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Travel satisfaction and subjective well-being : a behavioralmodeling perspectiveCitation for published version (APA):Gao, Y. (2018). Travel satisfaction and subjective well-being : a behavioral modeling perspective. Eindhoven:Technische Universiteit Eindhoven.

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Travel Satisfaction and Subjective Well-Being: A Behavioral Modeling Perspective

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 woensdag 11 april 2018 om 16.00 uur

door

Yanan Gao

geboren te Shaanxi, China

Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de promotiecommissie is als volgt:

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

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

2e promotor: prof.dr. Y. Wang (Chang’an University)

copromotor(en): dr. S. Rasouli

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

prof.dr. F. Witlox (Universiteit Gent)

prof.dr.ir. B. de Vries

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

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

ISBN: 978-90-386-4476-9

NUR: 955

Cover design: Feiyu Geng

Photograph: Xing Zheng

Printed by the Eindhoven University Press, Eindhoven, The Netherlands Published as issue 242 in de Bouwstenen series of the faculty of Architecture, Building and Planning of the Eindhoven University of Technology

Copyright © Y. Gao, 2018 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.

I hear and I forget, I see and I remember, I do

and I understand.

—Xun Zi

i

Acknowledgements

A long journey starts from Xi’an, an oriental city named Chang’an in ancient China, ends

in Eindhoven, a brilliant and innovative Dutch city in the west of Europe. Along the same

path with the “Ancient Silk Road”, this time we didn’t exchange silk, but we exchanged

culture and knowledge. Good things are a long time in coming. After six years of PhD

study, finally I completed the PhD thesis you are holding now, which probably was the

most challenging activity in my whole student career. Now, I am very glad that I have

chosen that long but fantastic path and came to Netherlands to share times and thoughts

with many people and to visit many places during the PhD period.

Hereby, I would like to express my thanks to people who have provided their direct

or indirect support to the completion of this doctoral dissertation. Your immense

contributions of ideas and time making my PhD research a success are highly appreciated.

First and foremost, my sincere gratitude to my supervisors Professor Harry

Timmermans and Professor Yuanqing Wang who gave me the golden opportunity to join

the Netherlands-China double degree PhD program. I am very grateful to my Dutch

supervisor, Professor Harry Timmermans from Eindhoven University of Technology, for

providing collaborations and interactions with excellent researchers working in the same

field with me. It brings me back to our first meeting in 2013, in a symposium held at

Chang’an University. The impressive lecture he gave and the inspiring communication

afterwards set my mind to continue my research under his supervision. His intelligence,

knowledge, optimistic and enthusiastic attitude towards his research has been

inspirational and motivational for me all along the way. His patient guidance provides

solid support during my research and scientific publications.

I would also like to thank my Chinese supervisor Professor Yuanqing Wang from

Chang’ an University for his continuous support during my PhD study. He encouraged me

to go abroad and to learn advanced techniques. He has the credit to open the door in my

research, to believe in me, and to give me the support from the home country which

always made me feel warm when I was studying overseas. With his support I finished

the survey design and completed the data collection, which is a very fundamental and

important aspect of this research.

ii

I am also very grateful to my co-supervisor in Eindhoven, dr. Soora Rasouli, a very

young and talented scientist. I very much appreciate her valuable work, her constructive

comments and suggestions for my PhD research. The excellent example she provided as

a successful female scientist is the role model for my future career.

I deeply appreciate the time working at the Urban Planning Group at Eindhoven

University of Technology. It was fantastic to have so many colleagues from different

countries working together. Co-workers from Netherlands, China, Iran, Indonesia, Turkey,

Greece, Italy etc., made this group international, young and enthusiastic. I would like to

thank my colleagues Aloys, Anna, Astrid, Bilin, Calvin, Dujuan, Elaheh, Elanie, Eleni, Fariya,

Feixiong, Gamze, Guangde, Iphigenia, Jia, Jianchuan, Jing, Jinhee, Lida, Linda, Nienke,

Pauline, Peng, Qi, Qing, Rainbow, Robert, Seheon, Sehnaz, Sunghoon, Tao, Tong,

Weiming, Wen, Wenshu, Widiyani, Xiaofeng, Xiaoming, Yang Ding, Yang Wang, Zahra,

Zhong for all their support. Thanks also go to Peter van der Waerden for the technical

support, and Mandy van de Sande and Marielle Kruizinga for all their help during my stay

at TUE.

I would also like to thank the graduate students Beiyan, Bo, Chao, Chenchen, Hui,

Ji, Junfeng, Junhong, Liu, Lixing, Qian, Rui, Shan, Shuo, Tengfei, Wenjie, Xiangyi, Xiaoxu,

Xing, Xinran, Xinxin, Xuan, Yanxing, Yuanyuan, Yuxiao and Zhouhao in department of

traffic engineering in Chang’an University. Your support and company make the last three

years of my PhD period in China so wonderful and memorable.

I acknowledge the China Scholarship Council for the financial support. I would also

like to thank the reviewers of my papers and the members of my PhD committee,

Professor Frank Witlox, Dr. Dick Ettema, and Professor Bauke de Vries for taking the time

and their valuable comments.

Besides, I would like to express my gratitude to my friends who bring me laughter,

joy and support in Netherlands, China and other parts of the world. I feel so lucky to

know many friends in Netherlands: Bao, Chi, Feiyu, Fenghua, Jiadun, Lei, Qinyu,

Shaoxiong, Shengnan, Shuli, Wei, Xiaoming, Xinrong, Xu, Yang, Zhijun. I am grateful for

our friendship and for all your moral support. I miss the time shared with my housemates

Adrien, Andrea, Cagil, Fabrizio, Francesca, Federica, Geoffrey, Giulio, Janos, Javier,

Jeroen, Juan, Leander, Lois, Lorenzo, Melis, Sophia, Stan, Viola, Xueting and Yves. I also

would like to thank all my friends from the Chinese dancing group and friends at SSCE

(Students Sports Centre Eindhoven). Thanks for filling my spare time with so many joyful

activities. I especially thank my wonderful friends who are the backbones of my life in

China: Menglei, Jingjing, Qin, Hao, Da, Ying, Qile, Fan, Lu, Xinwei and Bojie.

iii

My deepest gratitude goes to my beloved family in China, my dear parents, my

grandmother for their love and sacrifices not only during my PhD study, but also at every

step of my life. This dissertation is dedicated to you only for your smile when seeing this

book. I am very proud that I am the reason behind that smile. Thanks for my parents for

all the advices and support they give me to finish my work. Without you, I would never

have become the person I am today. I would like to thank my uncles and aunts, who

bring lots of happy times into my life.

高亚楠

12-2017

v

CONTENTS

Acknowledgements .................................................................................................. i

Contents ................................................................................................................ v

List of tables.......................................................................................................... ix

List of figures ........................................................................................................ xi

1 Introduction ........................................................................................................ 1

1.1 Background ....................................................................................... 1 1.2 Motivation ......................................................................................... 4 1.3 Research objectives ............................................................................ 4 1.4 Thesis outlines ................................................................................... 5

2 Concept and Literature Review ............................................................................. 7

2.1 Travel satisfaction .............................................................................. 7 2.1.1 What is travel satisfaction? ....................................................... 8 2.1.2 Measurement .......................................................................... 8 2.1.3 Causes and correlates .............................................................. 9

2.2 Subjective well-being ........................................................................ 18 2.2.1 Concept of subjective well-being ............................................. 18 2.2.2 Scales and different views of subjective well-being ................... 19 2.2.3 Relationship with travel satisfaction ........................................ 21

2.3 Theoretical framework ...................................................................... 21 2.4 Conclusions ..................................................................................... 26

vi

3 Data Collection .................................................................................................. 27

3.1 Motivation ........................................................................................ 27

3.2 Survey design .................................................................................. 27

3.2.1 Data consideration ................................................................ 28

3.2.2 Survey instrument ................................................................. 29

3.2.3 The questionnaire.................................................................. 29

3.3 Sample descriptives .......................................................................... 33

3.4 Conclusions ...................................................................................... 33

4 Objective and subjective correlates of satisfaction with daily trip stages .................. 35

4.1 Introduction ..................................................................................... 35

4.2 Correlates of travel satisfaction .......................................................... 37

4.2.1 Trip attributes ....................................................................... 37

4.2.2 Perceived service quality ........................................................ 38

4.2.3 Attitudes ............................................................................... 39

4.2.4 Mood ................................................................................... 39

4.2.5 Personality ............................................................................ 40

4.2.6 Socio-demographic characteristics ........................................... 40

4.3 Relationship between trip attributes, mood, personality traits and ratings

of satisfaction with daily trip stages .............................................................. 41

4.3.1 Key operational decisions ....................................................... 41

4.3.2 Structural hypotheses ............................................................ 43

4.3.3 Conceptual model .................................................................. 47

4.3.4 Measurements ...................................................................... 48

4.3.5 Sample recruitment and sample characteristics ........................ 50

4.3.6 Analysis and results ............................................................... 52

4.4 A reference-based model of trip stage satisfaction ............................... 57 4.4.1 Motivation ............................................................................ 57

4.4.2 Methodology ......................................................................... 58

4.4.3 Sample characteristics ........................................................... 63

4.4.4 Model formulation ................................................................. 64

4.4.5 Model estimation ................................................................... 66

4.4.6 Effects of trip attribute discrepancies and indifference tolerance 71

4.5 Conclusion ....................................................................................... 73

5 Prevalence of alternative processing rules in the formation of daily travel satisfaction in

different context ................................................................................................... 77

5.1 Introduction ..................................................................................... 77

5.2 Processing rules ............................................................................... 80

5.3 Sample characteristics....................................................................... 84

5.4 Analyzes and results ......................................................................... 86

5.4.1 Relationship between trip satisfaction and trip stage satisfaction 86

5.4.2 Relationship with daily travel satisfaction and trip satisfaction .... 91

5.5 Conclusion ....................................................................................... 96

vii

6 Linking travel satisfaction and subjective well-being .............................................. 99

6.1 Introduction ..................................................................................... 99

6.2 Structural hypotheses ..................................................................... 102

6.2.1 Travel satisfaction and subjective well-being .......................... 102

6.2.2 Personality and subjective well-being .................................... 103

6.3 Data collection and measurement scales .......................................... 104

6.3.1 Procedure ........................................................................... 104

6.3.2 Measurement ...................................................................... 105

6.3.3 Sample description .............................................................. 108

6.4 Structural equation model between travel satisfaction and subjective well-

being ...................................................................................................... 108

6.4.1 Conceptual framework ......................................................... 108

6.4.2 Validation ........................................................................... 109

6.4.3 Measurement model ............................................................ 115

6.4.4 Structural model ................................................................. 115

6.5 A latent class structural equation model of co-dependent relationships

between domain satisfaction and subjective well-being ................................ 117

6.5.1 Conceptual framework ......................................................... 118

6.5.2 Model estimation results ...................................................... 119

6.6 Discussion and conclusions .............................................................. 122

7 Conclusions and future work ............................................................................. 125

7.1 Summary ....................................................................................... 125

7.2 Contributions ................................................................................. 127

7.3 Limitations ..................................................................................... 129

7.4 Future research .............................................................................. 130

References ......................................................................................................... 133

Author Index ...................................................................................................... 149

Subject Index ..................................................................................................... 153

Summary ........................................................................................................... 155

Curriculum Vitae ................................................................................................. 159

List of Publications .............................................................................................. 160

ix

LIST OF TABLES Table 2-1 Summary of variables and applied methods in travel satisfaction research ... 11

Table 2-2 Summary of subjective well-being scales .................................................. 20

Table 2-3 Summary of the applied well-being constructs in travel behavior research ... 22

Table 3-1 Descriptive statistics of the sample (N = 1445) ......................................... 32

Table 4-1 Socio-demographic characteristics of the respondents (N = 1268) .............. 51

Table 4-2 Correlation matrix of predicting variables and outcome variables ................ 53

Table 4-3 Descriptive statistics of the variables used in the path analysis (N = 2916 trip

stages) ......................................................................................................... 54

Table 4-4 Goodness-of-fit of the model ................................................................... 54

Table 4-5 Standardized path estimates of direct, indirect and total effects.................. 56

Table 4-6 Socio-demographic characteristics of the respondents (N = 715 respondents)

.................................................................................................................... 64

Table 4-7 Regression results: trip stage satisfaction (Groups: 715; PT trip stages: 1402;

Average group size: 2) ................................................................................... 67

Table 4-8 Key points of the polynomial function ....................................................... 70

x

Table 5-1 Correlation between peak-end, summation and weighted averaging rules and

trip satisfaction .............................................................................................. 87

Table 5-2 Estimated resutls of conjunctive, disjunctive and linear processing rule for trip

satisfaction with panel effects ......................................................................... 87

Table 5-3 Estimated results of peak-end, summation and weighted averaging rules with

socio-demographic characteristics, mood, personality traits, and panel effects..... 89

Table 5-4 Estimated results of conjunctive, disjunctive and linear processing rule with

socio-demographic characteristics, mood, personality traits, and panel effects for trip

satisfaction .................................................................................................... 90

Table 5-5 Summary of Adjusted R2 for different models of trip satisfaction ................ 91

Table 5-6 Results of peak-end, summation and averaging rules for daily travel satisfaction................................................................................................................. 92

Table 5-7 Results of conjunctive, disjunctive, and linear processing rules without socio-

demographic, mood, and personality traits for daily travel satisfaction ................ 92

Table 5-8 Results of peak-end, summation and averaging rules with socio-demographics,

mood and personality traits for daily travel satisfaction ..................................... 94

Table 5-9 Results of conjunctive, disjunctive, and linear processing rule with socio-

demographic characteristics, mood and personality traits for daily travel satisfaction

.................................................................................................................... 95

Table 5-10 Summary of Adjusted R2 for different models of daily travel satisfaction .... 96

Table 6-1 Descriptive statistics of the sample (N = 1445) ....................................... 107

Table 6-2 Results of the exploratory factor analysis (N = 722) ................................ 110

Table 6-3 Results of the confirmatory factor analysis (N = 723) .............................. 111

Table 6-4 Standardized direct effect between subjective well-being variables (N=1445)

.................................................................................................................. 112

Table 6-5 Standardized direct effect of personality and socio-demographic variables on

subjective well-being (N=1445) .................................................................... 113

Table 6-6 Standardized direct effect of socio-demographic variables on personality traits

(N=1445) .................................................................................................... 114

Table 6-7 Goodness of fit measures for the structural equation model (N=1445) ...... 114

Table 6-8 Average latent class probabilities for residents’ most likely latent class

membership (Row) by latent class (Column) .................................................. 119

Table 6-9 Results of latent class structural equation model ..................................... 120

xi

LIST OF FIGURES

Figure 2-1 Theoretical Framework .......................................................................... 25

Figure 4-1 Conceptual model ................................................................................. 48

Figure 4-2 The Curve of Cubic Function .................................................................. 63

Figure 4-3The linear function of the difference between expected and experienced access

time ............................................................................................................. 70

Figure 4-4The linear function of the difference between expected and experienced waiting

time ............................................................................................................. 71

Figure 4-5 The cubic function of the difference between expected and experienced in-

vehicle time .................................................................................................. 72

Figure 4-6 The cubic function of the difference between expected and experienced egress

time ............................................................................................................. 72

Figure 5-1 Conjunctive rule .................................................................................... 83

Figure 5-2 Disjunctive rule ..................................................................................... 84

Figure 5-3 Sample characteristics (N = 1445) .......................................................... 85

Figure 6-1 Conceptual framework of SEM .............................................................. 109

Figure 6-2 Conceptual framework of LCSEM .......................................................... 119


1

1

INTRODUCTION

1.1 Background

Throughout history, the pursuit of happiness has never stopped with the development of

human civilization. Happiness has been considered to be the highest goal and ultimate

motivation for human actions. To get close to happiness through both thoughts and

behavior, first of all, we should make it clear what is happiness, and what state is

happiness. According to the experience of human beings, happiness is a subjective feeling

relative to objective happiness which depends on outward things to arouse happiness.

Over the years, philosophers are committed to explore the connotation of happiness and

tried to deeply understand the concept, expression and scope of it. In western philosophy,

the idea of happiness was originally developed by some Ancient Greek thinkers like

Epicurus and Aristotle. Early ideas equated happiness with eudaemonia and defined

happiness by external criteria such as virtue and holiness (Diener, 1984). Aristotle thought

Eudaemonia is gained leading a virtuous life which is a desirable state judged from a

particular value framework.

Chapter 1

2

In ancient China, the state of happiness is expressed by the word “乐” in most

cases. The meaning of “乐” can be summarized into two categories. First, it describes an

ideal state of happiness rather than the emotional reactions. Confucius said, “智者乐，仁

者寿。The wise are happy; the humane live long lives” (The Analects of Confucius). Zuo

Qiuming also said, “有德则乐，乐则能久。Virtue can make happiness, and this happiness

lives long” (The Commentary of Zuo). Second, it is hedonism, the word “乐” has been

used to express the hedonic or emotional feelings. For example, Confucius said, “有朋自

远方来，不亦乐乎? Is it not delightful having friends coming from faraway places?” (The

Analects of Confucius). The second meaning is similar with the happy mood, which

expresses a kind of positive emotion.

Starts from 1950s, many psychologists and social scientists have interested in the

formative studies of happiness or well-being mainly focusing on the definitions and how

to measure it (Jones, 1953; Wessman, 1957; Wilson, 1960). During the following decades,

many reviews of the history and philosophy of happiness have emerged (Chekola, 1975;

Culberson, 1977; Jones, 1953; Tatarkiewicz, 1976; Wessman, 1957; Wilson, 1960).

In the 1970s, researchers became interested in positive emotions rather than

negative emotional states, which were attracted more interests for decades, and the

theoretical and empirical work of happiness is emerging faster (Chekola, 1975; Culberson,

1977; Tatarkiewicz, 1976). The interpretation of happiness relates to multiple subjects

such as philosophy, psychology, sociology, and economics. Recent studies of happiness,

under the influence of “positive psychology” movement, are concerned happiness as an

individual and subjective state in which individuals judge their own happiness. There are

many terms used within the discipline describes self-reports on how well life is going,

including life satisfaction, hedonism, affective states, perceived desired satisfaction, a

hybrid of these accounts most commonly referred to as subjective well-being (commonly

abbreviated as SWB) (Haybron, 2000). The happiness or well-being resides within the

experience of the individual, although some objective conditions such as health and

wealth are seen as potential inflences on well-being (Campbell et al., 1976). Since the

subjectivity of well-being are recognized by the people, many researchers in the area of

subjective well-being avoid the term “happiness” because they are not same although the

terms are sometimes used synonymously.

Subjective well-being refers to how people experience the quality of their lives and

includes both affective and cognitive judgements (Diener, 1984). It is ‘a broad category

of phenomena that includes people’s emotional responses, domain satisfaction, and

global judgements of life satisfaction’ (Diener et al., 1999: p. 277). Life satisfaction


3

judgements refer to cognitive evaluations of an individual’s quality of life, called cognitive

well-being. Affective judgements refer to the moods and emotions people experience

through daily lives, called affective well-being. Relative to life satisfaction which is a global

cognitive evaluation of life, domain satisfaction refers to the evaluation of one’s

satisfaction with specific life domains, such as work, health, safety, social networks, etc.

Since the emergence of the subjective well-being studies over six decades ago,

the literature of satisfaction on other life domains has progressed rapidly especially in

social science and marketing research. One major stream is the study of customer

satisfaction. The definition, determinants and influence of customer satisfaction, which is

considered to be the key to a long-term relationship between buyers and sellers have

been studied by researchers for decades (e.g., Agnihotri et al., 2016; Anderson, 1973;

Churchill Jr and Surprenant, 1982; Fornell et al., 1996; Lin, 2007; Oliver, 1977; Parker

and Matthews, 2001; Yang and Peterson, 2004).

Although the study of customer satisfaction has a long history in fields such as

housing, tourism, psychology, business, economics and marketing (e.g., Anderson and

Sullivan, 1993; Churchill Jr and Surprenant, 1982; Fornell, 1992; Mouwen, 2015; Pitt et

al., 2014; Wirtz, 2001; Xiong et al., 2014), the concept of travel satisfaction has only

recently started to attract attention in transportation research (e.g., Abou-Zeid et al.,

2012; Aydin et al., 2015; Ettema et al., 2011; Friman et al., 2013; Stradling et al., 2007;

Susilo and Cats, 2014).

Travel satisfaction refers to people’s evaluation of transport services and their

experience during travel. The study towards travel satisfaction was put on the policy and

travel behavior research agenda since the last decade, reflecting the need to develop

improved instruments for assessing human well-being of transportation and transport

management policy. Transportation management decisions are not only evaluated in

terms of transportation objectives, but also in terms of traveler’s perception of transport

service. Therefore, understanding the causes and correlates of travel satisfaction and the

relationship between travel satisfaction and global subjective well-being is important not

only for transport companies to improve transport services, but also for governments and

policy makers to incorporate well-being into their public policies, for example to

encourage the widespread use of active and public transportation by measures which can

improve the satisfaction of public transport service (Bergstad et al., 2011; St-Louis et al.,

2014).

Chapter 1

4

1.2 Motivation

This research has been motivated by four main factors.

First, in general, developments in the subjective well-being studies and its

application to various life domains have called our attention to study this topic in

transportation field.

Second and more specifically, the strong dependence of travel behavior models

on objective factors, without systematic consideration of other subjective factors such as

mood and personality traits, motivated us to consider the effects of subjective factors on

travel satisfaction. Most prior studies have assumed that objective trip attributes influence

peoples’ travel satisfaction (Abou-Zeid, 2009; De Vos et al., 2015, 2016; Duarte et al.,

2009, 2010; Hussain et al., 2015; St-Louis et al., 2014; Susilo and Cats, 2014). Analysis

of the relationship between these objectives attributes and travel satisfaction allows

drawing conclusions with respect the relative contributions of the various attributes (trip

characteristics, vehicle attributes, etc.) to trip or travel satisfaction. The direct analysis of

satisfaction scores provides useful information about because it relates to the various

attributes of transport delivery. However, research in fields other than transportation has

suggested that subjective factors may also influence satisfaction and subjective well-

being (e.g., DeNeve and Cooper, 1998; Diener et al., 2003). It thus seems relevant to

investigate to what extent the effects of subjective factors such as personality traits and

mood bias the results of the effects of objective trip attributes on travel satisfaction.

Third, the fact that the daily trips become more complex as a result of multi-trip,

multi-modal accessible and rapid development of transport facilities, provided another

motivation for the study of the relationship between satisfactions in different context.

Forth, the studies of the relationship between domain-specific satisfaction and

subjective well-being have not considered travel satisfaction, even though travel is an

important daily consumption and experience. This motivated us to examine the

relationship between travel satisfaction and subjective well-being.

1.3 Research objectives

In light of the motivations described above, the objectives of the study are:

1. Develop our understanding of travel satisfaction and subjective well-being.

2. Investigate the corresponding effects of objective and subjective factors on

travel satisfaction


5

3. Examine the relationship between travel satisfactions in the context Multi-

trip, Multi-stage, Multi-attribute trip experiences.

4. Examine the relationship between travel satisfaction and subjective well-

being.

1.4 Thesis outlines

The thesis is organized in six succeeding chapters. Details of the chapter description are

discussed below.

Following this introductory chapter, Chapter 2 summarizes the contemporary

literature on travel satisfaction and subjective well-being. It reviews the concept,

measurement methods, and correlates of travel satisfaction in the transportation

literature. The concept, different views, measurement methods of subjective well-being

and the relationship with travel satisfaction have been viewed in this chapter. The

theoretical framework of this thesis is outlined in this chapter.

Chapter 3 will describe the data collection procedure and the sample

characteristics. A stepwise procedure of data collection is delineated starting from

questionnaire design via pilot studies to the final data collection. Descriptive statistics are

reported.

Chapter 4 studies the objective and subjective effect factors of travel satisfaction

in the context of trip stages. It describes a set of postulates about the causes and

correlates of trip stage satisfaction. It then describes the data we used in this chapter.

This is followed by the specification and estimation of a path analysis model that relates

travel satisfaction to its hypothesized correlates; and a reference-based model that relates

to examining the effects of difference between expected and experienced travel time and

cost.

Chapter 5 studies the processing rules in the formation of daily travel satisfaction

in the context multi-trip, multi-stage, multi-attribute travel experiences. It first introduces

and discusses the various procession rules that we investigate. Then it describes details

of the data collection and sample characteristics. It is followed by the discussion of the

analyzes and conclusions.

Chapter 6 studies the relationship between travel satisfaction and subjective well-

being. A structural equation models were built to examine the mutual dependency

between travel satisfaction and subjective well-being relative to satisfaction with other

life domains. Considering the heterogeneity of respondents, a latent structural equation

Chapter 1

6

model is formulated and estimates which assumes that the co-dependent relationships

between domain satisfaction and overall life evaluation vary between latent classes.

Chapter 7 concludes the thesis. It summarizes the research objectives, approach,

and findings, discusses the police implications and limitations of the research, and

presents directions for future research.


7

2

CONCEPT AND LITERATURE REVIEW

2.1 Travel satisfaction

The transportation literature has recently witnessed a rapid growth in the number of

studies on travel satisfaction in general and in the context of public transportation in

particular (e.g., de Ona et al., 2016; Susilo and Cats, 2014; Yang et al., 2015). Main

objects of previous studies are such as commuting stress (eg. Van Rooy, 2006; Wener et

al., 2005); travel liking (Ory and Mokhtarian, 2005); affect during travel (eg. Kahneman

et al., 2004); travel well-being (Abou-Zeid and Fujii, 2016); satisfaction with public

transportation or other mode (eg. Friman et al., 2001; Friman and Garling, 2001);

travelers’ evaluation of their travel (Ettema et al., 2016, De Vos and Witlox, 2016); and

customer satisfaction with public transport services /passengers’ perception of PT quality

(Mouwen, 2015). Table 2-1 summarizes studies that were published in the major journals

over the last five years. We focus on the last five years as these tend to better reflect the

state of the art in this field of inquiry.

Chapter 2

8

2.1.1 What is travel satisfaction?

According to the definition of customer satisfaction, satisfaction is a judgment that a

service provides a pleasurable level of consumption-related fulfilment. Measuring

satisfaction with travel services would entail travelers’ judgments of their satisfaction with

the whole or parts of the travel service. However, customer satisfaction is less general

than domain-specific subjective well-being and only applies to those using the service.

Therefore, if we regard travel as one of the various life domains, the travel satisfaction

has broader meaning than the satisfaction of transport services. The definition of travel

satisfaction can also be summarized in two categories similar as subjective well-being:

the cognitive part or utilitarian appraisals (Strading et al., 2007), especially for driving

conditions and pubic transport services; and the affective part like commute stress

(Gatersleben and Uzzell, 2007; Novaco and Gonzalez, 2009).

2.1.2 Measurement

Over the years, many different measurement approaches have been developed to

measure satisfaction. Both single-item and multi-item scales have been used for directly

measuring satisfaction during travel. For example, Abou-Zeid et al. (2012) used a single-

item scale to measure people’s satisfaction of their commute trip by car before and after

receiving a free transit pass and being required to take transit for at least 2-3 days.

More recently, dedicated travel scales have been developed. A typical example is

the Satisfaction with Travel Scale (STS) based on 9-items (Ettema et al., 2011, 2012).

Ettema and his colleagues (2011, 2012) designed a domain-specific scale for travel which

is based on the generic Swedish Core Affect Scale (SCAS) (Västfjäll et al., 2002; Västfjäll

and Gärling, 2007). The Satisfaciton with Travel Scale (STS) was proposed as a

combination of cognitive and affective evaluaitons. For example, it has three components:

a cognitive evaluation of the quality of travel, an affective evaluation of feelings during

travel ranging from stressed to relax and an affective evaluation of feelings during travel

ranging from bored to excited (Ettema et al., 2011). This scale was first used by Ettema

et al. (2011) and has been evaluated by others later (De Vos et al., 2015; Ettema et al.,

2012; Friman et al., 2013; Olsson et al., 2013). De Vos et al. (2015) tested the reliability

and structure of the Satisfaction with Travel Scale (STS) using data on leisure trips from

Ghent (Belgium) and concluded that the specification of a single underlying dimension for

affect rather than two offers a superior fit to the Ghent data, both for all modes combined

and for car use and cycling separately. For public transport and walking a three-

dimensional structure is more appropriate. Anthor example is the Satisfaction with Daily


9

Travel Scale (SDTS) designed by Bergstad and his colleagues (2011), based on the

Satisfaction with Life Scale (SWLS) (Diener et al., 1985; Pavot and Diener, 1993). Five

statements were used for this scale.

Trave satisfaction can be measured through satisfaction with a range of quality of

service attributes. For example, some studies measured satisfaction of mode-specific

attributes, such as accessibility, crowding, congestion and reliability of public transport,

instead of directly asking about the overall satisfaction (e.g., Lai and Chen, 2011). In this

case, overall satisfaction could be interpreted as a measure of how travelers assess the

whole package of quality of service attributes (Hensher et al., 2003).

The practice of using a single-item or a multi-item scale varies substantially across

disciplines. In housing research, for example, the vast majority of studies has typically

used a simple rating scale to evaluate a set of housing attributes. In contrast, in marketing

research, conventional wisdom is to use multi-item scales (Anderson and Gerbing, 1982;

Churchill Jr, 1979; Haws et al., 2011; Jacoby, 1978). It has led to the development and

application of many difference scales. Sometimes, one cannot escape the impression that

the quest for high reliability statistics has resulted in the use of semantically redundant

items. It led Bergkvist and Rossiter (2007, 2009) to challenge conventional wisdom on

both theoretical and empirical grounds. They argued that for concrete and singular

constructs and concrete attributes, single-item scales might be the best to use. They

reported evidence that single item scales demonstrated equally high predictive validity as

multi-item scales. Several replication studies were conducted, leading to mixed results.

Kwon and Trail (2005) found that sometimes there was no significant difference between

single and multiple-item scales, whereas in other cases the single item scales produced

better results. On the other hand, Diamantopoulos et al. (2012) concluded that multi-

item scales clearly outperformed single-item scales in terms of predictive validity.

2.1.3 Causes and correlates

2.1.3.1 Objective correlates

We develop our summary about the explanatory variables used in the studies in Table 2-

1. The most commonly used explanatory variables of travel satisfaction are trip attributes

such as travel time, travel distance and travel cost. These trip attributes have been

operationalized in different ways. Typically, the absolute value of travel time or distance

has been used. St-Louis et al. (2014), for example, developed six mode-specific models

of trip satisfaction to investigate how the determinants of commuter satisfaction differ

across modes. The results show that travel time variables are important variables

Chapter 2

10

accounting for commuter satisfaction of all transportation modes. Increased travel time

had a significant negative effect on the degree of satisfaction with all six modes. However,

comparing coefficients shows that pedestrians, cyclists, and bus users are less negatively

impacted by longer travel times than drivers, and metro and train users. Zhang et al.

(2016) presented evidence from 13 cities in China that travel time and frequency of public

transport are statistically significant variables influencing passenger satisfaction. De Vos

et al. (2016) also found that longer travel times reduce travel satisfaction while longer

travel distances increase travel satisfaction for leisure trips by car and public transit.

Besides travel time, several studies examined the effects of waiting time, transfer

time and travel time in specific travel contexts. For example, Yang et al. (2015) examined

the transfer/commute time ratio and transfer cost as determinants of travel satisfaction,

and found that this ratio has a negative effect for “Walk-Metro-Walk” users, suggesting

that longer transfer walks tend to decrease satisfaction. Higgins et al. (2017) studied the

effects of travel time on satisfaction while considering exposure to congestion and

travelers’ perception of congestion. Results indicated that increasing travel time causes a

significant increase in dissatisfaction, and that the probability of being very dissatisfied

increases with the duration of exposure to congestion conditions.

Thus, an examination of the literature suggests that most scholars treated the

concept of satisfaction very similar to the concept of utility. They estimated a functional

relationship between physical, objective attributes of transport modes and/or trips, and

uni or multi-dimensional measures of satisfaction. This approach differs from

operationalizations of the concept of satisfaction adopted in other streams of literature

such as for example service quality research. Based on satisfaction theory, service quality

research acknowledges that satisfaction for the same product may differ between people

with the same socio-demographic profile because their expectations may differ.

Satisfaction reflects the extent by which customer experiences meet their expectations.

The gap between experienced and expected trip attributes may explain travel satisfaction

better than absolute trip attribute values. In this paper, trip attributes refer to value of

travel time including access time, waiting time, in-vehicle time and egress time, and level

of travel cost. However, studies in travel behavior research tend not to include the

difference between experienced and expected trip attributes. The only exception is Carrel

et al. (2016), who included scheduled travel time, which is a somewhat similar concept

as expected time, and found that the coefficient for the difference between scheduled

and observed in-vehicle travel time is significant.


11

Table 2-1 Summary of variables and applied methods in travel satisfaction research

Study Dependent variable Independent variable Method/Model Linearity

Abenoza et al., 2017 Overall satisfaction with public transport

Quality of service attributes

14 year-specific dummy variables

5 region-specific dummy variables

Ordered logit model Nonlinear

Abou-Zeid and Fujii, 2016 Level of happiness/unhappiness

Time period Ordered logit model Nonlinear

Cao and Ettema, 2014 Travel satisfaction

Corridors

Socio-demographics

Transit use

Neighbourhood characteristics

Travel preferences

Residential preferences

Linear regression Linear

Carrel et al., 2016 Daily satisfaction with service

Baseline satisfaction ratings

Socio-demographics

Mode access

Mood

Subjective well-being

Scheduled and observed travel time


Cats et al., 2015 Overall satisfaction with public transport

Satisfaction with service quality

Frequency of using PT Ordered logit model Nonlinear

Chapter 2

12


de Ona et al., 2016 Satisfaction with transit

Perceived costs

Perceived benefits

Attractive alternatives

Behavioral intentions

Feeling towards transit

Service quality

Structural equation model Linear

De Vos et al., 2016 Satisfaction with leisure trip

Neighbourhood

Attitudes toward travel and land use

Socio-demographics

Transport mode access and

affordability

Travel distance and travel duration

Linear regression model Linear

Ettema et al., 2016 Service satisfaction

Service attributes

Travel Mode

Socio-demographics


Friman et al., 2017 Travel satisfaction Travel attributes

Socio-demographics Structural equation model Linear

Fu and Juan, 2017 Satisfaction with public transport

Perceived service quality

Subjective norm

Perceived behavioral control



13


Higgins et al., 2017 Commute satisfaction

Travel time

Congestion

Socio-demographics

Location

Multinomial logit model Nonlinear

Jaśkiewicz and Besta, 2014 Satisfaction with public transport or car

Quality of life

Place attachment

Identity scale

Correlation analyzes Linear

Mahmoud and Hine, 2016 Perception of users towards the overall bus service

Perceived quality of 29 bus indicators

Binary logistic regression model

Nonlinear

Mao et al., 2016 Travel satisfaction

Trip characteristics

Modal flexibility

Socio-demographics

Multimodal behavior

Multilevel regression model Linear

Mouwen, 2015 Measured satisfaction with public transport services

Service attributes

Socio-demographics Multiple regression model Linear

St-Louis et al., 2014 Commute satisfaction


Travel time

Socio-demographics

Travel preferences

Mode preferences


Chapter 2

14


Susilo and Cats, 2014 Travel satisfaction

Travel experience

Mood/Subjective well-being

Attitude

Past satisfaction

Socio-demographics


Survey type


Susilo et al., 2017 Travel satisfaction


Travel modes

Socio-demographics

Survey type

Location

Multivariate analyzes Linear

Wan et al., 2016a BRT rider satisfaction

Service quality

Station amenities

Satisfaction with service quality and

the ticket system

Satisfaction with the other SBS

attributes

Other attributes (e.g. Bus-only lanes)



15


Wan et al., 2016b BRT rider satisfaction

Service attributes

Frequency

Trip Purpose

Time

Weather

Socio-demographics

OLS linear regression Linear

Westman et al., 2017 Travel satisfaction Current mood

Cognitive performance Multivariate analyzes Linear

Ye and Titheridge, 2016 Satisfaction with commute

Travel mode choice

Travel attitudes

Built environment

Socio-demographics


Yang et al., 2015 Overall satisfaction of metro commuters

Socio-demographics

Journey details

Mode-specific service quality

Binary logistic regression Nonlinear

Zhang et al., 2016 Satisfaction with public transport

Public transport ownership

Contractual practices

Socio-demographics

Travel characteristics

City characteristics

Mixed logit model Nonlinear

Chapter 2

16

2.1.3.2 Subjective correlates

Satisfaction is also strongly linked with perceived service quality (Chen, 2008; Petrick,

2004). In other words, travelers who perceived good quality of public transit service are

more likely to have a higher level of satisfaction and continue to use this service. Most

studies found that service quality of public transport significantly affects customer

satisfaction, and consequently influences their behavioral intentions. Although the

selected service quality attributes differ between studies, frequency of service (e.g.,

Mouwen, 2015; Wan et al., 2016a), comfort (e.g., Mahmoud and Hine, 2016), information

(e.g., de Oña et al., 2016; Mouwen, 2015), accessibility (e.g., de Oña et al., 2016), and

reliability (e.g., Mahmoud and Hine, 2016), have been commonly examined attributes.

For example, Mouwen (2015) found that public transport users regarded on-time

performance, travel speed, and service frequency as the most important. Wan et al.

(2016a) also concluded that service quality measured in terms of frequency, on-time

performance and speed was the most important factor influencing overall satisfaction.

Cats et al. (2015) concluded that service provider responsiveness, service frequency and

reliability, and trip time were the key determinants of overall satisfaction using time series

data from Sweden. Mahmoud and Hine (2016) reported that 11 indicators had significant

impacts on user perception, including comfort of the bus, need for transfer, bus stop

location, availability of park and ride scheme, waiting and transfer time, reliability of the

service, frequency, security, bus fare, monthly discount, and information. de Oña et al.

