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The Role of Customer Value within the Service Quality,
Customer Satisfaction and Behavioural Intentions
Relationships: An Empirical Examination
in the Indonesian Higher Education Sector
Ratna Roostika
Thesis submitted in fulfilment of the
requirements for the degree of
Doctor of Philosophy
Faculty of Business and Enterprise
Swinburne University of Technology
2009
ii
ABSTRACT
All countries in the world are now moving towards a knowledge-based economy. The
performance of the higher education sector of any nation should be of great significance
as it influences the nation’s competitiveness. With respect to competitiveness, the issue
of quality and satisfaction has heightened concerns among higher education circles in
Indonesia. Nevertheless, both service quality and satisfaction constructs are not without
critiques and questions on the roles that both constructs play. In general services, quality
and satisfaction do not always relate to the economic returns. Customers do not always
concerns on offerings with the highest quality. Many customers express high
satisfaction ratings but still consider to spending elsewhere for different reasons. This
has prompted calls for the introduction of a more dynamic construct called ‘customer
value’ to better predict behavioural outcomes and assist competitive advantage. The
research gap in this thesis is therefore an empirically testing the integrative model of
service quality, customer satisfaction, customer value and behavioural intentions in the
higher education sector.
The proposed integrative model was empirically examined by collecting data from
undergraduate students in the five Universities in Yogyakarta, Indonesia. Exploratory
Factor Analysis and Confirmatory Factor Analysis using Partial Least Squares were
applied to test the measurement model and structural model proposed in this thesis. To
the academic literature, the proposed model provides a comprehensive picture of the
relationships among the key constructs (SQ, CS, CV and BI), and therefore allowing to
see the relative impacts of subsequent consequential variables. The findings identified
the determinants of service quality and customer value specific to higher education, as
well as confirm the relationships proposed from the conceptual model. In particular, the
findings showed the dominant role of customer value on behavioural intentions,
compared to that exerted by service quality and customer satisfaction. Customer value
also has a stronger influence on customer satisfaction than service quality. The inclusion
iii
of customer value has increased the predictive power in explaining behavioural
intentions.
The findings provide valuable guidelines for practitioners in this field by assisting them
to better understand the determinants and the nature of their relationships, in relation to
expected organisational outcomes and enhance competitive advantage of the university.
The ultimate contribution of this study would assist universities to provide their best
services according to the aspirations of university students at large.
iv
ACKNOWLEDGEMENTS
With the completion of this thesis, there are many people to whom I owe a great deal of
appreciation. I would like to thank to my mother, my husband, my son and my sisters
for their infinite love and encouragement, not only through the process of this thesis but
also throughout my life.
I would like to express my deepest gratitude to Associate Professor Siva Muthaly, my
supervisor, for his supervision from the start of this thesis until the end, for his constant
provision of constructive feedback and critical comments, and for the almost
instantaneous responses to any queries that I had during my PhD progress. He has given
me a substantial influence in conducting an independent research exercise as well as
guidance in data analysis. I would also like to thank Dr. Denny Meyer for her initial
assistance on research methodology and data analysis. I would like to thank AusAID
and International student liaison persons Emilia Tabrizi and Melina Wong for providing
invaluable support during my stay in Australia. In particular, I am grateful for AusAID
for providing the scholarship that allowed me to undertake this PhD adventure and learn
about Australian life. A special thanks also to Geoffrey Vincent and Nilay Patel for
language assistance. Finally, I would like to thank the academics, colleagues and
administrative staff at Swinburne University of Technology, Faculty of Business and
Enterprise, for providing a great academic atmosphere and services during the study.
Most importantly, I would like to thank God for the blessing and making this PhD
journey so very meaningful in my life.
v
DECLARATION
This thesis:
• Contains no material which has been accepted for the award to the candidate of
any other degree of diploma, except where due reference is made in the text of
the examinable outcome;
• To the best of the candidate’s knowledge contains no material previously
published or written by another person, except where due reference is made in
the text of the examinable outcome;
• Where the work is based on joint research or publications, discloses the relative
contributions of the respective workers or authors; and
• Has met all the requirements of the Ethics Approval from the Swinburne
University of Technology under SUHREC Project 0607/203.
Ratna Roostika
Melbourne, Australia
July 2009
vi
Table of Contents
Abstract ……………………………………………………………………………... ii
Acknowledgements ……………………………………………………………........ iv
Declaration ………………………………………………………………………….. v
Table of Contents ……………………………………………………………............. vi
List of Tables ………………………………………………………………………. xi
List of Figures ………………………………………………………………............. xiii
CHAPTER ONE: INTRODUCTION ................................................................................ 1
1.1 INTRODUCTION ........................................................................................................... 1
1.2 RESEARCH BACKGROUND AND JUSTIFICATION ................................................ 1
1.2.1 The Popularity of Service Quality and Satisfaction .................................................. 1
1.2.2 Customer Value: the Emerging Construct ................................................................ 2
1.2.3 Service Quality, Customer Satisfaction and Customer Value in the Indonesian
Higher Education Sector .................................................................................................... 3
1.2.4 The Rationale ............................................................................................................ 5
1.3 RESEARCH QUESTIONS .............................................................................................. 6
1.4 IMPORTANCE OF THE STUDY .................................................................................. 7
1.4.1 International Competition in the Higher Education Sector ....................................... 7
1.4.2 Local Competition in the Indonesian Higher Education Sector................................ 9
1.4.3 The Importance of Higher Education Competitiveness ............................................ 9
1.5 CONTRIBUTION OF THE STUDY............................................................................. 10
1.5.1 Theoretical Contributions ....................................................................................... 10
1.5.2 Practical Contributions ............................................................................................ 12
1.6 RESEARCH METHOD ................................................................................................. 13
1.6.1 Conceptual Model Development ............................................................................ 13
1.6.2 Operationalisation of the Constructs ....................................................................... 14
1.6.3 Data Collection........................................................................................................ 14
1.6.4 Data Entry and Analysis.......................................................................................... 15
1.7 THESIS OUTLINE ........................................................................................................ 15
1.8 LIMITATIONS .............................................................................................................. 16
CHAPTER TWO: LITERATURE REVIEW ................................................................. 18
2.1 INTRODUCTION ......................................................................................................... 18
2.2 SERVICE INDUSTRY .................................................................................................. 19
2.2.1 The Nature and Characteristics of Services ............................................................ 19
2.2.2 The Nature and Characteristics of Higher Education Services ............................... 21
2.3 CUSTOMERS OF THE EDUCATIONAL SYSTEM .................................................. 23
2.3.1 Higher Education Stakeholders’ Perceptions of Quality ........................................ 25
2.4 SERVICE QUALITY .................................................................................................... 27
2.4.1 Importance of Service Quality ................................................................................ 27
2.4.2 Quality a Difficult Construct ................................................................................... 27
2.4.3 Service Quality Defined from the Customer’s View Point ..................................... 28
vii
2.4.4 The Development of ‘SERVQUAL’ Measures ...................................................... 31
2.4.5 Critique of SERVQUAL ......................................................................................... 31
2.4.6 Service Quality in Higher Education ...................................................................... 34
2.4.6.1 The Importance of Service Quality in Higher Education ................................ 34
2.4.6.2 Quality Conceptualisation of Higher Education Sector ................................... 35
2.4.6.3 Service Quality Measurements of Higher Education Sector ........................... 36
2.5 CUSTOMER SATISFACTION..................................................................................... 40
2.5.1 Importance of Customer Satisfaction ...................................................................... 40
2.5.2 Concept and Dimensions of Satisfaction ................................................................ 40
2.5.3 Approaches to Customer Satisfaction ..................................................................... 41
2.5.4 Satisfaction in the Context of Higher Education .................................................... 44
2.5.4.1 Concept and Dimensions of Satisfaction in Higher Education ........................ 46
2.5.5 Comparing Service Quality and Customer Satisfaction ......................................... 46
2.5.6 Antecedents of Customer Satisfaction .................................................................... 48
2.5.7 Consequences of Satisfaction .................................................................................. 49
2.6 CUSTOMER VALUE ................................................................................................... 50
2.6.1 The Importance of Customer Value ........................................................................ 50
2.6.2 The Inclusion of Value in the Service Quality and Satisfaction Relationship ........ 52
2.6.3 Different Interpretation of Customer Value ............................................................ 54
2.6.4 An Approach to the Definition of Customer Value Construct ................................ 55
2.6.5 Customer Value versus Quality and Satisfaction .................................................... 56
2.6.5.1 The Distinction between Customer Satisfaction and Customer Value ............ 57
2.6.6 Measurements of Customer Value .......................................................................... 59
2.6.6.1 Unidimensional Conceptualisation of Customer Value ................................... 59
2.6.6.2 Multidimensional Conceptualisation of Customer Value ................................ 60
2.6.7 Customer Value in the Higher Education Context .................................................. 64
2.6.8 Antecedents of Customer Value.............................................................................. 65
2.6.9 Consequences of Customer Value .......................................................................... 66
2.7 CUSTOMER BEHAVIORAL INTENTIONS .............................................................. 67
2.7.1 The Theory of Behavioural Intentions .................................................................... 67
2.7.2 Customer Loyalty .................................................................................................... 70
2.7.3 Word-of-mouth Communication (WOM) ............................................................... 71
2.8 CONCLUSION .............................................................................................................. 72
CHAPTER THREE: THE RELATIONSHIPS AND CONCEPTUAL MODEL ........ 73
3.1 INTRODUCTION ......................................................................................................... 73
3.2 THE INTERRELATIONSHIP MODELS OF SERVICE QUALITY, CUSTOMER
SATISFACTION, CUSTOMER VALUE AND BEHAVIOURAL INTENTIONS ........... 73
3.2.1 Patterson and Spreng’s (1997) Model of Interrelationships ................................... 74
3.2.2 Oh’s (1999) Model of Interrelationships................................................................. 74
3.2.3 Cronin et al.’s (2000) Model of Interrelationships ................................................. 75
3.2.4 Choi et al.’s (2004) Model of Interrelationships ..................................................... 76
3.2.5 Alves and Raposo’s (2007) Model of Interrelationships ........................................ 77
3.2.6 Summary of the Integrative Models ........................................................................ 78
3.3 HYPOTHESIS DEVELOPMENT ................................................................................. 79
3.3.1 Part One: Dimensions of Service Quality in the Higher Education Sector............. 79
viii
3.3.2 Part Two: Relationships between Service Quality, Customer Satisfaction and
Behavioural Intentions ..................................................................................................... 81
3.3.2.1 The Antecedent Role of Service Quality and Customer Satisfaction .............. 83
3.3.2.2 The Interrelationships in the Higher Education Setting ................................... 85
3.3.2.3 Hypothesis Development: Service Quality, Customer Satisfaction and
Behavioural Intentions ................................................................................................. 87
3.3.3 Part Three: Dimensions of Customer Value in the Higher Education Sector ......... 88
3.3.4 Part Four: Relationships among Service Quality, Customer Satisfaction,
Customer Value and Behavioural Intentions. .................................................................. 90
3.3.4.1 The Direct Link ................................................................................................ 90
3.3.4.2 The Indirect Link ............................................................................................. 92
3.4 THE CONCEPTUAL MODEL ..................................................................................... 95
3.5 RESEARCH CONTEXT ............................................................................................... 97
3.6 CONCLUSION .............................................................................................................. 99
CHAPTER FOUR: METHODOLOGY ........................................................................ 101
4.1 INTRODUCTION ....................................................................................................... 101
4.2 RESEARCH PARADIGM .......................................................................................... 101
4.3 RESEARCH DESIGN ................................................................................................. 104
4.3.1 Research Approach ............................................................................................... 105
4.3.2 Methods of Collecting Data .................................................................................. 107
4.3.3 Research Tactics.................................................................................................... 109
4.3.3.1 Constructs Development and Operationalisation ........................................... 109
4.3.3.2 Pre-testing ...................................................................................................... 116
4.3.3.3 Scaling and Response Format ........................................................................ 118
4.3.3.4 Questionnaire Design ..................................................................................... 120
4.3.3.5 Sampling Plan ................................................................................................ 121
4.3.3.6 Statistical Analysis ......................................................................................... 129
4.4 ETHICS CONSIDERATIONS .................................................................................... 137
4.5 CONCLUSION ............................................................................................................ 138
CHAPTER FIVE: THE PRELIMINARY ANALYSIS ............................................... 140
5.1 INTRODUCTION ....................................................................................................... 140
5.2 DESCRIPTIVE ANALYSIS ....................................................................................... 140
5.2.1 Sample Characteristics .......................................................................................... 141
5.3 MISSING VALUE ANALYSIS .................................................................................. 142
5.4 NORMALITY AND OUTLIERS ................................................................................ 143
5.4.1 Normality .............................................................................................................. 143
5.4.2 Outliers .................................................................................................................. 144
5.5 REFLECTIVE VERSUS FORMATIVE MEASURES ............................................... 144
5.5.1 Service Quality ...................................................................................................... 146
5.5.2 Customer Value ..................................................................................................... 146
5.5.3 Second-Order Model of Service Quality and Customer Value ............................. 147
5.5.4 Satisfaction and Behavioural Intentions ............................................................... 148
ix
5.6 RELIABILITY AND VALIDITY ............................................................................... 149
5.6.1 Reliability Analysis (RA) ...................................................................................... 149
5.6.2 Exploratory Factor Analysis (EFA) ...................................................................... 150
5.6.2.1 Criteria for Interpreting the EFA Results ....................................................... 152
5.6.2.2 Reliability and EFA Findings from the Preliminary Analysis ....................... 153
5.6.2.3 Summary of Problematic Measures ............................................................... 163
5.7 CONCLUSION ............................................................................................................ 163
CHAPTER SIX: THE PARTIAL LEAST SQUARES ANALYSIS OF THE CONCEPTUAL MODEL ................................................................................................ 165
6.1 INTRODUCTION ....................................................................................................... 165
6.2 PLS APPROACH FOR CONSTRUCT DESIGN ....................................................... 165
6.2.1 The Operationalisation of First-order and Second-order Constructs .................... 166
6.3 THE EVALUATION OF MEASUREMENT MODELS ............................................ 167
6.3.1 Validity Analysis ................................................................................................... 167
6.3.1.1 Content Validity and Face Validity ............................................................... 167
6.3.1.2 Construct Validity .......................................................................................... 168
6.3.2 Evaluation of the Measurement Model using PLS ............................................... 169
6.3.2.1 Assessment of Convergent Validity ............................................................... 170
6.3.2.2 Assessment of Discriminant Validity ............................................................ 172
6.4 THE EVALUATION OF THE STRUCTURAL MODEL .......................................... 182
6.4.1 R-Squared (R2) ...................................................................................................... 182
6.4.2 Path Coefficients ................................................................................................... 184
6.4.3 t-Statistics .............................................................................................................. 184
6.4.4 Structural Paths ..................................................................................................... 186
6.4.4.1 Structural Model: Second-order and First-order Construct ........................... 186
6.4.4.2 Structural Model: The Main Constructs ........................................................ 187
6.4.4.3 Structural Model: The Mediating Effects ...................................................... 188
6.5 PARTIAL MEDIATION ANALYSIS ........................................................................ 193
6.6 RESULTS OF HYPOTHESES TESTING .................................................................. 196
6.7 CONCLUSION ............................................................................................................ 197
CHAPTER SEVEN: DISCUSSIONS ON THE EMPIRICAL ANALYSIS ............... 198
7.1 INTRODUCTION ....................................................................................................... 198
7.2 THE PRELIMINARY ANALYSIS ............................................................................. 198
7.2.1 Service Quality ...................................................................................................... 198
7.2.2 Customer Value ..................................................................................................... 199
7.2.3 Customer Satisfaction ........................................................................................... 200
7.2.4 Behavioural Intentions .......................................................................................... 201
7.2.5 Summary of the Preliminary Analysis .................................................................. 202
7.3 THE PLS ANALYSIS ................................................................................................. 203
7.3.1 The Measurement Model ...................................................................................... 204
7.3.1.1 Service Quality ............................................................................................... 204
7.3.1.2 Customer Value .............................................................................................. 214
x
7.3.2 The Structural Model ............................................................................................ 222
7.3.2.1 The Direct Relationships ................................................................................ 224
7.3.2.2 The Indirect Relationships ............................................................................. 228
7.3.2.3 Summary of the PLS Analysis ....................................................................... 233
7.4 CONCLUSION ............................................................................................................ 234
CHAPTER EIGHT: CONCLUSIONS AND IMPLICATIONS ................................. 236
8.1 INTRODUCTION ....................................................................................................... 236
8.2 SUMMARY OF STAGES OF THE RESEARCH ...................................................... 236
8.3 REVIEW OF OVERALL RESULTS .......................................................................... 239
8.4 THEORETICAL CONTRIBUTIONS ......................................................................... 243
8.5 IMPLICATIONS FOR PRACTITIONERS ................................................................. 247
8.6 LIMITATIONS ............................................................................................................ 249
8.7 SUGGESTIONS FOR FUTURE RESEARCH ........................................................... 251
8.8 CONCLUSION ............................................................................................................ 253
REFERENCES ................................................................................................................. 255
APPENDICES .................................................................................................................. 290
Appendix 1 Information Sheet ........................................................................................... 290
Appendix 2 Questionnaire.................................................................................................. 292
Appendix 3 Descriptive statistic ........................................................................................ 297
Appendix 4 Principal Component Analysis (PCA) ........................................................... 301
Appendix 5 Partial Least Squares (PLS Graph) ................................................................. 304
Appendix 6 Cross Loadings Matrix ................................................................................... 307
Appendix 7 PLS Graphic Output ....................................................................................... 314
Appendix 8 Owlia and Aspinwall’s (1996) Dimensions of Higher Education Service
Quality ................................................................................................................................ 319
Appendix 9 Ethics Clearance ............................................................................................. 321
Appendix 10 Published Supporting Paper ......................................................................... 323
xi
List of Tables
Table 1.1 Definitions of Key Constructs 13
Table 2.1 Customers in Higher Education 24
Table 2.2 Stakeholder Perspectives on Quality 26
Table 2.3 Quality Conceptualisation 27
Table 2.4 Service Quality Conceptualisation Based on Customers’ View 30
Table 2.5 Critiques of SERVQUAL 33
Table 2.6 Approaches to Quality Concepts in the Higher Education Sector 36
Table 2.7 Service Quality Dimensions in Higher Education 37
Table 2.8 Satisfaction Studies in Higher Education 45
Table 2.9 Key Differences between Service Quality and Satisfaction 48
Table 2.10 Role of Performance Expectations on Customer Satisfaction 49
Table 2.11 Consequences of Satisfaction 50
Table 2.12 Definitions of Customer Value 56
Table 2.13 Distinctions between Customer Value and Customer Satisfaction 57
Table 2.14 Multidimensional Approaches to Defining Customer Value 61
Table 2.15 Antecedents of Customer Value 65
Table 2.16 Consequences of Customer Value 67
Table 2.17 Positive Behavioural Expressions 69
Table 3.1 Findings on the Relationships between Service Quality,
Satisfaction and Behavioural Intentions
82
Table 3.2 Causal Ordering between Service Quality and Customer
Satisfaction
84
Table 3.3 Research on Service Quality, Satisfaction and Behavioural
Intentions in Higher Education
87
Table 3.4 Selected Empirical Studies on SQ-CS-CV-BI 90
Table 3.5 Summary of Research Questions and Hypotheses 97
Table 4.1 Research Paradigm 102
Table 4.2 The Differences between Exploratory and Conclusive Research 106
Table 4.3 Advantages and Disadvantages of Survey Types 108
Table 4.4 Sources of Questionnaire 115
Table 4.5 The Differences Between First-generation and Second-
generation Statistical Techniques
131
Table 4.6 Comparison between PLS and Covariance-based Approach 136
Table 5.1 The Respondents Characteristics 142
Table 5.2 The Standard Used in Performing and Interpreting EFA 153
Table 5.3 Exploratory Factor Analysis of Service Quality (28 items) 158
Table 5.4 Exploratory Factor Analysis of Customer Value (21 items) 161
Table 5.5 Problematic Items Identified in the Preliminary Analysis Using
PCA
163
Table 6.1 Criteria used as Rule-of-thumb in Measurement Model 170
Table 6.2 Problematic Items Identified Through PCA and PLS 174
Table 6.3 Reasoning for Indicators’ Removal or Retention 175
Table 6.4 Cross Loadings of First-order and Second-order Constructs 177
Table 6.5 Correlation between Latent Constructs and Square Root of AVE 179
Table 6.6 Summary of the Valid and Reliable Measurements 180
Table 6.7 Effect Size 183
xii
Table 6.8 Critical Z-value 185
Table 6.9 PLS Results of Direct Effect on the Structural Model 185
Table 6.10 Direct and Indirect Effects of the conceptual Model: PLS Results 191
Table 6.11 Direct and Indirect Effects of Partial Models 195
Table 6.12 Hypotheses and Summary of Findings 196
Table 7.1 Dimensions of Service Quality 214
Table 7.2 Dimensions of Customer Value 222
xiii
List of Figures
Figure 1.1 The Conceptual Model 7
Figure 2.1 Literature Review Structure 19
Figure 3.1 Patterson and Spreng’s (1997) Model of Interrelationships 74
Figure 3.2 Oh’s (1999) Model of Interrelationships 75
Figure 3.3 Cronin et al.’s (2000) “The Research Model” 76
Figure 3.4 Choi et al.’s (2004) Model of Interrelationships 77
Figure 3.5 Alves and Raposo’s (2007) Model of Interrelationships 78
Figure 3.6 Conceptual Model 96
Figure 4.1 The Research Process 104
Figure 4.2 Higher Education Students Growth in Yogyakarta 123
Figure 4.3 Favourite Subject at The National Scope 124
Figure 4.4 Student enrolments based on Discipline/National 125
Figure 4.5 Student enrolments based on Discipline/Yogyakarta 125
Figure 4.6 Measurement and Structural Models 133
Figure 5.1 Undergraduate Respondents from Five Universities 141
Figure 5.2 Respondents’ Characteristics Based on Discipline 142
Figure 6.1 First-order and Second-order Reflective Constructs of Service
Quality
165
Figure 6.2 First-order and Second-order Reflective Constructs of Customer
Value
166
Figure 6.3 Repeated Indicators Approach 166
Figure 6.4 Structural Model Result 184
Figure 6.5 Illustration of Direct Effect 189
Figure 6.6 Illustration of Mediating Effect 189
Figure 7.1 Second-order Reflective Constructs of Service Quality 205
Figure 7.2 PLS Loadings for Content Dimension 207
Figure 7.3 PLS Loadings for the Tangible Dimension 209
Figure 7.4 PLS Loadings for Competence Dimension 210
Figure 7.5 PLS Loadings for Attitude Dimension 211
Figure 7.6 PLS Loadings for the Delivery Dimension 213
Figure 7.7 Second-order Reflective Constructs of Customer Value 214
Figure 7.8 PLS Loadings for the Social Dimension 216
Figure 7.9 PLS Loadings for the Emotion Dimension 216
Figure 7.10 PLS Loadings for the Reputation Dimension 219
Figure 7.11 PLS Loadings for Price Dimension 221
Figure 7.12 Structural Model Result 224
Figure 8.1 The Structural Relationship of the Four Key Constructs 239
Figure 8.2 The Structural Relationship (Customer Value Excluded) 242
1
CHAPTER ONE
INTRODUCTION
1.1 INTRODUCTION
This chapter discusses the rationale for studying the topic of “The importance of
customer value to service quality, customer satisfaction and behavioural intentions
relationship in the Indonesian higher education sector”. The principal objectives of this
thesis are: 1) to investigate the determinants of customer value and service quality; 2) to
examine the interrelationships between service quality, customer satisfaction, customer
value and behavioural intentions; and 3) to analyse the relativeimpact of customer value
inclusion in the proposed conceptual model. More specifically, the conceptual model
proposed in this thesis simultaneously relates all of the four key constructs (service
quality/SQ, customer satisfaction/CS, customer value/CV and behavioural
intentions/BI) in order to provide a more comprehensive insight into the contributions
of all four key constructs in the Indonesian higher education sector.
This chapter commences with a brief discussion of the research background and
justification (Section 1.2). Section 1.3 presents the research questions. Section 1.4
addresses the importance of the study. The contribution made by the study is presented
in Section 1.5. The following Sections 1.6 and Section 1.7 focus on research method
and thesis outline respectively. The final section (1.8) presents the delimitations of this
thesis.
1.2 RESEARCH BACKGROUND AND JUSTIFICATION
1.2.1 The Popularity of Service Quality and Satisfaction
Service quality and customer satisfaction have been very popular and widely researched
in the general service marketing literature. Service quality was found to have significant
outcomes in many areas, particularly in the business sectors since it contributes to
competitive advantage (Parasuraman et al. 1988; Zeithaml et al. 1996), a differential
advantage (Vuorinen et al. 1998), financial performance (Rust et al. 1995), profitability
(Rust & Zahorik 1993) and customer satisfaction and behavioural intentions (Bolton &
2
Drew 1991; Cronin & Taylor 1992; Cronin et al. 2000; Brady & Robertson 2001;
Chumpitaz & Paparoidamis 2004; Olorunniwo et al. 2006). Closely related to the
service quality concept is customer satisfaction. Similarly, customer satisfaction has
been recognised as a major antecedent to several outcomes such as: business
performance (Van der Wiele et al. 2002), loyalty (Chumpitz & Paparoidamis 2004;
Olsen 2002; Tsoukatos et al. 2006) and purchase intentions (Labarbera & Mazursky
1983; Beardeen & Teel 1983; Lee & Hwan 2005; Tsoukatos et al. 2006). The direct
relationships among the constructs (SQ, CS and BI) and the indirect relationships
having customer satisfaction as a mediating variable have been empirically examined in
the service sectors (see Table 3.1), including the higher education sector (e.g Athiyaman
2000; Alves & Raposo 2007).
Nevertheless, both service quality and satisfaction constructs are not without critiques
and questions on the roles that both constructs play in the organisation’s
competitiveness. This has called for the introduction of a newer construct called
‘customer value’ as an important topic which is of growing interest to better predict the
behavioural outcomes and further competitive advantage (Slater 1996; Parasuraman
1997; Woodruff 1997; Slater & Narver 2000; Sweeney 2003).
1.2.2 Customer Value: the Emerging Construct
A fundamental based on the conceptualisation of customer value was developed by
Zeithaml (1988, p. 14) “The consumer’s overall assessment of the utility of a product
based on perceptions of what is received and what is given”. This definition has become
the most common definition of customer value in the marketing literature (Patterson &
Spreng 1997). Four diverse meaning of value are covered within this definition: (1)
value is low price, (2) value is whatever one wants in a product, (3) value is the quality
that the consumer receives for the price paid, and (4) value is what the consumer gets
for what they give. The majority of past research has focused on the fourth definition
(Petrick 2002).
The growing interest in customer value was triggered by the recognition that customer
value can be a further source of competitive advantage (Woodruff 1997; Slater 1997;
Slater & Narver 2000), customer satisfaction (Andreassen & Lindestad 1998; Oh 1999;
3
Tam 2004; Gill et al. 2007), re-purchase intentions, customer loyalty and relationship
commitment (Chang & Wildt 1994; Patterson & Spreng 1997; Andreassen & Lindestad
1998; Wang et al. 2004; Sweeney 2003) and long-term organisational profitability
(Woodruff & Gardial 1996). Anderson and Narus (1999, p. 5) maintain that, in the
business market, value is said to be the “cornerstone of business market management”.
Slater and Narver (1994) have identified the close relationship between customer value
and competitive advantage and maintain that competitive advantage is no longer based
on structural characteristics such as: market power, economies of scales and broad line,
but instead based on the capabilities that enable a business to consistently deliver
superior value to its customers. In order to achieve competitive advantage, firm must be
able to deliver customer value proposition, in which firm should (Rintamaki et al.
2008): 1) increase the benefits and decrease the sacrifices as relevant to customers, 2)
utilize more effectively on the competencies and resources as compared to its
competitors, 3) must maintain to be recognizably different (unique) from competition.
The significant role of customer value in many earlier studies has led academics to
include customer value in the model that formerly only focused on service quality,
customer satisfaction and behavioural intentions. A more comprehensive approach is
required to sustain and create competitive advantage since a traditional focus on service
quality and customer satisfaction is not sufficient in this highly competitive market
(Woodruff 1997; Slater 1997; Slater & Narver 2000).
1.2.3 Service Quality, Customer Satisfaction and Customer Value in the
Indonesian Higher Education Sector
The issues of quality and satisfaction have also been of heightened concern within the
higher education circles in Indonesia. Belonging to the service industry, higher
education is commonly defined by the quality of the service it provides (Slade et al.
2000). The service offered, and the way the service is delivered to customers are, two
important functions that form competitive differentiation across educational institutions
(Wright & O’Neill 2002). With the Indonesian government’s issuance of the “Basic
Framework for Higher Education Development KPPTJP IV 2003-2010”, higher
education in Indonesia should be organisationally sound, hence the higher education
sector may further support the nation’s competitiveness. In this KPPTJP IV 2003-2010
document, quality has been regarded as one of the requirements of a sound organisation.
4
The issuance of this document provides evidence that the Indonesian government has a
commitment to quality as an important aspect in improving higher education
competitiveness.
Despite acknowledging the importance of quality, customers do not always subscribe to
the same view as the service providers. Students, as the main customers of higher
education, have their own reasons and objectives in making decisions to study at the
most appropriate institution. As stated by Nizam (2006), wealth creation is the main
reason for attending tertiary education in Indonesia. Students are more concerned with
obtaining a degree as a ticket to enter the job market in the future. For this reason, not
all students seek high quality institutions for their study. Since students have different
reasons and objectives for undertaking their study in higher education (e.g., better
career, social approval, knowledge), higher education administrators must be able to
respond to critical student concerns and introduce initiatives that will satisfy these
concerns. In addition to the students’ points of view, rising competition, rising
operational costs and increasing student demand have forced higher education
institutions to apply differing marketing approaches in order to better deal with the new
conditions of the market. By practising marketing approaches, the institutions will get
closer to the market and understand better the demands of the market.
When considering value from the students’ perspective, students have spent money,
time, effort and opportunity costs to obtain the benefits of higher education experiences
offered by the institutions. Kotler & Fox (1995) maintain that customers expect a
significant return on any educational investment they make. Webb et al. (1997) maintain
that education is both a consumable as well as an investment of services/goods. By
making the educational investment, a question which is commonly raised relating to the
academic degree, financial expenditure and personal goals was, which of the institutions
will the students choose to obtain the services that will be best for them? (Kotler & Fox
1995). The above views justify the importance of examining service quality, customer
satisfaction and customer value in the higher education sector.
5
1.2.4 The Rationale
In the general service sectors, the perception of quality, value and satisfaction is likely
to have a strong impact on positive behavioural intentions. Since higher education
possesses all of the characteristics of the service industry, the proposed conceptual
model that simultaneously relates all of the four key constructs under investigation (SQ,
CS, CV and BI) should also be applicable and have similar positive impacts as had been
discovered in the general services sectors (e.g., Cronin et al. 2000; Andreassen &
Lindestad 1998; Mcdougall & Levesque 2000; Choi et al. 2004; Tam 2004). An
examination of the simultaneous interrelationships model in the higher education sector
would serve as the foundation to improve institutional health as well as meeting
students’ demands.
Furthermore, the marketing literature has identified a lack of research empirically
investigating the “simultaneous relationships” among service quality, customer
satisfaction, customer value and behavioural intentions (Cronin et al. 2000; Ostrom &
Iacobucci 1995). Most of the literature examines only either three constructs (SQ-CS-
BI, SQ-CS-CV, CV-CS-BI and SQ-CV-BI) and/or bivariate analysis (SQ-CS, SQ-CV,
SQ-BI, CV-CS, CV-BI and CS-BI). In addition, when employing all of the four
constructs, previous research only adopts the unidimensional measure. This thesis
extends ‘the Research Model’ as proposed by Cronin et al. (2000) by involving all of
the four key constructs and particularly measures service quality and customer value as
multidimensional constructs. ‘The Research Model’ as proposed by Cronin et al. (2000)
employed four constructs but unidimensional measures. The proposed conceptual model
as illustrated in Figure 1.1 is also called ‘the Integrative Model’ throughout this thesis.
The multidimensional conceptualisation of service quality and customer value is
important since it enables one to explain the complex nature of both constructs. The
extension of the Research Model would provide detailed determinants of service quality
and customer value in the higher education sector, while also enabling an examination
of the relative influence across the three constructs (service quality, customer
satisfaction and customer value) on behavioural intentions.
6
1.3 RESEARCH QUESTIONS
Notwithstanding the limited research that simultaneously relates service quality,
customer satisfaction and customer value on behavioural intentions in the general
services sectors, in the higher education sectors there were fewer empirical studies
investigating the simultaneous model relating these four key constructs than in the
general services sectors. The purpose of this thesis is to better understand the inclusion
of the customer value construct in the service quality, customer satisfaction and
behavioural intentions relationships in the higher education sector. The research gap in
this thesis is a need for empirically testing the integrative model of service quality,
customer satisfaction, customer value and behavioural intentions in the higher education
sector. Furthermore, to accommodate the complex nature of the key constructs, service
quality and customer value in particular are measured as multidimensional constructs.
Thus, this thesis is designed to answer the three key questions, as follows:
• Research question 1: What constitutes valid and reliable scales for measuring
service quality and customer value in the Indonesian higher education sector?
• Research question 2: How do service quality, customer satisfaction and
customer value relate to behavioural intentions in the higher education sector in
Indonesia?
• Research question 3: What are the effects of the inclusion of the customer
value construct in the relationships between service quality, customer
satisfaction and behavioural intentions?
In answering the above research questions, relevant issues relating to service quality,
customer satisfaction, customer value and behavioural intentions are examined and
discussed in Chapter Two and Chapter Three. The proposed conceptual model is
examined based on: 1) the causal direction proposed by Bagozzi (1992) and Oliver
(1997) in which cognitive response leads to emotive response, and 2) the Research
Model simultaneously relating those four constructs (SQ, CS, CV and BI) as previously
proposed by Cronin et al. (2000). In this respect, the determinants that build service
quality and customer value are examined; the direct relationships among the constructs
are evaluated; the indirect relationships with customer satisfaction and customer value
as mediating variables are investigated; and finally, the relative contributions of service
7
quality, customer satisfaction and customer value on behavioural intentions are also
tested. Figure 1.1 illustrates the conceptual model proposed in this thesis.
Figure 1.1 The Conceptual Model
1.4 IMPORTANCE OF THE STUDY
1.4.1 International Competition in the Higher Education Sector
Virtually all countries in the world are now moving towards knowledge-based
economies. This move has rendered higher education an important sector that supports
the nation’s overall strategy for survival and competitiveness. Porter (2002) states that
with the more open and integrated world economy, “competitiveness” has become a
central issue not only for advanced countries, but also for developing countries. As a
consequence, international recognition of the performance of higher education should
be of great significance for the competitiveness of any nations. Indonesia, as a
developing country, is no exception. The performance of Indonesian higher education
is challenged by other nations from both developed and developing countries. The
challenges are intensifying since it is geographically surrounded by countries with
extensive market penetration of their education industry into Indonesia. For this reason,
Tangibles
Competence
Delivery
Quality
Content
Reliability
Attitude
Emotion
Reputation
Social
Price
Service
Quality
Customer
Value
Customer Satisfaction
Behavioural Intentions
8
Indonesia must actively build its higher education competitiveness in this open and
integrated world economy.
In dealing with the intense competition in the higher education industry, one of the
strategies managed by the Indonesian government is encouraging local higher education
institutions to achieve a prominent international position, especially in Asia (Indonesia
Market Introduction 2008). With the approval of government policy on the legal
operation of overseas institutions such as twinning programs (Indonesian government
regulation PP 60 in1999), the Ministry of National Education (MoNE) permitted a joint
establishment between local and international institutions to allow the latter to establish
their offshore divisions in Indonesia. Furthermore, in order to support the international
position, the Directorate-General of Higher Education selected fifty promising
universities as part of an effort to introduce those institutions to the global academic
community (Mone 2009). The promotion of these fifty universities will enable both
local and international institutions to select an appropriate partner to establish further
collaboration. The selection of these fifty universities was based on institution awards,
student life, facilities, research and community service and international collaboration
(Indonesia Market Introduction 2008).
“Indonesia is and will become even more an attractive education market” (Ehef 2008, p.
1). Given that a foreign degree is still considered to be superior to a local credential and
provides an entry ticket to a better career, Indonesia is an interesting potential education
market. The local higher education institutions must carefully consider market
penetration from overseas higher education competitors. In 2004 it was estimated by the
Institute of International Education (IIE) that 0.9% of Indonesian higher education
students went to study abroad (Ehef 2008). This number is equal to 30,000 students (the
UNESCO estimate was 31,687 students) based on a total number of 3,441,429 tertiary
students in Indonesia in 2004.
In terms of overseas competitors in the education market, the USA and Australia remain
the market leaders (Indonesia Market Introduction 2008). By offering lower costs and
collaboration in offshore programmes with Australia, USA and UK universities,
Malaysia and Singapore have attracted and successfully maintained their popularity
within the Indonesian undergraduate student market. For post-graduate students,
9
Germany, the Netherlands and Japan are increasingly popular market leaders (Indonesia
Market Introduction 2008). The strength of Australia in the higher education industry is
due to its extensive use of agents and continuous visits and promotions as part of market
penetration strategies (Indonesia Market Introduction 2008). In addition, Australian
universities are also very active in creating links and partnerships with Indonesian
universities. The geographical proximity to Indonesia and a favourable study climate are
also extra benefits that Australia has over its competitors (USA, Japan and European
countries).
1.4.2 Local Competition in the Indonesian Higher Education Sector
The fact that the knowledge economy is an important driving force of wealth creation,
this has made access to higher education increasingly important. Even though demand
exceeds supply (Nizam 2006), this does not simplify the task of attracting students.
Despite the international issues facing higher education competition, the higher
education environment in Indonesia is also encountering intense competition among the
local institutions. Every year, more than 450,000 high school graduates compete for
entry into higher education (Nizam 2006). The public higher education sector only
serves about 10-20% of the applicants while the majority of high school graduates
enroll in private universities. Despite the common marketing problems, students are
now becoming more selective and rational on their choice of the programs and have
many options open to them than was previously the case. There is no guarantee that
public institutions are always preferred over private institutions, due to the intensive
international penetration of the Indonesian education market and more rational
consideration by students in choosing institutions. Both public and private higher
education in Indonesia must be aware of the nature of higher education competition.
Factors that contribute to the increase in higher education competitiveness must be
critically assessed.
1.4.3 The Importance of Higher Education Competitiveness
All of the aforementioned arguments provide a rationale for the importance of
identifying determinants that contribute to the increasing higher education
competitiveness. This thesis in particular examines determinants that have been
identified as closely related to building competitiveness as seen from marketing
10
perspectives. Previous literature has evidenced the contribution of service quality,
customer satisfaction and customer value to increasing organisational competitiveness
(Parasuraman et al. 1988; Zeithaml et al. 1996; Vuorinen et al. 1998; Woodruff 1997;
Slater 1997; Slater & Narver 2000). Although learning remains the mission of every
educational institution, the reality is that in order to survive, higher education
institutions must not merely maintain their traditional management system by
depending on government funding and students’ tuition fees. Different marketing
approaches are required to survive in the education market. A comprehensive model
relating to service quality, satisfaction and customer value has been examined in the
field of general services marketing and has been shown to exert significant influence on
behavioural intentions. Considering that it is critical to pursue marketing approaches in
managing higher education institutions, the higher education sector, as a service sector,
should also benefit from understanding the same marketing framework. Furthermore,
the empirical results should assist administrators and professionals in the higher
education industry in better managing higher education institutions and thereby increase
their own competitiveness.
1.5 CONTRIBUTION OF THE STUDY
1.5.1 Theoretical Contributions
This thesis is valuable to both academics and practitioners in the higher education
sector. The following discussion provides details on the contribution of the study.
The theoretical contribution of this thesis lies primarily in the application of the
integrative model as proposed in the conceptual model (Figure 1.1). This thesis
introduces and examines the applicability of the conceptual model in the higher
education industry. The earlier studies have mostly employed service quality, customer
satisfaction and behavioural outcomes. This thesis adds customer value to the
relationships model and simultaneously assesses all of the four constructs in order to see
the nature of the relationships. In addition, the relative effects of service quality,
customer satisfaction and customer value on behavioural intentions are also examined.
The application of the integrative model has been suggested by Ostrom and Iacobucci
(1995) and Cronin et al. (2000). Simultaneously investigating the relationships between
11
all of the four constructs (SQ, CS, CV and BI) might provide a more accurate and
comprehensive picture of the nature of the relationships. In addition, there were also
different opinions and findings relating to the causal ordering of service quality on
customer satisfaction and then on behavioural outcomes (Brady & Robertson 2001).
Service quality and customer value are considered to be a largely cognitive/evaluative
construct, while customer satisfaction is more of an affective/emotive construct. Since
the nature of the interrelationships across the constructs of interest is still the subject of
an ongoing debate, research in this area is very open. Empirical evidences would verify
the nature of the relationships, particularly in the Indonesian higher education sector.
The causal direction, the evaluative response leads to emotive response (Bagozzi 1992;
Oliver 1997), is adopted in the model to provide evidence that this causal direction is
robust across nations. Brady and Robertson (2001) provide support on the evaluative
leads to emotive response after examining the causal direction in two nations with
different cultures.
Secondly, the literature review in Chapter Two notes the context-specific nature of
service quality and customer value. Quality is considered as a difficult concept in the
social sciences since it means different things to different people (Sahney et al. 2004a;
LeBlanc & Nguyen 1997). Value is also a subjectively perceived concept and highly
personal since it is perceived differently from customer to customer (Zeithaml 1988;
Woodruff 1997; Kortge & Okonkwo 1993; Holbrook 1994). Since this thesis is focused
on the higher education industry, both measurements of service quality and customer
value must be adjusted to reflect the higher education context. In addition to the
context-specific nature, this thesis conceptualises service quality and customer value as
multidimensional constructs. Even though multidimensional conceptualisations of
service quality and customer value were common in general services marketing, the
evidences from empirical study examining customer value in the higher education
sector are still limited. By considering the context-specific and complex nature of
service quality and customer value, empirically testing the multidimensional concept of
service quality and customer value adds richness to the service quality and customer
value constructs in the higher education sector.
12
1.5.2 Practical Contributions
There are three broad consequences of this thesis for managers and administrators.
First, in the increasingly competitive environment, students have many more options
open to them. Factors that enable educational institutions to develop quality, value and
satisfying education experiences should be critically and continuously assessed. If the
inclusion of customer value on service quality and satisfaction relationships does
increase the predictive power to determine behavioural intentions, then it may be
necessary for institutions to not only focus on service quality and satisfaction per se, but
to also concentrate on activities that may increase the perception of the value of higher
education experiences. More specifically, by including value perceptions this thesis is
expecting to provide guidance to managers and administrators not only in assessing
benefits (quality) and satisfaction but also in considering the costs/sacrifices that
students have paid. The benefit and sacrifice valuation will provide a more realistic
picture of factors that motivate students to proceed to higher education.
Second, by identifying the structural relationships in the conceptual model, it will show
the relative degrees of importance among the three constructs (SQ, CS and CV).
Therefore, managers and administrators could focus on which factors contribute most to
the development of positive behavioural intentions. Simultaneously, managers and
administrators could also investigate and improve on the factors that may have the least
influence on the formation of behavioural intentions. The identification of the relative
importance of the factors under investigation will allow management and administrators
to have clearer understanding of further strategic actions that can enhance competitive
advantage.
Third, the multidimensional conceptualisation of service quality and customer value
will allow administrators to have a more detailed and clearer understanding of the
aspects of both constructs (SQ and CV). From the managerial perspective, an awareness
of the sources that improve service quality and customer value assists managers and
administrators in appropriately allocating resources to maximise an institution’s
competitive advantage. In addition to quality building, the results of this thesis should
also enrich the institutions’ value-creating process. Overall, by understanding the
13
quality and value concept in the higher education context thoroughly and its linkages to
satisfaction and behavioural intentions, this research offers managers and administrators
guidelines for designing a service strategy that reflects the quality and value of higher
education services.
1.6 RESEARCH METHOD
This section describes a summary of the research methodology adopted in this thesis. A
more detailed discussion and justification of the research methodology is presented in
Chapter Four.
1.6.1 Conceptual Model Development
The conceptual framework and hypotheses proposed in this thesis were developed based
on an extensive literature review relating to services industry, service quality, customer
satisfaction, customer value perceptions and customer behaviour in the general services
sector. Table 1.1 provides summary of definition of the key constructs. In particular, an
extensive review relating to the above-mentioned issues in the higher education context
was undertaken.
Table 1.1 Definitions of Key Constructs Constructs Definition Source
Service Quality Consumer’s judgment about a product’s overall excellence or superiority.
Zeithaml (1988)
Customer Value The consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given.
Zeithaml (1988)
Customer Satisfaction A short term attitude that results from the evaluation of their experience with the education service received.
Eliot and Healy (2001) in Navarro et al (2005)
Behavioural intentions Behavioural intentions represent a variety of customer responses and may indicate customers’ propensity to remain with or to defect from a company.
Zeithaml et al. (1996)
Service Quality Dimensions
Tangible Condition/appearance of physical facilities, equipment, personnel.
Sahney et al. (2004b)
Reliability ability to perform the promised service dependably and accurately.
Parasuraman et al. (1988)
Attitude The degree to which staff understand the customers (students) and have socially acceptable manners.
Sahney et al. (2004b)
Content The nature and relevance of product/service. Sahney et al. (2004b)
Competence The possession of the required skills and knowledge to perform the Service.
Sahney et al. (2004b)
Delivery The manner in which the product or service is being delivered and presented.
Sahney et al. (2004b)
14
Table 1.1 continued
Constructs Definition Source Customer Value Dimensions
Emotion Descriptive judgment regarding the pleasure that a product or service generates.
Petrick (2002) and Sweeney and Soutar (2001)
Price The price of a service as encoded by the consumer. Petrick (2002) and Sweeney and Soutar (2001)
Quality The utility derived from the perceived quality and expected performance of the product
Sweeney and Soutar (2001)
Reputation The prestige or status of a product or service, as perceived by the customer, based on the image of the supplier.
Petrick (2002) and Sweeney and Soutar (2001)
Social The utility derived from the product’s ability to enhance social self-concept.
Petrick (2002) and Sweeney and Soutar (2001)
1.6.2 Operationalisation of the Constructs
The development of the questionnaire that allows for the measurement of service
quality, customer satisfaction, customer value and behavioural intentions was based on
a review of the literature on these four key constructs (SQ, CS, CV and BI). Most
constructs used existing scales and some modifications were also made. A preliminary
test regarding the content was also conducted to increase the effectiveness in measuring
the key constructs under investigation. The features and composition of the
questionnaire were carefully designed so that completion will be more accurate and
effortless for students. All of these processes were followed to support the sound
psychometric properties of the survey instrument.
1.6.3 Data Collection
An on-site self-administered survey approach to selected universities using paper
questionnaires was pursued. The self-administered surveys by paper questionnaires
allows for gathering large samples. The unit of analysis was represented by students.
Students were chosen because that sample is most involved with the day-to-day services
offered by higher education institutions. It was presumed that students have the requisite
educational experiences to enable them to provide information, opinions and
perceptions of those aspects under examination. The sampling procedures were
carefully undertaken to ensure that the data collected closely represented the true
population. The questionnaires were distributed to five selected universities in
Yogyakarta, Indonesia.
15
1.6.4 Data Entry and Analysis
SPSS version 16 for Windows is used for the data entry and analysis. Factor analysis is
used to establish the psychometric properties of the measures. Additionally, Partial
Least Squares is applied to simultaneously explain the interrelationships among the
constructs under investigation.
1.7 THESIS OUTLINE
This thesis comprises eight chapters which are presented as follows:
Chapter One presents an overview of the thesis which includes the research background
and justifications, the research questions, the importance of the study, theoretical and
managerial contributions, research methodology, the outline of the thesis and
delimitations.
Chapter Two reviews the literature. This chapter commences with an introduction to the
notion of services industry. The role of students as customer in the higher education is
discussed. The sections following review the literature on service quality, customer
satisfaction, customer value and behavioural intentions. The discussions relate to the
concept and dimensionality of the constructs being studied, the nature of the
relationships and the application of the constructs in the higher education sector.
Chapter Three provides a review of the literature relating to the relationships and
hypothesis development across all of the constructs under investigation. This chapter is
an extension of the literature review in Chapter Two which specifically discussed the
logic and provided justification for the hypotheses and the conceptual model that forms
the basis of this thesis. This chapter commences with the discussions on earlier studies
that have applied integrative models in several different service sectors.
Chapter Four presents the research methodology applied in the present study. The
justification for conducting the quantitative method as an appropriate research design to
test the hypothesised model is provided. This chapter also details the research approach
and research tactics which cover measurement and questionnaire design, pre-testing,
sampling plan and statistical analysis. Ethics and confidentiality are also covered.
16
Chapter Five details the preliminary analysis to test the unidimensionality, reliability
and validity of the scales. Exploratory Factor Analysis using Principal Component
Analysis is employed. The assessment of reliability using Cronbach’s alpha is included.
Chapter Six details the processes used in Partial Lest Squares technique to verify the
psychometric properties and test the relationships in the proposed model. The valid and
reliable determinants used to measure the constructs are confirmed in this chapter. The
direct and indirect relationships proposed are examined.
Chapter Seven reviews and discusses the results derived from Chapter Five and Chapter
Six. The preliminary analysis is firstly reviewed and then followed by further
discussions and interpretation of the results from the main analysis. The discussions are
presented according to the proposed research questions and their related hypotheses
development.
Chapter Eight commences with a summary of stages of the research. The next section
covers brief reviews of overall results from the preliminary analysis (Chapter Five) and
the main analysis (Chapter Six). The contributions to the theory and academic
discipline, implications, limitations of the study and suggestions for future research are
also discussed.
1.8 LIMITATIONS
This thesis has its limitations. As has been mentioned in the section relating to
theoretical contributions, service quality and customer value are context-specific
constructs where different samples of people have different perceptions. This means
that there are aspects of the dimensions of service quality and customer value that may
vary slightly from the contextual perspectives. All of the dimensions considered valid in
this study may not suitable to measure service quality and customer value in other
geographical locations. All efforts were made to develop a measure that closely
represents service quality and customer value according to the Indonesian higher
education sector. This was done by a thorough literature review, interviews with
education experts and pre-testing.
17
Secondly, since the focus of this thesis is to examine the determinants of service quality
and customer value and the relationships across the constructs of interest, university
students were chosen as the main respondents. These students were chosen because they
have direct interaction with the respective education services provided by their
universities. There are other stakeholders of higher education institutions (government,
parents, society and industries). However, these stakeholders are less likely to be
involved with the day-to-day educational experiences. By surveying students’
perceptions and opinions, it is expected that the data collected will be highly enriched
by their hands on experience.
18
CHAPTER TWO
LITERATURE REVIEW
2.1 INTRODUCTION
With the increasing movement towards the knowledge-based economy, competition in
the higher education industry has become more intense. Many higher education
institutions are adopting marketing strategies to boost their competitiveness and
maintain their survival. As part of the drive to create competitiveness, research in the
higher education sector has primarily focused on service quality and satisfaction as key
factors for achieving competitiveness. However, in a highly competitive market where
customers are more demanding, service quality and satisfaction might no longer be
adequate sources of competitive advantage. This thesis addresses this gap by including
customer value in the service quality and satisfaction relationships, in order to provide a
more comprehensive model which will be better in explaining the behavioural
outcomes. To provide the body of knowledge regarding the nature of the relationships
among the four main constructs (service quality, customer satisfaction, behavioural
intentions and customer value), this chapter basically reviews the concepts,
dimensionalities, the structural model and the nature of the relationships as well as the
application of these constructs in the higher education sector. This review further
provides a foundation for the conceptual model proposed in this thesis. Figure 2.1
illustrates the structure of the literature reviews.
