke, J. 16

Makinde, O. O. 41, 42

Marans, R. W. 13, 17, 62, 64, 144, 161

Marcouyeux, A. 125

Maslow, A. H. 41, 45, 66, 67, 118

Mas-Machuca, M. 117

Mattisson, K. 99

Meegan, R. 64

Mitchell, A. 42, 64

Mohit, M. A. 42, 44, 45, 47

Mokhtarian, P. L. 92, 112

Molin, E. J. E. 128

Montero, R. 121, 122

Morris, E. A. 93, 96, 97

Moss, S. 55, 105, 133, 134, 154

Müller, T. 68

Mumford, L. 63

Munshi, T. 94

Muthén, L. K. 143

N

Nanjundeswaraswamy, T. S. 120

Newman, O. 67

Nguyen, P. D. 126

Novaco, R. W. 99, 127

Nyberg, A. J. 136

O

Oakil, A. T. M. 96

Ogu, V. I. 41, 43

Oh, J. 25, 44

Oktay, D. 18

Okulicz-Kozaryn, A. 9, 16

Olsen, J. R. 96

Olsson, L. E. 92, 93, 126, 127, 142

Orstad, S. L. 69

Orth, U. 146

Ory, D. T. 112

Oud, J. H. 28, 77

P

Pacione, M. 66

Parkes, A. 25, 159

Peiser, R. B. 16

Pekkonen, M. 47

Pietersma, S. 12

Pinto, S. 8

Pocock, B. 124

Proshansky, H. M. 11

Authors Index

219

Q

Qureshi, M. I. 124

R

Raymond, C. M. 65, 66, 68

Redman, L. 98

Rezvani, M. R. 17

Rice, R. W. 118

Rioux, L. 70, 125

Roberts-Hughes, R. 46

Rollings, K. A. 65

Rosenberg, M. J. 65

Russo, M. 122

Ryan, R. M. 144

S

Saeed, I. 117

Safi, M. 146

Sahin, H. 123

Salleh, N. A. 44

Santos, L. D. 65

Saraji, G. N. 120

Schalock, R. L. 145

Schepers, P. 70

Schimmack, U. 91

Schwanen, T. 96

Sealy–Jefferson, S. 47

Sedaghatnia, S. 17

Selvarajan, T. T. 119

Sen, A. 10

Senasu, K. 115, 148

Senlier, N. 18

Şimsek, Ö. F. 11

Sirgy, M. J. 8, 9, 11, 12, 19, 65, 69, 92, 115,

117, 118, 144, 145, 147, 148, 161, 162

Spears, S. 98

Spencer, E. S. 119

Stanley, J. 162

Steg, L. 99

Steger, M. F. 13

Steijn, B. 119, 121

Stimson, R. 13, 18

St-Louis, E. 92, 93, 96, 97, 100, 110

Stradling, S. G. 93, 98, 99

Stutzer, A. 97, 161

Sukriket, P. 117

Susilo, Y. O. 92, 93, 98, 110

T

Talen, E. 64, 65, 70

Tao, L. 42

Tay, L. 14

Teck-Hong, T. 161

Temelová, J. 42

Theodori, G. L. 45

Turcotte, M. 95

Türkoğlu, H. 17

Türksever, A. N. E. 18

U

Ullman, J. B. 55, 79, 133

Urry, J. 95

Authors Index

220

V

Van Aerden, K. 123

Van Assche, J. 67

Van Cauwenberg, J. 88

Van den Berg, P. 31

Van der Meer, P. H. 146

Van Praag, B. M. 145

Varghese, S. 117

Veenhoven, R. 10

Veitch, J. A. 124

Vera-Toscano, E. 41, 44, 47

Vestling, M. 12

Vischer, J. C. 125, 126

Visser, K. 88

Von Wirth, T. 17

W

Wan, D. 104

Wang, D. 43, 45, 47, 147

Wang, W. C. 43, 45, 47, 147

Washington, S. P. 55, 79, 105, 134, 154

Wasko, M. M. 124

Węziak-Białowolska, D. 62

Wittmer, J. L. 121

Woo, E. 11

Wyatt, T. A. 121

Y

Yavuz, N. 112

Ye, R. 93, 94, 100, 112, 113

Z

Zacher, H. 119

Zavei, S. J. A. P. 41

Zebardast, E. 39, 148

Zhang, C. 66, 97

Zhang, F. 39, 148

Zhang, Z. 62, 65, 69, 148

Zhu, Y. 117

Subject Index

221

Subject Index

A

Accessibility 18, 19, 65, 69, 94, 100, 103,

104, 105, 107, 108, 109, 110, 112, 113,

126, 132, 168, 169

Activity 4, 8, 11, 14, 15, 19, 25, 37, 44, 61,

63, 68, 69, 70, 71, 74, 77, 80, 81, 82, 83,

84, 85, 87, 88, 89, 95, 97, 107, 112, 113,

115, 116, 123, 148, 161, 162, 166, 167

Amos 53, 55, 79, 105, 107, 132, 134, 153,

154

Architecture 25, 28, 42, 69, 73, 77, 84

B

Biking 25, 37, 69, 71, 74, 76, 77, 80, 82, 83,

84, 85, 88, 95, 103, 108, 167

Bottom-Up Approach 142, 144, 153, 168

Built Environment 11, 14, 19, 20, 21, 26,

28, 40, 44, 65, 66, 92, 93, 94, 100, 104,

113, 114, 143, 168, 172