(2016) concluded that service quality is mostly explained by comfort, accessibility, and

timeliness, while information, availability and safety also had significant effects. Lai and

Chen (2011) examined the relationship between satisfaction, service quality, perceived

value, involvement and behavioral intentions. They found that passenger behavioral

intentions rely on passenger satisfaction. Service quality has a positive effect on overall

satisfaction and behavioral intentions. However, although similar terminology has been

used in these studies, the definitions and measurement methods are different.

Therefore, in this study, a scale, which depicts traveler’s satisfaction of transit service

quality and overall satisfaction of each trip stage is adopted, and the effects of traveler’s

satisfaction of transit service quality on trip stage satisfaction are estimated.

Other explanatory variables that potentially may influence travel satisfaction are

psychological dispositions such as attitudes, personality traits and moods. Attitudes

defined as travel-related opinions may influence satisfaction because, for example, people

who are more concerned with environmental issues may be more satisfied with active

modes for their commute (Manaugh and El-Geneidy, 2013). Cao and Ettema (2014) found


17

that attitudes towards a certain travel mode influence the level of satisfaction of using

that mode. Ye and Titheridge (2016) also concluded that most travel-related attitudes

have both direct and indirect effects on commute satisfaction.

Compared to the large literature on objective correlates of travel satisfaction, the

literature on the effects of moods and emotions is very limited. Previous studies indicate

that feeling stressed affects commute satisfaction (e.g., Ory and Mokhtarian, 2005).

Abou-Zeid (2009) argued that if the respondent is surveyed on a day with bad weather,

bad day at work, etc., ratings of travel satisfaction may be lower than on an average day.

Carrel et al. (2016) measured general feelings simultaneously with satisfaction ratings

and found that respondents’ mood on a given day is significantly associated with travel

time satisfaction.

Personality traits are other important psychological dispositions that may affect

satisfaction. Although significant associations between personality traits and subjective

well-being have been reported (e.g., Costa and McCrae, 1980; Diener et al., 2003; Steel

et al., 2008; Richard and Diener, 2009), few studies have examined the effects of

personality on satisfaction in the domain of transportation.

2.1.3.3 Socio-demographic characteristics

The literature shows that several socio-demographic variables are associated with

different degrees of satisfaction (e.g., Cao and Ettema, 2014; Carrel et al., 2016; De Vos

et al., 2016; Ettema et al., 2016; Higgins et al., 2017; Susilo and Cats, 2014; Yang et al.,

2015). Susilo and Cats (2014) found younger travelers to report lower travel satisfaction

levels for their walking trips, when compared to fellow travelers. Cao and Ettema (2014)

found that older residents are more satisfied with light rail transit. De Vos et al. (2016)

also concluded that older people have more positive feelings and a more positive

evaluation of travel modes (except for bicycle trips). These results are consistent with the

literature showing that older individuals report higher levels of subjective well-being in

general (Diener and Suh, 1997). St-Louis et al. (2014) estimated the effects of age on

satisfaction, found age was significant for pedestrians, cyclists, drivers and metro users.

Yang et al. (2015) estimated the effects of socio-demographic variables on satisfaction

for different travel stages of metro commutes, found that “Walk-Metro-Walk” users

between 40 and 49 years of age are more likely satisfied with their journey.

Gender is another important socio-demographic variable that has been examined

in many studies. Results are mixed. Some studies (e.g., Ettema et al., 2016; Carrel et al.,

2016) found that gender is not significantly related to satisfaction, while other studies

Chapter 2

18

reported significant effects for gender. For example, St-Louis et al. (2014) concluded that

gender was a significant covariate of metro and pedestrian satisfaction. Likewise, Higgins

et al. (2017) found that males were more likely to be ‘very satisfied’ with their commutes

compared to females.

Besides age and gender, other socio-demographic characteristics may also

influence travel satisfaction. Country of origin was significant in a study on drivers’

satisfaction (St-Louis et al., 2014). Respondents from North America were significantly

more satisfied with their car commute than people from other countries. Similarly, Cao

and Ettema (2014) found that bike ownership leads to a lower satisfaction with light rail

transit. De Vos et al. (2016) concluded that the possession of a driving license and car(s)

has a positive effect on travel satisfaction for active travel and public transit.

Thus, in summary, although these results are mixed, and the effects of socio-

demographic variables tend to be relatively small, they cannot be ignored when assessing

travel satisfaction. It is important to note, however, that although socio-demographic

variables may be significant covariates in the model, what causes heterogeneity

satisfaction ratings are not the socio-demographic characteristics but psychological

factors which may significantly differ across segments of the sample as a function of their

socio-demographics. As an example, if senior citizens on average tend to be more satisfied

with their trip experiences, it might not be due to their age but to the fact that people of

increasing age in general tend to be more easy-going and less stressed and tend to

provide higher satisfaction ratings in general. In that sense, the study of travel satisfaction

differs from the study of activity-travel behavior. In the latter case, differences in travel

demand reflect differences in underlying needs and desires and possibly space-time

constraints. In turn, needs are related to socio-demographic variables. Such a direct

relationship between travel satisfaction and socio-demographics is much less clear, but

there may be indirect relationships with personality traits.

2.2 Subjective well-being

2.2.1 Concept of subjective well-being

The literature on subjective well-being (SWB) has been steadily increasing in psychology,

sociology and economic research since the 1970s. Diener (1984) argued that several

related terms have been used in different literatures with fuzzy and different meanings.

Diener et al. (1985) posited that SWB consists of three components, positive affect (PA)

and negative affect (NA) of immediate experiences, and a cognitive component of satis-


19

faction with life as a whole. (Diener et al., 1985). Ryan and Deci (2001) identified two

main views of subjective well-being – the hedonic and the eudaimonic view. Kahneman

et al. (1999) defined hedonic psychology as the study of “what makes experiences and

life pleasant and unpleasant”. In contrast, the eudaimonic view contends well-being is

more than pleasure attainment and pain avoidance. The eudaimonic view of well-being

focuses on meaning of life, personal growth and self-realization, and defines well-being

in terms of the degree to which a person is fully functioning. The term eudaimonic well-

being is valuable because it refers to well-being as distinct from happiness. As an

alternative to utility, subjective well-being has been proposed as a measure of individuals’

benefits in a number of domains (Kahneman et al., 1999). Subjective well-being can also

be defined in the context of specific domains, such as work life, family life, and leisure

(Schimmack, 2008). A specific form of domain-specific SWB is customer satisfaction

(Oliver, 2010) that focuses on goods or services that fulfil specific needs.

2.2.2 Scales and different views of subjective well-being

Numerous scales have been designed to measure subjective well-being. Diener et al.

(1985) differentiated between single-item measurements, such as the self-anchoring

ladder (Cantril, 1965), Gurin Scale (Gurin et al., 1960) and the Delighted-Terrible Scale

(Andrews and Withey, 1976), and multi-item scales such as the Life Satisfaction Index

(Neugarten et al., 1961) and the General Well-being Schedule (Dupuy, 1980).Some

researcher (Diener, 1984, Abou-Zeid, 2009) made a distinction between cognitive and

affective evaluation. Cognitive evaluation involves respondents rating their satisfaction.

In contrast, affective evaluation may be measured by using psychological and

physiological measures. Psychological measures are obtained by self-reports or observer

ratings. They could be single-item or multiple-item measures and they are the most

common type of well-being measures. Physiological measures, such as facial expressions,

autonomic and brain measures, are not frequently used but provide an alternative for

assessing emotions.

Different scales reflect different views on well-being. In another review of

subjective well-being, De Vos et al. (2013) articulated that measurement of hedonic well-

being consists of affective components which capture shorter-term feelings such as the

Positive and Negative Affect Scale (PANAS) (Watson et al., 1988), the Scale of Positive

and Negative Experience (SPANE) (Diener et al., 2010), the Swedish Core Affect Scale

(SCAS) (Västfjäll et al., 2002; Västfjäll and Gärling, 2007) and cognitive evaluation, which

is mostly measured by the Satisfaction with Life Scale (SWLS) (Diener et al., 1985; Pavot

and Diener, 1993).

Chapter 2

20

Table 2-2 Summary of subjective well-being scales

Study Scales

Hedonic Well-being

Watson et al., 1988 Positive and Negative Affect Scale (PANAS)

Västfjäll et al., 2002

Västfjäll and Gärling, 2007 Swedish Core Affect Scale (SCAS)

Diener et al., 2010 Scale of Positive and Negative Experience (SPANE)

Ettema et al., 2011

Ettema et al., 2012

De Vos et al., 2015

Satisfaction with Travel Scale (STS)

Diener et al., 1985

Pavot and Diener, 1993 Satisfaction with Life Scale (SWLS)

Bergstad et al., 2011 Satisfaction with Daily Travel Scale (SDTS)

International Well-Being Group, 2006

Stanley et al., 2011a, 2011b Personal Well-Being Index (PWI)

Eudaimonic Well-being

Ryff, 1989

Ryff and Singer, 2008 Psychological Well-Being Scale (PWS)

Ingersoll-Dayton et al., 2004 Personal Well-being

Waterman et al., 2010 Questionnaire for Eudaimonic Well-Being (QEWB)

Diener et al., 2010 Flourishing Scale

Ryan and Deci, 2001 Self-determination Theory

As discussed, few studies have used a eudaimonic view of well-being, which

emphasises meaning of life, personal growth and the realisation of the best in oneself

(e.g., Stanley et al., 2011a, 2011b). The best-known scale to measure eudaimonic well-

being is the Psychological Well-being Scale (PWS) (Ryff, 1989), which consists of six

dimensions. Other scales include the Questionnaire for Eudaimonic Well-Being (QEWB)

(Waterman et al., 2010) and the Flourishing Scale (Diener et al., 2010). McMahan and

Estes (2011) combined hedonic and eudaimonic aspects of well-being using the Beliefs

about Well-Being Scale. Table 2-2 summarizes different measurements of hedonic and

eudaimonic well-being.


21

2.2.3 Relationship with travel satisfaction

Only recently, the travel behavior research community has jumped on the bandwagon of

satisfaction research and started to explore the relationship between travel satisfaction

and subjective well-being (Bergstad et al., 2011; Ettema et al., 2010; Hansson et al.,

2011; Martin et al., 2014; Sirgy et al., 2011). For example, Ettema et al. (2010) built a

theoretical framework arguing that participation in activities contributes to subjective

well-being and that the positive affect associated with travel has an impact on subjective

well-being. Bergstad et al. (2011) found that the effect of satisfaction with daily travel on

affective and cognitive subjective well-being is both direct and indirect. Sirgy et al. (2011)

developed a model, which described how positive and negative affects associated with

specific experiences of a trip influence tourists’ life satisfaction. Hansson et al. (2011)

found a significant relationship between commuting and health, while Martin et al. (2014)

concluded that active travel was correlated with psychological well-being. Therefore, this

literature seems to suggest that travel satisfaction is significantly related to subjective

well-being. Table 2-3 shows a summary of the applied well-being constructs.

2.3 Theoretical framework

As stated above, travel satisfaction and subjective well-being are presumably

interconnected. In terms of measuring travel satisfaction, respondents are prompted to

express their degree of satisfaction with their daily travel experiences. Some studies have

relied on simple rating scales, others have adopted more sophisticated multi-item travel

satisfaction instruments. Most of those scales are adapted in a trip level and not consider

the satisfaction of trip stage or integration of whole day’s trips. Therefore, to provide

these overall or summary satisfaction ratings, respondents need to retrieve and re-enact

their trips from memory, recall cognitive and affective aspects associated with their

experiences, process their evaluations of all attributes of the trip influencing their

satisfaction according to some processing rule and, finally express their degree of

satisfaction on the rating scale or in terms of the multi-item satisfaction instrument.

Reflecting on the sequence of events during a multi-stage trip, they need to process their

assessment of the bus stop environment, waiting time, on-time arrival, boarding, the

friendliness of the driver, ticket price, seat availability and quality, cleanness of the

vehicles, driving style of the driver, possible incidents during the trip, the alighting process,

and this is repeated for every stage of the trip, and for every trip on the day of interest.

Chapter 2

22

Table 2-3 Summary of the applied well-being constructs in travel behavior research

Study Sample area Constructs Method/Model

Abou-Zeid et al., 2012 Switzerland

Travel satisfaction

Attitudes towards car and

public transit

Correlation analysis

Abou-Zeid and Ben-Akiva,

2011 Web-based

Commute satisfaction

Overall well-being

Work well-being

Social comparative

happiness

Commute stress and

enjoyment

Personality

Quality of work environment

SEM

Abou-Zeid and Fujii, 2016 United States

Travel satisfaction

Attitudes towards car and

public transit

Ordered logit model

Archer et al., 2013 United States Well-being

Activity pattern

Multivariate ordered

response model

Bergstad et al., 2011 Sweden

Travel satisfaction

Activity satisfaction

Affective SWB

Cognitive SWB

Weekly mood

Regression analysis

Currie et al., 2009, 2010 Australia


Transport difficulties

Social exclusion

SEM

Delbosc and Currie, 2011a Australia


Transport difficulties

Social exclusion

Factor analysis

ANOVA

Diana, 2012 Italy

Satisfaction with public

transport

Frequency of transit use

Urban context

Correlations and

correspondence

analyzes


23

Study Sample area Constructs Method/Model

Ettema et al., 2011 Sweden

Travel satisfaction

Mood

Life satisfaction

ANOVA

Ettema et al., 2012 Sweden Travel satisfaction

In-vehicle activities Regression analysis

Olsson et al., 2013 Sweden Travel satisfaction

Life satisfaction Regression analysis

Ory and Mokhtarian, 2005 United States

Travel liking

Personality

Life style

Regression analysis

Ravulaparthy et al., 2013 United States Subjective well-being

Transport mobility

Ordered probit and

multinomial logistic

regression models

Spinney et al., 2009 Canada Transport mobility benefits

Quality of life ANOVA

Stanley et al., 2011a Australia


Social exclusion

Social capital

Connection with community

Ordered

polychotomous choice

model

Stanley et al., 2011b Australia

Risk of social exclusion

Social capital

Sense of community

Psychological well-being

Travel pattern

SEM

Accordingly, the conceptual framework is concerned with the following five

concepts:

1. Trip stage satisfaction

2. Trip satisfaction

3. Daily trip satisfaction

4. Travel satisfaction as a life domain

5. Subjective well-being

Chapter 2

24

The trip stage satisfaction is defined by the satisfaction of one segment of a whole

trip if they travel by multi-modes. One stage refers to the time spent on one specific travel

mode (exclude walking) including access time (walking time), waiting time, in-vehicle

time, possible transfer time and finally egress time. The trip satisfaction is defined by the

satisfaction of an entire trip from origin to destination including all the transfers between

travel modes. The daily travel satisfaction is defined by satisfaction of all trips in a full

day. Then a long-term evaluation of travel satisfaction is defined by the satisfaction of

travel which is regarded as a specific life domain like, for example, health and job. Finally,

subjective well-being is defined by a global evaluation of overall life covers all the life

domains together. Both hedonic well-being and eudaimonic well-being were entertained

in this study.

Subjective well-being has been proposed as a measure of individuals’ benefits in

different life domains. The relationship between overall subjective well-being and domain

life satisfaction has been a pertinent topic of research in social sciences for many decades.

On the one hand, the effect of satisfaction in a specific domain on overall subjective well-

being has been typically explained based on the bottom-up spill over theory of subjective

well-being (Sirgy, 2001). This theory posits that affect related to a consumption

experience contributes to affecting satisfaction in specific life domains, which in turn

influences satisfaction with life at large (Sirgy et al., 2011). In the context of travel, the

spill over theory would imply that high travel satisfaction would contribute to high

subjective well-being. For example, when a person has a good experience of one specific

trip (e.g. travel fast, no congestion, not crowd), he/she will have a good expression on

the whole day’s trip experiences. If this person continues experiencing fast and

comfortable trips every day, he/she overall subjective well-being increases.

On the other hand, there may also be an effect of overall well-being on travel

satisfaction, which would indicate a top-down approach in the study of subjective well-

being in the sense that their overall perspective on life may affect how people feel about

specific life domains (see, for example, Diener, 1984; Headey et al., 1991). For instance,

people who have a high level of subjective well-being are likely more satisfied with their

daily travel experiences. Few studies, however, have examined this top-down relationship

using empirical data. For example, Abou-Zeid and Ben-Akiva (2011) estimated the effects

of overall well-being on commute satisfaction using a structural equations model and

found that people who have a high level of overall well-being are likely more satisfied

with their commute. However, few studies have considered travel satisfaction although

travel is an important aspect among different life domains.


25

Objective factors

Subjective factors

Well-being constructs

Socio-demographic characteristics


Travel satisfaction

Satisfaction with different life

domains

Top-down approach

Daily travel satisfaction

Trip satisfactionTrip stage

satisfaction

Bottom-up approach

Other life domains

Interdomain transfer effects

Trip attributes

Travelers’ Personality

Mood

Attitude

Perceived service quality

Processing rules

Hedonic well-being

Eudaimonic well-being

Figure 2-1 Theoretical Framework

Only since the study of travel satisfaction has appeared on the agenda of travel

behavior researchers, researchers have started to explore the relationship between travel

satisfaction and subjective well-being (Bergstad et al., 2011; Ettema et al., 2010; Hansson

et al., 2011; Martin et al., 2014; Sirgy et al., 2011). Even so, studies in this field have not

emphasized the eudaimonic view of well-being. In this study, we focus on both hedonic

and eudaimonic well-being and their relationships with travel satisfaction.

Both objective and subjective factors may have effect on travel satisfaction and

subjective well-being. One aim of this study is to estimate the contribution of those factors

to travel satisfaction and subjective well-being.

The theoretical framework of this thesis is shown in Figure 2-1.

Chapter 2

26

2.4 Conclusions

Although, the literature of satisfaction has progressed rapidly especially in social science

and marketing research, the concept of travel satisfaction has only recently started to

attract attention in transportation research. This chapter reviewed the existing literature

on subjective well-being and travel satisfaction studies. It reviews the concept,

measurement methods, and correlates of travel satisfaction in the transportation

literature. The concept, different views of well-being, measurement methods of subjective

well-being and the relationship with travel satisfaction have also been viewed in this

chapter. The insights from this systematic literature reviews provide the theoretical basis

for the empirical analyzes in this dissertation.

The summary of a literature review on the current state-of-the-art have shown

that vast majority of current studies have examined the objective factors which influence

the travl satisfaction. Thus, subjective factors need to be investigated mainly from the

perspective of how the subjective factors contribute to travel satisfaction and subjective

well-being. Another potential limitation of the state-of-the-art research on travel

satisfaction assumes that satisfaction ratings in multi-trip, multi-stage and multi-attribute

travel experiences follow simple aggregation rules. It provides another motivation for the

study of the relationship between satisfactions in different context and by other

aggregation rules. Prior studies suggest that an understanding of how travel satisfaction

contributes to overall well-being and how overall well-being influences travel satisfaction

is still an open question.

To explore these questions, it is crucial to first collect empirical data of travel

satisfaction and related information. These data can then be used to model the effects of

factors on travel satisfaction and the relationship between travel satisfaction and

subjective well-being. The following chapters describe the data collection and analytical

findings based on this concept.


27

3

DATA COLLECTION

3.1 Motivation

The main objective of this study is to analyze the influence factors of travel satisfaction

and the relationship between travel satisfaction and subjective well-being. As discussed

in Chapter 2, the five domains, viz. trip attributes, perceived service quality, psychological

disposition, travel satisfaction, subjective well-being are interrelated. To fulfil the study

objectives, we need information regarding these five domains and how they influence

each other. In order to analysis the travel satisfaction in the context of multi-trip, multi-

modal, and multi-attribute, detailed trip dairy information of respondents is required. The

best way to obtain these data is to collect primary data by design a survey.

3.2 Survey design

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

manner under separate headings.

Chapter 3

28

3.2.1 Data consideration

The data requirements for this study can be divided into six segments, viz. socio-

demographic and general information, psychological disposition, trip diary data, travel

satisfaction data, perceived service quality data, subjective well-being data. The following

socio-demographic and general information were collected:

1. Age, gender, job type, work status, education level of the respondent.

2. Home location, household income, number of household members,

household car ownership.

3. Automobile and bicycle ownership and availability, frequency of using each

mode.

4. Attitude for each mode and environment.

Psychological disposition data includes personality traits of respondents and mood

data were collected by self-reported scales.

The central and the most crucial part of the survey concerns trip diary and travel

satisfaction. In this part, respondents were asked about their travel experience of a whole

day and the satisfaction of each stage of their trips, following information were collected:

1. The general information of the experienced day including the date, the day

of the week, and the satisfaction of the air quality.

2. Every trip of this day including the activity type before and after the trip, trip

origin and destination, start and end time, number of transfers.

3. Trip attributes include travel mode, access time, waiting time, in-vehicle time,

egress time, travel cost, mode-specific attributes and the expected value of

these attributes.

4. Satisfaction and expected satisfaction of each trip stage.

5. Perceived service quality of each mode.

6. Mode change intentions.

The next part of the survey is overall satisfaction and expected satisfaction of all

the trips and well-being of all the activities conducted in the experienced day.

The final part of the survey is about respondents’ subjective well-being data

includes overall life evaluation, overall life satisfaction, domain satisfaction and



29

3.2.2 Survey instrument

As this survey is quite demanding for respondents, face-to-face interviews were used for

conducting the survey. The survey was conducted in Xi’an City of China. Xi’an is the capital

city of Shaanxi Province, located in the northwest of China with a population of almost

8.46 million people. The data used in this study were collected in January 2015 using

random sampling. The interviewers consisted of graduate and master students of

Chang’an University who were specially trained for the interviews. Before going into the

field, several versions of the questionnaire were pre-tested in a pilot survey to ensure

respondents generally understood the questions. Changes in wording and especially

explanation were made until no more critical problems remained. The questionnaire was

designed into separate parts to suppress halo effects and reduce correlations.

3.2.3 The questionnaire

The questionnaire consisted of six modules. On the first page the respondents were

informed that the general aim was to investigate people’s travel satisfaction. The

questionnaire took between 1 and 2 hours to answer. The following modules were

included in the indicated order:

First, socio-demographic and general information module. In this module,

questions about age, gender, household car ownership, household income, education and

household size frequency of mode use, and attitudes included questions about if you

concern with the environment when travelling and if you are loyal users of public transport

or car were asked.

Second, personality traits module. Six personality traits were self-rated by

respondents including being critical, self-disciplined, impatient, reserved, easy-going and

calm. Being critical relates to people characterized by careful evaluation and judgment,

with a tendency to find and call attention to negative things. A person who is self-

disciplined could control himself and behave in a particular way without needing anyone

else to tell them what to do. An impatient personality concerns people who are restless

or short of temper. A reserved personality is marked by being self-restraint and reticence.

Easy-going refers to people who are relaxed and informal in attitude or standards. Being

calm relates to people who are not agitated without losing self-possession. These six

personality traits were selected from the Ten-Item Personality Inventory (Gosling et al.,

2003). Respondents were asked to rate the extent they agree or disagree with personality

statements such as “I see myself as critical”. Responses of personality involved an 11-

point Likert scale, ranging from “strongly disagree” to “strongly agree”.

Chapter 3

30

Third, trip dairy and trip information module. Respondents were asked to rate their

mood on the day of the trip. Nine different moods derived from the Gallup World Poll and

the European Social Survey (feeling enjoyment, relaxed, worried, sad, happy, depressed,

anger, stressed and tired) were measured on an 11-point scale, ranging from 0 to 10.

Zero means they did not experience the mood “at all”, while 10 means they experienced

the mood “all the time”. respondents were invited to recall their travel on the previous

day. The leading questions and instructions required respondents to re-enact their travel

on that day. Respondents were stimulated to discriminate between trips and trip stages.

We defined the trip as the whole period between origin and destination, including the

transfers between different travel modes. The trip stage was defined as the part of the

trip without a transfer. Walking was not regarded as a transfer but as access and egress.

It means that if a respondent transfers three times for completing a trip, this trip has

three trip stages. Before completing the requested information of each trip stage,

respondents were asked to provide information about trip origin, trip destination, start

time, end time and number of transfers. Next, questions about each trip stage took them

systematically and sequentially through the various stages of each trip: access, in-vehicle

travel, possible waiting, and egress time. For each stage of the trip, they were requested

to recall trip attributes such as travel time and travel costs and report the transport mode

they used for that stage. At the same time, their expectations about trip attributes were

asked. Having reconstructed the trip stages, respondents were asked to rate their degree

of satisfaction for every trip stage on an 11-point scale, ranging from “not satisfied at all”

to “extremely satisfied”.

Fifth, travel satisfaction and activity well-being module. Respondents were

requested to rate their satisfaction with each trip and activity well-being by asking the

questions: “Taking all things together, how satisfied would you say you are with your trip”

and “Taking all things together, how happy would you say you are with your activity”.

Sixth, perceived service quality of different modes. Public transport service quality

represented by 14 items including: frequency of service, punctuality, accessibility, number

of transfers, transfer time, on board duration, ticket price, seat availability, terminal/stop

conditions, in-vehicle environment, availability of information, driving skills of drivers, feel

safe from theft/attack and available at night by using a 11-point scale from “not satisfied

at all” to “extremely satisfied”. These items were mainly adopted from Lai and Chen

(2011), and amended according to the specific service characteristics in the Chinese

context.


31

Finally, subjective well-being including life satisfaction, life evaluation, domain

satisfaction and eudaimonic well-being was measured. Four questions and five statements

were used to measure life satisfaction. The four questions are: (1) Imagine a ladder with

steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents

the best possible life for you and the bottom of the ladder represents the worst possible

life for you. On which step of the ladder would you say you personally feel you stand at

this time? (2) Overall, how satisfied with your life were you five years ago? (3) As your

best guess, overall how satisfied with your life do you expect to feel in five years’ time?

(4) Overall, to what extent do you feel the things you do in your life are worthwhile? The

five statements about life evaluation are (1) In most ways my life is close to my ideal; (2)

The conditions of my life are excellent; (3) I am extremely satisfied with my life; (4) So

far I have gotten all important things I want in life; (5) If I could live my life over, I would

change almost nothing. Values again ranged from 0 (“strongly disagree”) to 10 (“strongly

agree”). Individuals are assumed to rate their overall life satisfaction by cognitively

integrating their satisfaction ratings for various life domains. A total of fourteen questions

about satisfaction with different life domains were included in our questionnaire: standard

of living, health, achievements in life, personal relationships, safety at home, safety out

of home, relationship with neighbours, relationship with friends, relationship with

colleagues, future security, the amount of time you do the things that you like, quality of

local environment, work and general travel experience. Values ranged from 0 (“not at all

satisfied”) to 10 (“extremely satisfied”). Eudaimonic well-being was measured on the

basis of eight statements: (1) I lead a purposeful and meaningful life; (2) My social

relationships are supportive and rewarding; (3) I am engaged and interested in my daily

activities; (4) I actively contribute to the happiness and well-being of others; (5) I am

competent and capable in the activities that are important to me; (6) I am a good person

and live a good life; (7) I am optimistic about my future; (8) People respect me.

Respondents were invited to answer these questions using a ten-point scale, ranging from

0 (“strongly disagree”) to 10 (“strongly agree”).

To reduce possible correlations due to halo effects between personality traits and

mood, and between these constructs and satisfaction, the measurements of these

constructs were separated as much as possible. As indicated, first, data on personality

traits were collected, immediately after the elicitation of socio-demographic information.

Moods were measured separately before the trip diary was constructed. Ratings of

satisfactions of trip stages were solicited after retrieving all trip attributes in the trip diary.

All interviewers worked with the same format of the questionnaire.

Chapter 3

32

Table 3-1 Descriptive statistics of the sample (N = 1445)

Socio-

demographics

Category Observations Percentage

Age <18 17 1%

18-25 410 28%

25-35 672 47%

35-45 211 15%

45-55 95 7%

>=55 40 3%

Gender Male 669 46%

Female 776 54%

Education Lower than high school 89 6%

High school 303 21%

Associate degree/Bachelor 910 63%

Master 128 9%

PhD 15 1%

Household size

(person)

1 337 23%

2-3 659 46%

>3 449 31%

Household

income

(RMB/Month)

<4000 513 36%

4000-8000 566 39%

>8000 366 25%

Household car

ownership

No car 721 50%

One car 708 49%

More than one car 16 1%

Job type Full time employed (fixed schedule) 738 51%

Full time employed (flexible schedule) 304 21%

Part time employed 58 4%

Full time student 280 19%

Part time student 60 4%

Unemployed 5 0%


33

3.3 Sample descriptives

The survey was administered face to face to a random sample of 1464 respondents in

2015 in Xi’an, China. Total of 1445 respondents completed the questionnaire, represents

98.7% of the total sample. Sample characteristics are presented in Table 3-1. It

demonstrates that 47% of the sample is between 25 and 35 years of age, 28% is between

18 and 25 years old, while only 3% of the sample is older than 55. Table 3-1 also shows

that 54% of the sample is female, while 46% is male. 31% of the sample is living in

households with more than 3 persons, while 23% is living alone. Even though 73% of the

sample has a professional education, income levels are medium. More than half of the

sample (51%) is employed full time (fixed schedule).

3.4 Conclusions

In this chapter, the design and application of a survey was presented. This survey aimed

at collecting data on travelers’ satisfaction of their travel experiences and subjective well-

being. The data collection was conducted in Xi’an of China in January 2015 through a

face-to-face random sampling interview by trained interviewers and pencil-paper

questionnaires. The respondents include individuals from all over the Xi’an city. It was a

successful experiment and we obtained significantly high response rate. We believe

several factors accounted for that. First, the survey was anonymous, and we had informed

the respondents upfront about the nature, purpose of the survey, who will own and

handle the data and the demands of the survey. Second, the interviewers are trained,

and they can explain to the respondents when they meet difficulties during the interview.

We offered some reward such as small gifts to the respondents upon completion. We

conducted pilot survey and changed or excluded confused questions based on the

feedback from the pilot survey. We believe that the two rounds of pilot survey have

improved the design and response rate of the survey. For replication purposes we

recommend taking local socio-cultural issues into account.

However, there were some limitations in choice and design of the variables. For

instance, the measurement of travel satisfaction could have been altered to many other

scales focus on specific aspects instead of single-item scales. The measurement of

personality traits could include more comprehensive items. Some specific limitations were

discussed in relevant sections and summarized in the last chapter.


35

4

OBJECTIVE AND SUBJECTIVE CORRELATES

OF SATISFACTION WITH DAILY TRIP

STAGES

4.1 Introduction

Travel satisfaction refers to people’s evaluation of transport services and their experience

during travel. As an potentially important factor contributing to subjective well-being, the

significance of travel satisfaction in the assessment of overall subjective well-being or

quality of life has been examined in several studies over the last couple of years (e.g.,

Abou-Zeid, 2009; Abou-Zeid and Ben-Akiva, 2011; Abou-Zeid and Fujii, 2016; Bergstad

et al., 2011; Cantwell et al., 2009; De Vos et al., 2013; Ettema et al., 2012; Olsson et al.,

2013; Reardon and Abdallah, 2013). These and other studies have generally concluded

that travel satisfaction plays a significant, but modest role in explaining quality of life.

Travel satisfaction has also played an important role in measuring the subjective

Chapter 4

36

evaluation of transport performance. These studies are similar to the dominant stream of

satisfaction research in housing, tourism and marketing. The aim is to examine the

relationship between housing, service or product attributes and consumer satisfaction.

The findings of these studies are useful to improve transportation systems and

technologies (Abou-Zeid et al., 2008). The analysis of travel satisfaction provides

management of transport companies and planning authorities direct feedback about their

service provision. Results may signal aspects of the transportation system and service

delivery that meet customer expectations and/or management targets, and aspects that

should be improved.

Therefore, understanding the causes and correlates of travel satisfaction is

important not only for transport companies to improve transport services, but also for

governments and policy makers to incorporate well-being into their public policies, for

example to encourage the widespread use of active and public transportation (Bergstad

et al., 2011; St-Louis et al., 2014).

The analyzes reported in this chapter are primarily motivated to improve the state

of practice in the application of travel satisfaction research to assess transport systems

and delivery. Most prior studies have assumed that objective trip attributes influence

peoples’ travel satisfaction (Abou-Zeid, 2009; De Vos et al., 2016; Duarte et al., 2009;

Hussain et al., 2015; St-Louis et al., 2014; Susilo and Cats, 2014). Analysis of the

relationship between these objectives attributes and travel satisfaction allows drawing

conclusions with respect the relative contributions of the various attributes (trip

characteristics, vehicle attributes, etc.) to trip or travel satisfaction. The direct analysis of

satisfaction scores provides useful information because it relates to the various attributes

of transport delivery. To draw valid conclusions, this approach implicitly assumes that the

variation in travel satisfaction ratings is only systematically influenced by the attributes of

the trip and vehicle. However, research in fields other than transportation has suggested

that subject factors may also influence satisfaction and subjective well-being (e.g.,

DeNeve and Cooper, 1998; Diener et al., 2003). It thus seems relevant to investigate to

what extent the effects of subjective factors bias the results of the effects of objective

trip attributes on travel satisfaction.

To the best of our knowledge, previous studies have not systematically

investigated the interrelationships between travel satisfaction, trip attributes, perceived

service quality and subjective factors such as personality traits. Rather, only the

relationships between two of these concepts has caught some attention (e.g., Morris and

Guerra, 2015; Ory and Mokhtarian, 2005; Steel et al., 2008). Consequently, if travel


37

satisfaction would be strongly correlated with psychological disposition such as

personality traits and moods, the estimated effects of trip attributes on travel satisfaction

may be misleading, implying that potentially wrong conclusions may have been drawn

and inadequate policy or management decisions may have been made. It is important,

therefore, to assess the strength and nature of the effects of subjective factors on travel

satisfaction before strong conclusions on the performance of travel services can be drawn.

In this chapter, therefore, we analyze the effects of trip attributes on travel

satisfaction simultaneously considering the effects of the mood and personality in order

to examine whether there are significant direct and indirect effects from mood and

personality on travel satisfaction. A path model is estimated to analyze these effects.

4.2 Correlates of travel satisfaction

4.2.1 Trip attributes

The most commonly used explanatory variables of travel satisfaction are trip attributes

such as travel time, travel distance and travel cost. These trip attributes have been

operationalized in different ways. Typically, the absolute value of travel time or distance

has been used. St-Louis et al. (2014), for example, developed six mode-specific models

of trip satisfaction to investigate how the determinants of commuter satisfaction differ

across modes. The results show that travel time variables are important variables

accounting for commuter satisfaction of all transportation modes. Increased travel time

had a significant negative effect on the degree of satisfaction with all six modes. However,

comparing coefficients shows that pedestrians, cyclists, and bus users are less negatively

impacted by longer travel times than drivers, and metro and train users. Zhang et al.

(2016) presented evidence from 13 cities in China that travel time and frequency of public

transport are statistically significant variables influencing passenger satisfaction. De Vos

et al. (2016) also found that longer travel times reduce travel satisfaction while longer

travel distances increase travel satisfaction for leisure trips by car and public transit.

Besides travel time, several studies examined the effects of waiting time, transfer

time and travel time in specific travel contexts. For example, Yang et al. (2015) examined

the transfer/commute time ratio and transfer cost as determinants of travel satisfaction,

and found that this ratio has a negative effect for “Walk-Metro-Walk” users, suggesting

that longer transfer walks tend to decrease satisfaction. Higgins et al. (2017) studied the

effects of travel time on satisfaction while considering exposure to congestion and

travelers’ perception of congestion. Results indicated that increasing travel time causes a

Chapter 4

38

significant increase in dissatisfaction, and that the probability of being very dissatisfied

increases with the duration of exposure to congestion conditions.

Thus, an examination of the literature suggests that most scholars treated the

concept of satisfaction very similar to the concept of utility. They estimated a functional

relationship between physical, objective attributes of transport modes and/or trips, and

uni or multi-dimensional measures of satisfaction. This approach differs from

operationalizations of the concept of satisfaction adopted in other streams of literature

such as for example service quality research. Based on satisfaction theory, service quality

research acknowledges that satisfaction for the same product may differ between people

with the same socio-demographic profile because their expectations may differ.

Satisfaction reflects the extent by which customer experiences meet their expectations.

The gap between experienced and expected trip attributes may explain travel satisfaction

better than absolute trip attribute values. In this paper, trip attributes refer to value of

travel time including access time, waiting time, in-vehicle time and egress time, and level

of travel cost. However, studies in travel behavior research tend not to include the

difference between experienced and expected trip attributes. The only exception is Carrel

et al. (2016), who included scheduled travel time, which is a somewhat similar concept

as expected time, and found that the coefficient for the difference between scheduled

and observed in-vehicle travel time is significant.

4.2.2 Perceived service quality

Satisfaction is also strongly linked with perceived service quality (Chen, 2008; Petrick,

2004). In other words, travelers who perceived good quality of public transit service are

more likely to have a higher level of satisfaction and continue to use this service. Most

studies found that service quality of public transport significantly affects customer

satisfaction, and consequently influences their behavioral intentions. Although the

selected service quality attributes differ between studies, frequency of service (e.g.,

Mouwen, 2015; Wan et al., 2016a), comfort (e.g., Mahmoud and Hine, 2016), information

(e.g., de Oña et al., 2016; Mouwen, 2015), accessibility (e.g., de Oña et al., 2016), and

reliability (e.g., Mahmoud and Hine, 2016), have been commonly examined attributes.

For example, Mouwen (2015) found that public transport users regarded on-time

performance, travel speed, and service frequency as the most important. Wan et al.

(2016a) also concluded that service quality measured in terms of frequency, on-time

performance and speed was the most important factor influencing overall satisfaction.