This chapter commences with a review of the nature of the service industry (Section
2.2) and is followed by discussions on ‘students as customers’ (Section 2.3). The next
sections discuss the key constructs used in the conceptual model and will be presented
in the following order: service quality/SQ (Section 2.4), customer satisfaction/CS
(Section 2.5), customer value/CV (Section 2.6) and behavioural intentions/BI (Section
2.7). Section 2.8 is the summary of the chapter.
19
Figure 2.1 Literature Review Structure
2.2 SERVICE INDUSTRY
2.2.1 The Nature and Characteristics of Services
In general, service is associated to behavioural activities than physical attributes
(Cubillo et al. 2006). Lovelock (2001, p.3) defines service as “an act or performance
offered by one party to another. Although the process may be tied to a physical product,
the performance is essentially intangible and does not normally result in ownership of
any of the factors of production”. The intangibility, heterogeneity, perishability and
inseparability of production-consumption are characteristics of service that are widely
accepted (Lovelock 2001). These four characteristics are further regarded as the main
characteristics of services. Unlike a product, the nature of service makes it unable to be
possessed, to be tasted, to be kept, to be touched, it remains intangible.
In order to understand the features of service, these four characteristics of services are
commonly used as proxies. The logic of understanding services is basically centred on
these four characteristics of service (Zeithaml et al. 1985). In practice, a particular
marketing strategy application is necessary to handle these special characteristics of
service (Kotler & Fox 1995). Theories and frameworks relating to service are also
The nature of service industry
Students as customers of
Higher education sector
Service Industry
Customer Value
• General services
• Higher education
Customer Satisfaction
• General services
• Higher education
Service Quality
• General services
• Higher education
Behavioural Intentions
20
mostly developed and driven according to these four characteristics of services. It is
essential to have a clear understanding of the features/characteristics of service since
these characteristics may justify diverse strategies that should be executed when
marketing services. A brief discussion of the attributes of service is presented in the
paragraphs below.
Intangibility is known to be the most obvious characteristic of service. Service is
considered intangible, as it is only imaginary in nature (Liechty & Churchill 1979). In
contrast to product, which is clearly tangible and exists in both time and space, service
tends to be reflected in social interactions and social acts and it only depends on time
(not space) (Berry 1980). The intangible nature of service is argued to be one of the
fundamental characteristics of service which differentiates services from goods
(Bateson 1979). This intangibility has also been described as one of the reasonably
stable generalisations that researchers can make about services (Hill 1995). In practice,
industries that tend to deal with intangible activities such as higher education, banking,
touring, consulting, internet services and insurance are considered to be a service sector.
Nevertheless, it is argued that no industry has either the pure characteristics of being a
service or a product. Industries are usually positioned somewhere along the continuum
between purely product or purely service.
The inseparability which reflects the simultaneous production and consumption
describes the majority of services (Gronroos 1978; Zeithaml et al. 1985). The deliveries
of services and products are different. Berry (1980) explains that services are usually
offered first; once a customer is interested, the production and consumption are carried
out at the same time. Unlike services, products are initially produced in the
manufactured, then inventoried and finally sold and consumed.
Despite the intangibility that was said to be the fundamental characteristics of service,
Parasuraman et al. (1985) argue that the hallmark of service is its heterogeneity. The
diversities that occur within each interaction (between customer and service provider)
make every transaction unique. The heterogeneity and uniqueness of each service
delivered lead to difficulties when it comes to assessing, comparing and evaluating
service. One possible reason for this difficulty is that since the nature of service is so
21
heterogeneous there is a lack of standardisation on which to base evaluation (Zeithaml
et al. 1985). This is typical in the service sector, where the service offerings are different
from one transaction to the next. The heterogeneous nature of service is especially
evident in the industry involving high labour content (Zeithaml et al. 1985). In the
industry with high labour content (e.g education, hospital, tourism and transport
services), the characteristics and the core of the services delivered vary between
providers, between customers and within different timing (Zeithaml et al. 1985). Each
service delivered is unique since it involves many people with different personalities.
The heterogeneity is also triggered by the fact that the end products depend on both
service provider and customer, since both sides are fundamental parts of the service
process, production and consumption.
The term perishable refers to the nature of service where it is impossible to save and use
in times of need (Bessom & Jackson 1975; Thomas 1978). This perishability of service
relates strongly to inseparability. Since services cannot be exactly recorded, it is not
always easy to match supply and demand. For example, the costs of running particular
programs or short courses must be covered regardless of whether there will be enough
participants or no participants at all. Therefore, service providers must be ready for any
losses that eventuate due to lack of demand for their offerings. On the other hand, it is
common practice in the service sector that making a booking does not always guarantee
customers for the service provided.
2.2.2 The Nature and Characteristics of Higher Education Services
In recognising that service is the main offering provided by higher education
institutions, meeting the expectations and needs of their customers should receive
greater emphasis (DeShields et al. 2005). Intense competition in educational market
forces higher education institutions to adopt marketing approaches in order to maintain
their existence and to differentiate their offerings from those of their competitors. Since
any marketing approach requires the institutions to understand their customers, it is
important to identify and assess the needs of the target market, modify the offerings to
adjust to the current trend of the market and deliver a superior quality service to
enhance customer satisfaction (Keegan & Davidson 2004).
22
The characteristics of services are noticeable in the higher education sector. There are
significant numbers of interactions between staff and students, interpersonal
consultations, customisations of products/services, heterogeneity of offerings and
inseparable participations between staff and students. This complex nature of offerings
creates particular challenges for the evaluation of services in the higher education sector
(Srikatanyoo & Gnoth 2002). For example, even though efforts have been made to
ensure the standard of the course materials that should be delivered, the quality of the
delivery depends on the knowledge of the tutors and lecturers. The same professor/staff
might also be differently perceived by students, since students have different
perceptions and service experiences.
It is known in the service sector that the quality of service performance depends, not
only on the service provider’s performance, but also on the quality and productivity of
the customer (Hill 1995). As discussed above, customers are an integral part of the
service process (production and delivery processes). In many services, it is usual that
customers are required to provide information or particular efforts before commencing
the service transaction (Kelley et al. 1990). In the higher education sector, a student is
not only in the position of a customer but also that of co-workers or clients (Eagle &
Brennan 2007). The quality, productivity and performance of higher education depend
on the participation of both staff and students. The concept of customer participation
should be encouraged in the higher education sector (Zeithaml & Bitner 1996). This
concept will work effectively when both staff and students understand and respect the
roles of the other.
In addition to the student and staff’s continuous participation to the education processes,
the perception of quality may vary among students as individuals, students of different
academic levels, different classes and different lecturers (Patterson et al. 1998; Owlia &
Aspinwall 1996). In the case of students, when faced with different circumstances, the
same individual may perceive the quality offered by the institution differently. Students
in their first year (having minimal experience with the institution) may perceive the
quality of service differently from final year students. The perception of the first year
students on quality might be affected by other people, since they do not have enough
experience with the institution. On the other hand, the final year students might have
23
different perceptions since they have much more experience with the education services.
Students with high academic achievement may also have different perceptions from
students with average academic achievement. The quality at this stage is dependent on
how the higher education institutions are able to identify and satisfy the needs of
students at all levels.
Satisfaction among students is also varied since there are many personal interactions
involved between higher education staff and students. The satisfaction derived by
students from such interactions may depend on a variety of factors, ranging from the
appearance, competence, personal characteristics of the staff and the interpersonal
interactions between staff and students. Overall, considering that the quality of services
and customer satisfaction depend on an organisation’s capability to manage their service
offerings, which is not as simple as the management of a product considering the
characteristics of service, therefore, research in the service sector remained a challenge.
2.3 CUSTOMERS OF THE EDUCATIONAL SYSTEM
Sahney et al. (2004b, p. 500) maintain that “a customer can be anyone who is being
served”. In the higher education sector, students are not the only customers since there
are other stakeholders identified as having particular interests in the service offered.
Table 2.1 summarises some studies regarding customers of higher education. Given the
varieties of higher education stakeholders, earlier research has appeared to show limited
conformity regarding the ’true’ customers of higher education.
24
Table 2.1 Customers in Higher Education Source Definition
Downey et al. (1994) The primary customer: students, performing as both an internal and external customers. Internal customers: Students and all employees. External customers: students, tertiary institutions, business industry,and society.
Madu et al. (1994) Input customers: parents and students. Transformation customers: the faculty. Output customers: business, industry and the society.
Spanbauer (1995) The primary customers: students. External customers: students, employers, community, taxpayers and educators at large. Internal customers: instructors and administrative staff.
Kanji et al. (1999) Primary internal: educator / employee. Secondary internal: student (as educational partner). Primary external: students. Secondary external: the government, industry and parents.
Sallis (1993) The primary external customer: the learner. The secondary external customers: parents and employers. The tertiary external customers: business, industry, government and society. The internal customers: lecturers, administrators and support staff.
Hill (1995) Student is the primary customer.
Galloway & Wearn (1998)
Student is the primary recipient of the services provided by higher education institutions.
Srikanthan & Dalrymple (2003)
Providers (funding bodies / universities and community at large / parents and society). Users of products (current and prospective students). Users of outputs (employers / industry). The employees of the sector (academics and administrators).
Source: Developed for the study and Sahney et al. (2004b)
Hill (1995) maintains that the student is a primary customer since students are the group
that interact most with higher education institutions. By examining the primary
customer, it is expected that the objective of analysing the key determinants that
contribute to higher education competitiveness will be more achievable.
There are debates regarding the position of students when being placed as the customers
of education services. When deciding that the student is the customer of higher
education services, careful interpretation must be made since the student cannot be
simply regarded similar to customers who consume commercial products (Eagle &
Brennan 2007). Treating students simply as commercial customer means that students
are allowed to simply assign blame to the service providers with regard to their
academic failure and poor performance. There are different forms of transactions
between students as customers and customers of commercial products. In the case of
students, even though they have paid overall tuition fees, this does not mean that
qualifications can be automatically acquired (Bejou 2005). There are certain academic
requirements to be met in order for students to obtain the desired qualification. Potential
problems may arise when simply treating students as commercial customers, since this
25
may lead to the possibility of damaging the student’s responsibility for their own
learning (Clayson & Haley 2005).
However, most students are rational and they do not regard highly the institutions that
easily grant a degree (Eagle & Brennan 2007). On the other hand, the majority of
students realise that they have to work hard in order to meet the requirements and
achieve their goals. Despite the arguments regarding students as customers of higher
education, treating students as partners in higher education appear to be more positive in
practice. Halbesleben et al. (2003) and Kotze and duPlessis (2003) suggest that students
be treated as contributors to and co-workers in the education process.
This thesis regards the students as the partners of higher education, not simply as a
customer of commercial products. As discussed above, the term ‘student as customer’ in
this thesis does not mean that students are entitled to the degree offered after all their
financial responsibilities have been met. Neither does it imply that students are always
correct in all aspects, so that institutions must arrange and direct all their resources
solely to fulfilling students’ needs. The term ‘customers’ is used simply for the reasons
of practicality and to ensure that the day-to-day education service deliveries meet
students’ needs and are in line with the requirements of the higher education
institutions.
2.3.1 Higher Education Stakeholders’ Perceptions of Quality
As illustrated in Table 2.1, students are not the only customers of the higher education
industry. There are also other groups that have vested interests in the educational
process, such as parents or carers (who are responsible for the tuition fees), government,
employers and societies. Since these different groups of higher education customers
have different interests, they have different criteria of higher education quality. Table
2.2 presents a summary of different definition of quality as perceived by different higher
education stakeholders.
26
Table 2.2 Stakeholder Perspectives on Quality. Stakeholder Group Quality Perspective
Funding bodies and society at large. Value for money, good return on investment and monetary aspect.
Current and potential students. Excellence and high standards services to ensure future employment.
Employers. Fitness for purposes and competency meets the functions.
Academic and administrators within universities.
Consistency and perfection.
Source: Srikanthan and Dalrymple (2003)
In addition to identifying different interests among stakeholders, studies have indicated
the significant contributions of these higher educational stakeholders to the success of
both students and the respective higher education institutions. For example: parents
financial contributions and non-financial contributions through providing positive
motivations and facilities to study; the industries provide jobs for future graduates; and
the government provides competitive funding, a stable political atmosphere and
facilitates broader academic networking which strategically vital for the success of
higher education institutions.
The different types of stakeholders and differing views of what constitutes quality have
contributed to the richness of the quality concept in higher education. Despite the
diversity of the stakeholders and the different perceptions of quality, this thesis only
focuses on students. Students were chosen as respondents since they are the group that
mostly have direct experiences with the higher education service offerings (Lagrosen et
al. 2004). Furthermore, other stakeholders of higher education are not examined due to
their less frequent or only occasional have direct interaction with the institutions. Table
2.1 provides support for studies that identified students as the primary customers of the
higher education sector.
By examining the primary customer, it is expected that the objective of analysing the
key determinants that contribute to higher education competitiveness will be more
achievable. The term ‘direct experiences’ is important in this thesis since it will provide
more objective and practical information for measuring the key factors (service quality,
customer value, customer satisfaction and behavioural intentions) specific to the higher
education context. The following sections are reviews of literature specific to all four
key constructs under investigation in this thesis.
27
2.4 SERVICE QUALITY
2.4.1 Importance of Service Quality
Service quality has been a common issue in marketing studies and is considered to be
the topic most researched. To date, service quality studies have not only covered the
conceptualisation and/or relationships with other variables, but also other aspects, as
categorised by Perez et al. (2007) into five major lines including: concept and nature of
service quality, measurement, strategic implications, effect on consumer behaviour and
how to improve service quality. Service quality has been widely discussed since it was
found to have significant positive outcomes in the business market. For example,
service quality has been identified as a source of competitive advantage (Ghobadian et
al. 1994; Zeithaml et al. 1996; Clow & Vorhies 1993), competitive corporate strategy
(Gronroos 2001), financial performance (Nelson et al. 1992; Rust et al. 1995; Anderson
et al. 1997), profitability and productivity (Hesket et al. 1994; Vuorinen et al. 1998;
Zeithaml 2000; Keiningham et al. 2005), business performance (Van der Wiele et al.
2002), satisfaction (Cronin & Taylor 1992; Boulding et al. 1993; Zeithaml 2000; Oliver
1996) and behavioural intentions (Bolton & Drew 1991; Cronin & Taylor 1992; Taylor
& Baker 1994; Olorunniwo et al. 2006; Cristobal et al. 2007).
2.4.2 Quality a Difficult Construct
Before service became the primary focus of researchers in the marketing area, most of
the early research on quality focused more on the quality of products and the
manufacturing process. Table 2.3 provides an early conceptualisation of quality mostly
based on the product approach.
Table 2.3 Quality Conceptualisation Sources Quality Conceptualization
Feigenbaum (1951) Product value.
Juran & Gryna (1988) Fitness for use.
Gilmore (1974) Conformance to specifications.
Crosby (1979) Conformance to requirements and defect avoidance.
Lehtinen & Lehtinen (1982) The physical and the interactive qualities.
Juran & Godfrey (2000) 1) Quality means products’ features meet customer needs, therefore provides satisfaction, 2) Quality means freedom from deficiencies.
Source: Developed for the study and Reeves and Bednar (1994)
‘Quality’ so far has been defined differently from a variety of perspectives. It is known
as a slippery concept because, while it seems easy to describe, however, it is
28
challenging to define (Garvin 1988; Galloway 1998). The term slippery concept means
that it has different meanings to different people (LeBlanc & Nguyen 1997; Ahmed et
al. 2002). Quality is also considered as a difficult and elusive construct to define
(Sahney et al. 2004a). The absence of general agreement has made the concept of
quality the subject of continuous debates and changes (Tam 1999). However, apart from
the ongoing debates, there is an agreement that quality should be determined and is
owned by stakeholders (Harvey & Green 1993; Ruben 1995).
Similar to ‘Quality’, ‘Service Quality’ is said to be an elusive construct (LeBlanc &
Nguyen 1997). Due to the lack of tangible evidence, the objective evaluation of service
quality is more difficult than product (Hong & Goo 2004). Moreover, the heterogeneous
nature of services, which involve many different people-based activities, makes
standardisation difficult hence increasing the complexity. Overall, despite being a well-
established construct (Zeithaml 2000), the uniqueness and complexity of service quality
features ensures that the conceptualisation and measurement remains a challenge.
2.4.3 Service Quality Defined from the Customer’s View Point
The idea that quality should be determined based on the customer’s view is based on the
reasoning that any attempt to create quality is commonly aimed at how to satisfy
customers (Juran et al. 1974). The major shift from the product-based quality
perspective to the customer-based quality perspective (customers’ view point) has been
caused by the ‘inability’ of the product-based perspective to provide the answer for
quality in the service sector. From the perspective of the manufacturing sector, quality is
the absence of defects and is measured by looking at the production process (Gronroos
1990). This view translates quality in terms of measures associated with internal
operations. The problem with the early concepts of quality, which commonly developed
from the manufacturing sector (internally generated measures of quality), was derived
from the inability to match product quality to customer perceptions of quality. An effort
to develop a new concept of service quality has been triggered by the more important
role of services areas and the inability of the manufacturing conceptualisation of quality
to be applied in the service sector (Reeves & Bednar 1994). This is why the current
focus of literature relating to service quality has been very much centred on the
customers’ views.
29
The unpopularity of the product-based approach does not mean that it is no longer
applicable in the service sector. Gatfield et al. (1999) argue that there are two main
schools of thought in determining service quality, the supply-side managerialist
approach and the demand-side customer approach. According to the managerialist
approach, the service provider is responsible for defining, stating, measuring, evaluating
and monitoring quality standards. The basis of the managerialist method was rooted in
the product-based approach which centres on internally generated measures of quality.
The demand-side approach refers to quality as defined by customers. In research, the
contribution of both quality approaches depends on the context and research objective.
For example, if the objective is to attract customers who are satisfied/dissatisfied with
service performance, researchers must be able to identify quality from the demand-side
approach. Alternatively, if the objective is to deliver service quality that offers the
highest capacity, the supply-side approach can be emphasised since the managers
certainly have the knowledge regarding the aspects of quality and how to improve their
products/organisations.
The majority of researchers from the service marketing discipline have favoured the
customers’ view of quality (Gatfield et al. 1999). The main reason in taking the
customers’ perspective is because the characteristics of service itself (e.g intangibility,
heterogeneity, perishability and inseparability) make an objective assessment effectively
impossible. For example, being intangible, the objective characteristics are not fully
present. When dealing with services, due to their complex nature, customer perception
is used as a proxy for objective assessment.
The support for the study of service based on customer perspectives has been recorded
in the majority of marketing literature. Babakus and Boller (1992) claim that customers
should be the ones who determine the features of services regarded as most valuable, as
opposed to the features which are determined by the service providers. The only
appropriate definition of service quality is in terms of whether or not the service
provided met customers’ expectations (Parasuraman et al. 1985; Reeves & Bednar
1994). Zeithaml et al. (1990) argue that defining quality should start with customers’
opinions. The fundamental position of customers in judging quality was supported by
Gronroos (1990), who also maintains that quality is meaningful when it is perceived by
30
customers. This means that no one but the customer is the only one that should judge
quality. Since customers are the end users and are faced with many choices, their
judgment should provide more reasonable and meaningful information to service
providers.
So far, the interest in the service quality study was basically inspired by the works from
Zeithaml et al. (1990) and Parasuraman et al. (1985, 1988), who are among the first to
introduce the concept of customers’ perception of service quality. Many studies on
perception of service quality centre on the comparison between customers’ expectations
and perceptions of suppliers’ performance. The concept of customers’ perception is then
known as perceived service quality which basically concerns the comparison between
expectations and perception of services’ performance. It is commonly described as
perceived service quality since it is based on the customers’ opinions when customers
make comparisons. Table 2.4 presents the literature on the service quality
conceptualisation based on customers’ view.
Table 2.4 Service Quality Conceptualisation Based on Customers’ View Source Service quality conceptualisation
Lewis & Booms (1983) in Parasuraman et al. (1985)
Service quality is a measure of how well the service level delivered matches customer expectations.
Parasuraman et al. (1988) Global judgement or attitude relating to the superiority of the service.
Zeithaml (1988) Customers’ assessment of the overall excellence or superiority of the service.
Gronroos (1990) The outcome of an evaluation process involving customers’ comparison of their expectations and experiences.
Asubonteng et al. (1996) The difference between customers’ expectations of service performance prior to the service encounter and their perceptions of the service perceived.
Source: Developed for the study
Considering the stronger services nature of offerings provided by education institutions,
the perceived service quality concept is used since the objective assessment of quality is
difficult to achieve in assessing higher education quality. Further, the judgment not only
relates to the service delivered at the point of transaction but also covers the overall
impression of the overall performance of education service providers. This is because
educational experiences involve not only one-time transactions but also day-to-day
academic experiences and wider aspects such as image and networking. For simplicity,
‘perceived service quality’ in this thesis is expressed with ‘service quality’ and is used
interchangeably. Both expressions are assumed to have the same meaning.
31
2.4.4 The Development of ‘SERVQUAL’ Measures
Due to the significant contribution made by service quality to most organisations (see
section 2.4.1 Importance of Service Quality), research on service quality has increased
in popularity where many service quality models were designed to capture the specific
context of industries. As previously discussed, service quality is a subjectively
perceived construct and dependent on time and context (Reeves & Bednar 1994). Since
every industry is unique, it necessitates developing the dimensions of service quality
according to the unique characteristics of the industry being examined. Across many
existing models and dimensions of service quality, SERVQUAL remains the most
popular and commonly used approach as the foundation in service quality research
(Asubonteng et al. 1996). SERVQUAL is a measure of service quality that was made
based on the expectations-perceptions of customers. Based on an exploratory study in
four different service industries (retail banking, credit card, securities brokerage and
product repair and maintenance), Parasuraman et al. (1985) developed a model of
service quality. This framework is further known as the “Gap analysis model”, which
leads to the definition of service quality as a degree of discrepancy between
expectations and service performance (Parasuraman et al. 1985). As part of their initial
exploratory study of service quality, ten dimensions of service quality were proposed
which include “reliability, responsiveness, competence, access, courtesy,
communication, credibility, security, understanding the customer and tangibles”. After
identifying that there was a potential overlap between the ten dimensions of service
quality, five dimensions of SERVQUAL were proposed. These five dimensions of
service quality cover “tangibles, reliability, responsiveness, assurance and empathy”,
and are measured by 22-item items scale (Parasuraman et al. 1988).
2.4.5 Critique of SERVQUAL
Since the development of the five dimensions of SERVQUAL, many studies have
examined in details the development of the dimensions of service quality from different
service settings. Even though this construct remains the most popular conceptualisation
of quality in the service sector, the five dimensions for measuring service quality have
received numerous criticisms (Sureshchandar et al. 2002). First among researchers who
questioned and criticised the validity of SERVQUAL measurement was Carman (1990).
There were two major concerns raised by Carman (1990). The first was a concern over
32
the validity and reliability of using the gap between expectations and perceptions
measures. Carman (1990) suggests that measures of both perceptions and expectations
can be collected in a combined survey format. The original scales of service quality
proposed by Parasuraman at al. (1988) consisted of two sets of 22 similar questions
measuring expectations and perceptions. Besides providing psychometric soundness,
the combined format would be less lengthy and hence it would be easier for customers
to complete the questionnaires. The second critique was related to the validity of
measuring subjects’ expectations, especially in the service areas where many customers
were first-time visitors and their expectations were often not quite realistic. Despite
questioning the relevance of expectations, there were also some doubts regarding the
relevance of the five dimensions of service quality. A more detailed critique of
SERVQUAL is provided in Table 2.5.
By empirically conducting research in electricity and gas utility companies, Babakus
and Boller (1992) also found some problems with the validities (convergent,
discriminant and content) for each of the dimensions of service quality. Their research
supports Carman’s (1990) scepticism on SERVQUAL psychometric soundness and they
also identified that the dimensionality varied according to the types of the service being
studied. The replication studies using factor analysis did not always confirm the five
distinctive dimensions of service quality as proposed by Parasuraman et al. (1988).
One of the critiques of SERVQUAL also addressed the problem concerning the
‘perceptions-minus expectations’ scores that were used to measure the gap in
SERVQUAL measure. To address this problem, Teas (1993) offers alternative models
called evaluated performance (EP) and normated quality (NQ) and claims that these
models could overcome the weaknesses of the expectation-perception gap model
developed by Parasuraman et al. (1988). Teas (1993) also investigated the validity of
the customer expectations component of SERVQUAL and found that respondents were
confused regarding the interpretation of the expectation measure compared with other
expectation concepts used in marketing.
Other critiques of the conceptualisation and measurement of service quality came from
Cronin and Taylor (1992, 1994). Their work provides evidence that the use of
33
SERVQUAL may potentially create confusion regarding the concept of service quality
and satisfaction. To overcome the confusion, Cronin and Taylor (1992) developed their
performance-based measure of service quality called SERVPERF (service
performance). In investigating the conceptualisation and operationalisation of the
SERVPERF measure, Cronin and Taylor (1992) examined a multi-industry sample
(banking, fast food, dry cleaning and pest control). In their study, four competing
models were assessed, namely un-weighted performance-based (SERVPERF),
SERVQUAL, weighted-SERVQUAL and weighted-SERVPERF. The study concluded
that the unweighted performance-based measure (SERVPERF) was the most
appropriate for measuring service quality.
The differing opinions regarding the strengths and weaknesses of SERVQUAL have
triggered an ongoing controversy in service quality research. In responding to the
critiques addressed to the measurement of expectations and perceptions in
SERVQUAL, Parasuraman et al. (1994) argue that the possibility of revising the
conceptualisation of service quality is very open. Nevertheless, there is no urgent need
to abandon the overall existing SERVQUAL measure in favour of the alternate
approaches. Further, Parasuraman et al. (1994) argued that the critiques being raised by
Cronin and Taylor (1992) and Teas (1993) on the expectation-perception gap were also
questionable and remain unresolved.
Table 2.5 Critiques of SERVQUAL Servqual Criticism
The conceptualisation and usefulness of the expectations side of the instrument have been questioned.
Carman (1990); Boulding et al. (1993); Cronin & Taylor (1992, 1994); Forbes et al. (1986); Tse & Wilton (1988); Wilton & Nicosia (1986)
The problems which expectations scores pose in terms of variance restriction have been highlighted.
Carman (1990); Babakus & Boller (1992); Brown et al. (1993)
Research has indicated problems associated with difference scores including showing that the performance items on their own explain more variance in service quality than difference scores.
Babakus & Boller (1992); Cronin & Taylor (1992, 1994)
The number of factors extracted has tended to vary from the five dimensions proposed.
Bouman & Van der Wiele (1992); Carman (1990); Cronin & Taylor (1992, 1994); Gagliano & Hathcote (1994)
Source: Caruana (2000, pp. 1340-1341)
The discussions above have evidenced the critiques concerning the effectiveness of
SERVQUAL as a valid and reliable measure of service quality. Nevertheless, there
remains a general agreement among marketing scholars that Parasuraman et al.’s (1988)
34
five dimensional measure of service quality is considered a reasonably good predictor in
explaining service quality (Sureshchandar et al. 2002). Further support was given by
Rust and Oliver (1994), who believed that the five dimensions of SERVQUAL covered
the basic concepts that should be captured in service quality. In addition, the notion that
SERVQUAL is very adaptable as a measure of service quality in different service
contexts (Weekes et al. 1996) ensures that SERVQUAL remains in favour and is
commonly used as a foundation for the measurement of service quality.
2.4.6 Service Quality in Higher Education
2.4.6.1 The Importance of Service Quality in Higher Education
A major concern of service quality has been given not only for the general service
industries, but also in the higher education sector (Athiyaman 2000). A number of
factors have changed in a recent social condition, including international and local
education competitions, changes in the government’s education policies, economic
down turn and the more rational decision-making that must be engaged in by customers
when investing in the higher education sector. Having more choices and offerings in
this highly competitive environment, students have become more critical, demanding
and rational in selecting higher education courses since investment in higher education
is considerable in terms of money, time, energy and effort expended. In order to survive,
higher education institutions must therefore decrease their reliance on government
funding and students’ tuition fees, as well as starting to understand their market.
Understanding the market means that administrators must know who their target
students are and always try to get close to their customers (higher education
stakeholders).
Since the core offerings of the higher education industry are in the form of services, the
quality of higher education is commonly determined by the performance of the service
it provides (Slade et al. 2000). Quality is fundamental to education institutions
especially when competition is intense. The current changes in the competitive
atmosphere in the higher education sector necessitate the higher education institutions to
adopt a marketing approach. To remain competitive, an institution must actively
monitor the quality of its service offerings based on both institution and government
standards of quality and, more importantly, customer perceptions of quality. Further
35
actions should be taken based on the subsequent quality assessment. In the education
sector, service quality can support excellence and it is believed to have a positive long-
term effect (LeBlanc & Nguyen 1997). The positive perception of quality may influence
further recommendations hence increasing the future financial position of an institution.
Although higher education is to some extent said to be a ‘pure’ service, especially when
it is seen as person-to-person interactions (Solomon et al. 1985), it consists of more than
merely offering services. The contribution of the physical facilities, such as the
classroom and its supporting facilities, computer laboratories, library and science
laboratories are all very critical to the education processes. The impacts of physical
attributes have been specifically investigated by Price et al. (2003), showing that a high
standard of facilities may influence students’ choices of institution. Douglas et al.
(2006, p. 252) comment that the varieties of the higher education offerings can no
longer be described as pure services but should be called “the service-product bundle”.
There are three elements in this bundle of products: 1) the physical or facilitating goods
(handout, module, slide, theatres, rooms, etc.); 2) the sensual service–explicit service
(knowledge levels and teaching ability of staff, ease of making an appointment, etc.);
and 3) the psychological-implicit service (friendliness, respect for feelings and opinions,
etc.). The quality of service in higher education should be determined by the overall
perceptions of the students of the set of product-bundle that the institution offers.
2.4.6.2 Quality Conceptualisation of Higher Education Sector
As previously discussed, the characteristics of services apply to higher education
offerings. All of the characteristics of services “intangibility, simultaneity, perishability
and heterogeneity” are evident. Although some quality studies exist, the concept of
what constitutes quality in the area of higher education has not been thoroughly
addressed (Srikanthan & Dalrymple 2003; Lagrosen et al. 2004). One of the attempts to
provide an in-depth discussion on the conceptualisation of service quality in the higher
education sector has been provided by Sahney et al. (2004a). Table 2.6 summarises
several approaches to quality conceptualisation in the higher education sector. Some of
the approaches might be too general to be operationalised since there are different
quality perceptions among higher education stakeholders.
36
Table 2.6 Approaches to Quality Concepts in the Higher Education Sector Quality in Higher Education
Fraser (1994) Quality definitions should be based on an international agreement on terms such as levels, standards, effectiveness and efficiency.
Martens & Prosser (1998) Quality concept should focus on quality learning.
Harvey & Green (1993) Quality is exceptional, quality is perfection or consistency, quality is fitness for purpose, quality is value for money, quality is transformation.
Green (1993) Quality is capacity, which whole organisations can be managed to have, to continually learn and implement customer wants.
Harvey & Knight (1996) Quality is something exceptional, consistent, fitness for purpose, valuable in terms of money and transformative.
Dahlgaard et al. (1995) Quality should be characterised by an increases in customer satisfaction through continuous improvement by all employees and students.
Cheng (1996) Quality is represented in the set of elements in the input, process, and output of the education system. This set of elements must satisfy both internal and external stakeholders through meeting their expectations.
QAA (2004) Quality describes how well the learning opportunities available to students help them to achieve their goals. Quality is about making sure that appropriate and effective teaching, support, assessment and learning opportunities are provided for students.
Source: Lagrosen et al. (2004) and Sahney et al. (2004a)
2.4.6.3 Service Quality Measurements of Higher Education Sector
There have been different ways of measuring the quality of service and the dimensions
applied in the higher education sector. Smith et al. (2007) examined SERVQUAL
dimensions (tangibles, reliability, responsiveness, assurance and empathy) and
compared the importance of service quality for both staff and students. Smith et al.’s
study appeared to support the existence of five dimensions of service quality as
proposed by Parasuraman et al. (1988). By applying the perception-expectation gap
model, Hill (1995) investigated how service quality theory impacts on students. Sahney
et al. (2004b) examined service quality (tangibles, content, attitude, competence,
delivery and reliability) across management and engineering institutions. Results
showed that the relative importance of these service quality dimensions was differently
perceived by student of management and engineering institutions.
37
Table 2.7 Service Quality Dimensions in Higher Education Sources Quality dimensions Sample
Athiyaman (2000) SERVPERF: Physical facilities, academic staff and learning outcomes.
Graduates of the university.
Alves & Raposo (2007)
SERVPERF: Technical and functional quality. Higher education students.
Sahney et al. (2004b) SERVPERF: Tangibles, competence, attitude, delivery, reliability and content.
Faculty, students, administrative staff, industry.
Oldfield & Baron (2000)
SERVPERF: Requisite, acceptable and functional.
Undergraduate students.
Gatfield et al. (1999) Importance: Academic instruction, campus life, guidance and recognition.
Students in business faculty.
Abdulah (2006) HEdPERF: Non-academic aspects, academic aspect, reputation, access and program issue.
Higher education students.
Galloway & Wearn (1998)
SERVPERF and SERVQUAL expectation-perception: assurance, empathy, reliability, responsiveness and tangibles.
Staff and students of higher education.
LeBlanc & Nguyen (1997)
SERVPERF: Contact personnel ‘faculty and administrative’, reputation, physical evidence, curriculum, responsiveness and access to facilities.
Higher education students.
Kwan & Ng (1999) SERVQUAL expectation-perception gap: course content, concern for students, facilities, assessment, instruction medium, social activities and medium.
Accounting and business students.
Smith et al. (2007) SERVQUAL expectation-perception gap: tangibles, reliability, responsiveness, assurance and empathy.
University students and staff.
Source: Developed for the study.
Numerous studies which focus on the dimensions of service quality have given
credence to the notion that service quality is a multidimensional concept. Table 2.7
illustrates varieties of service quality dimensions that have been used in the higher
education context. Some of the earlier studies of service quality have also confirmed the
existence of the multidimensional conceptualisation of service quality. For example,
Gronroos (1984, 1990) stated that quality should not be measured by a single dimension
and further proposed three dimensions: technical quality, functional quality and image.
Despite the consensus on the multidimensionality of the service quality construct, the
dimensionality of the service quality construct varies across studies due to the context
specific nature of service quality.
Despite an agreement on the multidimensionality of service quality, the application of
service quality in higher education has achieved mixed results. Some studies support the
application of expectation-perception of SERVQUAL, and other studies support the
service performance (SERVPERF) measure (see Table 2.7). Cuthbert (1996) claims that
the application of expectation-perceptions gap measure in higher education is not
38
appropriate due to a low reliability score. Galloway and Wearn (1998) showed that
expectation was found to contribute nothing to the predictive capabilities to the survey.
This led to the employment of other alternative measurements than SERVQUAL gap
analysis, such as service performance, the importance-performance gap analysis and
modifications of service quality adjusted to the specific context.
Service quality is said to be a context-specific construct. When measuring service
quality, it is important to take into account in the dimensions of service quality study
according to the specific situation of the industry (Lagrosen 2001). Although
SERVQUAL proposed by Parasuraman et al. (1988) offer general service quality
dimensions, in the higher education sectors, the five dimensions of service quality may
not be specific enough to sufficiently measure the quality of services within the to
higher education context. As a consequence, the five dimensions of service quality
proposed by Parasuraman et al. (1998) need be complemented, modified and adjusted to
the specific situation of higher education context.
2.4.6.3.1 Owlia and Aspinwall’s (1996) Dimensions of Service Quality
Despite several studies involving the multidimensional measurement of service quality
that have been discussed in Table 2.7, this thesis focuses on and employs the service
quality dimensions developed by Owlia & Aspinwall (1996). The choice of adopting
Owlia and Aspinwall (1996) dimensions of service quality was based on the following
justifications: 1) the dimensions of service quality in the Owlia and Aspinwall (1996)
framework are comprehensive enough in covering most common dimensions
considered important by students; 2) the dimensions appear to be quite informative for
the purposes of interpretation; 3) the dimensions were developed based on a thorough
examination and comparison across earlier dimensions of service quality; 4) a thorough
examination, interpretation and comparison of service quality measurements across
three different sectors (product, software and general service) were undertaken; and 5)
empirical research had been carried out to validate the measure (internal consistency,
construct validity and predictive validity) (see Owlia & Aspinwall 1998).
By making a comparison between three different sectors (product, software and general
service), Owlia and Aspinwall’s (1996) work was designed to make an important
39
contribution to the development of the more general dimensions of service quality in the
higher education sector. Common elements across the three different sectors were
examined and similar elements were identified to provide a more general set of quality
measurement. Since it was made to provide a general measurement for service quality in
the higher education sector, it is assumed that the dimensions will also be applicable for
measuring service quality in different locations/countries. Appendix 8 illustrates how
the dimensions of other sectors are interpreted according to the higher education
context. Owlia & Aspinwall (1996) argue that their framework of service quality could
provide a basis for measurement and further quality improvement in the higher
education sector. In addition, since Owlia & Aspinwall’s (1996, 1998) conceptual
framework of service quality emphasises the customer approach, it highlights the roles
of students in the higher education sector. This information will be valuable as a
platform on which to conduct a quality programme based on students’ perspectives.
Within this thesis, ‘the revised framework’ for service quality (Owlia & Aspinwall’s
1998), which consisted of six dimensions, was used as a basis as it provides more
comprehensive and more enlightening information for interpretation, as compared to
Owlia & Aspinwall’s (1998) ‘final work’ which consisted of only four dimensions. This
service quality as measured in the the revised framework include tangible, competence,
attitude, delivery, content and reliability, as dimensions used to assess service quality in
the Indonesian higher education sector. In addition, by considering findings from
Cuthbert (1996) and Galloway and Wearn (1998), this thesis employs the perception
only measure (SERVPERF).
The following section reviews the ‘customer satisfaction’ construct as one among four
key constructs under investigation in this thesis. The reviews cover the importance of
customer satisfaction, the concepts, dimensions and approaches of customer
satisfaction, satisfaction in the higher education context, the distinction between service
quality and customer satisfaction and customer satisfaction in the structural model.
40
2.5 CUSTOMER SATISFACTION
2.5.1 Importance of Customer Satisfaction
One of the primary goals among most players in the services industry is achieving
customer satisfaction (Jones & Sasser 1995). The importance of customer satisfaction
has been emphasised by Oliver (1997) who described customer satisfaction as
“fundamental”. Being defined as fundamental means that satisfaction is fundamental to
customers, to companies’ profits and political stability (Oliver 1997). Customer
satisfaction has ever since been a key management focus (Athanassapoulos &
Iliakopoulos 2003) and it has been a growing trend for organisations to undertake
studies in satisfaction as they become more customer focused (Oliver 1999).
Many studies have evinced the linkages between service quality and customer
satisfaction. Similar to service quality, customer satisfaction is highly popular in the
marketing literature and has been identified as a good predictor of many positive
consequences. Customer satisfaction has been identified as contributing to customer
retention and loyalty (Rust & Zahorik 1993; Anderson et al. 1994; Hallowell 1996;
Mittal & Kamakura 2001; Ranaweera & Prabhu 2003), business performance (Van der
Wiele et al. 2002) and financial performance and profit (Nelson et al. 1992; Rust et al.
1995; Heskett et al. 1994; Anderson & Mittal 2000; Chumpitaz & Paparoidamis 2004).
More details on the consequences of satisfaction are presented in Table 2.11.
Customers with high cumulative satisfaction were more likely to keep their
relationships with the relevant suppliers/organisations and appeared to be less sensitive
to expressing disappointment with under-performing products/services (Bolton 1998).
Loyal customers may exhibit a number of positive behavioural attributes that contribute
to increases in profitability such as higher levels of purchase, a decrease in price
sensitivity and a lower likelihood of switching to other products/services.
2.5.2 Concept and Dimensions of Satisfaction
Despite extensive studies, there has not been any consensus among researchers on the
definition of ‘customer satisfaction’ and the literature remains ambiguous (Giese &
Cote 2000). In their article, “Defining customer satisfaction”, Giese and Cote (2000)
reviewed and compared the existing definitions of satisfaction. They note that serious
problems may occur in customer satisfaction research if agreement among experts in
41
marketing on the definition of the satisfaction construct does not exist. The absence of
consensus may lead to these following problems: 1) limit the contribution made by the
research; 2) an inability to select a suitable definition according to a given study; 3) an
inability to develop a valid measure; and 4) an inability to compare and interpret the
results (Giese & Cote 2000; Yi 1990; Gardial et al. 1994; Peterson & Wilson 1992). In
their extensive work on satisfaction definition, Giese and Cote (2000, p.1) note that,
despite some significant differences across the definitions of satisfaction, there are held
in common some similar elements as follows: 1) customer satisfaction is a response
(emotional or/and cognitive); 2) the response pertains to a particular focus
(expectations; product; consumption experience); and 3) the response occurs at a
particular time (after consumption or after choice, based on accumulated experiences).
By reviewing extensive satisfaction definitions and validating the study through group
and personal interviews, Giese and Cote (2000, p.15) suggest that any definition made
of satisfaction must cover these following aspects: 1) summary affective response of
varying intensity; 2) focal aspects of product acquisition and/or consumption; and 3)
time-specific point of determination and limited duration.
The above discussions have highlighted the debates regarding the definitions of
customer satisfaction, specifically based on Giese and Cote (2000) reviews. The
following discussion addresses the issues on the approaches to customer satisfaction.
More specifically, cognitive and affective aspects of satisfaction and the nature of
transactions, whether cumulative or transaction-specific will be presented.
2.5.3 Approaches to Customer Satisfaction
An expectancy-disconfirmation model is commonly used to explain how customers
processed their experiences into a summary form that influenced satisfaction (Oliver
1993). This model posits that customers compare the actual product and service
performance with their prior expectations. Based on this process, Oliver (1997, p.13)
defined satisfaction as “customer’s fulfilment response or as a judgment about a product
or service”. Earlier definition by Tse and Wilton (1988) explain satisfaction as the
consumer’s response to the evaluation of the perceived discrepancy between prior
expectations and actual performance. Since it involves judgment based on a comparison
42
between expectations and actual performance, the cognitive aspect of satisfaction is
acknowledged in this definition.
The majority of studies also viewed satisfaction as an affective response as compared
with an expectancy-disconfirmation that involves a cognitive process (Oliver 1997;
Taylor 1994; Giese & Cote 2000). Many researchers have suggested that satisfaction is
an emotional response (e.g. Westbrook & Oliver 1991; Gotlieb et al. 1994; Babin &
Griffin 1998). The role that affect plays in customers’ post-purchase experiences has
been confirmed by Batra and Holbrook (1990). Yi (1990) also affirms the finding that
satisfaction results from evaluating affects in the consumption experience. Oliver (1993)
maintains that the positive/negative affect is a function of the amount of
positive/negative attribute-level of satisfaction. By examining the relationships between
positive/negative affect and positive/negative attribute-level of satisfaction, it was found
that affect mediates the relationship between cognitive evaluations and satisfaction and
also contributes independently to satisfaction. Considering that there is an ongoing
controversy over whether to view satisfaction as a cognitive or affective response
(McDougal & Levesque 2000), and given that it has also been argued that the
satisfaction construct can only be captured if its cognitive and affective perspective
were included (Oliver 1997; Strauss & Neuhaus 1997; Liljander & Starandvik 1997),
this thesis consequently accommodates both cognitive and affective aspects of
satisfaction to measure students’ experiences in the higher education sector. The reason
is that higher education experiences involve both aspects, feeling (affective) and
evaluative (cognitive). For example, students might be feeling happy with the programs
chosen, feeling socially more accepted or have made a good decision in entering the
programs chosen, etc.
Satisfaction may also be defined at the level of transaction-specific or as cumulative
judgment (Anderson et al. 1994; Bitner & Hubbert 1994; Oliver 1997). Transaction-
specific satisfaction is measured when customers evaluate a product/service
immediately after a consumption experience (Guolla 1999). The transaction specific
judgment particularly offers information on customer satisfaction regarding a particular
product or service encounter. Cumulative satisfaction, on the other hand, is satisfaction
that accumulates across a series of transactions or service encounters (Guolla 1999).
43
Cummulative satisfaction judgment is considered to provide a more fundamental
identification of indicators that represent the past, current and future performance of an
organisation (Anderson et al. 1994). The cumulative evaluation is especially useful for
predicting the consequences of satisfaction (Anderson et al. 1994).
In order to determine whether the cumulative or transaction-specific is more appropriate
for measuring satisfaction, Oliver (1997) suggests approaches at the intensity of the
product’s/service’s usage. The intensity or constant usage of the products/services will
determine whether or not researchers should take a short (transaction-specific) or long-
term approach (cumulative). Satisfaction can be measured at both the transaction-
specific and cumulative/global level for products/services that are consumed regularly.
For products/services that are consumed infrequently, it was suggested that the
transaction-specific measure of satisfaction be applied.
In this thesis, satisfaction at the overall or cumulative level is chosen since experiences
in education involve regularity and everyday involvement in a series of services
transactions. The focus is specifically upon satisfaction in relation to higher education
as a study destination. Additionally, and consistent with the objective of this thesis in
predicting the impact of service quality and customer value on student’s satisfaction as
well as how satisfaction will later impact on a student’s behaviour intentions, a
cumulative judgment is considered to be the most appropriate. As discussed previously,
a cumulative approach should be taken when the focus is on the organisation’s past,
present and future performance as well as on identifying the consequences of
satisfaction.
In analysing student satisfaction, this thesis supports both the cognitive and affective
aspect of satisfaction, as reflected in the questionnaire. Specific to higher education
context, this thesis adopts the conceptualisation of satisfaction developed by Elliot and
Healy (2001) in Navarro et al. (2005). Student satisfaction in this thesis is
conceptualised as a short term attitude that results from the evaluation of their
experience with the education service received. In addition, this thesis also focuses on
the post-choice evaluation. The post-choice evaluation in this thesis is largely related to
experiences and evaluations made by students after the choice has been taken (after
44
enrolling). Post-choice evaluations are of interest because they influence customers’
repeat purchases, as well as the purchases of other potential customers through word-of-
mouth communication (Guolla 1999). The satisfied students are likely to convey a
positive word-of-mouth, recommend the institution to their friends and relatives and
enrol for additional courses or return as post-graduate students. The post-choice
evaluations also assist higher education institutions to identify students concerns
regarding the performance of educational services and to develop strategic responses
through programs that satisfy the exact needs of students.
2.5.4 Satisfaction in the Context of Higher Education
Compared to historical contexts, the environment that higher educational institutions
have operated in has changed dramatically (DeShields et al. 2005). Today’s higher
education industry is experiencing in both national and international competition
(Velotsou et al. 2004). Global trends in the knowledge-based economy have created
new types and increased the numbers of competitors in the higher education industry
and have caused more difficulties for institutions in attracting new students (Nicholls et
al. 1995). Higher education institutions must take a proactive approach in dealing with
educational market realities. It is necessary for higher education institutions to adopt a
better orientation to the market in order to obtain competitive advantage over
competitors as well as to create a positive image in its’ target market (Petruzzellis et al.
2006). In order to survive, higher education institutions can no longer rely solely on
government support and on their students’ tuition fees. The issue of customer focus
become even more important since most higher education institutions derive their
income from students’ tuition fees. More and more higher education institutions are
becoming market and customer focused in order to attract and retain their students.
Satisfaction is considered to be one among several marketing approaches that can be
used to improving higher education performance. It is necessary to analyse and study
levels of student satisfaction in higher education, as institutions of higher education
could benefit greatly from being able to establish long-term relationships with their
students through satisfaction (Alves & Raposo 2007). Several satisfaction studies in the
higher education sector have identified factors or attributes that contribute to student
satisfaction and bring the benefits there of. Table 2.8 presents a summary of some
45
findings in satisfaction studies in higher education.
Table 2.8 Satisfaction Studies in Higher Education Source Findings
Athiyaman (1997) By linking service quality and satisfaction in the university sector, this study found that satisfaction is an antecedent of perceived service quality.
Guolla (1999) By investigating the impact of multiple teaching quality factors on course satisfaction and instructor satisfaction, the results suggest that most constructs used (learning, enthusiasm, organisation, interaction, rapport, assignments and material) have a positive significant effect on course and instructor satisfaction.
Arambewela & Hall (2001) By investigating the gap between pre-choice expectations and post-choice perceptions, this study concludes that despite being relatively satisfied with the university, students' expectations remained far above perceptions across all factors and variables investigated.
Coles (2002) The results of this study revealed that when the size of the class is big students are less satisfied. Furthermore, students are also less satisfied with the compulsory core modules as compared to optional modules.
Banwet & Datta (2003) This study found that students’ perceptions from attending the previous class/lectures influence their intentions to re-attend or recommend particular lectures. This study also provides support for Schneider & Bowen’s (1995) finding that the quality of the core service (lecture) represents the overall perception of quality.
Navarro et al. (2005) The finding of this study revealed that satisfaction is an important antecedent to securing students’ loyalty.
Douglas et al. (2006) The finding of this study revealed that the core services (lectures) are the most important aspects of a university’s offerings that generate satisfaction. The core services include: knowledge, class notes and materials and classroom delivery.
Alves & Raposo (2007) This study identified that there is a positive significant relationship between satisfaction and both loyalty and word-of-mouth communication.
Source: developed for the study
Some of the findings have shown that satisfaction has a positively significant
relationship with loyalty. When students are satisfied, they will be loyal and engage in
positive behaviours. Students with high levels of satisfaction engage in favourable
‘word-of-mouth’ communications such as recommendations to friends or other students
(Guolla 1999). The dissatisfaction of students, on the other hand, could have negative
consequences for the institutions and the students, namely students failing (Wiese 1994;
Walther 2000), quitting or transferring (Dolinsky 1994; Wiese 1994; Astin 2001) and
negative ‘word-of-mouth’ publicity (Dolinsky 1994; Walther 2000). From the aggregate
perspective (cumulative satisfaction), highly satisfied students tend to recommend
programs, return as graduate students, influence prospective students or regularly donate
as alumni (Guolla 1999). Given the characteristics of the current higher education
competitive environment and the importance of being customer focused, studying the
satisfaction concept is a necessary activity for the survival of higher education
institutions.
46
2.5.4.1 Concept and Dimensions of Satisfaction in Higher Education
As discussed in Section 2.5.2, there was an absence of a consensus regarding the
definition of satisfaction and, therefore, there was also a lack of generally accepted
measure of customer satisfaction (Hartman & Schmidt 1995). Compared to the general
services, the higher education sector has been the subject of relatively few satisfaction
studies (Athiyaman 1997; Arambewela & Hall 2001), and is consequently lacking
conceptualisation and measurement of satisfaction. One attempt to define satisfaction
with regard to higher education has been proposed by Athiyaman (1997, p.539), who
states that student satisfaction is “similar to attitude, but it is short time and results from
an evaluation of a specific consumption experience”. Since measuring student
satisfaction with all relevant education experiences is difficult, Athiyaman (1997)
suggests employing a set of general characteristics of a university that are manageable.