C

Cities 2, 7, 13, 18, 63, 69, 98, 165, 172

Cleanness 16, 25, 37, 73, 77, 84, 88, 98

Commute 92, 95, 96, 110, 112, 113, 126,

127, 138, 142

Conceptual Model 4, 40, 48, 52, 53, 54, 57,

70, 71, 77, 79, 80, 87, 104, 105, 114,

116, 132, 152, 153, 166, 167

Conceptualization 10, 11, 40, 52, 70, 77,

88, 104, 132, 144, 152

D

Design 2, 4, 21, 22, 24, 28, 34, 36, 38, 46,

47, 48, 50, 51, 52, 56, 57, 58, 59, 65, 67,

89, 140, 166, 170, 173

Domain-Specific 2, 3, 5, 14, 91, 92, 145,

168

Dutch 22, 28, 32, 64, 67, 89, 114, 138, 151,

154, 162

Dwelling 17, 18, 19, 24, 25, 30, 41, 43, 45,

46, 47, 53, 57

E

Economic 7, 13, 17, 18, 28, 41, 42, 43, 44,

59, 60, 67, 68, 70, 116, 124, 140, 145,

163, 171

222

Eindhoven 22, 28, 29, 31, 60, 172

Employee 117, 118, 125, 130, 138, 139,

171, 172

Environment 3, 10, 11, 12, 13, 14, 17, 18,

19, 20, 21, 26, 28, 40, 41, 42, 44, 65, 67,

77, 88, 92, 93, 94, 100, 103, 104, 113,

114, 117, 118, 120, 121, 122, 124, 125,

130, 132, 135, 136, 138, 140, 141, 142,

143, 163, 166, 168, 170, 172

Evaluation 9, 11, 13, 16, 17, 18, 26, 45, 47,

51, 53, 57, 64, 65, 66, 67, 68, 79, 88, 89,

96, 119, 123, 125

F

Family 3, 9, 12, 14, 16, 17, 18, 19, 22, 26,

27, 31, 34, 45, 100, 103, 105, 109, 110,

112, 113, 119, 121, 122, 124, 144, 145,

147, 148, 151, 152, 154, 155, 156, 157,

158, 159, 161, 162, 168, 169, 172, 173

Family Loss 159, 161, 162, 169

Framework 3, 4, 5, 8, 14, 15, 20, 38, 53, 62,

76, 77, 92, 144, 172

G

Gap 40, 42, 116

H

Happiness 1, 8, 9, 10, 11, 16, 17, 116, 117,

119, 123, 142, 146, 147

Health Well-Being 14, 27, 144, 145, 147,

159, 161, 162, 169

Histogram 48, 49, 71, 72, 100, 128, 129,

149, 150

Household Income 24, 28, 29, 33, 43, 54,

57, 59, 60, 67, 93, 99, 135, 141, 159,

166, 169

Housing Well-Being 3, 4, 14, 19, 21, 24,

143, 145, 148, 161, 166, 169, 170

I

Infrastructure 88, 89, 103, 108, 113

J

Job Satisfaction 5, 116, 117, 118, 119, 121,

122, 123, 124, 126, 127, 131, 134, 141,

145, 168, 171, 173

Job Well-Being 3, 19, 21, 27, 143, 159, 162,

166, 169, 171

L

Leisure 3, 12, 13, 14, 16, 17, 18, 19, 22, 27,

34, 91, 127, 144, 145, 147, 148, 151,

152, 154, 155, 158, 161, 172

Life Domains 2, 3, 4, 5, 9, 10, 11, 12, 14, 15,

19, 21, 22, 27, 34, 122, 144, 145, 146,

147, 148, 149, 152, 154, 155, 158, 159,

161, 162, 166, 168, 170, 172

Livability9, 10, 25, 37, 67, 71, 73, 76, 77, 84

Living Environment 12, 41, 163

M

Mobility 13, 41, 42, 63, 94, 126, 161

223

N

Neighborhood Satisfaction 4, 17, 18, 19,

25, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71,

72, 73, 76, 77, 79, 80, 81, 82, 83, 87, 88,

89, 95, 100, 104, 107, 108, 112, 113, 167

Netherlands 28, 69, 89, 110, 114, 138, 172

O

Objective 2, 3, 5, 7, 9, 10, 13, 16, 17, 21,

22, 47, 50, 51, 52, 53, 56, 66, 143, 147,

162, 166, 170

Ownership 24, 25, 32, 34, 35, 43, 45, 47,

50, 51, 53, 56, 57, 58, 59, 60, 93, 94,

112, 113, 166, 170

P

Parking 25, 26, 31, 34, 36, 46, 47, 48, 50,

51, 95, 96, 102, 103, 104, 108, 109, 110,

113, 167

Path Analysis 4, 5, 52, 55, 128, 131, 132,

134, 142, 152, 162, 166, 168, 169

Path Model 53, 55, 132, 133, 154, 166

Peak Hours 26, 33, 37, 103, 104, 107, 110,

112, 114, 167, 171

Perception 12, 16, 18, 40, 41, 63, 79, 94,

97, 123, 127, 135, 141, 142, 168, 170

Personality 7, 92, 96, 112, 113, 118, 136,

145, 147, 148, 154, 162