Cats et al. (2015) concluded that service provider responsiveness, service frequency and

reliability, and trip time were the key determinants of overall satisfaction using time series


39

data from Sweden. Mahmoud and Hine (2016) reported that 11 indicators had significant

impacts on user perception, including comfort of the bus, need for transfer, bus stop

location, availability of park and ride scheme, waiting and transfer time, reliability of the

service, frequency of the service, bus fare, monthly discount, security against crimes, and information at the station. de Oña et al. (2016) concluded that service quality is

mostly explained by comfort, accessibility, and timeliness, while information, availability

and safety also had significant effects. Lai and Chen (2011) examined the relationship

between satisfaction, service quality, perceived value, involvement and behavioral

intentions. They found that passenger behavioral intentions rely on passenger

satisfaction. Service quality has a positive effect on overall satisfaction and behavioral

intentions. However, although similar terminology has been used in these studies, the

definitions and measurement methods are different. Therefore, in this study, a scale,

which depicts traveler’s satisfaction of transit service quality and overall satisfaction of

each trip stage is adopted, and the effects of traveler’s satisfaction of transit service

quality on trip stage satisfaction are estimated.

4.2.3 Attitudes

Other explanatory variables that potentially may influence travel satisfaction are

psychological dispositions such as attitudes, personality traits and moods. Attitudes

defined as travel-related opinions may influence satisfaction because, for example, people

who are more concerned with environmental issues may be more satisfied with active

modes for their commute (Manaugh and El-Geneidy, 2013). Cao and Ettema (2014) found

that attitudes towards a certain travel mode influence the level of satisfaction of using

that mode. Ye and Titheridge (2016) also concluded that most travel-related attitudes

have both direct and indirect effects on commute satisfaction.

4.2.4 Mood

Compared to the large literature on objective correlates of travel satisfaction, the

literature on the effects of moods and emotions is very limited. Previous studies indicate

that feeling stressed affects commute satisfaction (e.g., Ory and Mokhtarian, 2005).

Abou-Zeid (2009) argued that if the respondent is surveyed on a day with bad weather,

bad day at work, etc., ratings of travel satisfaction may be lower than on an average day.

Carrel et al. (2016) measured general feelings simultaneously with satisfaction ratings

and found that respondents’ mood on a given day is significantly associated with travel

time satisfaction.

Chapter 4

40

4.2.5 Personality

Personality traits are other important psychological dispositions that may affect

satisfaction. Although significant associations between personality traits and subjective

well-being have been reported (e.g., Costa and McCrae, 1980; Diener et al., 2003; Richard

and Diener, 2009; Steel et al., 2008), few studies have examined the effects of personality

on satisfaction in the domain of transportation.

4.2.6 Socio-demographic characteristics

The literature shows that several socio-demographic variables are associated with

different degrees of satisfaction (e.g., Cao and Yang et al., 2015; Carrel et al., 2016; De

Vos et al., 2016; Ettema, 2014; Ettema et al., 2016; Higgins et al., 2017; Susilo and Cats,

2014). Susilo and Cats (2014) found younger travelers to report lower travel satisfaction

levels for their walking trips, when compared to fellow travelers. Cao and Ettema (2014)

found that older residents are more satisfied with light rail transit. De Vos et al. (2016)

also concluded that older people have more positive feelings and a more positive

evaluation of travel modes (except for bicycle trips). These results are consistent with the

literature showing that older individuals report higher levels of subjective well-being in

general (Diener and Suh, 1997). St-Louis et al. (2014), estimating, the effects of age on

satisfaction, found age was significant for pedestrians, cyclists, drivers and metro users.

Yang et al. (2015), estimating the effects of socio-demographic variables on satisfaction

for different travel stages of metro commutes, found that “Walk-Metro-Walk” users

between 40 and 49 years of age are more likely satisfied with their journey.

Gender is another important socio-demographic variable that has been examined

in many studies. Results are mixed. Some studies (e.g., Ettema et al., 2016; Carrel et al.,

2016) found that gender is not significantly related to satisfaction, while other studies

reported significant effects for gender. For example, St-Louis et al. (2014) concluded that

gender was a significant covariate of metro and pedestrian satisfaction. Likewise, Higgins

et al. (2017) found that males were more likely to be ‘very satisfied’ with their commutes

compared to females.

Besides age and gender, other socio-demographic characteristics may also

influence travel satisfaction. Country of origin was significant in a study on drivers’

satisfaction (St-Louis et al., 2014). Respondents from North America were significantly

more satisfied with their car commute than people from other countries. Similarly, Cao

and Ettema (2014) found that bike ownership leads to a lower satisfaction with light rail


41

transit. De Vos et al. (2016) concluded that the possession of a driving license and car(s)

has a positive effect on travel satisfaction for active travel and public transit.

Thus, in summary, although these results are mixed and the effects of socio-

demographic variables tend to be relatively small, they cannot be ignored when assessing

travel satisfaction. It is important to note, however, that although socio-demographic

variables may be significant covariates in the model, what actually causes heterogeneity

satisfaction ratings are not the socio-demographic characteristics but psychological

factors which may significantly differ across segments of the sample as a function of their

socio-demographics. As an example and related to findings in the literature, if senior

citizens on average tend to be more satisfied with their trip experiences, it might not be

due to their age but to the fact that people of increasing age in general tend to be more

easy-going and less stressed and tend to provide higher satisfaction ratings in general.

In that sense, the study of travel satisfaction differs from the study of activity-travel

behavior. In the latter case, differences in travel demand reflect differences in underlying

needs and desires and possibly space-time constraints. In turn, needs are related to socio-

demographic variables. Such a direct relationship between travel satisfaction and socio-

demographics is much less clear, but there may be indirect relationships with personality

traits.

4.3 Relationship between trip attributes, mood, personality

traits and ratings of satisfaction with daily trip stages

4.3.1 Key operational decisions

Before explaining the measurement of the central concepts and variables, we will first

motivate the key operational decisions underlying the present study. The first key decision

concerned the issue whether to analysis travel satisfaction at the trip level or at the trip

stage level. Most previous studies of travel satisfaction have considered the trip level. The

number of studies at the trip stage level is modest. We think that the choice of stage vs.

full trip primarily depends on the larger context of the study. If the focus is on studying

travel satisfaction in relation to loyalty or mode switching behavior, then the full trip level

seems the best choice as ultimately if there is any relationship between satisfaction and

loyalty/mode switching it is the overall satisfaction that probably matters most. On the

other hand, if the larger context is to identify the contribution of trip and vehicle

characteristics to satisfaction, multi-stage trips may involve too much variability, implying

that respondents need to process their potentially varying degrees of satisfaction into a

Chapter 4

42

single overall satisfaction rating. Therefore, in this study, we collected satisfaction data

for both the trip stage and overall trip level. In this specific paper, we focus our attention

at the trip stage level. As stages tend to be more homogeneous, ceteris paribus, a more

precise relationship between trip (stage) attributes and satisfaction is expected, providing

better information to managers and policy makers.

A second key operational decision concerns the timing of the measurement of

satisfaction. Traditionally, satisfaction has been measured retrospectively. For example,

in housing satisfaction studies, people or asked about their satisfaction with certain

attributes of their house and neighborhood. It reflects the belief that housing satisfaction

does not change suddenly and represents a state of mind, which can be prompted almost

any time in a sufficiently reliable manner. Similarly, a common way of measuring tourist

satisfaction is to interview tourist at airports after they finished their trip. Likewise, airlines

and hotel nowadays send forms to their customers some time after their trip. This process

may imply that respondents have forgotten about certain events and episodes, but this

may be an advantage in the sense that their satisfaction rating will be based on their

dominant, prevailing experiences they remember. Recently, we can witness a trend to

collect data about the emotional states of individuals using wearable wristbands, clip-ons

and smart phone based sensors and questionnaires. We think that this new technology

is especially useful for short-lived spontaneous emotional states (e.g. excitement, stress),

maybe to new experiences, that are difficult to retrieve from memory. Their advantage

may be less clear when measuring satisfaction related to routine behavior and

experiences. Another potential problem of the use of smart phone based instantaneous

measurement may be that respondents do not know when they are prompted for the first

time what to expect later during the trip. If they give a high satisfaction rating, there may

not be sufficient higher rating levels left to discriminate between better experiences later

during the trip. Similarly, if their first rating is too low, they may not have sufficient options

to discriminate and rate worse episodes of the trip. On the other hand, if respondents are

asked retrospectively to provide satisfaction rating of trip stages, they already

experienced the whole trip and thus can better use the rating scale. Thus, although this

is an important issue that needs further systematic research, we decided in this study to

apply a retrospective approach in which respondents were asked to express their degree

of satisfaction with the various stages of the trips they made the day before.

A third related operational decision is the measurement of mood. Unlike

personality, mood is a personal disposition that may last relatively short, compared to

personality. Particular incidents may sudden change a person’s mood. Thus, a first option

that we considered was to invite respondents according to sampling process to express


43

their mood throughout the day at the sampled moments. Respondents could be given a

report sheet and be asked at the end of each trip stage to report their mood and their

satisfaction with this part of the trip and any other information that is deemed relevant.

Experiences with a similar approach used to collected activity-diary data, however,

suggest that a relatively large percentage of respondents forgets to provide such data

during the trip and either does not report it at all or completes the data collection at the

end of the day. Alternatively, smart phones could be used to trace respondents and

prompt them to report mood and satisfaction when a trip end stage is detected. Although

we have access to some of the most powerful imputation algorithms, still such trip stage

ends are difficult to detect, particularly when respondents continue moving and/or waiting

times are short. If we would have sufficient budget, we probably would have implemented

a combination of these experience sampling methods and retrospective sampling. It

would allow cross-validation, and data fusion and imputation. However, because the

budget was limited, and the total questionnaire already requires around one hour to

complete, we decided to use the available budget for face to face interviews, which has

the advantage that the purpose of the data collection can be explained better and that

basic quality control can be conducted during the interview.

A potential disadvantage of retrospective measurement is that it may introduce

halo effects. Because of the time between the experiences and the rating of satisfaction,

the latter is more likely based on general impressions. On the other hand, the

juxtaposition of mood and satisfaction under the alternative measurement protocol may

induce or enhance correlations. Keeping in mind the aim of this study, avoiding the latter

was found to be more important that reducing halo affects. This was another reason to

choose retrospective measurement. The measurements of mood and trip satisfaction

were separated in the questionnaire in an attempt to avoid higher correlations due to

juxtaposition. Halo effects that occur due to insufficient discrimination between attributes

were suppressed as much as possible by instructing respondents to make provide

satisfaction ratings by comparison among the different attributes (clear breaks in the

interview) and using clearly defined anchors that were emphasized by the interviewers.

4.3.2 Structural hypotheses

4.3.2.1 Trip attributes and travel satisfaction

For evaluating the validity of reported travel satisfaction, a systematic review of the

literature suggests that travel satisfaction has been typically conceptualised as a function

of trip attributes such as travel time and cost. Some research suggests that travel

Chapter 4

44

satisfaction varies among travel modes and urban areas (e.g., Susilo and Cats, 2014).

Recent studies indicate that people who use active travel modes, like walking or cycling,

experience and evaluate their trips more positively compared to people who use other

means, like public transport and cars (e.g., Ettema et al., 2011; Olsson et al., 2013;

Friman et al., 2013). A few studies have examined mode-related attributes especially for

public transport services, for example real-time traveler information (Brakewood et al.

2014), crowding (Tirachini et al., 2013), reliability (Li et al., 2010) and multi-tasking when

using public transport (Ettema et al., 2012; Rasouli and Timmermans, 2014).

Few studies focus on the number of stages or mode transfers as potentially critical

factors in the commuting experience. Daily trips including commuter and leisure trips may

have more than one stage, and thus involve transfer(s). Previous studies mainly

considered the main trip stage of multi-staged trips, therefore neglecting the effects of

complexity and transfers of a multi-stage trip on travelers’ satisfaction. Only a few studies

focused on the satisfaction of different trip stages (e.g., Susilo and Cats 2014; Suzuki et

al., 2014). Moreover, as the trip mode is a key determinant of travel satisfaction, trip

stages using different trip modes should be analyzed separately. This study is therefore

based on measurements of travel satisfaction for different trip stages and examines the

relationship between trip stage travel satisfaction and the attributes of different stages,

and the effects of personality, moods and socio-demographic variables on trip stage

satisfaction.

Hence, given trip stage attributes as determinants of trip stage satisfaction and

the previous empirical support for trip-related attributes’ effects on travel satisfaction, we

hypothesize that trip attributes of each trip stage affect travelers’ travel satisfaction at

the trip stage level.

Hypothesis 1: Trip attributes of each trip stage are related to travel satisfaction of the trip

stages.

4.3.2.2 Mood and travel satisfaction

As satisfaction can be broadly viewed as a global retrospective judgement, it is

constructed only when asked. Therefore, satisfaction ratings are influenced not only by

the objective attributes, but potentially also by respondent’s mood and memory

(Kahneman and Krueger, 2006). Mood is a generic emotional state of the respondent that

tends to last for a certain duration. A positive mood may, ceteris paribus, result in higher

satisfaction ratings than a negative mood. Schwarz and Clore (1983) found that

satisfaction judgements were influenced by moods such as enjoyment and stress. The


45

relevant mood at the time of judgement such as enjoyment, relaxation, stress, and other

moods may affect the satisfaction ratings. Compared to the large amount of literature on

the objective correlates of travel satisfaction, the literature on subjective correlates, such

as mood and emotions, is relatively small. Abou-Zeid (2009) addressed context effects

on travel satisfaction ratings, which included the effect of mood. She thought that if the

respondent is surveyed on a day with coincident situations such as bad weather, bad day

at work, heavy congestion etc., the ratings of their travel satisfaction may change.

Although research on the relationship between mood and travel satisfaction is still in its

nascent stages, preliminary results support that mood is related to travel satisfaction.

Hypothesis 2: Mood is related to travel satisfaction.

4.3.2.3 Trip attributes and mood

In general, good or bad moods may be triggered by specific incidents and events in

different life domains. Friman (2004) found that different types of critical incidents elicited

affective responses. As discussed, we hypothesise that positive or negative moods may

influence a person’s ratings of satisfaction in the same and other domains, such as travel

satisfaction. In turn, however, travel experiences may cause mood changes. For example,

people may become feeling stressful and irritated when the travel time is long. Studies

found that reducing travel time reduces stress (Wener et al., 2003). Travel mode may

also influence mood. Ettema et al. (2011) found that mood is most positive for the car.

When traveling by bus, mood deteriorates with travel time and decreases with access to

bus stops. Morris and Guerra (2015) addressed the question how emotions, such as

happiness, pain, stress, sadness and fatigue, vary during travel and by travel mode,

controlling for demographics and other individual-specific attributes. Results found that

bicyclists experience more positive emotions than car passengers and car drivers. Bus

and train riders experience the most negative emotions. Travel stress, which often occurs

during commute trips, can be caused by long waiting and/or in-vehicle times,

unpredictability, traffic congestion and other conditions (Evans et al., 2002; Koslowsky et

al., 1995; Novaco et al. 1990; Novaco and Gonzales, 2009). Thus,

Hypothesis 3: Trip attributes are related to the mood.

4.3.2.4 Personality traits and travel satisfaction

As another potentially influential variable, affecting satisfaction, personality has emerged

during the last four decades of research on subjective well-being (e.g., Costa and McCrae,

1980; Diener et al., 2003; Steel et al., 2008; Richard and Diener, 2009). Relative to moods,

Chapter 4

46

personality traits are longer lasting. In fact, the correlation between subjective well-being

and personality traits such as extraversion and neuroticism has been found stronger than

correlations with any demographic predictor (Lucas and Fujita, 2000; Steel et al., 2008;

Richard and Diener, 2009). Other studies have examined associations between

personality traits and satisfaction in domains like job and marriage and found some close

relationships (Kelly and Conley, 1987; Judge et al., 2000; Judge et al., 2002). Kelly and

Conley (1987) found personality characteristics were important predictors of both marital

stability and marital satisfaction. Judge et al. (2000) found that core self-evaluations

measured in childhood and in early adulthood were linked to job satisfaction measured in

middle adulthood. Therefore, we hypothesize that personality traits also play a role in the

context of travel satisfaction. Thus,

Hypothesis 4: Individuals’ personality traits are related to travel satisfaction.

4.3.2.5 Personality traits and mood

Although the concepts of personality and mood refer to different time horizons, there is

also some evidence that between personality and mood are correlated. Some other

studies finding evidence of this relationship include Hennessy and Wiesenthal (1997),

Matthews and Gilliland (1999), Ilies and Judge (2002), Matthews et al. (2009) and

Zajenkowski et al. (2012). For example, individuals who are impatient and get stressed

out easily are likely to get irritated by transportation stressors more quickly than others

(Hennessy and Wiesenthal, 1997). Matthews and colleagues reviewed research devoted

to the personality–mood relationship and indicated that correlation coefficients vary

across studies and range from 0.1 to 0.62 (Matthews and Gilliland, 1999; Matthews et al.,

2009). Ilies and Judge (2002) found Neuroticism and Extraversion were associated with

average levels of mood. Zajenkowski et al. (2012) found that the relationship between

personality and mood varies across situations. As we focus on the relative short-term

mood of a specific day, personality is relatively more stable than mood while personality

is regarded as a long-term characteristic. Therefore, in this study, we hypothesize that

personality traits are related to moods. Thus,

Hypothesis 5: Individuals’ personality traits are related to moods.

4.3.2.6 Socio-demographic characteristics and travel satisfaction

The literature shows that socio-demographic variables are associated with satisfaction.

For example, Rittichainuwat et al. (2002) found a significant difference in travel


47

satisfaction of “environment and safety” between male and female travelers: male

respondents had higher satisfaction levels than female respondents. Furthermore, single

and married travelers showed significant differences in travel satisfaction. Married

travelers were more satisfied than single travelers. Regarding travelers’ age groups, they

found significant differences in travel satisfaction of “environment and safety” for different

age groups. Perović et al. (2012) conducted an empirical study about the effects of socio-

demographic characteristics on tourist travel behavior and satisfaction during their stay.

Results show that wage level was positively and significantly correlated with satisfaction.

Bergstad et al. (2011) found that socio-demographic variables accounted for 2% of the

variance in travel satisfaction. Results also indicated that older people have higher travel

satisfaction, whereas travel satisfaction tends to be lower for households with children at

home than for households with no children at home. Although the effects of socio-

demographic variables are relatively small, they cannot be ignored when assessing the

variance in travel satisfaction. In addition, there are correlations between mood and socio-

demographic variables. For example, Cameron (1975) found persons of higher

socioeconomic status reported more happy moods than those of lower status. Similarly,

we hypothesize that personality traits vary among people with different personal profile

(age, gender, education etc.). Thus,

Hypothesis 6: Individuals’ socio-demographics are related to their travel satisfaction of

trip stages.

Hypothesis 7: Individuals’ socio-demographics are related to their moods.

Hypothesis 8: Individuals’ socio-demographics are related to their personality traits.

4.3.3 Conceptual model

Based on the hypotheses formulated in the previous paragraphs, our conceptual model

is built as shown in Figure 4-1. Recalling that the aim of our study is to examine the

effects of personality traits and mood on travel satisfaction rating, we add moods and

personality to the commonly used framework, which explains travel satisfaction as a

function of trip attributes and socio-demographic variables. Thus, we hypothesize that

personality traits and moods affect ratings of satisfaction for different stages of a trip.

Further, we allow for the possibility that trip attributes may also influence moods. Finally,

we assume that individuals’ socio-demographic variables may be correlated with mood,

personality and travel satisfaction.

Chapter 4

48

Figure 4-1 Conceptual model

4.3.4 Measurements

4.3.4.1 Travel satisfaction

Respondents were asked to rate their degree of satisfaction for every trip stage on an 11-

point scale, ranging from “not satisfied at all” to “extremely satisfied”. Note that the use

of the 11 point scale is not very common is satisfaction research as most studies use 5 or

7 point scales. However, as respondents have the tendency to avoid extreme levels of

the scale, very few levels remain to discriminate between different degrees of satisfaction.

Although this may arbitrarily increase the strength of correlations, it also means that less

data points are available to identify and/or test the nature of the relationships between

the relevant constructs and trip satisfaction. Although it would also be relevant to analyze

overall satisfaction with the full trip, such focus would imply that respondents have to

trade-off the different aspects and experiences and express this in a single rating. To

allow for more variability in the ratings, in this paper we focus on trip stages.


49

4.3.4.2 Trip attributes

Trip attributes included general trip attributes for each trip stage such as travel mode

(car, public transport, motorbike, bike or taxi), travel time (including access time, waiting

time, in-vehicle time and egress time), travel cost in general, cost by different modes and

mode-specific attributes. For walking, we collected data about walking conditions (on the

street, on the side walk, in a pedestrian street or others), walking safety at night, and

walking safety when crossing the street. For car users, car parking conditions (at home,

on street free, on street paid, parking garage free, parking garage paid or others), parking

time (less than one minute, 1-5 minutes or more than five minutes), and number of

passengers excluding driver (0, 1, 2 or more than 2) were measured. For public transport

users, seat availability (not crowded and seated, not crowded and not seated, crowded

and seated, crowded and not seated) and in-vehicle activities (working/studying, reading

for leisure, listening to music/radio, using internet, sleeping/resting, email/SMS/phone

call, gaming, talking to others, window gazing/people watching) were measured in this

study.

4.3.4.3 Mood

As we want to capture the effect of mood on people’s travel satisfaction, mood was

measured by inviting respondents to respond to nine items, representing different moods

including feeling enjoyment, relaxed, worried, sad, happy, depressed, anger, stressed

and tired. These mood questions were derived from the Gallup World Poll and the

European Social Survey. The questions ask respondents to express their moods on the

day for which satisfaction was measured on an 11-point scale, ranging from 0 to 10. Zero

means they did not experience the emotion “at all”, while 10 means they experienced the

emotion “all of the time”. Note that according to this scale, the perseverance of the mood

is measured. Alternatively, one could measure the frequency of different changes in mood.

However, this alternative was not chosen because moods are more difficult to recall,

especially if they lasted for short moments, and because retrospective measurements by

their very nature are more focused on dominant depositions.

4.3.4.4 Personality

For the measurement of personality traits, researchers have relied on a variety of

psychometric scales including the Ten-Item Personality Inventory (TIPI), which is a short

instrument based on the Big-Five personality domains (Gosling, et al., 2003). On the basis

of reliability and convergence tests, this 10-item measure of the Big Five dimensions is

suitable for this study in the situations when fewer measures are needed, or researchers

Chapter 4

50

can tolerate the somewhat diminished psychometric properties associated with very brief

measures. For each item, we systematically assessed whether we could reason the

relationship between the personality trait and trip satisfaction. Ultimately, we selected six

out of the ten-items and rewording some of them into Chinese for easy understanding:

critical, self-discipline, impatient, reserved, easy-going and calm. We assumed that these

personality traits might systematically affect people’s experience of travel and/or their

use of the measurement scale. Respondents were asked to rate the extent they agree or

disagree with the personality statements such as “I see myself as critical.” Responses of

personality involved an 11-point Likert scale, ranging from “disagree strongly” to “agree

strongly”. Higher scores reflect higher levels of the relevant personality dimension. Thus,

for reasons similar to the measurement of satisfaction, single-item scales were used to

measure different personality traits. While this approach is clearly problematic in

diagnostic, psychometric studies, we argue this approach offers sufficiently robust

information to analyze to effect of self-reported personality traits on travel satisfaction.


As socio-demographic variables are important explanatory variables of travel satisfaction,

mood and personality traits, we collected the data with respect to the following socio-

demographic characteristics of respondents: age (year), gender, household size (three

classes ranging from one person to more than three persons), household income (three

classes ranging from lower than 4000 RMB to more than 8000 RMB per month), education

(five classes ranging from lower than high school to doctoral degree) and household car

ownership (three classes ranging from no car to more than one car).

4.3.5 Sample recruitment and sample characteristics

Data used in this study were collected in the city of Xi’an, China in 2015 from a random

sample using a face-to-face paper-and-pencil survey. This survey concerned information

about all trips and trip stages on a specific day, including trip attributes (e.g., travel mode,

travel time and travel cost) and travel satisfaction. In order to examine the relationship

between travelers’ mood, personality and travel satisfaction, socio-demographic

characteristics, mood on that specific day and respondents’ personality traits were also

measured in the survey. Total of 1464 respondents participated in the study. Since

missing values and outliers may affect the results of the path model it is important to

detect them. Finally, satisfaction ratings from 1268 respondents of 2916 trip stages were

used in the analysis after data cleaning.


51

Table 4-1 Socio-demographic characteristics of the respondents (N = 1268)

Socio-demographics Category Value Percentages

Age <18 17 1%

18 - 24 385 30%

25 - 34 587 46%

35 - 44 166 13%

45 - 54 83 7%

>=55 30 2%

Gender Female 705 56%

Male 563 44%


High school 273 22%


Master 106 8%

PhD 11 1%

Household size 1 person 314 25%

2 - 3 persons 555 44%

More than 3 persons 399 31%

Household income

(RMB/month)

Less than 4000 478 38%

4000 – 8000 494 39%

More than 8000 296 23%

Household car

ownership

No car 675 53%

One car 525 41%


Chapter 4

52

Table 4-1 gives an overview of the socio-demographic characteristics of the

sample. It shows that 56% of the sample is female, while 44% is male. As for age, 46%

of the sample is between 25 and 34 years old and 30% is between 18 and 24 years old.

The youngest and oldest age categories have few observations. More than half of the

sample does not have a car in their household. Even though 72% of the sample has a

professional education, the prevailing income levels were medium (4000-8000 RMB) and

low (less than 4000 RMB). As for household size, 25% of the respondents is single. 44%

of the sample is living in 2 or 3-person households.

4.3.6 Analysis and results

Based on the conceptual model framework, explained in section 4.3.3, and considering

the nature of the measured constructs as explained in section 4.3.4, a path analysis model

was estimated to analyze the relationship between mood, personality traits, socio-

demographic variables, trip attributes and travel satisfaction of each trip stage. Before

estimating the model, however, we calculated the correlation between all variables to

check any problems of multicollinearity. Results of correlation matrix of predicting

variables and outcome variables are shown in Table 4-2. It shows that multicollinearity is

not an issue in the current data. Table 4-3 shows the descriptive statistics including mean,

standard deviation, Skewness and Kurtosis of the variables, which were finally used in

the path analysis model.

Next, the path model was estimated using MPLUS, a flexible co-variance based

latent variable modeling program (Muthén and Muthén, 2015). First, we built the model

based on the stipulated conceptual framework using all observed variables. As our

conceptual model does not say anything about the correlation of the error terms, we used

the modification index to decide on the inclusion of correlated errors. In particular,

correlated error between moods and personality traits was added to the model. As

discussed, for both mood and personality, we included a number of correlated errors

between different moods and personality traits. Path coefficients for moods and

personality traits that were not significant were deleted from the final model. The

estimated results of the final path model are shown in Table 4-5. The various statistics

such as Chi-square, degree of freedom. RMSEA, CFI, TLI and SRMR suggest a good model

fit, see Table 4-4. All path coefficients of the final model shown in Table 4-5 are nonzero

and significant at the 95% confidence level.


53

Table 4-2 Correlation matrix of predicting variables and outcome variables

Variables Satisfaction Relax Happy Anger Critical Self-

disciplined Reserved

Easy-

going PT Taxi

Waiting

time

Walking safety at

night

Cost of

PT Age Gender

Satisfaction --

Relax 0.186** --

Happy 0.140** 0.443** --

Anger -0.095** -0.141** 0.013 --

Critical 0.050** 0.117** 0.069** 0.123** --

Self-disciplined 0.031 0.191** 0.110** -0.002 0.386** --

Reserved 0.000 0.137** 0.130** 0.091** 0.296** 0.304** --

Easy-going 0.065** 0.156** 0.150** 0.048** 0.029 0.103** -0.005 --

Public transport -0.192** -0.087** -0.066** -0.022 0.001 -0.007 0.001 -0.024 --

Taxi -0.135** -0.056** -0.037* -0.058** -0.042* -0.060** -0.069** 0.005 0.734** --

Waiting time -0.213** -0.092** -0.053** 0.052** 0.049** 0.001 0.016 -0.013 0.463** 0.410** --

Walking safety

at night 0.067** -0.023 -0.008 0.011 0.001 -0.011 -0.058** 0.009 0.173** 0.008 0.045* --

Cost of PT -0.082** -0.058** -0.057** 0.019 0.002 -0.030 -0.008 -0.021 0.544** 0.283** 0.203** 0.133** --

Age 0.087** 0.074** 0.010 -0.031 -0.016 0.155** 0.157** -0.144** -0.136** -0.149** -0.101** -0.001 -0.108** --

Gender 0.006 -0.029 -0.013 0.060** 0.079** 0.096** -0.046* 0.053** -0.030 -0.027 -0.026 0.086** -0.005 0.016 --

Chapter 4

54

Table 4-3 Descriptive statistics of the variables used in the path analysis (N = 2916 trip stages)

Mean

Std. Deviation

Skewness Kurtosis

Trip stage satisfaction 6.81 1.987 -0.588 0.331

Mood

Happy 6.26 3.204 7.699 180.909

Relax 6.02 2.404 -0.413 -0.292

Anger 2.03 2.602 1.276 0.596

Personality

Critical 3.51 2.686 0.300 -0.858

Self-disciplined 5.75 2.421 -0.434 -0.311

Reserved 5.24 2.469 -0.248 -0.374

Easy-going 6.15 2.232 -0.373 -0.041

Socio-demographics

Age 29.54 9.240 1.561 2.797

Gender (1 if Male, -1 otherwise)

-0.12

0.248 -1.940

Table 4-4 Goodness-of-fit of the model

Goodness of Fit Indices Value

Degrees of freedom 65

Chi-square 161.333

Chi-square / degrees of freedom 2.482

Root mean square error of approximation (RMSEA) 0.023

CFI 0.981

TLI 0.962

Standardized root mean square residual (SRMR) 0.018

Results provide support for all hypotheses shown in Figure 4-1. All path estimates

in the final model are statistically significant (p < 0.05). Table 4-5 show the standardized

path estimates of direct, indirect and total effects. Results show that trip attributes of

each trip stage including public transport, waiting time, and walking safety at night

significantly affect travel satisfaction. The results show that using public transport

negatively affects travel satisfaction, supporting H1. People who predominantly use public


55

transport are less satisfied than users of other transport means, suggesting that service

quality of public transport needs to be improved. Long waiting times lead to lower travel

satisfaction, which is an expected result. Moreover, walking safety at night positively

affects travel satisfaction. This indicates that safety when walking at night is an important

indicator of higher travel satisfaction.

Three moods including feeling happy, relaxed and anger have significant effects

on travel satisfaction. As we can see from the coefficients, positive moods like feeling

relaxed and happy will lead to higher ratings of travel satisfaction while negative moods

like anger will lead lower ratings. The magnitudes of the three coefficients are almost the

same. These results support our hypothesis that mood affects the ratings of travel

satisfaction (H2).

Trip attributes also affect mood as expected (H3). In other words, trip attributes

have both direct and indirect effects on travel satisfaction. This indicates that trip

attributes may be important determinants of a day’s mood. Results indicate that waiting

time has a negative effect on being relaxed. Results also show that cost by public ransport

negatively affects the mood of feeling happy. This suggests that reducing the waiting

time and cost will be an effective way to improve travel satisfaction.

Personality traits have been shown to have direct effect on travel satisfaction, thus

supporting H4. One interesting result is the positive influence of a critical personality score

on travel satisfaction. Generally, a person who is critical is sensitive to changes in their

daily life and always gives negative opinions if something doesn’t go perfectly. But our

study shows that more critical people may give higher ratings of travel satisfaction. This

may indicate that thinking more clearly and critically may reduce stress and anxiety and

make a person more positive when expectations are met. Another personality trait, which

affects travel satisfaction, is being easy-going. An easy-going personality also affects

travel satisfaction positively, indicating that people who are not easily worried or angered

end to give high ratings of travel satisfaction.

Beside the direct effect, results show that personality does affect travel satisfaction

indirectly, through mood, which in turn influences travel satisfaction. Results about the

effect of personality on mood show that being critical, self-disciplined, reserved and easy-

going positively affects the mood of feeling relaxed. Being self-disciplined, reserved and

easy-going has a significant positive effect on happy mood. Being critical, reserved and

easy-going positively affects the mood of anger. These results support our hypothesis

that personality affects mood (H5).

Chapter 4

56

Table 4-5 Standardized path estimates of direct, indirect and total effects

Dependent variables

Paths Direct effect

Indirect effects

Total effects

Travel satisfaction

Happy → Travel satisfaction 0.074 0.074

Relaxed → Travel satisfaction 0.097 0.097

Anger → Travel satisfaction -0.084 -0.084

Critical → Travel satisfaction 0.049 -0.003 0.046

Easy-going → Travel satisfaction 0.044 0.022 0.066

Public transport → Travel satisfaction

-0.128

-0.128

Taxi → Travel satisfaction

0.007 0.007

Waiting time → Travel satisfaction -0.134 -0.014 -0.148

Walking safety at night → Travel satisfaction

0.090

0.090

Age → Travel satisfaction 0.062 0.005 0.067

Gender → Travel satisfaction -0.004 -0.004

Self-disciplined → Travel satisfaction

0.015 0.015

Reserved → Travel satisfaction 0.011 0.011

Cost by public transport → Travel satisfaction

-0.003 -0.003

Happy Self-disciplined → Happy 0.062 0.062

Reserved → Happy 0.112 0.112

Easy-going → Happy 0.143 0.143

Cost by public transport → Happy -0.047 -0.047

Age → Happy

0.009 0.009

Relaxed Critical → Relaxed 0.050 0.050

Self-disciplined → Relaxed 0.112 0.112

Reserved → Relaxed 0.081 0.081

Easy-going → Relaxed 0.153 0.153

Waiting time → Relaxed -0.071

-0.071

Age → Relaxed 0.056 0.010 0.066

Gender → Relaxed

0.004 0.004

Anger Critical → Anger 0.091 0.091

Reserved → Anger 0.060 0.060

Easy-going → Anger 0.043 0.043

Taxi → Anger -0.082 -0.082

Waiting time → Anger 0.082 0.082

Gender → Anger 0.045 0.007 0.052

Age → Anger -- 0.004 0.004

Critical Gender → Critical 0.077 0.077

Self-disciplined

Age → Self-disciplined 0.167 0.167

Reserved Age → Reserved 0.164 0.164

Easy-going Age → Easy-going -0.141 -0.141


57

Results also indicate that socio-demographic variables such as age and gender

affect travel satisfaction, personality and mood, thus supporting H6, H7 and H8. The

results of effects of age positively affect travel satisfaction ratings. Like Ory and

Mokhtarian (2005), we found that older people feel more satisfied with their travel

experience than young people. Older people more likely feel relaxed and being self-

disciplined than young people. Younger people are more easy-going than old people.

Gender (male) has a positive effect on being critical and the mood of anger. It indicates

that males are more likely to being critical and angry than females.

4.4 A reference-based model of trip stage satisfaction

4.4.1 Motivation

Despite extensive research on consumer satisfaction over the past decades in many

disciplines, the study of travel satisfaction has only recently attracted interest in

transportation research. Although the number of studies has rapidly increased recently,

this line of research is still in its infancy, mostly focusing on the development of

measurement methods and the identification of correlates of travel satisfaction (e.g.,

Cantwell et al., 2009; St-Louis et al., 2014; Susilo and Cats, 2014; De Vos et al., 2015;

Abou-Zeid and Fujii, 2016; Ettema et al., 2016; De Vos et al., 2016; Abenoza et al., 2017).

Based on satisfaction theory (Miller, 1977; Churchill Jr and Suprenant, 1982; Berry

and Parasuraman, 2004), satisfaction is defined as a psychological state that reflects the

evaluation of the relationship between customer expectations and product/service

delivery. The key notion is thus that individuals evaluate choice alternatives by comparing

experienced attributes against a set of reference points, defining their expectations. If

satisfaction is assumed to be a function of the discrepancy between expectations and

experienced attributes, any measurement and analysis of satisfaction should include some

function of the discrepancy between the experienced attributes and an individual’s

expectations. This approach is in contrast with common practice in travel behavior

research, which is almost invariantly based on linear regression and structural equations

analysis using the absolute values of the experienced attributes as explanatory variables.

Expectations are usually ignored.

In this study, we assume that trip satisfaction systematically varies in a non-linear

way with the discrepancy between expectations and actually experienced attributes of

the trip. Also, we assume an indifference tolerance around the difference between

Chapter 4

58

expectations and experienced attributes, reflecting the contention that small deviations

from expectations will have a minor effect on satisfaction.

We argue that although direct questioning of trip satisfaction triggers responses

that reflect the mapping of experienced trip attributes on some satisfaction scale, the

expressed satisfaction rating may also be influenced by attitudes, moods and personality

traits of the respondent at the time of measurement. Few studies in travel behavior

research do not filter out or control for the fact that respondents may differ in terms of

their assessment of experiences in general: some people are more critical than others, or

more disciplined or more/less patient. Ignoring such psychological dispositions implies

that any differences in satisfaction ratings due to personality traits and mood are wrongly

attributed to differences in trip or vehicle characteristics and/or the effects of socio-

demographic variables.

Thus, in contributing to the rapidly increasing literature on travel satisfaction, the

aim of this study is to develop a reference-based model of travel satisfaction that takes

attitudes, moods, personality traits and indifference tolerance into account. The

suggested model allows testing whether travelers feel indifferent within a certain range

of differences between the expected and experienced attribute level. Polynomial

expressions of differences between expected and experienced attributes are used to find

the best representation of the relationship between satisfaction and the discrepancy

between expected and experienced trip attributes.