2.5.5 Comparing Service Quality and Customer Satisfaction
Service quality and satisfaction are two constructs identified as having both similarities
and differences between them. In particular, since both involve evaluation between two
specific expressions; 1) expectancy-perception as in service quality; and 2) expectancy-
disconfirmation as in satisfaction, there have been debates over whether or not service
quality and satisfaction are sufficiently distinct from one another (Johnston 1995). Both
the service quality and satisfaction concepts are often being treated interchangeably by
some service researchers (Kleinsorge & Koenig 1991; Choi et al. 2004). However,
despite the debates regarding the similarities and differences between service quality
and satisfaction, marketing researchers have come to a consensus that both constructs
are separate and unique and share a close relationship (e.g. Boulding et al. 1993; Taylor
& Baker 1994; Cronin & Taylor 1992).
Both service quality and satisfaction involve evaluation and comparison between two
different expressions (expectancy-perception and expectancy-disconfirmation). Even
though both compare two different expressions, the difference lies in the standard that is
used in making a comparison (Spreng & Mackoy 1996). The creation of a standard to
measure ‘expectation’ is particularly important since without a standard, customers may
consistently require high rating/high expectations and, therefore, it will be very difficult
to achieve satisfaction as expectations can never be met. The use of a standard has
47
received wide support in the academic literature (Bearden & Teel 1983; Oliver 1996;
Anderson & Sullivan 1990). In the case of the difference between satisfaction and
service quality, Reeves and Bednar (1994) explain that expectation in a satisfaction
study reflects predictions of what ‘would’ happen during future transactions, while in
the case of service quality, expectation reflects what customers’ feel a service ‘should’
offer. Similarly, Cronin and Taylor (1992) explain that expectancy as in the case of
satisfaction reflects ‘something that will happen’ and in the case of service quality
reflects more on ‘something that should be provided by the firm’. Boulding et al. (1993)
remind researchers that the term ‘expectations’ should be treated differently in the
satisfaction and service quality studies, since the term ‘expectations’ can be a potential
source of confusion.
The intensity of the transaction between encounter-specific and cumulative judgment
are also identified as aspects that may contribute to difficulties in distinguishing
between service quality and satisfaction constructs. Anderson et al. (1994) have
identified the similarity of overall service quality and cumulative/overall
conceptualisations of satisfaction. Furthermore, in another case, Bitner and Hubbert
(1994) found that customers are unable to distinguish between ‘overall satisfaction or
dissatisfaction’ based on all encounters and ‘an overall impression of firm’s’ based on
inferiority or superiority. Oliver (1997) also determined that both service quality and
satisfaction similarly contribute to the process of how encounter-specific transactions
may lead to accumulative judgment. In the case of service quality, encounter-specific
transactions may accumulate over time and will eventually form an impression of
overall quality. As is the case with satisfaction, repeated satisfaction may lead to a more
global evaluation of satisfaction.
Another dominant view in distinguishing between the satisfaction and service quality
issues is that service quality is often represented as a cognitive judgment, whereas
satisfaction is more an affect judgment (Oliver 1993, 1997; Gooding 1995). The
majority of past studies of satisfaction formation view it as an affective response to an
expectancy disconfirmation that involves a cognitive process (see Section 2.5.3) (Oliver
1997; Taylor 1994; Tse & Wilton 1988). The different views of treating customer
satisfaction as an affective or a cognitive construct may affect how both constructs are
48
being modelled in the structural relationship. Choi et al. (2004) explain that an
acceptance of the conceptualisation of service quality as a cognitive construct and
satisfaction as an affective construct would lead to a causal direction in the structural
relationship that service quality will influence satisfaction. Although early service
quality researchers defined satisfaction as an antecedent of service quality (see
discussion Section 3.3.2.1 and Table 3.2), it has now generally been accepted that
satisfaction is a consequence of service quality. Despite several similarities, Rust and
Oliver (1994) have identified some key elements that differentiate service quality from
satisfaction and this identification adds a logical support to the notion that both
constructs are distinct. Table 2.9 lists the key differences.
Table 2.9 Key Differences between Service Quality and Satisfaction Service Quality Satisfaction
The dimensions underlying quality judgments are rather specific.
The dimensions of satisfaction can result from any dimension (whether or not it is quality related).
Expectations of quality are based on ideals or perceptions of excellence.
A large number of non-quality issues can help to form satisfaction judgments (e.g. needs, equity, and perceptions of “fairness”).
Quality perceptions do not require experience with the service or provider.
Satisfaction judgments require experience with the service or provider.
Quality is believed to have fewer conceptual antecedents.
More conceptual antecedents.
Source: Rust and Oliver (1994)
2.5.6 Antecedents of Customer Satisfaction
Although service quality is often considered to be the most important antecedent to
customer satisfaction, prior studies have also found that customer satisfaction is
influenced by other factors. Empirical studies concerning the disconfirmation paradigm
generally show that performance expectations and perceived performance are important
antecedents to customer satisfaction. Voss et al. (1998) examined how expectations
affect satisfaction judgments when price and performance are in consistent condition
(and/or in inconsistent condition). Table 2.10 provides previous studies on the role of
performance on satisfaction. Perceived performance was used in the Patterson and
Spreng (1997) study in the business-to-business context involving dimensions such as
outcomes, methodology, service, relationships, global and problem identification as
antecedents of satisfaction. In addition to service quality and performance, recent
interests have also identified the customer value construct as an important antecedent of
satisfaction (Eggert & Ulaga 2002; McDougal & Levesque 2000; Spiteri & Dion 2004)
49
(also see Section 2.5.7).
Table 2.10 Role of Performance Expectations on Customer Satisfaction Sources Context Link between Performance and Satisfaction
Swan & Trawick (1981) Restaurant. Positive.
Cadotte et al. (1987) Restaurant. Not significant.
Westbrook (1987) Cable television. Not significant.
Gupta & Stewart (1996) Banks. Not significant.
Spreng & Mackoy (1996) Undergraduate students advising.
No direct link (but indirect positive link through disconfirmation).
Patterson et al. (1997) Business-to-business professional services.
No direct link (but indirect negative link through disconfirmation).
Source: Voss et al. (1998)
In the higher education sector, service quality has been identified as the antecedent of
satisfaction (Browne et al. 1998; Guolla 1999; Alves & Raposo 2007). Similarly, recent
studies have also identified the contribution of customer value to satisfaction (Sakhtivel
& Raju 2006; Alves & Raposo 2007). Further details on customer satisfaction research
in higher education sector is summarised in Table 3.3.
2.5.7 Consequences of Satisfaction
Customer satisfaction has been recognised as a major antecedent to people’s attitude
towards an organisation and is, thus, an important determinant of future behaviour
(Soderlund 2002; Oliver 1999; Zeithaml et al. 1996). In terms of higher education, the
main consequences found by some researchers were: loyalty (Webb & Jagun 1997),
action taken as a consequence of word-of-mouth communication (Athiyaman 1997;
Alves & Raposo 2007), retention (DeShields et al. 2005) and complaints (Webb &
Jagun 1997). Table 2.11 illustrates varieties of consequences of satisfaction and shows
the broad contributions of satisfaction in different industries. This underlines the
importance of customer satisfaction, even though to have the most effective outcomes,
additional variables might be necessary to increase the influence on organisational
outcomes.
50
Table 2.11 Consequences of Satisfaction Source Context Consequences
Oliver (1980) Consumer & non-consumer of flu inoculations.
Repurchase intent.
LaBarbera & Mazursky (1983)
Residential telephone customers. Repurchase intent.
Bearden and Teel (1983) Consumers of automobile repairs. Repurchase intent and complaint behaviour.
Oliver & Swan (1989) Automobile purchasers. Repurchase intent.
Woodside et al. (1989) Hospital patients. Intention to come back.
Bolton & Drew (1991) Residential telephone customers. Post-purchase attitude.
Rust & Zahorik (1993) Retail banking customers. Retention & market share/profitability.
Anderson et al. (1994) Firms participating in Swedish Customer Satisfaction Barometer.
Loyalty, decreasing price elasticity, protecting current market shares, attracting new customers, and helping companies to build a positive corporate image.
Patterson et al. (1997) Business to Business professional services.
Repurchase intention.
Bernhardt, et al. (2000) Fast food restaurants. Repurchase intent & profitability.
Brady & Robertson (2001)
Fast food customers. Repurchase intent & word of mouth.
Mittal & Kamakura (2001)
Automotive customers. Repurchase intent.
Van der Wiele et al. (2002)
Customers of Start Flexcompany (Service organisation).
Business Performance.
Ranaweera & Prabhu (2003)
Fixed line telephone users. Retention & word-of-mouth to others who have no relation to a specific transaction.
Chumpitaz & Paparoidamis (2004)
Customers of information systems. Loyalty, financial performance and profit.
Lee & Hwan (2005) Banking customers. Purchase intentions (customers) & profitability (managers).
Tsoukatos & Rand (2006)
Customers of Greek insurance. Repurchase intent, word of mouth.
Source: Developed for the study
The next sections review the ‘customer value’ construct, which covers the following
issues: the importance of customer value; the inclusion of customer value construct; the
concept, approaches, dimensions and measures of customer value; the distinction
between customer value, satisfaction and quality; customer value in the higher
education context; and finally the antecedents and consequences of customer value in
the structural model.
2.6 CUSTOMER VALUE
2.6.1 The Importance of Customer Value
Similar to service quality and satisfaction, customer value is considered one of most
popular topics among marketing practitioners (Sweeney 2003). Anderson and Narus
(1999, p. 5) maintain that, in the business market, value is said to be the “cornerstone of
business market management”. The interest in customer value was triggered by the fact
that customer value significantly contributes to the creation of competitive advantage
51
(Woodruff 1997; Slater 1997; Parasuraman 1997; Slater & Narver 2000; Flint et al.
2002), is a determinant of customer satisfaction (Churchill & Surprenant 1982;
Andreassen & Lindestad 1998; Oh 1999; Eggert & Ulaga 2002; Tam 2004; Gill et al.
2007), is a key strategic variable which facilitates re-purchase intentions, loyalty and
relationship commitment (Ravald & Gronros 1996; Patterson & Spreng 1997;
Andreassen & Lindestad 1998; Wang et al. 2004; Sweeney 2003) and is essential for the
long-term profitability of organisations (Woodruff & Gardial 1996).
Competitive advantage is a term commonly discussed in the organisational studies and
strategic management. Competitive advantage is defined as strategic benefits gained
over competing firms that enable the firm to compete more effectively in the market
place (Jap 1999). The attainment of sustainable competitive advantage has become the
heart of economics, management and marketing studies (Voola 2005). Sustainable
competitive advantage refers more to the possibility that the competitors can imitate and
competitive advantages can be duplicated (Voola 2005). Various theoretical
perspectives have attempted to explain sustainable competitive advantage such as the
industrial organisation economics (IO) theory, which specifically expanded by the work
of Porter ‘Structure-conduct-performance (SCP)’framework (Porter 1980) and the more
recent theory called as Resource-based view (RBV). The RBV points out that
organisation can develop sustained competitive advantage only by promoting value in a
way that is rare and difficult to imitate by competitors (Barney 1991, 1995; Peteraf
1993; Teece et al. 1997). Even though the RBV has been the major paradigm in
explaining competitive advantage, in general, marketing theorists have not applied the
RBV to understanding important issues in marketing (Srivastava et al. 2001). Further,
Srivastava et al. (2001) argue that the application of RBV in marketing studies is
essential as it emphasises developing and leveraging resources and capabilities to create
value for customers and other stakeholders.
Rintamaki et al. (2007) argue that competitive advantage and customer value are linked
through value delivery (or value creation), and this should be reflected in the value
proposition. Three kinds of customer value propositions have been defined by Anderson
et al. (2006): all benefits, favorable points of difference, and resonating focus. All
benefits concerns on the positive features and outcomes of buying and utilising the
52
product or service. In order to differentiate itself from its competitors, the firm needs to
have points of difference. “Points of difference are elements that make the supplier’s
offering either superior or inferior to the next best alternative” (Anderson et al. 2006, p.
94). The resonating focus is based on points of parity and points of difference from the
competitive offerings. According to Anderson et al. (2006, p. 94) “Points of parity are
elements with essentially the same performance or functionality as those of the next best
alternative”. Points of parity and points of difference refer to different customer value
dimensions that aim at competitive advantage. In other way, it can also be said that
customer value proposition is “a strategic management decision on what the company
believes its customers value the most and what it is able to deliver in a way that gives it
competitive advantage” (Rintamaki et al. 2008, p. 624).The competitive advantage and
customer value theories also recognise that customers are seen as market-based assets
and firm’s resources as providing capabilities (ex. Intellectual capabilities) that will
contribute to competitive advantage. “A firm can be said to have a customer-based
advantage when (some segment of) customers prefer and choose its offering over that of
one or more rivals” (Srivastava et al., 2001, p. 783).
Based on the above argument, the close relationship between competitive advantage and
customer value provide a strong base on the importance that organisations need to
employ their resources and capabilities more effectively than their competitors in order
to better deliver customer value proposition.
2.6.2 The Inclusion of Value in the Service Quality and Satisfaction Relationship
Aside from the popularity of service quality and satisfaction constructs, in a highly
competitive market the role of both constructs is increasingly being questioned. There
appears to be no guarantee of positive behavioural outcomes even though efforts have
been made to increase quality and satisfy customers (Anderson et al. 1994).
Furthermore, the efforts that have been made to improve quality do not always relate to
the economic returns (Anderson et al. 1994). Quality also sometimes causes problems in
some industries (Slater 1996) since customers do not always buy offerings of the
highest quality (Olshavsky 1985). Research models of service quality have been
criticised particularly since they did not consider aspects that may lead to sustainable
competitive advantage (Butz & Goodstein 1996). The weaknesses of service quality
53
models were also identified as neglecting the effect of perceived price or cost when
evaluating customers’ judgment on quality (Iacobucci et al. 1994). The earlier studies
noted the contributions of quality as a strategic tool to increase competitive position and
profitability (Reicheld & Sasser 1990). However, in a current highly competitive
market, which is characterised by more demanding customers, economic downturn and
intense competition, quality is no longer adequate as a source of competitive advantage
(Woodruff 1997).
Similarly, the role of satisfaction is also being questioned. Empirical evidence from the
retail setting has suggested that many customers express high satisfaction ratings but
remain spending elsewhere for different reasons (Jones & Sasser 1995). Firms that
seemed to do well in satisfaction evaluations often fail to outperform their competitors
and consequently lose their market. The earlier marketing literature has recorded that
customer satisfaction is the major driver of loyalty. However, Jones & Sasser (1995)
found that satisfaction does not always create customer loyalty. For example, even
though satisfied with a particular hotel, it is possible that the same customers will not
use the same hotel in the future since competitors might provide better value in terms of
price, social perceptions or emotional attachment, and additionally, customers may have
different reasons for using competitors’ offerings.
Research on the relationship between customer satisfaction and performance have also
been criticised in the sense that the research only evaluates ‘existing’ customer and,
therefore, ignores potential customers, non-customers and competitors (Gale 1994).
Eggert & Ulaga (2002) argue that customer perceptions of a product’s price should be
taken into account when assessing an organisation’s performance. Customer satisfaction
is considered as only tactical in nature since it suggests only simple improvements and
corrections of defects and errors that have been identified (Eggert & Ulaga 2002).
Petrick (1999) identified in a tourism study that potential mistakes may occur when
predicting purchase intentions solely based on satisfaction or service quality. This
potential mistake may happen because customers may be satisfied but they are still not
considering that the services offered represent a good value for money. In monitoring
organisational performance, it is suggested that managers should not only be
considering satisfaction but also assessing customer value (Gill et al. 2007). Given the
54
limitations of customer satisfaction as a good predictor of organisation outcomes, Gross
(1997) suggests that value should be used in place of satisfaction as it is a better
predictor of consumer behaviour.
Recognising the strategic relevance of maintaining loyal customers for survival, growth
and organisational performance, marketing scholars and organisations highlight the
delivery of customer value as a key strategy for fostering customer loyalty and
increasing competitive advantage. Research in marketing should have a more
comprehensive approach than merely focusing on service quality or customer
satisfaction (Vargo & Lusch 2004; Woodruff 1997) in order to adequately explain what
creates and sustains a competitive advantage.
2.6.3 Different Interpretation of Customer Value
There has not been any single widely accepted definition of customer value and
research findings remain fragmented (Anderson et al. 2006; Wang et al. 2004).
Customer value is also one of the constructs that is difficult to define and measure
(Zeithaml 1988; Holbrook 1994; Woodruff 1997). Customer value tends to be highly
personal, subjectively perceived and vary widely from one customer to another
(Parasuraman et al. 1985; Zeithaml 1988; Kortge & Okonkwo 1993; Holbrook 1994).
In addition to being differently perceived by each individual, customer value also varies
according to the context being studied (Dodds et al. 1991; Sweeney 1994; Sweeney
2003; Patterson & Spreng 1997). In the economics discipline, value refers to utility or
desirability; in the social sciences, value refers to human values; and in industrial
settings, value refers to designing cost-effective processes on the condition that
standards are also maintained (Patterson & Spreng 1997). Based on the above
arguments, it is clear that value is considered as an abstract and context-specific
construct (Dodds et al. 1991; Sweeney 1994; Sweeney 2003; Whittaker et al. 2007).
Further support for the proposition that value is interpreted differently by different
people has been given by DeSarbo et al. (2001, p. 864) who states that “there are indeed
heterogeneous interpretations of customer-perceived value, and multiple customer
segments may assign differential importance weights to the value drivers (perceived
quality and price)”. Based on an exploratory study, Zeithaml (1988, p. 13) states that
55
different customers have different conceptions of value and further, these conceptions of
value can be categorised as: (1) value is low price; (2) value is whatever one wants in a
product; (3) value is the quality that the consumer receives for the price paid; and (4)
value is what the consumer gets for what they give. From Zeithaml’s (1988)
categorisation of value conception, clearly the definitions of value differ among
customers, from identifying the product attributes (value is low price) to identifying the
effects of consumption experiences (value is what the consumer gets for what they give)
(Rintamaki et al. 2007). Whittaker et al. (2007) claim that the same products/services
may be differently perceived since value varies across different situations, different
times and experiences, different offerings, competition and, furthermore, value is
dependent on the characteristics of the customer.
2.6.4 An Approach to the Definition of Customer Value Construct
In the early approach to customer value construct, Monroe (1973) and Monroe and
Khrisnan (1985) introduced the concept of acceptable price range and assumed that
most customers use some acceptable price range to evaluate the value of a product. The
concept of acceptable price range holds that the value of products will increase when the
price is offered within or slightly above or below the acceptable price range. When the
price is set far above the acceptable level, the value tends to decrease. Nevertheless,
when the price is set well below the acceptable price range, the value may not be
automatically increased since customers become suspicious regarding the quality
attached (Cooper 1969, in Nasution 2005).
In the marketing discipline, the definition of value is typically based on customer points
of view. The important role of the customer in determining value was confirmed by
Rintamaki et al. (2007, p. 622), who stated “it is always the customer who defines what
is valuable and what is not”. For this reason, all efforts to create value must be
addressed to support customers in enhancing opportunity costs (Vargo & Lusch 2004).
Among the different approaches and conceptualisations of customer value (see Table
2.12), there are two common areas in which most of value definitions agree. First,
customer value should be defined based on the customers’ perspective (Rintamaki et al.
2007). Second, most definitions emphasise the importance of a trade off between
benefits and sacrifices. It is clear from this definition that the terms “what is received
56
and what is given”, or “benefits and sacrifices” are essential in determining the value of
products/services. The “benefits” refers to economic, social and relational advantage,
while the “sacrifices” refers to price spend, time lost, effort and risk (Zeithaml 1988).
Table 2.12 Definitions of Customer Value Author(s) Definitions of Customer Value
Zeithaml (1988) The consumer’s overall assessment of the utility of a product based on a perception of what is received and what is given.
Monroe (1990) Ratio of perceived benefits relative to perceived sacrifice.
Anderson et al. (1993) Perceived worth in monetary units of the set of economic, technical, service, and social benefits received by a customer firm in exchange for the price paid for a product offering, taking into consideration the available alternative suppliers’ offerings and price.
Woodruff & Gardial (1996)
Trade-off between desirable attributes and sacrifice attributes.
Flint et al. (1997) The customers’ assessment of the value that has been experienced, given the trade-offs between all relevant benefits and sacrifices in a specific-use situation.
Woodruff (1997) Customer’s perceived preference for and evaluation of those product attributes, attributes performances and consequences arising from use that facilitates achieving the customer’s goals and purposes in use situations.
Source: Woodruff (1997, p. 141) and Ulaga and Chacour (2001, p. 529)
This thesis adopts the definition of customer value developed by Zeithaml (1988) as
stated in Table 2.12 and uses the terms ‘customer perceived value’ and ‘customer value’
interchangeably. No particular differentiation is given to the meaning of both terms.
Both terms are used as an expression of value as perceived by the customer. For
consistency, the term ‘customer value’ will mostly be used in the rest of the discussions.
2.6.5 Customer Value versus Quality and Satisfaction
Research on customer value often examines the relationships between service quality,
price, customer satisfaction and purchase intentions (e.g. Bolton & Drew 1991; Ostrom
& Iacobucci 1995; Chang & Wildt 1994; Tam 2004). However, the concept of customer
value is often confused with other related concepts, especially the earlier concept of
‘quality and satisfaction’. There have been several attempts made to explain the
difference between these three concepts (quality, satisfaction and value). Service quality
is confirmed by Monroe and Khrisnan (1985) as a purely an evaluative measure. The
similarity between service quality and customer value is that both constructs are
cognitive. However, the difference between service quality and value is that unlike
service quality assessment (overall excellence), value requires a trade-off between
benefits and sacrifices (Choi et al. 2004). Even though both service quality and
customer value are cognitive (evaluative) constructs, the concept of value should be
57
considered distinct from the concept of service quality (Cronin et al. 1997; Whittaker et
al. 2007).
By conceptualising customer value as a trade-off between benefits and sacrifices,
customer value is also clearly different from satisfaction. Apart from having a different
conceptualisation and being distinct, both satisfaction and customer value may relate in
different ways (Eggert & Ulaga 2002; Woodruff & Gardial 1996). The distinction
between satisfaction and customer value is also clearly shown when both constructs are
examined in the structural model. All existing models involving customer value and
satisfaction always defined customer value as an antecedent of satisfaction and state that
satisfaction mediated the relationship between customer value and behavioural
intentions. The following Table 2.13 details the distinctions between customer
satisfaction and customer value constructs as identified by Eggert and Ulaga (2002).
Table 2.13 Distinctions between Customer Value and Customer Satisfaction Satisfaction Sources Customer Perceived
Value Sources
Affective construct. Oliver (1996);Woodruff (1997) Cognitive Construct. Woodruff (1997); Cronin et al. (1997); Patterson & Spreng, (1997)
Post-purchase perspective.
Hunt (1977); Oliver (1981); Sweeney & Soutar (2001)
Pre/post purchase perspective.
Woodruff (1997); Sweeney & Soutar (2001)
Unidimensional construct .
Cronin et al. (2000); Fornell et al. (1996); Halowell (1996)
Multidimensional construct .
Sweeney & Soutar (2001)
Consequence to value.
Cronin et al. (2000); Fornell et al. (1996; Halowell (1996); Parasuraman (1997)
Antecedent to value. Cronin et al. (2000); Fornell et al. (1996); Halowell (1996); Parasuraman (1997)
Tactical orientation. Eggert & Ulaga (2002) Strategic Orientation. Eggert & Ulaga (2002)
Present customer. Eggert & Ulaga (2002) Present and Potential customers.
Eggert & Ulaga (2002)
Suplier’s offerings. Eggert & Ulaga (2002) Suppliers’ and Competitors’ offerings.
Eggert & Ulaga (2002)
Source: Eggert and Ulaga (2002)
2.6.5.1 The Distinction between Customer Satisfaction and Customer Value
Customer value is the result of a cognitive comparison process (Woodruff 1997; Cronin
et al. 1997; Patterson & Spreng 1997). Customer value is categorised as a cognitive
construct since it evaluates benefits and sacrifices. Unlike customer value and service
quality, satisfaction is predominantly conceptualised as an affective construct by
researchers (e.g. Yi 1990; Westbrook & Oliver 1991; Giese & Cote 2000), even though
58
some researchers also focus on the both cognitive and affective aspects of satisfaction
(Oliver 1996, 1997; Taylor 1994).
Customer value occurs at both the pre-purchase and post-purchase stages (Woodruff
1997; Eggert & Ulaga 2002). Nevertheless, satisfaction is commonly evaluated at the
post-purchase transaction (e.g. Woodruff 1997; Sweeney & Soutar 2001; Eggert &
Ulaga 2002). Since customer value may be created before a transaction, perception of
value can occur without purchasing the products/services; as with satisfaction,
customers must have experience of the products/services offered before expressing
whether they are satisfied or dissatisfied (Sweeney & Soutar 2001).
The majority of marketing literature has conceptualised satisfaction as a unidimensional
construct and modelled it as the outcome or consequences of customer value (Cronin et
al. 2000; Fornell et al. 1996; Halowell 1996). On the other hand, customer value is
predominantly conceptualised as a multidimensional construct, and in the structural
model it is conceptualised as a predictor or antecedent of satisfaction (Sweeney &
Soutar 2001).
Both customer value and satisfaction have different objectives (Eggert & Ulaga 2002).
Customer satisfaction concerns more the performance at the point of purchase. Since it
generally only involves the one point of purchase (it does not consider the past and
future point of purchase). Therefore, satisfaction is claimed to only cover the tactical
aspect. Customer satisfaction is purported to be only concerned with tactical orientation
since it is only directed to improve and correct the performance of products/services
(Eggert & Ulaga 2002). Customer value construct, on the other hand, is seen to be more
future-oriented and strategic, since it focuses on value creation and meeting former,
present and future customers’ requirements (Eggert & Ulaga 2002).
The last distinction is that while satisfaction only focuses on a supplier’s offerings,
customer value measurement involves the assessment of competitors’ offerings in order
to facilitate competitive benchmarking (Eggert & Ulaga 2002).
59
2.6.6 Measurements of Customer Value
2.6.6.1 Unidimensional Conceptualisation of Customer Value
A number of studies that measure customer value have used unidimensional measures
and operationalised the construct directly using multiple items (e.g. Patterson & Spreng
1997; Cronin et al. 2000; Tam 2004; Choi et al. 2004). The unidimensional approach
measures value by using a limited number of items to measure the overall perception of
value.
So far, nearly every study involving customer value in the service context uses
Zeithaml's (1988) framework or trade-off model to resolve which determinants of value
need to be included (Table 2.14 lists some studies that have involved the trade-off
model ‘benefits versus sacrifices’). Zeithaml’s (1988, p. 14) conceptualisation of
customer value is “The consumer’s overall assessment of the utility of a product based
on a perception of what is received and what is given”. As previously discussed, the
trade-off includes comparison between benefits (what customers get from the exchange)
and sacrifices (what customers have given up). As part of the early development of the
benefits and sacrifices evaluation, the early conceptualisations of customer value have
relied on pricing literature (Dodds & Monroe 1985) and only used perceived quality and
price as the main determinants of customer value. Quality is seen as benefits received
and price is seen as the sacrifices made to acquire products/services. Nevertheless,
viewing customer value as being based on quality and price is considered to be
somewhat too simplistic and too narrow (Bolton & Drew 1991). This view suggests that
other dimensions should be employed than just price and quality.
The unidimensional conceptualisation of customer value is criticised as it ignores the
conceptual richness of the customer value construct, which is considered too complex to
be operationalised as a unidimensional construct (Sweeney & Soutar 2001; Lam et al.
2004; Wang et al. 2004). Even though the unidimensional conceptualisation of
customer value is commonly effective in the structural model, its weaknesses lie in its
inability to cover the complex nature of the customer value construct (Sweeney and
Soutar 2001). The unidimensional approach does not seem to fit as a measure of
customer value since value perceptions differ among customers (Sweeney 2003). Since
the unidimensional approach assumes that customers have a common definition of
60
value, it ignores the fact that there are other elements of customer value such as price,
quality, social and emotion, that are important and have an impact on perceptions of
value (Petrick 2002). In recognition of the complexity that customer value has, and
considering the problems encountered when using unidimensional approach, attempts
have been made to conceptualise customer value as a multidimensional construct.
2.6.6.2 Multidimensional Conceptualisation of Customer Value
In order to incorporate its complex nature, it has been suggested that customer is a
multidimensional construct. So far, varieties of customer value dimensions have been
proposed in the marketing studies. Little consensus has been reached among researchers
on the dimensionality of customer value. The lack of agreement might be due to the
abstract and context-specific nature of customer value, which require specific
adjustment according to the context. Table 2.14 identifies varieties of multidimensional
approach to defining customer value.
61
Table 2.14 Multidimensional Approaches to Defining Customer Value Author(s)/context Types of
Components Components of Customer Value
Benefit Components Sacrifice Components
De Ruyter et al (1997) – Hotel service.
Reflective Emotional value, practical value and logical value.
Grewal et al. (1998) – Bicycles.
Reflective Perceived acquisition value. Perceived transaction value.
Lapierre (2000) – (ICE: information, communication, entertainment), distribution, and finance services.
Reflective Alternative solutions, product quality, product customization, responsiveness, flexibility, reliability, technical competence, supplier’s image, trust and solidarity.
Price, time/effort/energy, conflict.
Mathwick et al. (2001) – Internet and catalog shopping.
Reflective Visual appeal, entertainment, escapism, enjoyment, efficiency and economic value.
Sweeney & Soutar (2001) – Durables.
Reflective Emotional value, social value and functional value due to quality.
Functional value due to price.
Petrick (2002) – Fast-food restaurant service.
Reflective Quality, emotional response and reputation.
Monetary price, behavioural price.
Kristina (2004) – Online bill payment service.
Reflective Technical value, functional value, temporal value and spatial value.
Wang et al. (2004) – Security firms.
Reflective Functional value, social value and emotional value.
Perceived sacrifice.
Liu et al. (2005) – Financial staffing. services
Reflective Core service and support service.
Economic value.
Pura (2005) – Directory services.
Reflective Social value, emotional value, epistemic value and conditional value.
Monetary value, convenience value.
Lin et al. (2005) – Web services.
Reflective & Formative
Web site design, fulfilment /reliability, security/privacy and customer service.
Monetary sacrifice.
Roig et al. (2006) – Banking services.
Reflective & Formative
Functional value (installations, professionalism & quality), emotional value and social value.
Price.
Whittaker et al. (2007) – business services.
Reflective & Formative
Functional, epistemic, emotional, social and image.
Price/quality.
Source: Ruiz et al. (2008)
A broad dimensionality of customer value was initially developed by Sheth et al. (1991)
who proposed five dimensions of customer value, consisting of social, emotional,
functional, epistemic and conditional value. The Sheth et al. (1991) framework of
customer value has been widely used as a foundation to develop the measurement of
customer value. This theoretical framework is believed to provide the best foundation
for extending existing unidimensional value constructs since the validity of this
framework has been tested through an intensive investigation in a variety of areas
include economics, the social sciences and clinical psychology (Sweeney & Soutar
62
2001). The five dimensions of customer value as identified by Sheth et al. (1991, p.
160-162) are discussed below.
Functional value refers to he perceived utility acquired from an alternative’s capacity
for functional, utilitarian or physical performance. Functional value is often related to
the attributes of the services such as reliability, durability and monetary value. It is also
focused on the ability to perform the function that it has been promoted to provide
(Whittaker et al. 2007).
Social value is the perceived utility acquired from an alternative’s association with one
or more specific social group. Social value derives mostly from usage of
products/services when they are shared with others. Social value represents the benefits
derived from social interactions, hence the improvement of self-image among other
individuals (Bearden & Netemeyer 1999). Social value and emotional value together are
considered to provide further relational benefits (Whittaker et al. 2007).
Emotional value is the perceived utility acquired from an alternative’s capacity to
arouse feelings or affective states. Emotional value refers to the benefits derived from
obtaining services/products that stimulate feeling and/or affective states (Whittaker et
al. 2007). Service value and emotional value together represent the affective aspect of
customer value (Roig et al. 2006).
Epistemic value is the perceived utility acquired from an alternative’s capacity to arouse
curiosity, novelty, and/or gained knowledge. Sheth et al. (1991) claim that new products
may arouse curiosity and curiosity will encourage the purchase of certain
products/services. Epistemic value refers more to offerings that give experience from
curiosity, novelty and satisfaction from obtaining particular knowledge (Whittaker et al.
2007).
The final dimension, the conditional value, is the perceived utility acquired by an
alternative as the result of the specific situation or set of circumstances which impact
choice. Conditional value is value benefits according to the condition. It is dependent on
context and only occurs in a specific situation (Pura 2005). The situation could be
63
seasonal, emergency or special once-in-a-lifetime occasions (Sheth et al. 1991). For
this reason, conditional value is rarely applied in the customer value model, because it
must be attached to a specific condition to provide value.
2.6.6.2.1 PERVAL and SERV-PERVAL Measures of Customer Value
The superiority of the multidimensional concept as opposed to the unidimensional
concept of customer value has been verified by Sweeney and Soutar (2001). They have
developed and tested their four dimensions of customer value and confirmed that the
results were found to be better than when only using a unidimensional conceptualisation
“overall value for money” item. Sweeney and Soutar’s (2001) study highlights and
encourages the need to operationalise customer value in multidimensional terms. This
multidimensional customer value measure was then called as PERVAL. The PERVAL
scale was developed based on Sheth et al.’s (1991) theoretical framework of customer
value. Instead of proposing five dimensions, as recommended by Sheth et al. (1991),
Sweeney and Soutar (2001) established four dimensions of customer value specifically
designed for the retail setting (durable goods). The four dimensions of customer value
cover: functional value (price/value for money), functional value (performance/quality),
emotional value and social value. They omitted the conditional value as it was regarded
as only for specific (exclusive) and temporary situations (e.g. illness, seasonal related
needs, disaster, etc). Since their study was meant to develop a general value measure,
conditional value was not included due to it being less critical than the other value
dimensions.
The service industry sector is different from product-based industry and the strategies to
assess the variables related to the services sector are hence also different. While the
Sweeney and Soutar (2001) customer value scales were tested in the retail setting which
was tangible product in nature, Petrick (2002) suggests that a different scale specifically
designed for service sector is necessary. Petrick (2002) argues that the scales developed
for measuring tangible products are relatively difficult to employ for measuring
services. To overcome the PERVAL limitation, Petrick developed multi-items -
multidimensional scales for measuring customer value specific to the service sector
called SERV-PERVAL. The SERV-PERVAL was tested empirically on cruise line
passengers. The measurement consists of five dimensions: behavioural price, monetary
64
price, emotional response, quality and reputation. Behavioural price represents the non-
monetary aspects of obtaining the service. Customers spend time and effort as part of
their search to find the service they want (Petrick 2002; Zeithaml 1988). Monetary price
refers to the price of a service (Petrick 2002). Emotional response reflects the pleasure
acquired from consuming the services (Sweeney & Soutar 2001; Petrick 2002). Quality
refers to customer’s judgment regarding the excellence of overall services’ provided
(Petrick 2002; Zeithaml 1988). Finally, reputation refers to prestige or status received,
based on the image that the service providers have developed (Petrick 2002).
This thesis applies the combination of the multidimensional approaches of the customer
value scale developed by Petrick (2002) and Sweeney and Soutar (2001) to measure
customer value of service in the higher education setting. Accordingly, this thesis
applies five components in the customer value measurement. These are: quality;
emotional value and monetary price (Petrick 2002; Sweeney & Soutar 2001); reputation
(Petrick 2002); and social value (Sweeney & Soutar 2001). The inclusion of reputation
is supported in the studies of Lapierre (2000) and reputation in higher education by
LeBlanc and Nguyen (1999).
The justification in using PERVAL and SERV-PERVAL is that these measurements
can be considered as a general value that can be applied in diverse situations (Sweeney
& Soutar 2001), including the higher education service sector. Moreover, the
multidimensional aspect of customer value is expected to be more effective in
explaining the complex nature of the customer value construct, rather than applying the
unidimensional approach. In addition, the measures developed by Sweeney and Soutar
(2001) and Petrick (2002) have been tested across different sectors, thus confirmed their
validity and reliability as a good measurement of customer value.
2.6.7 Customer Value in the Higher Education Context
Several studies in the higher education sector have examined the contribution of
customer value in relation to satisfaction, image, loyalty, etc. (e.g. Hartman & Schmidt
1995; Webb & Jagun 1997; LeBlanc & Nguyen 1999; Alves & Raposo 2007). LeBlanc
and Nguyen (1999) further discovered that the value perceived by the students may
involve dimensions which relates to the quality received, image, emotional values and
65
even social values. Alves and Raposo (2007) have proven empirically tested that service
quality, expectations and image were antecedents of value and satisfaction was a
consequence of customer value. Even though Alves and Raposo (2007) have included
the structural model of antecedent and consequence of customer value, their research
did not evaluate customer value as a multidimensional construct.
There is a profusion of research examining service quality and satisfaction in the higher
education sector. While service quality and satisfaction have certainly proved to offer an
understanding of how higher education institutions should perform, it is still regarded as
not providing a comprehensive picture of what students value regarding their
educational experiences. The motivation to study and to choose particular institutions or
programs cannot be explored only by assessing service quality and satisfaction. Since
customer value covers a more comprehensive evaluation such as benefits and sacrifices,
it is argued that customer value would provide better measures and information on how
an organisation should perform. In the higher education sector, the influence of
customer value is still limited and remains unexplored. This highlights the importance
of customer value research in the higher education sector.
2.6.8 Antecedents of Customer Value
In the marketing literature, service quality has been commonly identified as an
antecedent of customer value (see Table 2.15). Spiteri and Dion (2004) have completed
extensive studies and identified sixteen legitimate antecedents to the comprehensive and
complex concept of customer value. Despite the service quality, other determinants are
less frequently examined as antecedents of customer value.
Table 2.15 Antecedents of Customer Value Antecedents Sources
Perceived service quality. Dodds et al. (1991); Cronin et al. (1997, 2000); Fornell et al. (1996); Gooding (1995); Wakefield & Barnes (1996); Oh (1999); Agarwal & Teas (2001); Imrie et al. (2002); Choi et al. (2004); Tam (2004); Alves & Raposo (2007)
Performance. Patterson & Spreng (1997); Hartline & Jones (1996)
Trust and commitment. Sirdeshmukh et al. (2002); Walter & Ritter (2003)
Perceived price. Rust et al. (2000); Kumar & Grissafe (2004); Oh (1999); Tam (2004)
Risk. Sweeney et al. (1999); Agarwal & Teas (2001); Snoj et al. (2004)
Sacrifices. Cronin et al. (2000); Spiteri & Dion (2004); Wang & Lo (2003)
Image or reputation. Teas & Agarwal (2000); Agarwal & Teas (2001); Andreassen & Lindestad (1998); Alves & Raposo (2007)
Company’s resources. Mital & Sheth (2001)
Perceived relationship benefits.
Spiteri & Dion (2004)
Source: Developed for the study and Whittaker et al. (2007)
66
When examining research involving customer value in the structural model, there have
been different ways in which researchers configuring the dimensions of customer value.
Some studies measure sacrifice (price, time, effort and risk) and benefits (economic,
social and relational) together as part of a customer value construct (Sweeney & Soutar
2001; Petrick 2002; LeBlanc & Nguyen 1999; Pura 2005). Other studies treat
dimensions of customer value such as price, sacrifices, reputation, etc. as antecedents of
customer value. For example, ‘sacrifice’ in the models of Cronin et al. (2000), Spiteri
and Dion (2004) and Wang and Lo (2003) was treated separately as an antecedent of
value despite the consensus that sacrifice is part of the overall value itself. ‘Price’ was
separately modelled as an antecedent of customer value (Oh 1999; Kumar & Grisaffe
2004; Tam 2004). ‘Reputation’ was independently modelled as an antecedent of
customer value (Teas & Agarwal 2000; Agarwal & Teas 2001; Andreassen & Lindestad
1998; Alves & Raposo 2007).
Regardless of the existence of several competing models treating the dimensions of
customer value, it should be noted that there is no right or wrong in differently
modelling customer value (whether as part of or as antecedents of the customer value
construct). What is more important is that the model and the dimensions used must
appropriately represent the context being studied and address the research objective.
Even though only a general set of measurement is offered (e.g. Sheth et al. 1991;
Sweeney & Soutar 2001; Petrick 2002), it is certainly useful as a foundation, but it must
also be carefully adjusted according to the context. This is why marketing literature has
very rich models and conceptualisations of customer value. This construct is abstract,
subjectively perceived, and context-specific.
2.6.9 Consequences of Customer Value
Previous studies have identified the fact that even though customers are satisfied, there
is no guarantee that customers will be loyal and stick to the company (Jones & Sasser
1995). Fredericks and Salter (1995) identify the contribution of perception of value to
the creation and maintenance of customer loyalty. Zeithaml and Bitner (1996) claimed
that customers remain loyal when the perceived value received exceeds competitors’
offerings. Despite satisfaction being commonly observed as a direct consequence of
customer value, past studies have also shown ample evidence of customer value as an
67
important determinant of behavioural intentions including purchase decisions, loyalty
intention, willingness to buy and word-of-mouth communication. Reichheld and Sasser
(1990) have further identified why increasing customer loyalty should lead to higher
profitability. Having been related to satisfaction and loyalty, customer value has also
been identified as influencing organisational performance (Weinstein & Pohlman 1998).
Table 2.16 summarises some consequences of customer value.
Table 2.16 Consequences of Customer Value Consequences Sources
Re/purchase intention. Zeithaml (1988); Dodds et al. (1991); Chang & Wildt (1994); Patterson & Spreng (1997); Cronin et al. (1997); Sweeney et al. (1999); Petrick (2002); Brady & Robertson (2001); Gill et al. (2007)
Loyalty. McDougall & Levesque (2000); Frederick & Salter (1995); Zeithaml & Bitner (1996); Andreassen & Lindestad (1998); Alves & Raposo (2007)
Satisfaction. Churchill & Surprenant(1982); Bojanic (1996); Woodruff (1997); Flint et al. (1997); Patterson & Spreng (1997); McDougall & Levesque (2000); Eggert & Ulaga (2002); Lam et al. (2004)
Feedback/word-of-mouth/recommendation.
LeBlanc and Nguyen (2001); Petrcik (2002); Lam et al. (2004); Tam (2004); Gill et al. (2007)
Willingness to buy. Sweeney et al. (1999)
Customer retention. Flint et al. (1997)
Organisational performance (sales, profit, market share, net present value).
Weinstein & Pohlman (1998)
Source: Whittaker et al. (2007).
The following review concerns the behavioural intentions constructs, which covers the
theory of behavioural intentions and the types of behavioural intentions commonly
related to the higher education sector (loyalty and word-of-mouth).
2.7 CUSTOMER BEHAVIORAL INTENTIONS
2.7.1 The Theory of Behavioural Intentions
The ability to explain how attitudes predict behavioural intentions had been the focus of
many prior studies (Ajzen 2001). The theory of reasoned action (TRA) proposed by
Ajzen and Fishbein (1980) is the theory most commonly used as a foundation for
studying how a person’s attitude shapes their behaviour. The TRA provides by far the
most sustained explanation of the intention-behaviour relationship. The TRA suggests
that a given behavioural performance is primarily determined by the strength of
someone’s intention to perform a specific behaviour (Ajzen & Fishbein 1980). Based on
68
the TRA, the attitude of the individual toward and the social norms regarding particular
behaviour both influence someone’s intention to perform an action. The attitude toward
a particular behaviour reflects the degree to which people feel favourably or
unfavourably disposed to that behaviour (Ajzen 1987). The norm refers to how the
society would perceive the behaviour and the social perceptions may also force
someone to consider whether it is necessary to perform or not to perform (Ajzen 1987).
Ajzen and Fishbein (2000) suggest that researchers should focus on behavioural
intentions rather than on attitudes when the objective of the research is on predicting
behaviour.
Behaviour is seen as the immediate consequence of intention (Ajzen 1987). Ajzen
(1992) explains that there are some motivational factors covered in intentions that may
influence behaviour. Further, Ajzen (1992) states that intention also indicates the degree
to which people are willing to try engaging in, and the amount of effort that should be
given to that behaviour. The stronger the intention, the more likely someone will be to
carry out that behaviour. For example, the stronger the student’s intention to enrol in a
particular programme, the more committed that student is to actively search for
information regarding that programme (cost, schedule, content, etc.). However, the
success of behavioural predictability depends not only on the intention, but also on non-
motivational factors such as the number of opportunities and resources available (e.g.
time, skills, money, etc.) (Ajzen 1987). These non-motivational factors provide actual
control for someone (transformation from intention into action) over the particular
behaviours (Ajzen 1987).
Behavioural intentions represent a variety of customer responses and may indicate
customers’ propensity to remain with or to defect from a company (Zeithaml et al.
1996). Further, Zeithaml et al. (1996) classify behavioural intentions into two
categories: favourable and unfavourable. Favourable behavioural intentions signal that
customers show a preference for one particular company over others, engage in positive
word-of-mouth observations, recommend a provider to others, increase spending on the
company’s products/services and are willing to pay premium prices. All of these
behaviours indicate that customers are bonding with the company. Burton et al. (2003)
maintain that customer experiences may also influence behavioural intentions. If the
69
experience is positive, customers would tend to re-use the service (Olorunniwo et al.
2006). Previous studies have indicated varieties of favourable behaviour intentions
across service sectors. Table 2.17 presents studies on positive behavioural intentions.
Table 2.17 Positive Behavioural Expressions Source Positive behavioural intentions
Boulding et al. (1993) Saying positive things about the company to others.
Parasuraman et al (1991, 1988); Reicheld & Sasser (1990)
Recommending the company or service to others.
LaBarbera & Mazursky (1983); Newman & Werbel (1973); Rust & Zahorik (1993)
Willingness to pay a premium price and remaining loyal to the company.
Zeithaml et al. (1996) Being loyal to a company: a preference for one company over others, continuing to make purchases, and increasing business with the company in the future.
Source: Zeithaml et al. (1996, p. 34)
Unfavourable behavioural intention is shown when the performance of the service is
seen as inferior by customers, as a consequence of which customers are likely to ignore,
make a shift or spend less with the company (Zeithaml et al. 1996). Complaining is one
example of unfavourable behaviour which may lead to switching behaviour. A decrease
in the number of transactions that a customer usually has with the company may also be
categorised as an indicator of unfavourable behavioural intentions (Zeithaml et al.
1996). Complaining is usually the result of dissatisfaction with and disaffection from
particular offerings and it may lead to a negative response to the company (Richins
1983; Scaglione 1988). Practitioners found that customer complaint is a useful
dimension to understand marketplace dissatisfaction (Ross & Oliver 1984). With
respect to marketing theory, customer complaint studies and its consequences also
critical in explaining and predicting consumers’ loyalty and repurchase intentions
(Singh 1988).
A number of studies demonstrate the existence of a linkage between service quality,
customer satisfaction, customer value and behavioural intentions (e.g., Boulding et al.
1993; Cronin et al. 2000; Chumpitaz & Paparoidamis 2004; Choi et al. 2004).
Behavioural intention is often regarded as an outcome of many constructs in marketing
studies. Therefore, the majority of studies emphasise what really causes (the antecedents
of) behavioural intentions. The details of discussions relating to variables that influence
behavioural intentions and the nature of the relationships in the structural model are
explained in Chapter Three, Section 3.3.2 and 3.3.4. The following discussion focuses
70
on loyalty and word-of-mouth communication since both aspects are most commonly
related to behavioural intentions in the higher education sector.
2.7.2 Customer Loyalty
There have been many ways of defining and measuring customer loyalty (Jacoby &
Chesnut 1978, Zeithaml et al. 1996). Loyalty covers all the behavioural and attitudinal
aspects (Rowley 2003). Loyalty can be expressed in many ways depending on the
products/services and situations, such as retention, making re-purchase and
financial/non-financial contributions. In business markets, loyalty can be shown through
the willingness to spend more, to pay a premium and consequently increasing a
company’s profitability. Rust et al. (1999) found that experience will have direct effect
on behavioural intentions, by possibly giving lower level of uncertainty. Customers
would be happy to pay premium price to the same provider because of positive
experiences and for the risk avoidance (prevention for uncertainty). In a majority of
cases, loyalty is often seen as the willingness of the customer to show positive
behaviours and maintain a relationship with the company/supplier. Loyalty usually
derives from the customers’ belief that one particular supplier offers better value than
others (Rowley 2003).
In the case of higher education, loyal students play an important role in supporting the
institution and encouraging other students to choose to join or to stay. This is why
loyalty has been defined as one of the key elements in higher education strategy, since
loyalty may strengthen and expand relationships and may lead to improving financial
performance. The main motivations to build students’ loyalty are the advantages that
such loyalty offers to the institutions. The advantages of loyalty can be categorised into
three main contributions (Henning-Thurau et al. 2001), including:
1. Loyalty can be managed to increase students’ enrolments; therefore, it provides
additional sources of income for the institutions.
2. In the education process, participation of both students and staff is necessary. Loyal
students would positively contribute to improving teaching and learning quality by
becoming active participants (Rodie & Kleine 2000).
3. Students – institution relationship. This can be done by providing donations to the
institution, positive communication, recommendation and through co-operation.
71
Consequently, alumni may also offer internships opportunities to current students,
cooperation in research projects, etc.
2.7.3 Word-of-mouth Communication (WOM)
Word-of-mouth communication is one type of behaviour which is commonly expressed
by informing friends, colleagues, neighbours, relatives and other acquaintances about
particular satisfactory experience (Soderlund 1998). As discussed previously, service is
characterised by its intangibility, simultaneity of production and consumption and
heterogeneity. Due to this complex nature, consumers have difficulties understanding
and very limited information regarding the quality of the services (Moogan et al. 1999).
It is often in the service area, when service is being purchased, that alternatives are
evaluated without the benefit of any direct experience of the service (Moogan et al.
1999). This phenomenon is typical in the service transactions where personal source
such as word-of-mouth communication is considered to be a reliable source to help
consumers to make decisions. Compared to the product-based market, Murray (1991)
found that consumers of services rely greatly on personal sources of information and
that personal sources have a greater impact on services-based consumers than on
product-based consumers in making purchases. Mangold (1999) also identified that
there was a positive significant influence of word-of-mouth communication on
consumer purchasing behaviour.
Despite offering tangible products such as lecture notes, books, reports and other
materials, many believe that higher education institutions deal more with services as its
core business. Typical of the nature of services in which there is simultaneity between
production and consumption, an advance experience of the service is impossible
(Solomon et al. 1985). Making an investment in higher education is not a simple matter.
Besides involving a very considerable high monetary investment, the consumption
process in higher education may last for several years and there are also risks that may
lead to students not performing well in the education process (Mitchell 1995; Murray
1991). For these reasons, students and their families rely on prior information from
trustworthy and knowledgeable sources/friends. The most effective information comes
particularly from students who already have the education experiences and understand
the quality of the education services offered (Mortimer 1997). Word-of-mouth
72
information is particularly effective as a risk-reducing strategy for students embarking
on the higher education experience. This is because, by their nature, potential students
require information and more involvement with students who are already customers of
particular higher education institutions (Friedman & Smith 1993; Zeithaml et al. 1985).
This thesis focuses on loyalty and word-of-mouth communication since these
dimensions are among the most relevant behaviours in the higher education sector
(Athiyaman 1997; Alves & Raposo 2007). The nature of higher education service has
made word-of-mouth communication a reliable source of information for decision
making. The institutions may benefit from endorsement by word-of-mouth
communication. Similarly, examining loyalty is important considering the positive
behaviours of loyal customers as well as long-term relationship effects.