Physical Activity 19, 25, 37, 44, 69, 70, 71,

74, 77, 80, 87, 88, 89, 107, 112, 113, 167

Planning 7, 11, 64, 65, 89, 95, 165, 170

Policy 1, 2, 42, 163, 165, 170, 171

Preference 59, 122

Privacy 27, 125, 128, 130, 132, 135, 138,

139, 140, 142, 168, 171

Private Transport 96, 98, 99, 102, 103, 104,

105, 107, 108, 110, 167

Psychology 1, 10, 11, 14, 42, 62, 65, 117,

124, 132

Public Transport 17, 18, 26, 27, 89, 94, 95,

96, 97, 98, 99, 102, 103, 104, 105, 107,

108, 110, 112, 130, 132, 138, 139, 140,

167

Q

Quality Of Life 1, 8, 10, 16, 17, 39, 40, 62,

92, 116, 117, 148, 162, 165, 170

R

Regression 4, 5, 48, 49, 50, 51, 53, 70, 71,

72, 73, 76, 99, 100, 101, 102, 105, 128,

129, 130, 132, 149, 151, 152, 153, 166

Residential Satisfaction 12, 41, 42, 44, 46,

60

Residents 2, 8, 13, 17, 19, 21, 22, 24, 28,

29, 38, 40, 42, 43, 45, 46, 47, 48, 50, 52,

56, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69,

70, 71, 77, 87, 88, 89, 94, 95, 100, 105,

107, 138, 148, 149, 152, 154, 162, 163,

165, 166, 167, 170, 171

Rush Hours 93, 96, 114, 167

S

Safety Satisfaction 88, 97, 108

224

Salary Satisfaction 132, 135, 136, 141, 168

Social Satisfaction 68, 76, 77, 80, 87

Social Well-Being 144, 145, 148, 159, 161,

169

Society 8, 40, 97, 115, 147

Socio-Demographic Variables 48, 50, 52,

57, 67, 93, 99, 104, 105, 107, 112, 113,

128, 144, 145, 149, 152, 153, 154, 155,

158, 166, 168

Socio-Economic 28, 42, 43, 68

Spatial 12, 28, 41, 44, 61, 63, 67, 69, 89

Stress 16, 93, 95, 96, 97, 99, 120, 123, 124,

125, 127, 146

Subjective Well-Being 2, 3, 4, 5, 8, 14, 21,

28, 42, 92, 117, 144, 145, 146, 147, 162,

165, 172

Survey 3, 8, 18, 21, 22, 23, 24, 27, 33, 35,

38, 77, 136, 199

T

Theory 3, 9, 10, 11, 14, 16, 19, 41, 42, 45,

65, 89, 97, 118, 136, 144, 153, 168

Time Schedule 26, 70, 89

Traffic Congestion 17, 25, 26, 62, 79, 95,

96, 97, 110, 171

Transport Satisfaction 95, 102, 104, 105,

107, 108, 110, 130, 138, 139

Travel Cost 26, 93, 96, 97

Travel Mode 33, 93, 94, 95, 98, 104, 112,

113, 114, 167

Travel Satisfaction 5, 26, 91, 92, 93, 94, 95,

96, 97, 98, 99, 100, 101, 102, 104, 107,

108, 110, 112, 113, 114, 127, 148, 167

Travel Time 26, 27, 71, 83, 93, 95, 97, 110,

112, 113, 114, 127, 132, 135, 136, 138,

142, 167, 168

Trip Attributes 19, 26, 93, 96, 97, 104, 110,

113, 167

U

Unemployment 28, 145, 146, 158, 159,

161, 162, 169, 171

Urban Life Domains 3, 4, 15, 19, 21, 166,

168, 172

Urbanism 16, 65, 95

W

Walking 25, 31, 37, 68, 70, 74, 77, 79, 80,

82, 83, 85, 88, 95, 98, 103, 108, 167

Work Location 19, 27, 117, 124, 127, 128,

132, 134, 135, 136, 168

Work Satisfaction 19, 27, 38, 115, 116, 117,

118, 119, 124, 127, 128, 129, 130, 131,

132, 134, 135, 136, 138, 139, 140, 141,

142, 168, 172

Workload 27, 119, 120, 121, 122, 123,

127, 130, 132, 135, 136, 140, 141, 168

Workspace 19, 27, 38, 124, 125, 128, 130,

132, 134, 135, 136, 138, 139, 140, 141,

168

225

Summary

Modelling and Measuring Quality of Urban Life: Housing, Neighborhood,

Transport and Job

Research on Quality of Life (QOL) has a long history in social science. It tends to focus

on specific domains and how people’s satisfaction about that domain is related to their

quality of life. Considering that most of the world’s population is living in cities and that