4.4.2 Methodology

The method of analysis that has been used in prior travel satisfaction research depends

on the assumed functional relationship between the selected explanatory variables and

satisfaction, and on the nature of the dependent variable: the measurement of

satisfaction. Prior research has tended to be strongly empirically driven; the functional

form chosen was at the liberty of the researcher and was not logically deduced to capture

a particular theoretical construct or representation of a cognitive decision process.

Dominant are the studies that measured satisfaction on an interval scale (or assumed it

was) and hypothesized that satisfaction was linearly related to the selected explanatory

variables. These studies have typically used linear regression models (e.g., Cao and

Ettema, 2014; De Vos et al., 2016; Wan et al., 2016b), in case of single relationships or

structural equation modeling (e.g., de Ona et al., 2016; Ye and Titheridge, 2016) or path

analysis in case of estimating direct and indirect effects between satisfaction and the

selected explanatory variables


59

Other studies did not measure satisfaction on a scale with (assumed) interval

properties, but rather measured satisfaction on an ordinal scale. To do justice to the

ordinal scale, these studies have used discrete choice models to estimate the relationship

between attributes and satisfaction. For example, binary logistic regression (Mahmoud

and Hine, 2016; Yang et al., 2015), and ordered logit models (Abenoza et al., 2017; Cats

et al., 2015; Ettema et al., 2016) have been used in these cases.

Thus, in conclusion, it has been assumed in most studies that satisfaction is a

linear function of trip attributes possibly controlling for socio-demographic variables. A

smaller number of studies assumed that the satisfaction ratings were measured on an

ordinal scale and therefore methods such as ordered logit models. The latter models show

an S-shaped form between the sum of explanatory variables (which thus is linear) and

ordered categories of satisfaction. The former category of models is fundamentally linear

in nature. Virtually all these studies in travel behavior research conceptualised satisfaction

to reflect discrepancies between traveler expectations and experiences. Consequently,

the estimated model did not contain a reference point. While a linear model may be useful

when satisfaction is directly estimated as a function of attribute levels, a linear

specification seems more difficult to defend when a reference point is assumed or when

the model of satisfaction should be congruent with satisfaction theory. Thus, it is

worthwhile to examine whether nonlinear satisfaction models better replicate

observations of travel satisfaction.

Our literature review has revealed that traditionally travel satisfaction has been

measured as a linear function of attribute levels or intensities, such as travel time, waiting

time and travel costs. On the other hand, as commonly assumed in the consumer behavior

literature, satisfaction may be conceptualised to reflect the evaluation of the relationship

between customer expectations and service delivery. If a traveler is facing a delay, but is

also expecting a delay, his travel time satisfaction may be less negatively affected

compared to the situation that the delay is not expected.

Regardless of the decision to view satisfaction as a function of objective attribute

values or as a function of the discrepancy between objective attribute values and

expectations, theoretical motivations should drive the specification of the satisfaction

function. The dominant linear specification assumes that the change in satisfaction due

to a unit change in an attribute value is constant across the full attribute range. It may

be more realistic to assume that the negative impact on satisfaction becomes

disproportionally larger with increasing attribute values in an undesired direction.

Chapter 4

60

The inclusion of expectations complicates matters. Expectations serve as a

reference point. The curvature of the function may be symmetric around the reference

point, indicating that the same departure from the two sides of the reference point has

an identical impact on the degree of satisfaction. This may be an unrealistic assumption

because a worse than expected experienced attribute value may have a larger negative

effect than the positive effect of a better than expected attribute level. Accounting for

such an effect requires the specification of an asymmetric function. Kano (1984)

differentiated between attractive versus must be attributes. Attractive attributes for which

the experience surpasses expectations trigger satisfaction. The bigger the positive

surprise, disproportionally more satisfaction may be derived. In contrast, in case of must

be attributes, the increase in satisfaction may be increasingly smaller until the expected

value is reached. The use of a reference point also brings in the concept of consumer

tolerance. Are the marginal effects of discrepancies from the expected attribute level

monotonically decreasing or increasing according to a single underlying curve or are

travelers tolerant about small differences with the reference point, indicated by small

changes in satisfaction (asymptotically equal to zero)?

In this study, we hypothesize that satisfaction is a non-linear function of the

difference between the experienced attribute level and expectation. If the experienced

attribute level outperforms the expected attribute level, satisfaction increases non-

proportionally to the difference. Similarly, if the experienced attribute level underperforms

the expected value, satisfaction decreases non-proportionally to the differences.

Moreover, we hypothesize that individuals may tolerate small deviations from the

expected attribute value.

Elaborating Finn (2011), we use polynomials to estimate the satisfaction function.

The estimated parameters indicate whether theoretical hypotheses are supported. More

specifically, we estimate a cubic polynomial function that can flexibly accommodate all

mentioned behavioral mechanisms. Moreover, we assume that beyond socio-

demographics, attitudes, moods, service quality and personality traits may systematically

shift the satisfaction function. Thus, the cubic polynomial function of satisfaction was

specified as:

𝑆𝑖𝑛 = 𝐶𝑖 + ∑ (𝛼𝑘(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘)3 + 𝛽𝑘(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘)2 + 𝛾𝑘(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘))𝐾𝑘=1 +

∑ 휃𝑑𝐷𝑑𝑛𝐷𝑑=1 + ∑ 𝜓𝑚𝑀𝑚𝑛

𝑀𝑚=1 + ∑ 𝜔𝑝𝑃𝑝𝑛 + ∑ 𝜆𝑎𝐴𝑎𝑛

𝐴𝑎=1

𝑃𝑝=1 + ∑ 𝜑𝑞𝑄𝑞𝑛

𝑄𝑞=1 + 𝜖𝑖𝑛 4.1


61

where,

𝑆𝑖𝑛 is the degree of satisfaction of individual n for choice alternative i;

C is a constant;

𝐸(𝑋𝑖𝑘) is the individual’s expectation of the value of attribute k for choice alternative i;

𝑋𝑖𝑘 is the experienced value of attribute k for choice alternative i;

𝐷𝑑𝑛 describes individual n on socio-demographic variable d;

𝑀𝑚𝑛 describes individual n on mood m;

𝑃𝑝𝑛 describes individual n on personality trait p;

𝐴𝑎𝑛 describes individual n on attitude variable a;

𝑄𝑞𝑛 is the score of individual n on service quality variable q;

𝛼, 𝛽, 𝛾, 휃, 𝜓, 𝜔, 𝜆, 𝜑 are parameters to be estimated;

𝜖 is a random disturbance term assumed to be normally distributed with zero mean and

standard deviation 𝜎.

To demonstrate the potential of the cubic function, note it may have 1, 2 or 3

distinctive roots. For our discussion, the case of three distinctive roots suffices to

demonstrate the flexibility of the suggested model. The interval between two critical

points can be viewed as the attribute range in which individuals tolerate the difference

between their expectation and experience. Taking the first derivative of equation 4.1

gives the critical points. Thus,

∂𝑆𝑖𝑛

∂(𝐸(𝑋𝑖𝑘)−𝑋𝑖𝑘)= 0 4.2

3𝛼𝑘(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘)2 + 2𝛽𝑘(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘) + 𝛾𝑘 = 0 4.3

(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘) = −2𝛽𝑘±√4𝛽𝑘

2−12𝛼𝑘𝛾𝑘

6𝛼𝑘 4.4

It implies that people are indifferent between

Chapter 4

62

(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘) = −2𝛽𝑘+√4𝛽𝑘


6𝛼𝑘 4.5

and

(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘) = −2𝛽𝑘−√4𝛽𝑘


6𝛼𝑘 4.6

This property allows the function to capture any indifference tolerance within a

certain continuous attribute range (if such a phenomenon exits) without a priori assuming

or explicitly estimating a certain threshold. Obviously, the interpretation of the results in

terms of tolerance requires a judgement beyond the mathematical result in the sense

that one needs to judge whether the difference in satisfaction is small enough,

considering the application domain, to interpret it as a tolerance.

Another property of the polynomial allows capturing a change in the satisfaction

function from convexity to concavity. This property can be investigated by determining

the inflection point and the sign of the second derivative at any point. In order to derive

the inflection point, the second derivative needs to be equal to zero. That is,

∂2𝑆𝑖𝑛

∂(𝐸(𝑋𝑖𝑘)−𝑋𝑖𝑘)= 0 4.7

6𝛼𝑘(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘) + 2𝛽𝑘 = 0 4.8

(𝐸(𝑋𝑖𝑘) − 𝑋𝑖𝑘) =−𝛽𝑘

3𝛼𝑘 4.9

Figure 4-2 shows the curve of this case, with the critical points and inflection point.


63

Figure 4-2 The Curve of Cubic Function

4.4.3 Sample characteristics

The survey was conducted in the city of Xi’an, China in 2015 using random sampling.

Total of 1464 respondents participated in the study. In this paper, the trip stages involving

public transport were used for analysis. Outliers were removed as they may affect the

results of the polynomial estimation. Finally, satisfaction ratings of 1402 trip stages

involving public transport from 715 respondents were used in the analysis.

Table 4-6 gives an overview of the socio-demographic characteristics of the

sample. It shows that the percentage females in the sample is slightly higher than the

percentage males. This overrepresentation may be due to the fact that women were more

willing to complete the questionnaire, and/or they use public transport more often than

males do. As for age, almost half of the sample consists of respondents between 25 and

34 of age. The percentage mid-aged respondents and elderly is relatively small. One

reason may be that the survey was conducted in the evening, when a disproportionally

higher number of young people are traveling, while elderly like staying at home in the

evening. Close to 60 per cent does not have a car and thus depends on public transport.

Another 37% only has one car. Because the share of 2-3 persons household is 46%, the

sample includes a substantial share of car-deficient households. In fact, cross-tabulation

shows these households represent 25% per cent of the sample. Close to 20 per cent of

the sample has an income larger than 8000 RMB/month.

Chapter 4

64

Table 4-6 Socio-demographic characteristics of the respondents (N = 715 respondents)

Socio-demographic characteristics Sample Percentages

Age

<18 7 1%

18 - 24 229 32%

25 - 34 346 48%

35 - 44 81 11%

45 - 54 41 6%

>=55 11 2%

Gender

Female 406 57%

Male 309 43%

Household size

1 person 180 25%

2 - 3 persons 326 46%

More than 3 persons 209 29%

Household income (RMB/month)

Less than 4000 289 40%

4000 – 8000 284 40%

More than 8000 142 20%

Household car ownership

No car 428 60%

One car 263 37%


4.4.4 Model formulation

Combining the general principles which we discussed and the specific data that we

collected, we estimated three regression models with random effects to account for the

fact that one respondent may have provided several satisfaction ratings. First, we

estimated a linear regression model (Model 1) with access time, waiting time, in vehicle

time, egress time and travel cost for a trip stage, plus socio-demographic variables (Eq.


65

10) as explanatory variables. This model is the benchmark model in the sense that it best

represents a common approach in transportation research. Second, we estimated the

same regression model (Model 2) but added mood, personality traits, attitudes and

perceived service quality (Eq. 4.11). Third, we estimated a regression model (Model 3)

using the cubic polynomial function of the differences between expected and experienced

values for access time, in vehicle time and egress time, a linear function of the difference

between expected and experienced values for waiting time, a categorical variable of travel

costs for a trip stage, plus a series of socio-demographic characteristics, attitudes, service

quality, moods and personality traits (Eq. 4.12). After removing some non-significant

variables, the satisfaction functions were specified as follows, where D is the difference

between expectation and experience:

4.10

𝑆𝑖𝑛𝑡 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛿T𝑎𝑐𝑐𝑒𝑠𝑠,𝑖𝑡 + 휁T𝑤𝑎𝑖𝑡𝑖𝑛𝑔,𝑖𝑡 + 𝜇T𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒,𝑖𝑡 + 휂T𝑒𝑔𝑟𝑒𝑠𝑠,𝑖𝑡 + 𝜏𝐶𝑜𝑠𝑡𝑖𝑡 +

𝜃𝑑𝐷emographic𝑛𝑡 +휀𝑖𝑡 + 𝑢𝑖

𝑆𝑖𝑛𝑡 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛿T𝑎𝑐𝑐𝑒𝑠𝑠,𝑖𝑡 + 휁T𝑤𝑎𝑖𝑡𝑖𝑛𝑔,𝑖𝑡 + 𝜇T𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒,𝑖𝑡 + 휂T𝑒𝑔𝑟𝑒𝑠𝑠,𝑖𝑡 + 𝜏𝐶𝑜𝑠𝑡𝑖𝑡 +

휃𝑑𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑛𝑡 + 𝜓𝑚𝑀𝑜𝑜𝑑𝑛𝑡 + 𝜔𝑝𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑛𝑡 + 𝜆𝑎𝐴𝑡𝑡𝑖𝑡𝑢𝑑𝑒𝑛𝑡 + 𝜑𝑞𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑛𝑡 +

휀𝑖𝑡 + 𝑢𝑖 4.11

𝑆𝑖𝑛𝑡 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛼1 𝐷𝑎𝑐𝑐𝑒𝑠𝑠,𝑖𝑡3 + 𝛽1𝐷𝑎𝑐𝑐𝑒𝑠𝑠,𝑖𝑡

2 + 𝛾1𝐷𝑎𝑐𝑐𝑒𝑠𝑠,𝑖𝑡 + 𝛼2 𝐷𝑤𝑎𝑖𝑡𝑖𝑛𝑔,𝑖𝑡3 +

𝛽2𝐷𝑤𝑎𝑖𝑡𝑖𝑛𝑔,𝑖𝑡2 + 𝛾2𝐷𝑤𝑎𝑖𝑡𝑖𝑛𝑔,𝑖𝑡 + 𝛼3𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒,𝑖𝑡

3 + 𝛽3𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒,𝑖𝑡2 + 𝛾3𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒,𝑖𝑡 +

𝛼4𝐷𝑒𝑔𝑟𝑒𝑠𝑠,𝑖𝑡3 + 𝛽4𝐷𝑒𝑔𝑟𝑒𝑠𝑠,𝑖𝑡

2 + 𝛾4𝐷𝑒𝑔𝑟𝑒𝑠𝑠,𝑖𝑡 + 𝜏𝐶𝑜𝑠𝑡𝑖𝑡 + 휃𝑑𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑛𝑡 + 𝜓𝑚𝑀𝑜𝑜𝑑𝑛𝑡 +

𝜔𝑝𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑛𝑡 + 𝜆𝑎𝐴𝑡𝑡𝑖𝑡𝑢𝑑𝑒𝑛𝑡 + 𝜑𝑞𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑛𝑡 + 휀𝑖𝑡 + 𝑢𝑖 4.12

where,

Daccess,it is the difference between expected and experienced access time for trip stage t

of individual i;

Dwaiting,it is the differences between expected and experienced waiting time for trip stage

t of individual i;

Din-vehicle,it is the differences between expected and experienced in-vehicle time for trip

stage t of individual i;

Degress,it is the differences between expected and experienced egress time for trip stage t

of individual i;

Chapter 4

66

Costit is the experienced cost for trip stage t of individual i;

αk is the coefficient of cubic term of attribute differences;

βk is the coefficient of square term of attribute differences;

γk is the coefficient of single term of attribute differences;

𝜏 is the coefficient of travel cost;

휃𝑑 is the coefficient of socio-demographic variables;

𝜓𝑚 is the coefficient of mood;

𝜔𝑝 is the coefficient of personality traits;

𝜆𝑎 is the coefficient of attitude;

𝜑𝑞 is the coefficient of service quality.

Random effect versions of the models were estimated.

4.4.5 Model estimation

Table 4-7 shows the estimated coefficients of the regression models. According to the

goodness of fit index, the r-squared of model 3 is highest, followed by model 2. Model 1

has the lowest r-squared. It implies that the proposed reference-based polynomial model

using differences between expectations and experiences is better than the commonly

applied linear regression, only using experienced values. It also shows that adding

attitudes, perceived service quality, moods and personality traits improves the goodness-

of-fit of the model.

Results of model 3 show that the difference between expected and experienced

waiting time is significant at the 0.01 level (γ2 = 0.04637, p = 0.0000). The cubic,

quadratic and single term of the difference between expected and experienced in-vehicle

time are significant (α3 = 0.00008, p = 0.1332; β3 = 0.00407, p = 0.1451; γ3 = 0.07144,

p = 0.0078). It implies that people are more sensitive to deviations from their

expectations for waiting time and in-vehicle time than access time and egress time when

they rate their satisfaction levels. Travel cost is significant at the 0.01 level (τ = 0.17806,

p = 0.0059). As the cost of public transport stage in Xi’an reflects differences in comfort

of public transport services, satisfaction is higher when travel costs are higher.


67

Table 4-7 Regression results: trip stage satisfaction (Groups: 715; PT trip stages: 1402; Average group size: 2)

Model 1 Model 2 Model 3

Variable Estimate P Estimate P-

value Estimate P

Constant C 6.83895*** 0.0000 3.75917*** 0.0000 3.72196*** 0.0000

Trip attributes

Daccess3 α1 -- -- -- -- 0.00064 0.3928

Daccess2 β1 -- -- -- -- 0.00457 0.7539

Daccess γ1 -- -- -- -- -0.02252 0.7369

Dwaiting γ2 -- -- -- -- 0.04637*** 0.0000

Din-vehicle3 α3 -- -- -- -- 0.00008* 0.1332

Din-vehicle2 β3 -- -- -- -- 0.00407* 0.1451

Din-vehicle γ3 -- -- -- -- 0.07144*** 0.0078

Degress3 α4 -- -- -- -- 0.00009 0.5352

Degress2 β4 -- -- -- -- 0.00160 0.5688

Degress γ4 -- -- -- -- 0.02044 0.3792

Experienced

access time δ -0.00340 0.7038 0.00049 0.9560 -- --

Experienced

waiting time ζ -0.04358*** 0.0000 -0.03846*** 0.0000 -- --

Experienced

in-vehicle

time

μ -0.00746** 0.0434 -0.00851** 0.0187 -- --

Experienced

egress time η -0.00340 0.7040 -0.00188 0.8304 -- --

Experienced

cost τ 0.18911*** 0.0056 0.19574*** 0.0036 0.17806*** 0.0059

Social-demographic variables

Age 휃1 0.00979 0.2270 0.00953 0.2203 0.00880 0.2563

Gender 휃2 0.09684 0.1576 0.10318 0.1060 0.11125* 0.0804

Household

size1 휃3 -0.05849 0.6110 -0.08009 0.4526 -0.05630 0.5967

Household

size2 휃4 -0.03536 0.7121 -0.09803 0.2674 -0.10398 0.2388

#Household

car (no car) 휃5 0.13445 0.3510 0.25090* 0.0727 0.27071* 0.0514

#Household

car (one car) 휃6 0.12929 0.3811 0.23941* 0.0780 0.28869** 0.0331

Household

income 휃7 -0.10728 0.3038 0.01296 0.8943 0.04103 0.6735

Chapter 4

68



value Estimate P

Constant C 6.83895*** 0.0000 3.75917*** 0.0000 3.72196*** 0.0000

Household

income 휃8 0.08834 0.3528 0.09641 0.2714 0.08997 0.3035

Mood

Enjoyment 𝜓1 -- -- 0.08023** 0.0296 0.08154** 0.0266

Relax 𝜓2 -- -- -0.00267 0.9421 -0.00173 0.9622

Worry 𝜓3 -- -- 0.02656 0.5137 0.01351 0.7395

Sad 𝜓4 -- -- -0.02848 0.4899 -0.01916 0.6415

Happy 𝜓5 -- -- 0.05547* 0.0769 0.05570* 0.0755

Depress 𝜓6 -- -- 0.04624 0.3008 0.06330 0.1569

Anger 𝜓7 -- -- -0.09848** 0.0108 -0.08874** 0.0212

Stress 𝜓8 -- -- -0.01328 0.6947 -0.01825 0.5887

Tired 𝜓9 -- -- 0.02841 0.3216 0.02718 0.3410

Personality

Critical 𝜔1 -- -- 0.03047 0.2640 0.02749 0.3124

Self-

disciplined 𝜔2 -- -- -0.08110** 0.0117 -0.08330*** 0.0094

Impatient 𝜔3 -- -- -0.00222 0.9374 -0.00895 0.7507

Reserved 𝜔4 -- -- -0.00743 0.7926 0.00350 0.9012

Easy-going 𝜔5 -- -- 0.05430* 0.0734 0.05997** 0.0474

Calm 𝜔6 -- -- 0.04690 0.1911 0.04259 0.2341

Attitude

Environment

concern 𝜆1 -- -- -0.01060 0.8802 -0.00362 0.9589

Loyal user of

public

transport

𝜆2 -- -- 0.15108* 0.0597 0.15967** 0.0458

Loyal user of

car 𝜆3 -- -- 0.12534 0.1463 0.12682 0.1412

Satisfaction of service quality

Frequency 𝜑1 -- -- 0.06669 0.1362 0.05862 0.1902

Punctuality 𝜑2 -- -- 0.02379 0.5868 0.02723 0.5317

Accessibility 𝜑3 -- -- -0.04525 0.2745 -0.05481 0.1868

Number of

transfer 𝜑4 -- -- -0.03253 0.4521 -0.04251 0.3240

Transfer

time 𝜑5 -- -- 0.01191 0.7973 0.02482 0.5911


69



value Estimate P

Constant C 6.83895*** 0.0000 3.75917*** 0.0000 3.72196*** 0.0000

In-vehicle

time 𝜑6 -- -- 0.02952 0.4754 0.00681 0.8692

Price 𝜑7 -- -- 0.06347** 0.0492 0.06797** 0.0346

Seat

availability 𝜑8 -- -- 0.16603*** 0.0000 0.15616*** 0.0000

Station

environment 𝜑9 -- -- -0.04096 0.3152 -0.02570 0.5274

In-vehicle

environment 𝜑10 -- -- 0.09625** 0.0220 0.09808** 0.0191

Information 𝜑11 -- -- -0.07793* 0.0518 -0.06516 0.1034

Driver skill 𝜑12 -- -- 0.02496 0.5066 0.01264 0.7358

Safety 𝜑13 -- -- 0.04298 0.2139 0.04390 0.2039

Night service 𝜑14 -- -- -0.01251 0.6894 -0.00877 0.7785

Random effects model: v(i, t) = e(i, t) + u(i)

Var[e] 1.15635 1.21087 1.14652

Var[u] 2.49671 1.89505 1.88737

Corr[v(i, t),

v(i, s)] 0.68346 0.60762 0.62210

Sum of

Squares 5089.95732 4200.63663 4139.07658

R2 0.04054 0.20751 0.21916

Adjusted R2 0.03129 0.18168 0.19009

Note: ***, **, * ==> Significance at 1%, 5%, 10% level.

Moreover, we estimated the effects of socio-demographic variables, attitudes,

perceived service quality, mood and personality traits. In model 3, results show that

gender effects are significant at the 0.1 level (휃2 = 0.11125, p = 0.0804). It implies that

the satisfaction ratings for public transport trip stages of men are higher than those of

women. Households’ car ownership has significant effects on trip stage satisfaction. The

satisfaction ratings of people who live in households owning less than two cars are

substantially higher than the average. Feeling enjoyment, happy mood and anger mood

have significant effects on trip stage satisfaction. Coefficients show that feeling enjoyment

and happy have positive influences on trip stage satisfaction (𝜓1 = 0.08154, p = 0.0266;

𝜓5 = 0.05570, p = 0.0755). Anger has a negative influence on trip stage satisfaction (𝜓7

= -0.08874, p =0.0212).

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70

Table 4-8 Key points of the polynomial function

Root D1 Root D2 Root D3 Critical point D4 Critical point D5 Inflection point D6

Function: 𝑺 = 𝟎. 𝟎𝟎𝟎𝟔𝟒𝑫𝒂𝒄𝒄𝒆𝒔𝒔𝟑 + 𝟎. 𝟎𝟎𝟒𝟓𝟕𝑫𝒂𝒄𝒄𝒆𝒔𝒔

𝟐 − 𝟎. 𝟎𝟐𝟐𝟓𝟐𝑫𝒂𝒄𝒄𝒆𝒔𝒔 + 𝟑. 𝟕𝟐𝟏𝟗𝟔

-21.4370 +

0.0000i

7.1482 +

14.8388i

7.1482 -

14.8388i -6.5509 1.7905 -2.3802

Function: 𝑺 = −𝟎. 𝟎𝟎𝟎𝟎𝟖𝑫𝒊𝒏−𝒗𝒆𝒉𝒊𝒄𝒍𝒆𝟑 + 𝟎. 𝟎𝟎𝟒𝟎𝟕𝑫𝒊𝒏−𝒗𝒆𝒉𝒊𝒄𝒍𝒆

𝟐 + 𝟎. 𝟎𝟕𝟏𝟒𝟒𝑫𝒊𝒏−𝒗𝒆𝒉𝒊𝒄𝒍𝒆 + 𝟑. 𝟕𝟐𝟏𝟗𝟔

-51.1862 +

0.0000i

0.1556 +

30.1480i

0.1556 -

30.1480i -16.9583 + 3.1752i -16.9583 - 3.1752i -16.9583

Function: 𝑺 = 𝟎. 𝟎𝟎𝟎𝟎𝟗𝑫𝒆𝒈𝒓𝒆𝒔𝒔𝟑 + 𝟎. 𝟎𝟎𝟏𝟔𝟎𝑫𝒆𝒈𝒓𝒆𝒔𝒔

𝟐 + 𝟎. 𝟎𝟐𝟎𝟒𝟒𝑫𝒆𝒈𝒓𝒆𝒔𝒔 + 𝟑. 𝟕𝟐𝟏𝟗𝟔

-39.0642 +

0.0000i

10.6432 +

30.7468i

10.6432 -

30.7468i -5.9259 + 6.3708i -5.9259 - 6.3708i -5.9259

Figure 4-3The linear function of the difference between expected and experienced access time

Personality traits are also shown to affect trip stage satisfaction. The self-

disciplined personality trait has a negative effect on trip stage satisfaction (𝜔2 = -0.08330,

p = 0.0094). The Easy-going personality trait has a positive effect on trip stage

satisfaction (𝜔5 = 0.05997, p = 0.0474). Being loyal to using public transport has a

positive effect on trip stage satisfaction (𝜆2= 0.15967, p = 0.0458). Results show that

service quality variables including price, seat availability, and in-vehicle environment have

positive effects on trip stage satisfaction (𝜑7 = 0.06797, p = 0.0346; 𝜑8 = 0.15616, p =

0.0000; 𝜑7 = 0.09808, p = 0.0191).

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

-20 -15 -10 -5 0 5

S

Daccess = Expected access time - Experienced access timeD4 D6

S = 0.00064𝐷𝑒𝑔𝑟𝑒𝑠𝑠^3 + 0.00457𝐷𝑒𝑔𝑟𝑒𝑠𝑠^2 - 0..02252𝐷𝑒𝑔𝑟𝑒𝑠𝑠 + 3.72196


71

Figure 4-4The linear function of the difference between expected and experienced waiting time

4.4.6 Effects of trip attribute discrepancies and indifference tolerance

Based on the estimated coefficients, we draw the graphs of the satisfaction function to

show the effects of the discrepancy between expected and experienced trip attributes

access time, waiting time, in-vehicle time, and egress time on trip stage satisfaction

(Figure 4.2 – Figure 4.5). Waiting time is linear, the others are all cubic functions. In order

to better understand the properties of the estimated cubic function and formally interpret

the function, the three roots, critical points and inflection point were derived (Table 4-8).

Figure 4-3 graphs the estimated cubic function for access time. The cubic function

has the form S = 0.00064*𝐷𝑎𝑐𝑐𝑒𝑠𝑠^3 + 0.00457*𝐷𝑎𝑐𝑐𝑒𝑠𝑠^2 + 0.02252*𝐷𝑎𝑐𝑐𝑒𝑠𝑠 +

3.72196. The first derivative of the estimated equation gives us two critical points (D4 =

-6.55, D5 = 1.79). The second derivative of the estimated function gives the inflection

point (D6 = -2.38). According to Figure 4-3, the curvature of this function changes slowly,

implying that satisfaction ratings are not very sensitive to the difference between

expected and experienced access time. This may be interpreted as that respondents are

indifferent if the experienced access time is less than 6.55 minutes longer than the

expected access time. When the gap between experienced access time and expectation

is longer than 6.55 minutes, the satisfaction decreases rapidly.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

-35 -30 -25 -20 -15 -10 -5 0 5

S

Dwaiting = Expected waiting time - Experienced waiting time

S = 0.04637𝐷waiting + 3.72196

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72

Figure 4-5 The cubic function of the difference between expected and experienced in-vehicle time

Figure 4-6 The cubic function of the difference between expected and experienced egress time

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

-50 -40 -30 -20 -10 0 10

S

Din-vehicle = Expected in-vehicle time - Experienced in-vehicle time

D6

S = 0.00008 𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒^3 + 0.00407𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒^2 + 0.07144𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒 + 3.72196

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

-25 -20 -15 -10 -5 0 5 10 15 20

S

Degress = Expected egress time - Experienced egress time

D6

S = 0.00009 𝐷𝑒𝑔𝑟𝑒𝑠𝑠^3 + 0.00160𝐷𝑒𝑔𝑟𝑒𝑠𝑠^2 + 0.02044𝐷𝑒𝑔𝑟𝑒𝑠𝑠 + 3.72196


73

Figure 4-4 graphs the estimated function for difference between expected and

experienced waiting time. The estimated satisfaction function has the form S =

0.04637𝐷𝑤𝑎𝑖𝑡𝑖𝑛𝑔 + 3.72196. As can be seen in Figure 4-4, the function is linear, implying

that satisfaction increases with decreasing difference between experienced and expected

waiting time (when experienced waiting time is larger than expected waiting time). The

estimated function does not suggest any indifference threshold.

Figure 4-5 graphs the estimated satisfaction function for the difference between

expected and experienced in-vehicle time. The cubic function has the form S = 0.00008

𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒^3 + 0.00407𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒^2 + 0.07144𝐷𝑖𝑛−𝑣𝑒ℎ𝑖𝑐𝑙𝑒 + 3.72196. The first

derivative of the estimated equation gives us the two critical points, which are

unobservable, while taking the second derivative gives the inflection point (D6 = -16.96).

As can be seen in Figure 4-5, the function is concave between -40 and -16.96 minutes

discrepancy and convex between -16.96 and 5 minutes. There is no evidence of tolerance

for this function. As can be seen in most cases the experienced in-vehicle time is higher

than the expectation. The closer the two values become, the higher the satisfaction.

Figure 4-6 graphs the estimated function for the difference between expected and

experienced egress time. The estimated function reads S = 0.00009 𝐷𝑒𝑔𝑟𝑒𝑠𝑠^3 +

0.00160𝐷𝑒𝑔𝑟𝑒𝑠𝑠^2 + 0.02044𝐷𝑒𝑔𝑟𝑒𝑠𝑠 + 5.74736. The first derivative of the estimated

equation gives us the two critical points, which are unobservable, while taking the second

derivative gives the inflection point (D6 = -5.93). According to the inflection point, the

function is concave between -20 and -5.93 minutes discrepancy and convex between -

5.93 and 15 minutes. There is no evidence of tolerance for this function. The effect of

differences between expected and experienced egress time on trip stage satisfaction is

positive for the majority of cases where experienced egress time is larger than expected

egress time. It can be concluded that the closer the experienced egress time is to the

expected egress time, the more satisfied respondents are.

4.5 Conclusion

In this chapter, we developed separate models to estimate the effects of objective and

subjective factors on travel stage satisfaction. One of the main aims of this study is to

understand how satisfaction is related to attributes of trips and transport modes.

Techniques such as linear regression analysis and structural equations modeling provide

insight into the strength of the effects of such attributes on satisfaction, which is relevant

information for managers and policy makers. This reasoning, however, assumes that the

Chapter 4

74

estimated relationship is not confounded by any omitted variables. This paper is

motivated by the contention that results of most prior research may be biased because

they ignored measuring subjective factors such as mood and personality traits that both

influenced ratings of travel satisfaction.

First, this study examined this issue through a path analysis based on cross-

sectional survey data collected in China in 2015. The main findings of this study suggest

that moods such as feeing happy and relaxed lead to higher travel satisfaction while a

bad mood such as anger leads to a lower travel satisfaction. By including the effect of

trip attributes in the same model, results indicate that travel satisfaction is not only

affected by objective elements such as trip attributes, but also by subjective factors such

as mood. This finding suggests that results of the effect of trip attributes on travel

satisfaction may be biased when researchers ignore the effect of mood. Personality has

both direct and indirect significant effect on travel satisfaction. Personality traits such as

being critical and easy-going have a direct effect on travel satisfaction. Being critical and

easy-going leads to high satisfaction. Personality has a direct effect on mood. Therefore,

mood is also a mediator of personality and travel satisfaction.

Regarding the relationship between trip attributes and travel satisfaction, as we

specify the effect of mood and personality simultaneously with trip attributes in the model,

the effect of trip attributes can be estimated more purely. The use of public transport has

a negative effect on travel satisfaction. How to encourage people to use public transport

considering that people have a lower satisfaction when using public transport becomes

an essential issue to be discussed by policy makers. Therefore, service quality such as

accessibility, punctuality, frequency and environment needs to be improved. Moreover,

the waiting time negatively affects travel satisfaction, suggesting that using smartphones

to get real time information may reduce waiting time to encourage people to use public

transport. This requires the improvement of information systems and in-vehicle wireless

technology. Results found that trip attributes can affect mood, which in turn affects travel

satisfaction. The results showed significant effects of age and gender on travel

satisfaction, mood and personality. Older people are found to be more satisfied in general

with their travel experience, and more likely feel relaxed, are self-disciplined and reserved.

Younger people are more easy-going. Men are more likely feel anger and are critical.

This analysis has provided empirical evidence that personality traits and moods

influence travel satisfaction. Omitting these factors in analyzes of travel satisfaction which

aim at estimating to the contribution of travel attributes to travel satisfaction may lead to

biased effects, which in turn may lead to misguided recommendations to managers and


75

policy makers to improve consumer satisfaction. It is therefore recommended to include

personality traits and moods in cross-sectional studies of travel satisfaction. Omitting

these variables in monitoring studies may be less problematic if it can be validly assumed

that the effect of personality and mood on travel satisfaction is invariant across time.

Second, a reference-based polynomial regression approach was used to

construct the model of trip stage satisfaction incorporating trip attributes, perceived

service quality, psychological disposition and indifference tolerance. Whereas in many

other disciplines customer satisfaction has been conceptualised as a function of the gap

between expected and experienced service delivery, travel satisfaction in transportation

research has been predominantly measured as a function of objective or subjective

attribute levels. Therefore, the another aim of this study has been to examine the

performance of models of travel satisfaction that include this discrepancy versus models

that do not. Furthermore, because different attribute potentially play a different role in

judging satisfaction, we allow non-linear relationships between the difference between

expected and experienced attributes and satisfaction. In this chapter, we built a model of

the impact of differences between expected and experienced trip attributes on trip stages

satisfaction using polynomial regression models, controlling for perceived service quality,

attitudes, moods, personality traits and socio-demographic characteristics of respondents.

We find that the difference between expected and experienced waiting time, in-

vehicle time, experienced cost, perceived service quality, attitude, mood, personality and

socio-demographic characteristics have significant effects on trip stage satisfaction. In

addition, adding perceived service quality, attitudes, moods and personality traits

improves the goodness of fit of the model. Results show that loyalty to public transport,

price, seat availability, in-vehicle environment, enjoyment mood, happy mood, and easy-

going personality lead to higher ratings of trip stage satisfaction. An angry mood and self-

disciplined personality have negative effects on satisfaction. We also found that

satisfaction ratings of men are higher than satisfaction ratings of women. This result is at

variance with findings of some previous studies of trip satisfaction, which found that

gender has no significant effect on satisfaction with public transport services (De Vos et

al., 2016, Carrel et al., 2016). Household car ownership has significant effects on trip

stage satisfaction. The satisfaction ratings of people who live in households owning less

than one car are substantially higher than the average. Unlike previous studies, we

explored the existence of a tolerance range in the discrepancy between expected and

experienced attributes. Findings indicate that respondents tolerate small differences in

access time.

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76

The contributions of this analysis are 1) we include customer expectations in the

analysis of travel satisfaction, controlling for perceived service quality, attitude, mood,

personality traits and socio-demographic characteristics; 2) we applied a polynomial

regression model to avoid making a priori assumptions about the form of the satisfaction

function; 3) we investigated the existence of a tolerance range reflecting people’s

sensitivity to gaps between experiences and expectations when evaluating their travel

experience. Results support the potential value of incorporating these ideas into travel

satisfaction analyzes. The major advantage of using the polynomial form is that the

researcher does not need to a priori decide on the form of the satisfaction function.

Linearity/non-linearity, convexity/concavity and tolerance are now the result of the

estimated cubic function. It should be mentioned in this context that of course there is

always an element of personal judgment and interpretation of the result.


77

5

PREVALENCE OF ALTERNATIVE

PROCESSING RULES IN THE FORMATION OF

DAILY TRAVEL SATISFACTION IN

DIFFERENT CONTEXT

5.1 Introduction

The study of travel satisfaction has rapidly increased in popularity in recent years. In part,

this increasing popularity reflects a response to the growing importance of quality of life,

subjective well-being, happiness and similar concepts in urban policies. Transportation

researchers have jumped on the bandwagon with the desire to make a contribution to

this emerging line of research, focusing on transportation and travel. Consequently, the

body of knowledge about the determinants and co-variates of travel satisfaction has

rapidly increased. In addition, methodological advances related to the construction and

validation of travel satisfaction scales have been made (Ettema et al., 2011).