2.8 CONCLUSION
This chapter provided extensive literature reviews regarding key constructs (service
quality/SQ, customer satisfaction/CS, customer value/CV and behavioural
intentions/BI) and served as the foundation for the development of the conceptual
model proposed in this thesis. The nature of the service industry relating to the
intangibility, heterogeneity, inseparability of production-consumption and perishability
were briefly presented. An important issue concerning the position of students as the
main customers of higher education was also examined.
A review of the application of the four key constructs (SQ, CS, CV and BI) in the
general service and higher education sectors were presented in Section 2.4 to 2.7. More
specifically, the reviews cover: the importance of the key constructs in the general
service and higher education sectors; the conceptualisation, dimensionality and
measurement; critiques of SERVQUAL; the distinctions between service quality,
customer satisfaction and customer value; and the nature of the relationships in the
structural model (antecedents and consequences). The following chapter will review in
more detail the nature of the relationships among the constructs of interest and the
hypotheses development.
73
CHAPTER THREE
THE RELATIONSHIPS AND CONCEPTUAL MODEL
3.1 INTRODUCTION
Recognising the strategic importance of maintaining customers’ positive behaviour,
loyalty and increasing competitive advantage, customer value is increasingly becoming
a subject of interest among marketing scholars. A more comprehensive approach than a
simple focus on service quality or customer satisfaction is necessary in order to better
explain what creates and sustains competitive advantage (Vargo & Lusch 2004;
Woodruff 1997). Chapter Two provided an extensive discussion on a set of constructs
(service quality/SQ, customer satisfaction/CS, customer value/CV and behavioural
intentions/BI) identified as having strategic importance for the building of competitive
advantage.
Based on the literature review presented in Chapter Two, this chapter further discusses
how the relationships exist among the four key constructs under investigation (SQ, CS,
CV and BI). This chapter begins with discussions on five previous works that have
included customer value in the model and simultaneously analysed the relationships.
This is done in order to provide an overview of contributions of the models that
simultaneously relate SQ, CS, CV and BI in the different services sectors. The
relationships among the constructs and the hypothesis development are presented in the
following discussions. Finally, the conceptual model is proposed that forms the basis for
this thesis.
3.2 THE INTERRELATIONSHIP MODELS OF SERVICE
QUALITY, CUSTOMER SATISFACTION, CUSTOMER VALUE
AND BEHAVIOURAL INTENTIONS
This section illustrates the five earlier works in different service sectors that have
simultaneously related service quality, customer satisfaction, customer value and
behavioural intention in one model. An examination of these five earlier models is
undertaken to provide a basis for the nature of the relationships in the structural model
74
and causal direction relating to the four key constructs that will be examined
simultaneously in this thesis.
3.2.1 Patterson and Spreng’s (1997) Model of Interrelationships
Patterson and Spreng (1997) empirically examined the relationship between service
performance, perceived value, satisfaction and repurchase intentions in a business-to-
business service sector. Three consultancy firms and an active user of consultants were
used as a sampling frame for the study. Six service performance indicators (outcomes,
methodology, service, relationship, global and problem identification) were identified as
antecedents of satisfaction and perceived value. The bivariate relationships showed that
all of the six indicators proposed were found to have a significant effect on satisfaction
and perceived value. Satisfaction was strongly influenced by perceived value, and
satisfaction in turn has significant effects on purchase intentions. The mediations tests
showed that, first, the effect of perceived value on purchase intentions was completely
mediated by satisfaction and, second, that the effects of the performance dimensions on
purchase intentions were mediated by perceived value and customer satisfaction.
Figure 3.1 Patterson and Spreng’s (1997) Model of Interrelationships
3.2.2 Oh’s (1999) Model of Interrelationships
An integrative model of perceived service quality, customer satisfaction and perceived
value was also constructed by Oh (1999). By using a sample from the luxury segment of
the hotel industry, this study supports the use of the integrative model to determine
customers’ post-purchase decision-making process. In the measurement of perceived
quality, Oh’s (1999) study excluded the expectations measure and only included a
Outcomes
Methodology
Service
Relationship
Global
Problem Identification
Satisfaction
Value
Intentions
75
perception measure of service quality. Oh’s (1999, p. 78) study offers several important
findings: (a) the integrated model provides a framework to better understand the
customer decision-making processes as well as evaluating company performance more
accurately; (b) perceived value is an important variable to be considered in service
quality and consumer satisfaction studies or vice versa; (c) service quality and perceived
value in combination may completely mediate perceptions on customer satisfaction; (d)
perceived price has a negative impact on perceived value; and (e) perceived price was
found to have no relationship with perceived service quality.
Figure 3.2 Oh’s (1999) Model of Interrelationships
3.2.3 Cronin et al.’s (2000) Model of Interrelationships
The study by Cronin et al. (2000) is one among many pieces of marketing literature that
have provided extensive reviews of the service quality, customer satisfaction, customer
value and behavioural intentions interrelationships. The convergent literature (the
interrelationships) and the divergent literature (direct effects) were highlighted to
provide overviews of varieties of relationship models existing in the literature review.
Cronin et al. (2000) compared three competing models involving those four key
constructs and proposed their fourth model called ‘The Research Model’. The Research
Model specifically highlights the importance of simultaneously analysing the relative
impacts of the three exogenous constructs (SQ, CS, CV) on behavioural intentions. By
proposing the Research Model, it is not being stated that the previous studies were
incorrect, but rather that most previous studies were considered “limited in scope” (p.
196). The Research Model incorporated both the direct and indirect relationships across
the four key constructs (SQ, CS, CV and BI). The direct effect explains the bivariate
-
Actual Price
Perceived Price
Perceived Service Quality
Perceptions
Repurchase
Intentions WOM
Customer satisfaction
Perceived Customer Value
76
links between each construct. Based on the empirical assessment across the six different
service industries, this study found significant direct links between each of the
constructs (SQ-BI, SQ-CS, SQ-CV, CV-CS, CV-BI, CS-BI) when all of these
constructs were analysed collectively.
The theoretical justification for the causal direction of the indirect relationships was
drawn from the work of Bagozzi (1992) and Oliver (1997), who suggest that the initial
evaluation (i.e. appraisal) of the services provided leads to an emotional reaction and
consecutively guides behaviour. By using the procedure suggested by Bollen (1989) to
examine the mediating effects of ‘service value and satisfaction’ on the service quality
and behavioural intentions relationship, the result was also verified as significant.
Except for health care, the mediating effect of satisfaction on the relationship between
service value and behavioural intentions was also significant across the six industries
under investigation. Specifically, Cronin et al.’s (2000) study suggests that the indirect
effect through service value enhanced the impact of service quality on behavioural
intentions.
Figure 3.3 Cronin et al.’s (2000) “The Research Model”
3.2.4 Choi et al.’s (2004) Model of Interrelationships
Using the data collected from 537 South Korean health care customers, Choi et al.’s
(2004) research examined the validity of the integrative model in the health care
industry. The service quality scale consisted of a modified SERVQUAL which covers
four dimensions relating to medical services: (1) convenience of the care process; (2)
health care providers’ (other than physicians) concern; (3) physician’s concern; and (4)
tangibles. Choi et al. (2004) adopted the SERVPERF measure (the perception-only
measure) as suggested by Cronin and Taylor (1992). To operationalise behavioural
intentions, Choi et al. (2004) included the more informative aspects of behavioural
Sacrifice
Service
Quality
Satisfaction
Service
Value
Behavioural
Intention
77
intentions covering willingness to recommend, intention to repurchase and positive
word-of-mouth communication. The results affirm the causal sequence among service
quality, satisfaction, value and behavioural intentions constructs as suggested by
Bagozzi (1992) or Oliver (1997). Oliver (1997) proposed that ‘cognition affect affective
then conation’. Service quality and customer value are considered more as
cognitive/evaluative constructs, satisfaction is more reflecting an affective/emotive
construct and behavioural intentions is more a conative construct. Based on the causal
directions shown in Figure 3.4, service quality and value were also shown to have a
significant direct impact on behavioural intentions. Service quality was also shown to
influence value assessments.
Figure 3.4 Choi et al.’s (2004) Model of Interrelationships
3.2.5 Alves and Raposo’s (2007) Model of Interrelationships
Using structural equations modelling, Alves and Raposo (2007) empirically tested an
explanatory model of student satisfaction in higher education. Alves and Raposo‘s
(2007) work is interesting since it is pioneering the integrative assessment on service
quality, customer satisfaction, perceived value, image, customer expectations and
behavioural intentions in the higher education sector. Having been defined as the most
important customers of education services, students were used as the target population
(all students were from Portuguese state universities and belong to Conselho de Reitores
das Universidades Portuguesas/CRUP – the Council of Rectors of Portuguese
Universities). The results suggest that image was a variable which has the greatest
influence on student satisfaction, while it also directly influences loyalty and indirectly
influences perceived value and quality through the raising of expectations. The
relationship between quality and value to loyalty/word-of-mouth communication were
also indirect through satisfaction. Finally, Alves and Raposo’s (2007) model also
determined that students’ loyalty was the main consequence of satisfaction and further
loyalty influences word-of-mouth communication.
Service Quality
Value
Satisfaction Behavioural Intentions
78
Figure 3.5 Alves and Raposo’s (2007) Model of Interrelationships
3.2.6 Summary of the Integrative Models
Based on the five works that have modelled service quality, customer value, customer
satisfaction and behavioural intentions collectively across different areas of services, it
can be concluded that most of the works support the assertion that there are direct and
indirect relationships across the four constructs. As has been identified by Cronin et al.
(2000), the indirect relationship covers the relationships between service quality and
behavioural intentions by placing customer value and customer satisfaction as
mediating variables. The direct relationships also existed relating each of the four
constructs (SQ-CS, SQ-CV, SQ-BI, CS-BI, CV-BI, CV-CS) to one another. However,
only some of the abovementioned studies proposing the direct relationships between
service quality and customer value to behavioural intentions.
The five works used different approaches to measure the constructs being studied.
Parasuraman et al.’s (1988) SERVQUAL, was used as a foundation in most studies
measuring service quality. However, none of the five studies discussed employed the
expectations-perceptions gap but rather used the performance perception-only measure
of service quality. More specifically, different approaches were proposed among those
five studies regarding the multidimensionality of service quality. Despite the use of
service quality, other constructs (customer satisfaction, customer value and behavioural
intentions) were measured as unidimensional constructs employing multi-items or a
single-item.
Image
Customer
Expectation
Perceived
Value
Student’s global
satisfaction
Image
Student
loyalty
Technical Quality
Perceived Functional Quality
Perceived
79
Customer value, which is recognised as a new key management tool that predicts
behavioural intentions better than service quality and satisfaction, is argued to be a
multidimensional construct (see discussion Section 2.6.6.2). There is only a limited
amount of research collectively relating service quality, customer value, customer
satisfaction and behavioural intentions in which customer value is examined as a
multidimensional construct. In addition, research collectively relating those four key
constructs in the higher education sector is relatively scarce (Alves & Raposo 2007). It
is, therefore, important that the integrative model, for example, the ‘Research Model’ as
proposed by Cronin et al. (2000), be empirically examined in the higher education
sector. This integrative model will provide more comprehensive information as well as
show the relative degrees of influence exerted by service quality, customer satisfaction
and customer value on behavioural intentions in the higher education context.
3.3 HYPOTHESIS DEVELOPMENT
This section reviews the nature of the relationships across the four key constructs (SQ,
CS, CV and BI) in the general services and higher education sectors. This review is
essential in providing the basis for hypotheses development in this thesis. In addition,
since service quality and customer value are conceptualised as multidimensional
constructs, the dimensionality of both constructs in the higher education sector is
examined and proposed as part of the development of the hypotheses.
3.3.1 Part One: Dimensions of Service Quality in the Higher Education Sector
The application of service quality in the higher education sector has produced a mixed
result (see Section 2.4.6.2 and 2.4.6.3). This inconsistency might be caused by the
different definitions of ‘service quality’ being used (Choi et al. 2004) and, therefore,
have further effects on the development of the dimensions measuring the construct.
Giese and Cote (2000) claim that the lack of consensus over the definition will lead to
the inability to select an appropriate definition according to the given context, develop
valid measures and/or compare and interpret empirical results. Several works that have
been undertaken on developing the dimensions of service quality in higher education
have mostly used SERVQUAL (Parasuraman et al. 1985, 1988) as a foundation.
However, as has been discussed in Section 2.4.6.3, alternative measurements such as
SERVPERF and modifications of SERVQUAL are endorsed. This thesis uses the
80
multidimensional approach to measuring service quality since it provides a more
comprehensive means of explaining the complex nature of service quality than just
using the unidimensional approach employing single/multiple item(s) asking the overall
service quality perception.
More specifically, this thesis uses the performance perception-based only measure of
service quality (SERVPERF) because this scale was proven to be superior to three other
competing scales investigated including the SERVQUAL scale (Cronin & Taylor 1992)
(see section 2.4.5). Cuthbert (1996) also identified problems regarding the use of the
expectation-perceptions gap measure in the higher education sector. Furthermore,
Galloway and Wearn (1998) identified that no contribution of ‘expectation scale’ was
found to the predictive capabilities to the survey (see Section 2.4.6.3).
In examining service quality in the higher education sector, this thesis adopts the
conceptualisation “Customers’ assessment of the overall excellence or superiority of the
service” as proposed by Zeithaml (1988, p. 3). This thesis examines the dimensionality
of service quality in the higher education sector based on the framework that has been
proposed by Owlia and Aspinwall (1998) (see discussion in Section 2.4.6.3.1). Despite
being informative in explaining the aspects of service quality in the higher education
sector, Owlia and Aspinwalls’ (1998) model is argued to be sufficiently general to
measure service quality in higher education.
In their framework of service quality, Owlia and Aspinwall (1998) proposed six
dimensions of service quality for the higher education sector, including tangible,
attitude, competence, content, reliability and delivery. The importance of reliability has
been identified in most service quality studies including those conducted on the higher
education sector (Galloway & Wearn 1998; Smith et al. 2007). Tangible was considered
the most important factor of service quality in general services (Tsoukatos et al. 2006,
Perez et al. 2007) and in higher education (Athiyaman 2000; LeBlanc & Nguyen 1997;
Kwan & Ng 1999; Price et al. 2003; Lagrosen et al. 2004; Sahney et al., 2004b). The
importance of content in the higher education sector has been identified by Navarro et
al. (2005), Sahney et al. (2004b) and Kwan and Ng (1999). In an industry involving
“people-processing” such as education, knowledge and practical skills which reflect the
81
competence of the staff are important (Owlia & Aspinwall 1996, 1998; Lovelock 1981).
Attitude, which is reflected in social manners, courtesy, empathy, warmth and
sympathy, is considered as the main aspect of service quality (Parasuraman et al. 1988;
Haywood-Farmer 1988; Hill 1995; Sakhtivel et al. 2005). Delivery relates to how the
product or service is being delivered and presented, and it is also considered to be one
of the dimensions of service quality in the higher education sector (Sahney et al.
2004b). Based on the above arguments, this thesis hypothesises the following:
H1: Service quality is a multidimensional construct and it can be defined in terms of
tangible, competence, attitude, delivery, content, and reliability.
H1a: Tangible is associated with service quality.
H1b: Competence is associated with service quality.
H1c: Attitude is associated with service quality.
H1d: Delivery is associated with service quality.
H1e: Content is associated with service quality.
H1f: Reliability is associated with service quality.
3.3.2 Part Two: Relationships between Service Quality, Customer Satisfaction and
Behavioural Intentions.
Globalisation has changed the nature of competition in that firms must offer high-
quality services and at the same time meeting/exceeding customer needs. In order to
survive, the question is then ‘how can firms enhance their service quality that rises
customer satisfaction and thereby their economic gains?’ In the pursuit of answering
this question, researchers have given considerable time and effort in modeling service
quality and satisfaction due to their significant contributions to behavioral intentions
(e.g. re/purchase intent, retention, loyalty, word-of-mouth communication) and
improved organisational performance. Previous studies have shown that service quality
has a positive effect on satisfaction and subsequently on organisational profitability.
However, there seems to be no clear message in the previous literature on the causal
ordering of service quality and customer satisfaction, and on which of the two
constructs is a better predictor of behavioral intentions (Cronin & Taylor 1992;
Olorunniwo et al. 2006). Table 3.1 and 3.2 provide some findings on service quality and
customer satisfaction in explaining behavioural intentions as well as previous research
82
on causal ordering between service quality and customer satisfaction. Similar to the
general services, in the higher education sector, the nature of relationships between
service quality and customer satisfaction needed to be assessed in order to better
understand how these two key constructs contribute to behavioural intentions and hence
organisational performance.
Table 3.1 Findings on the Relationships between Service Quality, Satisfaction
and Behavioural Intentions Sources Findings
Parasuraman et al. (1988) Even though initially proposing that the higher levels of perceived service quality will have an increased the influence on consumer satisfaction, it was found in their final evidence that service quality is a consequence of satisfaction. This study therefore supports the assertion that satisfaction leads to service quality.
Bitner (1990) By using the path analysis, the findings showed that satisfaction leads to service quality and further service quality leads to service loyalty.
Bolton & Drew (1991) By assuming that service quality is equivalent to attitude, they suggest that satisfaction is an antecedent of service quality.
Woodside et al. (1989) The empirical findings suggest that customer satisfaction is a mediating variable in the relationship between the judgments of service quality and purchase intentions.
Cronin & Taylor (1992) Although initially hypothesising that satisfaction leads to service quality, the empirical result that was analysed using LISREL-based analyses indicates that satisfaction is a consequence of service quality in a multi-industry sample. Further findings also confirmed that satisfaction has a stronger and significant effect on purchasing intentions as compared to service quality.
Anderson et al. (1994) This study demonstrates the economic benefits of customer satisfaction. It also acknowledges the positive contribution of service quality to satisfaction and also to profitability.
Dabholkar (1995) This study suggests that the role of service quality as an antecedent of satisfaction is situation-specific. In the situation where the consumer is cognitive-oriented, the consumer will perceive service quality as giving satisfaction. On the other hand, if a consumer is affective-oriented, the consumer will perceive satisfaction as causing service quality.
Brady & Robertson (2001) This study was specifically conducted to test whether the causal direction of service quality-satisfaction-behavioural intention is robust across national borders. The results suggest that the causal direction where service quality leads to satisfaction is robust across diverse cultures (US & Ecuador were taken as samples). Satisfaction then leads to behavioural intentions. This causal direction supports the dominant theory in the literature and confirmed its robustness across nations (Cronin & Taylor 1992; Gotlieb et al. 1994).
Burton et al. (2003) The results suggest that actual performance influences customer satisfaction via perceived performance. Customer experience is shown to relate to satisfaction via an interaction effect, and also to have a significant impact on behavioral intentions.
Olorunniwo et al. (2006) Results indicate that there is a significant direct effect of service quality on behavioural intentions. However, when satisfaction is placed as a mediating variable, the indirect effect between service quality and behavioural intentions is stronger. In the service factory, they found that the dominant dimensions of service quality constructs are: tangibles; recovery; responsiveness; and knowledge.
Olsen (2002) Using a relative attitudinal framework and treating satisfaction as a having a mediating role, they indicate that the relationship between quality, satisfaction and loyalty is significant.
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Table 3.1 continued (Findings on the Relationships among Service Quality, Satisfaction
and Behavioural Intentions) Sources Findings
Tsoukatos et al. (2006) Not all of the dimensions of Parasuraman et al.’s (1988) SERVQUAL were significant in the Greek insurance sector. By using GIQUAL, the causal relations between service quality perceptions, satisfaction and loyalty were confirmed. They also identified that the causal ordering of service quality – customer satisfaction – loyalty is valid.
Cristobal et al. (2007) By designing service quality as a multidimensional construct consisting of: web design; customer service; assurance; and order management, perceived quality is found to influence satisfaction, and further satisfaction influences customer loyalty.
Chumpitaz & Paparoidamis (2004)
They developed accessibility, delivery and product reliability as antecedents of industrial satisfaction. Industrial satisfaction fully mediates the relationship between accessibility and loyalty, while it only partially mediates the relationship with technical assistance and delivery service. This research also provides evidence that there is a direct effect of industrial satisfaction on loyalty.
Source: Developed for the study
As indicated in Table 3.1, there were different opinions on the causal direction of the
relationships between service quality, customer satisfaction and behavioural intentions.
Most studies except Parasuraman et al. (1988), Bitner (1990) and Bolton and Drew
(1991), concluded that service quality determines customer satisfaction and, further, that
customer satisfaction has a significant effect on behavioural intentions. This sequence
follows Bagozzi’s (1992) approach of causal ordering “the appraisal→emotional
response→coping framework” and Oliver’s (1997) “cognitive→affective→conative”
causal sequence as a basis. However, even though the dominant theory supports the
Bagozzi (1992) and Oliver (1997) frameworks which feature the “cognitive leads to
affective” causal direction, it is worth discussing the previous debates that have
occurred on the causal relationships between quality and satisfaction. Discussions on
the causal ordering between service quality and satisfaction are presented in the
following Section 3.3.2.1.
3.3.2.1 The Antecedent Role of Service Quality and Customer Satisfaction
Apart from being recognised as a cornerstone of effective services management (Rust &
Oliver 1994), the conceptualisation and the causal order between service quality and
customer satisfaction remain debatable in marketing studies (Taylor & Baker 1994;
Brady & Robertson 2001). The matter of directions is especially significant for
managers since it provides information on which aspects should be given priority, the
quality of service or emotional satisfaction. Brady and Robertson (2001) have identified
three competing theories which attempt to explain the relationship between service
quality and satisfaction. The first is the theory which suggests the direct link between
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service quality and behavioural intentions, and places satisfaction as an antecedent of
service quality (Parasuraman et al. 1988; Bitner 1990; Bolton & Drew 1991). The
definition of service quality as ‘overall excellence’ (Parasuraman et al. 1985) was used
as the basis of this theory. By defining service quality as overall excellence, service
quality is treated as a cumulative terms (global construct) which should be linked
directly to behavioural intentions (Bitner 1990; Brady & Robertson 2001).
The second theory is that in which service quality is modeled as an antecedent of
satisfaction, and satisfaction leads to behavioural intentions. As has been discussed
above, this theory uses Bagozzi’s (1992) and Oliver’s (1997) causal ordering as a basis
which is: the appraisal→emotional-response→coping framework or cognitive→
affective→conative framework. Service quality is considered to be a largely cognitive
construct (Parasuraman et al. 1985, 1988) and conversely customer satisfaction is more
of an affective reaction to a service encounter (Oliver 1997). This infers that the more
cognitive evaluation (service quality) leads to emotive assessment (satisfaction) and in
turn, drives behavioural intentions. Empirical evidence supporting the debate on this
causal ordering has been found in many studies in the marketing literature, and Table
3.2 summarises some studies that have reported different findings on the causal ordering
between service quality and satisfaction.
Table 3.2 Causal Ordering between Service Quality and Customer Satisfaction Sources Measures Conclusion
Parasuraman et al. (1985) SERVQUAL SAT → QUA
Parasuraman et al. (1988) SERVQUAL SAT → QUA Bolton & Drew (1991) Multiple item scales SAT → QUA Bitner & Hubert (1994) Multiple item scales SAT → QUA Mohr & Bitner (1995) Multiple item scales SAT → QUA These studies suggest direct links between service quality and behavioural intentions, while satisfaction acts as an antecedent of service quality.
Cronin & Taylor (1992) SERVQUAL, SERVPERF QUA → SAT
Oliver (1993) Multiple item scales QUA → SAT Parasuraman et al. (1994) Not applicable QUA → SAT
Anderson et al. (1994) Not available QUA → SAT Rust & Oliver (1994) Not available QUA → SAT Strandvik & Liljander (1994) Multiple item scales QUA → SAT Brady & Robertson (2001) Multiple item scales QUA → SAT Oloruniwo et al. (2006) Modified SERVQUAL QUA → SAT Cristobal et al. (2007) SERVQUAL perception-only QUA → SAT These studies are based on Bagozzi’s (1992): evaluative→response→coping framework and Oliver’s (1997): cognitive→affective→conative causal sequence.
Taylor & Cronin (1994) SERVQUAL & Multiple item scales
A non-recursive relation
Dabholkar (1995) Not available QUA → SAT (depending on the context)
Using contingency approach, the type of customer may impact on the causal sequence of service quality and satisfaction.
Source: Adapted from deRuyter et al. (1997) and study development
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The third theory, despite the dominant consensus which identifies satisfaction as the
consequence of service quality (Cronin & Taylor 1992; Anderson et al. 1994; Brady &
Robertson 2001), suggests that the relationship is situation-specific (Dabholkar 1995).
This theory acknowledges that the antecedent role of service quality and satisfaction
will vary depending on the context in which the service encounter takes place and the
customers’ rational tendencies (Brady & Robertson 2001). Dabholkar (1995) states that
customer types may impact the causal sequence. For example, a more cognitively
oriented customer may evaluate quality first and then develop their satisfaction
judgment (service quality → Satisfaction). Conversely, customers who are more
emotionally driven will develop their affective assessment before encountering their
cognitive evaluation (satisfaction → service quality).
The discussions about the directional relationships presented above were not meant to
show anything that those proposing ‘the cognitive leads to affective’ framework were
wrong. More importantly, clarification on which framework to follow is important since
it helps the interpretation and implementation of the model chosen in practice. This
thesis follows the dominantly accepted framework (the cognitive leads to affective
framework) as proposed by Bagozzi (1992) and Oliver (1997) in explaining the causal
direction between service quality and satisfaction in the higher education sector. In
addition, people are more cognitively oriented when it comes to a decision of making
investment in the higher education. By following this framework, it is suggested that in
order to effectively create favourable behavioural intentions, managers or practitioners
should emphasise quality in order to improve the satisfaction judgment (Brady &
Robertson 2001). In other words, quality appraisal should be managed in order to be
able to manage affective (satisfaction) assessment. From the academic perspective, the
Bagozzi (1992) and Oliver (1997) frameworks indicate that, despite acknowledging the
significant contribution of service quality on behavioural intentions, the relationship is
indirect through satisfaction.
3.3.2.2 The Interrelationships in the Higher Education Setting
The marketing concept which emphasises the satisfaction of both customer and
organisational needs has been applied in higher education studies (Navarro et al. 2005;
Athiyaman 1997). An increasing number of higher education institutions have gradually
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adopted marketing approaches to attract and retain quality students. With knowledge
delivery as its core service, higher education has all of the features of a service. The
concepts of service quality and customer satisfaction are, therefore, directly applicable
as a strategy to enable the institutions to respond more effectively the needs of the
market.
In the higher education sector, the study which examines the relationships between
service quality and satisfaction is relatively new (Athiyaman 1997). In the higher
education sector, it is not easy to study the relationship between quality perception and
satisfaction since many groups are involved in providing this service and there are many
demands from different stakeholders (Bigne et al. 2001 in Navarro et al. 2005). Except
for Athiyaman’s (1997) work, studies that have been done in the education sector have
indicated that service quality influences customer satisfaction (Guolla 1999; Alves &
Raposo 2007; Navarro et al. 2005). Table 3.3 presents some findings from earlier
studies involving service quality, customer satisfaction and behavioural intentions in the
higher education sector.
In the higher education sector, interest has been focused more on relating the service
quality and satisfaction constructs with customer retention, loyalty and word-of-mouth
communication. This is because these four dimensional aspects of behavioural
intentions are the most relevant consequences of service quality and satisfaction in the
higher education sector (Alves & Raposo 2007). The benefit of the positive word-of-
mouth communication of satisfied customers is that satisfied customers may attract new
customers. In addition to showing loyalty, satisfied customers will spread a positive
impression by word-of-mouth to other people who have no relation to the service
provider (Ranaweera & Prabhu 2003). This in turn could influence their purchasing
intentions (Silverman 2001). Reicheld & Sasser (1990) maintain that positive word-of-
mouth communication reduces marketing expenditure and may increase revenues when
customers show an interest in the product/services provided. The benefits from loyal
students are also significant for the institutions’ short-term and long-term survival.
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Table 3.3 Research on Service Quality, Satisfaction and Behavioural Intentions in
Higher Education Source Findings
Athiyaman (1997) Satisfaction is an antecedent of service quality.
Browne et al. (1998) Perceived service quality leads to customer satisfaction.
Guolla (1999) Perceived service quality is an antecedent of customer satisfaction in class teaching.
Mavondo et al.(2000) The findings suggest that the drivers of student satisfaction have complex inter-relations among themselves. Academic reputation, quality of teaching staff and market orientation are antecedents to satisfaction and satisfaction is central to student recommendations. The study also finds that all variables investigated are positively related to recommending prospective students (except administration) and are important for attracting and retaining students.
Arambewela & Hall (2001)
The study concludes that although the students are relatively satisfied, the expectations are far above the perceptions across all factors and variables investigated. There were also significant variances in the expectations and perceptions among students from different countries, meaning that the impact of culture in decision-making requires further investigation.
Tsarenko & Mavondo (2001)
By analysing the organisation resources and capabilities, the study reveals that to satisfy students, local students require more resources than international students. Similarly, there were fewer resources found to be significant for the international students when the relationship between resources and recommendation was examined. Student satisfaction also mediates the relationship between all resources and recommendation (for local students). However, half of the hypotheses were not supported for international students.
Banwet and Datta (2003)
The result indicates that perceptions of quality and the satisfaction received from the previous lectures influence students’ intentions to re-attend the class and/or recommend the lectures.
Petruzzellis et al. (2006) This study does not specifically address the relationship. However, it suggests that quality improvement in the area of teaching and non-teaching is important since it will foster better relationships with surrounding economic and productive systems.
Alves & Raposo (2007) By using a structural equation, this study finds that image is the strongest influence on satisfaction, followed by value and perceived quality. Satisfaction was also shown to have consequences for word-of-mouth communication.
Sakhtivel & Raju (2006) There is a strong correlation between education service quality and customer value, and between customer value and customer satisfaction.
Source: Developed for the study
3.3.2.3 Hypothesis Development: Service Quality, Customer Satisfaction and
Behavioural Intentions
Despite the diversity of opinions in the three different perspectives viewing the causal
order of service quality and customer satisfaction (see Section 3.3.2.1), the service
quality→satisfaction causal order has overall, received the strongest support in the
literature as well as the most frequent empirical validation (Gotlieb et al. 1994; Cronin
& Taylor 1992). Accepting the proposition that service quality and satisfaction are
distinct constructs (Boulding et al. 1993; Taylor & Baker 1994; Cronin & Taylor 1992)
(see Section 2.5.5) and the service quality→satisfaction causal ordering, the
relationships between service quality, customer satisfaction and behavioural intentions
in this thesis follow the cognitive→affective causal ordering and are built based on
Cronin et al.’s (2000) ‘Research Model’. On the basis of the evidence that there are
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direct and indirect relationships between service quality, satisfaction and behavioural
intentions, the following hypotheses are proposed:
The direct link:
H2: Service quality is positively associated with customer satisfaction.
H3: Service quality is positively associated with customer behavioural intentions.
H4: Customer satisfaction is positively associated with customer behavioural intentions.
The indirect link:
H5: Customer satisfaction mediates the relationship between service quality and
behavioural intentions.
3.3.3 Part Three: Dimensions of Customer Value in the Higher Education Sector
Customer value is a relatively new concept in the marketing literature (Sweeney 2003)
compared to service quality and satisfaction. It has been increasingly researched due to
the limitations of service quality and satisfaction in explaining behavioural intentions
and further organisational outcomes. There are only limited numbers of customer value
studies that have been done in the higher education sector (Alves & Raposo 2007).
Previous studies have employed both the unidimensional and the multidimensional
approaches to studying customer value. In the higher education sector, the use of the
multidimensional approach in measuring customer value is still limited (e.g LeBlanc &
Nguyen 1999).
Customer value is also acknowledged as a context-specific construct, meaning that
different persons have different perceptions of value (see Section 2.6.3). Considering
the significant contributions of customer value to explain behavioural intentions, and
since there has been limited literature produced regarding the perception of customer
value in the higher education sector in Indonesia, it is necessary to examine how
Indonesian higher education students perceived the value of their higher education
experiences. By using the conceptualisation of customer value based on Zeithaml (1988,
p. 14), “The consumer’s overall assessment of the utility of a product based on
perceptions of what is received and what is given”, this thesis uses the multidimensional
approach to examine customer value in the Indonesian higher education sector. The
examination of the dimensionality of customer value is important since it provides a
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broad picture of what Indonesian students perceive as valuable regarding their overall
education experiences and it also explains the motivation behind studying in the
particular institutions or programs chosen.
As previously discussed (Section 2.6.6.2.1), this thesis adopts the dimensionality of
customer value as proposed by Sweeney and Soutar (2001) and Petrick (2002). The five
dimensions of customer value include quality, reputation, price, social and emotion.
Reputation has been identified as a dimension of customer value in Petrick (2002) and
LeBlanc and Nguyen (1999). Functional value in terms of quality was employed by
several studies in customer value (e.g. Sweeney & Soutar 2001; Petrick 2002; LeBlanc
& Nguyen 1999; Wang et al. 2004; Roig et al. 2006). Price, which is commonly
categorised as, sacrifice aspect of customer value, was employed in the majority of
customer value studies (e.g. LeBlanc & Nguyen 1999; Lapierre 2000; Sweeney &
Soutar 2001; Petrick 2002; Wang et al. 2004; Roig et al. 2006; Whittaker et al. 2007).
Social and emotional value, which represent the affective aspect of customer value,
were employed in a number of customer value studies (e.g. DeRuyter et al. 1997;
LeBlanc & Nguyen 1999; Sweeney & Soutar 2001; Petrick 2002; Wang et al. 2004;
Pura 2005; Roig et al. 2006; Whittaker et al. 2007). Based on the identification of
significant dimensions that build customer value in the retail and service sectors, the
following hypotheses are proposed:
H6: Customer value is a multidimensional construct and it can be defined in terms of
quality, social, price, emotion and reputation.
H6a: Quality is associated with customer value.
H6b: Social is associated with customer value.
H6c: Price is associated with customer value.
H6d: Emotion is associated with customer value.
H6e: Reputation is associated with customer value.
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3.3.4 Part Four: Relationships among Service Quality, Customer Satisfaction,
Customer Value and Behavioural Intentions.
3.3.4.1 The Direct Link
Customer value has been attributed in various fields of study, such as marketing,
strategic management, social science and information system (Patterson & Spreng
1997). In services marketing, many studies focused on examining the nature of the
interrelationships and the way customer value may increase the predictive power of the
model that examines the influence of service quality and satisfaction on behavioural
intentions. Considering merely service quality and satisfaction alone is not sufficient to
explain customer’s behaviours (see discussion in Section 2.6.2). Good quality and
satisfied customers do not always provide a guarantee of positive behavioural
intentions. As part of the attempt to improve the understanding of the relative
contributions of service quality and satisfaction to the formation of behavioural
intentions, customer value has been added to explain customer behaviours. Selected
empirical studies of customer value in relation to service quality, satisfaction and
behavioural intentions are summarised in Table 3.4 to provide an overview of the
findings from the previous studies.
Table 3.4 Selected Empirical Studies on SQ-CS-CV-BI
Study Constructs & Setting
Findings
Bolton & Drew (1991)
SQ, CS, CV, BI (Residential phone services)
They identified service value as a function of service quality, sacrifice, customer characteristics, expectations, disconfirmation and performance. Their results show that service value is predominately influenced by service quality, customers’ disconfirmation experiences and customer characteristics (e.g., age and income).
Chang & Wildt (1994)
SAC, SQ, CV, BI (Apartments & PCs)
They found that value is positively influenced by service quality and negatively impacted on perceived price. Most importantly, they find that perceived value is the most important factor leading to purchase intent.
Ostrom & Iacobucci (1995)
SAC, SQ, CS, CV, BI (Hotel)
Their findings revealed that judgments of satisfaction and value vary. While customisation is important to the making of satisfaction judgments, friendliness is more critical to value judgments.
Sweeney et al. (1997)
SQ, CV, BI (Electrical appliances retail setting)
They found that both technical service quality and product quality positively affect perceived value, while relative price negatively affects perceived value. In addition, perceived value and relative price are found to influence consumers’ willingness to buy.
Andreassen & Lindestad (1998)
SQ, CS, CV, BI (Tour industry)
They found mixed results on the interrelationship between perceived quality, value, corporate image, customer satisfaction and customer loyalty. In cases where the industry is complex and customers infrequently purchased services, corporate image has a stronger influence on customer loyalty than satisfaction.
Wang et al. (2004)
CV, CS, Brand Loyalty (Chinese securities)
All dimensions of customer value (functional, social, emotional and sacrifice) were found to have a significant effect on satisfaction. However, all dimensions of customer value have indirect effect on brand loyalty through satisfaction.
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Table 3.4 continued (Selected Empirical Studies on SQ-CS-CV-BI)
Study Constructs & Setting
Findings
Cronin et al. (2000)
SQ, CS, CV, BI (6 industries)
In studying the effect of quality, satisfaction, value and behavioural intentions, when collectively investigated, they found that quality, satisfaction and value may relate directly to behavioural intentions. Further, the results also suggest that value will enhance the indirect relationship between service quality and behavioural intentions. (6 industries: spectator sports, participation sports, entertainment, health care, long distance carriers, fast food).
McDougall & Levesque (2000)
SQ, CS, CV, BI-loyalty& switching intentions (4 Services )
They investigated the relationship between service quality, relational quality, customer satisfaction and perceived value. It was found from this study that the most important drivers of satisfaction were service quality and value. Relational quality was shown to have less influence on satisfaction. There was also evidence of a direct link between customer satisfaction and future intentions. Even though there were different results across the four services regarding the service quality and perceived value relationship, they suggest the important role of perceived value and service quality on customer satisfaction. (4 services: dentist, auto service, restaurant, and haircut).
Choi et al. (2004)
SQ, CS, CV, BI (Health care market)
This study revealed that service quality and perceived value affect customer satisfaction, then further affect behavioural intentions. Service quality appeared to have a stronger role than value in influencing satisfaction. In addition to the evidence of a direct effect between service quality and customer value, both constructs have a direct impact on behavioural intention.
Tam (2004) SQ, CS, CV, BI (Restaurant industry)
An integrative model involving service quality, perceived value, customer satisfaction and post-purchase behaviour was developed. Perceived service quality was found to have a positive effect on satisfaction and perceived value. Customer satisfaction and perceived value further influence post-purchase behaviour.
Oh (1999) SQ, CS, CV, BI, Price, WOM, Perceptions (The hotel industry)
The results revealed that the customer value construct should be considered in service quality and customer satisfaction studies. Customer value may mediate the link between service quality and satisfaction. Price has a negative impact on perceived value and has no relationship with service quality.
Gill et al. (2007) CV, CS, BI (Winery visitors)
The study revealed that across five dimensions proposed, only four were identified as dimensions of perceived value and have a positive impact on behavioural intentions. Overall satisfaction was found to partially mediate the relationship between value and behavioural intentions.
SQ: service quality; CS: customer satisfaction; CV: customer value; BI: behavioural intentions; SAC: sacrifice; WOM: word-of-mouth.
Source: Developed for the study
As shown in Table 3.4, studies have shown that service quality is an important predictor
of satisfaction and customer value. Furthermore, customer value is also shown to be an
important antecedent of satisfaction and behavioural intentions. The direct relationship
between customer value and behavioural intentions has been confirmed in a number of
different service contexts (Chang & Wildt 1994; Oh 1999; Cronin et al. 2000; Choi et
al. 2004; Gill et al. 2007). In addition, the direct relationship between customer value
and satisfaction has also been found in numerous empirical studies (e.g. Patterson &
Spreng 1997; Andreassen & Lindestad 1998; McDougall & Levesque 2000; Cronin et
al. 2000). Based on previous evidence of the direct relationships between service quality
on customer value, customer value on customer satisfaction and customer value on
behavioural intentions, this thesis proposes that:
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H7: Service quality is positively associated with customer value.
H8: Customer value is positively associated with customer satisfaction.
H9: Customer value is positively associated with behavioural intentions.
3.3.4.2 The Indirect Link
Besides the existence of the direct relationships, some studies have posited the existence
of an indirect relationship between customer value and behavioural intentions with
satisfaction mediating the relationship (Patterson & Spreng 1997; Cronin et al. 2000;
Wang et al. 2004; Choi et al. 2004; Gill et al. 2007). Eggert and Ulaga (2002) explored
the relationship between customer satisfaction and customer value using two alternative
models. The first model suggests that customer value has a direct impact on repurchase
intentions and word-of-mouth communication. The second model suggests that
customer value is mediated by customer satisfaction in relation to its impact on
repurchase intentions and word-of-mouth communication. Results suggest that both
direct and indirect relationships were significant.
The relationship between customer value, satisfaction and behavioural intentions is
verified empirically. However, there remain different opinions regarding the nature of
the mediation process. A full mediation effect of satisfaction on the customer value and
behavioural intentions link was evidenced by Patterson and Spreng’s (1997) and Eggert
and Ulaga’s (2002) studies. In contrast, other studies have concluded that satisfaction
only partially mediates the relationship between customer value and behavioural
intentions (e.g. Cronin et al. 2000; Petrick 2004; Gill et al. 2007). The different findings
might be a result of the different consumption settings and different dimensional or
measurement systems being employed (unidimensional or multidimensional measure of
customer value) (Gill et al. 2007). When examining the relationships among customer
value, satisfaction and behavioural intentions, a majority of the studies used
unidimensional measurement. Petrick’s (2004) study, however, provided evidence on
the partially mediating effect of satisfaction on the customer value and behavioural
intentions relationship using a multidimensional measure of customer value.
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This thesis employs the multidimensional approach to measuring customer value. Since
the number of studies employing a multidimensional approach to customer value is
small particularly in the higher education sector, testing the mediating effect of
satisfaction on the customer value and behavioural intention relationship will enrich the
evidence of the indirect relationship in the situation when customer value is treated as a
multidimensional construct. Therefore, this thesis hypothesises that:
H10: Customer satisfaction mediates the relationship between customer value and
behavioural intentions.
Apart from the relationship with customer satisfaction and behavioural intentions,
customer value has been found to mediate the relationship between service quality and
behavioural intentions. Roundtree (1996) addresses the extent to which customers’
perceptions of value mediate the relationship between customers’ service quality and
their behavioural intentions (e.g. willingness to buy and search intentions). Cronin et al.
(1997) also argue that the relationship between service quality and purchase intentions
is mediated by service value. Cronin et al. (2000) further identified the existence of
direct and indirect relationships among service quality, customer value and customer
satisfaction to behavioural intentions. Their results support the view that the addition of
service value to the model enhances the ability to explain the variances in behavioural
intentions. Oh (1999) and Choi et al. (2004) also suggest that service quality together
with value will influence satisfaction and subsequently influence customer behaviours.
However, Caruana et al. (2000) provide a slightly different result compared to other
studies. In contradiction to the majority of findings, which have agreed on the positive
customer value link to satisfaction, Caruana et al.’s (2000) finding showed that
customer value has a negative moderating effect between service quality and customer
satisfaction. Overall, the majority of empirical evidences and theoretical arguments
support the argument that cognition leads to affect and, furthermore, it drives
behavioural outcomes (Oliver 1997; Bagozzi 1992). This suggests that service quality
and value influence satisfaction and that satisfaction influences customer’s behavioural
intentions. Based on previous empirical evidences and theoretical arguments, this thesis
proposes the following hypotheses:
94
H11: Customer value mediates the relationship between service quality and customer
satisfaction.
H12: Customer value mediates the relationship between service quality and
behavioural intentions.
Finally, apart from the potential significance of the interrelationships among service
quality, customer value, customer satisfaction and behavioural intentions, the
frameworks explaining the relationships are different. Some studies emphasis the direct
relationships (SQ-BI, SQ-CS, SQ-CV, CV-BI, CV-CS and CS-BI), while other studies
emphasis the indirect relationships, having customer value and customer satisfaction as
mediating or moderating variables (SQ-CS-BI, SQ-CV-BI, CV-CS-BI and SQ-CV-CS).
Cronin et al. (2000), Rust and Oliver (1994) and Ostrom and Iacobucci (1995) suggest
that simultaneously investigating the relationships among all four constructs might
provide a more accurate and comprehensive picture of the nature of the relationships. In
addition, the integrative model will enable the researchers to analyse the relative
impacts of service quality, customer satisfaction and customer value on behavioural
intentions. By suggesting the integrative model incorporating the four constructs (SQ,
CS, CV and BI), it is not implied that the direct relationships and the indirect
relationships which only included three constructs (SQ-CS-BI, SQ-CV-BI, CV-CS-BI
and SQ-CV-CS) is incorrect, but rather that they are limited in scope.
This thesis is designed to address the gap (models with limited scope) as suggested by
Cronin et al. (2000), to simultaneously examine service quality, customer value,
customer satisfaction and behavioural intentions in the conceptual model. In order to
provide clearer evidence of the robustness of the conceptual/integrative model, the four
partial models (SQ-CS-BI, SQ-CV-BI, CV-CS-BI and SQ-CV-CS) are also analysed as
a comparison. By examining the four competing partial models, it is hoped that this
thesis will provide more insights (by providing empirical evidence) into whether or not
the integrative model (involving 4 key constructs: SQ, CS, CV and BI) is more
meaningful than the other four competing models (involving 3 key constructs: SQ-CS-
BI, SQ-CV-BI, CV-CS-BI and SQ-CV-CS).
95
The four competing partial models are:
• The first model examines the relationship between service quality, customer
value and behavioural intentions (SQ-CV-BI).
• The second model examines the relationship between service quality, customer
value and customer satisfaction (SQ-CV-CS).
• The third model examines the relationship between service quality, customer
satisfaction and behavioural intentions (SQ-CS-BI).
• The fourth model examines the relationship between customer value, customer
satisfaction and behavioural intentions (CV-CS-BI).
3.4 THE CONCEPTUAL MODEL
This section presents the conceptual model of this thesis and a summary of all the
hypotheses proposed. The conceptual model proposed, as shown in Figure 3.6, adopts
the ‘Research Model’ as proposed by Cronin et al. (2000). Rust and Oliver (1994),
Ostrom and Iacobucci (1995) and Cronin et al. (2000) maintain the importance of
simultaneously measuring service quality, customer value and customer satisfaction to
predict behavioural intentions. The difference from the Cronin et al.’s ‘Research Model’
is that this conceptual model incorporates a second-order multidimensional approach to
both service quality and customer value constructs. In addition, sacrifice (in terms of
price) is configured as part of the customer value dimension as suggested by Sweeney
and Soutar (2001) and Petrick (2002) (not as an antecedent of customer value).
96
Figure 3.6 Conceptual Model
Tangibles
Competence
Delivery
Quality
Content
Reliability
Attitude
Emotion
Reputation
Social
Price
Service
Quality
Customer
Value
Customer
Satisfaction
Behavioural Intentions
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Table 3.5 Summary of Research Questions and Hypotheses
Research Questions Research Hypotheses What constitutes valid and reliable scales for measuring the service quality construct and customer value in the Indonesian higher education sector?
More specifically, relating to service quality, the research question is: Do the six dimensions of service quality (tangibles, competence, attitude, delivery, content and reliability) apply in the higher education sector in Indonesia?
H1: Service quality is a multidimensional construct and it can be defined in terms of tangible, competence, attitude, delivery, content, and reliability. H1a: Tangibles is associated with service quality. H1b: Competence is associated with service quality. H1c: Attitude is associated with service quality. H1d: Delivery is associated with service quality. H1e: Content is associated with service quality. H1f: Reliability is associated with service quality.
What constitutes valid and reliable scales for measuring the service quality construct and customer value in the Indonesian higher education sector?
More specifically, relating to customer value, the research question is: Do the five dimensions of customer value (quality, social, price, emotion and reputation) apply in the higher education sector in Indonesia?
H6: Customer value is a multi-dimensional construct and it can be defined in terms of quality, social, price, emotion and reputation. H6a: Quality is associated with customer value. H6b: Social is associated with customer value. H6c: Price is associated with customer value. H6d: Emotion is associated with customer value. H6e: Reputation is associated with customer value.
How do service quality, customer satisfaction and customer value relate to behavioural intentions in the higher education sector in Indonesia?
H2: Service quality is positively associated with customer satisfaction.
H3: Service quality is positively associated with behavioural intentions.
H4: Customer satisfaction is positively associated with behavioural intentions.
H5: Customer satisfaction mediates the relationship between service quality and behavioural intentions.
H7: Service quality is positively associated with customer value.
H8: Customer value is positively associated with customer satisfaction.
H9: Customer value is positively associated with behavioural intentions.
H10: Customer satisfaction mediates the relationship between customer value and behavioural intentions.
H11: Customer value mediates the relationship between service quality and customer satisfaction.
H12: Customer value mediates the relationship between service quality and behavioural intentions.
What are the effects of the inclusion of customer value variable in the relationships between service quality, customer satisfaction and behavioural intentions?
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3.5 RESEARCH CONTEXT
The service industry tends to drive the global economy more than the manufacturing
industry. Many countries have been seriously investing in and developing their higher
education sectors as important sources of income. Increasing numbers of higher
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education institutions compete internationally and nationally (Velotsou et al. 2004).
Since the global competition in the higher education sector has increased the number of
competitors, attracting new students has become even more difficult (Nicholls et al.
1995). Surrounded by countries which have achieved extensive penetration of their
education market (e.g., Singapore, Australia, Malaysia), Indonesian higher education
institutions should be aware of their performance and the possibility of losing their local
market.
The competition in the higher education industry is not only at the international level,
but the Indonesian local higher education industry competition is also intense. In
addition to the classical marketing problems such as increasing operational costs,
decreasing sales and the economic downturn, students are now becoming more selective
and rational in their choice of programs and students have many more options open to
them than at any previous time. In 1999, the Indonesian government introduced the
transformation of four most well known public universities into autonomous
universities or as they are called “state legal entity universities”. The introduction of
autonomous universities in fact increases competition especially among the private
universities. This is because the public universities that changed into autonomous
universities should be more independent in financing themselves. Therefore, they are
operating in a manner that is somewhat similar to the ways of private universities.
Education is considered as both a consumption and an investment good (Webb et al.
1997). By making the education investment, students and parents should expect that it
will provide future benefits through the acquisition of knowledge, skills and a degree, as
well as a good career. However, since investment in higher education is considered
expensive, and there are also some risks associated with education processes (wrong
choice, time consumption, opportunity costs), a common concern among students and
parents usually relates to the best institution to be selected when considering the
benefits and costs (Kotler & Fox 1995). Students and their families are becoming more
rational and, thus, the benefits and costs are of most common concern when choosing
higher education institutions.
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Even though learning remains the mission of every education institution, to survive, it is
certainly not enough to maintain the traditional management system by depending on
government funding and the students’ tuition fee. Marketing approaches are required to
survive in the education market. Since the role of service quality and satisfaction in the
highly competitive market has been questioned, a more comprehensive model relating
to service quality, customer satisfaction, customer value and behavioural intentions
should provide better solutions to an increase in institution’s competitive advantage.
Higher education, as a service sector, should also benefit from understanding the
marketing approach. From this argument it can be concluded that, to win the student
market, having good quality and satisfied students is not enough. Students (and their
families) will consider the benefits and costs of having experiences in higher education.
Indonesian students and their families (who support the tuition fees) are assumed to
have the same regard. Therefore, the above arguments confirm the importance of
examining service quality, customer satisfaction, customer value and behavioural
intention simultaneously in the higher education sector.
This research is conducted in Yogyakarta, a city known as the student city in Indonesia.