the share of the urban population continues to increase leading to various problematic

negative externalities, it is not surprising that the concept of QOL re-emerged on the

academic agenda, also of engineering disciplines as the Quality of Urban Life (QOUL)

concept. The concept quickly found its way into main research streams and rapidly

gained momentum. Particularly in urban planning, researchers attempted to investigate

the effect of various aspects of cities on citizens’ well-being, and achieved considerable

progress in developing methodological and theoretical approaches concerning QOUL.

However, for a long time, these studies were based on aggregate data, focusing on

aspects such as crime and economic challenges without considering the role of

subjective judgments of residents. Borrowing subjective evaluation methods from social

and behavioral sciences, urban planning researchers expanded the scope and

instrumentality of this line of research. Now, it is evident that in order to understand

QOUL, it is essential to investigate how residents judge urban places.

However, despite these promising methodological advances in QOUL studies,

there are still very few studies that provide a comprehensive framework to measure

residents’ wellbeing in urban settings. The majority of studies focuses on a single urban

life domain. Although these studies have been instrumental in optimizing the

determinants of urban life, they may not reflect the extent by which various life

domains influence each other and overall QOUL. In fact, by ignoring other life domains,

the estimates of quality of life and the marginal effects of the attributes of the

considered life domain may be seriously biased, potentially leading to wrong policy and

design recommendations.

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This limitation of most prior urban planning and transportation research

motivated the present PhD study. The main aim is to develop a comprehensive model of

QOUL, expanding and integrating concepts that have been suggested over the last few

decades to predict subjective well-being of residents in urban settings. Thus, the main

objective of this dissertation is to estimate the relationships between QOUL and

satisfaction in different life domains with an explicit focus on the role of the built

environment, while controlling for other life domains.

In order to achieve these aims, an extensive literature review of the study of

QOL in different life domains was conducted. The insights from the review provided the

theoretical foundation for the development of the conceptual framework of this thesis.

As subjective well-being is a multi-dimensional concept involving many life domains, a

domain-specific approach based on bottom-up spillover theory was used to develop the

initial model. Moreover, overall QOUL is hypothesized to be a function of the person-

place-activity relationships, and thereby the main life domains were derived from the

concept. Regarding place, housing well-being and neighborhood well-being were

included in the model as both house and neighborhood are key living places. For the

movement between places, transport was included, while work, social, shopping and

leisure activities were added to the model to represent daily activities. Finally, health,

family and income were included in the model.