Chapter 5

78

Examples of such studies include Abou-Zeid and Ben-Akiva (2011), Abou-Zeid and

Fujii (2016), Archer et al. (2013), Currie et al. (2009), Ettema et al. (2010), Ravulaparthy

et al. (2013), Spinney et al. (2009), to name a few. A review of this line of research can

be found in De Vos et al. (2013). Results of these studies have provided mixed evidence

about the contribution of travel satisfaction to assessments of quality of life. It seems that

travel satisfaction contributes marginally to subjective well-being; the reverse relationship

seems stronger in the sense that subjective well-being more strongly affects travel

satisfaction ratings.

A second motivation underlying the study of travel satisfaction is to better

understand service provision, particularly of public transportation. To limit the growth in

car use, a competitive public transportation system is of utmost importance. An

understanding of customer satisfaction and its determinants is highly instrumental in

optimizing service delivery and stimulating travelers to use public transportation.

Examples of such studies include Abenoza et al. (2017), Cao (2013), de Oña et al. (2013),

De Vos et al. (2016), Dell’Olio et al. (2010, 2011), Del Castillo and Benitez (2012, 2013),

Diana (2012), Eboli and Mazzulla (2007, 2008, 2010, 2011, 2012), Edvardsson (1998),

Ettema et al. (2012), Fellesson and Friman (2008), Friman and Fellesson (2009), Lai and

Chen (2010), Sumaedi et al. (2012), Vicente and Reis (2016) and Wu et al. (2016). Such

studies provide information about the relative importance of different trip and vehicle

attributes on customer satisfaction.

Regardless of the theoretical and policy orientations of these studies on travel

satisfaction, respondents are prompted to express their degree of satisfaction with their

daily travel experiences. Some studies have relied on simple rating scales, others have

adopted more sophisticated multi-item travel satisfaction instruments (e.g., Bergstad et

al., 2011; Ettema et al., 2011). Recently, smart phones have been used to measure

satisfaction instantaneously (experience sampling) (Susilo et al., 2017), but the vast

majority of the studies on travel satisfaction has relied on memory recall. To provide these

overall or summary satisfaction ratings, respondents need to retrieve and re-enact their

trips from memory, recall cognitive and affective aspects associated with their

experiences, process their evaluations of all attributes of the trip influencing their

satisfaction according to some processing rule and, finally, and finally express their degree

of satisfaction on the rating scale or in terms of the multi-item satisfaction instrument.

Reflecting on the sequence of events during a multi-stage trip, they need to process their

assessment of the bus stop environment, waiting time, on-time arrival, boarding, the

friendliness of the driver, ticket price, seat availability and quality, cleanness of the

vehicles, driving style of the driver, possible incidents during the trip, the alighting process,


79

and this is repeated for every stage of the trip, and for every trip on the day of interest.

Moreover, to further complicate matters, previous research has shown that multi-tasking

i.e. the activities conducted when traveling affect trip satisfaction (Ettema et al., 2012),

and that in addition the kind of activity conducted before and after the trip influences

travel satisfaction (Rasouli and Timmermans, 2014).

An interesting and relevant research question then is which processing rule best

captures this process of articulation of travel satisfaction. Do travelers simply average

their satisfaction across trips or trip segments? Or, do they put higher weight on more

recent experiences? Rather than averaging, do they base their overall daily travel ratings

on the best experience they had, or maybe on their worse experience? Do they apply

some combination of these rules? It goes without saying that this research question has

direct theoretical relevance because it provides insight into complex cognitive processes.

The question also has applied relevance because the answer to this question may indicate

how researchers best analyze travel satisfaction data, while the implications for

practitioners in the context of attracting loyal customers who are satisfied with the service

delivery depend on the processing rule that best represents traveler satisfaction.

An examination of the literature reveals that this research question did not receive

much attention in transportation research. The only study in transportation research we

could trace is Suzuki et al. (2014). The theoretical orientation of this study suggests the

authors were inspired by Kahneman (2000) as they did not take a wider perspective. In

several domains other than travel satisfaction, it has been found that summary

judgements of sequences tend to be a function of the satisfaction with the peak

experience (the experience with the largest satisfaction difference with the average

satisfaction) and the last experience. Suzuki et al. (2014) compared this so-called peak

and end rule (Kahneman, 2000) with summation, averaging and weighted duration rules.

As said, the peak-end rule contends that the satisfaction of an event sequence

corresponds to the average of the satisfaction with the peak experience in the sequence

and the satisfaction with the experience of the end of the sequence. The summing rule

in contrast suggests that overall satisfaction equals the sum of the satisfaction of each

stage/event in the sequence. The equal weight averaging rule divides this total by the

number of stages. The duration weighted averaging rule is similar but weighs each stage

by its duration. In case of an equal number of stages of equal length, the latter two rules

are identical.

In light of the scant attention to this research question, the aim of our study is to

compare the predictive performance of different processing rules that represent how

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80

travelers arrive at overall travel satisfaction ratings. Rather than focusing specifically on

the theoretical foundations underlying this prior research, we take a broader perspective

and also consider rules that are founded on other theories. Using data on travel

satisfaction collected in Xian, China, we examine a set of processing rules that differ in

terms of the nature of the underlying decision process and allow investigating recency

effects. In order to more clearly communicate the importance of the different explanatory

variables, we first estimated models with satisfaction only. Then, assuming that

personality and mood may systematically affect travel satisfaction ratings, we added

mood and personality traits to the equation. Finally, because unobserved heterogeneity

may be present, we estimated random parameters regression models. This series of

analyzes is repeated twice, once to analyze how satisfactions about trip stages are

processed to arrival at the satisfaction for the overall trip, and once to analyze how trip

satisfactions are processed to generate overall satisfaction with a day’s travel.

In the remainder of this chapter, we first introduce and discuss the various

processing rules that we investigate. Next, we provide details of sample characteristics.

This is followed by the discussion of the analyzes and their results. We complete this

chapter by drawing conclusions and highlighting implications for travel satisfaction

research and management of public transport systems.

5.2 Processing rules

Let 𝑖𝑛 = {𝑖𝑛(𝑗) ∈ 𝐼𝑛}; 𝐼𝑛 > 0; 𝑗 = 1, … , 𝐽 be the sequence of chronologically ordered

daily trips individual n makes. Each trip i consists of a sequence of one or more trip stages

j. Associated with each stage is a set of K attributes affecting trip satisfaction. Let 𝑆𝑛𝑖(𝑗)𝑘

denote the satisfaction of individual n with attribute k characterizing stage j of trip i. In

order to arrive at trip stage satisfaction, individuals need to process and integrate these

attribute-level satisfactions into a summary satisfaction rating according to some

processing rule. That is,

𝑆𝑛𝑖(𝑗) = 𝑓𝑛𝑘(𝑆𝑛𝑖(𝑗)𝑘) 5.1

where 𝑓𝑛𝑘 is the processing rule that individual n applies to the attributes.


81

This equation describes the functional form of the satisfaction. We mention it here

for completeness, but do not further discuss it in this chapter. Our study is concerned

with the following phases.

Similarly, the segment level summary satisfactions need to be integrated to arrive

at the satisfaction for the full trip. That is,

𝑆𝑛𝑖 = 𝑓𝑗′(𝑆𝑛𝑖(𝑗)) 5.2

Finally, if overall satisfaction with a full day’s travel is solicited, individuals have to

process their trip satisfaction and combine these into an overall travel satisfaction rating

for the full day. That is,

𝑆𝑛 = 𝑓𝑖′′(𝑆𝑛𝑖) 5.3

In order to answer our research questions, alternative processing rules need to be

specified. Starting with the rules tested in previous research, Kahneman’s (2000) peak

and end rule for trip satisfaction may be expressed as:

𝑆𝑛𝑖 = (𝑆𝑛𝑖(𝑗∗) + 𝑆𝑛𝑖(𝐽))/2 5.4

𝑗∗ = argmax𝑗(|𝑆𝑛𝑖(𝑗) − 𝑆�̅�𝑖(𝑗)|) 5.5

where 𝑆�̅�𝑖(𝑗) is the average satisfaction across the trip stages.

Note that this rule assumes that the elicited satisfaction rating equals the average

of the experiences that deviates most from the average (regardless of whether it is

positive or negative) and the satisfaction with most recent experience.

The summing rule for trip satisfaction assumes that

𝑆𝑛𝑖 = ∑ 𝑆𝑛𝑖(𝑗)𝐽𝑗=1 5.6

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82

while the averaging rule can be expressed as

𝑆𝑛𝑖 = ∑ 𝑆𝑛𝑖(𝑗)𝐽𝑗=1) /𝐽𝑖 5.7

Similarly, the duration weighted averaging rule equals

𝑆𝑛𝑖 = ∑ Δ𝑖(𝑗)𝑆𝑛𝑖(𝑗)𝐽𝑗=1 / ∑ Δ𝑖(𝑗)

𝐽𝑗=1 5.8

where Δ𝑖(𝑗) is the duration of stage j of trip i.

All previous models are special or limited cases of the linear additive processing

rule.

𝑆𝑛 = ∑ ∑ 𝑆𝑛𝑖(𝑗)𝐽𝑗

𝐼𝑛𝑖 5.9

This processing rule captures a compensatory decision-making process in which a

low satisfaction of an attribute of a stage can be at least partially be compensated by

high satisfactions with one or more other attributes or stages. Likewise, high satisfactions

can be reduced by low satisfactions on one or more other attributes or stages. Overall

satisfaction is a weighted sum of attribute satisfactions across trip stages. The model is

consistent with Anderson’s classical theory of information integration (Anderson, 1981).

Other processing rules allow the examination of non-compensatory decision-making

processes. The conjunctive processing rule assumes that overall satisfaction is based on

the minimum attribute satisfaction, while the disjunctive rule assumes that overall

satisfaction is based on the maximum attribute satisfaction, regardless of the satisfaction

ratings of the other attributes. These non-compensatory processing rules may adequately

capture the formation of satisfaction either when individuals attempt to simplify the

integration process by focusing on some satisfactions/attributes only or when the very

bad or very good experience dominates the assessment process. The first would be

indicative of bounded rationality (Rasouli and Timmermans, 2015).


83

Figure 5-1 Conjunctive rule

Although these principles can be tested at the individual level, usually non-linear

approximations are used to present conjuctive and disjunctive processing rules. Einhorn

(1970, 1971) suggested non-linear approximations of the conjunctive and disjunctive

processing rules. The conjunctive rule can be approximated as:

𝑆𝑛 = ∏ ∏ 𝑆𝑛𝑖(𝑗)𝛽𝐽

𝑗𝐼𝑛𝑖 5.10

By taking the log, the following model is obtained

log 𝑆𝑛 = ∑ ∑ 𝛽 log 𝑆𝑛𝑖(𝑗)𝐽𝑗

𝐼𝑛𝑖 + 휀𝑛𝑖 5.11

Figure 5-1 provides a graphical representation of this processing rule. Note it

represents a parabolic curve, implying that low satisfaction values related to an attribute/

stage cannot be compensated by higher value on one or more of the other attributes/

stages.

A nonlinear approximation of the disjunctive model is given by

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84

Figure 5-2 Disjunctive rule

𝑆𝑛 = ∏ ∏ (1

𝛼𝑖(𝑗)−𝑆𝑛𝑖(𝑗))

𝛽𝐽𝑗

𝐼𝑛𝑖 5.12

By taking the log,

log 𝑆𝑛 = − ∑ ∑ 𝛽 log(𝛼𝑖(𝑗) − 𝑆𝑛𝑖(𝑗))𝐽𝑗

𝐼𝑛𝑖 + 휀𝑛𝑖 5.13

where 𝛼𝑖(𝑗) is a reference point, in our study the maximum score of satisfaction is 10,

the value 𝛼𝑖(𝑗) is set as 11.

Figure 5-2 graphs this processing rule. It shows the equation represents a

hyperbolic response curve.

5.3 Sample characteristics

Distributions of characteristics of the sample are shown in Figure 5-3. It shows that 46%

of the sample is between 25 and 35 years of age, and that 28% is between 18 and 25

years old. Only 3% of the sample is older than 55. This distribution shows the self-

selection in the use of public transport. It may be amplified by the fact that the survey

was conducted at the end of the day (evening) when the number of young people

travelling is larger than the number of respondents in other age groups.


85

Figure 5-3 Sample characteristics (N = 1445)

Figure 5-3 also shows that 54% of the sample is female, while 46% is male. 31%

of the sample is living in households with more than 3 persons, while 23% is living alone.

Even though 73% of the sample has a professional education, 39% of the sample has

medium income level. More than half of the sample (51%) is employed full time (fixed

schedule).

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86

5.4 Analyzes and results

The aim of this study is to examine the predictive performance of alternate processing

rules that individuals may use to arrive at overall satisfaction ratings based on their

assessment of components of their travel. Our data allows exploring this research

question for two different decision problems: (i) how do individuals arrive at their

satisfaction ratings for trips based on their ratings of trips stages? (ii) how do individuals

arrive at their daily travel satisfaction ratings based on their evaluations of the trips? It

should be emphasized from the outset that the number of episodes that need to be

processed and integrated differs between the two problems. Because the complexity of

the trips we observed in our data set is limited, the number of stages was typically just

2. The number of episodes for a full day was mostly four.

Based on our theoretical considerations, in particular we will analyze the

prevalence of the distinguished processing rules and focus our attention on the existence

of any recency effects in the formation process. In a second, more elaborated analysis,

we investigate the effects of including selected socio-demographic attributes (age and

gender), a set of personality traits and mood.

5.4.1 Relationship between trip satisfaction and trip stage satisfaction

We first report the results of the analyzes concerned with the relationship between trip

satisfaction and trip stage satisfaction. As discussed, satisfaction was measured for whole

trip and for each stage including the access, waiting and transfer part. The last stage thus

includes egress. Consequently, the sequence length is relatively short.

5.4.1.1 Models with stage satisfactions only

Because we use Suzuki et al. (2014) as a benchmark, we first report the findings of the

processing rules they examined for our data set. We use pearson correlation analyzes to

estimate the performance of each processing rule. The results of pearson correlation

coefficients are listed in Table 5-1. It shows that the performance of the rules, measured

in terms of the pearson correlation coefficient does not differ much. The summation rule,

peak-end rule and equal-weight averaging rule show a slightly higher correlation than the

duration-weighted averaging rule. The duration-weighted rule performed best for Suzuki’s

data which is different from the results in our study. This difference may be caused by

the fact they explicitly differentiated between access and egress, which tends to take less

time. In our study, each stage has access and egress.


87

Table 5-1 Correlation between peak-end, summation and weighted averaging rules and trip satisfaction

Processing Rule Peak-end

Rule

Summation

Rule

Equal-weight

Averaging Rule

Duration-weighted

Averaging Rule

Pearson Correlation

coefficient (r) 0.595*** 0.595*** 0.595*** 0.584***

r2 0.354 0.354 0.354 0.341

*** ==> Correlation is significant at the 1% level (2-tailed).

Table 5-2 Estimated resutls of conjunctive, disjunctive and linear processing rule for trip satisfaction with panel effects

Processing Rule Conjunctive Model Disjunctive Model Linear Model

Coefficient SE Coefficient SE Coefficient SE

Constant 0.253*** 0.052 1.058*** 0.037 2.387*** 0.422

β1 (Stage 1

Satisfaction) 0.450*** 0.058 0.292*** 0.054 0.400*** 0.060

β2 (Stage 2

Satisfaction) 0.226*** 0.054 0.156*** 0.052 0.224*** 0.058


Var[e] 0.009 0.010 1.289

Var[u] 0.007 0.009 0.898

Corr[v(i, t), v(i, s)] 0.444 0.476 0.411

Sum of Squares 3.597 4.237 472.210

R2 0.388 0.279 0.353

Adjusted R2 0.382 0.272 0.347

***, **, * ==> Significant at 1%, 5%, 10% level.

Table 5-2 reports the results of the non-linear processing rules. We decided to

estimate the constant of the regression model; it would not affect the coefficient of the

stages, but allows for possible scaling. Considering the panel nature of trip satisfaction

data, we estimated panel effects of the non-linear processing rules. The explained

variance of the linear processing rule is, as expected, in the same order of magnitude as

the processing rules listed in Table 5-1. The conjunctive rule seems to slightly better

capture the way the satisfaction for the stages are processed to derive at an overall

satisfaction for the trip. This suggests that the stage with the lowest satisfaction exerts

most influence on overall trip satisfaction. The estimated coefficients suggest that the

contribution of the first stage to overall trip satisfaction is higher than the contribution of

Chapter 5

88

the second stage. Note that this finding is at variance with the peak-end rule, which

suggests that the final stage should have a higher contribution. Finally, the disjunctive

processing rule clearly performs worst of all considered processing rules.

5.4.1.2 Models with socio-demographic characteristics, mood and personality traits

As we have argued, trip satisfaction ratings may vary considerably between individuals

with different socio-demographic profiles. Moreover, ratings may be influenced by mood

and personality traits. If a person is in a good mood when the satisfaction ratings were

provided, the good mood may carry over to the satisfaction ratings and we might observe

a higher rating. Also when the actual trip was made if the person is in a good mood he

may have a good memory of that trip when he needs to recall it. Similarly, a bad mood

may result in a lower rating. Similar effects may be reflected in satisfaction ratings from

personality traits. Some people are more critical than others and less tolerant and such

personality traits may therefore affect measured satisfaction ratings. To control for such

effects, we estimated a second set of processing rules which in addition to trip stage

satisfaction also included socio-demographic characteristics, mood and personality traits.

We estimated the models in two steps. First step, we estimated regression models

using discrepancy of trip satisfaction and processing rules with socio-demographics,

moods, personality traits and panel effects. Second step, we calculated partial correlation

coefficient between trip satisfaction and processing rules controls for the socio-

demographics, moods and personality traits. Then we added the r-square of two steps to

get the total explained variance of processing rules. Table 5-3 shows the results of peak-

end, summation and averaging rules. One interesting finding is that trip satisfaction

ratings of more critical travelers turn out to be a little higher. This may appear

counterintuitive at first glance. However, in line with other research in social psychology,

it may indicate that more critical travelers have lower expectations and therefore their

satisfaction ratings may be higher. Travelers feeling enjoyment have slightly higher

significant ratings. The signs of the estimated effects are the same across all processing

rules. The results of adjusted R-square do not differ much.

Examination of Table 5-4 shows that the explained variance in trip satisfaction

ratings increases for conjunctive/disjunctive/linear processing rules after adding mood,

personality traits and socio-demographic variables. The increase in explained variance

compared to the base model (Table 5-2) is in the same order as the explained variance.

Exception includes critical is not significant in disjunctive model. Happy mood in

disjunctive model, depressed mood in all models, anger mood in all models turn out to

be significant. This suggests that mood have a structural effect.


89

Table 5-3 Estimated results of peak-end, summation and weighted averaging rules with socio-demographic characteristics, mood, personality traits, and

panel effects

Processing Rule

Peak-end Rule

(Same as summation

rule in this case)

Equal-weight Averaging

Rule

Duration-weighted

Averaging Rule

Step 1 Regression analyzes

Coefficient SE Coefficient SE Coefficien

t SE

Age -0.022 0.014 -0.022 0.014 -0.025* 0.013

Gender -0.340 0.221 -0.340 0.221 -0.361 0.224

Critical 0.131** 0.053 0.131** 0.053 0.131** 0.053

Self-disciplined 0.046 0.063 0.046 0.063 0.049 0.063

Impatient 0.034 0.060 0.034 0.060 0.037 0.060

Reserved -0.052 0.053 -0.052 0.053 -0.066 0.053

Easy-going 0.026 0.066 0.026 0.066 0.019 0.069

Calm -0.077 0.076 -0.077 0.076 -0.055 0.078

Enjoyment 0.185** 0.073 0.185** 0.073 0.197*** 0.074

Relax -0.100 0.083 -0.100 0.083 -0.107 0.083

Worry 0.063 0.085 0.063 0.085 0.080 0.084

Sadness -0.087 0.085 -0.087 0.085 -0.098 0.081

Happy -0.054 0.059 -0.054 0.059 -0.049 0.058

Depressed 0.093 0.081 0.093 0.081 0.067 0.083

Anger -0.074 0.082 -0.074 0.082 -0.066 0.082

Stress 0.037 0.060 0.037 0.060 0.047 0.059

Tired 0.061 0.054 0.061 0.054 0.054 0.054

Variance parameter given is sigma

Std. Dev. 1.463*** 0.066 1.463*** 0.066 1.501*** 0.064

R2 0.158 0.158 0.158

Adjusted R2 0.082 0.082 0.082

Step 2 Partial correlation analyzes

Partial correlation

coefficient 0.574*** 0.574*** 0.568***

Square of partial

correlation coefficient 0.330 0.330 0.323

Total explained variance of processing rules

Total R2 0.412 0.412 0.415


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90

Table 5-4 Estimated results of conjunctive, disjunctive and linear processing rule with socio-demographic characteristics, mood, personality traits, and

panel effects for trip satisfaction

Processing Rule Conjunctive Model Disjunctive Model Linear Model


Constant 0.242** 0.079 1.055*** 0.081 2.276*** 0.813

β1 (Stage 1

Satisfaction) 0.420*** 0.059 0.284*** 0.056 0.377*** 0.061

β2 (Stage 2

Satisfaction) 0.245*** 0.057 0.162*** 0.055 0.212*** 0.061

Age -0.002 0.001 -0.001 0.001 -0.023 0.015

Gender -0.019 0.019 -0.018 0.022 -0.281 0.220

Critical 0.008* 0.004 0.007 0.005 0.086* 0.049

Self-disciplined 0.004 0.004 0.004 0.005 0.010 0.051

Impatient 0.001 0.004 0.002 0.005 0.022 0.050

Reserved -0.005 0.004 -0.006 0.005 -0.053 0.050

Easygoing 0.002 0.005 0.001 0.006 0.044 0.056

Calm -0.004 0.005 -0.003 0.006 -0.033 0.059

Enjoyment 0.019*** 0.005 0.020*** 0.006 0.233*** 0.062

Relax -0.004 0.006 -0.004 0.006 -0.079 0.065

Worry 0.003 0.006 0.001 0.007 0.035 0.069

Sadness -0.004 0.006 -0.002 0.006 -0.065 0.063

Happy -0.007 0.004 -0.010* 0.005 -0.041 0.050

Depressed 0.014** 0.006 0.018*** 0.006 0.156** 0.065

Anger -0.016*** 0.006 -0.015** 0.007 -0.146** 0.067

Stress 0.002 0.004 0.011 0.005 0.018 0.050

Tired 0.005 0.004 0.002 0.005 0.059 0.047


Var[e] 0.012 0.013 1.655

Var[u] 0.003 0.004 0.252

Corr[v(i, t), v(i, s)] 0.175 0.251 0.132

Sum of Squares 2.841 3.490 377.673

R2 0.517 0.406 0.482

Adjusted R2 0.471 0.349 0.432



91

Table 5-5 Summary of Adjusted R2 for different models of trip satisfaction

Model

Without socio-demographic

characteristics, personaliity,

and mood

With socio-demographic

characteristics, personality,

and mood

Peak-end rule 0.354 0.412

Summation rule 0.354 0.412

Equal-weight averaging rule 0.354 0.412

Duration-weighted averaging rule 0.341 0.415

Conjunctive trip satisfaction model 0.382 0.471

Disjunctive trip satisfaction model 0.272 0.349

Linear trip satisfaction model 0.347 0.432

5.4.1.3 Summary

Table 5-5 provides an overview of the explained variance of the different models. It shows

that the explained variance systematically increases by adding the scio-demographic

characteristics, personality traits and mood. The conjunctive processing rules best

describes the integration of trip stage satisfaction into trip satisfaction across the sample.

Regardless of the complexity of the models, the first stage more affects trip stage

satisfaction than the second stage. Controlling for personal characteristics, mood and

personality traits magnifies the influence of the second trip stage.

5.4.2 Relationship with daily travel satisfaction and trip satisfaction

A limitation of the analyzes reported in section 4.1 is that the sequences are short. It is

therefore not immediately clear whether the obtained results generalize to longer

sequences. Therefore, we decided to include in this paper the discussion of a second

analysis, which is concerned with the relationship between daily travel satisfaction and

trip satisfactions. Because most respondents made more trips on the survey data, this

sequence is longer. The analyzes are the same as for the analysis of the processing of

trip stage satisfaction. We start discussing the results of the pure processing strategies.

Next, we report the results of regression analysis in which reported trip satisfactions were

complemented with these control variables.

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92

Table 5-6 Results of peak-end, summation and averaging rules for daily travel satisfaction

Processing Rule Peak-end

Rule Summation Rule

Equal-weight

averaging Rule

Duration-weighted

Averaging Rule

Pearson Correlation

Coefficient (r) 0.655*** 0.691** 0.691*** 0.691***

r2 0.429 0.477 0.477 0.477

*** ==> Correlation is significant at the 1% level (2-tailed).

Table 5-7 Results of conjunctive, disjunctive, and linear processing rules without socio-demographic, mood, and personality traits for daily travel

satisfaction

Conjunctive Model Disjunctive Model Linear Model


Constant 0.286** 0.135 1.139*** 0.052 1.811* 0.975

β1 (Trip 1 Satisfaction) 0.366*** 0.024 0.242*** 0.021 0.403*** 0.028


β3 (Trip 3 Satisfaction) 0.062 0.050 0.069* 0.039 0.100* 0.053

β4 (Trip 4 Satisfaction) -0.113 0.159 0.009 0.085 -0.061 0.139

R2 0.551 0.401 0.509

Adjusted R2 0.549 0.398 0.506


5.4.2.1 Models with trip satisfactions only

As in the previous section, we first discuss the results of the peak-end, summation and

average rules. We use pearson correlation analyzes to estimate the performance of each

processing rule. Because the number of sequences is higher for this case, the rules may

show differences. This is also shown in Table 5-6, which indicates that the peak-end rule

performs the worst of the considered rules. The summation, equal-weight averaging rule

and duration-weighted averaging rule perform slightly better.

The results of the conjunctive, disjunctive and linear processing rules are listed in

Table 5-7. Consistent with the case of the integration of trip stage satisfaction, the

conjunctive rule has the highest explained variance, following by the linear rule. Both

perform better than peak-end, summation and average rules. Also in this case, the

disjunctive processing rule performs worst. If we examine the contribution of the various

trips to the overall daily trip satisfaction for the conjunctive rule, results indicate that


93

from-home trip has more influence than the return-home trip. Moreover, first trip has

more impact than the second trip. This order is also found for the disjunctive and linear

processing rules.

5.4.2.2 Models with socio-demographics, mood and personality traits

The next model that was estimated added socio-demographic variables, mood and

personality traits to the processing rules. We estimated the models in two steps. First

step, we estimated regression models using discrepancy of total travel satisfaction and

processing rules with socio-demographics, moods, personality traits and panel effects.

Second step, we calculated partial correlation coefficient between total travel satisfaction

and processing rules controls for the socio-demographics, moods and personality traits.

Then we added the r-square of two steps to get the total explained variance of processing

rules. As shown in Table 5-8, adding these variables increases the explained variance of

the models, without affecting their relative performance. The peak-end rule is least

successful in explaining the relationship between trip satisfactions and daily travel

satisfaction, followed by the duration-weighted averaging rule, and the summation rule

same as equal-weight averaging which is slightly doing better than the duration-weighted

averaging rule. As for the socio-demographics, the effects of age and gender is not

significant. Reserved is the only personality trait that has a significant effect on daily travel

satisfaction. Being reserved is just significant at the 5 per cent level for all models. Being

enjoyment has a significant positive effect on daily travel satisfaction. It is significant for

four processing rules. Being sad has a positive effect but not significant at the 1 per cent

level for four models. Feeling depressed has a significant negative effect on daily travel

satisfaction. The significant negative coefficient can be observed for four processing rules.

Thus, on average, the more depressed respondents stated to feel, the lower their daily

travel satisfaction rating.

Table 5-9 displays the estimation results of the model adding socio-demographic

variables, mood and personality traits for the conjunctive, disjunctive and linear

processing rule. Similar to the results in Table 5-8, adding these variables increases the

explained variance of the models, without affecting their relative performance. The

disjunctive processing rule is least successful in explaining the relationship between trip

satisfactions and daily travel satisfaction, while the conjunctive model has the highest

explained variance, which is 0.562 (adjusted R-square). The marginal effect on daily

travel satisfaction of the from-home trip is higher than the marginal effect of the return-

home trip

Chapter 5

94

Table 5-8 Results of peak-end, summation and averaging rules with socio-demographics, mood and personality traits for daily travel satisfaction

Processing

rule Peak-end Rule Summation Rule

Equal-weight

Averaging Rule

Duration-

weighted

Averaging Rule

Step 1 Regression analyzes

Coef SE Coef SE Coef SE Coef SE

Constant 0.094 0.231 -0.335 0.225 -0.335 0.225 -0.230 0.223

Age 0.000 0.001 0.000 0.001 0.000 0.001 0.001 0.001

Gender 0.116 0.089 0.108 0.087 0.108 0.087 0.087 0.086

Critical 0.030 0.019 0.026 0.019 0.026 0.019 0.025 0.019

Self-disciplined -0.028 0.022 -0.011 0.022 -0.011 0.022 -0.004 0.022

Impatient 0.016 0.019 0.012 0.019 0.012 0.019 0.008 0.019

Reserved -0.042** 0.020 -0.039** 0.020 -0.039** 0.020 -0.040** 0.019

Easygoing -0.027 0.021 -0.009 0.021 -0.009 0.021 -0.011 0.020

Calm 0.009 0.025 -0.005 0.025 -0.005 0.025 -0.009 0.025

Enjoyment 0.085*** 0.026 0.099*** 0.025 0.099*** 0.025 0.096**

* 0.025

Relax 0.002 0.026 -0.004 0.025 -0.004 0.025 -0.003 0.025

Worry 0.015 0.029 0.006 0.028 0.006 0.028 0.001 0.028

Sadness 0.074** 0.030 0.061** 0.029 0.061** 0.029 0.071** 0.029

Happy 0.017 0.022 0.019 0.022 0.019 0.022 0.014 0.021

Depressed -0.082*** 0.032 -0.073** 0.031 -0.073** 0.031

-

0.085**

*

0.031

Anger 0.001 0.027 -0.023 0.027 -0.023 0.027 -0.017 0.026

Stress 0.004 0.024 0.015 0.023 0.015 0.023 0.016 0.023

Tired -0.010 0.021 -0.004 0.020 -0.004 0.020 0.000 0.020

R2 0.064 0.074 0.074 0.074

Adjusted R2 0.043 0.054 0.054 0.053

Step 2 Partial correlation analyzes

Partial

correlation

coefficient

0.620*** 0.655*** 0.655*** 0.658***

Square of partial

correlation

coefficient

0.384 0.430 0.430 0.433

Total explained variance of processing rules

Total R2 0.427 0.484 0.484 0.486



95

Table 5-9 Results of conjunctive, disjunctive, and linear processing rule with socio-demographic characteristics, mood and personality traits for daily

travel satisfaction

Processing rule Conjunctive Model Disjunctive Model Linear Model

Coef SE Coef SE Coef SE

Constant 0.330** 0.137 1.068*** 0.059 2.257** 1.013



β3 (Trip 3 Satisfaction) 0.058 0.050 0.056 0.039 0.092 0.053

β4 (Trip 4 Satisfaction) -0.156 0.162 -0.025 0.089 -0.123 0.143

Age 0.000 0.000 0.000 0.000 0.001 0.001

Gender 0.005 0.006 0.002 0.007 0.075 0.083

Critical 0.003** 0.001 0.004** 0.002 0.031* 0.018

Self-disciplined -0.003* 0.002 -0.001 0.002 -0.032 0.021

Impatient 0.000 0.001 0.001 0.002 0.005 0.018

Reserved -0.003** 0.001 -0.004** 0.002 -0.040** 0.019

Easygoing -0.001 0.001 -0.000 0.002 -0.008 0.020

Calm 0.001 0.002 0.001 0.002 0.006 0.024

Enjoyment 0.005*** 0.002 0.006*** 0.002 0.075*** 0.024

Relax -0.000 0.002 0.001 0.002 -0.004 0.024

Worry 0.003* 0.002 0.003 0.002 0.030 0.024

Sadness -0.000 0.000 -0.000 0.000 -0.001 0.001

Happy 0.001 0.002 -0.000 0.002 0.013 0.021

Depressed -0.004** 0.002 -0.003 0.002 -0.063** 0.028

Anger -0.000 0.002 -0.002 0.002 0.001 0.006

Stress 0.001 0.002 0.001 0.002 0.020 0.022

Tired -0.000 0.001 -0.001 0.002 0.004 0.019

R2 0.573 0.428 0.534

Adjusted R2 0.562 0.412 0.521


As for the socio-demographics, same for the other processing rules shown in table

5-8, both age and gender have non-significant effect on daily travel satisfaction. Being

critical is significant at the 5 per cent level for conjunctive and disjunctive model and at

the 10 per cent level for linear model. Its coefficient is positive, implying that on average

respondents who are more critical show a higher daily travel satisfaction rating. Being

self-disciplined is significant at the 10 per cent level only for conjunctive model and has

negative impact on the level of satisfaction. Being reserved is now significant and negative

at the 5 per cent level for conjunctive, disjunctive and linear rule.

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96

Table 5-10 Summary of Adjusted R2 for different models of daily travel satisfaction

Processing Rule

Without Socio-demograpic

Characteristics, Personaliity,

and mood

With Socio-demograpic

Characteristics, Personality,

and mood

Peak-end Rule 0.429 0.427

Summation Rule 0.477 0.484

Equal-weight Averaging Rule 0.477 0.484

Duration-weighted Averaging Rule 0.477 0.486

ConjunctiveTrip Satisfaction Model 0.549 0.562

Disjunctive Trip Satisfaction Model 0.398 0.412

Linear Trip Satisfaction Model 0.506 0.521

As with the other processing rules, mood of enjoyment has a positive significant

effect on daily travel satisfaction. It is significant for all three processing rules. Mood of

worry is significant at the 10 per cent level only for conjunctive model. Feeling depressed

has a significant negative effect. The significant negative coefficient can be observed for

conjunctive and linear rule.

5.4.2.3 Summary

Table 5-10 provides an overview of the explained variance of the different models in the

context of daily travel satisfaction. It shows that the explained variance systematically

increases by increasing the complexity of the model except for the peak-end rule.

Regardless of the complexity of the model, the conjunctive processing rules best describes

the integration of trip satisfaction into daily travel satisfaction across the sample. The first

trip more affects travel satisfaction that the second trip.

5.5 Conclusion

Satisfaction ratings are routinely asked in many industries. Nowadays, customer

satisfaction questionnaires are distributed after every purchase. Also in public

transportation, a growth in the use of such scales to monitor service quality can be

observed. In addition to monitoring general trends in customer satisfaction, the ratings

also allow managers and policy makers to identify aspects of service delivery that can be

improved and maybe should be improved to attract new customers and/or enhance

loyalty among current customers.


97

When ratings of trip and travel satisfaction appear easy to provide, the task is in

fact cognitively quite demanding. Respondents need to retrieve the various trips and trip

stages from their memory, re-enact the trip to recall their experience and then judge the

various attributes influencing their satisfaction and integrate these into an overall

satisfaction rating. It is important and relevant therefore to understand the processing

rules that customers apply in this process. If wrong assumptions about the process are

made, it likely results in biased conclusions, which in turn may lead to counterproductive,

ineffective or inefficient policy or managerial recommendations.

Research in travel behavior on this topic has been very scarce, and moreover the

few scholars uniformly used Kahneman (2000) peak-end rule as the source of inspiration.

It suggests that the travel satisfaction research community is fascinated by this rule.

Indeed, the rule seems appealing. It states that satisfaction assessment of a long series

of episodes is best predicted by the average of the last satisfaction and either the highest

of lowest satisfaction, deviating most from the average. The recency effects may be

explained because it was the last experienced and thus closest to the moment of

elicitation. The most deviating experience may have made most impact.

To contribute to this still largely unexplored area of research in travel behavior

research, we compared the performance of a set of processing rules that assume distinct

underlying decision-making processes using data on public transport use collected in Xian,

China. These processing rules range from taking only the best attribute satisfaction into

account or the worst to some weighted averaging process. Thus, these rules differ in

terms of the degree of rationality that is assumed.

Based on the results of this study, the following conclusions may be drawn. First,

we found little support for the prevalence of the peak-end rule. In fact, except for the

disjunctive rule, it consistently had the lowest explained variance, also after controlling

for socio-demographics, mood and personality traits. Rather, for all estimated models,

the conjunctive processing rule had the highest associated explained variance. It suggests

that the episode respectively trip with the lowest satisfaction dominates overall

satisfaction. Second, we also did not find much evidence of recency effects. Rather, for

both the trip stages and the trips, the satisfaction of the first trip and first stage has higher

marginal effects on overall satisfaction that more recent trips and stages.

Suzuki et al. (2014) also did not find much support for the peak-end rule and left

open the possibility that the length of the sequence may cause the difference. We contend

that it is travel itself that may explain the difference. If we have an activity with a relatively

homogeneous experience, it is likely that an extreme experience (negative or positive) to

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be strongly correlated with overall satisfaction and is a better predictor than the

satisfaction of the longest episode. Consider as an example a long haul night flight. The

experience is rather homogeneous, maybe except take off, landing and meal. Now

suppose there is heavy turbulence. Such episodes tend to last for only few minutes, but

likely strongly impact traveler satisfaction. Compared to such activities, daily travel shows

much more variation and many more factors potentially influence satisfaction ratings.

Consequently, it may be that individuals retrieve more episodes and/or a wider set of

factors influencing their satisfaction when providing satisfaction ratings.

More replicative studies are needed to accumulate evidence of the predictive

performance of the difference processing rules. We hope this study will stimulate such

efforts.