The reason for conducting the study in Yogyakarta is that it is an important destination
for higher education and it hosts thousands of students from all areas of Indonesia.
Having these characteristics, students studying in Yogyakarta can be considered as
representative of higher education students from all over Indonesia.
3.6 CONCLUSION
By considering the suggestion made by Cronin et al. (2000), Rust and Oliver (1994) and
Ostrom and Iacobucci (1994) that the implementation of model relating: service
quality/SQ, customer satisfaction/CS and customer value/CV simultaneously will
provide a more accurate and comprehensive picture of the relationships in predicting
behavioural intentions/BI, this research is designed to answer the gap, by examining the
relationships among the key constructs (SQ, CS, CV and BI) in one integrative model.
This chapter started with an overview of the five earlier studies that have examined the
integrative model in the different service sectors. This overview provides the basis for
developing the conceptual model proposed in this thesis that simultaneously relates SQ,
CS, CV and BI.
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More specifically, as part of the hypothesis development, four sections (parts) relating
to the four key constructs were discussed. The first part covered discussion of the
dimensionality of service quality in the higher education sector. The second part
discussed the interrelationships among service quality, customer satisfaction and
behavioural intentions in both the general services and the higher education sectors. In
this section, the causal direction between service quality and satisfaction was
specifically reviewed bearing in mind the absence of agreement regarding the direction
of the relationship between these two constructs. The third section reviewed the
dimensionality of customer value. The last section examined the inclusion of customer
value in the service quality, customer satisfaction and behavioural intentions
relationships. The direct and indirect relationships among the four key constructs under
investigation were reviewed to provide the basis of hypothesis development and the
proposed conceptual model. Four competing models (SQ-CS-BI, SQ-CV-BI, CV-CS-BI
and SQ-CV-CS) are also investigated as a comparison with the integrative model. In
addition, a brief review of the research context provided confirmation of the importance
of examining the four main key constructs and their relationships in the Indonesian
higher education sector.
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CHAPTER FOUR
METHODOLOGY
4.1 INTRODUCTION
The previous chapters identified the research problems and undertook an extensive
review of the relevant theoretical frameworks for this thesis. The methodology
employed to test the hypotheses proposed in Chapter Three is presented in this chapter.
This thesis adopts the sequence of the research process as suggested in the model of a
systematic approach by Kumar et al. (1999) (see Figure 4.1). The research process for
this thesis systematically follows the three recommended phases. Phase One of this
research process, preliminary planning, is covered in Chapters One to Three. This
chapter discusses Phase Two of Kumar et al.’s systematic research model by providing
a detailed discussion of the research approach and research tactics. Phase Three
discusses the implementation of research which covers analysis of the collected data,
conclusions and recommendations. In addition, to providing a foundation for the
research methodology, a philosophical discussion of the methodology is also undertaken
prior to discussing research design in Phase Two.
4.2 RESEARCH PARADIGM
A paradigm as currently defined is an idea which has been made famous by Kuhn
(1970). Babbie (2004, p. 33) describes a paradigm as “a model or framework for
observation and understanding, which shapes both what we see and how we understand
it”. In the scientific disciplines, a paradigm reflects the whole system of thinking
(Neuman 2006). In the context of conducting research, a paradigm provides a
framework which consists of a set of theories, methods and ways in which researchers
can define their data (Collis & Hussey 2003). Within the marketing literature, there has
been a variety of accepted paradigms. Perry et al. (1999) acknowledge four paradigms
in social sciences (positivism, critical theory, constructivism and realism). Neuman
(2006) identifies three paradigms (positivist social science, interpretive social science
and critical social science), while Barker et al. (2001) and Collis and Hussey (2003)
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identify two paradigms (positivist and phenomenological/interpretivist). Each paradigm
has different traditions and requires diverse research techniques (Voola 2005). Table 4.1
summarises accepted paradigms that have been adopted in the social sciences.
Table 4.1 Research Paradigm Research Paradigm Approach
Positivism An approach which emphasises seeking causal laws, careful empirical observations and value-free research.
Critical theory An approach which emphasises meaningful social action, socially constructed meaning and value relativism. It incorporates historically situated structures and ethnographic.
Interpretivism (phenomenological)
An approach which allows for a more intensive and flexible relationship with the respondents. It provides a more in-depth understanding of the phenomenon of interest and is often described as as qualitative research.
Constructivism An approach which suggests that truth is based on a particular belief system in a specific context. Realities are varied and are socially based. It attempts to understand the values that underlie a research finding.
Source: Neuman (2006), Perry et al. (1999) and Voola (2005)
Perry et al. (1999) argue that critical theory and constructivism are not suitable as a
foundation on which to conduct research in the marketing area. The Critical Theory is
inappropriate in business/marketing studies since it is not common conduct to ask
respondents to release information regarding “historical, mental, emotional and social
structures” (Guba & Lincoln 1994 p. 112). Constructivism is also not suitable because it
does not consider real economic and technological dimensions of business (Hunt 1991
in Voola 2005).
In marketing research, two main research paradigms have been identified and thought to
have a significant impact (Collis & Hussey 2003). Those two paradigms are the
positivist paradigm and the interpretivist paradigm. So far, both positivist and
intepretivist paradigms have been described under a variety of different names: the
positivist is commonly named as quantitative and objectivist while the interpretivist is
defined as qualitative and subjectivist.
Interpretivism, which is commonly known as qualitative research, is a type of research
that involves a more intensive approach than the standardised method usually applied in
positivism. In so doing, interpretivism requires a more in-depth technique to understand
the phenomenon of interest. The inductive approach is usually preferable if the
objective is to understand the phenomenon (Blaikie 1993). The methodologies used
include observations, field study, in-depth interviews, focus groups and case studies.
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Positivism is known to be the oldest and the most widely used approach (Neuman
2006). Most people assume that the positivist approach is the method used in science
and is used widely as the approach to natural science (Neuman 2006). However, it is not
specific to science, and many social theories are also associated with positivism.
Positivist researchers commonly prefer to gather exact quantitative data. In doing so,
experiments, survey and statistics are conducted. As positivists, researchers commonly
emphasis exact measurement and ‘objective’ research as well as testing hypotheses by
carefully analysing the behaviour of the raw data from the measurement (Neuman
2006). To answer the research questions, this thesis adopts an objective approach by
conducting a survey to collect quantitative data. By analysing the quantitative data, it is
expected that more exact and objective answers could be obtained. As such, this thesis
can be considered to be adopting a positivist approach.
A quantitative method is designed to identify and confirm research hypotheses which
are formed on the basis of existing theory (Cavana et al 2001). A certain size of survey
is required for a statistical analysis to be able to be applied to analyse any hypotheses
proposed (Malhotra et al. 2004). According to Burns and Bush (2000), when involving
a large number of data respondents, a structured questionnaire is normally designed for
predetermined responses in quantitative research. In doing so, quantitative research
usually involves a large number of surveys using questionnaires, statistical analysis or
experimental testing of hypotheses using categorical or numerical data (Malhotra et al.
2004; Neuman 2006; Punch 2005).
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Figure 4.1: The Research Process
Source: Adapted from Kumar et al. (1999, p. 72)
4.3 RESEARCH DESIGN
A research design is the framework of a study, and is used as a guide for data collection
and analysis (Churchill & Iacobucci 2005). It is also considered to be a blueprint for a
study that guides the examination of the research objectives (Churchill & Iacobucci
2005; Malhotra et al. 2004). Chisnal (1997) argues that one fundamental part of any
Research Purpose
• Problem or Opportunity
• Research Users Phase 1 Preliminary
Planning
Phase 2 Research
Design
Phase 3
Implementation
Research Tactics
• Constructs & operationalisation
• Pre-testing
• Scaling & response format
• Questionnaire design
• Sampling plan
• Anticipated analysis
Research Approach
• Exploratory/Descriptive/Causal
• Choice of data collection research method
Data Collection and Analysis
• Data Collection
• Fieldwork
• Data Processing
• Data Analysis
• Statistical Analysis
• Interpretation
Conclusion and Recommendations
Research Purpose
• Problem or Opportunity
Research Objective
• Research Question
• Development of Hypotheses
Research Process
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research activity is developing an effective research design. A clear understanding of
the research design will make the study relevant to the research problem and help the
research procedures to be simple and economical (Churchill 1991). The next section
discusses the research approach and research tactics.
4.3.1 Research Approach
Choosing the research approach is important since it effects how the data will be
obtained (Kumar et al. 1999). A research approach can be described as exploratory,
conclusive and descriptive (Boyce 2003). Exploratory research is concerned more with
exploring the real nature of research problems and sometimes involving hypotheses to
be tested later (Chisnall 1997). The exploratory research method is usually adopted for
the following purposes: to formulate or define the problems, identify alternatives,
develop hypotheses, gain insight for developing an approach to the problem and
establish priorities for future research (Malhotra et al. 2004, p. 64). Exploratory research
is necessary when researchers do not have sufficient information on which to base the
plan for a research project (Boyce 2003). In order to gather information regarding the
general nature of the research problem, the exploratory research usually involves
informal and unstructured research methods.
Conclusive research is research that seeks to obtain reliable information that is used as a
basis for decision-making (Boyce 2003). Conclusive research consists of: (1)
descriptive research which provides an in depth description of the phenomena of an
existing situation. This is done by offering a profile of the factors (Cavana et al. 2001);
and (2) causal research which aims to explore the reason a phenomenon occurs and,
thus, it goes further than description (Neuman 2006; Punch 2005). Causal research is
applicable to understanding the phenomenon in terms of the validity of causality such as
‘X causes Y’ (Churchill & Iacobucci 2005). In other words, as Kumar et al. (1999, p.
75) suggest, causal research should be used when “it is necessary to show that one
variable causes or determines the values of the other variables”.
The difference between exploratory and descriptive research is that the descriptive
research commonly requires prior formulation of specific hypotheses (Malhotra et al.
2004). This means that descriptive research must already have “a clear statement of the
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problem, specific hypotheses and detailed information” (Malhotra et al. 2004, p. 66).
The major difference between descriptive research and causal research is that the
objective of descriptive research is the description of phenomena such as aspects of
market environment, while the objective of causal research is more to obtain some
evidences concerning the cause and effect relationships (Malhotra et al. 2004).
When compared with exploratory research, conclusive research is said to be more
quantitative in nature. Even though the aim of exploratory research is to increase
understanding of people’s behaviour, attitudes and opinions, it does not quantify the
information collected (Boyce 2003). In brief, the objective of exploratory research is to
provide an understanding of the nature of the problem, while the conclusive research is
intended to test the hypotheses and the relationships proposed (Malhotra et al. 2004).
All three approaches (exploratory, descriptive and causal) should not be seen as
separated, but they often complement each other (Malhotra et al. 2004). Table 4.2
summarises the differences between exploratory and conclusive research.
Table 4.2 The Differences between Exploratory and Conclusive Research Exploratory research Conclusive research
Seeks information that increases understanding people and situations, but not in a reliable numerical form. Produces qualitative data.
Seeks data expressed in numbers. The data can therefore be analysed using quantitative processes.
Findings are often from focus groups or in-depth interviews.
Findings are typically from a survey or census.
Research methods are informal and flexible. Research methods are tightly planned, structured and formal.
Findings are unlikely to be sufficient for decision-making. Often needs to be followed by a conclusive research project.
Findings are intended to be suitable for decision-making.
Source: Boyce (2003)
The selection of a research approach depends on the research question (Hair et al. 2003;
Voola 2005). An exploratory research approach should be chosen when little is known
and an in-depth clarification of business phenomena is necessary. Exploratory research
provides the initial step for the overall research design. Descriptive research should be
undertaken when the research question requires description of some phenomena.
Finally, causal research is appropriate when the research question involves causality
between constructs to be researched. As has been discussed in Chapter Three regarding
the proposed hypotheses, this thesis attempts to explain the relationships among the
constructs (service quality, customer value, customer satisfaction and behavioural
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intentions). Therefore, a causal research approach is a more appropriate design for this
thesis.
4.3.2 Methods of Collecting Data
Data collection can be broadly characterised as being primary or secondary in nature
(Kumar et al. 1999). Primary data are those collected from the actual site of the
occurrence of events, while secondary data are data that already exist and no collection
is necessary (Sekaran 2003). In the context of this thesis, the conceptual model relating
to the interrelationships among service quality, customer value, customer satisfaction
and behavioural intentions necessitates obtaining primary data, as it requires specific
direct information for the verification the hypotheses proposed.
Primary data can be collected in several ways such as through surveys, experiments and
case studies/interviews (Neuman 2006). Surveys are the most common method used for
collecting primary data by researchers (Kumar et al. 1999). This survey method obtains
information by questioning respondents regarding their attitudes, intentions, behaviour,
motivations and demographic/lifestyle characteristics (Malhotra et al. 2004). Survey
methods are also called as quantitative methods in the sense that it involves a large
sample from the population of interest to be collected to obtain a number of answers
through questionnaires (Malhotra et al. 2004). Since a large sample may represent the
target population as a whole, it is believed that data collected through this survey
method can be used to make generalised conclusions regarding the defined target
population (Hair et al. 2003). Surveys offer greater ease in collecting large amounts of
data, as well as in tabulating and analysing that data (Neuman 2006). Other advantages
of survey research include economy and anonymity for respondents (Malhotra et al.
2004). This thesis uses survey methods to gather personal opinions from respondents.
By conducting the survey, it is expected that researchers will be able to sample many
respondents who answer the same structured questions. Based on the data collected
through the survey, empirical analysis can be undertaken, variables can be measured
and the proposed hypotheses can be tested.
There are three main ways of administering a survey dependent upon whether they are:
1) relying on self-administered questionnaire; 2) person-to-person interview; and 3)
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using computer assistance (Malhotra et al. 2004, p. 131). The advantages and
disadvantages of the survey types are summarised in the following Table 4.3.
Table 4.3 Advantages and Disadvantages of Survey Types Survey method of administration
Advantages Disadvantages
Self-administered. Involves addressing the questionnaire to predetermined respondents.
• Ease of presenting questions requiring visual aids.
• Facilitates questions with long or complex response categories.
• Large sample size.
• Provides anonymity.
• Respondent does not have to share answers with the interviewer.
• Careful questionnaire is needed.
• Open questions usually are not useful.
• Respondents must have good reading and writing skills.
• Control is difficult with respect to all questions being answered, meeting questions’ objectives and the quality of the answers.
Person to person. Involves face-to-face contact with respondents.
• Rapport and confidence-building are possible.
• Can probe complex issues
• Flexibility of data collection
• High response rate.
• Sample control and control data are high.
• Perceived anonymity of the respondent is low.
• Expensive in time and cost
• Potential for interviewer bias is high.
• Difficult to obtain wide access
• Large sample size is difficult.
Computer assistance. Involves using the computer-internet to contact the respondents.
• Facilitates diversity of questions.
• Global reach and moderate quantity of data.
• The speed of the data collection is high.
• The cost is low.
• Facilitates questions requiring sensitive information.
• Very low response rate.
• Control on sample is low.
• Control of data collection environment is low.
• Sample bias due to the lack of access.
• Difficult to assure anonymity and confidentiality.
Source: Aaker et al. (2001, p. 251) and Malhotra et al. (2004, p. 142)
Understanding the objective of the study is crucial before researchers choose the survey
types. As described in the Table 4.3, conducting face-to-face surveys usually takes time,
is costly and generates only a relatively small sample. The internet/electronic assistance
is being increasingly adopted. However, this method is not appropriate for this thesis
since the accessibility of internet connections in Indonesia is limited and not without
cost for student respondents. Considering that the primary objective of this thesis is to
investigate the relationships of the constructs being researched, the self-administered
surveys were determined to be an appropriate survey type.
A self-administered survey by paper questionnaire facilitates the gathering of large
samples. It is also a simple method for both researchers and respondents, as it only
requires respondents to read the questionnaires and provide their answers without
needing any assistance from a trained interviewer (Hair et al. 2006). A self-administered
survey can be mailed or completed ‘on-site’ in classrooms, waiting rooms, or offices
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(Fink 2006). Since the main group of respondents in this research are students, an on-
site self-administered survey approach to selected higher education institutions using
paper questionnaire was pursued. By distributing the survey through this method, there
are considerable advantages including: the ability to reach the targeted institutions; the
ability to collect varieties of respondents in terms of disciplines; the anticipated
response time; achieving the desired response rate; the affordable cost of obtaining the
data; and an acceptable means of dealing with ethical matters in terms of not disturbing
the academic activities. The detailed procedure for reaching the targeted respondents is
discussed in Section 4.3.3.5.3 regarding “Selecting the Sample Procedure”.
4.3.3 Research Tactics
The following step after data collection method is selecting appropriate research tactics.
The research tactics cover: construct development and operationalisation, pre-testing,
questionnaire design, determining the scaling and response format, designing a
sampling plan and identifying anticipated statistical analysis. A preliminary interview
and a literature review provided a foundation for developing the first draft for the
constructs development and how they should be measured. This initial draft was further
subjected to language translation and a pre-test procedure. The pre-test provided a
refinement of the translated first draft questionnaire development.
4.3.3.1 Constructs Development and Operationalisation
4.3.3.1.1 Service Quality
Numerous studies which focus on the dimensions of service quality have supported the
assertion that service quality is a multidimensional concept. The multidimensional
measurement model helps researchers discern the complex nature of service quality. A
warning against using single item as an indicator of a complex construct has been stated
by Jacoby (1978). Gronroos (1984) also argues that quality should not be measured by a
single dimension. Support for the notion of the multidimensionality of the service
quality concept has also been given by many researchers (e.g. Lehtinen & Lehtinen
1982; Parasuraman et al. 1985; Cronin & Taylor 1992; Babakus & Boller 1992). In
addition to the multidimensional approach, the multi-items approach was applied to
measure each construct as suggested by Churchill (1979) and Jacoby (1978).
Consequently, in order to better explain the complex nature of the service quality
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construct, this thesis adopts multi-item scales and the multidimensional concept in
operationalising service quality.
Dimensions of service quality (SERVQUAL) developed by Parasuraman et al. (1985,
1988) have mostly been used as a basis for measuring service quality in marketing
research. However, Aldridge and Rowley (1998) argue that the application of
SERVQUAL in higher education research has achieved little success. This has led many
scholars to use an alternative measurement model such as service performance
(SERVPERF), the importance-performance gap analysis, or a modified version of
SERVQUAL adjusted to the specific research context (see also discussion in the
literature review Section 2.4.6.3).
Considering that service quality is a context-specific construct (Lagrosen 2001), it is
important that the dimensions of a service quality study are designed according to each
specific situation. Since the application of the original SERVQUAL by Parasuraman et
al (1988) did not always seem to work well in the higher education setting, this thesis
carefully completed a thorough literature review to determine the most appropriate
service quality dimensions for the Indonesian higher education setting. After a thorough
literature review, it was decided that the revised framework for service quality
dimensions in the higher education setting proposed by Owlia and Aspinwall (1998)
was the most appropriate means of measuring service quality in Indonesia’s higher
education setting. The choice of Owlia and Aspinwall’s framework was made because a
majority of the items were applicable to the Indonesian context and it was
comprehensive and informative for interpretation. Furthermore, the validity of the scales
has also been tested. In developing the service quality measure for higher education,
Owlia and Aspinwall (1998) used the models previously proposed for non-educational
environments as a guideline for developing a new framework in which the service
quality dimensions and their corresponding characteristics were identified. Owlia &
Aspinwall (1998) argue that their measurement can be generalised in any education
sectors.
Even though Owlia and Aspinwall’s final framework proposes only four constructs
(academic resources, competence, attitude and content), this research adopts the revised
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framework of service quality consisting of six constructs (tangibles, competence,
attitude, delivery, content and reliability) as they are more informative in explaining
aspects of service quality in higher education. The revised framework will be further
analysed for its validity and reliability. Competence was measured using six items,
attitude using three items and delivery using three items. The scales used for these three
variables ranged from (1) ‘strongly disagree’ to (7) ‘strongly agree’. Variable content
was measured using six items while reliability using three items, having scales ranged
from (1) ‘very low’ and (7) ‘very high’. Finally, the variable tangible was measured
using six items and the scales used were ranged from (1) ‘very poor’ and (7) ‘very
excellent’.
4.3.3.1.2 Customer Value
Like service quality, customer value is known as a complex construct which cannot be
simply measured by means of unidimensional approach. However, despite some
critiques, a unidimensional approach in measuring customer value is still employed in
some studies (e.g. Patterson & Spreng 1997; Andreassen & Lindestad 1998; Caruana et
al. 2000; Cronin et al. 2000; Kumar & Grisaffe 2004; Tam 2004). The use of a
unidimensional measure is not supported by some experts in the marketing area (e.g.
Woodruff & Gardial 1996) as it lacks validity. The unidimensional construct has also
been criticised as being inadequate to capture measurement errors (Churchill 1979;
Parasuraman et al. 1994; Petrick 2002). Sweeney (2003) also affirms that a
unidimensional measure is not appropriate since the determinants of value differ among
customers. Even though the unidimensional conceptualisation provides specific and
effective measurements, it unable to dissect the complex nature of customer value (Lin
et al. 2005). In recognition of the complexity of the customer value construct, there
have been attempts to develop a multidimensional measure. The first multidimensional
customer value measure, known as PERVAL, was developed by Sweeney and Soutar
(2001) for the retail setting. The PERVAL scale was primarily developed based on the
conceptual framework of value proposed by Sheth et al. (1991) (See discussion Section
2.6.6.2.1). Petrick (2002) later developed a multidimensional scale for measuring the
customer value of a ‘service’ called service performance value (SERV-PERVAL).
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In the higher education sector, the customer value construct is not well developed. One
study applying the customer value construct has been conducted by LeBlanc and
Nguyen (1999), involving: functional value (want satisfaction and price/quality);
epistemic value; emotional value; social value; and image of customer value. This thesis
applies a combination of the customer value scales developed by Petrick (2002) and
Sweeney and Soutar (2001) to measure customer value of service in the higher
education setting. Five dimensions in the customer value measurement were used.
These are: quality, measured using four items (Petrick 2002); emotional, measured
using five items (Petrick 2002); price, measured using four items (Sweeney & Soutar
2001); social, measured using three items (Sweeney & Soutar 2001); and reputation,
measured using five items (Petrick 2002; Sweeney & Soutar 2001). The argument in
justifying the combined constructs developed by Petrick (2002) and Sweeney and
Soutar (2001) of customer value is that these dimensions can be considered as a general
value construct that can be applied in any situation (Sweeney & Soutar 2001).
Moreover, the multidimensional aspect of customer value is expected to be better in
explaining the model rather than applying the unidimensional approach. A total of
twenty-one items were used to measure customer value and the scales ranged from (1)
‘strongly disagree’ to (7) ‘strongly agree’.
4.3.3.1.3 Customer Satisfaction
As discussed in Section 2.5.2, reviews of the literature have shown that there is no
consensus regarding the definition of satisfaction and, therefore, there is a lack of
agreement on the generally accepted measurement of satisfaction (Hartman & Schmidt
1995). In the marketing literature, the emotion-based measure has been widely
employed by many services marketing scholars to represent the satisfaction construct
(e.g. Westbrook & Oliver 1991; Voss et al. 1998; Cronin et al. 2000). In addition to the
emotion-based measures, the evaluative measures of satisfaction are also popular since
the emotion-based are not always appropriate for use in the service areas. For example,
the decisions to enrol in higher education are not only based on the emotional aspects,
but also on the economic reasons and other functional aspects. The original cognitive
(evaluative) model of satisfaction scale was developed by Oliver (1980) and has been
widely adopted in marketing research (e.g. Cronin et al. 2000; Caruana et al. 2000;
Athiyaman 1997; Olorunniwo et al. 2006).
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Some studies in marketing use a single item to measure overall satisfaction, such as
stating “overall, I am satisfied with….” (e.g. Spreng & Mackoy 1996). In many cases,
however, it is argued that the use of a single item scale usually cannot capture the
complexity of the particular construct being measured. The customer satisfaction
construct is believed to be an abstract and complex construct which is unable to be
measured directly by a single item (Fornell 1992; Oliver 1981). The increasing use of
multi-item scales to capture the overall satisfaction construct is an attempt to overcome
criticism of the limited depth of single item measurement. Some studies focus more on
the evaluative judgments and others use the combination between cognitive and
affective aspects of customer satisfaction. For example, McDougall and Levesque
(2000) developed two items of overall satisfaction (e.g., expectations met and
satisfaction with the service provider); Caruana et al. (2000) proposed the use of three
items to measure overall satisfaction adapted from Oliver (1980) and Taylor and Baker
(1994); Cronin et al. (2000) proposed a combination of affective and cognitive aspects
adapted from Westbrook and Oliver’s (1991) and Oliver’s (1997) works; and
Ranaweera and Prabhu (2003) also adopted combination of affective and cognitive
aspects of customer satisfaction consisting of a three-item measure.
So far, studies involving the measurement of customer satisfaction in the education
sector have commonly adopted a combination of measurements from the
abovementioned studies. An example of this approach is Athiyaman (1997), who
employed six items adopted from Oliver (1980) and measuring the items using a five-
point Likert scale. After a thorough review of satisfaction studies, especially in the
service marketing area, it was decided to employ a combination of both the emotion and
evaluation-based measures. More specifically, this thesis combined the instruments
developed by Athiyaman (1997), Cronin et al. (2000) and Mc Dougall and Levesque
(2000). A total of nine items were used to measure cumulative customer satisfaction and
the scales ranged from (1) ‘strongly disagree’ to (7) ‘strongly agree’.
4.3.3.1.4 Behavioural Intentions
This thesis employs the dimensions of behavioural intentions proposed by Boulding et
al. (1993) and Athiyaman (1997). The reason behind this is that the dimensions
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employed in Boulding et al. (1993) and some parts in Athiyaman (1997) incorporate
dimensions (e.g. word-of-mouth recommendation and loyalty) which are most relevant
to the higher education sector. Alves and Raposo (2007) also posited that word-of-
mouth recommendations and loyalty were the most appropriate dimensions of
behavioural intentions in the higher education setting. The use of multi-items
questionnaire to measure behavioural intentions is expected to better capture the
varieties of intention. All items were measured using the scale ranging from (1)
‘strongly disagree’ to (7) ‘strongly agree’.
4.3.3.1.5 General Information
The following general information section was designed to gather data on respondents’
details such as gender, age, reason for choosing an institution, how respondents found
information about an institution as well as open-ended questions regarding service
quality, customer satisfaction, customer value and behavioural intentions constructs.
These questions were not specifically designed to answer the research questions and
proposed hypotheses. In addition to providing qualitative and quantitative information
and better insights in understanding the proposed conceptual model, these questions
were included for the purpose of the further research in the same context.
4.3.3.1.6 Overall Items Generated from the Literature Review
Overall, to answer the hypotheses proposed from the conceptual model, four main
constructs (service quality, customer value, customer satisfaction and behavioural
intentions) consisting of 66 items were employed. The summary of the overall survey
instruments is provided in the following Table 4.4. Details of the questionnaire are
provided in Appendix 2.
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Table 4.4 Sources of the Questionnaire
Section A: Service Quality
Tangible Sources
Sufficiency of academic equipment (e.g. laboratories, workshops). • Owlia & Aspinwall (1998) Ease of access to equipment.
Degree to which the equipment looks modern.
Ease of access to information sources (e.g. books, journals, software, information networks).
Degree to which environment is visually appealing.
Adequacy of support services (e.g. common room).
Competence Sources
Competence of support staff (e.g. technicians, receptionists, secretaries). • Owlia & Aspinwall (1998) Sufficiency (number) of academic staff.
Theoretical (relevant) knowledge of academic staff.
Practical (relevant) knowledge of academic staff.
Extent to which academic staff are up-to-date in their subjects.
Expertise of academic staff in teaching/communication.
Attitude Sources
Extent to which academic staff understand a student’s academic needs. • Owlia & Aspinwall (1998) Degree to which academic staff are willing to help.
Availability of academic staff for guidance and advice.
Extent to which academic staff give personal attention.
Delivery Sources
Extent to which course material is timely/sequentially presented. • Owlia & Aspinwall (1998) Degree to which exams are representative of courses taught.
Extent to which courses are stimulating.
Content Sources
Degree to which the programme contains primary knowledge/skills. • Owlia & Aspinwall (1998) Degree to which the programme contains ancillary knowledge/skills.
Extent to which students learn communication skills.
Extent to which students learn team working.
Relevance of curriculum to the future jobs of students.
Applicability of knowledge learnt in other fields.
Reliability Sources
Credibility of degree awarded to the students. • Owlia & Aspinwall (1998) Degree to which school/department handles feedback from students.
Extent to which personal (confidential) information is secure.
Section C: Customer Value
Quality & Price Sources
Is outstanding quality. • QUALITY • Petrick (2002)
Is very reliable.
Is very dependable.
Is very consistent.
Is reasonably priced. • MONETARY PRICE
• Sweeney & Soutar (2001)
Offers value for money.
Is a good product for the price.
Would be economical.
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Table 4.4 Continued (Sources of Questionnaire) Social, Emotion & Reputation Sources
Would improve the way I am perceived. • SOCIAL
• Sweeney & Soutar (2001)
Would make a good impression on other people.
Would give its owner social approval.
Makes me feel good. • EMOTIONAL
• Petrick (2002)
Gives me pleasure.
Gives me a sense of joy.
Makes me feel delighted.
Gives me happiness.
Has good reputation. • REPUTATION
• Petrick (2002) • Sweeney & Soutar (2001)
Is well respected.
Is well thought of.
Has status.
Is reputable.
Section C: Customer Satisfaction
Satisfaction Sources
I am satisfied with my decision to attend…. • Athiyaman (1997); Cronin et al. (2000) If I had to do it all over again, I would not enroll in……*
My choice to enroll in…..is a wise one
I feel bad about my decision to enroll in…..*
I think I did the right thing when I decided to enroll in …..
I am not happy that I enrolled in………..*
This facility is exactly what is needed for this service. • Cronin et al. (2000) The (provider) meet my expectations. • Mc Dougall & Levesque
(2000) Considering everything, I am extremely satisfied with the service.
* reverse coded.
Section D: Behavioural Intentions
Behavioural Intentions Sources
I like talking about…………..to my friends. • Athiyaman (1997) I like helping potential students by providing them with information about…..and its courses.
When talking to people about this organisation outside the school, I say positive things.
• Boulding et al. (1993)
I would recommend this organisation to my employer as a place to recruit students.
I would recommend this organisation as a place to get a degree.
I plan to contribute money to this organisation after graduation.
Would you recommend this organisation to a friend applying to study business.
I will consider providing non-monetary contributions to this organisation once I become a graduate (e.g. on the job training, guest lecture, advisory).
• New item
4.3.3.2 Pre-testing
Since the research for this thesis was conducted in Indonesia, the original questionnaire
was translated into the Indonesian language. The questionnaire was originally
formulated in English, and then translated into Indonesian, and translated back into
English again. Finally, the translated questionnaire was evaluated against the original
questionnaire to check for any inconsistencies. There are two reasons for adopting the
back-translation method (Nasution 2005). First, this method is applied to ensure
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consistency of meaning for each item in the original questionnaire. Second, since the
target respondents in this thesis are Indonesian students, it would be appropriate for the
final translation to be done by an Indonesian in order to get the real meaning from each
item in the questionnaire.
Pre-testing is one of the most important parts of conducting research since it helps the
researcher to design a more accurate measure and, therefore, increasing the quality of
the research. Prior to the major survey distribution, it is essential to conduct pre-testing
in order to develop a more specific questionnaire that is clear and rational, so that it can
be appropriately answered by the respondents as well as free from bias (Chisnall 1997).
In addition, pre-testing enables researchers to reveal errors in questionnaire design
(Burns & Bush 2003; Cavana et al. 2001). As suggested by Chisnall (1997), pre-testing
may involve several experts in the field and several ways of re-constructing the
questionnaires such as re-writing questions, or changing the sequence of the content or
the style of composition. Fink (2003) asserts that collecting expert feedback prior to
administering the survey could allow the instrument to be fine-tuned.
The pre-testing involved two marketing academics and two doctoral students in
marketing and management. This process highlighted the need for improvements in
translation accuracy, content validity and the relevance of every questionnaire item
being asked in the Indonesian higher education setting. In addition, the questionnaire
was also subjected to another test by respondents with similar characteristics
(undergraduate students) as those intended for the later final/major survey. In terms of
the size of the sample population to be used for pre-testing, there does not appear to be
any widely agreed number (Voola 2005). However, Burns and Bush (2000) suggest that
five to ten respondents are sufficient to conduct an effective pre-test. Malhotra et al.
(2004) recommend that about 15 to 30 respondents are sufficient for pre-testing. In this
research, the researcher distributed 50 questionnaires for the participating higher
education institutions. The respondents were requested to provide clear indications of
the content, wording, sequence, form and layout of the questionnaire. A total of 34
usable questionnaires were returned.
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The main feedback from these pre-tests was as follows:
From academic experts:
1. The definitions of constructs used should be clearly explained according to the
context since students might confuse, and be unable to differentiate between,
service quality, customer value and satisfaction constructs.
2. The Indonesian translation of the term emotion value (delighted, happiness, joy,
etc.) should be made carefully, since words with several synonyms might cause
redundancy.
3. In general, the Indonesian version was easy to understand, content was
representative and the length was sensible.
From students:
1. The explanation on the meaning of the ‘scale’ should be clearer and simpler.
2. There were several inconsistencies in the wording such as the use of the terms
‘lecturer’ or ‘academic staff’ when referring to a lecturer.
3. There was a confusion regarding the scope of the evaluation, whether to
comment the university as a whole or just the faculty.
4. The majority of the respondents agreed that the questionnaire was simple and
clear in its wording and presented no difficulty in understanding the meaning of
each of the questions being asked.
In accordance with the research process described in Figure 4.1, after conducting the
pre-test the following section discusses the issues of questionnaire design, sampling
plan and the anticipated statistical analyses.
4.3.3.3 Scaling and Response Format
A scale is crucial when developing a questionnaire. It is crucial since the scale will be
used as a means to find out and to record respondents’ opinions on a particular matter
and in expressing how strongly the respondents hold that opinion (Boyce 2003). For
ease of construction and administration, this thesis employs the non-comparative scales
with the itemised rating of Likert scales. In order to have a better levels of measurement
in social research, the Likert scale, is commonly used (Babbie 2004). Babbie (2004)
further explains that since the response categories in survey questionnaires are
standardised, the relative intensity of different items can be determined. Likert scales
allow respondents to demonstrate their attitudes or perceptions towards the object of
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interest by indicating the extent to which they agree or disagree. The response
categories such as strongly agree, agree, disagree and strongly disagree were used in
this thesis to measure attitudes or perceptions. Other scales (very low (1) – very high (7)
and very poor (1) – very excellent (7)) were also adopted. The Likert scale was adopted
since it is easy to administer and easy for respondents to understand what it is required.
Although there is no single optimal number of categories for use in the Likert scaling
system selected, seven categories of Likert scale from ‘1’ (strongly disagree) to ‘7’
(strongly agree) were applied. Jacoby and Matell (1971) argue that there is no effect on
the number of categories that should be used when employing Likert scale and
considered that two or three categories should be adequate enough. Such scales are
considered sufficient for reliability and validity tests (Jacoby & Matell 1971). However,
Hair et al. (2006) argue that the larger the number of categories the greater the accuracy
of the scale, even though it may be burdensome for the respondent. Hair et al. (2006)
suggest that the scaling should be no fewer than five categories. Malhotra et al. (2004)
suggest the use of between five and nine categories. This thesis adopts the
recommendation by Malhotra et al. (2004) and Hair et al. (2006) by applying a seven-
point Likert scale.
There is still a debate over whether or not the Likert scale is considered as an ordinal or
interval scale. This thesis investigates the relationships (cause and effect) among the
constructs being researched. Therefore, an interval scale should be used for the
statistical analysis. A Likert scale is commonly treated as an ordinal scale. The ordinal
scale does not provide information regarding the distance between the categories in the
scale (Boyce 2003). Boyce (2003) further explains that, in practice, the researcher often
assumes that the distance between each pair of adjacent categories is equal (for
example, between ‘strongly disagree’ and ‘disagree’, and between ‘neither agree nor
disagree’ and ‘agree’). Following Boyce’s (2003) argument and assuming that the equal
distance is accepted, the data from Likert scale can be treated in the same manner as that
from an interval scale. Furthermore, by treating it as an interval scale, the mean score
for each statement for the respondent sample can be legitimately calculated.
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4.3.3.4 Questionnaire Design
A questionnaire is commonly used to collect and record primary research data in order
to satisfy the objective of the research (Boyce 2003). Questionnaires consist of a series
of questions for respondents to answer. There are several reasons why a questionnaire is
a popular survey method (Boyce 2003): (1) questions can be asked in exactly the same
manner (e.g. the same words and sequence), which enables data comparison; (2) it is
easier to control the questionnaire compared to controlling surveys using interviewers;
(3) it can be designed to cover everything in an exact manner as required by the
researcher to meet the research objective; and (4) there is an efficient and correct data
input and, furthermore, the data obtained can be recorded in the same way during data
processing. The questionnaire should be carefully designed in order to ensure that the
data collected is both relevant and accurate (Zikmund 2003). A well-designed
questionnaire is important since researchers rarely have a second chance to go to the
respondents, data collected cannot be changed and eventually the quality of the results
depends on the quality of the questionnaire (Boyce 2003).
The general nature of a questionnaire lies between two extremes: the structured
questionnaire and the unstructured questionnaire. The structured questionnaire was
utilised since it is easier for respondents to complete the questionnaire and in
correspond with the causal approach followed by this thesis. The causal approach
requires quantitative data which can be obtained from a structured questionnaire. The
questionnaire can be structured in various approaches, and this thesis adopts the
Sections Approach (Burn & Bush 2000) in structuring the questionnaire. For example,
the sequence included, firstly, service quality, followed by customer value, customer
satisfaction and finally behavioural intentions. Each section started with its associated
instructions.
Since evidence shows that long questionnaires tend to increase non-response rates, this
thesis emphasises the importance of designing a short questionnaire as suggested by De
Vaus (2002) and Dillman et al. (1993). These authors strongly recommend that the
questionnaire should be kept short and attention should be focused on the relevance of
the content. Following Burn and Bush (2000) and Zikmund (2003) suggestions,
demographic information was placed at the end of the questionnaire. There are debates
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regarding the best way of placing demographic information. Bourque and Fileder
(2003) propose that surveys should commence with the easiest questions such as
demographic background. However, other support for asking demographic questions at
the end of the questionnaire was acknowledged by Fraser and Lowley (2000). The
questionnaire designed for this thesis placed the demographic section in the last stage of
the survey.
It is essential to take care with the physical design of the survey instrument since it
impacts on the presentation and administration of the survey (Aaker et al. 2001).
Parasuraman (1986) suggests that written instructions should be provided, and that the
presentation of the questionnaire must be clear and appealing to the respondents.
Consequently, all efforts were made to ensure minimal error in the instrument, and the
instructions were clear and easily comprehensible.
4.3.3.5 Sampling Plan
Conducting sampling, as opposed to a census, is appropriate for this thesis since the
population size (undergraduate students in Yogyakarta, Indonesia) is large and the cost
and time associated with collecting the information from the population are both
considerable. Sampling itself is the process of selecting observations (Babbie 2004).
When designing sampling, a clear sample plan is essential because poor sample design
may distort the findings through systematic biasing (Short et al. 2002). Accurate and
reliable information from the sample will allow generalisations of the relationships from
the sample to the population (Scheaffer et al. 1996).
4.3.3.5.1 Defining the Target Population
As sampling is intended to gain information about a population, a clear prior
understanding of the population that the sample is intended to represent is essential
(Kalleberg et al. 1990). It is also crucial that the sample be as representative as possible
to the population, so that it enables one to approximate those characteristics of the
population that are relevant to the research question (Kerlinger 1986). After determining
the population of interest, it is then important to determine the unit of analysis. The unit
of analysis refers to the level of investigation the study addresses (Malhotra et al. 2004).
The unit of analysis can be individual, group or organisational. The unit of analysis in
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this thesis is undergraduate students at the individual level. Students are expected to
provide true information, opinions and perceptions of their experiences when studying
in the higher education institution.
Since the purpose of this thesis is to analyse students’ perceptions of service quality,
value, satisfaction and behavioural intentions in the higher education sector in
Yogyakarta, Indonesia, the following considerations were taken to determine the target
population:
1) Location for conducting research. The justification for selecting the city of
Yogyakarta as a location to conduct the survey is as follows. Yogyakarta is known
to be ‘an academic city, a university city, a student city as well as a culture city’
(Kopertis Wilayah V 2006). Yogyakarta hosts 123 tertiary education institutions
consisting of universities, institutes, schools of higher learning, academies and
polytechnics (Mone 2009). As a student city of Indonesia, Yogyakarta provides a
variety of choices for students to continue their higher education. As a student city,
the academic atmosphere in Yogyakarta is stronger than in other cities in Indonesia.
Being a student city, Yogyakarta also hosts thousands of students from all over
Indonesia and abroad. Due to the numerous centres presence of higher learning,
many of the inhabitants are students who have a significant impact on the economic
prospects of the majority of the population in Yogyakarta. Yogyakarta can be
described as a melting pot where different cultures from throughout Indonesia
converge (Kopertis Wilayah V 2006). Considering that Yogyakarta is unique, in the
sense that: 1) It is considered an important destination for Indonesian youngsters to
undertake further study in higher education; 2) the students are drawn from a wide
diversity of backgrounds (origins and cultures); and 3) given the existence of
numerous higher education institutions in Yogyakarta, thus, those characteristics
specific to Yogyakarta provides adequate characteristics of sample varieties for this
research. Figure 4.2 provides data from students studying in Yogyakarta from 2004-
2007, based on the four main islands (Java, Kalimantan, Sumatra and Sulawesi) and
some areas of eastern Indonesia. Even though the figures do not show significant
growth in the number of students from 2004 to 2007, they clearly show the
significant number of student backgrounds from all over Indonesia. This provides
evidence for Yogyakarta as an important destination for Indonesian students
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pursuing tertiary education. Please also see Section 3.5 ‘Research Context’ and
section 1.2.3 ‘Service Quality, Customer Satisfaction and Customer Value in the
Indonesian Higher Education Sector’ for justification of research in Indonesia.
Figure 4.2 Higher Education Students Growth in Yogyakarta
Source: Kopertis Wilayah V (2006), accessed 28 July 2008
Java Island
Kalimantan Island
Sumatra Island
Sulawesi Island
Eastern Java
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2) Discipline of the study. Considering the time and cost limitations inherent in
attempting to sample undergraduate students from all disciplines, this thesis
focused on the Business Faculty since business study is always considered the
favourite discipline in Indonesia. Figure 4.3 identifies the proportion of most
favourite subjects based on student enrollments in national scope in Indonesia.
Figure 4.3 Favourite Subject at the National Scope
3) Type of higher education. The university was chosen, despite other types of
higher education existing in Indonesia (institutes, schools of higher learning,
academies and polytechnics), for the following reasons: 1) the 19 Universities
(2 public and 17 private) existing in Yogyakarta have generally been established
for more than 10 years. Therefore, their future existence is expected to be more
reliable; 2) universities are well known to students all over Indonesia compared
to other smaller types of tertiary education institution; therefore, it provides a
wider variety of students’ backgrounds/origins; 3) universities commonly have a
larger student population and varieties of programs; and 4) most universities
operate Business Faculties, as compared to other types of higher education
institution. Tables 4.4 and 4.5 provide figures which indicate that universities
accommodate the largest number of students as compared to other types of
higher education. These figures not only apply in Yogyakarta but also in most
other areas of Indonesia.
Source: Mone (2009)
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Based on the characteristics of Indonesian higher education, the target population of this
thesis is undergraduate students enrolled in the Business Faculties in universities in
Yogyakarta, Indonesia.
4.3.3.5.2 Determining the Sample Frame
After the target population and the unit of analysis have been determined, the sample
frame can be identified. The sample frame is a list of the population members from the
location where the sample was obtained (Zikmund 2003). Kumar et al. (1999) suggest
that it is not necessary to list all members of a population. However, it is considered
sufficient to specify the procedures by which each sampling unit can be located.
Figure 4.4 Student Enrolments Based on Discipline/National
Figure 4.5 Student Enrolments Based on Discipline/Yogyakarta
Source: Mone (2009)
Source: Mone (2009)
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The sample frame for this thesis was obtained from the databases of the directorate-
general of Higher Education of the Republic of Indonesia. This website (Mone 2009)
provides public access to all higher education institutions existing in all provinces in
Indonesia. Similarly, all the higher education institutions in Yogyakarta can also be
accessed in detail through the same source. More specifically, the sample frame was
taken from the information sources relating to students studying in the Business
Faculties in the five selected universities in Yogyakarta. The choice of the five selected
universities was made based on preliminary interviews with two local (Yogyakarta)
experts in the higher education sector regarding the representativeness of the
universities to be selected for the research. The five selected universities were chosen
based on the following considerations: 1) varieties of students’ cultural backgrounds; 2)
existence of a Business Faculty; and 3) accreditation status.
4.3.3.5.3 Selecting the Sample Procedure
The representativeness of the sample is important since the researcher will use the
sample (a subgroup of the population) to produce accurate generalisations about the
population (larger group) (Neuman 2006). Sampling techniques are classified into non-
probability sampling and probability sampling. Non-probability sampling techniques are
essentially based on the subjective judgment of the researcher, whereas in probability
sampling, the sampling items are selected by chance (Neuman 2006). Although it may
be possible to use non-probability sampling procedures to obtain a representative
sample, probability sampling procedures are recommended.
This thesis does not specifically follow one single technique; nevertheless, it applies a
series of sample designs incorporating a mix of sampling methods. The reasons behind
this are that this approach: (1) considers the time and cost (resource limitations); and (2)
considers the accuracy of data representativeness. The following stages of sampling
procedures are described:
1. Judgmental cluster selection at the participating universities. By conducting
interviews with two academic experts as a preliminary exploratory research to
select appropriate universities as samples, and by determining that the
universities selected should run three major disciplines in the Faculty of
Business (management, accounting and economics), five universities were
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selected. The five universities selected were University of Pembangunan
Nasional Yogyakarta/UPNVY, Atmajaya University/UAJ, Gadjah Mada
University (GMU), Yogyakarta State University (UNY), and Islamic University
of Indonesia/UII. The existence of three different disciplines in the Faculty of
Business is important to reflect the variety of disciplines.
2. The stratification method in terms of discipline was then applied since the three
disciplines in the Business Faculty from each of the participating universities
must be represented in the sample. By using stratification method, the faculty
assisted the provision of the three departments/disciplines as well as ensuring the
classes consisting of students who are at least in the second year.
3. A random selection of students from more than one class from each of the
departments/discipline was implemented. The questionnaire was randomly
distributed to the class that had been selected using stratified method.
Respondents may directly return the questionnaire to researcher and/or returning
to the box provided in the administrative office for the duration of one month.
Preliminary contact with the Deans of the selected universities was made to ensure that
access was granted to the researcher to conduct the necessary research. Since the
objective of this research is to test the research model and hypotheses based on
students’ perspectives, undergraduate students were selected as the main respondents.
To ensure the variability of the sample and the capacity to describe academic
experiences, the sample was drawn from at least three different departments and
respondents were in at least the second year of their study. The university staff provided
assistance in managing which students’ sample would meet the criteria determined by
the researcher. The students participated in the survey voluntarily after a class session.
The average time required to complete the survey was approximately 20-30 minutes.
The questionnaire was able to be submitted directly to the researchers. Alternatively, a
deposit box was also provided in the administrative office to collect those
questionnaires not directly returned to the researchers.
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4.3.3.5.4 Determining Sample Size
Sample size relates to the number of elements that must be covered in the study
(Malhotra et al. 2004). Determining sample size is not a simple matter and may require
several qualitative and quantitative considerations (Malhotra et al. 2004). Several
approaches can be used to determine sample size, either by applying statistical
techniques or through some ad-hoc methods to determine the required sample size
(Voola 2005). This means that the statistical method chosen may influence sample size
decisions. As will be discussed in the following Section 4.3.3.6, Partial Least Squares
(PLS) was chosen as a statistical method to analyse the conceptual model and answer
the research questions. PLS can be used to analyse cases with small sample sizes (Chin
& Newsted 1999). Based on the results of two Monte Carlo simulations, Chin and
Newstead (1999) showed that PLS can provide appropriate estimates with a sample size
as small as 20 cases. Chin (1998a) offers three rules-of-thumb to determine sample size
that can be used as a guide when applying PLS. Nevertheless, since PLS is not the only
statistical technique used in this thesis, the sample size decision was also made
considering the other statistical techniques employed in this thesis and the nature of the
target population.
This thesis does not specifically follow Chin’s (1998a) suggestion (three rules-of-
thumb) for sample size for the following reasons:
1. This thesis employs factor analysis using Principal Component Analysis (PCA)
technique prior to the main analysis using PLS. In order to produce a reliable
factor, a figure of 200 should be presented as the minimum figure, although 100
may be sufficient in cases of clear factor structure (Kline 1994). Other source
suggests that 300 cases provide greater certainty, unless there are several high-
loading marker variables (> 0.80) (Tabachnick & Fidell 2001).
2. When determining the sample size, it is also important to consider the nature of
the targeted population. The population of undergraduate students in Yogyakarta
is considerably large. According to Malhotra et al. (2004), in regional studies,
research typically uses 200-1000 or more consumers as samples. By considering
the size of the student population and the statistical techniques employed, this
thesis follows Malhotra et al.’s (2004) view and adopts the commonly
recommended sample size for PCA & Structural Equation Modelling (SEM)
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techniques. Kline (2005) suggests that sample sizes should be in excess of 200
for SEM analyses. Hoyle (1995) recommends at least 100 cases. This thesis
adopts a minimum number for sample size of approximately 200 (Kline 2005),
which is considered sufficient based on both the statistical method used and the
student population from the participating universities.
4.3.3.6 Statistical Analysis
Considering the proposed conceptual framework (testing the relationships), the nature
of the data and complexity of the research model, the SEM methodology in general and
PLS in particular was considered to be an appropriate statistical method for this thesis.
Previous satisfaction studies have identified that satisfaction scores were frequently
negatively skewed (Fornell et al. 1996; Anderson & Fornell 2000), in which PLS can
accommodate this nature of data since PLS does not require normally distributed data.
The research model proposed is considered complex since it involves two higher order
constructs measured by their associated first order constructs and also involves several
direct and indirect relationships. PLS is suitable for the loyalty study since this area
often involves complex relationships and employs numerous variables (Ryan et al.
1999). The use of PLS has received support from literature in loyalty and satisfaction
studies (Murgulets et al. 2001; Westlund et al. 2001). The following discussion
specifically reviews the advantages of using SEM and why it is appropriate to use the
PLS methodology.
4.3.3.6.1 Exploratory Factor Analysis
Before conducting the SEM analysis using PLS methodology, Exploratory Factor
Analysis (EFA) and Reliability Analysis (RA), are carried out using SPSS to purify the
measures. Running EFA is important in order to assess the unidimensionality of the
measures and in identifying the internal consistency of the items. The unidimensionality
and internal consistency are important to achieve a good measure of each construct
(Marimuthu 2008). The ultimate aim of conducting the EFA is to specify a valid and
reliable measurement model (Venaik 1999).