To operationalize the conceptual framework, data were collected through an

online questionnaire distributed among households in four neighborhoods in the city of

Eindhoven in the summer and autumn of 2016. The questionnaire included questions

about subjective well-being in the four selected domains of housing, neighborhood,

transport and job, plus questions about financial situation, health, leisure, social life,

family life and socio-demographic characteristics. Considering the influence of personal

attributes and the characteristics of the residential area on QOUL evaluations, the study

areas were chosen such as to reflect differences in socio-economic position and

differences in the characteristics of the built environment.

Different types of models are used to analyze the complex relationships

embedded in the developed theoretical framework. For each domain, considering the

nature of the dependent variable, interrelationships among various indicators are

estimated using regression analysis, path models and structural equation models.

Results indicate the main determinants of QOUL in each life domain. The estimation

results of the models lead to the following conclusions. First, in the housing well-being

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model, satisfaction with ownership, type of house and design are the most important

predictors of overall housing satisfaction. Regarding the objective characteristics of the

house, lack of safety facilities, and living in low cost rental houses significantly and

negatively influence residential satisfaction of households. Among the socio-

demographic variables, length of residency and household income influence residential

satisfaction positively, mediated through ownership.

Second, in the neighborhood well-being model, social aspects such as

neighborhood attachment and safety turn out to be more important than environmental

and physical aspects. From neighborhood services, only grocery shopping is found to be

influential. Both walking and biking conditions are found to significantly influence overall

neighborhood satisfaction, with satisfaction with walking being more important. Among

the socio-demographic variables, being single influences neighborhood satisfaction

negatively, mediated through aspects such as neighborhood attachment.

Third, in transport well-being model, traveling in peak hours is found to be the

main predictor of travel satisfaction. More specifically, rush hours avoidance has a

positive and significant effect on travel satisfaction. Moreover, mode choice directly

influences commuting satisfaction. Accessibility satisfaction is a strong predictor of

transport well-being. Considering that socio-demographic characteristics of individuals

directly influence commuting choices, which in turn influence travel satisfaction, results

show that younger people and males are more likely to travel during peak hours, and

therefore they have lower travel satisfaction compared with other groups. As a group of

employed people in our sample avoids peak hour commuting, which leads to higher

travel satisfaction, we can see the positive role of teleworking and work flexibility on

travel satisfaction.

Fourth, in the job well-being model, the results indicate a strong influence of

work conditions satisfaction on overall work satisfaction. Satisfaction with quality of

work from the work condition category is found to have a strong influence on work

satisfaction. Satisfaction with privacy in the work space and satisfaction with travel time

to work are other important factors influencing job satisfaction.

Finally, the results of estimating overall QOUL reveal interesting findings as

well. Income well-being, health well-being and social well-being are the most important

predictors of QOUL, respectively. This means that physical domains have less impact

compared to the aforementioned domains, which is in line with the findings of

psychological and happiness research. Moreover, transport well-being is the only

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domain with an indirect influence on QOUL. Considering the multiple associations

among the domains, there are many more findings related to the interrelationships

among the domains, such as the impact of transport well-being on neighborhood well-

being, or the effect of neighborhood well-being on house well-being. All in all, as the

four major urban domains that we investigated have significant influence on QOUL, we

can conclude that our results confirm the relevance of the conceptual framework

underlying this thesis.

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Curriculum Vitae

After completing her Master degree in Architecture in 2008 at Faculty of Architecture of

Mashhad University, Lida worked in architecture consultancies and firms as a landscape

designer and architect. Also, she taught some courses related to theory and practice of

architecture and urban design at different institutes. Moreover, she collaborated with

Iranian architecture magazines in writing and editing articles and was active in

organizing talks and seminars.

Later, she moved to Norway and worked as a designer and developer for three

years. Having interest in interdisciplinary research and gaining knowledge in urban

studies, she started conducting research on urban regeneration as a guest researcher at

Delft University of Technology. In October 2013, she joined the Urban Planning Group

at TU/E to pursue her PhD research and started working on the topic “Quality Of Urban

Life”. During the PhD study, she broadened her knowledge of many urban related topics

such as housing, neighborhood, transportation and job and gained vast knowledge on

theory and analysis of urban systems. She has presented her work at many conferences

and workshops such as AESOP, ERES and INGRID. Her research interests include urban

planning, quality of life and urban design.

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Documents

Modelling and measuring quality of urban life : housing, neighbourhood, transport and … · the quality of my dissertation from different angles. My appreciation also goes to the