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6

LINKING TRAVEL SATISFACTION AND

SUBJECTIVE WELL-BEING

6.1 Introduction

Since long, subjective well-being, a concept closely related to life satisfaction, happiness

and fulfilment has been a topic of research in social and psychological sciences. Subjective

well-being expresses people’s cognitive and emotional evaluations of their lives. These

evaluations include people’s emotional reactions to events, their moods, judgments of life

satisfaction and fulfilment, and satisfaction with different domains of life such as marriage

and work (Diener et al., 2003). Thus, subjective well-being is a multi-dimensional concept

that covers many life domains. The concept has been measured using a variety of

different scales (Diener et al., 1985; Diener and Suh, 1997). As an alternative to the

concept of utility, subjective well-being has been proposed as a measure of individuals’

benefits in a number of different life domains (Kahneman et al., 1999).

People’s satisfaction with different domains of their life thus influences subjective

well-being. The effect of satisfaction in a specific domain on overall subjective well-being

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has been typically explained on the basis of the bottom-up spill over theory of subjective

well-being (Sirgy, 2001). This theory posits that affect related to a consumption

experience contributes to affecting satisfaction in specific life domains, which in turn

influences satisfaction with life at large (Sirgy et al., 2011). Many scholars advocated this

bottom-up approach. For example, Mohan-Neill (1995) predicted life satisfaction using

variables such as work satisfaction and satisfaction with personal relationships. The

results indicated that satisfaction with personal relationships is more predictive of life

satisfaction than work satisfaction, although both were significant predictors of life

satisfaction. Similarly, Oishi et al. (1999) found that value orientation moderates the

effects of domain satisfaction on overall life satisfaction. Dasgupta and Majumdar (2000),

using the bottom-up approach, found that satisfaction with material possessions, family

life, self-development, and local government administration have a significant effect on

life satisfaction of Calcutta residents. As a final example, Grzeskowiak et al. (2006)

concluded that satisfaction with housing influences satisfaction in various other life

domains, which in turn affects satisfaction with life. In the context of travel, the spill over

theory would imply that high travel satisfaction would contribute to high subjective well-

being.

In principle, there may also be an effect of overall well-being on travel satisfaction,

which would indicate a top-down approach in the study of subjective well-being in the

sense that their overall perspective on life may affect how people feel about specific life

domains (see, for example, Diener, 1984; Headey et al., 1991). Few studies, however,

have examined this top-down relationship using empirical data. Abou-Zeid and Ben-Akiva

(2011) estimated the effects of overall well-being on commute satisfaction using a

structural equations model and found that people who have a high level of overall well-

being are likely more satisfied with their commute.

Thus, these studies suggest that an understanding of how domain-specific

satisfaction contributes to overall well-being and how overall well-being influences domain

satisfaction has been a pertinent topic of research in social sciences and marketing

research for many decades. However, the studies in these fields of study have not

considered travel satisfaction, even though travel is an important daily consumption and

experience. Knowledge about the interrelationship between subjective well-being and

travel satisfaction has only recently accrued since the study of travel satisfaction has

appeared on the agenda of travel behavior researchers. Since the last seven years,

transportation researchers have examined determinants and effects of travel behavior

(see, for example, Abenoza et al., 2017; De Vos et al., 2016; Yang et al., 2015). The

interest in the topic is fast expanding.


101

Although the rapidly growing number of studies in travel behavior research on

travel satisfaction has substantially increased our knowledge about travel satisfaction,

limitations of prior research leave open sufficient room for additional research. In this

chapter, we focus our attention on the following relatively unexplored aspects of travel

satisfaction in travel behavior research. First, few studies examined the interrelationships

between travel satisfaction and overall well-being by simultaneously considering the

relationship between satisfaction with other life domains and overall subjective well-being.

The existing partial conceptualisation and analysis may introduce bias in the results,

particularly when domains other than travel influence subjective well-being and

satisfactions with various life domains are correlated. Thus, in this study, we adopt this

more general approach.

Second, prior travel behavior research has predominantly adopted a hedonic view

of well-being. According to this view, researchers have equated well-being with hedonic

pleasure based on the contention that people’s goal of life is maximizing their amount of

pleasure. However, travel and activities during trip also allow people to achieve purpose

and meaning of life. This so-called eudemonic well-being has been under-researched in

travel behavior analysis. Thus, rather than focusing on a specific view of well-being,

different views were entertained in this study.

Third, personality traits may influence the degree of experienced travel satisfaction

and responses to travel satisfaction scales. Diener and Lucas (1999) argued that the

strong influence of personality is seen as one of the most replicable and most surprising

findings in subjective well-being research. In fact, the correlation between subjective well-

being and personality such as extraversion and neuroticism is stronger than correlations

with any demographic predictor (Lucas and Fujita, 2000; Richard and Diener, 2009; Steel

et al., 2008). Personality may capture structural response patterns of individuals. However,

personality traits have been largely ignored in studies of travel satisfaction. Thus, in the

current analysis, we included personality scales in the measurement and analysis to allow

for personality traits effects moderating the relationships.

Thus, this chapter first examines the mutual dependency between travel

satisfaction and subjective well-being relative to satisfaction with other life domains, while

controlling for personality traits and selected socio-demographic variables. To analyze the

direct and indirect relationships between these constructs, a structural equation model is

estimated. Furthermore, this chapter formulates and estimates a latent class structural

equation model. It assumes that the co-dependent relationships between domain

satisfaction and overall life evaluation vary between latent classes. In that sense, the

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latent class structural equations are similar in idea to the latent class models that have

been widely applied in transportation research. Latent classes are identified that differ in

terms of the parameters of the kind of model that is estimated.

6.2 Structural hypotheses

6.2.1 Travel satisfaction and subjective well-being

Since the 1970s, the concept of subjective well-being has caught the attention of

sociologists, economists and psychologists. Although the term subjective well-being has

been recognized in some studies, related terms such as happiness, life satisfaction, quality

of life were sometimes also used in different literatures (Diener, 1984). Among those

definitions of subjective well-being, two main views have been identified – the hedonic

view and the eudaimonic view (Ryan and Deci, 2001). Kahneman et al. (1999) defined

the hedonic view as the study of “what makes experiences and life pleasant and

unpleasant”. In contrast, the eudaimonic view of subjective well-being emphasizes the

meaning of life, personal growth and self-realization. Related scales have been designed

to measure different views of subjective well-being. We summarized the scales of hedonic

and eudaimonic views of subjective well-being that have appeared in the recent literature

(Table 2-2). Examples of measurement of hedonic well-being are scales such as the

Positive and Negative Affect Scale (PANAS) (Watson et al., 1988), the Scale of Positive

and Negative Experience (SPANE) (Diener et al., 2010), the Swedish Core Affect Scale

(SCAS) (Västfjäll et al, 2002; Västfjäll and Gärling, 2007) mainly captures affective

feelings, while the Satisfaction with Life Scale (SWLS) (Diener et al., 1985; Pavot and

Diener, 1993) is mostly used to measure cognitive evaluation. In this study, we put

emphasis on the cognitive evaluation of hedonic well-being normally known as life

satisfaction. Relatively fewer scales have been designed to measure eudaimonic view of

subjective well-being such as the Personal Well-being Scale (PWS) (Ryff, 1989), the

Questionnaire for Eudaimonic Well-being (QEWB) (Waterman et al., 2010) and the

Flourishing Scale (Diener et al., 2010).

Scales mentioned above were used when researchers focused on the global

evaluation of life. In addition, subjective well-being has been proposed as a measure of

individuals’ benefits in different life domains. The relationship between overall subjective

well-being and domain life satisfaction has been a pertinent topic of research in social

sciences for many decades. On the one hand, the effect of satisfaction in a specific domain

on overall subjective well-being has been typically explained based on the bottom-up spill

over theory of subjective well-being (Sirgy, 2001). This theory posits that affect related


103

to a consumption experience contributes to affecting satisfaction in specific life domains,

which in turn influences satisfaction with life at large (Sirgy et al., 2011). Many scholars

advocated this bottom-up approach. On the other hand, there may also be an effect of

overall well-being on travel satisfaction, which would indicate a top-down approach in the

study of subjective well-being in the sense that their overall perspective on life may affect

how people feel about specific life domains (see, for example, Diener, 1984; Headey et

al., 1991). Few studies, however, have examined this top-down relationship using

empirical data. Only since the study of travel satisfaction has appeared on the agenda of

travel behavior researchers, researchers have started to explore the relationship between

travel satisfaction and subjective well-being (Bergstad et al., 2011; Ettema et al., 2010;

Hansson et al., 2011; Martin et al., 2014; Sirgy et al., 2011). Even so, studies in this field

have not emphasized the eudaimonic view of well-being. In this study, we focus on both

hedonic and eudaimonic well-being and their relationships with travel satisfaction. We

estimated the interdependency between travel satisfaction, overall life satisfaction and


6.2.2 Personality and subjective well-being

Some studies have addressed the research question “Who is happy”. The majority of

studies on subjective well-being examined biosocial indicators such as gender and age.

Other studies have suggested that personality may be one of the strongest determinants

of subjective well-being. For example, DeNeve and Cooper (1998) examined 137 distinct

personality constructs as correlates of subjective well-being. Many personality traits were

significantly associated with subjective well-being. For instance, of the “Big Five”

personality traits (Openness to experience, Conscientiousness, Extraversion,

Agreeableness and Neuroticism) (Costa and McCrae, 1992), extraversion and

agreeableness were positively associated with subjective well-being, whereas neuroticism

was negatively associated with it. McCrae and Costa (1991) found that extraversion leads

to a positive affect, while neuroticism leads to a negative affect. Openness correlates

positively with both positive and negative affect. Agreeableness and conscientiousness

have instrumental effects on subjective well-being.

Diener and Lucas (1999) suggested that these big five findings should not come

as a surprise because extraversion is characterized by a positive affect and neuroticism is

virtually defined by a negative affect. Some researchers have examined the relation of

the big five personality traits to psychological well-being. Schmutte and Ryff (1997) found

that extraversion, conscientiousness, and low neuroticism were linked with the

eudaimonic dimensions of self-acceptance, mastery, and life purpose; openness to

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experience was linked to personal growth; agreeableness and extraversion were

connected to positive relationships; and low neuroticism was associated with autonomy.

Although personality has been shown to be a major determinant of subjective

well-being in the literature, few studies explored the correlation between personality and

satisfaction in the travel domain. Ory and Mokhtarian (2005) found that attitudes,

personality, and lifestyle are key variables affecting travel liking. Abou-Zeid and Ben-Akiva

(2011) found a positive correlation between an organized personality and work commute

satisfaction.

6.3 Data collection and measurement scales

6.3.1 Procedure

Data for this study were collected in Xi’an city, China. Xi’an is the capital city of Shaanxi

province, located in the northwest of China with a population of almost 8.46 million people.

The data used in this study were collected in January 2015 using random sampling. A

face-to-face interview, based on a structured questionnaire, was used to collect the data.

The interviewers consisted of a pool of graduate and master students of Chang’an

University, who were specially trained for the interviews. Several versions of the

questionnaire were pre-tested. Changes in wording and especially explanation were made

until no more critical problems remained. The quality of the returned data, differentiated

by interviewer, was monitored and appropriate action was taken if needed. A total of

1445 respondents completed the questionnaire.

The operationalization of the conceptual framework requires data on subjective

well-being, which included life satisfaction/evaluation, domain satisfaction and

eudaimonic well-being; personality traits and socio-demographic characteristics. The

survey included questions pertaining to each of these concepts. First, the questionnaire

asked about the respondent personal characteristics including age, gender, type of job,

education, household income, etc. Second, respondents were asked to rate themselves

on a set of personality traits. Next, trip information and the mood experienced during the

travel day were elicited, followed by a segment on overall trip satisfaction and well-being.

The next segment prompted respondents to judge the service quality of different

transport modes. The last part concerned the measurement of subjective well-being.

To reduce possible correlations due to halo effects between personality traits and

mood, and between these constructs and satisfaction, the measurements of these

constructs were separated as much as possible. As indicated, first, data on personality


105

traits were collected, immediately after the elicitation of socio-demographic information.

Moods were measured separately before the trip diary was constructed. Ratings of

satisfactions of trip stages were solicited after retrieving all trip attributes in the trip diary.

The minimum duration to completion of a survey was around 1 hour, the longest was

about two hours.

6.3.2 Measurement

Our literature review indicated that previous research has relied on different scales to

measure subjective well-being. Methodologically, the notion of a scale presumes its

generalizability has been proven. In reality, at best partial evidence can be given in the

sense that one can never be sure that when a new sample of respondents completes a

set of validated items the same or similar reliability will be obtained (see, for example,

De Vos et al., 2015). Existing scales have been developed in the American and European

context. To the best of our knowledge, the scales have never been validated in a Chinese

context. Therefore, rather than uncritically applying an existing scale, we systematically

evaluated the relevance of the items of the various scales, translated these into Chinese

trying to find the same subtlety and connotations in the choice of words, and explicitly

re-analyzed the validity and reliability of the resulting scales. Based on the literature, this

study measured subjective well-being in terms of three categories: overall life evaluation,

eudaimonic well-being and domain satisfaction.

6.3.2.1 Overall life satisfaction/evaluation

Four questions and five statements were used to measure life satisfaction. The four

questions are: (1) Imagine a ladder with steps numbered from 0 at the bottom to 10 at

the top. The top of the ladder represents the best possible life for you and the bottom of

the ladder represents the worst possible life for you. On which step of the ladder would

you say you personally feel you stand at this time? (2) Overall, how satisfied with your

life were you five years ago? (3) As your best guess, overall how satisfied with your life

do you expect to feel in five years’ time? (4) Overall, to what extent do you feel the things

you do in your life are worthwhile?

The five statements about life evaluation are (1) In most ways my life is close to

my ideal; (2) The conditions of my life are excellent; (3) I am extremely satisfied with my

life; (4) So far, I have gotten all important things I want in life; (5) If I could live my life

over, I would change almost nothing. Values again ranged from 0 (“strongly disagree”)

to 10 (“strongly agree”).

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6.3.2.2 Eudaimonic well-being

Eudaimonic well-being was measured on the basis of eight statements: (1) I lead a

purposeful and meaningful life; (2) My social relationships are supportive and rewarding;

(3) I am engaged and interested in my daily activities; (4) I actively contribute to the

happiness and well-being of others; (5) I am competent and capable in the activities that

are important to me; (6) I am a good person and live a good life; (7) I am optimistic

about my future; (8) People respect me. Respondents were invited to answer these

questions using a 11-point scale, ranging from 0 (“strongly disagree”) to 10 (“strongly

agree”).

6.3.2.3 Domain satisfaction

Individuals are assumed to rate their overall life satisfaction by cognitively integrating

their satisfaction ratings for various life domains. A total of fourteen questions about

satisfaction with different life domains were included in our questionnaire: standard of

living, health, achievements in life, personal relationships, safety at home, safety out of

home, relationship with neighbours, relationship with friends, relationship with colleagues,

future security, the amount of time you do the things that you like, quality of local

environment, work and general travel experience. Values ranged from 0 (“not at all

satisfied”) to 10 (“extremely satisfied”).

6.3.2.4 Personality traits

Researchers have relied on a variety of psychometric scales for measuring personality

traits, including the Ten-Item Personality Inventory (TIPI), which is a very brief measure

of the Big-Five personality domains (Gosling et al., 2003). This instrument is useful in

situations when fewer items are needed, or researchers can tolerate the somewhat

diminished psychometric properties associated with such limited items instruments. For

each item, we systematically assessed whether we could reason the relationship between

the personality trait and travel satisfaction. Ultimately, we selected six out of the ten-

items: critical, self-disciplined, impatient, reserved, easy-going and calm. We assumed

that these personality traits may systematically affect people’s experience of travel and/or

their ratings of travel satisfaction.

Respondents were asked to rate the extent they agree or disagree with personality

statements such as “I see myself as critical.” on an 11-point scale, ranging from “disagree

strongly” to “agree strongly”. Thus, for reasons similar to the measurement of satisfaction,

single-item scales were used to measure the different personality traits.


107

Table 6-1 Descriptive statistics of the sample (N = 1445)

Socio-demographics Category Observations Percentage

Age <18 17 1%

18-25 410 28%

25-35 672 47%

35-45 211 15%

45-55 95 7%

>=55 40 3%

Gender Male 669 46%

Female 776 54%


High school 303 21%


Master 128 9%

PhD 15 1%

Household size

(person)

1 337 23%

2-3 659 46%

>3 449 31%

Household income

(RMB/Month)

<4000 513 36%

4000-8000 566 39%

>8000 366 25%

Household car

ownership

No car 721 50%

One car 708 49%


Job type Full time employed (fixed schedule) 738 51%

Full time employed (flexible schedule) 304 21%

Part time employed 58 4%

Full time student 280 19%

Part time student 60 4%

Unemployed 5 0%


The following socio-demographic characteristics were measured: age (year), gender (0

= female; 1 = male), household size (number of persons living in your family), household

income (three classes ranging from lower than 4000 RMB to more than 8000 RMB),

education (five classes ranging from lower than high school to doctoral degree), job type

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(six classes including fix schedule full time employed, flexible schedule full time employed,

part time employed, full time student, part time student and unemployed).

6.3.3 Sample description

1445 respondents were used in the analyzes. Distributions of characteristics of the sample

are shown in Table 6-1. It demonstrates that 46% of the sample is between 25 and 35

years of age, 28% is between 18 and 25 years old, while only 3% of the sample is older

than 55. Table 6-1 also shows that 54% of the sample is female, while 46% is male. 31%

of the sample is living in households with more than 3 persons, while 23% is living alone.

Even though 73% of the sample has a professional education, income levels are medium.

More than half of the sample (51%) is employed full time (fixed schedule).

6.4 Structural equation model between travel satisfaction and

subjective well-being

6.4.1 Conceptual framework

Based on our review of the satisfaction literature in travel behavior research and other

fields of inquiry, and a critical reflection of the relevance and applicability of these studies

to examine the relationship between travel satisfaction and subjective well-being in a

methodologically rigorous manner that avoids various sources of bias, we developed a

conceptual framework. Figure 6-1 shows the conceptual model that was used to develop

the structural equation model that we estimated to examine the influence of travel

satisfaction on subjective well-being, and the reverse relationship. As the figure shows,

we assume that overall life satisfaction is influenced by domain-specific satisfaction, and

vice-versa that domain satisfaction may also be influenced by overall life satisfaction. This

reflects the idea that the examination of the relationships between travel satisfaction and

overall life satisfaction should include satisfaction with other life domains to avoid bias.

Satisfaction ratings of life domains may be influenced by judgements of overall life

satisfaction. Hence, we assume that overall life satisfaction, domain satisfaction and life

evaluation are mutually correlated. In addition, following findings in social psychology,

we assume that personality may influence well-being, or at least the subjective rating of

well-being. Depending on particular personality traits, people may differently rate their

satisfaction with different life domains and overall life satisfaction. Finally, we allow for

correlations between socio-demographic variables, and personality traits and subjective

well-being. Figure 6-1 depicts the hypothetical causal model.


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Figure 6-1 Conceptual framework of SEM

6.4.2 Validation

In order to validate the scales and assess the formulated conceptual framework, an

exploratory factor analysis (EFA) was conducted using half of the sample based on three

theoretical constructs: (1) eudaimonic well-being; (2) life evaluation; and (3) the different

domain-specific satisfactions. The results of the EFA, using varimax rotation and a factor

loading of 0.40 as the threshold to retain items in a factor, led to three independent

factors as shown in Table 6-2: (1) eudaimonic well-being; (2) social network satisfaction;

and (3) life evaluation.

Important in these results is the fact that respondents seem to differentiate

between hedonic and eudaimonic interpretations of subjective well-being. Moreover, the

different questions related to satisfaction with social network relationships loaded on a

single different factor. Other domain-specific satisfactions do not show strong

communalities, suggesting they may be judged independently from other domain

satisfactions, which is a positive finding.

After the EFA procedure, a confirmatory factor analysis (CFA) specifying the

posited relationships of the observed indicators to the latent variables, was conducted

using the other half of the sample (Table 6-3). The following goodness of fit indices were

obtained: Chi square/df = 4.098, Comparative Fit Index (CFI) = 0.944, Tucker Lewis

Index (TLI) = 0.934, Standardized Root Mean Square Residual (SRMR) = 0.042. These

statistics support the validity of the constructed scales.

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Table 6-2 Results of the exploratory factor analysis (N = 722)

Latent variables and Measured indicators Factor

loading

Explained

variance

Mean

(St. Dev)

Eudaimonic well-being 21.038

I lead a purposeful and meaningful life. ** 0.606 6.95 (1.963)

My social relationships are supportive and rewarding. ** 0.555 6.93 (1.971)

I am engaged and interested in my daily activities. ** 0.634 6.68 (2.085)

I actively contribute to the happiness and well-being of

others. ** 0.636 6.80 (1.938)

I am competent and capable in the activities that are

important to me. ** 0.630

6,92 (1.778)

I am a good person and live a good life. ** 0.566 7.55 (1.771)

I am optimistic about my future. ** 0.547 7.65 (1.808)

People respect me. ** 0.603 7.61 (1.597)

Social network satisfaction 16.044

How satisfied are you with your friends? * 0.856 8.32 (1.416)

How satisfied are you with your colleagues? * 0.675 7.95 (1.668)

How satisfied are you with your personal relationships? * 0.633 7.81 (1.611)

How satisfied are you with your neighbours? * 0.486 7.36 (2.173)

Life evaluation 14.604

In most ways my life is close to my ideal. ** 0.699 6.34 (1.960)

The conditions of my life are excellent. ** 0.801 5.99 (1.955)

I am extremely satisfied with my life. ** 0.727 6.33 (2.047)

Please imagine a ladder with steps numbered from 0 at

the bottom to 10 at the top. The top of the ladder represents

the best possible life for you and the bottom of the ladder

represents the worst possible life for you. On which step of

the ladder would you say you personally feel you stand at this

time?

0.551 6.94 (1.667)

Total variance explained 51.687

Notes: Rotation method: Varimax.; Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.916.

*Scale: 0 = not at all satisfied, 10 = extremely satisfied.

**Scale: 0 = strongly disagree, 10 = strongly agree.


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Table 6-3 Results of the confirmatory factor analysis (N = 723)

Latent variables and Measured indicators STDYX Standardized

Loadings (S.E.)

Eudaimonic well-being

I lead a purposeful and meaningful life. ** 0.691 (0.022)

My social relationships are supportive and rewarding. ** 0.698 (0.021)

I am engaged and interested in my daily activities. ** 0.724 (0.020)

I actively contribute to the happiness and well-being of others. ** 0.673 (0.023)

I am competent and capable in the activities that are important to

me. ** 0.698 (0.021)

I am a good person and live a good life. ** 0.705 (0.021)

I am optimistic about my future. ** 0.750 (0.019)

People respect me. ** 0.739 (0.019)

Social network satisfaction

How satisfied are you with your friends? * 0.735 (0.023)

How satisfied are you with your colleagues? * 0.758 (0.022)

How satisfied are you with your personal relationships? * 0.692 (0.025)

How satisfied are you with your neighbours? * 0.594 (0.029)

Life evaluation

In most ways my life is close to my ideal. ** 0.823 (0.025)

The conditions of my life are excellent. ** 0.769 (0.018)

I am extremely satisfied with my life. ** 0.879 (0.013)

Please imagine a ladder with steps numbered from 0 at the bottom to

10 at the top. The top of the ladder represents the best possible life for

you and the bottom of the ladder represents the worst possible life for

you. On which step of the ladder would you say you personally feel you

stand at this time?

0.669 (0.023)

Notes:

*Scale: 0 = not at all satisfied, 10 = extremely satisfied.

**Scale: 0 = strongly disagree 10 = strongly agree.

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Table 6-4 Standardized direct effect between subjective well-being variables (N=1445)

Variables Life

evaluation

Social

network

Living

standard Health

Life

achievement

Safety at

home

Safety out

of home

Future

security

Amount of time do what you like

Environme

nt quality Work Travel

Overall well-being

Eudaimonic well-

being 0.694 0.496 0.291 0.213 0.209 0.434

Life evaluation 0.282 0.650 0.471 0.260 0.229 0.270 0.262 0.287

Domain

satisfaction

Social network 0.290

Health 0.106

Life achievement 0.321 0.144

Safety at home 0.165

Future security 0.144

Amount of time do

what you like 0.125

Environment quality 0.157 0.111 0.154

Work 0.185

Travel 0.086 0.067

Note: Significant at 0.05 level.


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Table 6-5 Standardized direct effect of personality and socio-demographic variables on subjective well-being (N=1445)

Variables Eudaimonic

well-being Living standard Health Safety at home

Safety out of

home

Amount of time do

what you like

Environment

quality Travel

Personality

Self-disciplined 0.188 0.064 0.073 0.106

Impatient -0.064 -0.073

Easy-going 0.138 0.074 0.061

Reserved 0.128

Calm 0.102

Socio-

demographics

Age 0.129 -0.119 -0.060

Gender 0.083

Note: Significant at 0.05 level.

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Table 6-6 Standardized direct effect of socio-demographic variables on personality traits (N=1445)

Variables Self-disciplined Reserved Easy-going Calm

Socio-demographics

Age 0.150 0.122 -0.120 0.226

Gender 0.095 0.066 0.082

Note: Significant at the 0.05 level.

Table 6-7 Goodness of fit measures for the structural equation model (N=1445)

Goodness of fit measure Index Criteria

χ2/df 3.573 < 5.0

RMSEA 0.042 < 0.08

SRMR 0.038 < 0.05

CFI 0.937 > 0.9

TLI 0.925 > 0.9

Notes:

χ2 = Chi-square；df = degree of freedom；RMSEA = root mean square error of approximation；

SRMR = standardized root mean square residual；CFI = comparative fit index；TLI = Tucker Lewis

index.

Based on our conceptual framework and the results of the factor analyzes, we

estimated the corresponding structural equation model. The primary interest is in the

relationship between travel satisfaction and overall subjective well-being without making

any a priori claims regarding the direction of causality. Because subjective well-being may

also be influenced by satisfaction with other life domains, these were also included in the

analysis. As discussed, a distinction was made between hedonic and eudemonic

subjective well-being. Personality was included in the model based on the hypothesis that

particular personality traits may influence satisfaction ratings. Finally, we examined the

effect of several socio-demographic variables. As we only found consistent effects for age

and gender, the reported results of the structural equation model only include these

variables.


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The estimated structural equation model and the standardized coefficients of the

estimated relationships are shown in Table 6-6. Table 6-7 list the calculated goodness of

fit measures. It shows that all measures meet commonly used criteria for a good fit.

Table 6-6 show the results of the estimated structural equation model. All

coefficients of the measurement components of the SEM are nonzero and significant at

conventional levels. The final model consists of 16 endogenous variables: life evaluation,

eudaimonic well-being, social network satisfaction, satisfaction with the various domains,

including travel satisfaction, and personality traits self-disciplined, calm, reserved, and

easy going. Age and gender are the exogenous variables in the model.

6.4.3 Measurement model

The SEM model measures the latent variable eudaimonic well-being based on 8

statements; all coefficients exceed 0.6, indicating that all statements are strongly

correlated with eudaimonic well-being. The latent variable “Overall life

satisfaction/evaluation” is measured in terms of three life evaluation statements: “In most

ways my life is close to my ideal (λ = 0.769)”, “I am extremely satisfied with my life (λ =

0.758)” and “The conditions of my life are excellent (λ = 0.739)”; and one life evaluation

question “On which step of the ladder would you say you personally feel you stand at this

time (λ = 0.726)”. The latent variable “Social network satisfaction” is measured in terms

of four domain satisfaction items: personal relationships (λ=0.771), relationship with

friends (λ = 0.722), relationship with colleagues (λ = 0.684) and relationship with

neighbours (λ = 0.579).

6.4.4 Structural model

Table 6-4 shows the interrelations between the subjective well-being constructs. First,

eudaimonic well-being is positively related to life evaluation (β = 0.694). Because life

evaluation measures hedonic well-being, this result is consistent with our hypothesis that

eudaimonic well-being affects hedonic well-being. For the relationship between

eudaimonic well-being and life domain satisfaction, results show that eudaimonic well-

being has positive and significant effects on social network satisfaction (β = 0.496), living

standard satisfaction (β = 0.291), future security satisfaction (β = 0.213), satisfaction of

the amount of time you do what you like (β = 0.209), and work satisfaction (β = 0.434).

Individuals with higher eudaimonic well-being are more likely to be satisfied with their

social network, living standard, future security, the amount of time do what you like and

work. Likewise, the correlations between life evaluation and domain satisfaction are

positive. Results show that life evaluation positively impacts satisfaction with health (β =

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0.282), life achievement (β = 0.650), safety at home (β = 0.471), safety out of home (β

= 0.260), future security (β = 0.229), the amount of time you do what you like (β =

0.270), environment quality (β = 0.262), and travel (β = 0.287). It indicates that the

effect of life evaluation on life achievement satisfaction is the strongest of all life domains.

Work satisfaction significantly influences life evaluation (β = 0.185), but satisfaction for

other life domains does not show significant effects. This indicates people who are more

satisfied with their work will achieve higher overall life satisfaction.

Travel satisfaction, as a domain of life satisfaction, is the focal point of this study.

Results show that travel satisfaction does not significantly influence life evaluation;

however, life evaluation strongly influences travel satisfaction (β = 0.287).

Table 6-4 also shows positive relationships between satisfactions for different life

domains. Results show that satisfaction of social network is correlated with health

satisfaction (β = 0.290). In turn, heath satisfaction is correlated with satisfaction of safety

out of home (β = 0.106). Similarly, satisfaction with life achievements shows a positive

relationship with satisfaction with living standard (β = 0.321) and future security (β =

0.144). Satisfaction of safety at home shows a significant relation with satisfaction with

social network (β = 0.165). The satisfaction with future security is significantly correlated

with the satisfaction with safety out of home (β = 0.144). The satisfaction of amount of

time you do what you like has a significant relationship with satisfaction with social

network (β = 0.125). The satisfaction with environment quality is significantly related to

satisfaction with safety out of home (β = 0.157), future security (β = 0.111) and the

amount of time you do what you like (β = 0.154). Finally, travel satisfaction shows a

significant relationship with the satisfaction with future security (β = 0.086) and the

amount of time you do what you like (β = 0.067).

Table 6-5 shows the effect of personality traits and socio-demographic variables

on subjective well-being. The model shows that personality traits do have a significant

effect on eudaimonic well-being and domain satisfaction. Particularly, eudaimonic well-

being is positively influenced by personality traits self-disciplined (β = 0.188), easy-going

(β = 0.138) and calm (β = 0.102). In contrast, the personality trait “impatient” negatively

influences eudaimonic well-being (β = -0.064). This suggests that self-disciplined, easy-

going and calm people are more likely to lead a meaningful and purposeful life. As might

be expected, people who are impatient are less likely to achieve their life purposes. As

for the effects of personality on domain satisfactions, a self-disciplined personality has

positive effects on satisfaction with the living standard (β = 0.064) and health (β = 0.073).

An impatient personality has a negative effect on satisfaction with health (β = -0.073).


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An easy-going personality has positive effects on satisfaction with health (β = 0.074) and

the amount of time you do what you like (β = 0.061). Finally, a reserved personality has

a positive effect on environment satisfaction (β = 0.128).

Age and gender also influence subjective well-being constructs. Age is positively

correlated with eudaimonic well-being (β = 0.129) and negatively correlated with health

satisfaction (β = -0.119) and safety at home (β = -0.060). Gender positively influences

satisfaction with safety out of home (β = 0.083).

There is also evidence of effects of age and gender on personality traits (Table 6-

6). Age has a positive effect on a self-disciplined personality (β = 0.150), a reserved

personality (β = 0.122), and a calm personality (β = 0.226) while it has a negative effect

on an easy-going personality (β = -0.120). Gender has a positive effect on a self-

disciplined personality (β = 0.095), an easy-going personality (β = 0.066), and a calm

personality (β = 0.082). Older people tend to be more self-disciplined, reserved and calm,

but less easy-going than younger people. Men tend to be more self-disciplined, easy-

going and calm than women.

6.5 A latent class structural equation model of co-dependent

relationships between domain satisfaction and subjective

well-being

Methodologically, a potential advantage of the structural equation and path models that

are primarily associated with satisfaction research is that these models allow the

estimation of quite complex direct and indirect relationships between socio-demographics,

personality traits, attributes of transportation models, satisfaction and well-being.

Interestingly, the evolution of choice models is also characterized by increasingly

complexity. For example, hybrid choice models (Ben-Akiva et al., 2002), add attitudes

and possibly other psychological constructs to utility in an attempt to better explain choice

behavior. Recently, these models have been extended to include social influence (Kim et

al., 2017). Hybrid choice models, however, do not allow estimating indirect and reciprocal

relationships. If such complexity would dictate the choice of model, research should prefer

estimating structural equation or path models.

On the other hand, advances in discrete choice modeling have led to several

alternatives to incorporate unobserved heterogeneity in choice models. Mixed logit

models allow estimated the parameters of an assumed distribution of estimated

parameters expressing the marginal effects of attribute differences on choice probabilities

(McFadden and Train, 2000). Similarly, latent class choice models identify latent segments

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of the population (Boxall and Adamowicz, 2002), exhibiting different utility functions or

even different decision rules (Hess and Stathopoulos, 2013; Kim et al., 2016). These

approaches have also been combined (Hensher, et al., 2013).

To the best of our knowledge, transportation researchers have not formulated

models that include heterogeneity into structural equation or path models. Rather, a

single structural model is estimated, which is assumed to apply to all respondents (of a

particular socio-demographic profile and a set of personality traits). Thus, the analytical

power of researchers would be enhanced if latent class structural equation models would

become an integral part of the set of models that can be used in travel satisfaction

research.

The aim of this analysis therefore is to formulate and estimate such a latent class

structural equation model. It assumes that the co-dependent relationships between travel

satisfaction and social well-being vary between latent classes. In that sense, the latent

class structural equations is similar in idea to the latent class models that have been

widely applied in transportation research. Latent classes are identified that differ in terms

of the parameters of the kind of model that is estimated.

6.5.1 Conceptual framework

In this section, we model the socio-demographic indicators and personality traits as a

categorical latent variable to investigate heterogeneity in the relationship between domain

satisfaction and subjective well-being by employing a latent categorisation approach

within a SEM framework. This approach goes beyond the methods using continuous latent

variables, it imposes the assumption that there are certain numbers of latent segments

among individuals. In our study, we assumed the latent class variable is influenced by

some indicators such as socio-demographic characteristics and personality traits.

SEM is an approach that estimates direct and indirect effects between latent

concepts using qualitative causal assumptions. It requires a conceptual model, which

hypothesises causal effects, and then tests the model on specific data to determine how

valid the hypotheses are. A conceptual framework was adopted in this study to examine

the effects of domain satisfaction on overall life evaluation.

Figure 6-2 depicts the hypothetical model structure. We include the socio-

demographic characteristics and personality traits as the indicators of the latent variable

in the SEM.


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Domain Satisfaction

CIndicators

Overall Life Evaluation

Indicator 1

Indicator 2

Indicator 3

Indicator 4

Figure 6-2 Conceptual framework of LCSEM

Table 6-8 Average latent class probabilities for residents’ most likely latent

class membership (Row) by latent class (Column)

Class 1 Class 2

Class 1 membership 0.892 0.108

Class 2 membership 0.005 0.995

6.5.2 Model estimation results

The latent class structural equation model shown in Figure 6-2 is estimated. The models

are estimated by using the statistical software Mplus 7.31 (Muthén and Muthén, 2015).

We estimated the model incrementally increasing the number of classes. The two-class

solution gave the best interpretable results. The subsequent model with incorporate

effects of the class membership functions is estimated based on two classes. After

removing insignificant socio-demographic and personality variables, we got the final

model with results shown in Table 6-9. The model with two latent classes has an entropy

index of 0.965. This suggests that the latent classes are well defined. A cross-tabulation

of the most likely latent class membership (row) by latent class (column) in Table 6-8

corroborates the high entropy value.

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Table 6-9 Results of latent class structural equation model

Constrained

model Two classes model

Class 1 Class 2

Estimate P Estimate P Estimate P

Indicators

Overall life evaluation

In most ways my life is close to

my ideal. ** 0.789 0.000 0.845 0.000 0.778 0.000

The conditions of my life are

excellent. ** 0.805 0.000 0.742 0.000 0.771 0.000

I am extremely satisfied with my

life. ** 0.821 0.000 0.636 0.000 0.819 0.000

On which step of the ladder

would you say you personally

feel you stand at this time?

0.691 0.000 0.885 0.000 0.730 0.000

Direct influence

Domain satisfaction

Living standard 0.226*** 0.000 0.155 0.465 0.249*** 0.000

Health 0.014 0.591 0.455*** 0.000 -0.009 0.745

Achievements in life 0.243*** 0.000 -0.418 0.056 0.239*** 0.000

Personal relationships 0.022 0.417 0.014 0.908 0.057 0.055

Safety at home 0.039 0.129 0.484** 0.011 0.013 0.681

Safety out of home 0.050 0.073 -0.214 0.134 0.067** 0.027

Relationship with neighbors -0.014 0.581 -0.145 0.248 -0.017 0.520

Relationship with friends 0.000 0.989 0.563*** 0.000 0.020 0.538

Relationship with colleagues 0.035 0.208 -0.136 0.263 0.027 0.393

Future security 0.145*** 0.000 -0.119 0.560 0.144*** 0.000

The amount of time you do

what you like 0.145*** 0.000 -0.002 0.989 0.166*** 0.000

Quality of local environment 0.004 0.900 0.305*** 0.008 0.005 0.888

General travel experience 0.022 0.444 -0.247 0.062 0.045 0.142

Work 0.131*** 0.000 0.339*** 0.009 0.130*** 0.000

Categorical latent variable

Calm -- -0.231 0.015 -- --


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Constrained

model Two classes model

Class 1 Class 2

Estimate P Estimate P Estimate P

Indicators

Loglikelihood

H0 Value -10112.491 -9954.218

H0 Scaling Correction Factor for

MLR -- 1.4318

Information Criteria

AIC 20276.982 20002.437

BIC 20414.155 20250.402

Sample-Size Adjusted BIC 20331.562 20101.099

Classification Quality

Entropy -- 0.965

Chi-Square Test of Model Fit

Value 232.818 --

Degrees of Freedom 44 --

P-Value 0.0000 --

RMSEA 0.054 --

Probability RMSEA <=.05 0.135 --

CFI 0.948 --

TLI 0.929 --

SRMR 0.024 --

The estimation results of this latent class model are reported in Table 6-9. To

identify the additional insights of incorporating a categorical variable in the SEM model,

we compared the results from our new model with those from a constrained SEM where

the model parameters do not vary across the latent classes. Goodness of fit indicators

AIC, BIC and adjusted BIC decreases in the latent class model which indicated that the

latent class model performance better than the constrained model. The log likelihood of

the SEM is somewhat lower than that of the latent class model. The LCSEM estimation

shows that different segments of travelers are characterized by different effects of these

variables. In other words, the LCSEM estimation provides insights into the nature and

origin of the heterogeneity.