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4.3.3.6.2 Structural Equation Modelling (SEM)
SEM is considered as a new approach for testing empirical data involving multivariate
models. SEM can be traced back to 1970, when it was initiated by Karl Joreskog, who
integrated features of econometrics and psychometrics into one model called the
Structural Equation Modelling technique (Klem 2000). SEM as a statistical technique is
basically an integration of path analysis and factor analysis (Byrne 2001). In the 1970s,
LISREL was the only widely available SEM statistical program (Kline 2005). However,
computer technology has rapidly advanced, and the number of software packages
designed for the purpose of analysing SEM techniques has increased dramatically.
There are currently many choices of SEM computer programs such as the covariance-
based (e.g. LISREL, EQS, AMOS, SEPath, CALIS and RAMONA) (Kline 2005; Chin
& Newsted 1999) and the component-based (e.g. PLS-GUI, Visual PLS, SPAD-PLS,
Smart PLS and PLS graph) (Temme et al. 2006).
SEM techniques such as LISREL, AMOS, EQS and PLS are known as second-
generation data analysis techniques (Bagozzi & Fornell 1982; Fornell 1987). As a
second-generation technique, the SEM-based procedure offers additional advantages
over the first-generation statistical techniques (e.g. factor analysis, discriminant analysis
and regression analysis), in the sense that they are able to analyse a set of interrelated
research questions simultaneously and systematically (Gefen et al. 2000). SEM
techniques enable to simultaneously model the various interrelationships between
multiple constructs (Anderson & Gerbing 1988). SEM is more powerful, illustrative and
robust than multiple regression, since it covers the modeling of interactions,
nonlinearities, correlated independents, measurement error, correlated error terms, etc.
(Pirouz 2006).
SEM also offers researchers a greater flexibility in the interaction between the theory
and data (Chin & Newsted 1999; Chin 1998a). In general, SEM provides flexibility to:
1) model relationships among multiple predictor and criterion variables; 2) construct
unobservable latent variables; 3) model errors in measurements for observed variables;
and 4) measure statistically a priori substantive/theoretical and measurement
assumptions against empirical data (e.g confirmatory analysis) (Chin & Newstead 1999,
pp. 308; Chin 1998a, p. 297). Table 4.5 provides an overview of the first-generation and
second-generation statistical techniques.
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Table 4.5 The Differences Between First-generation and Second-generation
Statistical Techniques Capabilities Second-generation First-
generation LISREL PLS Regression
Objective of variance analysis.
Maps paths to many dependent variables and analyses simultaneously.
Yes Yes No
Maps specific and error variance of the observed variables into the research model.
Yes No No
Maps reflective observed variables. Yes Yes Yes
Maps formative observed variables. No Yes No
Analyses all the paths, both measurement and structural in one analysis.
Yes Yes No
Can perform confirmatory factor analysis. Yes Yes No
Requires sound theory base. Yes No No
Required minimal sample size. At least 100-150 cases.
At least 10 times the number of items in the most complex construct.
At least 30 is required.
Objective of variance analysis. Overall model fit (e.g. insignificant X2 or high AGFI).
Variance explanation (high R2).
Variance explanation (high R2).
Assumed distribution. Multivariate normal.
Relatively robust to deviations from a multivariate distribution.
Relatively robust to deviations from a multivariate distribution.
Source: Gefen et al. (2000) and Voola (2005)
4.3.3.6.3 Structural Model and Measurement Model
The basic composition of SEM can be divided into two sub-models: the measurement
model and structural model (Byrne 2001).
4.3.3.6.3.1 Structural Model
The structural model addresses the relationships among the latent variables. It focuses
on how latent variables directly or indirectly influence other latent variables in the
model (Byrne 2001). The latent variable is also known as: constructs, unobserved
variables, unmeasured variables, concepts or factors. The assessment of latent variables
has a long tradition in social science (e.g. Churchill 1979; Duncan 1984; Nunally 1978),
since research often involves variables that cannot be measured directly. More
precisely, Joreskog (1993, p.295) states that latent variables are “ theoretical creations
that cannot be observed or measured directly”. By using SEM, researchers may
develop latent variables. There are two types of latent variables in the structural model:
exogenous latent variables and endogenous latent variables. Exogenous latent variables
are independent latent variables that act as predictors or causes of other latent variables
in the model. Endogenous latent variables are dependent latent variables, which are
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influenced by one ore more exogenous latent variables. Arrows in the structural model
go from exogenous latent variables towards endogenous latent variables. In Figure 4.6,
for example, service quality is an exogenous variable that predicts customer value,
customer satisfaction and behavioural intentions. The arrows (H1 to H6) address the
relationship among latent variables in the structural model. These direct and indirect
relationships can be assessed by path coefficient analysis that is already covered in PLS.
The full structural model which combines both measurement model and structural
model for this thesis is also illustrated in Figure 4.6.
4.3.3.6.3.2 Measurement Model
Correspondingly, for each latent variable in the structural model, there is a measurement
model to specify the relationships between the observed variables and their respective
latent variables. The measurement model addresses the relationship between the
observed variable to the underlying latent variable. The measurement model is also
described as a block structure (Wold 1980). The observed variables are also commonly
called indicators, items, measured variables or manifest variables. An overview of the
relationships in the measurement model can be provided by using an example in Figure
4.6. For example, “satisfaction” as one of latent variables is inferred through its
indicators, C1 – C3, which are displayed as rectangles or squares. The measurement
model addresses the relationships between indicators (C1-C3) to satisfaction as their
respective latent variable. Similarly, the same measurement models also apply to service
quality with indicators A1-A3, customer value with indicators B1-B3 and behavioural
intentions with indicators D1-D3.
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4.3.3.6.4 SEM Techniques
Two different types of SEM techniques have been acknowledged to exist. The first type
is the covariance-based method that can be operated using software such as LISREL,
AMOS and EQS (Bollen 1989). Another type is variance-based or component-based
method that can be operated with some existing PLS software (e.g. PLS Graph, Smart
PLS) (Chin 1998b). The following sections review the applications and the differences
between the methods.
4.3.3.6.4.1 Covariance-based SEM (CBSEM) Technique
The best known SEM technique is covariance-based SEM (CBSEM). The CBSEM
approach uses a Maximum Likelihood (ML) function that attempts to minimise the
differences between the sample covariances and those implied by the theoretical model
(Chin & Newsted 1999; Chin et al. 2003). In practice, the CBSEM should have a
structural model that has a strong theoretical background. Therefore, the theory used to
support the model is the most important issue in applying CBSEM. CBSEM has mostly
been applied in confirmatory studies to determine whether or not a certain model is
valid. In order to provide a valid model, the CBSEM must produce a non-significant
result. The non-significant result shows that there is no difference between the implied
covariance and the sample data. Nevertheless, Chin (1998b) argues that a non-
= Observed variable/item/indicator
= Latent variable/construct
Measurement model
H1
H3
H4
H2
H5
H6
Perceived Service
Quality
Customer
Value
Behavioural
Intentions
Satisfaction
C1
C2
C3
B3
B2
B1
D3
D2
D1
A3
A2
A1
Full structural model
Figure 4.6 Measurement and Structural Models
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significant difference can also mean that there is an inability to detect model
misspecification (e.g. statistical power which relates to the ability to detect and/or reject
a poor model).
It is known that the statistical objective of the CBSEM method is to obtain goodness-of-
fit and therefore, requires indicators that must be in reflective mode. According to Chin
(1998b), by being over-reliant upon the goodness-of-fit, it may undermine other SEM
procedures that have different criteria (e.g. the construct that require formative model).
Furthermore, Chin (1998b) states that models with good fit may still have poor R2 and
factor loadings.
“The fit measures only relate to how well the parameter estimates are able to match the
sample covariances. They do not relate to how well the latent variables or item measures
are predicted. The SEM algorithm takes the specified model as true and attempts to find
the best fitting parameter estimates. If, for example, error terms for measures need to be
increased in order to match the data variances and covariances, this will occur.” (Chin
1998b, p. 6).
In addition to the goodness-of-fit issue, due to the ML algorithm, the CBSEM approach
not only needs an adequate sample size, but also requires strict assumptions that the
observed variables follow a specific multivariate distribution (normal distribution) and
that the observations are independent of one another (Joreskog & Wold 1982). This is
why CBSEM is usually called ‘hard’ modelling (Falk & Miller 1992). Furthermore,
CBSEM is more likely to have problems in obtaining a good fit for complex models
with more indicators (Chin & Newsted 1999). The increasing number of
indicators/latent variables will increase the degree of freedom, and as a consequence,
the various model fit indices will likely be positively biased relative to simpler models
(Mulaik et al. 1989; Chin & Newsted 1999).
4.3.3.6.4.2 Variance-based Technique (PLS)
PLS regression was originally developed by Herman O.A. Wold in the late 1960’s for
use in the field of econometrics (Pirouz 2006). It was initially used in analytical,
physical and clinical chemistry studies (Geladi & Kowalski 1986; Pirouz 2006). When
developing PLS, Wold specifically sought to address the analysis of a weak theory and
weak data (Wold 1982). Wold designed PLS to cope with limitations in Ordinary Least
Squares (OLS) regression when data are problematic such as small datasets, missing
values, non-normality, and multicollinearity (Pirouz 2006). OLS regression yields
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unstable results when data is derived from a small sample, or has missing values and
multicollinearity between predictors since it increases the standard error of their
estimated coefficients (Field 2000).
PLS is often described as ‘soft’ modelling since it softens the assumptions of the OLS
regression that requires hard assumptions (e.g. large sample size, no missing values,
normal distribution and no multicollinearity) (Falk & Miller 1992; Pirouz 2006). Wold
(1982) states that the PLS approach is distribution-free. The practicality of the PLS
application is supported by Fornell and Bookstein (1982, p.440) who argue that
“marketing data often do not satisfy the requirements of multi-normality and interval
scaling or attain the sample size required for maximum likelihood estimation”.
Considering its ability to avoid multivariate normality assumptions, PLS is argued to
have important advantages for non-experimentalists (Kroonenberg 1990).
PLS is a prediction-oriented technique. The PLS approach is particularly useful for
predicting a set of dependent variables when a large set of independent variables is
involved (Chin 1995; Wold 1980). The covariance-based model can only handle the low
to moderate complexity of a model with fewer indicators (Chin 1995). One of the goals
of PLS is to predict Y (dependent variables) from X (independent variables) and to
describe the common structure underlying the two variables (Abdi 2003). Since it is
useful for the prediction of the model, PLS has been applied not only in confirmatory
analysis but also in exploratory study where the theoretical background might be weak.
4.3.3.6.5 Differences in Approaches between PLS and CBSEM
There are several substantial differences between the approaches of PLS and CBSEM.
Both the PLS and the CBSEM approaches have advantages in testing research models.
PLS was developed as, and can be viewed as, a complementary analysis to the CBSEM
(Chin & Newstead 1999), since it has a prediction-based orientation. A further detailed
comparison of these two methods is given in Table 4.6.
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Table 4.6 Comparison between PLS and Covariance-based Approach Criterion Component-based (PLS) approach Covariance-based approach
Objective. Prediction oriented. Parameter oriented.
Required theory base. Applicable is scarcity of prior theory. Supports both exploratory and confirmatory research.
Requires sound theory base. Supports confirmatory research.
Approach. Variance based. Covariance based.
Assumptions. Fewer assumptions: predictor specification (non-parametric) distribution free.
Stringent Assumption: normal distribution and independent observations (parametric).
Parameter estimates. Consistent as both indicators and sample size increase.
Consistent in all conditions.
Latent variable scores. Explicitly estimated. Indeterminate.
Epistemic relationship between LV and its indicators.
Can be modelled in either formative or reflective mode.
Can be modelled in reflective mode only.
Observations on indicators.
Nominal, ordinal and interval scaled. Ratio preferred.
Implications. Optimal for prediction accuracy. Optimal for parameter accuracy.
Model evaluation. High R2, cross-validation test for predictive relevance, jack-knifing or bootstrapping for significance test.
Goodness-of-fit (overall model fit, e.g. insignificant x2).
Model complexity. Large complexity (e.g. 100 constructs and 1000 indicators).
Small to moderate complexity (e.g. fewer than 100 indicators).
Model identification. No identification problem.
Sample size. Minimal recommendations range from 30 to 100 cases.
Minimal recommendations from 200 to 800 cases.
Source: Chin & Newstead (1999, p. 314)
4.3.3.6.6 PLS Model Evaluation
This section provides discussion of the evaluation techniques using PLS. Similarly to
CBSEM, the broad categories of the PLS model evaluation can be expressed as
examining the structural model and the measurement model. The capability of each
multi-item scale in capturing its construct is examined using the measurement model.
The data in the measurement model is evaluated to determine the validity and reliability
of the survey instruments. In this thesis, the data is evaluated by examining the
individual loading of each item, internal composite reliability, average variance
extracted (AVE) and discriminant validity (Chin 1998a). After some necessary
adjustment of items and acceptance of the measurement model, the second step, a
structural model, is evaluated to assess the relationship between constructs (latent
variables). In the structural model, the hypotheses are tested by assessing the path
coefficients (standardised beta), t-statistics and r-squared value (Chin 1998a). Details on
the operationalisation of PLS evaluation to test the proposed conceptual model and
hypotheses of this thesis will be discussed in Chapter Six.
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4.3.3.6.7 Justifications for Applying PLS in This Research
1. The conceptual model proposed in this thesis can be considered complex since it
incorporates four main constructs; two of these are second-order constructs,
measured by several first-order constructs. PLS is better suited for understanding
complex relationships (Fornell et al. 1990; Chin 1995, Chin & Newsted 1999).
2. The objective of this thesis is to understand the nature of the relationship among
service quality, customer value, customer satisfaction and behavioural
intentions. The relative influence of service quality, customer satisfaction and
customer value on behavioural intentions is examined simultaneously in the
model. Since PLS is a second-generation model, an advanced technique which
enable the prediction of a large set of independent to dependent variables
simultaneously, it is considered as most suitable for this thesis.
3. PLS does not require normally distributed data and works well in coping with
small sample size. In marketing research and social science, it is common that
data do not satisfy the requirements of normally distributed data and difficulties
in attaining adequate number of respondents.
4.3.3.6.8 Computer Software used for Analysis
Statistical Program for the Social Sciences (SPSS) version 16 is a software that is used
for the preliminary data analysis and factor analysis in this thesis. While SPSS is a
statstical program that has been widely used in social science, PLS is less widespread.
PLS Graph, a software application for path modelling with latent variables, is used to
carry out the data analysis for this research. PLS Graph was developed by Professor
Wynne Chin. In the marketing and management areas, the use of PLS software has been
noted in a number of studies (Ryan et al. 1999; Murgulets et al. 2001; Westlund et al.
2001; Helm 2005; Venaik et al. 2005; Witt & Rode 2005, Ulaga & Eggert 2006,
Whittaker et al. 2007, Wang et al. 2004, Wang et al. 2007, Miller 2007).
4.4 ETHICS CONSIDERATIONS
The issue of ethics and confidentiality is important in doing marketing research (Ferrel
& Fraedrich 1991; Tsalikis & Fritzsche 1989). Churchill (1995) states that ethics relates
to the development of moral standards that usually apply in a situation where actual or
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potential harm in the physical, mental or economic spheres may occur to some
individual or group. Ethics basically considers whether an action is considered as right
or wrong or good or bad (Malhotra & Miller 1998). In the context of this research, the
primary concern is the ethical consideration when dealing with the respondents. Some
important ethical issues in this context are the right of the respondents to be voluntarily
involved, the right to be informed and to the right to privacy. Additionally, when
conducting research, the researcher must be truthful, objective and ensure the
confidentiality of the information provided by the respondents (Zikmund 2003).
The approval for this research was granted by Swinburne University’s Human Research
Ethics Committee (SUHREC) on 1 June 2007, as SUHREC Project No. 0607/203
(Appendix 9). Swinburne University of Technology requires research that involves
human intervention to obtain clearance from the University Human Research Ethics
Committee. At all stages of the research, the information sheets were used to ensure the
right of the respondents regarding the clarity of information, privacy, confidentiality and
voluntariness, and the obligations of the researcher to maintain ethical standards and to
provide accessible contact.
4.5 CONCLUSION
This chapter discusses Phase Two of Kumar et al.’s (1999) systematic research model
by providing a detailed discussion on the research design (see Figure 4.1). The research
design provides a foundation for the research methodology adopted in this thesis. In
addition to providing the research design, this chapter also reviews a philosophical
discussion of the methodology employed in this thesis. A philosophical argument of the
positivistic paradigm of research was decided to be appropriate and this paradigm is
used as the foundation for subsequent discussions of the research process.
The research design consists of two main parts, the research approach and research
tactics. Given that the primary object of this thesis is to examine the relationships
among service quality, customer value, customer satisfaction and behaviour intentions,
the approach that is followed by this research is a causal research. A quantitative survey
is employed to test the hypotheses that had been developed in Chapter Three. A self-
administered on-site survey is chosen as the data collection method. A detailed review
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of research tactics is further presented which covers: construct developments and
operationalisation; pre-testing; scaling; sample planning and statistical methods. Partial
Least Squares (PLS) in particular was chosen as an appropriate statistical method for
answering the research questions and testing the proposed hypotheses. Discussions on
the results of the statistical analysis to answer the research questions and test the
hypotheses proposed will be presented in detail in the following chapters.
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CHAPTER FIVE
THE PRELIMINARY ANALYSIS
5.1 INTRODUCTION
A broad discussion of the methodology employed in this thesis was undertaken in the
previous chapter. This chapter presents the preliminary empirical results of the
quantitative study and is examined according to the following broad structure:
1. General demographic description and descriptive analysis
2. Data screening
3. Reliability Analysis (RA)
4. Exploratory Factor Analysis (EFA) using Principal Component Analysis (PCA)
The preliminary analysis is intended to provide a broad overview of the data that has
been collected, in order to explore its general characteristics and to highlight any
shortcomings. After the data had been screened and prepared for analysis, a PCA was
undertaken on all of the key constructs. A PCA was employed to test the
unidimensionality of the measures since the conceptual model involves second-order
factors. This served as preparation for a more thorough examination of the proposed
structural model using Structural Equation Modelling (SEM) with Partial Least Squares
(PLS) technique.
5.2 DESCRIPTIVE ANALYSIS
The surveys were distributed to the Business Faculties at the five selected universities in
Yogyakarta: University of Pembangunan Nasional (UPN), Islamic University of
Indonesia (UII), Gadjah Mada University (UGM), Yogyakarta State University (UNY)
and Atmajaya University (UAJ). Different disciplines that characterised the Business
Faculties in Indonesia (management, accounting and economics) were accessed in order
to increase the varieties of backgrounds among respondents and the generalisability of
the findings. Samples were also collected from students who were already in their
second year or above. The reason for selecting second year students or above was
because of their considerable experience in making appropriate evaluations of their
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institutions. Figure 5.1 shows the number of respondents according to the participating
universities.
Of the 647 surveys that have been collected, four were considered unsatisfactory since
respondents only completed the first and second pages of the total six pages of survey
documents. Using the recommendation from Hair et al. (2006), those four surveys were
omitted from the analysis since more than 20% of the items were unanswered. The total
response rate for this research was relatively high, at 647 out of 750 total survey forms
distributed (85.73%).
Figure 5.1 Undergraduate Respondents from Five Universities
5.2.1 Sample Characteristics
Table 5.1 provides a summary of respondent characteristics. The number of male
respondents was 6% higher than the number of female respondents. This survey data
also provided almost equal proportions of samples in terms of disciplines, providing
samples from management (39%), accounting (32%) and economics (29%). More
specifically, Figure 5.2 illustrates the proportion of disciplines in each university. The
provision of samples from varieties of disciplines will facilitate the generalisation of the
results. Among the five universities involved (UPN, UII, UGM, UNY and UAJ), three
were private (UPN, UII, UNY) and the other two were public universities (UGM and
UNY). The survey collected of 61% its samples from private universities and 39% from
public universities. The higher proportion of samples from the private universities was
obtained because there were only two public universities running a Business Faculty in
Yogyakarta, compared to seventeen private universities. Section 4.3.3.5.1 gives the
reasoning behind the selection of the universities.
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Table 5.1 The Respondents’ Characteristics
Variable Number (%)
Gender Male 343 (53%)
Female 300 (47%)
University status Public 253 (39%)
Private 390 (61%)
Total 643 (100%)
Age Ranging from 18 to 27 years old, mean: 21 years old.
Disciplines of study Management 253 (39%)
Accounting 208 (32%)
Economics 186 (29%)
Figure 5.2 Respondents’ Characteristics Based on Discipline
5.3 MISSING VALUE ANALYSIS
As stated in Section 5.2, four cases were deleted due to missing responses accounting to
more than 20% of the total items (Hair et al. 2006) and, thus, a sample of 643 provided
the data for the comprehensive empirical analysis. Hair et al. (2006) state that cases with
a missing value ratio of less than 20% can be retained in the data set since this
proportion would not affect the overall results. Cohen and Cohen (1983) suggest that up
to 10% of missing data was considered not large and unlikely to be problematic.
Tabachnick and Fidell (2001), however, recommend that missing values should not be
more than 5%. A preliminary analysis of missing data was carried out to produce clean
data. Among the 643 cases, there was no evidence of missing values exceeding 5% per
case. Further analysis based on the input data matrix (SPSS input data), the conceptual
model contained 643 cases and 69 indicators for each respondent. From the total of
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44,367 data points (643 x 69 = 44,367), there were 53 points missing or unanswered.
This indicated that the missing value in this survey was not a major problem.
In handling the missing data, this thesis uses the EM (Expectation-Maximation) method
for the replacement of missing data since it offers significant methodological
advantages (Hair et al. 2006; Tabachnick & Fidell 2001). The EM method is an iterative
process which uses all other variables relevant to the construct of interest to predict the
values of the missing variables (Cunningham 2008). Based on the Monte Carlo
experiments, the EM method of data imputation was found to be more consistent and
accurate in predicting parameter estimates than other methods such as list-wise deletion
and means substitution (Graham et al. 1997 in Cunningham 2008). Cohen and Cohen
(1983) suggest that researchers should use missing data technique that preserves data,
rather than techniques such as list-wise deletion.
The EM can also be used to check whether the missing data occurs in a random manner.
The randomness of missing data can be found in the “Little’s MCAR” test (Missing
Completely At Random) shown in the ‘EM estimated statistics’. To be considered as
missing completely at random, the missing data should have the χ2 (chi-square) which is
not significant at an alpha level of 0.001 (Cunningham 2008). The result of the missing
data estimation process in this thesis showed the value of χ2 statistic of 0.841, which
means that it was not significant and, therefore, the missing values in this thesis had
occurred in a random manner.
5.4 NORMALITY AND OUTLIERS
5.4.1 Normality
Even though normality is not a necessary condition for PLS evaluation, Tabachnick and
Fidell (2001) argue that solutions will be improved when indicators display normal
distributions. All of the indicators in this thesis were subjected to a test of normality
using histograms, box-plots, skewness and kurtosis. Many of the indicators had
significant levels of negative skewness, as measured by a statistic greater than two
standard errors of skewness. Similarly, kurtosis of these data was also problematic. The
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individual histogram showed a similar result where many of the data were negatively
skewed.
Anderson and Fornell (2000) have identified a tendency for the data to be negatively
skewed in the study involving customer perception on satisfaction. Therefore, having
negatively skewed data in the measure is not surprising. Many respondents tend to give
a neutral to very good response, which causes value at the lower end of the scale to be
under-represented. The same case appeared to occur in this thesis where there was
tendency for students to choose the higher end value (above 3) of the Likert scale which
led to the skewing of the data.
5.4.2 Outliers
Outliers are extreme observations and may create great difficulty (Neter et al. 1996).
Outliers should be removed or modified in order to reduce their influence (Coakes &
Steed 2003). In this thesis, after employing univariate and multivariate detection of
outliers, results showed some existence of outliers. However, after a thorough
examination of the outliers, the researcher decided to retain the outliers since they
represented a segment of the population. Hair et al. (2006, p.73) asserts that “outliers
should be retained unless there is demonstrable proof that they are truly abnormal and
not representative of any observations in the population”. In addition, Neter et al. (1996)
suggest that outliers can be discarded only if there is direct evidence that they represent
an error in recording, miscalculations or similar types of circumstance. Having
scrutinised the data from data screening, the next step is to specify whether the model is
reflective or formative before the reliability and validity of the measures can be further
analysed.
5.5 REFLECTIVE VERSUS FORMATIVE MEASURES
As has been proposed in the conceptual framework (Figure 3.6), this thesis attempts to
identify the structural relationships among the latent constructs. The employment of
latent constructs in social science has been a common practice (Diamantopoulos et al.
2008). A latent variable is a variable that cannot be directly measured, thus requiring
indicators (measures) which are more observable to operationalise the construct (see
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Section 4.3.3.6.3.1). The application of SEM methodology enables latent constructs to
be examined. In SEM, there are two basic compositions which are known as the
measurement model and the structural model (Byrne 2001). The measurement model
concerns the relationships between latent constructs and indicators, while the structural
model concerns the relationships among latent constructs. Anderson and Gerbing (1988)
suggest that the distinction between the measurement and structural models must be
clear, in the sense that a proper specification for the measurement model is important to
enable the assignment of the meaning for the relationships implied in the structural
model. In this respect, the direction of the relationship between latent construct and
indicators must be clearly specified. Whether the direction of the relationship is from
indicators to latent construct (formative indicators), or from latent construct to
indicators (reflective indicators) must be evident.
When applying SEM, the decision to model indicators as formative or reflective is
crucial since the direction of the relationship between the indicators and their respective
latent variable in the formative and reflective models is different. To support the proper
specification of the measurement model (Anderson & Gerbing 1988), researchers must
carefully consider whether the constructs under investigation are reflective or formative.
In addition, since this thesis employs the Partial Least Squares (PLS) technique which
enables a researcher to analyse both reflective and formative models along with the
structural model, clear specification of model indicators is essential.
The reflective measures/indicators reflect the latent variable/construct. The reflective
indicators are called effect indicators as they indicate the effect of the latent variable
(Bollen & Lennox 1991). When assigning the model indicators as reflective, Chin
(1998a) argues that two considerations should be borne in mind. First, it should be
possible to conceptually argue that the indicators are believed to reflect the latent
construct. In other words, the latent construct is causing the indicators. Secondly, the
measures (latent constructs and indicators) should be positively correlated (theoretically
and empirically).
In the formative model, the indicators create the latent variable and are called ‘cause
indicators’ since they cause the latent variable (Bollen & Lennox 1991). When
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assigning the indicators to the formative model, it is important to ensure that the
indicators are relatively independent, that is, in the condition where the sample is large
enough, no multicollinearity problems exist (Venaik 1999). Further, since the indicators
cause the latent construct, it is not necessary to examine the correlation or internal
consistency (Bollen 1984; Venaik 1999).
5.5.1 Service Quality
The service quality construct is measured in six dimensions (tangible, competence,
content, attitude, reliability and delivery), and each dimension is further measured with
three to six items (see Table 4.4). Theoretically, the indicators reflect the latent
variable/construct. For example, understanding of students’ need and/or willingness of
the staff to help reflect the attitude as their latent construct. At the same time, tangible,
competence, content, attitude, reliability and delivery also reflect service quality.
Therefore, all measures and dimensions of the service quality construct are considered
as reflective. Previous studies that have modelled service quality as a reflective second-
order construct include Caruana et al. 2000, Agus et al. 2007, Cristobal et al. 2007, Choi
et al. 2004, Tsoukatos et al. 2006 and Olorunniwo et al. 2006. Appendix 3, Table B,
illustrates the correlation results among the service quality constructs.
5.5.2 Customer Value
The customer value construct is measured with five dimensions (reputation, emotion,
social, price and quality), and each dimension is further measured with three to five
items (see Table 4.4). Unlike service quality, in which all studies employed reflective
conceptualisation of the construct, there are still debates over whether the formative or
reflective conceptualisations should be used in studies of the customer value construct.
On the condition that customer value is defined as a trade-off between benefits and
costs, the group that supports the formative concept argues that the dimensions of
customer value are independent and should not correlate to each other. Even though
benefits and costs may correlate, there was no theoretical support for the existence of
the relationship. Benefits and costs are not interchangeable, as they do not satisfy the
requirement of reflective concept (Lin et al. 2005).
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Nevertheless, a study by Sweeney and Soutar (2001) found that the functional and
emotional aspects of customer value may not be independent. In other words, the
hedonic and utilitarian components of a human’s attitude may be correlated (Osgood et
al. 1957 in Sweeney & Soutar 2001). An example of the non-independent nature of
functional and emotional value was explained through the case of carpet purchase
(Sweeney & Soutar 2001). When an individual purchases an attractive carpet, the
purchase may increase the opportunities for both a favourable emotional and functional
responses. There were also some other multidimensional constructs that have been
found to have separate but correlated dimensions. “Indeed, many other
multidimensional constructs, including organizational commitment (Mowday et al.
1979), wellbeing at work (Warr 1990), retail service quality (Dabholkar et al. 1996) and
communication-evoked mental imagery (Babin & Burns 1998), have been found to
have separate but correlated dimensions” (Sweeney & Soutar 2001, p. 206). In
conformity with the Sweeney and Soutar (2001) argument that it is acceptable for all of
the dimensions of customer value be interrelated, the reflective approach is followed.
In addition to the theoretical justification for assigning customer value as a reflective
construct, empirical or statistical tests were used to confirm that all of the measures in
the model are reflective. The reflective measure requires that all of the indicators
correlate with other indicators within the construct that they are supposed to measure.
Appendix 3, Table C, illustrates the correlation results for the customer value
constructs. Furthermore, Exploratory Factor Analysis (EFA) and Confirmatory Factor
Analysis (CFA) will be employed to test the psychometric properties of all measures
used in this thesis. A summary of the previous studies that have conceptualised
customer value as a reflective model were presented in Table 2.14.
5.5.3 Second-Order Model of Service Quality and Customer Value
As stated earlier, past studies have identified the multidimensional conceptualisations of
service quality (see Table 2.7) and customer value (Table 2.14). Both service quality
and customer value constructs in this thesis were measured as multidimensional
constructs. In addition to conceptualising service quality and customer value as
multidimensional constructs, this thesis also models both as second-order constructs
measured by their respective first-order constructs (see Conceptual model Figure 3.6).
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The service quality construct consists of six first-order constructs (tangible,
competence, content, attitude, reliability and delivery). The customer value construct
consists of five first-order constructs (reputation, emotion, social, price and quality).
According to Podsakoff et al. (2003), higher-order models are recommended for social
researchers in a condition when the construct is complex. This is because each
dimension in the higher-order models are treated as an important component of the
higher construct. The dimensions of a multidimensional construct can be conceptualised
under an overall abstraction (Law et al. 1998). This overall abstraction is usually called
the second-order factor. It is theoretically meaningful and parsimonious to use this
overall abstraction as a representation of the dimensions (Law et al. 1998, p. 741).
When the model is hypothesised as a second-order model, the argument for the second-
order is essentially theoretical (Cunningham 2008). Cunningham (2008) further argues
that in most cases, first-order and second-order models are equivalent, thus only a little
is gained from specifying the second-order and first-order correlations. The objectives
in applying the multidimensional concept and the second-order approach in this thesis
were to facilitate the incorporation of a comprehensive aspect of service quality and
customer value, as well as creating a more parsimonious model when simultaneous
structural relationships among several constructs are involved.
Equally, when involving second-order model, the reflective and formative
conceptualisation assigned to the model is also an important issue. The theoretical
justifications for assigning a reflective conceptualisation of service quality and customer
value have been provided in the previous Sections (5.5.1 and 5.5.2). The theoretical
justifications need to be further supported by several statistical analyses, to ensure that
the measures are valid and reliable. Since this thesis conceptualises service quality and
customer value as reflective second-order measures, the statistical test must follow the
required test for the reflective model.
5.5.4 Satisfaction and Behavioural Intentions
Customer satisfaction and behavioural intentions were also conceptualised as reflective
models since the indicators reflect the latent construct. Other than the service quality
and customer value constructs, the correlation analysis among the indicators of
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satisfaction and behavioural intentions has also been examined and showed that the
indicators were correlated in the expected direction within the underlying construct, thus
satisfying the requirement for reflective indicators.
A fundamental characteristic of the reflective model is that all measures must be
positively intercorrelated (Bollen 1984). As a consequence, the correlation or internal
consistency must be examined. In order to examine the validity and reliability of the
measures, this thesis not only employs RA and EFA, but also more importantly, CFA
with PLS technique is applied as the main statistical techniques. Both the EFA and CFA
provide an adequate means of examining the measurement model, covering the 1)
testing of item reliabilities; 2) convergent validity and 3) discriminant validity.
Since this thesis uses PLS for examining the main analysis, running EFA is only
optional. This is because PLS already combines factor analysis and path analysis.
Nevertheless, conducting EFA before CFA is useful as an initial strategy to help
develop the proposed measurement model (Hair et al. 2006) (see discussion Section
5.6.2). More specifically, applying EFA before CFA is important, when the model
involves the operationalisation of a second-order construct measured by its associated
first-order constructs. The rule-of-thumb for measuring the second-order constructs is
that the items in each of the first-order construct should be unidimensional (Gerbing &
Anderson 1984).
Having discussed that all measures are specified as reflective model, the next step is to
analyse the reliability and validity of the measures as necessary requirements for
reflective model.
5.6 RELIABILITY AND VALIDITY
5.6.1 Reliability Analysis (RA)
The selected measures need to demonstrate good psychometric properties. That is, the
measures need to be both ‘reliable’ and ‘valid’. A measure is considered reliable when it
provides consistent or repeatable results. Based on internal consistency, the term
reliability refers to the degree to which responses generated from all items to measure a
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construct are consistent (Kline 2005). On the other hand, validity is concerned with the
soundness of the inferences based on the scores; more specifically, it considers
“whether the scores measure what are they supposed to measure” (Kline 2005, p. 59).
Overall, when measures demonstrate poor reliability and/or validity properties, the
measures are said to be statistically biased.
In the initial analysis, it is important to examine the internal consistency by using
Cronbach alpha. To determine the quality of the measurement, Churchill (1979)
suggests that coefficient alpha should be employed. In addition, Nunnaly (1978, p. 230)
suggests that “coefficient alpha provides a good estimate of reliability in most situations
since a major source of measurement error is caused by the sampling content”. A high
coefficient alpha indicates that the set of items performs well in explaining the construct
(Churchill 1979).
There are different views of what are acceptable scores for assessing internal
consistency using Cronbach’s alpha. Nunnaly and Bernstein (1994) recommend a
reliability value greater than 0.7. However, based on recommendations from Hair et al.
(2006) and Aiken (2006), several marketing studies have accepted reliability greater
than 0.6. Rossiter (2002) suggests that a construct with three to five items that yield
alpha between 0.7 and 0.8 is considered ideal. For exploratory research, an appropriate
level of coefficient alpha is 0.5 to 0.6, for theoretical (basic) research the level is 0.8,
and for applied (decision) research the acceptable level is up to 0.9 (Finn & Kayande
1997; Nunnaly 1978). This thesis follows the recommendations from Hair et al. (2006)
suggesting alpha greater than 0.6 is acceptable.
5.6.2 Exploratory Factor Analysis (EFA)
Factor analysis is a statistical technique which is designed to identify the dimensions
that underlie the relationship among a set of observed variables (Pedhazur 1991). This
technique is commonly used to summarise the information contained in a large number
of observed variables and to explain the common underlying dimensions in these
variables (Hair et al. 2006). Factor analysis is valuable when data are complex and a
researcher is uncertain of what the most important variables in the field are (Kline
1994).
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There are two basic types of factor analysis: EFA and CFA (Hair et al. 2006). EFA is a
multivariate analysis technique used to analyse the structure of the correlations among a
large number of variables (Hair et al. 2006). In order to achieve a good measure of each
construct, EFA is important in assessing the unidimensionality of the measures as well
as in identifying the internal consistency of the items. However, EFA has two
limitations. First, the process is statistically rather than theoretically driven; second, “all
the items load into all latent variables” (items which are not intended to load into any
particular latent variable are still specified to load into that latent variable) (Cunningham
2008, p. 3-5). For these reasons, CFA is becoming popular for its ability to redress
EFA’s limitations.
CFA is a method used to empirically test theories about measurement models when
there is a strong rationale for specifying the factors and the items that should define
each factor (Cunningham 2008). It is common in the CFA that researchers already have
a priori assumptions about the structure of the data and the interrelationships in the
model. In this instance, the application of CFA should be undertaken to test the extent to
which data fits the expected outcomes.
There is extensive debate in the literature regarding the use of EFA and CFA. The main
difference between EFA and CFA is that models in CFA must be specified a priori
(Hair et al. 2006). CFA may be more appropriate when the measurement models have a
well-developed underlying theory (Hurley et al. 1997). CFA, however, is criticised for
being over-applied and used in inappropriate situations (Hurley et al. 1997). In contrast,
EFA is considered appropriate in instances in which theoretical understanding of the
interrelationships between latent and measured variables is less known (Cunningham
2008). Kelloway (1995) provides further agreement that EFA is often considered to be
more appropriate than CFA in the early stage of the scale development. EFA is
appropriate to identify multiple variables and the results can be useful to develop
theories that will lead to proposed measurement model (Hair et al. 2006). Hair et al.
(2006) further recommend that CFA can be used to confirm the measurement model
that has been developed using EFA. Coakes and Steed (2003) suggest that EFA can be
used as an exploratory technique to summarise the structure of a set of variables, while
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CFA is appropriate for testing a theory regarding the structure of a particular variable.
Anderson and Gerbing (1988) suggest that the distinction between EFA and CFA can be
considered as an ordered progression in which EFA should be examined prior to CFA.
EFA was recommended as a useful initial strategy to determine the unidimensionality of
the model. CFA was then recommended for evaluating the model derived from EFA.
Based on the arguments above, it is appropriate to employ both EFA and CFA in this
thesis. EFA was used to summarise the structure of the constructs of service quality,
customer value, customer satisfaction and behavioural intentions. The EFA technique
used is PCA. When doing EFA, there are two broad categories of rotation that can be
applied: orthogonal and oblique. The choice in rotation is aimed at meeting the criterion
of ‘simple structure’. This thesis employs the orthogonal rotation using varimax, since it
is considered to be the most efficient procedure for achieving the simple structure
(Tabachnick & Fidell 2001). The varimax rotation simplifies factors by maximising the
variance of the loadings within factors (Tabachnick & Fidell 2001).
5.6.2.1 Criteria for Interpreting the EFA Results
As was discussed in Chapters Three and Four, the model and the measures employed in
this thesis were not newly developed but based on previous studies. This thesis
modified Cronin et al’s. (2000) “Research Model” by proposing the service quality and
customer value constructs into a multidimensional construct involving first-order and
second-order constructs. The measurements involved combinations of existing measures
in the general services and higher education sectors. A newly developed item was also
proposed. The indicators were modified from the original sources to capture the specific
context of the higher education sector. Since several modifications and adjustments
were made to the indicators to ensure that they would work with the context of the
Indonesian higher education sector, it is important to examine the validity and reliability
of the measures to ensure that the proposed measurements have an acceptable
psychometric property.
There are various opinions regarding the rule-of-thumb for determining the minimum
requirement for reliable and valid measures. Table 5.2 provides a brief summary of the
standards used in this thesis for performing the measurements and interpreting the
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results of EFA using PCA. The standards provide guidance to indicate that the item
used passed all the requirements and can be considered as having an acceptable level of
validity.
Table 5.2 The Standard Used in Performing and Interpreting EFA Rules of Thumb Sources
Exploratory Factor Analysis Use at least two multi-item measures together, not single measures. Use principal component analysis if 20 or more variables. Need eigenvalues >1 and evidence from scree plot to accept factors. Cross loading when > 0.30 on two or more factors (recommended to drop). Communality should be >0.5.
Hinkin (1995) Nunnaly & Bernstein (1994) Tinsley & Tinsley (1987) Nunnaly & Bernstein (1994) Hair et al. (2006)
Loading Factor Need factor loading >0.32. Factor loading 0.3 - 0.4 (considered to meet the minimal level). Factor loading ± 0.5 (considered practically significant). Factor loading > 0.7 (considered as a well defined structure).
Tabachnick & Fidell (2001) Hair et al. (2006) Hair et al. (2006) Hair et al. (2006)
Source: Marimuthu (2008)
5.6.2.2 Reliability and EFA Findings from the Preliminary Analysis
Running EFA is optional when PLS is employed for the main analysis. As in the case of
this thesis, applying EFA before CFA is important since the model involves the
operationalisation of a second-order construct measured by its associated first-order
constructs. The PCA in this thesis was specifically used to examine the
unidimensionality of the first-order constructs of service quality (tangible, competence,
attitude, delivery, reliability and content) and customer value (reputation, emotion,
quality, price and social). Customer satisfaction and behavioural intentions were also be
subjected to RA and PCA for the examination of their psychometric properties. In
addition, even though EFA provides the test of unidimensionality and construct validity
for the measures used in this thesis, the final decision regarding the removal or retention
of the measures will be confirmed by CFA using PLS in the following chapter.
5.6.2.2.1 Measures of Service Quality
As has been discussed in Section 2.4.6.3.1, this thesis adopts the dimensions of service
quality proposed by Owlia and Aspinwall (1998) who developed six dimensions to
measure service quality: tangibles; reliability; delivery; content; competence; and
attitude. Before being subjected to PCA, the reliability test using Cronbach’s alpha
showed alpha values ranging from 0.676 (reliability) to 0.917 (tangible), which were
considered acceptable/satisfactory (Hair et al. 2006; Aiken 2006).
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The results from PCA showed a formation of five factors of service quality (tangible,
content, competence, attitude and delivery). As an initial step, Kaiser’s latent root
criterion (Eigenvalues>1) was applied as a guide for extracting factors, which gave an
indication of the existence of five factors. The scree-plot was also examined and
similarly indicated the presence of five factors. The result from PCA showed a different
structure from “the revised framework” for service quality dimensions in higher
education as proposed by Owlia and Aspinwall (1998). Instead of five factors, Owlia
and Aspinwall’s (1998) revised framework suggested six dimensions/factors of service
quality.
Based on the communality table, there were seven items/indicators (A5, A6, A11, A14,
A19, A20 and A22) that were indicated with values lower than 0.5. This means that
these items did not fit well with the factor solution. Communalities refer to the
estimation of variance in each item explained by the factors (components) in the factor
solution (Factor Analysis 2009). Low communality value indicates that the item does
not fit well with the factor solution. The communality must account for > 0.50 to have
sufficient explanatory power (Hair et al. 2006, p. 117). However, it should be noted that
communality coefficient per se is not of singular importance, but the extent to which the
item plays a role in the interpretation of the factor is more important (Factor Analysis
2009). Communalities must be interpreted in relation to the interpretability of the
factors. A high value of communality (>0.5) can be meaningless unless the factor on
which the indicator is loaded is interpretable. On the other hand, a low communality
indicator (<0.5) may be meaningful if the indicator is contributing to a well-defined
factor.
The rotated component matrix of all 28 items of service quality (Table 5.3) has
identified some items that had a factor loading of < 0.5. Even though item loading >
0.32 is acceptable, as suggested by Tabachnic and Fidell (2001), the items that have
loadings of less than 0.5 were subjects of particular concern, since low loadings indicate
problems with convergent validity. The discussions of the results of the Rotated
Component Matrix will be presented according to the dimensions that have been
identified from the PCA on the service quality construct.
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Factor 1 (Tangible)
There were nine items loaded in Factor 1 as a result of PCA (see Table 5.3). Factor 1
was named as tangible since the majority of the items that loaded in this Factor 1 were
among the items that were initially designed to measure tangible. This factor showed a
very strong internal consistency as identified with the high value of Cronbach’s alpha
0.902. Six of the items (A23-A28) were all loaded with values higher than 0.6 while
three items (A5, A21 and A22) were loaded below 0.5, a rule-of-thumb to be considered
as practically significant measures (Hair et al. 2006). In the initial stages, items with
small loadings of 0.50 to 0.60 may be used (Hair et al. 2006). All of the three items
with low loadings were initially designed to measure other dimensions. Items A21
(degrees school handle feedback) and A22 (personal information is secure) were
supposed to measure reliability, whereas item A5 (sufficient number of staff) was
designed to measure competence. However, since they loaded into tangible, the
interpretability of these three items has become problematic and should be carefully
assessed.
Despite being identified as having a low loading, items A21 and A5 also cross-loaded.
Cross-loading is a condition under which a variable (item) is found to have more than
one significant loading (Hair et al. 2006). When there is an evidence of cross-loading, it
is recommended that the item be dropped (Nunally & Bernstein 1994). Dropping all of
the three items (A5, A21 and A22) increased the internal consistency from 0.902 to
0.917. As a consequence, it appears that there is potential for these three items to be
removed based on the evidence of low loading, cross-loading and possible problems
with interpretability. Nevertheless, since EFA in this thesis is employed to support the
main analysis using CFA, no final decisions were made about removing or retaining all
of the items identified as problematic. The final decision will be confirmed by CFA
analysis using PLS.
Factor 2 (Attitude)
The reliability of this dimension was 0.844 which is considered acceptable (>0.6)
according to the recommendation of Hair et al. (2006). All of the items that loaded into
Factor 2 (attitude) were higher than 0.5, with the lowest being 0.584 for item A6
(support staff competent). Despite having a satisfactory level of reliability and
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practically significant factor loadings, there were also no evidences of cross loading.
This means that all of these items measured the attitude dimension better than other
dimensions. However, careful attention (in terms of interpretability) must be given to
item A6 since this item was initially designed to measure the competence dimension.
Factor 3 (Content)
Similar to tangible, Factor 3 (content) contained some items that were not supposed to
load with Factor 3. The result of the reliability test using Cronbach’s alpha was 0.834,
showing an ideal internal consistency (Rositer 2002). The items that loaded in Factor 3
(content) came mostly from the content dimension (A14-A19). All of the content items
loaded with values > 0.5 which is considered practically significant.
Nevertheless, there were also two new items which were initially not designed to load
into Factor 3 (content) (A20 and A11). These items were all have low loading (< 0.5).
Item A20 (credibility of degree awarded) was initially designed to load into the
reliability, whereas item A11 (courses offered are stimulating) was designed to load into
delivery. In addition, despite being identified as having low loading, items A20 and A11
also cross-loaded. Accordingly, even though there was an ideal internal consistency,
these two items cannot be considered as valid based on the evidence of low factor
loadings and cross-loading. Furthermore, since both items did not load into the
designated dimensions, the meaning will be potentially problematic and should be
carefully examined.
A further examination of Factor 3 (content) has identified that items A17 and A18 were
also cross-loaded. However, the cross-loading in these items can be ignored based on
the following arguments: 1) both of these items were conceptually meaningful to
measure content since they were developed based on a thorough literature review and
have been undergone through pre-testing; 2) both items have acceptable loading levels;
and 3) together with other items, they produced an ideal level of internal consistency.
Overall, there were two items (A20 and A11) identified as problematic, since they
showed evidence of low item loadings, cross-loading and possible problems with
interpretability.
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Factor 4 (Competence)
The reliability of Factor 4 (competence) was 0.734 which was considered as acceptable
(>0.6) according to the recommendation of Hair et al. (2006). The items that loaded into
Factor 4 (competence) were all higher than 0.5. Item A2 (academic staff up-to-date) has
the highest loading (0.764) and item A4 (academic staff has relevant practical
knowledge) has the lowest loading (0.636). There were also no evidences of cross-
loading. This means that all of these items measure the competence dimension better
than other dimensions. Based on these satisfactory evidences, all items (A1-A4) that
loaded into competence can be considered as valid and reliable measures.
Factor 5 (Delivery)
The items that loaded into Factor 5 (delivery) consisted only of two items. It is
recommended by Hinkin (1995) that there should be at least two multi-item measures
together, not single item in order to be valid for a measurement. Nevertheless, there is a
risk of having a limited number of items since it may lead to a lack of validity. The
reliability of Factor 5 (delivery) was the smallest compared to the reliability values of
the other dimensions. The reliability of Factor 5 was 0.662. This value, however, still
meets the standard recommend by Hair et al. (2006). The lack of reliability in the
delivery dimension might be due to the limited number of items (two items). Malhotra
(1999) in Nasution (2005) observes an increase in the alpha value when the number of
items increases.
All of the loadings in Factor 5 (delivery) were >0.5, meaning that the loadings were
above the recommended rule-of-thumb. However, it was found that item A12 was
cross-loaded. Although item A12 cross-loaded, having the evidences of acceptable item
loading, internal consistency and meaningful concept, the cross-loading can be ignored.
In addition, since this factor consists only of two items, it is likely that item A12 will be
retained to maintain the content validity.
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Table 5.3 Exploratory Factor Analysis of Service Quality (28 items) No Measures/Items Factor
1 Factor
2 Factor
3 Factor
4 Factor
5 Tests
Tangible KMO: 0.929 Barlett Significance: 0.000
A23 Sufficiency of academic equipment. .843 A24 Ease of access to equipment. .838
A26 Access to information sources. .818 A25 Equipment is modern. .801 A27 Environment is appealing. .739 A28 Availability of support services. .664 A21 Degrees to which school handles feedback. .466 .402 .392 A22 Personal information is secure. .465 A5 Sufficient number of staff. .392 .316
Cronbach Alpha: 0.902
Attitude
A9 Provide clear guidance-advice. .805
A8 Willing to help. .770
A10 Provide adequate personal attention. .762
A7 Understand student's needs. .740
A6 Support staff competent. .584
Cronbach Alpha: 0.844
Content
A15 Degree which programs incorporate additional content.
.689
A17 Students learn communication skills. .648 .406
A16 Relevance of curriculum for future jobs. .643
A18 Students learn team working. .621 .358
A19 Applicability of knowledge to other fields. .621
A14 Degree to which programs contain basic knowledge.
.507
A20 Credibility of degree awarded. .439 .476
A11 Courses offered are stimulating. .354 .333 .370
Cronbach Alpha: 0.834
Competence
A2 Academic staff up-to-date. .764
A3 Relevant theoretical knowledge. .692
A1 Academic staff expertise. .688
A4 Relevant Practical knowledge. .636
Cronbach Alpha: 0.734
Delivery
A13 Exams cover materials presented in class. .734
A12 Presentation in logical-timely manner. .302 .541
Items loaded <0.3 were suppressed. Cronbach Alpha: 0.662
5.6.2.2.2 Measures of Customer Value
The measure of customer value in this thesis was developed by combining measures
proposed by Sweeney and Soutar (2001) and Petrick (2002). The reliability of each
dimension before being analysed using PCA ranged from 0.848 (emotion) to 0.924
(price). Further analysis using PCA performed almost similar structure between the
proposed measure in Table 4.4 and the PCA result shown in Table 5.4. By employing
21 items which reflect the five factors of customer value, the communality table showed
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a satisfactory figure with all of the items having no value less than 0.50, as the limit for
variables that should be considered for exclusion (Hair et al. 2006).
By comparison with the original measure proposed to measure customer value (see
Table 4.4), the result from PCA analysis provided a figure which was slightly different.
The discussions of the results of the Rotated Component Matrix (Table 5.4) are
presented according to the dimensions that have been identified from the PCA of the
customer value construct.
Factor 1 (Reputation)
As can be seen from Table 5.4, the reliability of Factor 1 (reputation) was 0.906 which
is well above the recommended value suggested by Hair et al. (2006). The items that
loaded into Factor 1 (reputation) were all higher than 0.5, the lowest being 0.625 (item
B21) and the highest 0.844 (item B19). However, there was evidence of cross-loading
with item B17 (this institution has a good reputation) which cross-loaded with one of
the items in Factor 3 (quality). There is a possibility that the cross-loading in this case
can be ignored based on these following arguments: 1) item B17 is conceptually
meaningful to measure reputation since it was developed from a grounded literature
review; 2) item B17 has an acceptable loadings level; and 3) together with other items,
it produced a high level of internal consistency. Overall, the validity of the measures
employed will be further examined by CFA analysis using PLS.