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Table 6-9 shows some of the coefficients of the direct relationships between

domain satisfaction and overall life evaluation are significant at the 5 per cent level. The

signs and magnitudes of these influences vary across the latent classes indicating that

there is significant unobserved heterogeneity on the evaluation of overall life evaluation

and life domain. It means that the two latent classes mainly differ in terms of the relative

importance of some variables. The effects of a calm personality on latent class 1 is -0.231.

It indicates the calm personality could be one of the causes of heterogeneity between

individuals.

From the results of constrained model, we found that satisfaction of living standard,

achievements in life, future security, the amount of time you do what you like and work

have significant effects on overall life evaluation. However, both in the constrained model

and in the latent class model, the effect of travel satisfaction on overall life evaluation is

not significant which is consistent with the conclusions in the previous section. As we can

see from the results of the latent class model, the signs and magnitudes of the coefficients,

the effects of domain satisfaction on overall life evaluation in each class are discriminatory.

This indicates the heterogeneity among the individuals is significant. In latent class one,

the effects of health (0.455), safety at home (0.484), relationship with friends (0.563),

environment (0.305), and work (0.339) on overall life evaluation are significant at the 5

per cent level. In latent class two, the effects of living standard (0.249), the achievements

in life (0.239), safety out of home (0.067), future security (0.144), the amount of time

you do what you like (0.166), and work (0.130) on overall life evaluation are significant

at 5 per cent level. Class one can be considered as people who more care about health,

environment and relationship with friends while class two are the ones more ego centric

people with putting importance on their individual satisfaction with respect to living

condition, life achievements, future security etc. Noticeably, the majority belong to class

two. The direct influence of work satisfaction on overall life evaluation in the latent class

1 (0.339) is stronger than which in the latent class 2 (0.130). Besides travel satisfaction,

some other domains also do not significantly affect overall life evaluation, for example

personal relationships, relationships with neighbors and relationships with colleagues.

6.6 Discussion and conclusions

Understanding the relationship between travel satisfaction and subjective well-being is

essential, not only because it fulfils a research gap, but also because it becomes

increasingly relevant to the policy and operations of transport. By recognizing segment

specific characteristics and different sensitivity to the evaluation of travel experiences,


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effective strategies for different travelers can be made by transport companies or policy

makers.

The empirical results of this analysis show that travel satisfaction does not

significantly influence life evaluation, but life evaluation strongly influences travel

satisfaction. This may be a disappointing result in the sense it suggests daily travel is less

influential for subjective well-being than other domains such as work, health and personal

relationships. On the other hand, we would be surprised if a different result would have

been obtained.

This finding is not in line with selective earlier research. Even though some

empirical work studies the impact that transport may have on well-being (Archer et al.,

2013; Bergstad et al., 2011; de Groot and Steg, 2006; Delbosc, 2012; Delbosc and Currie,

2011a, 2011b, 2011c; Steg and Gifford, 2005), very little work has directly studied the

relationship between travel satisfaction and overall well-being. Despite the small body of

extant studies regarding the effect of travel satisfaction on overall well-being, some

studies have empirically demonstrated that travel well-being contributes to overall well-

being or quality of life. For example, Spinney et al. (2009) found significant association

between transport mobility benefits and quality of life. Bergstad et al. (2011) found the

effect of satisfaction with daily travel on affective and cognitive subjective well-being is

both direct and indirect. However, few studies treated subjective well-being as an

exogenous variable (Abou-Zeid and Ben-Akiva, 2011). Abou-Zeid and Ben-Akiva (2011)

found a positive impact of overall well-being on commute satisfaction, but the effect is

not significant.

Our study focused on general satisfaction with daily travel and thus extends the

knowledge derived from prior research on travel satisfaction, which focused on the

relationship between subjective well-being and types of trips, such as leisure trips (Sirgy

et al., 2011) and commute trips ( Mao et al., 2016; Olsson et al., 2013; Ye and Titheridge,

2016).

Personality traits are found to impact travel satisfaction in this study. The inclusion

of personality traits may in part explain our main finding, relative to other studies.

Moreover, only some socio-demographic variables are shown to affect travel satisfaction,

but only indirectly. For example, age and gender indirectly influence travel satisfaction

through self-disciplined personality.

Travel satisfaction is shown to affect satisfaction with other life domains such as

satisfaction with the amount of time you do what you like. This supplements previous

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findings that commute satisfaction has a positive and significant effect on work well-being

(Abou-Zeid and Ben-Akiva, 2011).

Finally, we use a latent class structural equation model to gain new insights into

the relationship between domain satisfaction and subjective well-being. The latent classes

are defined based on the personality traits, which provides the insights into their joint

influences upon domain satisfaction and subjective well-being. Results of a model without

latent classes serve as comparison. This analysis has revealed heterogeneity in the

relationship between evaluation of overall life and life domain.

However, several problems are worth considering in future research. In this

chapter, we measured the satisfaction of travel experiences, which is regarded as a life

domain. Future research could further investigate the relationship between satisfaction

of trip/trip stage and subjective well-being based on revealed data. Moreover, although

a range of personality items was tested in this study, it is also interesting to apply more

comprehensive measures for personality instead of the current six-item scales. This study

is only concerned with a city in China, it is worthwhile to repeat the experiment in different

cities and countries to better understanding any spatial, cultural differences in travel

satisfaction and subjective well-being.


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7

CONCLUSIONS AND FUTURE WORK

7.1 Summary

Despite extensive research on consumer satisfaction over the past decades, the study of

travel satisfaction has only recently been discussed in transportation research. The results

are used for supporting urban planners and transportation policy makers in their

endeavours to create transport strategies that enhance subjective well-being or helping

transportation providers to evaluate their service provision.

Previous studies mostly focused on the development of measurement methods

and the identification of correlates of travel satisfaction. However, most studies did not

consider the subjective correlates of travel satisfaction even though the effects of those

attributes are significant and cannot be ignored. Moreover, with the development of travel

behavior models and subjective psychology research, researchers try to better understand

the effects of travel satisfaction on subjective well-being or quality of life.

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The aim of this thesis is to enhance our understanding of travel satisfaction and

its correlation with subjective well-being, contributing to both modeling and data

collection challenges. Rather than the common focus on trips, it assumes that individuals’

responses to travel satisfaction are distinguished in the context of different trip attributes,

travel modes, and different trip stages. Therefore, a new survey was designed to collect

data about travel satisfaction on overall travel satisfaction, whole day travel satisfaction,

satisfaction for every trip and trip stage.

To explore the effects of both objective and subjective attributes on travel

satisfaction, both a path model and a reference-based model was developed and applied.

Chapter 4 aimed at exploring the effects of objective and subjective attributes of vehicles

and trips on trip stage satisfaction. The empirical results of the path analysis provided

evidence that subjective factors such as mood, and personality traits have significant

effects on trip stage satisfaction. Moreover, contrasted with the state of the art in travel

behavior research, which is almost invariantly based on linear regression and structural

equations analysis, we explored the performance of non-linear models of trip satisfaction.

We assumed that trip satisfaction systematically varies in a non-linear fashion with the

discrepancy between expectations and actual experienced attributes of the trip. We

developed a reference-based model of trip satisfaction that takes perceived service quality,

attitudes, moods, personality traits and indifference tolerance into account. Results

support the contentions underlying the study: several relationships between attributes

and satisfaction appear non-linear. There is also evidence of indifference tolerance, but

results differ between attributes.

However, these results are limited by the fact that we only focus on the trip stage

satisfaction. Requesting respondents to provide satisfaction ratings for multi-stage trips

or daily travel experiences implies they have to value each stage respectively based on

memory recall and then cognitively integrate these judgments into the requested

satisfaction rating of trip or whole day trip experience. Our knowledge about the

prevalence of alternate processing rules that may be used to arrive at trip satisfaction

ratings is very limited. Research on this topic in travel behavior analysis is very scarce. In

contributing to the research on travel satisfaction related to public transport, chapter 5

compares the performance of different processing rules using data on satisfaction with

public transport trips from Xian, China. Based on the results of chapter 5, we found the

popular peak-end rule, except for the disjunctive rule, consistently had the lowest

explained variance, also after controlling for socio-demographics, mood and personality

traits. Rather, for all estimated models, the conjunctive processing rule had the highest

associated explained variance. It suggests that the episode respectively trip with the


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lowest satisfaction dominates overall satisfaction. Second, we also did not find much

evidence of a recency effect. Rather, the satisfaction of the first trip or stage has higher

marginal effects on overall satisfaction than more recent trips or stages.

Chapters 4 and 5 analyzed travel satisfaction of trip stage level, trip level and even

day level, the correlates which affect them, and the correlations with each other. Chapter

6 shifted the focus of attention from trip or trip stage satisfaction to individuals’

satisfaction of travel as a specific domain and their relationships with subjective well-

being. In Chapter 6, the question to what extent subjective well-being or life satisfaction

is influenced by travel satisfaction will be addressed. To identify the relative importance

of travel satisfaction to subjective well-being, we collected data about overall life

satisfaction, life evaluation, eudaimonic well-being and satisfaction in various other life

domains, reflecting the notion that the relationship between travel satisfaction and

subjective well-being may also be influenced by satisfaction related to other domains.

This specific focus and coverage differentiates the present study from earlier work. A

structural equation model was estimated to find the direct and indirect relationships

between domain-specific well-being, including travel satisfaction, and subjective well-

being. Socio-demographic variables and the personality of individuals were considered to

investigate their influence on travel satisfaction and subjective well-being.

In addition, in order to investigate heterogeneity in the relationship between travel

satisfaction and subjective well-being, a latent class structural equation model of the

relationships between travel satisfaction, overall life satisfaction and eudaimonic well-

being is estimated to identify heterogeneous segments of respondents, controlling for the

effects of personality traits on the latent variables in Chapter 6. The results indicate that

the effects of travel satisfaction on overall life satisfaction and eudaimonic well-being are

significant. The calm personality can be identified as the indicator of latent categories

that relate to people’s evolution of life and travel experiences. The influence of travel

satisfaction on eudaimonic well-being and the influence of eudaimonic well-being on

overall life satisfaction differ across travelers.

7.2 Contributions

The research contributes to a better understanding of travel satisfaction in a travel

behavior modeling perspective. To the best of our knowledge, this is the first attempt to

explore the interrelationships between travel satisfaction, trip attributes, mood,

personality, service quality and subjective well-being systematically. Several studies in

travel behavior research have only investigated the relationships between two or three of

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these concepts (e.g., Ory and Mokhtarian, 2005; Steel et al., 2008; Morris and Guerra,

2015). First of all, we analyze the effects of trip attributes on travel satisfaction

simultaneously considering the effects of the moods and personality traits by estimating

a path model. The empirical results of this study provide tenable evidence that the

proposed model is acceptable. The main findings of this study suggest that mood directly

influences travel satisfaction, while the effects of personality are both direct and indirect.

Secondly, in this study, we developed a model of the impact of differences

between expected and experienced trip attributes on trip stages satisfaction using

polynomial regression models, controlling for perceived service quality, attitudes, moods,

personality traits and socio-demographic characteristics of respondents. The contributions

are (i) we included customer expectations in the analysis of travel satisfaction, controlling

for perceived service quality, attitude, mood, personality traits and socio-demographic

characteristics; (ii) we applied a polynomial regression model to avoid making a priori

assumptions about the form of the satisfaction function; (iii) we investigated the existence

of a tolerance range reflecting people’s sensitivity to gaps between experiences and

expectations when evaluating their travel experience. Results support the potential value

of incorporating these ideas into travel satisfaction analyzes. The major advantage of

using the polynomial form is that the researcher does not need to a priori decide on the

form of the satisfaction function. Linearity/non-linearity, convexity/concavity and

tolerance are now the result of the estimated cubic function. It should be mentioned in

this context that of course there is always an element of personal judgment and

interpretation of the result.

Thirdly, this research makes a contribution to the literature by providing insights

into the processing rules that best capture the process of articulation of travel satisfaction.

With that motivation this research compared the predictive performance of different

processing rules that represent how travelers arrive at overall travel satisfaction ratings.

Rather than focusing specifically on the theoretical foundations underlying this prior

research, we take a broader perspective and also consider rules that are founded on other

theories.

Fourthly, this study was one of the first of its kind and scope in China conducted

to better understand the relationship between travel satisfaction and subjective well-

being. Taking a more comprehensive perspective than typical previous studies on this

topic in transportation research, this study (i) includes both hedonic and eudaimonic view

of subjective well-being; (ii) allows for mutual dependency between travel satisfaction

and subjective well-being; (iii) acknowledges that subjective well-being is influenced by


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people’s interdependent satisfactions with different domains of life; and (iv) allows for

possible influence of personality traits on satisfaction ratings. Furthermore, a latent class

structural equation model of the relationship between travel satisfaction, overall life

satisfaction and eudaimonic well-being is estimated to identify heterogeneous segments

of respondents, controlling for the effects of personality traits on the latent variables.

7.3 Limitations

The conclusions of this study are directly linked to the decisions we made in the beginning

of designing the study. Most of the study limitations relate to the data, assumptions

related to the data collection and our choice of method of analysis. In that sense, our

study is not free of limitations.

Starting with the easy potential limitations associated with retrospective surveys,

some typical errors and biases could be generated. As real-time data is difficult to collect,

we used a retrospective measurement to collect travel satisfaction data. However, studies

have shown that retrospective data can be useful if carefully collected. In order to reduce

errors, the survey was carefully designed and administrated with the aim of reducing

respondent burden, focusing on the whole day travel experiences closest to the time

when answering the questions. Several rounds of pilot testing were organized and

feedback reports from respondents and academics were taken into account to improve

the design. Although we attempted to ensure the responses have better consistency and

as few errors as possible, it is likely there are errors and memory biases when reporting

all the detailed information of trips because of the long questionnaire. Real-time data of

travel satisfaction could be more precise and reduce measurement bias.

Another potential limitation of the current study are the limited measurement

methods used in travel satisfaction, subjective well-being, mood, and personality traits.

Our analyzes are only based on selected scales and limited items used in the survey.

The results of this study are further limited by the fact that the data used only

allow cross-sectional analysis. Potential variables such as environmental conditions,

weather condition may affect travel satisfaction. The systematic analysis of these

additional effects ideally requires panel data and a dedicated sampling of days to

systematically vary environmental conditions and weather conditions. Panel data would

also have the advantage of additional intra-personal variability in mood and travel

experience, allowing more sophisticated analysis. There are some other potential

variables which may affect travel satisfaction such as built environment attributes,

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departure frequency, traffic control, the information system and other facilities could be

considered in the future analysis.

Lastly, this study is only concerned with a single city in China, and therefore these

data do not reflect any general differences.

7.4 Future research

In recent decades, researchers have addressed several aspects of the influence on travel

satisfaction and subjective well-being, such as travel time, service attributes, congestion

and socio-demographics, etc. However, it is a long way to completely understand travel

satisfaction and subjective well-being. The research presented in this thesis also raised

some further questions concerning the results of these studies. There are several possible

lines of research arising from this work.

First, the limitations of retrospective measurement of travel satisfaction data

require collecting new kinds of data. It may be more valid to collect observations during

actual travel using modern technology such as mobile phone apps. This is not meant to

say that such experience sampling is errorless. However, it would be of great interest to

compare the validity and reliability of retrospective measurement and experience

sampling in future research.

A big challenge of investigating travel satisfaction is obtaining the panel data as

the travel satisfaction data we collected pertain to a particular day. To get a good

impression of the heterogeneity of trips, it is desirable to collect longitudinal diary data.

While cross-sectional travel diary surveys are useful in determining the overall average

travel behavior of the regional population, they do not capture repetitive patterns in travel

activities. New technologies and use of smartphones may provide a promising way of

collecting longitudinal travel data. For instance, a dynamic analysis could be conducted

to explore whether mood changes over time co-vary with differences in travel satisfaction.

This study focuses on the effects of mood, personality traits on travel satisfaction.

Particularly, nine items representing mood and six items representing personality have

been analyzed in this research project. More comprehensive measurement methods and

scales can be used to collect the relevant information.

In addition, the study of travel satisfaction and subjective well-being in this study

concern general travel satisfaction and its relationship with subjective well-being. Further

research should explore the relationship between trip/stage satisfaction and subjective

well-being. Furthermore, this research mainly discusses the cognitive aspect of subjective


131

well-being. Affective or emotional aspects of subjective well-being can be added in future

studies.

A further challenge is to link travel satisfaction to travel behavior such as modal

choice behavior and residential location selection. Although this topic has been studied

recently, newly emerging travel modes, as well as the changing built environment, will

affect daily transport mode choice, making additional research necessary.

Finally, further investigation of travel satisfaction in different cities and countries

is necessary to better understand any spatial, cultural and wealth differences in travel

satisfaction and its relative influence on subjective well-being.

133

References

Abenoza, R. F., Cats, O., & Susilo, Y. O. (2017). Travel satisfaction with public transport:

Determinants, user classes, regional disparities and their evolution. Transportation

Research Part A: Policy and Practice, 95, 64-84.

Abou-Zeid, M. (2009). Measuring and modeling activity and travel well-being (Doctoral

Dissertation, Massachusetts Institute of Technology).

Abou-Zeid, M., & Ben-Akiva, M. (2011). The effect of social comparisons on commute

well-being. Transportation Research Part A: Policy and Practice, 45(4), 345-361.

Abou-Zeid, M., Ben-Akiva, M., & Bierlaire, M. (2008). Happiness and travel behavior

modification. In European Transport Conference (No. TRANSP-OR-CONF-2006-080).

Abou-Zeid, M., & Fujii, S. (2016). Travel satisfaction effects of changes in public transport

usage. Transportation, 43(2), 301-314.

Abou-Zeid, M., Witter, R., Bierlaire, M., Kaufmann, V., & Ben-Akiva, M. (2012). Happiness

and travel mode switching: Findings from a Swiss public transportation experiment.

Transport Policy, 19(1), 93-104.

Agnihotri, R., Dingus, R., Hu, M. Y., & Krush, M. T. (2016). Social media: Influencing

customer satisfaction in b2b sales. Industrial Marketing Management, 53, 172-180.

Anderson, E. R. (1973). Consumer dissatisfaction: The effect of disconfirmed expectancy

on perceived product performance. Journal of Marketing Research, 10, 38-44.

Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of

customer satisfaction for firms. Marketing Science, 12(2), 125-143.

Anderson, J. C., & Gerbing, D. W. (1982). Some methods for respecifying measurement

models to obtain unidimensional construct measurement. Journal of Marketing

Research, 453-460.

Anderson, N. H. (1981). Foundations of information integration theory. American Journal

of Psychology, 95(4).

Andrews, F. M., & Withey, S. B. (1976). Social Indicators of Well-Being. New York:

PlenumPress.

Archer, M., Paleti, R., Konduri, K., Pendyala, R., & Bhat, C. (2013). Modeling the

connection between activity-travel patterns and subjective well-being. Transportation

Research Record: Journal of the Transportation Research Board, (2382), 102-111.

134

Aydin, N., Celik, E., & Gumus, A. T. (2015). A hierarchical customer satisfaction framework

for evaluating rail transit systems of Istanbul. Transportation Research Part A: Policy

and Practice, 77, 61-81.

Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., Bolduc, D.,

Boersch-Supan, A., Brownstone, D., Bunch, D. S., Daly, A., Palma, A. D., Gopinath,

D., Karlstrom, A., & Munigaza, M. A. (2002). Hybrid choice models: Progress and

challenges. Marketing Letters, 13(3), 163-175.

Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus

single-item measures of the same constructs. Journal of Marketing Research, 44(2),

175-184.

Bergkvist, L., & Rossiter, J. R. (2009). Tailor-made single-item measures of doubly

concrete constructs. International Journal of Advertising, 28(4), 607-621.

Bergstad, C. J., Gamble, A., Gärling, T., Hagman, O., Polk, M., Ettema, D. F., Friman, M.,

& Olsson, L. E. (2011). Subjective well-being related to satisfaction with daily travel.

Transportation, 38(1), 1-15.

Berry, L. L., & Parasuraman, A. (2004). Marketing Services: Competing Through Quality.

Simon and Schuster.

Boxall, P. C., & Adamowicz, W. L. (2002). Understanding heterogeneous preferences in

random utility models: A latent class approach. Environmental and Resource

Economics, 23(4), 421-446.

Brakewood, C., Barbeau, S., & Watkins, K. (2014). An experiment evaluating the impacts

of real-time transit information on bus riders in Tampa, Florida. Transportation

Research Part A: Policy and Practice, 69, 409-422.

Cameron, P. (1975). Mood as an indicant of happiness: Age, sex, social class, and

situational differences. Journal of Gerontology, 30(2), 216-224.

Campbell, A., Converse, P. E., & Rogers, W. L. (1976). The Quality of American life:

Perceptions, Evaluations, and Satisfactions. New York: Russel Sage.

Cantril, H. (1965). Pattern of Human Concerns. New Jersey: Rutgers University Press.

Cantwell, M., Caulfield, B., & O’Mahony, M. (2009). Examining the factors that impact

public transport commuting satisfaction. Journal of Public Transportation, 12(2), 1-21.

Cao, J. (2013). The association between light rail transit and satisfactions with travel and

life: Evidence from Twin Cities. Transportation, 40(5), 921-933.

Cao, J., & Ettema, D. F. (2014). Satisfaction with travel and residential self-selection: How

do preferences moderate the impact of the Hiawatha Light Rail Transit line?. Journal

of Transport and Land Use, 7(3), 93-108.

135

Carrel, A., Mishalani, R. G., Sengupta, R., & Walker, J. L. (2016). In pursuit of the happy

transit rider: Dissecting satisfaction using daily surveys and tracking data. Journal of

Intelligent Transportation Systems, 20(4), 345-362.

Cats, O., Abenoza, R. F., Liu, C., & Susilo, Y. O. (2015). Evolution of satisfaction with

public transport and its determinants in Sweden: Identifying priority areas.

Transportation Research Record: Journal of the Transportation Research Board,

(2538), 86-95.

Chekola, M. G. (1975). The Concept of Happiness (Doctoral Dissertation, University of

Michigan, 1974).

Chen, C. F. (2008). Investigating structural relationships between service quality,

perceived value, satisfaction, and behavioral intentions for air passengers: Evidence

from taiwan. Transportation Research Part A: Policy and Practice, 42(4), 709-717.

Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing

constructs. Journal of Marketing Research, 64-73.

Churchill Jr, G. A., & Surprenant, C. (1982). An investigation into the determinants of

customer satisfaction. Journal of Marketing Research, 491-504.

Costa, P. T., & McCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and

NEO five-factor inventory (NEO-FFI): Professional manual. Psychological Assessment

Resources, Incorporated.

Costa, P. T., & McCrae, R. R. (1980). Influence of extraversion and neuroticism on

subjective well-being: Happy and unhappy people. Journal of Personality and Social

Psychology, 38(4), 668.

Culberson, C. E. (1977). A Holistic View of Joy in Relation to Psychotherapy Derived From

Lowen, Maslow, and Assagioli (Doctoral dissertation, California School of Professional

Psychology, 1977).

Currie, G., Richardson, T., Smyth, P., Vella-Brodrick, D., Hine, J., Lucas, K., Stanley, J.,

Morris, J., Kinnear, R., & Stanley, J. (2009). Investigating links between transport

disadvantage, social exclusion and well-being in Melbourne—Preliminary results.


Currie, G., Richardson, T., Smyth, P., Vella-Brodrick, D., Hine, J., Lucas, K., Stanley, J.,

Morris, J., Kinnear, R., & Stanley, J. (2010). Investigating links between transport

disadvantage, social exclusion and well-being in Melbourne–Updated results.

Research in Transportation Economics, 29(1), 287-295.

Dasgupta, S. K., & Majumdar, S. (2000). Sense of well-being and perceived quality of life

in Calcutta. In Advances in Quality of Lfe Theory and Research (pp. 65-79). Dordrecht:

Springer.

136

de Groot, J., & Steg, L. (2006). Impact of transport pricing on quality of life, acceptability,

and intentions to reduce car use: An exploratory study in five European countries.

Journal of Transport Geography, 14(6), 463-470.

de Oña, J., de Oña, R., Eboli, L., Forciniti, C., & Mazzulla, G. (2016). Transit passengers’

behavioural intentions: The influence of service quality and customer satisfaction.

Transportmetrica A: Transport Science, 12(5), 385-412.

de Oña, J., de Oña, R., Eboli, L., & Mazzulla, G. (2013). Perceived service quality in bus

transit service: A structural equation approach. Transport Policy, 29, 219-226.

De Vos, J., Mokhtarian, P. L., Schwanen, T., Van Acker, V., & Witlox, F. (2016). Travel

mode choice and travel satisfaction: Bridging the gap between decision utility and

experienced utility. Transportation, 43(5), 771-796.

De Vos, J., Schwanen, T., Van Acker, V., & Witlox, F. (2013). Travel and subjective well-

being: A focus on findings, methods and future research needs. Transport Reviews,

33(4), 421-442.

De Vos, J., Schwanen, T., Van Acker, V., & Witlox, F. (2015). How satisfying is the Scale

for Travel Satisfaction?. Transportation Research Part F: Traffic Psychology and

Behaviour, 29, 121-130.

De Vos, J., & Witlox, F. (2016). Do people live in urban neighbourhoods because they do

not like to travel? Towards a new residential self-selection hypothesis. Travel

Behaviour and Society, 4, 29-39.

Del Castillo, J. M., & Benitez, F. G. (2012). A methodology for modeling and identifying

users satisfaction issues in public transport systems based on users surveys. Procedia-

Social and Behavioral Sciences, 54, 1104-1114.

Del Castillo, J. M., & Benitez, F. G. (2013). Determining a public transport satisfaction

index from user surveys. Transportmetrica A: Transport Science, 9(8), 713-741.

Delbosc, A. (2012). The role of well-being in transport policy. Transport Policy, 23, 25-33.

Delbosc, A., & Currie, G. (2011a). Exploring the relative influences of transport

disadvantage and social exclusion on well-being. Transport Policy, 18(4), 555–562.

Delbosc, A., & Currie, G. (2011b). Piecing it together: A structural equation model of

transport, social exclusion and well-being. In G. Currie (Ed.), New Perspectives and

Methods in Transport and Social Exclusion Research. Emerald: Bingley.

Delbosc, A., & Currie, G. (2011c). Transport problems that matter–social and

psychological links to transport disadvantage. Journal of Transport Geography, 19(1),

170-178.

Dell’Olio, L., Ibeas, A., & Cecín, P. (2010). Modelling user perception of bus transit quality.


137

Dell’Olio, L., Ibeas, A., & Cecín, P. (2011). The quality of service desired by public

transport users. Transport Policy, 18(1), 217–227.

DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137

personality traits and subjective well-being. Psychological Bulletin, 124(2), 197–229.

Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines

for choosing between multi-item and single-item scales for construct measurement:

A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3),

434-449.

Diana, M. (2012). Measuring the satisfaction of multimodal travelers for local transit

services in different urban contexts. Transportation Research Part A: Policy and

Practice, 46(1), 1–11.

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542-575.

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life

scale. Journal of Personality Assessment, 49(1), 71-75.

Diener, E., & Lucas, R. E. (1999). Personality and subjective well-being. In Well-Being:

The Foundation of Hedonic Psychology (pp. 213-229). New York: Sage Foundation.

Diener, E., Oishi, S., & Lucas, R. E. (2003). Personality, culture, and subjective well-being:

Emotional and cognitive evaluations of life. Annual Review of Psychology, 54(1), 403-

425.

Diener, E., & Suh, E. (1997). Measuring quality of life: Economic, social, and subjective

indicators. Social Indicators Research, 40(1), 189-216.

Diener, E., Suh, E., Lucas, R. E., & Smith, H. L. (1999). Subjective Well- Being: Three

Decades of Progress. Psychological Bulletin, 125 (2), 276-302.

Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D. W., Oishi, S., & Biswas-Diener, R.

(2010). New well-being measures: Short scales to assess flourishing and positive and

negative feelings. Social Indicators Research, 97(2), 143-156.

Duarte, A., Garcia, C., Giannarakis, G., Limão, S., Polydoropoulou, A., & Litinas, N. (2010).

New approaches in transportation planning: Happiness and transport economics.

Netnomics, 11(1), 5-32.

Duarte, A., Garcia, C., Limao, S., & Polydoropoulou, A. (2009). Experienced and expected

happiness in transport mode decision making process. Transportation Research Board

88th Annual Meeting.

Dupuy, H. J. (1980). The research edition of the general psychological well-being schedule.

Hyattsville: National Center for Health Statistics.

Eboli, L., & Mazzulla, G. (2007). Service quality attributes affecting customer satisfaction

for bus transit. Journal of Public Transportation, 10(3), 21-34.

138

Eboli, L., & Mazzulla, G. (2008). A stated preference experiment for measuring service

quality in public transport. Transportation Planning and Technology, 31(5), 509-523.

Eboli, L., & Mazzulla, G. (2010). How to capture the passengers’ point of view on a transit

service through rating and choice options. Transport Reviews, 30(4), 435-450.

Eboli, L., & Mazzulla, G. (2011). A methodology for evaluating transit service quality based

on subjective and objective measures from the passenger’s point of view. Transport

Policy, 18(1), 172-181.

Eboli, L., & Mazzulla, G. (2012). Structural equation modelling for analysing passengers’

perceptions about railway services. Procedia-Social and Behavioral Sciences, 54, 96-

106.

Edvardsson, B. (1998). Causes of customer dissatisfaction—Studies of public transport by

the critical-incident method. Managing Service Quality, 8 (3), 189–197.

Einhorn, H. J. (1970). The use of nonlinear, noncompensatory models in decision making.

Psychological Bulletin, 73(3), 221-230.

Einhorn, H. J. (1971). Use of nonlinear, noncompensatory models as a function of task

and amount of information. Organizational Behavior and Human Performance, 6(1),

1-27.

Ettema, D. F., Abenoza, R. F., & Susilo, Y. O. (2016). Satisfaction with intermodal trips in

Stockholm: How do service attributes influence satisfaction with the main mode and

with the journey as a whole?. Transportation Research Board 95th Annual Meeting.

Ettema, D. F., Friman, M., Gärling, T., Olsson, L. E., & Fujii, S. (2012). How in-vehicle

activities affect work commuters’ satisfaction with public transport?. Journal of

Transport Geography, 24, 215-222.

Ettema, D. F., Gärling, T., Eriksson, L., Friman, M., Olsson, L. E., & Fujii, S. (2011).

Satisfaction with travel and subjective well-being: Development and test of a

measurement tool. Transportation Research Part F: Traffic Psychology and Behaviour,

14(3), 167-175.

Ettema, D. F., Gärling, T., Olsson, L. E., & Friman, M. (2010). Out-of-home activities, daily

travel, and subjective well-being. Transportation Research Part A: Policy and Practice,

44(9), 723-732.

Evans, G. W., Wener, R. E., & Phillips, D. (2002). The morning rush hour: Predictability

and commuter stress. Environment and Behavior, 34(4), 521-530.

Fellesson, M., & Friman, M. (2008). Perceived satisfaction with public transport service in

nine european cities. Journal of the Transportation Research Forum, 47(3), 93-103.

Finn, A. (2011). Investigating the non-linear effects of e-service quality dimensions on

customer satisfaction. Journal of Retailing and Consumer Services, 18(1), 27-37.

139

Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience.

Journal of Marketing, 56 (1), 6-21.

Fornell, C., Johnson, M. D., Anderson, E., Cha, J., & Bryant, B. E. (1996). The American

Customer Satisfaction Index: Nature, purpose, and findings. Journal of Marketing,

60(4), 7-18.

Friman, M. (2004). The structure of affective reactions to critical incidents. Journal of

Economic Psychology, 25(3), 331-353.

Friman, M., Edvardsson, B., & Gärling, T. (2001). Frequency of negative critical incidents

and satisfaction with public transport services. I. Journal of Retailing and Consumer

Services, 8(2), 95-104.

Friman, M., & Fellesson, M. (2009). Service supply and customer satisfaction in public

transportation: The quality paradox. Journal of Public Transportation, 12(4), 4.

Friman, M., Fujii, S., Ettema, D. F., Gärling, T., & Olsson, L. E. (2013). Psychometric

analysis of the satisfaction with travel scale. Transportation Research Part A: Policy

and Practice, 48, 132-145.

Friman, M., & Gärling, T. (2001). Frequency of negative critical incidents and satisfaction

with public transport services. II. Journal of Retailing and Consumer Services, 8, 105–

114.

Friman, M., Gärling, T., Ettema, D. F., & Olsson, L. E. (2017). How does travel affect

emotional well-being and life satisfaction?. Transportation Research Part A: Policy and

Practice, 106, 170-180.

Fu, X., & Juan, Z. (2017). Understanding public transit use behavior: Integration of the

theory of planned behavior and the customer satisfaction theory. Transportation,

44(5), 1021-1042.

Gatersleben, B., & Uzzell, D. (2007). Affective appraisals of the daily commute comparing

perceptions of drivers, cyclists, walkers, and users of public transport. Environment

and Behavior, 39(3), 416–431.

Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the Big-

Five personality domains. Journal of Research in Personality, 37(6), 504-528.

Grzeskowiak, S., Sirgy, M. J., Lee, D. J., & Claiborne, C. B. (2006). Housing well-being:

Developing and validating a measure. Social Indicators Research, 79(3), 503–541.

Gurin, G., Veroff, J., & Feld, S. (1960). Americans View Their Mental Health: A Nationwide

Interview Survey. Basic Books.

Hansson, E., Mattisson, K., Björk, J., Ö stergren, P. O., & Jakobsson, K. (2011).

Relationship between commuting and health outcomes in a cross-sectional population

survey in southern Sweden. BMC Public Health, 11(1), 834.

140

Haws, K. L., Bearden, W. O., & Netemeyer, R. G. (2011). Handbook of Marketing Scales:

Multi-Item Measures for Marketing and Consumer Behavior Research. Sage.

Hayborn, D. M. (2000). Two philosophical problems in the study of happiness. Journal of

Happiness Studies, 1, 207-225.

Headey, B., Veenhoven, R., & Wearing, A. (1991). Top-down versus bottom-up theories

of subjective well-being. Social Indicators Research, 24(1), 81-100.

Hennessy, D. A., & Wiesenthal, D. L. (1997). The relationship between traffic congestion,

driver stress and direct versus indirect coping behaviours. Ergonomics, 40(3), 348-

361.

Hensher, D. A., Greene, W. H., & Chorus, C. G. (2013). Random regret minimization or

random utility maximization: An exploratory analysis in the context of automobile fuel

choice. Journal of Advanced Transportation, 47(7), 667-678.

Hensher, D. A., Stopher, P., & Bullock, P. (2003). Service quality- developing a service

quality index in the provision of commercial bus contracts. Transportation Research

Part A: Policy and Practice, 37(6), 499–517.

Hess, S., & Stathopoulos, A. (2013). A mixed random utility—random regret model linking

the choice of decision rule to latent character traits. Journal of Choice Modelling, 9,

27-38.

Higgins, C. D., Sweet, M. N., & Kanaroglou, P. S. (2017). All minutes are not equal: Travel

time and the effects of congestion on commute satisfaction in Canadian cities.

Transportation, 1-20.

Hussain, R., Al Nasser, A., & Hussain, Y. K. (2015). Service quality and customer

satisfaction of a UAE-based airline: An empirical investigation. Journal of Air Transport

Management, 42, 167-175.

Ilies, R., & Judge, T. A. (2002). Understanding the dynamic relationships among

personality, mood, and job satisfaction: A field experience sampling study. Organ

Behav Hum Decis Process, 89(2), 1119-1139.

Ingersoll-Dayton, B., Saengtienchai, C., Kespichayawattana, J., & Aungsuroch, Y. (2004).

Measuring psychological well-being: Insights from Thai elders. The Gerontologist,

44(5), 596–604.

International Well-being Group (2006). Personal Well-being Index. Australian Centre on

Quality of Life, Melbourne, Deakin University.

Jacoby, J. (1978). Consumer research: A state of the art review. Journal of Marketing,

42(2), 87-96.

141

Jaśkiewicz, M., & Besta, T. (2014). Heart and mind in public transport: Analysis of motives,

satisfaction and psychological correlates of public transportation usage in the Gdańsk–

Sopot–Gdynia Tricity Agglomeration in Poland. Transportation Research Part F: Traffic

Psychology and Behaviour, 26, 92-101.

Jones, H. M. (1953). The Pursuit of Happiness. Cambridge,MA: Harvard University Press.

Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction: The

mediating role of job characteristics. Journal of Applied Psychology, 85(2), 237.

Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of personality and job

satisfaction: A meta-analysis. Journal of Applied Psychology, 87(3), 530-541.