Factor 2 (Emotion)
The items that loaded in Factor 2 (emotion) numbered four (B13 – B16). The reliability
of Factor 2 (emotion) was 0.928 which was well above the recommended rule-of-thumb
(Hair et al. 2006). All of the items loaded very well with the values ranged from 0.742
(item B13) to 0.849 (item B15). There was also no evidence of cross-loading. Having
satisfactory results in terms of high reliability and item loadings, and there being no
evidence of cross-loading, meant that these measures can be considered as valid and
reliable to measure Factor 2 (emotion).
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Factor 3 (Quality)
As can be seen from Table 5.4, the reliability of Factor 3 (quality) was 0.923 which was
well above the recommended value (0.6) according to Hair et al. (2006). The items that
loaded into Factor 3 (quality) were all well above 0.5. Nevertheless, item B3 (institution
is dependable) was cross-loaded with one of the items in Factor 1 (reputation). Despite
directly removing the item that cross-loaded, this thesis will further examine the validity
of item B3 in the CFA as the main analysis. In addition, considering that B3 has a well-
defined factor loading (0.746) and is conceptually meaningful, the evidence of cross-
loading could be ignored.
Factor 4 (Price)
The reliability of Factor 4 (price) was not as high as the previous three dimensions,
being only 0.848. However, this value was still considered ideal (Rositer 2002) since it
was well above the recommended value (>0.6) (Hair et al. 2006). All of the items were
also loaded very well into Factor 4 (price) with the lowest value being 0.696 (item B7)
and the highest 0.817 (item B5). Nevertheless, among the four items measuring price,
item B6 (courses offer good value for money) and item B7 (institution has good
services for the price) were cross-loaded. In sum, despite the satisfactory level of factor
loadings and internal consistency, the decision to remove or retain the items that cross-
loaded will be later confirmed in the CFA.
Factor 5 (Social)
The reliability of Factor 5 (social) was 0.896. This value is considered acceptable (Hair
et al. 2006) since it was well above the recommended value (>0.6). All of the items (B9
– B12) were also loaded very well into Factor 5 (social) with the lowest value being
0.560 (item B12) and the highest 0.806 (item B10). This means that all of the items in
Factor 5 (social) loaded with values >0.5, and thus considered to have practically
significant loading (Hair et al. (2006).
Nevertheless, item B12 (makes me feel good) was cross-loaded with the item in Factor
2 (emotion). This item was also the only item that was not initially designated to load in
Factor 5 (social) since it was initially designed to load into Factor 2 (emotion). Even
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though item B12 loaded >0.5, the interpretability must be carefully examined since it
may not reflect the social aspect of customer value.
Table 5.4 Exploratory Factor Analysis of Customer Value (21 items) No Measures Factor
1 Factor
2 Factor
3 Factor
4 Factor
5 Tests
Reputation KMO: 0.938 Barlett Significance: 0.000
B19 This institution is well thought of. .844 B18 This institution is well respected. .822 B20 This institution has a good status. .800 B17 This institution has a good reputation. .668 .428 B21 This institution is reputable. .625
Cronbach Alpha: 0.906
Emotion B15 Makes me feel delighted. .849 B16 Gives me happiness. .829 B14 Gives me a sense of joy. .801 B13 Gives me pleasure. .742
Cronbach Alpha: 0.928
Quality B1 Institution has outstanding quality. .812 B2 Institution is reliable. .805 B3 Institution is dependable. .310 .746 B4 Institution has consistent quality. .720
Cronbach Alpha: 0.923
Price B5 Courses are reasonably priced. .817 B6 Courses offer good value for money. .358 .780 B8 Studying here is economical. .751 B7 Institution has good services for the
price. .366 .696
Cronbach Alpha: 0.848
Social B10 Give me good impression to other
people. .806
B11 Provides social approval. .803 B9 Studying here improves the way I am
perceived. .776
B12 Makes me feel good. .484 .560
Cronbach Alpha: 0.896
Items loaded <0.3 were suppressed.
5.6.2.2.3 Measures of Customer Satisfaction
The customer satisfaction construct was measured by using the combined instruments
developed by Athiyaman (1997), Cronin et al. (2000) and Mc Dougall & Levesque
(2000) adjusted to the higher education context. A total of eight items were used to
measure cumulative customer satisfaction. Item C9 was not included since it measured
overall satisfaction. Except for the measurement taken from Athiyaman (1997), the
measures of customer satisfaction were taken from the general marketing area. Since the
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measures of customer satisfaction in this thesis involved some modifications and
combinations of previous measures that were not specifically designed for the higher
education sector, these measures were subjected to exploratory analysis using PCA.
The Cronbach’s alpha of customer satisfaction constructs was 0.910. This value was
considered high for internal consistency. An analysis of eigenvalues and the screeplot
also suggested that one factor was formed to measure customer satisfaction. The values
shown in the extraction of the communality were not strong, even though most of them
were above 0.5 except for item C2 with value of 0.402. All of the eight items that were
used to measure customer satisfaction were loaded between 0.634 and 0.807 (Appendix
4 Table C). These values were considered acceptable and practically significant for the
initial stages (Hair et al. 2006). At this preliminary stage, there were no items
considered for removal from the customer satisfaction construct since all the items
satisfied the requirements for the reliability and validity test.
5.6.2.2.4 Measures of Behavioural Intentions
The behavioural intentions construct was measured using the combined instruments
developed by Boulding et al. (1993) and Athiyaman (1997). There were seven items
used to measure the behavioural intentions construct. The last item (D7) was newly
developed for this thesis to capture the non-monetary contribution of students as a
reflection of their loyalty. The behavioural intentions construct was also subjected to
PCA since the instruments used had been slightly modified for the higher education
context as well as the inclusion of a new measure.
The Cronbach’s alpha measuring internal consistency showed a satisfactory level
(0.821) for study in social science. The communality table from PCA indicated that a
majority of the values were slightly less than 0.5. However, since the instruments
measuring behavioural intentions were developed based on a grounded literature review
and have undergone a pre-test, this is acceptable in terms of content and face validity.
As discussed above, the item of importance is the interpretability not the communality
coefficient per see. The analysis of eigenvalues and the scree plot suggested that one
factor was formed. All of the seven items loaded with values ranged between 0.668 and
0.748 (Appendix 4 Table D), which were higher than the rule-of-thumb of 0.5. Overall,
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at this initial stage, all of the seven items measuring behavioural intentions were
retained for further examination in the main analysis using PLS.
5.6.2.3 Summary of Problematic Measures
Throughout the processes in the exploratory analysis using PCA, some items were
identified as problematic and needed further confirmation for possible deletion (Table
5.5). This is due to the inability of the problematic items identified to fulfil the
recommended criteria such as low factor loadings, cross-loading and interpretability.
The validity and reliability examination using PLS will confirm the deletion of the
problematic items in the measurement model.
Table 5.5 Problematic Items Identified in the Preliminary Analysis Using PCA Components Item Low Loadings
(<0.5) Cross
Loadings Interpretability
SERVICE QUALITY
Content A20 Credibility of degree awarded.
√√√√ √√√√ √√√√
A11 Courses offered are stimulating.
√√√√ √√√√ √√√√
A17 Students learn communication skills.
√√√√
A18 Students learn team working.
√√√√
Tangible A21 Degrees to which school handles feedback.
√√√√ √√√√ √√√√
A22 Personal information is secure.
√√√√ √√√√
A5 Sufficient number of staff. √√√√ √√√√ Delivery A12 Presentation in logical-
timely manner. √√√√
CUSTOMER VALUE
Reputation B17 This institution has a good reputation.
√√√√
Price B6 Courses offer good value for money.
√√√√
B7 Institution has good services for the price.
√√√√
Social B12 Makes me feel good. √√√√
Quality B3 Institution is dependable. √√√√
5.7 CONCLUSION
Even though an EFA analysis is optional when using PLS for the main analysis, the
purpose of running the RA and PCA included in this thesis was to examine the
unidimensionality of the measures and to give a clearer understanding of the items and
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dimensions that made up the scales. The RA and PCA also provide guidance as to those
items which should be considered for either retention for further analysis or removal,
and accordingly provide a starting point for the PLS analysis.
By using 643 of undergraduate students as respondents, this thesis identified five
dimensions of service quality (competence, content, delivery, attitude and tangible), five
dimensions of customer value (reputation, price, quality, social and emotion) and one
factor formation of satisfaction and behavioural intentions that have been validated
through the RA and PCA techniques. Nevertheless, EFA was only used to provide a
preliminary stage of analysis in order to produce a more concise data set for progress to
the next stage of the analysis. The CFA using PLS will be used to confirm the validity
and reliability of the items in the measurement model.
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CHAPTER SIX
THE PARTIAL LEAST SQUARES ANALYSIS
OF THE CONCEPTUAL MODEL
6.1 INTRODUCTION
The preliminary analysis of the data involving descriptive and Exploratory Factor
Analysis (EFA) was undertaken in the previous chapter. This chapter presents the main
data analysis. The purified measures from the EFA were further analysed and tested by
using the Structural Equation Modeling (SEM) with the Partial Least Squares (PLS)
technique. Since all of constructs in this study are of reflective nature, as such, the PLS
evaluations follow the required tests for reflective model. This chapter begins with a
brief discussion of the procedures used in PLS. This is followed by a discussion of the
evaluation of the measurement model. Finally, the assessment of the structural model is
presented to answer the research hypotheses.
6.2 PLS APPROACH FOR CONSTRUCT DESIGN
There are three general methodological issues that must be considered when PLS is
used as the main tool for analysis (Hulland 1999). These issues are: 1) determining the
nature of the relationships between indicators and latent constructs (reflective or
formative); 2) assessing the measurement model (validity and reliability); and 3)
interpreting the structural model. With respect to point one, the discussions and
arguments regarding the reflective model assigned to all measures used in this thesis
have been provided in the previous chapter, Section 5.5. Figures 6.1 and 6.2 illustrate
the reflective second-order conceptualisation of service quality and customer value in
this thesis.
Figure 6.1 First-order and Second-order Reflective Constructs of Service Quality
Service
Quality
Tangible (6 items)
Attitude (4 items)
Delivery (3 items)
Content (6 items)
Competence (6 items)
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Figure 6.2 First-order and Second-order Reflective Constructs of Customer Value
With regard to the second issue, the preliminary analysis provided an initial means of
testing the validity and reliability of the measures using EFA. More importantly, the
validity and reliability of the measures are confirmed in this chapter using Confirmatory
Factor Analysis (CFA) with PLS technique. The following section discusses the
measurement model, where validity and reliability will be further verified. However,
before going into more detailed analysis of the measurement model, it is also important
to understand the processes by means of which PLS operationalises the second-order
construct which is measured by multiple first-order constructs.
6.2.1 The Operationalisation of First-order and Second-order Constructs
As a second-generation of multivariate analysis, PLS allows the operationalisation of
the second-order construct. This thesis adopts the hierarchical component model
suggested by Wold which is also known as the repeated indicators approach (Chin
1996; Venaik 1999). The approach is illustrated in Figure 6.3.
Figure 6.3 Repeated Indicators Approach
Note: I1, I2, I3, I4 = items/indicators; C1& C2 = component measures (first-order
component); Latent construct = second-order construct.
Source: (Venaik 1999)
I1
I2
I4
I3
I1
I4
I3
I2
Latent
Construct
C1
C2
First-order construct
Second-order construct
Customer
Value
Quality (4 items)
Price (4 items)
Social (3 items)
Emotion (5 items)
Reputation (5 items)
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This approach is named the ‘repeated approach’ since items/indicators (I1 to I4) are
used twice (Venaik 1999). Firstly, they are used to measure the latent construct (second-
order construct). In this case, I1 to I4 together measure latent construct. Secondly, I1 to
I4 are used to measure the first-order components. That is, I1 and I2 are used to measure
C1. Similarly I3 and I4 are used to measure C2.
6.3 THE EVALUATION OF MEASUREMENT MODELS
PLS allows the measurement and structural models to be analysed at the same time
(Chin 1998a). However, the analyses using PLS are usually conducted in two stages: 1)
the assessment of the measurement model, which focuses more on the reliability and
validity of the measures; and 2) the assessment of the structural model which is more
concerned with the path coefficients, model adequacy and selecting the best final model
(Hulland 1999). These two-step approaches are commonly employed to ensure that the
measures have good psychometric properties before conclusions can be drawn regarding
the nature of the structural relationships. The following sections discuss the reliability
and validity analyses adopted in this thesis.
6.3.1 Validity Analysis
To ensure the accuracy of the structural model analysis, the validity and reliability of
the scale development needs to be tested (Churchill 1979). The validity test of the scale
was conducted in order to evaluate whether or not the instruments used to measure the
concept does in fact measure the intended concept (Kline 2005). The validity of the
measures is discussed in the following sections.
6.3.1.1 Content Validity and Face Validity
Content validity is a validity test which is designed to ensure that the measures cover an
adequate and representative set of items that cover all aspects of the concepts being
measured (Sekaran 2003). It is “a qualitative type of validity where the domain of a
concept is made clear and the analyst judges whether the measures fully represent the
domain” (Bollen 1989, p. 185). A measure is said to have content validity if there is an
acceptance among experts/researchers that the measure includes items that tap the
concept (Bohrnstedt 1983). Content validity can be examined by using a panel of judges
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who are considered to be experts in the area that is being investigated (Maxim 1999).
According to Hair et al. (2006), content validity is also called as face validity, a kind of
subjective assessment which examines the correlation between the items and the
concept based on ratings made by experts in the field, pre-test or other reliable means.
The object of conducting content/face validity testing is basically to ensure that the
measures not only cover past empirical issues but also expand to cover both theoretical
and practical concerns.
In this thesis, content validity was confirmed as most of the measurement items have
been validated through previous studies and the scale development was based on a
thorough literature review. In addition, the feedback from discussions with experts in
the relevant field (higher education officials) enabled further refinement of the scales.
The pre-testing received from 34 undergraduate students in order to appraise the face
validity has resulted in some slight changes in the wording of the questionnaire (see
Section 4.3.3.2).
6.3.1.2 Construct Validity
Construct validity concerns whether or not the instrument operates the construct as
theorised (Sekaran 2003). More specifically, the purpose of conducting the construct
validity test is to show that particular constructs which consisted of certain
measurement items are, in fact, made up of designated item, and not made up of items
which are supposed to measure other constructs (Nasution 2005). For example, this
method will demonstrate how strongly the measurement item correlates with the
construct it is related to, while correlating weakly or insignificantly with other
constructs. The valid construct should contain relatively high correlations between
measures of the same construct. When using PLS, the issues in construct validity occur
in two major ways: convergent validity and discriminant validity. In this thesis, both
convergent validity and discriminant validity were examined to assess construct
validity.
Convergent validity focuses on convergence among items that measure the same
construct (Pedhazur 1991). Bagozzi (1981 p. 375) describes the notion of convergence
in the measurement as follows “measures of the same construct should be highly
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intercorrelated among themselves and uniform in the pattern of correlation”. This means
that in order to have convergent validity, the indicators that measure the construct
should be highly correlated. On the other hand, discriminant validity is “the extent to
which the scale is certainly a narrative of the measure and not simply a reflection of
some other variable” (Churchill 1979, p. 70). Discriminant validity is demonstrated by
showing that the indicators are better associated with their respective construct than they
are with other constructs. In other words, the indicators should have a low correlation
with an unrelated construct (Sekaran 2003). Discriminant validity is also considered a
necessary test of construct validity and is even held to be a stronger test than convergent
validity since it investigates the distinctions between constructs (Wainer & Braun 1988
in Cunningham 2008)
6.3.2 Evaluation of the Measurement Model using PLS
Both EFA and CFA techniques can be used to estimate convergent and discriminant
validity (Anderson & Gerbing 1988). Chapter Five provided some preliminary analysis
of the reliability and convergent validity using internal consistency (Cronbach’s alpha)
and the loadings from the PCA. The level where loadings (with PCA analysis) and
internal consistency (with Cronbach’s alpha) were above the recommended rule-of-
thumb indicates that validity and reliability were acceptable in the exploratory analysis.
To verify the validity and reliability of the data collected in the main study, the CFA
using PLS was employed. In PLS, reliability and construct validity were assessed by
examining the measurement model. The measurement model specifies the relationships
between the indicators and their respective constructs (see Section 4.3.3.6.3). The
measurement model is important in identifying good measures of each construct. The
measurement model in PLS is evaluated by examining: (1) the individual loading of
each item; (2) Internal Composite Reliability (ICR); (3) Average Variance Extracted
(AVE); and (4) discriminant validity (Chin 1998a).
In order to produce a satisfactory measurement model, the results from the PCA that
have been tested for the unidimensionality were incorporated into the measurement
model. Table 6.1 summarises the rule-of-thumb in performing the measurement model
analysis.
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Table 6.1 Criteria used as Rule-of-thumb in Measurement Model PLS Evaluation Rule-of-thumb Sources
Item Loadings Loadings > 0.7 adequate Loadings > 0.5 acceptable
(Chin 1998a; Fornell & Larcker 1981) (Chin 1998a; Chin & Newstead 1999)
ICR Composite reliability > 0.7 (Chin 1998a; Fornell & Larcker 1981) AVE Need AVE > 0.50 (Chin 1998a; Fornell & Larcker 1981) AVE (Diagonal) For evidence of validity, the square
root of the AVE is expected to be greater than the inter-scale correlation between constructs.
(Fornell & Larcker 1981; Staples et al. 1999)
Source: Marimuthu (2008)
6.3.2.1 Assessment of Convergent Validity
6.3.2.1.1 Item Loadings
PLS produces loading and weight scores for identification of the importance of
indicators to their relevant latent variable. The loading score in PLS output is used to
explain the effects of reflective indicators, while the weight explains the formative
indicators. The effectiveness of the reflective indicators in measuring the latent variable
can be assessed by the loading scores (Marimuthu 2008). Each of the loading scores
determines the correlation between indicators and their respective constructs. As a
consequence, the loading scores can be used to determine the contribution of each
indicator to the relevance of its respective construct. The higher the loadings indicate
the stronger the relationships in terms of shared variance with the construct. On the
other hand, the weight scores are more suitable for interpreting the formative indicators.
The indicators in the formative construct are weighted based on the relative importance
of every indicator in forming the constructs (Marimuthu 2008). By analysing the
weight, researchers can determine how each indicator makes its contribution to the
development of the constructs (Sambamurthy & Chin 1994). The significance of the
loadings and weights can be obtained in PLS by running the bootstrapping technique.
There are different procedures to follow when examining the construct validity of the
reflective and formative constructs and/or indicators using PLS. Since this thesis has no
formative construct, this discussion focuses on the PLS procedures for reflective
constructs. Item loading is also known as item reliability. In the reflective model, high
loading is important in order to ensure that all items are measuring the same construct.
Chin (1998a) suggests that the standardised loadings should be higher than 0.707,
meaning that the indicators relate at least 50 percent with their latent variables. In
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practice, it is common that some of the loadings are found to be below the threshold of
0.7. Chin (1998a) further recommends that a loading of 0.5 or 0.6 may still be
acceptable in the early stage of scale development. In addition, it should be noted that
even if the items with extremely low loadings have been included based on a strong
theoretical rationale, in general, items with loadings below 0.4 or 0.5 should be removed
(Hulland 1999).
The PLS results in this thesis identified some items that have loadings of less than 0.70
(see Table 6.6). From a total of 64 items used in the main analysis, only two item
measures (A5 and A14) have loadings less than 0.6, while 13 measures loaded between
0.6 and 0.7. All of the item loadings were above 0.7, the level which is considered
adequate (Chin 1998a). Accordingly, based on the results of the item loadings generated
by PLS, all of the item loadings were above 0.5 thus satisfying the requirement as listed
in Table 6.1. No items were dropped for further examinations.
6.3.2.1.2 Internal Composite Reliability (ICR)
Since the reflective model assumes that all measures must be positively interrcorrelated
(Diamantopoulos et al. 2008) (see also Section 5.5), the internal consistency (composite
reliability) should be assessed. In addition to the internal consistency examination using
Cronbach’s alpha in the previous Reliability Analysis (RA), PLS provides a reliability
test using Internal Composite Reliability (ICR). ICR can be used as a measure for
convergent validity since it seeks to ensure that the indicators that measure the
respective construct are highly correlated. The reliability (internal consistency) of the
reflective construct measured by ICR should produce a value of 0.7 or higher (Fornell &
Larcker 1981; Chin 1998a). In addition, Nunnaly (1978) also recommends 0.7 as a
‘modest’ composite reliability score for research at the early stages. Table 6.6 presents
the results of the ICR. Having all ICR scores higher than 0.7 and ranging between 0.843
(competence) and 0.949 (emotion), overall the composite reliabilities estimated using
ICR were satisfactory. Thus, the reflective measures in this thesis were considered to
have reasonable convergent validity.
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6.3.2.1.3 Average Variance Extracted (AVE)
AVE measures the average variance that is shared between a set of items and their
respective construct (Hulland 1999). It is used to assess how well a latent construct
explains the variance of a set of items that are supposed to measure that latent construct.
A construct displays convergent validity if its AVE value is at least 0.50, which
explains that at least 50% variance of the indicators are captured by the construct
(Fornell & Larcker 1981; Chin 1998a).
In this thesis, all constructs have AVEs above 0.5 (Table 6.6) except content (0.496)
and behavioural intentions (0.484). Considering that AVE is not the only measure of
convergent validity and that other measures of convergent validity such as the item
loadings and ICR produced satisfactory results for both content and behavioural
intentions constructs, both of these constructs can be considered as having an adequate
support of convergent validity. In addition, by referring to Bagozzi’s (1981) argument
regarding whether or not convergent validity is shown by having indicators of the same
construct that are highly intercorrelated among themselves, the cross-loading analysis is
conducted to confirm the convergent validity as well as to further examine the
discriminant validity.
6.3.2.2 Assessment of Discriminant Validity
In order to test the discriminant validity, two methods will be discussed in detail as
follows:
1. Examine the correlation between item loadings and construct.
2. Examine the correlation among construct scores and square root of the AVE
6.3.2.2.1 Correlation between Item Loadings and Construct
The discriminant validity is shown when the indicators are better associated with their
respective construct than they are with other constructs. Discriminant validity can be
evaluated by examining whether or not there are evidences of cross-loadings between
the indicators and their constructs (Gaski 1984; Venaik 1999). When checking the
cross-loadings, researchers must ensure whether each group of indicators/items should
load higher for its respective construct than indicators/items of other constructs
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(Cunningham 2008). The cross-loading matrix of the measures showing the correlations
between all items and constructs are displayed in Appendix 6 Table A and B.
Testing the correlation between indicators is important to determine whether the latent
constructs predict the indicators in their block better than they do the indicators in any
other block. This is shown by checking the loadings on every column in Appendix 6;
the correlations of the constructs with their indicators should be higher than with the
indicators of any other constructs. Similarly, an examination across the rows in
Appendix 6 should reveal that the correlations of the indicators with their constructs are
higher than with any other constructs.
The cross-loading examination indicated that some of the measures/indicators did not
load uniquely and higher on their respective construct compared with indicators of any
other construct. These problematic indicators are summarised in Table 6.2 (column
PLS). An examination of the PLS loadings reveals that there were six items identified as
cross-loading (A5, A14, A20, A21, A22, A28 and C2). In addition, Table 6.2 also
provides identification of problematic indicators from PCA as a comparison. Based on
PCA, indicators loaded less than 0.5 and/or cross-loaded were identified as problematic.
There were ten indicators identified as cross-loaded through PCA. The detailed results
of PCA loadings, PLS loadings and cross-loadings are provided in Appendix 4 to 6.
However, the summary of the problematic indicators is provided in Table 6.2. When
making a decision to remove or retain any indicator, three factors (low loading, cross-
loading and content validity) were taken into consideration.
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Table 6.2 Problematic Items Identified Through PCA and PLS
PCA PLS Indicators Loadings Problematic
identification Indicators Loadings Problematic
identification
Tangible Tangible
A21 0.466 Low loading Cross loading
A21 0.6615 Cross-loading
A22 0.465 Low loading A22 0.6038 Cross-loading A5 0.392 Low loading A5 0.5488 Cross-loading A28 0.664 No problem A28 0.7120 Cross-loading
Content Content
A14 0.5883 Cross-loading
A20 0.476 Low loading Cross loading
A20 0.7130 No problem
A11 0.370 Low loading Cross loading
A11 0.6766 No problem
A17 0.648 Cross loading A17 0.6722 No problem
A18 0.621 Cross loading A18 0.6832 No problem
Delivery
A12 0.541 Cross loading A12 0.9067 No problem
Satisfaction Satisfaction
C2 0.634 No problem C2 0.618 Cross-loading
Reputation Reputation
B17 0.668 Cross loading B17 0.8598 No problem
Price Price
B6 0.780 Cross loading B6 0.911 No problem B7 0.696 Cross loading B7 0.8486 No problem
Social Social
B12 0.560 Cross loading B12 0.8367 No problem
By considering both PCA and PLS results, three indicators (A5, A21, A22) were
dropped due to cross-loading, low loading and the problem of interpretability since
these indicators did not measure the designated constructs, but instead loaded into other
constructs (see Table 6.3). A28 and C2 were dropped due to cross-loading as shown by
the PLS result. A14 was retained, despite being cross-loaded, since this indicator (the
degree to which a program contains basic knowledge/skills) is theoretically grounded
and conceptually meaningful. Even though the PLS loadings presented no issues,
indicators A11 and A20 were dropped due to problems with interpretability since they
did not load into their designated constructs. In addition, both of these indicators were
also identified by PCA as problematic (low loadings and cross-loading). Further
examination has determined that no cross-loading occurred to indicator A14 when
indicator A11 and A20 were deleted. Eastlick and Lotz (2000) maintained the
importance of retaining poorly performing indicators in order to maintain content/face
validity. Accordingly, it is important to retain indicator A14, which measures the
‘content’ dimension of the service quality construct.
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Overall, based on the analysis of correlations between indicator/item loadings and
constructs, seven indicators were removed from the original measurement. These
indicators were A5, A11, A20, A21, A22, A28 and C2. Table 6.3 provides a summary
regarding the decision to remove or retain problematic indicators found through PCA
and PLS analysis.
Table 6.3 Reasoning for Indicators’ Removal or Retention
Items Problem identification Decision to retain/remove EFA CFA
A5 Low loading Cross-loading Remove, due to low loading, cross-loading and problem with content validity (interpretability). This indicator should measure ‘competence’ dimension; however, it loaded into the ‘tangible’ dimension.
A11 Low loading Cross-loading
np Remove, due to low loading, cross-loading and problem with content validity (interpretability) when measuring ‘content’, since this indicator focuses more on measuring ‘delivery’ dimension.
A12 Cross-loading np Retain, due to high loading and no cross-loading in PLS.
A14 Low loading Cross-loading Retain, due to having important content validity and being theoretically grounded. Nevertheless, when A11 and A20 were deleted, no cross-loading problem occurred.
A17 Cross-loading np Retain, due to acceptable loading and no cross-loading in PLS.
A18 Cross-loading np Retain, due to acceptable loading and no cross-loading in PLS.
A20 Low loading Cross-loading
np Remove, due to low loading, cross-loading (EFA) and problem with content validity (interpretability) since this indicator should measure the ‘reliability’. However, it turned out loaded into ‘çontent’ dimension.
A21 Low loading & Cross-loading
Cross-loading Remove, due to low loading, cross-loading and problem with content validity (interpretability). This indicator should measure the ‘reliability’ dimension. However, it loaded into the ‘tangible’ dimension.
A22 Low loading Cross-loading Remove, due to low loading, cross-loading and problem with content validity (interpretability) since this indicator should measure ‘reliability’ instead of ‘tangible’.
A28 np Cross-loading Remove, due to cross-loading in PLS.
B6 Cross-loading np Retain, due to high loading and no cross-loading in PLS.
B7 Cross-loading np Retain, due to high loading and no cross-loading in PLS.
B12 Cross-loading np Retain, due to high loading and no cross-loading in PLS.
B17 Cross-loading np Retain, due to high loading and no cross-loading in PLS.
C2 np Cross-loading Remove, due to cross-loading in PLS.
np = no problem
In addition to identifying problematic items/indicators, there were also two indicators
identified as having no problems in terms of CFA and EFA analysis. However,
indicators A6 and B12 were loaded into other constructs into which they were not
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initially designed to load. As has been discussed in Chapter Five, item A6 was supposed
to measure competence but it turned up to load into attitude. Similarly, item B12 was
supposed to measure emotion not social. Even though these items did not load as
expected, both items were retained for the following reasons:
• By carefully examining the meaning of the question being asked in item A6,
“the competence of the support staff”, it is evident that the competence of the
staff could be expressed by positive attitudes such as clear advice or willingness
to help. This means that item A6 is also meaningful in explaining attitude
despite initially being designated for measuring competence. The reason why it
did not load with the competence might be because the measures in competence
were all focused on the academic staff while item A6 was focused more on the
support staff. Based on this reasoning, it was decided to retain item A6 since it is
considered reasonably meaningful in measuring attitude.
• By carefully examining the meaning of item B12 (makes me feel good), it can be
argued that this item is meaningful in measuring the social dimension. This is
because this item can also mean “feeling socially good” or “feeling good in
terms of being accepted socially”. For this reason, it provides meaningful
interpretability in measuring the social dimension. In addition, both the
‘emotion’ and ‘social’ dimensions measure the affective aspect of customer
value. As a consequence, there is a possibility of close meaning among the
measures of both dimensions (emotion and social).
6.3.2.2.2 Correlation among Construct Scores and Squares Root of the AVE
6.3.2.2.2.1 Correlation among Construct Scores
Following the correlation analysis between item loadings and their respective
constructs, the correlation analysis among constructs is further conducted to provide
evidences of whether the first-order constructs measure their respective second-order
construct. As can be seen in Appendix 6, Table C, the results showed that ‘quality’
loaded high to customer value construct and also loaded high in the ‘service quality’
construct (0.720). Interestingly, the loading of the ‘quality’ dimension on the service
quality construct was even higher than the three dimensions of service quality: attitude
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(0.676), competence (0.688) and delivery (0.628). This condition led to the decision to
remove the ‘quality’ component as a measure of the ‘customer value’ construct due to
the possibility of redundancy when being used together with dimensions of ‘service
quality’ construct. Table 6.4 provides the correlation among first-order constructs and
second-order constructs after the ‘quality’ dimension was removed from the model. No
existence of cross-loadings was discovered after the removal of the ‘quality’ dimension
(Appendix 6, Table B presents the final result of cross-loading examination and
Appendix 5 shows the significance of the loadings and weights after the ‘quality’
construct has been dropped).
Table 6.4 Cross Loadings of First-order and Second-order Constructs Constructs SQ Value
Attitude 0.662 0.364
Competence 0.618 0.367
Content 0.81 0.569
Delivery 0.61 0.430
Tangible 0.81 0.551
Emotion 0.554 0.828
Price 0.507 0.683
Reputation 0.511 0.778
Social 0.52 0.863
6.3.2.2.2.2 Square Root of the AVE
The last procedure in testing the discriminant validity is checking the square root of the
AVE. This can be demonstrated by comparing the square root of the AVE for each
construct/dimension with the correlations between the construct and other constructs in
the model. The evidence of discriminant validity is shown when the square root of the
AVE of each construct is larger than the correlations between the construct and any
other constructs (Staples et al. 1999). The rule-of-thumb states that the square root of
the AVE of each construct should be larger than the correlations of the specific
construct with any other constructs (Chin 1998a). Unfortunately, the guidelines
regarding how much larger the AVE should be as compared to the correlations among
constructs are not available (Gefen & Straub 2005).
Table 6.5 illustrates the correlations among construct scores and the square root of the
AVE. The diagonal bold values illustrate all the square roots of the AVE values. The
square roots of the AVE values should be greater than the inter-construct correlations, a
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circumstance which will provide evidence of discriminant validity. As shown in Table
6.5, the results indicated that all the square roots of the AVE values were greater than
the inter-construct correlations; therefore, the constructs in the model were different
from each other and satisfy the requirement of discriminant validity.
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Table 6.5 Correlation between Latent Constructs and Square Root of AVE
Tangible Content Attitude Competence Delivery Reputation Emotion Price Social Satisfaction BI
Tangible 0.8735
Content 0.588 0.7043
Attitude 0.365 0.388 0.7849
Competence 0.382 0.472 0.434 0.7576
Delivery 0.370 0.449 0.407 0.448 0.8643
Reputation 0.516 0.549 0.260 0.371 0.357 0.8567
Emotion 0.470 0.499 0.375 0.350 0.395 0.539 0.9077
Price 0.427 0.465 0.353 0.311 0.373 0.456 0.533 0.8313
Social 0.445 0.502 0.295 0.355 0.415 0.623 0.652 0.484 0.8729
Satisfaction 0.701 0.551 0.316 0.376 0.416 0.607 0.629 0.495 0.539 0.7874
BI 0.418 0.493 0.319 0.368 0.374 0.515 0.600 0.448 0.531 0.585 0.6957
The diagonal (in bold) shows the square root of AVE
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Overall, after applying the recommended methods for evaluating the PLS measurement
model, Table 6.6 provides the final summary of the valid and reliable measurements that
will be used for the structural model testing.
Table 6.6 Summary of the Valid and Reliable Measurements Items Individual Items
Loadings (PLS) Standard Error
Average Variance Extracted (AVE)
Internal Composite Reliability (ICR)
Cronbach Alpha
Original Data
Refined Data
Tangible 0.763 0.941 0.922
A23 0.8529 0.8867 0.01
A24 0.8644 0.8947 0.01
A26 0.8565 0.8896 0.01
A25 0.8427 0.8991 0.01
A27 0.7876 0.7917 0.01
A28 0.7120 -
A21 0.6615 -
A22 0.6038 -
A5 0.5488 -
Attitude 0.616 0.889 0.844
A9 0.8279 0.8269 0.01
A8 0.8180 0.8196 0.02
A10 0.7461 0.7432 0.02
A7 0.8170 0.8171 0.02
A6 0.7103 0.7119 0.02
Content 0.496 0.855 0.795
A15 0.6961 0.7283 0.03
A17 0.6722 0.7139 0.03
A16 0.7685 0.7580 0.02
A18 0.6833 0.7268 0.03
A19 0.6468 0.6748 0.03
A14 0.5834 0.6164 0.04
A20 0.7131 - -
A11 0.6765 - -
Competence 0.574 0.843 0.734
A2 0.7852 0.7870 0.02
A3 0.7702 0.7724 0.03
A1 0.7657 0.7631 0.03
A4 0.7080 0.7066 0.03
Delivery 0.747 0.855 0.662
A12 0.9065 0.9015 0.01
A13 0.8190 0.8256 0.02
Reputation 0.734 0.932 0.906
B19 0.8913 0.8933 0.02
B18 0.9094 0.9107 0.01
B20 0.9047 0.9050 0.01
B17 0.8598 0.8579 0.02
B21 0.7003 0.6982 0.04
Note: Original data illustrates the loadings before all problematic indicators identified being removed. Refined data illustrates the loadings after all problematic indicators identified being removed.
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Table 6.6 cont’d Items Individual Items
Loadings (PLS) Standard Error
Average Variance Extracted (AVE)
Internal Composite Reliability (ICR)
Cronbach Alpha
Original Data
Refined Data
Emotion 0.824 0.949 0.928
B15 0.9176 0.9184 0.01
B16 0.9099 0.9110 0.01
B14 0.9151 0.9145 0.01
B13 0.8871 0.8858 0.01
Quality - - -
B1 0.9191 - -
B2 0.9220 - -
B3 0.9035 - -
B4 0.8619 - -
Price 0.691 0.899 0.848
B5 0.8251 0.8271 0.02
B6 0.9109 0.9077 0.01
B8 0.7300 0.7385 0.03
B7 0.8486 0.8438 0.02
Social 0.762 0.927 0.896
B10 0.9058 0.9054 0.01
B11 0.8785 0.8806 0.01
B9 0.8681 0.8665 0.02
B12 0.8367 0.8368 0.02
Satisfaction 0.620 0.919 0.894
C1 0.8635 0.8174 0.02
C2 0.5316 - -
C3 0.7897 0.7935 0.02
C4 0.6455 0.7817 0.02
C5 0.7857 0.8140 0.02
C6 0.7278 0.7909 0.02
C7 0.7732 0.7465 0.02
C8 0.7706 0.7650 0.02
Behavioural Intentions
0.484 0.867 0.821
D1 0.7165 0.7138 0.02
D2 0.6933 0.6914 0.03
D3 0.6865 0.6859 0.04
D4 0.6730 0.6738 0.03
D5 0.7532 0.7529 0.03
D6 0.6584 0.6609 0.03
D7 0.6818 0.6852 0.03
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6.4 THE EVALUATION OF THE STRUCTURAL MODEL
By using the valid and reliable output from the measurement model, the structural model
was analysed and used to test the validity of the hypothesised relationships among the
constructs, as proposed in Chapter Three and the conceptual model (Figure 3.7). An
overview of the structural model resulting from PLS analysis is presented in Figure 6.4
(The PLS graphic output is shown in Appendix 7 Figure A). The following sections will
evaluate the structural model by using: (1) R-squared or variance explained; (2) structural
path coefficients; and (3) t-statistics.
6.4.1 R-Squared (R2)
The use of R-squared (R2) is important to determine the predictive ability of the model. PLS
produces R2
for each of dependent construct in the model. R2 in the structural model is
similar to R2
in the regression model, which measures the percentage of the construct’s
variation and also explains the extent to which the independent constructs predict the
dependent construct (Chin 1998a). The bigger the R2, the more predictive power the model
implies. The rule-of-thumb for the significance of R2 of the predicted variables should be
greater than 0.10 (Falk & Miller 1992). Across the three key dependent constructs
(customer value, customer satisfaction and behaviour intentions), the R2
of the predicted
constructs in the model were greater than the recommended value 0.10 (R2
of customer
value = 47.4%; satisfaction = 56.7% and behavioural intentions = 45.6%). In addition, in
order to understand the relative magnitudes of each independent construct on a dependent
construct, an effect size analysis was conducted.
When several independent variables are employed in a multiple regression model, the effect
size (f2) can be used to determine the strength of the effect of a particular independent
construct on the dependent construct. According to Cohen and Cohen (1983), the effect size
of the independent constructs on a dependent construct can be categorised into 0.02 (small
effect), 0.15 (medium effect) and 0.35 (large effect). This can be done by examining the
change in the R2 following the exclusion or inclusion of the independent construct (Chin
1998a). The effect size can be calculated by using the following equation:
183
2
222
1included
excludedincluded
R
RRf
−
−=
The change of R2 will determine the value of f
2. R
2included is the R
2 of the dependent
construct when all independent constructs are assigned in the model. R2
excluded is the R2
of
the dependent construct when particular independent construct is removed (Vatanasakdakul
2007). Since this thesis particularly focused on the addition of customer value in predicting
the dependent variables in the model (customer satisfaction and behavioural intentions),
only two effect sizes were analysed: 1) relating to the effect on behavioural intentions; and
2). relating to the effect on customer satisfaction. In order to examine the effect size of
customer value on behavioural intentions, the R2
included was the behavioural intentions as
dependent construct (R2included = 0.456) (see Appendix 7 Figure A). R
2excluded was generated
by removing the path between customer value and behavioural intentions (R2
excluded =
0.378) (see Appendix 7 Figure B). As with the effect size of customer value on customer
satisfaction, the R2included was the customer satisfaction as dependent construct (R
2included =
0.584) (see Appendix 7 Figure E). R2
excluded was generated by removing the path between
customer value and customer satisfaction (R2
excluded = 0.480) (see Appendix 7 Figure G).
Table 6.7 Effect Size Construct Removed
Dependent Construct
R2included R2excluded f2 Effect Size
Customer Value Behavioural Intentions
0.456 0.378 0.143 Small Effect (<0.15)
Customer Satisfaction
0.584 0.480 0.25 Medium Effect (>0.15)
The effect size calculation showed that customer value has a medium effect on customer
satisfaction (>0.15) and a small to medium effect on behavioural intentions (between
0.02/small effect and 0.15/medium effect). In addition to the relative strength of the effect
as shown by the effect size, the effect of independent constructs in this thesis on their
respective dependent constructs can be also be analysed by their path coefficients and will
be described in the following section.
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6.4.2 Path Coefficients
Path coefficients are used to indicate the strength of the relationship between two constructs
(Wixom & Watson 2001). Path coefficients are similar to standardised beta coefficients in
regression analyses (Karahanna et al. 2002). In PLS, the bootstrap procedure is used to
estimate the t-statistic and the significance levels for the structural path coefficients (Chin
1998a). The results of the path coefficients and their significance are summarised in Table
6.9.
Figure 6.4 Structural Model Result
6.4.3 t-Statistics
The statistical significance of the pathways was assessed by examining the t-statistics that
were calculated through the bootstrap re-sampling techniques. As explained by Chin
(1998a, p.320), the calculation technique of bootstrapping can be explained as follows: “N
samples sets are created in order to obtain N estimates for each parameter in the PLS
model. Each sample is obtained by sampling with replacements from the original data set”.
Before running the bootstrapping facility, the total number of sub-samples needs to be
defined by users in a PLS software application. This thesis takes 100 sub-samples for the
Tangible R2=0.669
Content R2=0.674
Attitude R2=0.458
Competence R2=0.475
Delivery R2=0.396
Reputation
R2 =0.685
Emotion
R2 =0.714
Social
R2 =0.724
Price
R2 =0.520
Behavioural Intentions R2=0.456
Satisfaction
R2=0.567
Service Quality
Customer Value R2=0.474
0.688****
0.368****
0.217****
0.427****
0.451****
0.097**
0.818****
0.821****
0.677****
0.689****
0.629****
0.845****
0.828****
0.851****
0.721****
Note: ****p<0.001; ***p<0.01; **p<0.05; *p<0.1
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bootstrapping procedures. Once the bootstrapping is generated, the standard errors can be
obtained which indicate the significance level of the path coefficient.
The results from the PLS analysis structural model are summarised in Figure 6.4 and Table
6.9. Consistent with the published literature which supports the direction of the
relationships of the constructs (Chapter Three), a one-tailed test was used to test path
significance. Table 6.8 contains the critical z-value that is used for specifying the
significance levels of both one-tailed and two-tailed tests. Even though two-tailed tests are
not employed, they are included for the purpose of comparison. As an illustration, the
observed z-value should be greater than 2.326 (one-tailed test) or 2.576 (two-tailed test) for
rejecting the null hypothesis at the 0.01 level.
Table 6.8 Critical Z-value Significance level
(p-value) Symbol Critical Z-values
1-tailed test 2-tailed test 0.001 **** 3.090 3.290 0.010 *** 2.326 2.576 0.050 ** 1.645 1.960 0.100 * 1.282 1.645
Not significant ns -- --
Table 6.9 PLS Results of Direct Effect on the Structural Model Constructs Proposed
Effect
Path
Coefficients
Standard
Error
t-Value
Effects of service quality on
Satisfaction + 0.368 0.040 9.1142****
Customer value + 0.688 0.025 28.0897****
Behavioural
intentions
+ 0.097 0.044 2.1959**
Effects of customer value on
Satisfaction + 0.451 0.042 10.6334****
Behavioural
intentions
+ 0.427 0.045 9.5190****
Effects of satisfaction on
Behavioural
intentions
+ 0.217 0.050
4.3297****
Note: **** p<0.001, *** p<0.01, * *P<0.05, * p<0.1, ns = not significant
Since all of the criteria used to evaluate the structural model have been discussed, the
following sections interpret the results of the structural model.
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6.4.4 Structural Paths
6.4.4.1 Structural Model: Second-order and First-order Construct
As mentioned in Section 6.2, the conceptual model of this thesis employs the
operationalisation of first-order and second-order constructs. The validity and reliability
tests have been examined in the measurement model towards all of the first-order and
second-order constructs. Satisfactory results were obtained from the reliability and validity
tests showing evidences of convergent validity and discriminant validity. This section in
particular discusses the significance of the relationship between the first-order constructs
and the second-order constructs. The relationships will be interpreted through an
examination of the relevant path coefficients and t-statistics.
As shown in Figure 6.4, service quality as a second-order construct was measured by its
five first-order components (tangible, content, delivery, competence and attitude). All paths
for the five first-order constructs constituting the service quality construct contributed
strongly at a significance level of 0.001. Among the five dimensions of service quality,
content (0.821) and tangible (0.818) exerted a strong influence (above 0.8) over service
quality, whereas other dimensions were only less than 0.7 with delivery shown as having
the least influence (0.629).
Similarly, all of the four first-order dimensions of customer value also indicated strong
relationships with the customer value construct at the significance level of 0.001 (Figure
6.4). In the absence of the ‘quality’ dimension, which was commonly regarded as most
important dimension in explaining customer value in previous studies, this thesis shows
that ‘social’ (0.851) has the highest influence ahead of ‘reputation’ (0.828). Reputation was
considered the most important dimension of customer value in past studies (Alves &
Raposo 2007, Hill et al. 1995; LeBlanc & Nguyen 1999). However, reputation was only
placed third among the dimensions of customer value in this thesis. Interestingly, ‘price’
(0.721) was identified as the least important of dimensions in explaining customer value.
Considering that price and reputation have been shown to exert less influence than other
dimensions of customer value (social and emotion) in the higher education sector, this
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provides an important view of the importance of emphasising the affective aspects of higher
education experiences.
After validating the relationships between first-order and second-order constructs, the next
section reports the findings with respect to the relationships between the four key constructs
(service quality, customer value, customer satisfaction and behavioural intentions) in the
main structural model.
6.4.4.2 Structural Model: The Main Constructs
This section specifically analyses the direct relationships in the structural model (inner
model) relating the four main constructs (service quality, customer satisfaction, customer
value and behavioural intentions). Table 6.9 presents the PLS results for the six direct
paths proposed in the structural model. All of the six path coefficients were found to be
positively significant at the 0.001 level, except for one path relating to service quality and
behavioural intentions which was significant at the 0.05 level for a one-tailed test. As
shown in Figure 6.4, one can see the dominant role of customer value, when simultaneously
analysed together with service quality, satisfaction and behavioural intentions. Service
quality exerts a strong influence on customer value (0.688). Customer value further has
significant influence on satisfaction (0.451) and behavioural intentions (0.427). The
relationship between service quality and satisfaction was less strong (0.368). The
relationship between satisfaction and behavioural intentions was 0.217. Interestingly,
although past studies have evidenced the significant contribution of service quality to the
shaping of behavioural intentions, this study identified that the path coefficient was
significant but very weak (0.097). This might be because of the impact of simultaneous
examinations involving satisfaction and customer value.
Having determined that most of the path coefficients were positive and significant, these
research provide support for H2, H3, H4, H7, H8 and H9 relating to the direct relationships
proposed in Chapter Three. These evidences also imply the importance of service quality,
satisfaction and customer value in explaining behavioural intentions. Therefore, it is
suggested that all three key variables should be incorporated when examining behavioural
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intentions. Similarly, the results also revealed the importance of service quality and
customer value as drivers of customer satisfaction. Consequently, both constructs should be
incorporated into a customer satisfaction study.
By examining the R2, service quality, customer value and satisfaction together explained
behavioural intentions at the desirable level variance of 0.456%. This means that all the
predictor variables in the model explained 46% of the behavioural intentions. This value is
far above the rule-of-thumb recommended by Falk & Miller (1992) that the R2
should be
greater that 0.10. Service quality also has significant explanatory power regarding customer
value with R2
= 0.474. Both service quality and customer value explains satisfaction
strongly with R2 = 0.567. In sum, all of the four constructs interact with each other
significantly and have a relatively high predictive power (45.6%) on behavioural intentions
when analysed simultaneously.
In addition to analysing the direct effects, this thesis also examines indirect relationships.
By analysing the indirect effects, it is expected that the more meaningful result of the
relationships among the constructs in the structural model can be explained. More
specifically, there were four indirect relationships being examined in this thesis: 1) service
quality – customer value – behavioural intentions (SQ-CV-BI); 2) service quality –
customer satisfaction – behavioural intentions (SQ-CS-BI); 3) customer value – customer
satisfaction – behavioural intentions (CV-CS-BI); and 4) service quality – customer value –
customer satisfaction (SQ-CV-CS).
6.4.4.3 Structural Model: The Mediating Effects
6.4.4.3.1 Indirect Effect
The relationship between latent variables (constructs) in the structural equation models can
be investigated through a mediator variable. A mediator is a variable that exists between the
independent variable and dependent variable (Baron & Kenny 1986; Mackinnon et al.
1995). The mediation analysis allows for measurement and understanding the existence of a
significant intervening mechanism between independent variables and the dependent
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variables. The application of mediating models is widely conceptualised and tested in
marketing research (e.g. Han et al. 1998; Im & Workman 2004).
The mediation analysis in this section involves the examination of direct and indirect
relationships among the constructs. The direct effect involves directional relation between
two constructs. On the other hand, the indirect effect is the effect of an independent
variable on a dependent variable through one or more mediating variables (Hoyle 1995).
Using the illustration provided in Figures 6.5 and 6.6, the direct and indirect relationships
can be explained as follows: 1) Figure 6.5 illustrates the direct effect between X
(independent variable) and Z (dependent variable); and 2) Figure 6.6 illustrates the indirect
effect where the effect of X on Z is mediated by a mediating variable Y. In other words, if x
has a direct effect on y, and y has direct effect on z, then x is said to have an indirect effect
on z through y. The total sum of direct and indirect effects is termed as the ‘total effect’
(Hoyle 1995). In the structural model (Figure 6.4), two constructs (customer value and
customer satisfaction) were conceptualised as mediating variables.
Figure 6.5 Illustration of Direct Effect
Figure 6.6 Illustration of Mediating Effect
Source: Baron and Kenny (1986)
In testing the mediation hypotheses, there are several approaches followed by social
researchers. The more advance statistical software allows using the “bootsrap” facility to
generate the standards errors that permit directly testing the significance of the indirect
effects. Other studies apply the approach suggested by Baron and Kenny (1986), by which
the size and significance of the direct and indirect effects are examined (Ettlie & Pavlou
Y
Z X
a
c
b
Z X c
190
2006; Thatcher & Perrewe 2002). Baron and Kenny’s (1986) method is also widely
accepted and applied in marketing studies (e.g. Agarwal et al. 2003; Matear et al. 2002).
There are three requirements highlighted by Baron and Kenny (1986) to test the mediation
effect: 1) the independent variable (X) must affect the mediating variable (Y); 2) the
independent variable (X) must affect the dependent variable (Z); and 3) the mediating
variable (Y) must affect the dependent variable (Z). Two types of mediation have been
identified (Baron & Kenny 1986): 1) partial mediation is the case in which the path from X
to Z is reduced in absolute size but it is still bigger than zero when the mediator is
controlled; and 2) complete mediation holds if the link between the X and Z shows zero
effect with the introduction of the mediator variable(s). This thesis adopts Baron and
Kenny’s (1986) recommendation for examining the mediating effects as proposed in the
hypotheses.
Based on the proposed hypotheses, there are four mediating models which will be
examined in this thesis (see Table 6.10). The effects of two mediating variables (customer
satisfaction and customer value) were tested based on the requirements suggested by Baron
and Kenny (1986). Based on the discussion regarding the direct relationships across all
constructs in the structural model (Section 6.4), it can be concluded that all of the three
conditions required for mediation testing were fulfilled. As illustrated in Figure 6.4, all
constructs have significant positive correlations to each other, thus satisfying all of the
conditions for mediation. In support of Baron and Kenny’s (1986) approach, the unique
bivariate relationships and the partial models were tested and the results were presented in
Appendix 7 (Figure G to L and B to E). Due to satisfactory results being obtained in
fulfilling the requirements of testing the mediation effects, further analyses involving
direct, indirect and total effects were carried out.