Kahneman, D. (2000). Evaluation by moments: Past and future. Choices, Values, and

Frames, 693-708.

Kahneman, D., Diener, E., & Schwarz, N. (1999). Well-Being: Foundations of Hedonic

Psychology. Russell Sage Foundation.

Kahneman, D., & Krueger, A. B. (2006). Developments in the measurement of subjective

well-being. The Journal of Economic Perspectives, 20(1), 3-24.

Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A

survey method for characterizing daily life experience: The day reconstruction method.

Science, 306, 1776–1780.

Kano, N. (1984). Attractive quality and must-be quality. Journal of the Japanese Society

for Quality Control, 4(2), 147-156.

Kelly, E. L., & Conley, J. J. (1987). Personality and compatibility: A prospective analysis

of marital stability and marital satisfaction. Journal of Personality and Social

Psychology, 52(1), 27-40.

Kim, J., Rasouli, S., & Timmermans, H. (2016). A hybrid choice model with a nonlinear

utility function and bounded distribution for latent variables: Application to purchase

intention decisions of electric cars. Transportmetrica A: Transport Science, 12(10),

909-932.

Kim, J., Rasouli, S., & Timmermans, H. (2017). Investigating heterogeneity in social

influence by social distance in car-sharing decisions under uncertainty: A regret-

minimizing hybrid choice model framework based on sequential stated adaptation

experiments. Transportation Research Part C: Emerging Technologies, 85.

Koslowsky, M., Kluger, A. N., & Reich, M. (1995). Commuting Stress: Causes, Effects, and

Methods Of Coping. Plenum Press.

Kwon, H., & Trail, G. (2005). The feasibility of single-item measures in sport loyalty

research. Sport Management Review, 8(1), 69-88.

142

Lai, W. T., & Chen, C. F. (2011). Behavioral intentions of public transit passengers—The

roles of service quality, perceived value, satisfaction and involvement. Transport Policy,

18(2), 318-325.

Li, Z., Hensher, D. A., & Rose, J. M. (2010). Willingness to pay for travel time reliability

in passenger transport: A review and some new empirical evidence. Transportation

Research Part E: Logistics and Transportation Review, 46(3), 384-403.

Lin, W. B. (2007). The exploration of customer satisfaction model from a comprehensive

perspective. Expert Systems with Applications, 33(1), 110-121.

Lucas, R. E., & Fujita, F. (2000). Factors influencing the relation between extraversion

and pleasant affect. Journal of Personality and Social Psychology, 79(6), 1039-1056.

Mahmoud, M., & Hine, J. (2016). Measuring the influence of bus service quality on the

perception of users. Transportation Planning and Technology, 39(3), 284-299.

Manaugh, K., & El-Geneidy, A. M. (2013). Does distance matter? Exploring the links

among values, motivations, home location, and satisfaction in walking trips.

Transportation Research Part A: Policy and Practice, 50, 198-208.

Mao, Z., Ettema, D. F., & Dijst, M. (2016). Commuting trip satisfaction in Beijing: Exploring

the influence of multimodal behavior and modal flexibility. Transportation Research

Part A: Policy and Practice, 94, 592-603.

Martin, A., Goryakin, Y., & Suhrcke, M. (2014). Does active commuting improve

psychological wellbeing? Longitudinal evidence from eighteen waves of the British

Household Panel Survey. Preventive Medicine, 69, 296-303.

Matthews, G., Deary, I., & Whiteman, A. (2009). Personality Traits. Cambridge:

Cambridge University Press.

Matthews, G., & Gilliland, K. (1999). The personality theories of HJ Eysenck and JA Gray:

A comparative review. Personality and Individual Differences, 26(4), 583-626.

McCrae, R. R., & Costa Jr, P. T. (1991). Adding Liebe und Arbeit: The full five-factor model

and well-being. Personality and Social Psychology Bulletin, 17(2), 227-232..

McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of

Applied Econometrics, 447-470.

McMahan, E. A., & Estes, D. (2011). Measuring lay conceptions of well-being: The beliefs

about well-being scale. Journal of Happiness Studies, 12(2), 267-287.

Miller, J. A. (1977). Studying satisfaction, modifying models, eliciting expectations, posing

problems, and making meaningful measurements. Conceptualization and

Measurement of Consumer Satisfaction and Dissatisfaction, 72-91.

Mohan-Neill, S. I. (1995). The influence of firm's age and size on its environmental

scanning activities. Journal of Small Business Management, 33(4), 10-21.

143

Morris, E. A., & Guerra, E. (2015). Mood and mode: Does how we travel affect how we

feel?. Transportation, 42(1), 25-43.

Mouwen, A. (2015). Drivers of customer satisfaction with public transport services.


Muthén, L. K., & Muthén, B. O. (2015). Mplus Version 7.31 [Computer program]. Los

Angeles: Muthén & Muthén.

Neugarten, B. L., Havighurst, R. J., & Tobin, S. S. (1961). The measurement of life

satisfaction. Journal of Gerontology, 16, 134-143.

Novaco, R. W., & Gonzalez, O. I. (2009). Commuting and well-being. Technology and

Well-Being, 3, 174-205.

Novaco, R. W., Stokols, D., & Milanesi, L. (1990). Objective and subjective dimensions of

travel impedance as determinants of commuting stress. American Journal of

Community Psychology, 18(2), 231-257.

Oishi, S., Diener, E., Suh, E., & Lucas, R. E. (1999). Value as a moderator in subjective

well‐being. Journal of Personality, 67(1), 157-184.

Oliver, R. L. (1977). Effects of expectation and disconfirmation on postexposure product

evaluations: An alternative interpretation. Journal of Applied Psychology, 64(3), 480–

486.

Oliver, R. L. (2010). Satisfaction: A Behavioral Perspective on the Consumer, Second Ed.

New York: McGraw-Hill.

Olsson, L. E., Gärling, T., Ettema, D. F., Friman, M., & Fujii, S. (2013). Happiness and

satisfaction with work commute. Social Indicators Research, 111(1), 255-263..

Ory, D. T., & Mokhtarian, P. L. (2005). When is getting there half the fun? Modeling the

liking for travel. Transportation Research Part A: Policy and Practice, 39(2), 97-123.

Parker, C., & Matthews, B. P. (2001). Customer satisfaction: Contrasting academic and

consumers’ interpretations. Marketing Intelligence and Planning, 19(1), 38–44.

Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological

Assessment, 5(2), 164-172.

Perović, Đ., Stanovčić, T., Moric, I., & Pekovic, S. (2012). What socio-demographic

characteristics do influence the level of tourist’s satisfaction in Montenegro? Empirical

analysis. Revista De Turism-Studii Si Cercetari In Turism, (14), 5-10.

Petrick, J. F. (2004). The roles of quality, value, and satisfaction in predicting cruise

passengers’ behavioral intentions. Journal of Travel Research, 42(4), 397-407.

Pitt, M., Chotipanich, S., Issarasak, S., Mulholland, K., & Panupattanapong, P. (2014). An

examination of facility management, customer satisfaction and service relationship in

the Bangkok healthcare system. Indoor and Built Environment, 29(11), 765-769.

144

Rasouli, S., & Timmermans, H. (2014). Judgments of travel experiences, activity

envelopes, trip features and multi-tasking: A panel effects regression model

specification. Transportation Research Part A: Policy and Practice, 63, 67-75.

Rasouli, S., & Timmermans, H. (2015). Models of bounded rationality under certainty. In:

Rasouli, S., Timmermans, H. (Ed.), Bounded Rational Choice Behavior: Applications in

Transport. Chennai: Emerald Publishers.

Ravulaparthy, S., Yoon, S., & Goulias, K. (2013). Linking elderly transport mobility and

subjective well-Being: A multivariate latent modeling approach. Transportation

Research Record: Journal of the Transportation Research Board, (2382), 28-36.

Reardon, L., & Abdallah, S. (2013). Well-being and transport: Taking stock and looking

forward. Transport Reviews, 33(6), 634-657.

Richard, E., & Diener, E. (2009). Personality and subjective well-being. In The Science of

Well-Being (pp. 75-102). Springer Netherlands.

Rittichainuwat, B. N., Qu, H., & Mongknonvanit, C. (2002). A study of the impactof travel

satisfaction on the likelihood of travelers to revisit Thailand. Journal of Travel and

Tourism Marketing, 12(2-3), 19-43.

Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of

research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1),

141-166.

Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of

psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069-

1081.

Ryff, C. D., & Singer, B. H. (2008). Know thyself and become what you are: A eudaimonic

approach to psychological well-being. Journal of Happiness Studies, 9(1), 13-39.

Schimmack, U. (2008). The structure of subjective well-being. In: Eid, M., Larsen, R.J.

(Ed.), The Science of Subjective Well-Being (pp. 97–123). New York: Guilford Press.

Schmutte, P. S., & Ryff, C. D. (1997). Personality and well-being: Reexamining methods

and meanings. Journal of Personality and Social Psychology, 73(3), 549-55.

Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being:

Informative and directive functions of affective states. Journal of Personality and

Social Psychology, 45, 513-523.

Sirgy, M. J. (2001). Handbook of Quality-of-Life Research: An Ethical Marketing

Perspective. The Netherlands: Kluwer.

Sirgy, M. J., Kruger, P. S., Lee, D. J., & Yu, G. B. (2011). How does a travel trip affect

tourists’ life satisfaction?. Journal of Travel Research, 50(3), 261-275.

145

Spinney, J. E., Scott, D. M., & Newbold, K. B. (2009). Transport mobility benefits and

quality of life: A time-use perspective of elderly Canadians. Transport Policy, 16(1),

1-11.

Stanley, J., Hensher, D. A., Stanley, J., Currie, G., Greene, W. H., & Vella-Brodrick, D.

(2011a). Social exclusion and the value of mobility. Journal of Transport Economics

and Policy, 45(2), 197-222..

Stanley, J., Hensher, D. A., Stanley, J., & Vella-Brodrick, D. (2011b). Mobility, social

exclusion and well-being: Exploring the links. Transportation Research Part A: Policy

and Practice, 45(8), 789-801.

Steel, P., Schmidt, J., & Shultz, J. (2008). Refining the relationship between personality

and subjective well-being. Psychological Bulletin, 134(1), 138-161.

Steg, L., & Gifford, R. (2005). Sustainable transportation and quality of life. Journal of

Transport Geography, 13, 59-69.

St-Louis, E., Manaugh, K., van Lierop, D., & El-Geneidy, A. (2014). The happy commuter:

A comparison of commuter satisfaction across modes. Transportation Research Part

F: Traffic Psychology and Behaviour, 26, 160-170.

Stradling, S. G., Anable, J., & Carreno, M. (2007). Performance, importance and user

disgruntlement: A six-step method for measuring satisfaction with travel modes.

Transportation Research Part A: Policy and Practice, 41(1), 98-106.

Sumaedi, S., Bakti, I. G. M. Y., & Yarmen, M. (2012). The empirical study of public

transport passengers’ behavioral intentions: The roles of service quality, perceived

sacrifice, perceived value, and satisfaction (Case study: Paratransit passengers in

jakarta, Indonesia). International Journal for Traffic and Transport Engineering, 2(1).

83-97.

Susilo, Y. O., & Cats, O. (2014). Exploring key determinants of travel satisfaction for multi-

modal trips by different traveler groups. Transportation Research Part A: Policy and

Practice, 67, 366-380.

Susilo, Y. O., Woodcock, A., Liotopoulos, F., Duarte, A., Osmond, J., Abenoza, R. F.,

Anghel, L. E., Herrero, D., Fornari, F., Tolio, V., O’Connell, E., Markuceviciute, I.,

Kritharioti, C., & Pirra, M. (2017). Deploying traditional and smartphone app survey

methods in measuring door-to-door travel satisfaction in eight European cities.

Transportation Research Procedia, 25, 2262-2280.

Suzuki, H., Fujii, S., Gärling, T., Ettema, D. F., Olsson, L. E., & Friman, M. (2014). Rules

for aggregated satisfaction with work commutes. Transportation, 41(3), 495-506.

Tatarkiewicz, W. (1976). Analysis of Happiness. Martinus Nijhoff, The Hague, Netherlands.

146

Tirachini, A., Hensher, D. A., & Rose, J. M. (2013). Crowding in public transport systems:

Effects on users, operation and implications for the estimation of demand.


Van Rooy, D. L. (2006). Effects of automobile commute characteristics on affect and job

candidate evaluations: A field experiment. Environment and Behavior, 38(5), 626–655.

Västfjäll, D., & Gärling, T. (2007). Validation of a Swedish short self‐report measure of

core affect. Scandinavian Journal of Psychology, 48(3), 233-238.

Västfjäll, D., Friman, M., Gärling, T., & Kleiner, M. (2002). The measurement of core

affect: A Swedish self ‐ report measure derived from the affect circumplex.

Scandinavian Journal of Psychology, 43(1), 19-31.

Vicente, P., & Reis, E. (2016). Profiling public transport users through perceptions about

public transport providers and satisfaction with the public transport service. Public

Transport, 8(3), 387-403.

Wan, D., Kamga, C., Hao, W., Sugiura, A., & Beaton, E. B. (2016a). Customer satisfaction

with bus rapid transit: A study of New York City select bus service applying structural

equation modeling. Public Transport, 8(3), 497-520.

Wan, D., Kamga, C., Liu, J., Sugiura, A., & Beaton, E. B. (2016b). Rider perception of a

“light” bus rapid transit system-The New York City select bus service. Transport Policy,

49, 41-55.

Waterman, A. S., Schwartz, S. J., Zamboanga, B. L., Ravert, R. D., Williams, M. K., Bede

Agocha, V., Kim, S. Y., & Brent Donnellan, M. (2010). The Questionnaire for

Eudaimonic Well-Being: Psychometric properties, demographic comparisons, and

evidence of validity. The Journal of Positive Psychology, 5(1), 41-61.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief

measures of positive and negative affect: The PANAS scales. Journal of Personality

and Social Psychology, 54(6), 1063-1070.

Wener, R. E., Evans, G. W., Phillips, D., & Nadler, N. (2003). Running for the 7: 45: The

effects of public transit improvements on commuter stress. Transportation, 30(2),

203-220.

Wessman, A. E. (1957). A Psychological Inquiry into Satisfactions and Happiness

(Doctoral Dissertation, Princeton University, 1956).

Westman, J., Olsson, L. E., Gärling, T., & Friman, M. (2017). Children’s travel to school:

Satisfaction, current mood, and cognitive performance. Transportation, 44(6), 1365-

1382.

147

Wilson, W. R. (1960). An Attempt to Determine Some Correlates and Dimensions of

Hedonic Tone (Doctoral Dissertation, Northwestern University, 1960).

Wirtz, J. (2001). Improving the measurement of customer satisfaction: A test of three

methods to reduce halo. Managing Service Quality: An International Journal, 11(2),

99-112.

Wu, J., Yang, M., & Rasouli, S. (2016). Exploring passenger assessments of bus service

quality using bayesian networks. Journal of Public Transportation, 19(3), 36-54.

Xiong, B., Skitmore, M., Xia, B., Masrom, M. A., Ye, K., & Bridge, A. (2014). Examining

the influence of participant performance factors on contractor satisfaction: A structural

equation model. International Journal of Project Management, 32(3), 482-491.

Yang, M., Zhao, J., Wang, W., Liu, Z., & Li, Z. (2015). Metro commuters’ satisfaction in

multi-type access and egress transferring groups. Transportation Research Part D:

Transport and Environment, 34, 179-194.

Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty:

The role of switching costs. Psychology and Marketing, 21(10), 799–822.

Ye, R., & Titheridge, H. (2017). Satisfaction with the commute: The role of travel mode

choice, built environment and attitudes. Transportation Research Part D: Transport

and Environment, 52, 535-547.

Zajenkowski, M., Goryńska, E., & Winiewski, M. (2012). Variability of the relationship

between personality and mood. Personality and Individual Differences, 52(7), 858-

861.

Zhang, C., Juan, Z., Lu, W., & Xiao, G. (2016). Do the organizational forms affect

passenger satisfaction? Evidence from Chinese public transport service.


149

Author Index

A

Abdallah, S., 35

Abenoza, R. F., 11, 57, 59, 78, 100

Abou-Zeid, M., 3, 4, 7, 8, 11, 17, 19, 22,

24, 35, 36, 39, 45, 57, 78, 100, 104,

123, 124

Adamowicz, W. L., 118

Agnihotri, R., 3

Anderson, E. R., 3

Anderson, E. W., 3

Anderson, J. C., 9

Anderson, N. H., 82

Andrews, F. M., 19

Archer, M., 22, 78, 123

Aydin, N., 3

B

Ben-Akiva, M., 22, 24, 35, 78, 100, 104,

117, 123, 124

Benitez, F. G., 78

Bergkvist, L., 9

Bergstad, C. J., 3, 9, 20-22, 25, 35, 36,

47, 78, 103, 123

Berry, L. L., 57

Besta, T., 13

Boxall, P. C., 118

Brakewood, C., 44

C

Cameron, P., 47

Campbell, A., 2

Cantril, H., 19

Cantwell, M., 35, 57

Cao, J., 11, 16-18, 39, 40, 58, 78

Carrel, A., 10, 11, 17, 38-40, 75

Cats, O., 3, 4, 7, 11, 14, 16, 17, 36, 38,

40, 44, 57, 59

Chekola, M. G., 2

Chen, C. F., 9, 16, 30, 38, 39, 78

Churchill Jr, G. A., 3, 9, 57

Clore, G. L., 44

Conley, J. J., 46

Cooper, H., 4, 36, 104

Costa, P. T., 17, 40, 45, 103

Culberson, C. E., 2

Currie, G., 22, 78, 123

D

Dasgupta, S. K., 100

de Groot, J., 123

de Oña, J., 16, 38, 39, 78

De Vos, J., 4, 7, 8, 10, 12, 17-19, 20,

35-37, 40, 41, 57, 58, 75, 78, 100,

105

Deci, E. L., 19, 20, 102

Del Castillo, J. M., 78

Delbosc, A., 22, 123

Dell’Olio, L., 78

DeNeve, K. M., 4, 36, 103

Diamantopoulos, A., 9

Diana, M., 22, 78

Diener, E., 1, 2, 4, 9, 17-20, 24, 36, 40,

45, 99-103

Duarte, A., 4, 36

Dupuy, H. J., 19

150

E

Eboli, L., 78

Edvardsson, B., 78

Einhorn, H. J., 83

El-Geneidy, A. M., 16, 39

Estes, D., 20

Ettema, D. F., 3, 7, 8, 11, 12, 16-18, 20,

21, 23, 25, 35, 39, 40, 44, 45, 57-59,

77-79, 103

Evans, G. W., 45

F

Fellesson, M., 78

Finn, A., 60

Fornell, C., 3

Friman, M., 3, 7, 8, 12, 44, 45, 78

Fu, X., 12

Fujii, S., 7, 11, 22, 35, 57, 78

Fujita, F., 46, 101

G

Gärling, T., 7, 8, 19, 20, 102

Gatersleben, B., 8

Gerbing, D. W., 9

Gifford, R., 123

Gilliland, K., 46

Gonzalez, O. I., 8

Gosling, S. D., 29, 49, 106

Grzeskowiak, S., 100

Guerra, E., 36, 45, 128

Gurin, G., 19

H

Hansson, E., 21, 25, 103

Haws, K. L., 9

Hayborn, D. M., 2

Headey, B., 24, 100, 103

Hennessy, D. A., 46

Hensher, D. A., 9, 118

Hess, S., 118

Higgins, C. D., 10, 13, 17, 18, 37, 40

Hine, J., 13, 16, 38, 39, 59

Hussain, R., 4, 36

I

Ilies, R., 46

Ingersoll-Dayton, B., 20

International Well-being Group, 20

J

Jacoby, J., 9

Jaśkiewicz, M., 13

Jones, H. M., 2

Juan, Z., 12

Judge, T. A., 46

K

Kahneman, D., 7, 19, 44, 79, 81, 97, 99,

102

Kano, N., 60

Kelly, E. L., 46

Kim, J., 117, 118

Koslowsky, M., 45

Krueger, A. B., 44

Kwon, H., 9,

L

Lai, W. T., 9, 16, 30, 39, 78

Li, Z., 44

Lin, W. B., 3

Lucas, R. E., 46, 101, 103

M

Mahmoud, M., 13, 16, 38, 39, 59

Majumdar, S., 100

Manaugh, K., 16, 39

Mao, Z., 13, 123

151

Martin, A., 21, 25, 103

Matthews, B. P., 3

Matthews, G., 46

Mazzulla, G., 78

McCrae, R. R., 17, 40, 45, 103

McFadden, D., 117

McMahan, E. A., 20

Miller, J. A., 57

Mohan-Neill, S. I., 100

Mokhtarian, P. L., 7, 17, 23, 36, 39, 57,

104, 128

Morris, E. A., 36, 45, 128

Mouwen, A., 3, 7, 13, 16, 38

Muthén, B. O., 52, 119

Muthén, L. K., 52, 119

N

Neugarten, B. L., 19

Novaco, R. W., 8, 45

O

Oishi, S., 100

Oliver, R. L., 3, 19

Olsson, L. E., 8, 23, 35, 44, 123

Ory, D. T., 7, 17, 23, 36, 39, 57, 104,

128

P

Parasuraman, A., 57

Parker, C., 3

Pavot, W., 9, 19, 20, 102

Perović, Đ., 47

Peterson, R. T., 3

Petrick, J. F., 16, 38

Pitt, M., 3

R

Rasouli, S., 44, 79, 82

Ravulaparthy, S., 23, 78

Reardon, L., 35

Reis, E., 78

Richard, E., 17, 40, 45, 46, 101

Rittichainuwat, B. N., 46

Rossiter, J. R., 9

Ryan, R. M., 19, 20, 102

Ryff, C. D., 20, 102, 103

S

Schimmack, U., 19

Schmutte, P. S., 103

Schwarz, N., 44

Singer, B. H., 20

Sirgy, M. J., 21, 24, 25, 100, 102, 103,

123

Spinney, J. E., 23, 78, 123

Stanley, J., 20, 23

Stathopoulos, A., 118

Steel, P., 17, 36, 40, 45, 46, 101, 128

Steg, L., 123

St-Louis, E., 3, 4, 9, 13, 17, 18, 36, 37,

40, 57

Stradling, S. G., 3

Suh, E., 17, 40, 99

Sullivan, M. W., 3

Sumaedi, S., 78

Surprenant, C., 3

Susilo, Y. O., 3, 4, 7, 14, 17, 36, 40, 44,

57, 78

Suzuki, H., 44, 79, 86, 97

T

Tatarkiewicz, W., 2

Timmermans, H. J. P., 44, 79, 82

Tirachini, A., 44

Titheridge, H., 15, 17, 39, 58, 123

Trail, G., 9

Train, K., 117

U

152

Uzzell, D., 8

V

Van Rooy, D. L., 7

Västfjäll, D., 8, 19, 20, 102

Vicente, P., 78

W

Wan, D., 14-16, 38, 58

Waterman, A. S., 20, 102

Watson, D., 19, 20, 102

Wener, R. E., 7, 45

Wessman, A. E., 2

Westman, J., 15

Wiesenthal, D. L., 46

Wilson, W. R., 2

Wirtz, J., 3

Withey, S. B., 19

Witlox, F., 7

Wu, J., 78

X

Xiong, B., 3

Y

Yang, M., 7, 10, 15, 17, 37, 40, 59, 100

Yang, Z., 3

Ye, R., 15, 17, 39, 58, 123

Z

Zajenkowski, M., 46

Zhang, C., 10, 15, 37

153

Subject Index

A

Activity, 18, 22, 28, 30, 41, 43, 79, 97

Affective well-being, 3

C

Cognitive well-being, 3

Conjunctive Rule, 11, 83, 87, 92

Correlation, 9, 10, 13, 22, 29, 31, 43, 46,

47, 48, 52, 53, 86-89, 92-94, 101,

104, 108, 11, 126, 127

Cost, 5, 9, 10,12, 28, 30, 37, 38, 43, 49,

50, 53, 55, 56, 59, 64-67, 75

D

Data, 5, 8, 16, 24, 26-29, 31, 33, 39, 42,

43, 48-50, 52, 64, 74, 79, 80, 86, 87,

91, 97, 100, 103, 104, 124, 126, 127,

129, 130

Delighted-Terrible Scale, 19

Disjunctive Rule, 11, 82, 84, 97, 126

Duration-weighted Averaging Rule, 86,

87, 89, 91-94, 96

E

Equal-weight Averaging Rule, 86, 87, 89,

91, 92, 94, 96

Eudaimonic well-being, 19, 20, 24, 25,

28, 31, 102-106, 115-117, 127, 129

G

General Well-being Schedule, 19

Gurin scale, 19

H

Hedonic well-being, 19, 20, 24, 102, 115

Heterogeneity, 5, 18, 41, 80, 117, 121,

122, 124, 127, 130

Household, 28, 29, 32, 33, 47, 50-52, 63,

64, 67-69, 75, 85, 104, 107, 108

L

Latent class, 6, 101, 102, 117-122, 124,

127, 129

Latent variable, 52, 109-111, 115, 118,

120, 127, 129

Life satisfaciton, 2, 3, 19, 21, 23, 24, 28,

31, 99, 100, 102-106, 108, 115, 116,

127, 129

Limitations, 6, 33, 101, 129, 130

M

Mood, 2-4, 11, 14-17, 22, 23, 28, 30, 31,

37, 39, 41-47, 49, 50, 52, 54, 55, 57,

58, 60, 61, 65, 66, 68, 69, 74-76, 88,

86, 88-97, 99, 104, 105, 126-130

Multi-item scales, 8, 9,19

O

Observations, 32, 52, 59, 107, 130

P

Path model, 37, 50, 52, 117, 118, 126,

128

154

Peak-end Rule, 79, 86-89, 91-94, 96, 97,

126

Personality, 4, 16-18, 22, 23, 28, 29, 31,

33, 36, 37, 39-42, 44-47, 49, 50, 52,

54, 55, 57, 58, 60, 61, 65, 66, 68-70,

74-76, 80, 86, 88-97, 101, 103, 104,

106, 108, 113-119, 122-124, 126-

130

Positive and Negative Affect Scale, 19,

20, 102

Public transport, 3, 7-13, 15, 16, 22, 29,

30, 36-38, 44, 49, 53-56, 63, 166,

68-70, 74, 75, 78, 80, 84, 96, 97,

126

R

Reference-based model, 5, 57, 58, 126

S

Satisfaction with Life Scale, 9, 19, 20,

102

Satisfaction with Travel Scale, 8, 20

Self-anchoring ladder, 19

Service quality, 10-12, 14-16, 27, 28, 30,

36, 38, 39, 55, 60, 61, 65, 66, 68-70,

74-76, 96, 104, 126-128

Single-item scales, 8, 9, 33, 50, 106

Structural equation model, 5, 12, 14, 15,

58, 101, 108, 114, 115, 117-120,

124, 127, 129

Subjective well-being, 2-5, 8, 11, 14,

17-28, 31, 33, 35, 36, 40, 45, 46, 77,

78, 99-105, 108, 109, 112-118, 122-

131

Summation Rule, 86, 87, 89, 91-94, 96

Swedish Core Affect Scales, 8, 19, 20,

102

T

Transport mode, 10, 12, 30, 38, 73, 104,

131

Travel behavior, 3, 4, 10, 18, 21, 22, 25,

38, 41, 47, 57-59, 97, 100, 101, 103,

108, 125-127, 130, 131

Travel satisfaction, 3-5, 7-18, 21-30, 33,

35-41, 43-50, 52, 54-59, 74-81, 86,

91-97, 99-103, 106, 108, 114-116,

118, 122-131

Travel time, 5, 9, 10, 13, 17, 37-39, 43,

45, 49, 50, 59, 130

Trip attributes, 4, 9, 10, 27, 28, 30, 31,

36-38, 41, 43-45, 47, 49, 50, 52, 54,

55, 58, 59, 71, 74, 105, 126-128

Trip stage, 5, 16, 21, 23, 24, 28, 30, 31,

35, 39, 41-44, 47-50, 52, 54, 57, 63-

67, 69-71, 73, 75, 80-86, 88, 91, 92,

96, 97, 105, 124, 126-128

U

Utility, 10, 19, 38, 99, 117, 118

155

Summary


Since the emergence of the subjective well-being studies now over six decades ago, the

study of domain satisfaction has progressed rapidly especially in social science and

marketing research. As an essential domain of human’s life, travel satisfaction has

recently become a popular topic in travel behavior research. A better understanding of

travel satisfaction and its determinants is highly instrumental in optimizing service delivery

and guiding transport policy. The strong dependence of travel behavior models on

objective factors, without systematic consideration of other subjective factors, motivated

us to consider the effects of subjective factors on travel satisfaction. The fact that daily

trips become more complex provided another motivation for the study of the relationship

between satisfactions in different context. As subjective well-being is a multi-dimensional

concept that covers many life domains, travel satisfaction contributes to subjective well-

being and vice-versa subjective well-being also influences travel satisfaction. Thus,

understanding the relationship between travel satisfaction and subjective well-being is

the main objective of this dissertation.

The thesis aims at developing our understanding of the concept of subjective well-being,

measurement methods and determinants of travel satisfaction and subjective well-being.

The current literature comprises a variety of conceptualizations and interpretations of

travel satisfaction and subjective well-being. In order to assess the collective

understanding of both concepts, a systematic literature review of travel satisfaction and

subjective well-being in terms of definitions, measurements and main determinants was

conducted. The insights from this systematic literature reviews provide the theoretical

basis for the empirical analyzes in this dissertation, which lead to main findings and

implications that are of scientific value for scholars and provide valuable insights for

governments and transport companies.

156

Based on the theoretical framework, a path model of travel satisfaction, trip attributes,

moods and personality traits was proposed in simultaneously analyzes of the effects of

objective and subjective attributes of vehicles and trip stages on travel satisfaction. The

proposed framework considered the trip stage level as the trip attributes. A limitation of

this approach, which follows the state-of-the-art in transportation, is that satisfaction is

assumed a linear function of the attributes of the choice alternatives. Therefore, we test

models that assume that trip satisfaction systematically varies in a non-linear fashion as

a function of the discrepancy between expectations and actual experienced attributes of

the trip stage. This analysis formulates and estimates a reference-based model of trip

satisfaction that takes attitude, mood, personality, service quality and indifference

tolerance into account. Estimation results indicate that the difference between expected

and experienced waiting time, in-vehicle time, experienced cost, perceived service quality,

attitude, mood, personality and socio-demographic characteristics have significant effects

on trip stage satisfaction. Findings also indicate that respondents tolerate minor

differences in access time.

Another potential limitation of the state-of-the-art research on travel satisfaction assumes

that satisfaction ratings in multi-trip, multi-stage and multi-attribute travel experiences

follow simple aggregation rules. In contrast, we examine a set of alternative processing

rules that differ in terms of the nature of the underlying decision process and allow

investigating recency effects. We found that the peak-end rule was not supported by our

data. Second, we found that satisfaction of the first trip and first trip stage have higher

marginal effects on overall satisfaction than more recent trips and stages.

Finally, prior studies suggest that an understanding of how domain satisfaction

contributes to overall well-being and how overall well-being influences domain satisfaction

is still an open question. We examine the mutual dependency between travel satisfaction

and subjective well-being relative to satisfaction with other life domains, while controlling

for personality traits and selected socio-demographic variables using a structural equation

model. The empirical results of this analysis show that travel satisfaction does not

significantly influence life evaluation, but life evaluation strongly influences travel

satisfaction. Another contribution is a latent class structural equation model was examined

to gain new insights into the co-dependent relationships between domain satisfaction and

subjective well-being. This analysis has revealed heterogeneity in the relationship

between evaluation of overall life and life domains.

157

The data used in this study were collected in January 2015 in Xi’an, China among a

random sample of respondents. The questionnaire involved questions about the

characteristics of a full day of travel, expressed travel satisfaction for the full day, every

trip, and every stage, perceived service quality of travel mode, plus questions about

travelers’ attitude, mood, personality trait, and socio-demographic characteristics.

This dissertation contributes to a shift in focus from objective correlates of travel

satisfaction to subjective correlates, from evaluation of indistinct travel experience to

satisfaction of specific day/trip/stage. To the best of our knowledge, this is the first

attempt to explore the subjective correlates of travel satisfaction, different aggregation

rules of travel satisfaction, and the relationship between travel satisfaction and subjective

well-being, in a comprehensive framework. In the context of policy formulation and

practical application, this dissertation contributes to shaping and appraising transport and

social policy.

159

Curriculum Vitae

Yanan Gao was born on 28-06-1989 in Shaanxi, China. She obtained her bachelor degree

in Traffic Engineering at Chang’an University in 2010. She graduated with honours and

continued her master study in Traffic Engineering at Chang’an University. During her

master education, she received National Scholarships for Graduate Students. She focused

on research on public transport, built environment and commute carbon emissions. She

obtained her master degree in 2013.

She became a PhD candidate recommended for admission in Transportation

Planning and Management at Chang’an University in 2012. From September 2013, she

became a joint PhD student of Chang’an University and Eindhoven University of

Technology and started a PhD project at the Urban Planning Group of Eindhoven

University of Technology in the Netherlands under the support from the China Scholarship

Council. Since September 2015, she continued her research at Chang’an University in

China. Her research project focused on travel satisfaction and subjective well-being. The

objectives of her research are to explore the relationship between domain satisfaction,

eudaimonic well-being, life evaluation, personalities and overall life satisfaction, and to

examine factors such as trip attributes, mood, and personality traits. Her publications

have appeared in Transportation Research Part F and Travel Behaviour and Society.

160

List of Publications

Journal articles:

Gao, Y., Rasouli, S., Timmermans, H.J.P., & Wang, Y. (2018). A reference-based model

of trip stage satisfaction incorporating trip attributes, perceived service quality,

psychological disposition and indifference tolerance. Transportation Research Part

A: Police and Practice. (Under review)

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2017). Understanding the relationship

between travel satisfaction and subjective well-being considering the role of

personality traits: a structural equation model. Transportation Research Part F:

Traffic Psychology and Behaviour, 49, 110-123.

Gao, Y., Rasouli, S., Timmermans, H.J.P., & Wang, Y. (2017). Effects of traveller’s mood

and personality on ratings of satisfaction with daily trip stages. Travel Behaviour and

Society, 7, 1-11.

Wang, Y., Li, Z., & Gao, Y. (2015). Minimum revenue guarantee and toll revenue cap

optimization for ppp highways: Pareto optimal state approach. Baltic Journal of Road

& Bridge Engineering, 10(4), 365-371.

Wang, Y., Wang, Z., Li, Z., Staley, S. R., Moore, A. T., & Gao, Y. (2012). Study of modal

shifts to bus rapid transit in Chinese cities. Journal of Transportation Engineering,

139(5), 515-523.

Book chapters:

Gao, Y. (2014) Study of urban neighborhood household commute carbon emissions,

Chapter 8, pp. 164-181. In: Wang, Y., Han S., & Liu, Y. (eds.), Low carbon Xi’an

Research. Xi’an Jiaotong University Press.

Gao, Y., Ma, J., & Wang, Z. (2018) Trip generation. In: Wang, Y., & Ma, S. (eds.),

Transportation Planning. Beijing Jiaotong Press (In press).

Gao, Y., Zhang, J., & Wu, R. (2018) Trip distribution models. In: Wang, Y., & Ma, S.

(eds.), Transportation Planning. Beijing Jiaotong Press (In press).

161

Gao, Y., Yu, Z., Li, D., & Zhu, L. (2018) Modal split models. In: Wang, Y., & Ma, S. (eds.),


Gao, Y., & Yang, X. (2018) Traffic Assignment. In: Wang, Y., & Ma, S. (eds.),


Conference Proceedings and Presentations:

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2017). A study of the relationship

between subjective well-being and travel satisfaction. In: Proceedings of 95th

Annual Meeting of TRB, Washington, DC.

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2016). Mode-switching intentions of

public transport users: the influence of perceived service quality, travel satisfaction

and psychological disposition. In: Proceedings of the 21th International Conference

of HKSTS, Hong Kong.

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2015). A study of the relationship

between subjective well-being and travel satisfaction. In: S. Rasouli and H.J.P.

Timmermans (eds.), Proceedings of 2015 BIVEC-GIBET Transport Research Day,

Eindhoven.

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2015). Analyzing effects of personality

and mood on travel satisfaction: A path analysis model. In: Proceedings of the 20th

International Conference of HKSTS, Hong Kong.

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2014). Motives underlying intentions

to buy a car: Simultaneous probit selection-Multinomial logit choice model. In:

Proceedings of the 19th International Conference of HKSTS, Hong Kong.

Gao, Y., Rasouli, S., Timmermans, H., & Wang, Y. (2014). Reasons for not buying a car:

A probit selection multinomial logit choice model. In: Proceedings of 12th

International Conference on Design and Decision.

Wang, Y., Gao, Y., Wang, Z., Staley, S. R., Moore, A. T., & Li, Z. (2011). The impact of

bus rapid transit development on travel choices in Chinese Cities. In: Proceedings

of the 16th International Conference of HKSTS, Hong Kong.

Wang, Y., Wang, Z., Li, Z., Staley, S. R., Moore, A. T. & Gao, Y. (2012) A study of modal

shifts to bus rapid transit in Chinese cities. In: Proceedings of 90th Annual Meeting

of TRB, Washington, DC.

Bouwstenen is een publikatiereeksvan de Faculteit Bouwkunde,Technische Universiteit Eindhoven.Zij presenteert resultaten vanonderzoek en andere aktiviteiten ophet vakgebied der Bouwkunde,uitgevoerd in het kader van dezeFaculteit.

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nr 228nog niet bekend / gepubliceerd


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Travel satisfaction and subjective well-being : a ... · Travel satisfaction and subjective...

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