One reasons for testing the mediation effect is to understand the mechanism through which
the independent variable affects the dependent variable. The effect of mediating variables
can be identified by analysing the direct, indirect and total effects (Mackinnon et al. 1995).
The indirect effect can be calculated by multiplying the path coefficients of (a) and (b) in
Figure 6.7. The direct effect is reflected by (c). Finally, the total effect is obtained by
191
summing up the direct and indirect effects. Table 6.10 summarises the direct, indirect, total
effects, and effect ratio of all the four mediating relationships.
Table 6.10 Direct and Indirect Effects of Conceptual Model: PLS Results Independent Intervening Dependent Direct
effect Indirect effect
Total effect
Effects ratio
Service Quality Behavioural Intentions 0.097 na 1.213 Satisfaction Behavioural Intentions Na 0.08 0.177
Service Quality Behavioural Intentions 0.097 na 0.029 Customer value Behavioural Intentions Na 0.294 0.391
Service Quality Satisfaction 0.368 na 1.187 Customer value Satisfaction Na 0.310 0.678
Customer value Behavioural Intentions 0.427 na 4.36 Satisfaction Behavioural Intentions Na 0.098 0.525
Note: Refer to Figure XX: Indirect effect = (a) x (b). Direct effect = (c),
Total effect = direct effect + indirect effect
Based on Table 6.10, the results suggest:
1) SQ-CS-BI
Service quality has a direct positive effect on behavioural intentions (0.097) and an
indirect positive effect on behavioural intentions through satisfaction (0.08). The
role of satisfaction as a mediating variable has increased the total effect to 0.177.
2) SQ-CV-BI
Service quality has a direct positive effect on behavioural intentions (0.097) and an
indirect positive effect on behavioural intentions through customer value (0.294).
The role of customer value as a mediating variable has increased the total effect to
0.391.
3) SQ-CV-CS
Service quality has a direct positive effect on satisfaction (0.368) and an indirect
positive effect on satisfaction through customer value (0.310). The role of customer
value as a mediating variable has increased the total effect to 0.678.
4) CV-CS-BI
Customer value has a direct positive effect on behavioural intentions (0.427) and an
indirect positive effect on behaviour intentions through satisfaction (0.098). The
role of satisfaction as a mediating variable has increased the total effect to 0.525.
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Overall, these mediating models provide evidence suggesting that hypotheses relating to the
mediating effects (H5, H10, H11 and H12) in Chapter Three are supported.
In order to understand the relative magnitudes of direct and indirect effects, an effect ratio
analysis was conducted. The effects ratio was calculated by dividing the direct effect into
the indirect effect to yield an effects ratio (Voola 2005). When the effects ratio is greater
than 1.0, the direct effect is greater than the indirect effect. On the other hand, when the
effects ratio is less than 1.0, the direct effect is less than the indirect effect. When the
effects ratio is equal to 1, the direct effect and the indirect effect are equal.
By examining the effect ratio in Table 6.10, the results indicate:
1) SQ-CS-BI
The direct effect of service quality on behavioural intentions is greater than its
indirect effect.
2) SQ-CV-BI
The direct effect of service quality on behavioural intentions is less than its indirect
effect.
3) SQ-CV-CS
The direct effect of service quality on customer satisfaction is greater than its
indirect effect.
4) CV-CS-BI
The direct effect of customer value on behavioural intentions is greater than its
indirect effect.
6.4.4.3.2 Relative Impacts of Service Quality and Customer Value
In order to determine the relative impact of service quality and customer value on
behavioural intentions and satisfaction, their direct and indirect effects were examined
(based on standardised structural path coefficients). To provide an equal comparison,
satisfaction was used as the only mediating variable on the relationships between service
quality and customer value to behavioural intentions (see Appendix 7 Figure F). The direct
effect of service quality on satisfaction was 0.367, whereas value had a direct effect of
193
0.452 on satisfaction. These results point to customer value as a stronger antecedent to
students’ satisfaction than service quality. The total effects of service quality and customer
value on behavioural intentions were 0.178 and 0.526 respectively. The direct effect of
service quality on behavioural intentions was 0.098, whereas the direct effect of customer
value on behavioural intentions was 0.427. Again, customer value emerged as a more
important determinant of behavioural intention than service quality.
Based on the information contained in Figure 6.4 and Table 6.10, as compared to
satisfaction, customer value showed the higher mediating effect with respect to the service
quality and behavioural intentions relationship. There was a total effect of 0.391 when the
relationship between service quality and behavioural intentions was mediated by customer
value. When satisfaction mediates the service quality and behavioural intention
relationship, the total effect was 0.177.
Overall, this thesis supports the mediation effects across the four hypotheses on indirect
relationships. Customer value has a stronger role as mediating effect on the service quality
and behavioural intentions relationship than does customer satisfaction. Based on the direct
effect, indirect effect and total effect examinations, it revealed that customer value also has
a stronger effect on satisfaction and behavioural intentions than does service quality. In
order to provide further understanding of the implications of the indirect relationships, the
following four partial mediation models were also examined.
6.5 PARTIAL MEDIATION ANALYSIS
In addition to the mediation analysis in the integrative model, a mediation analysis was
conducted on each of the four proposed mediating models in the partial models. As
explained in section 3.3.4.2, testing the partial models (SQ-CS-BI, SQ-CV-BI, CV-CS-BI
and SQ-CV-CS) in this thesis is to provide a comparison and an empirical evidence,
whether or not the integrative model may contribute to better results/explanations. The
partial mediation analysis is used to test the unique effect of each mediating variable. PLS
is used to examine the mediating effects of customer value and satisfaction across the four
194
indirect relationships in the partial models. The standardised beta scores produced by PLS
regression were used to estimate the path coefficients shown in the models. Appendix 7,
Figure B to E, illustrates the four partial models of the indirect relationships. Table 6.11
provides a summary of the partial mediation effects when the models were analysed
partially.
Similar to the simultaneous model, the partial mediation analysis was undertaken by
following the requirements suggested by Baron and Kenny (1986). As can be seen in
Appendix 7, Figure B to E, all of the path coefficients across the four indirect models were
significant and therefore, they satisfy the mediation rule-of-thumb recommended by Baron
and Kenny (1986).
Based on Table 6.11, the results suggest:
1) SQ-CS-BI
Service quality has a direct positive effect on behavioural intentions (0.264) and an
indirect positive effect on behavioural intentions through satisfaction (0.274). The
role of satisfaction as a mediating variable increased the total effect to 0.538.
2) SQ-CV-BI
Service quality has a direct positive effect on behavioural intentions (0.158) and an
indirect positive effect on behavioural intentions through customer value (0.380).
The role of customer value as a mediating variable has increased the total effect to
0.538.
3) SQ-CV-CS
Service quality has a direct positive effect on satisfaction (0.326) and an indirect
positive effect on satisfaction through customer value (0.360). The role of customer
value as a mediating variable has increased the total effect to 0.686.
4) CV-CS-BI
Customer value has a direct positive effect on behavioural intentions (0.450) and
indirect positive effect on behavioural intentions through satisfaction (0.189). The
role of satisfaction as a mediating variable has increased the total effect to 0.639.
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The results from the partial models of mediation effects have provided the same results as
the conceptual model. Therefore, the partial models provide support for H5, H10, H11 and
H12.
Table 6.11 Direct and Indirect Effects of Partial Models Independent Intervening Dependent Direct
Effect Indirect Effect
Total Effect
SQ-CS-BI Service Quality Behavioural Intentions 0.264 n/a*
Satisfaction Behavioural Intentions n/a* 0.274 0.538
SQ-CV-BI Service Quality Behavioural Intentions 0.158 n/a*
Customer Value Behavioural Intentions n/a* 0.380 0.538
SQ-CV-CS Service Quality Satisfaction 0.326 n/a*
Customer Value Satisfaction n/a* 0.360 0.686
CV-CS-BI Customer value Behavioural Intentions 0.450 n/a*
Satisfaction Behavioural Intentions n/a* 0.189 0.639
*n/a: not applicable
Interestingly, when satisfaction and customer value were introduced as mediating variables
between service quality and behavioural intentions, the total effects were similar (0.538)
(Table 6.11). This showed evidences of the different effects of satisfaction and customer
value as mediating variables when examined differently in the partial models (Table 6.11
and Appendix 7, Figure B to E) or in the conceptual model (Table 6.9 and Figure 6.4).
In addition to examining the direct and indirect relationships, the analysis of R2 also
provides a key understanding of the proportion of the variance in the endogenous constructs
that can be attributed to the exogenous constructs. As can be seen from the simultaneous
model (Figure 6.4), the R2 of behavioural intentions was 0.456. The R
2 of 0.456 of
behavioural intentions indicates that service quality, customer value and satisfaction
accounted for 45.6% of the variance of the behavioural intentions construct. However,
when the three partial models relating to the impacts of the exogenous variables on
behavioural intentions were examined (excluding SQ-CV-SAT), the results were all smaller
than 45.6%. The R2
of SQ-SAT-BI was 37.8%, the R2 of SQ-CV-BI was 41.9% and the R
2
of CV-SAT-BI was 44%. As discussed above, the bigger the R2, the more predictive power
the model implies. This means that the conceptual model predicts behavioural intentions
better than do the partial models. The R2
of SQ-CV-SAT was 58.4%, meaning that service
quality and customer value account for 58.4% of the variance of customer satisfaction. This
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indicates the significant contribution of service quality and customer value to satisfaction in
the Indonesian higher education sector.
6.6 RESULTS OF HYPOTHESES TESTING
Overall, there were 12 hypotheses proposed to test the relationships based on the
underlying theories that have been presented in Chapter Two and Chapter Three. Table
6.12 displays the proposed hypotheses and findings. The preliminary analysis through PCA
provides evidence that one dimension (reliability) was not valid and significant as a
measure of service quality; therefore H1f was not supported. Further, the PLS analysis did
not support the quality dimension as a valid measure of customer value; therefore H6a was
not supported. Other than H1f and H6a, all of the direct and indirect relationships in the
structural model were supported.
Table 6.12 Hypotheses and Summary of Findings Number Hypotheses S/NS
H1 Service quality is a multidimensional construct and it can be measured by tangible, competence, attitude, delivery, content and reliability.
Partly Supported
H1a Tangible is associated with service quality. S H1b Competence is associated with service quality. S H1c Attitude is associated with service quality. S H1d Delivery is associated with service quality. S H1e Content is associated with service quality. S H1f Reliability is associated with service quality. NS
H6 Customer value is a multidimensional construct and it can be measured by quality, social, price, emotion and reputation.
Partly Supported
H6a Quality is associated with customer value. NS
H6b Social is associated with customer value. S
H6c Price is associated with customer value. S H6d Emotion is associated with customer value. S H6e Reputation is associated with customer value. S H2 Service quality is positively associated with customer satisfaction. S H3 Service quality is positively associated with behavioural intentions. S H4 Customer satisfaction is positively associated with behavioural intentions. S H5 Customer satisfaction mediates the relationship between service quality and
behavioural intentions. S
H7 Service quality is positively associated with customer value. S H8 Customer value is positively associated with customer satisfaction. S H9 Customer value is positively associated with behavioural intentions. S H10 Customer satisfaction mediates the relationship between customer value and
behavioural intentions. S
H11 Customer value mediates the relationship between service quality and customer satisfaction.
S
H12 Customer value mediates the relationship between service quality and behavioural intentions.
S
Note: S/NS: (S) Supported or (NS) Not Supported
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6.7 CONCLUSION
This chapter has confirmed the results of the preliminary analysis and examined the main
analysis. The validity and reliability of the measures have been evaluated and confirmed by
PLS analysis through the measurement model. One dimension of service quality
(reliability) and one dimension of customer value (quality) were dropped due to the
unsatisfactory results in the measurement model. Based on the results of PLS analysis in
the measurement model, only five dimensions (tangible, competence, attitude, delivery and
content) were associated with service quality and four dimensions (social, price, emotion
and perception) were associated with customer value. The structural model involving direct
and indirect relationships was examined. All paths were found to be positive and
significant, therefore supporting all of the hypotheses regarding the direct relationships.
There were partial mediation effects across all of the indirect relationships proposed in the
hypotheses, since the introduction of the mediating variables did not cause zero effect on
the relationship between independent and dependent variables. All of the hypotheses
relating to the mediation effects were also supported. In addition, the results also show the
superior role of customer value as compared to service quality on customer satisfaction and
behavioural intentions. Customer value also has stronger mediating effect than customer
satisfaction.
In order to test the robustness of the conceptual model, the partial models (SQ-CS-BI, SQ-
CV-BI, SQ-CV-CS and CV-CS-BI) were analysed as comparison. Both of the conceptual
and partial models provided similar results regarding the significance of the path
coefficients of the direct and indirect effects. The R2 of the simultaneous model was found
to be higher than any of the proposed three partial models when behavioural intention was
modelled as a dependent variable. This suggests the importance of simultaneously
examining the influence of service quality, satisfaction and customer value on behavioural
intentions. Finally, a summary of all of the proposed hypotheses was provided.
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CHAPTER SEVEN
DISCUSSIONS ON THE EMPIRICAL ANALYSIS
7.1 INTRODUCTION
This chapter presents a detailed discussion of the findings from both the preliminary and
main analyses in Chapters Five and Six. Although the PCA in Chapter Five was primarily
intended as preparatory work for the PLS analysis, there were a number of interesting
findings that emerged in the course of assessing the PCA and are worthy of further
comment. Discussions of the main analysis (Chapter Six) cover the results from the PLS
analysis and are presented in alignment with the three research questions and their related
hypotheses.
7.2 THE PRELIMINARY ANALYSIS
7.2.1 Service Quality
As discussed in Chapter Two, service quality is a context-specific construct. Testing the
dimensions of the service quality study according to each specific situation is important to
ensure its validity and reliability (Lagrosen 2001). The service quality dimensions proposed
by Parasuraman et al. (1988), which, developed for general service marketing, may not be
sufficient to represent the specific context of the higher education sector. Accordingly, this
thesis adopts Owlia and Aspinwall’s (1998) scale, which is developed particularly for
measuring service quality in the higher education sector.
The results from the PCA (Chapter Five) revealed that the set of items in the service quality
scale did not perfectly factorise into six factors as initially proposed. The finding did not
conform exactly to the Owlia and Aspinwall (1998) revised framework. In their revised
framework, Owlia and Aspinwall (1998) proposed six dimensions (tangible, competence,
content, attitude, delivery and reliability). The PCA produced five dimensions of service
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quality (tangible, competence, content, attitude and delivery), which were identified as
valid and reliable measures. This means that only these five dimensions of service quality
that are valid and reliable as a measure of service quality in Indonesian higher education
sector based on PCA.
The results showed that all items measuring the reliability dimension were loaded into
other dimensions. Two items, A21 and A22, loaded into the tangible dimension while item
A20 loaded into the content dimension. The same unsatisfactory result in the reliability
dimension also occurred with the Owlia and Aspinwall (1998) study, which found a low
alpha value for the reliability dimension. Despite the low number of items in the reliability
dimension, the low alpha values could be the result of the different issues being canvassed
in the reliability dimension. These three items conceptually reflect some aspects of
reliability, however, they dealt with different issues (credibility of degrees, handling
feedback and security information). Overall, the PCA has revealed the formation of five
dimensions of service quality. The results from the PCA in the Indonesian higher education
sector confirm that the reliability dimension is not a robust measure of service quality. This
means that a more specific and consistent scale of reliability in the higher education sector
is needed to better measure reliability aspect.
7.2.2 Customer Value
Customer value is also a context-specific construct where conceptualisations may vary
according to the context being studied (Dodds et al. 1991). Similar to the service quality
construct, customer value in this thesis was designed as a multidimensional construct.
Customer value is measured using a combined scale of PERVAL (Sweeney & Soutar 2001)
and SERVPERVAL (Petrick 2002) (see Section 4.3.3.1.2). Considering that this thesis
employs a combination of scales from previous studies, that customer value is a context-
specific construct (Sweeney 1994) and that no previous multidimensional customer value
construct was measured in the higher education sector in Indonesia, examining the validity
and reliability of the customer value construct is necessary. The psychometric test is
designed to ensure that the customer value measure used in this thesis is a valid and reliable
measure of customer value in the Indonesian higher education sector. The scale was
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conceptualised as consisting of five dimensions (emotional, social, price, reputation and
quality) (Sweeney & Soutar 2001; Petrick 2002).
In the customer value literature, Sweeney and Soutar (2001) discovered that when assessing
a product, customers are not only concerned with the functional aspects such as rational or
economic valuations. Customers are also concerned with the emotional aspect (the
enjoyment or pleasure obtained from the product/service), the social aspect (social
consequences of what the product/service communicates to others) and the symbolic aspect
(the prestige of the service provider based on image). Based on the PCA result, this thesis
shows that there existed five factors covering not only functional aspects (e.g. quality and
price) but also social, emotional and symbolic (reputation) aspects of customer value in the
Indonesian higher education sector. This means that Indonesian students assess the value of
the higher education services offered based not only on the functional aspects, which only
consider on the rational and economic valuation, but also consider the symbol, enjoyment
and the social aspects.
The considerable importance of non-functional dimensions in the higher education sector
has also been empirically examined by LeBlanc and Nguyen (1999). For example, the
existence of social value is shown where students build friendships with others in their
classes, become members of groups and join in social activities which add value to
students’ learning experience. Emotional value is shown where learning experiences should
be enjoyable and symbolic value where students obtain pride from their belonging to or
being part of, their chosen institution. In sum, based on the PCA, the combined PERVAL
and SERVPERVAL scale translated very well in the Indonesian higher education sector.
7.2.3 Customer Satisfaction
The customer satisfaction construct was measured by using the combined instruments
developed by Athiyaman (1997), Cronin et al. (2000) and Mc Dougall and Levesque
(2000), adjusted to suit the higher education context. These instruments consisted of a
combination of the evaluative and affective aspects of customer satisfaction. Both
evaluative and affective aspects are applicable in the higher education sector since the
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service industry involves intensive human interactions. Consistent with the objective of this
thesis which is to examine the relationships across the four key constructs, customer
satisfaction was designed more as an overall cumulative perception measured by single
dimension and multi-items (Oliver 1980, 1981; Cronin et al. 2000; Caruana et al. 2000;
McDougal & Levesque 2000) (see Chapter Four Section 4.3.3.1.3). Based on the PCA, the
one factor was formed and this formation was in alignment with the unidimensional
conceptualisation of customer satisfaction. The high internal consistency value (Cronbach’s
alpha = 0.910 – see Section 5.6.2.2.3) showed support for the one factor formation and its
unidimensionality. The significance of all eight items (C1-C8) in measuring customer
satisfaction implies that student satisfaction in the Indonesian higher education sector is
influenced by both the evaluative and affective aspects of satisfaction. As a consequence,
when assessing customer satisfaction, higher education managers are recommended to
consider both evaluative and affective aspects of satisfaction.
7.2.4 Behavioural Intentions
The behavioural intentions construct is of interest in this thesis since it reflects the strategic
outcomes after all efforts to increase service quality, customer value and customer
satisfaction have been made. Behavioural intentions, rather than customer satisfaction, is
increasingly popular and is seen as the more meaningful construct (Dick & Basu 1994;
Durvasula et al. 2004; Hong & Goo 2004; Singh & Sirdeshmukh 2000). The behavioural
intentions construct was measured using the combined instruments developed by Boulding
et al. (1993) and Athiyaman (1997). The behavioural intentions scale in this thesis had been
slightly modified to adjust to the higher education context and was augmented by newly
developed items. The analysis of PCA suggested that one factor was formed. All of the
seven items measuring behavioural intentions loaded higher than 0.5, a rule-of-thumb for
showing convergent validity. It also has an internal consistency of 0.821(Section 5.6.2.2.4)
which means that it has a satisfactory level of reliability for study in social science.
To measure behavioural intentions, this thesis incorporates word-of-mouth
recommendations and loyalty dimensions as these dimensions are most relevant in the
higher education sector (Alves & Raposo 2007). The word-of-mouth recommendation is
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suitable for the behavioural intentions measure in the higher education sector since higher
education can be categorised as an industry where trust is highly important. In addition to
trust, the substantial investment (money, time and efforts) that must be made to study in
higher education causes heavy reliance on person-to-person communication to reduce the
risks of making the wrong choice or of failure. Potential students (stakeholders) often
require word-of-mouth information from reliable persons before making a decision on
selecting a higher education institution.
Student loyalty, like customer loyalty, is concerned with positive attitude such as intention
to contribute to financial and/or non-financial support, intention to return, etc. As pointed
out in the study by Zeithaml et al. (1996), loyalty is posited as one of the dimensions of
behavioural intentions. Since this thesis does not specifically focus on behavioural
intentions, this construct was designed as a unidimensional measure covering aspects
commonly relates to students’ behaviour in the higher education sector (e.g. loyalty and
word-of-mouth). Overall, based on PCA analysis, one factor formation consisting of loyalty
and word-of-mouth aspects was formed to measure behavioural intentions in the Indonesian
higher education sector.
7.2.5 Summary of the Preliminary Analysis
In summary, the preliminary analysis using PCA produced a number of interesting findings
as follows:
1. There were five factors forming the service quality construct specific to higher
education, consisting of: tangible, content, competence, attitude and delivery. The
reliability dimension was not robust since all of its items loaded into other
dimensions. The formation of five factors was slightly different from the Owlia and
Aspinwall (1998) scale that was used as a foundation on which to develop the
service quality scale in this thesis. The formation of the factors also confirmed the
context-specific nature of the service quality scale, where Owlia and Aspinwall
(1998) scale also significant for higher education in Indonesia. As explained by the
founder of the SERVQUAL scale (Parasuraman et al. 1988), the SERVQUAL scale
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can be used as a foundation, though adjustment is necessary to better explain its
context-specific nature.
2. Since there were no previous customer value scale specifically developed for higher
education sector, this thesis combined the scale from PERVAL and SERV-
PERVAL. The PERVAL and SERVPERVAL scales, which originally developed in
a retail setting and the general service sector, performed reasonably well in the
Indonesian higher education sector. There were five formations of customer value
scales consisting of reputation, price, social, emotion and quality. Even though the
customer value scale used was developed from the retail and general services, the
PLS loading in the following discussions allow the examination of specific
contributions of each dimension in the higher education sector.
3. A one-factor formation was formed for the customer satisfaction and behavioural
intentions constructs. One-factor formation representing the evaluative and affective
measure of customer satisfaction is applicable to the Indonesian higher education
sector. Similarly, behavioural intention is measured by one dimension mainly
consisting of loyalty and word-of-mouth communication which are considered to be
reliable measures of behavioural intentions in the Indonesian higher education
sector.
The discussion above indicates that there were interesting findings, since few changes were
found in the formation of the scale compared to the original scales. This means that the
context-specific nature of service quality and customer value was evidenced in this thesis.
In order to provide a more meaningful result, a study that involves the service quality and
customer value constructs must be carefully adjusted according to the context being
studied. Nevertheless, the major purpose of this preliminary PCA was only to provide a
preparation for the following PLS analysis. A discussion of the findings from the PLS
analysis follows below.
7.3 THE PLS ANALYSIS
The Confirmatory Factor Analysis (CFA) using PLS analysis is the focus of this research
study. The PLS was employed to investigate whether there were evidence of relationships
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as had been theorised from the literature review. More specifically, this thesis is to
investigate whether or not the relationships as proposed in the conceptual model are
broadly applicable in the higher education sector. The main analysis is divided into two
broad areas. The first procedure is called the measurement model, where in all items are
being examined for their validity and reliability. Once they showed satisfactory
psychometric properties, the items were included in the next procedure. The second
procedure is called the structural model and it examines the relationships amongst the key
constructs.
7.3.1 The Measurement Model
The validity and reliability of the scales were tested in the measurement model by
examining the item loadings, Internal Composite Reliability (ICR), Average Variance
Extracted (AVE), cross-loadings and the square root of AVE. Based on the result of the
PLS analysis (Chapter Six, Table 6.2 and Table 6.3) several problematic items were
identified. Table 6.3 provides justifications regarding the removal of problematic items and
Table 6.6 summarises the valid and reliable scales for this thesis. The results from the
measurement model provide the answer for the proposed research question 1 and its related
hypotheses.
Research question 1: What constitutes valid and reliable scales for measuring service
quality and customer value in the Indonesian higher education sector?
In order to answer research question 1, PCA and PLS were employed. The discussions on
the preliminary analysis were provided in the previous Section 7.2. The following sections
discuss the main findings from the PLS analysis, regarding the examination for hypothesis
1 and hypothesis 2.
7.3.1.1 Service Quality
Hypothesis 1 was proposed to examine the relationships between all of the dimensions of
service quality (tangible, competence, attitude, delivery, content and reliability). Figure 7.1
illustrates the final result of the PLS outer model of the service quality construct.
205
Hypothesis 1: Service quality is a multidimensional construct and it can be defined
in terms of tangible, competence, attitude, delivery, content and reliability.
Hypothesis 1a: Tangible is associated with service quality.
Hypothesis 1b: Competence is associated with service quality.
Hypothesis 1c: Attitude is associated with service quality.
Hypothesis 1d: Delivery is associated with service quality.
Hypothesis 1e: Content is associated with service quality.
Hypothesis 1f: Reliability is associated with service quality.
The following section will discuss each of the dimensions that have been identified as valid
and reliable by the PLS analysis.
Figure 7.1 Second-order Reflective Constructs of Service Quality
7.3.1.1.1 Reliability
The results from PCA revealed that, rather than six, there were five formations of factors
measuring service quality. Further examination using PLS also confirmed that items
designed to measure the reliability dimension (A20, 21, 22) did not show satisfactory
psychometric properties. In summary, the result from PCA was supported by the PLS
analysis, where the reliability dimension is not a robust measure; therefore, this dimension
was dropped.
delivery
2 item
s
Competence
4 item
s
Content
6 item
s
Attitude
5 item
s
Service Quality
0.818****
0.821**** 0.677****
0.689****
0.629****
Note: ****p<0.001; ***p<0.01; **p<0.05; *p<0.1
Tangible
5 item
s
206
Removing the reliability dimension as a measure of service quality is not common in the
service sector since reliability is extensively described as an important determinant of
service quality. The importance of reliability as a determinant of service quality has been
identified in most of the service sectors including higher education (Parasuraman et al.
1985; Garvin 1983; Watts 1987; Haywood-Farmer 1988; Gronroos 1988; Cronin &Taylor
1992; Owlia &Aspinwall 1997). Parasuraman et al. (1988) specifically reported that
generally customers demand reliability in the private sector. Zeithaml et al. (1990)
identified customers’ consistency in ranking service quality attributes with reliability as the
most important dimension and tangible as the least important. In the higher education
sector, Smith et al. (2007) and Galloway and Wearn (1998) found the same result.
Reliability was the most important dimension of service quality and tangible was the least
(Galloway & Wearn 1998).
Although previous studies have discovered the significant role of reliability in explaining
service quality in both general services and in higher education, the results thus far were
inconsistent. Reliability was found to be the least important factor in the higher education
sector based on the overall sample (engineering and management students), and content
was the most important dimension (Sahney et al. 2004b). The Owlia and Aspinwall (1998)
study has found a similar pattern in which reliability dimensions appeared to be less
important in higher education than other dimensions. Other studies have found that tangible
was considered the most important dimension of service quality in both general services
(Olorunniwo et al. 2006, Tsoukatos et al. 2006, Perez et al. 2007) and higher education
(Athiyaman 2000; Owlia & Aspinwall 1998, 1996; Lagrosen et al. 2004; Sahney et al.
2004b; LeBlanc & Nguyen 1997). The inconsistencies of the results could be explained by
the different contexts being studied and the different scales made up to measure the
reliability dimension.
Based on the path coefficients as shown in Figure 7.1, this thesis identified that all of the
five dimensions (content, tangible, competence, attitude and delivery) are positively
associated with service quality. Content is considered to have the highest path coefficient
among the dimensions of service quality, followed in order by tangible, competence,
207
attitude and delivery. This implies that content is the strongest dimension to reflect service
quality in the Indonesian higher education sector, as perceived by students. As a
consequence, managers could respond accordingly, by focusing firstly on content and later
on other dimensions.
7.3.1.1.2 Content
Content as a dimension refers to the nature and relevance of the product/service being
offered. The importance of content in the higher education sector has been identified by
Sahney et al. (2004b) and Kwan and Ng (1999). Based on Sahney et al.’s (2004b) relative
ranking of customer requirements, they found that content was ranked first by the entire
sample of students and reliability was ranked last. Kwan and Ng’s (1999) study on quality
indicators found different results between Asian students (Hong Kong and Chinese) and
North American students (US). Hong Kong and Chinese students are found to be more
practical and more focused on study-related matters, than their North Amreican
counterparts including ‘content’. The reason might be that Hong Kong and Chinese
students regard university education as an investment and consequently stressed the
importance of course content and facilities (Kwan & Ng 1999). A different result was
found with students from the United States who were more interested in campus life or
social life on campus (Kwan & Ng 1999). Figure 7.2 illustrates the PLS loadings for
content dimension.
By looking at the tenets of the content dimension used in this thesis (the programs contain
basic (A14) and additional (A15) contents, relevance of the curriculum for future jobs
(A16), the programs contain communication skills (A17) and team working (A18) and the
applicability of the learning in other fields (A19), it can be seen that Owlia and Aspinwall
(1998) have emphasised the importance of skills and knowledge that will be useful for
Figure 7.2 PLS Loadings for Content Dimension
A15
0.7283
Content
A14
0.6164
A16
0.7580
A17
0.7139
A18
0.7268
A19
0.6748
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students’ future careers. This dimension takes into consideration not only the learning for
basic knowledge/skills, but also relative skills and knowledge, which are applicable to the
Indonesian higher education context. For Indonesians, pursuing education in the tertiary
level is mostly an initiative designed to increase the potential for wealth creation (Nizam
2006). Since the emphasis is primarily on careers, many students do not quite openly
demand the highest quality from higher education institutions. Many students just want to
get a degree as a ticket to enter the job market, while also acquiring skills that are useful for
their future careers. In addition, since the respondents in this research were students in their
second year and above, the significance of this scale appeared to reflect the importance
aspects of content that will be useful for students’ future careers. In particular, items A16
and A15, which focus on additional content and relevance for future jobs, were found to
have the highest loadings. This implies that the attractions or quality perceived by students
can be related to how institutions provide a better access for future jobs/better careers.
7.3.1.1.3 Tangible
Tangible in the higher education context is defined as the physical state, sufficiency and
availability of equipment and facilities (Owlia & Aspinwall 1996). In the higher education
sector, apart from the core product which is intangible, the education service is always
represented by the engagement of some tangible forms (Kotler & Fox 1995). Lecture
theatres, handouts, libraries and information technology (IT) laboratories are facilities
commonly provided by the institutions and those tangible forms are fundamental in the
education processes. Since there is highly involvement of some tangible attributes, it is
likely that higher education students’ perceptions are influenced by the tangible facilities
(Oldfield & Baron 2000). Price et al.’s (2003) study found that the tangible or physical
environment of the service production is a factor that may influence the potential students
in making selections for their future study. This means that the contribution of tangible
form to the education process is not important only for the enrolled students, but also for
potential students who are affected by the physical facilities offered by the institution
through advertisements and word-of-mouth communications. Figure 7.3 illustrates the PLS
loadings for the tangible dimension.
209
Among the five items considered valid and reliable for measuring the tangible dimension
(see Figure 7.3), items related to ‘equipment’ were found to be the two strongest items.
These items are ‘the degree to which equipment is modern’ (A25) and ‘the ease of access to
the equipment’ (A24). Students highly appreciate the equipment being up to date and
modern since higher education is the place where students expect to always learn new
things and are aware of the development of the most current technology. In addition,
outside the campus, the dynamism of technological development has made technology
more affordable and accessible to everyone. The second highest loading (item A24) is
related to access to equipment. Interestingly, it is followed by item A26 which relates to
ease of access to information sources (books, journals, software, etc.). This implies that
while the institutions might have sufficient tangible resources, unless well managed, such
an advantage will be useless or will fail to create positive perceptions on the quality. For
example, even though IT and computer facilities are modern, without clear schedules for
booking, clear rules governing use, and competent staff who take charge of the amenities,
all of those sophisticated physical facilities might not be well-appreciated. The sufficiency
of the equipment (A23) does have an effect on accessibility. When there are limited
resources, the staff must be able to control the capacity utilisation so that physical facilities
can be shared by a majority of the students. The involvement of the human factor is more
important than the physical aspect per se. Despite the fact that the loadings were not far
different, the degree to which the environment is visually appealing (A27) came last. This
implies that the appearance of the environment is of less concern for students than the issue
of equipment being accessible and modern. Again, this suggests that, particularly for
enrolled students who have the real day-to-day experience with the service offerings, the
tangible product per se will be meaningless unless accessible and well-managed.
Figure 7.3 PLS Loadings for the Tangible Dimension
A24
0.8947
Tangible
A23
0.8867
A25
0.8991
A26
0.8896
A27
0.7917
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7.3.1.1.4 Competence
Competence refers principally to the possession of the skills and knowledge required to
perform the service. Owlia and Aspinwall (1998) argue that the knowledge of the academic
staff is a vital factor in the higher education sector, which must be accompanied by
familiarity with practical applications as well as presentation skills. Lovelock (1981) found
that in “people processing” services, such as hospitals and education, there is very frequent
involvement in personal contact. Customers often evaluate the staff who takes part in the
provision of these services (in hospitals or education where there are high levels of personal
contact) in terms of their technical or customer-related skills, consistency of performance,
personality and appearance. Figure 7.4 illustrates the PLS loadings for the competence
dimension.
Based on the PLS result (see Figure 7.4), it can be seen that students rated item A2 (being
up-to-date) as the most important item, followed by having relevant theoretical knowledge
(A3), having expertise in the teaching area (A1) and incorporating practical knowledge
according to the teaching area (A4). This outcome implies that in order to be positively
perceived as having quality, in terms of competence, the staff must be up-to-date in their
fields of expertise. This is very logical since higher education is becoming more IT-related,
information technologies are becoming more affordable, information is more accessible and
consequently students demand the skills and competencies that enable them to follow the
current trend. Therefore, in class, the academic staff must not only use the same resources
year-by-year, but must also be willing to always learn new things. Since the core service of
higher education is knowledge delivery, the academic staff must possess the appropriate
theoretical and practical knowledge as well as expertise in teaching. Administrative staff
must also remain informed about the most modern management systems. The manager
should be selective in the recruitment process, since the competence of the staff reflects the
Figure 7.4 PLS Loadings for Competence Dimension
A2
0.7870
Competence
A1
0.7631
A3
0.7724
A4
0.7066
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perceived quality of the institution. To increase the levels of competence, there are many
measures that can be adopted for staff, such as providing opportunities to study for a higher
degree, short courses, training, conferences, forums for knowledge sharing, etc.
7.3.1.1.5 Attitude
Attitude in this thesis relates to understanding the customers (students) and social manners.
Attitude is also commonly described as courtesy and empathy (Parasuraman et al. 1988)
and warmth (Haywood-Farmer 1988). Empathy is described as caring or individualised
attention given to customers (Parasuraman et al. 1988). Courtesy, which is an emotive and
positive attitude towards students will lead to the creation of pleasant learning
environments (Sakhtivel et al. 2005).
The staff who directly deliver services play key roles in shaping the decisions made by the
students they serve. The staff's ability and willingness to satisfy students, by showing a
positive manner and neat appearance, plays a significant part in determining levels of
student satisfaction. In many ways, the staff can be the source of differentiation for the
service provider (Palmer 1994 in Oldfield & Baron 2000), for example, politeness,
patience, knowledge and helpfulness. While an educational sector could be said to be
utilitarian (emphasis on the functional aspect), at the personal (one-to-one) level, it is
important that students are served with sensitivity and sympathy (Hill 1995). Assistance
should be provided where possible and even the simple act of listening is often appreciated
(Hill 1995). Figure 7.5 illustrates the PLS loadings for the attitude dimension.
As has been identified through the questionnaire, clear guidance (A9), willingness to help
(A8), understanding students’ needs (A7), adequate personal attention (A10) and support
staff competencies (A6) are all valid and reliable manifestation of service quality in terms
Figure 7.5 PLS Loadings for Attitude Dimension
A7
0.8171
Attitude
A6
0.7119
A8
0.8196
A9
0.8269
A10
0.7432
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of attitude. Similar to the previous dimensions (tangible and competence), the service
sectors require that human aspects play most important role. Despite having the skills and
expertise, it is important that the staff exhibit good manners and a willingness to care. This
is particularly important since quality in higher education is produced by both students and
staff (institution). Students are not purely customers of higher education, but they are
partners of the institution. The success of both the institution and the students depends on
how staff and students support each other. Lammers et al. (2005) point out that in the
education process, it is important that students understand whether or not the role of
academic and administrative staff is only to act as facilitators in the learning process and
for helping students to achieve their goals. Students themselves hold the power to achieve
their goals (Eagle & Brennan 2007).
7.3.1.1.6 Delivery
Delivery refers to the extent to which and how well the product or service is being delivered
and presented. Sahney et al. (2004b) argue that professors should not only have the
expertise in particular fields, but they must also be capable of transferring that knowledge.
In other words, the commitment to acquiring the knowledge must correspond with the
commitment to deliver it. In this thesis, two items were valid as a measure of the delivery
dimension. These related to the tenets “the presentations should be logical and in a timely
manner (A12)” and “the coverage of the contents of the exam (A13)”, as shown in Figure
7.6. As identified in Table 6.6, this dimension has the least reliability (internal consistency)
compared to the other dimensions of service quality. This might be due to the limited
number of items (two items). By taking a closer look at the items that measure delivery
(‘presentation of course material’ and ‘exam coverage’), these different aspects of delivery
might be revealed as the cause of low internal consistency, despite being statistically
significant as a measure. Figure 7.6 illustrates the PLS loadings for the delivery dimension.
213
The significance of the delivery dimension implies that the higher education sector in
Indonesia must be concerned with the delivery aspects of teaching. A brilliant professor
will not be appreciated when he or she cannot deliver the knowledge that they possess to
their students. The class is not a place to show off expertise, but rather to share and to
deliver knowledge. A two-way communication system must be encouraged, rather than the
traditional one-way communication. A regular evaluation should be made in order to ensure
that the course materials are all covered, delivered properly and remain up-to-date as well
as being reflected in all exams. A course coordinator should ensure that the course
materials are appropriately and logically scheduled, which will help students in the learning
process.
7.3.1.1.7 Summary of Discussions on Service Quality
Overall, the PLS analysis has identified five dimensions as valid and reliable for the
measurement of the service quality construct. In addition, the path coefficients have also
shown the positive significant effects of tangible, competence, content, attitude and
delivery on service quality. The reliability dimension was dropped due to its unsatisfactory
psychometric properties. Based on these results, Hypothesis 1 is partly supported since
there were only five dimensions found to be valid and reliable mechanisms for measuring
service quality in the Indonesian higher education sector namely. In this case, hypotheses
H1a to H1e were supported while H1f was not. A further look into the contribution of each
dimension of service quality (Figure 7.1) revealed that content has the highest loading,
followed by tangible, competence, attitude and delivery. This order reflects the specific
condition on the contribution of each service quality dimension in the Indonesian higher
education sector. This figure is Table 7.1 provides a summary of the terminology of the
dimensions used to measure service quality in this thesis.
Figure 7.6 PLS Loadings for the Delivery Dimension
Delivery
A12
0.9015
A13
0.8256
214
Table 7.1 Dimensions of Service Quality Dimensions Definitions
Tangible The state, sufficiency and availability of equipment and facilities.
Attitude The degree to which staff understand the customers (students) and have socially acceptable manners.
Content The nature and relevance of product/service.
Competence The possession of the required skills and knowledge to perform the Service.
Delivery The manner in which the product or service is being delivered and presented.
Source: Sahney et al. 2004
7.3.1.2 Customer Value
The following hypothesis 6 is proposed relating to the validity and reliability of the
customer value scale. Figure 7.7 illustrates the final result of the PLS outer model of the
customer value construct.
Figure 7.7 Second-order Reflective Constructs of Customer Value
Hypothesis 6: Customer value is a multidimensional construct and it can be defined in
terms of quality, social, price, emotion and reputation.
Hypothesis 6a: Quality is associated with customer value.
Hypothesis 6b: Social is associated with customer value.
Hypothesis 6c: Price is associated with customer value.
Hypothesis 6d: Emotion is associated with customer value.
Hypothesis 6e: Reputation is associated with customer value.
Similarly to service quality, customer value in this thesis was also designed as a
multidimensional construct. Testing the validity and reliability of the multidimensional
Reputation
5 item
s
Emotion
4 item
s
Social
4 item
s
Price
4 item
s
Customer Value
0.845**** 0.828****
0.851****
0.721****
Note: ****p<0.001; ***p<0.01; **p<0.05; *p<0.1
215
customer value construct is important since customer value is argued to be a context-
specific construct (Sweeney 1994; Dodds et al. 1991) and no previous customer value
construct has been measured in the higher education sector in Indonesia. Both the PCA and
PLS analyses have examined five dimensions of customer value (reputation, emotion,
quality, price and social). However, after considering the ‘correlation among the construct
scores’ (Section 6.3.2.2.2.1), only four dimensions were identified as valid and reliable
measures of the customer value construct based on the PLS analysis. The path coefficients
as illustrated in Figure 7.7 showed that there were positive relationships between the
reputation, emotion, price and social dimensions and customer value construct. Therefore,
based on this result, H6 is partially supported since there were only four dimensions which
were proven to be valid and reliable mechanisms for measuring customer value. More
specifically, this thesis only supported hypotheses H6b to H6e and did not support H6a. In
addition, a further look into the contribution of each dimension of customer value (Figure
7.7) showed that Social dimension has the highest loading, followed by emotion, reputation
and price. This order reflects the specific condition on the contribution of each customer
value dimension in the Indonesian higher education sector. The following section discusses
in detail every dimension of customer value identified as valid and reliable by PLS.
7.3.1.2.1 Affective Aspects (Social and Emotional Value)
The significance of affective dimensions in the higher education sector has been
empirically examined by LeBlanc and Nguyen (1999). As can be seen in Figure 7.7, it was
shown that among all four dimensions of customer value, the social dimension had the
highest path coefficient and was followed respectively by emotion, reputation and price.
This finding shows the importance of the affective aspects of value, especially in the higher
education sector. The significance of the affective aspects is logical since, as a service
provider, higher education involves a lot of human interactions. It is, therefore, feeling or
emotion that plays an important part. Higher education sectors such as hospital, banking or
insurance is a service sector where ‘trust’ plays a vital role. Figure 7.8 and 7.9 illustrate the
PLS loadings for the social dimension and the emotion dimension.
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A further look into students’ motivation to enroll in higher degrees will also provide us
with the insight that students are not merely paying for the services that they receive
directly. The social aspect also relates to the degree gained from completing a series of
courses in higher education. The degree will allow students to pursue better careers, status,
lifestyles, etc. Bogler and Somech (2002) have documented a change in student motivation
for entering higher education, from gaining knowledge to employment prospects and social
mobility. A higher education degree is seen as enhancing both employment prospects and
the opportunities for social mobility. When considering the core benefits of entering higher
education, students are not specifically buying their degrees, but rather are more concerned
with the benefits to be gained after being granted the degree, e.g. employment, status and
lifestyle (Binsardi & Ekwulugo 2003). This implies that there are important aspects beyond
the core education offerings (lecturing and degree), through which the higher education
sector must also facilitate students’ social mobility. A passive reproduction of learned
knowledge is no longer sufficient, especially in fulfilling the social value aspect of higher
education experiences.
The importance of the social aspect was also reflected by the significance of the items
measuring the social dimension which covered issues such as whether studying in higher
education improves the way students are perceived (B9), providing good impressions
(B10), social approval (B11) and making students feel good socially (B12). This implies
that higher education institutions should be concerned with developing a strategy which
will enable them to increase the social acknowledgement of their students. In particular, the
two highest loading items (B10 and B11) which relate to positive impression and social
approval, could be used as guidelines for a strategy designed to increase value for students.
Figure 7.8 PLS Loadings for the Social Dimension
B10
0.9054
Social
B9
0.8665
B11
0.8806
B12
0.8368
Figure 7.9 PLS Loadings for the Emotion Dimension
B14
0.9145
Emotion
B13
0.8858
B15
0.9184
B16
0.9110
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This can be done by expanding networks with industrial sectors, communities and the
government. Institutions can raise their profiles through involvement in many public or
academic events, etc.
Despite that tuition fees being continuously increased from year to year, especially in the
private university sector in Indonesia, it is generally believed that higher education is a
‘public good’. This means that higher education not only belongs to the staff and the
students, but also to the community, industrial employers and the government. For this
reason, the social aspect of the higher education sector can also be enhanced by actively
promoting public awareness of the existence of the institutions. Several ways can be done
and students can be involved to improve the positive social image of the institutions. For
example, this could be done by improving the corporate social responsibility, publishing
brief regular reports to nearby communities outlining the achievements of the institutions
and providing free public lectures or short courses. More specifically in Indonesia, where
there are student unions and many student clubs exist (religion, sports, language-based
clubs, etc.), the institutions should allow and provide positive support in order to increase
opportunities for students’ social development. It is quite common, even though this has not
been subject to academic research, for students who are actively participating in the school
organisation to have more confidence and recognisability in the world of work.
Emotional value relates to positive feelings towards and enjoyment of the higher education
services being delivered. Based on the PLS loadings on all items that made up the emotion
dimension, all items were found to have very high loadings (see Figure 7.9). Positive
feelings can be influenced by both academic and non-academic aspects such as class
delivery, teaching methods, enrollment processes and relationships between students and
staff. Based on a study in the business school, emotional value was found to become more
positive as students advanced in their studies (LeBlanc & Nguyen 1999). In other words,
emotional responses were found to be less favourable among first year students in the
business school. LeBlanc and Nguyen (1999) posited that the increase in the emotional
value may be due to the ability to choose an area of specialisation which corresponds to
students’ best interests and needs.
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In this thesis, the emotional dimension is stated to be the second most important dimension
as identified by PLS analysis. Since this thesis only gathered the sample from students in
their second year and above, comparison with the younger class was not possible and it was
not the aim of this thesis to make such a comparison. This thesis, however, identified the
significance of the emotional dimension. This means that emotion represents one of the
aspects of students’ value perception. Similar to the social dimension, the emotional
dimension is also part of the affective aspects. Therefore, feeling plays a strong role
particularly in a service sector industry like higher education where face-to-face contact,
customisation and personal support are very strong.
The emotional attachment is essential in a service involving a high level of personal contact
and trust. This emotional attachment may motivate students on their learning. Once the
students have a positive, strong emotional bond with the institution, it will usually be
enduring and, as theories indicate, students become loyal partners of the institution. This
implies that institutions should encourage all staff to provide positive emotional support for
students.
7.3.1.2.2 The Reputation Benefits
Reputation has potential applicability to the Indonesian higher education sector. Figure 7.10
illustrates the PLS loadings of all items that were used to measure reputation in this thesis.
All items showed high loadings (>0.857), except B21 (0.698) which is still considered
adequate (Chin 1998a) (see Table 6.1). In general, the validity of these items (B17-B21)
has indicated the significance of the reputation dimension as a measure in Indonesian
higher education sector. As a service industry, the prestige or reputation of the higher
education sector is strongly attached to all stakeholders and particularly to students,
especially relating to its capacity for delivering social advantage. For example, one of the
reasons that students enter higher education is to increase social approval and to create a
good impression. As in many societies, Indonesians still regard education as the only viable
choice for anyone attempting to achieve vertical mobility in economic and social status
(Nizam 2006).
219
The literature reviews have shown that, due to the intangible nature of services, indirect
elements are commonly used by customers as a proxy to evaluate the service. One of the
indirect elements is image or reputation. For the enrolled students/current students, the
impact of institutional reputation may influence perceptions across all their academic years.
This is because reputation relates to the social benefits of being a higher education student.
Reputation can also be relevant for the pride of being a member of prestigious institution.
Students who belong to a more reputable institution usually have more loyalty to, and
confidence in mentioning, the institution at which they study. As is the case with higher
education in Indonesia, the reputation of public institutions is higher than that of their
private counterparts. However, currently, more private institutions in Indonesia are
differentiating themselves and are able to build positive profiles in specific areas such as
information technology, law practice, mining and English literature.
LeBlanc and Nguyen (1999) also found that customer value is influenced by perceived
image. However, apart from the majority agreement on the significance of reputation,
LeBlanc and Nguyen’s (1999) findings have also revealed that perceptions on reputation
were less favourable in the more advanced students than among the first year students.
They argue that perceptions of image among students changed as they experienced the
service. This thesis gathered samples from students in their second year and above. Thus, a
comparison with first year students was not possible. This thesis, however, identified the
significance of the reputation dimension. This means that reputation represents one of the
aspects of students’ value perception.
The reputation of a higher education institution is also particularly important for potential
students as it may influence the decision to choose an educational institution (Bourke 2000;
Figure 7.10 PLS Loadings for the Reputation Dimension
B18
0.9107
Reputation
B17
0.8579
B19
0.8933
B20
0.9050
B21
0.6982
220
Gutman & Miaoulis 2003; Mazzarol 1998). Word-of-mouth promotion and the marketing
activities of the institution play important roles in developing opinion about the reputation
of the institution (Ivy 2001). This implies that institutions need to maintain and develop a
distinctive reputation since reputation generates positive value perceptions among current
and potential students.
7.3.1.2.3 Price Value
Previous studies have recognised the key influence of functional value on consumer choice
(Berry Yadav 1996; LeBlanc & Nguyen 1999; Sheth et al. 1991; Sweeney & Soutar 2001;
Tellis & Gaeth 1990; Zeithaml 1988). Functional value involves dimensions related to the
utilitarian function of education in the sense that students believe they are receiving
something from the service for which they have paid (LeBlanc & Nguyen 1999). As shown
in Figure 7.7, price has the weakest path coefficients compared to other customer value
dimensions. There are several explanations as to why price is somewhat less important than
the affective aspects, including reputation. Since the respondents in this research were
undergraduate students who were mostly supported financially by their parents, price could
be less sensitive for this group compared to post-graduate students who are mostly
financially independent. A cross-sectional survey might also cause this finding (weakest
path coefficient) and longitudinal study might provide different results. As in the case of
Indonesia, the price for enrolled students in public universities is usually less sensitive
compared to the private institutions, since there are more substantial supports for students at
government (public) institutions (this research involved two public universities).
Nevertheless, students do place more emphasis on price when making decisions to enrol in
private universities. Since students and their families are becoming more aware of the
importance of education, at least within reasonable bound, price may no longer be a very
sensitive issue compared to reputation, emotion and social benefits that students and their
families receive.
The results from the LeBlanc and Nguyen (1999) study in a business school suggest that a
significant relationship exists between students’ overall evaluation of service value and
perceptions of price. Price remains one of the most important drivers of customer value
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(Rintamaki et al. 2007). Based on the tenets of the price dimension in this thesis (see Figure
7.11), whether the services provided offer good value for money (B6) and has an acceptable
price level (B5), the management must continuously strive to ensure that tuition fees are
within an acceptable price range as well as maintaining the quality of the service offerings.
7.3.1.2.4 Quality
Unlike previous studies on customer value, the PLS analysis has revealed that there was a
cross-loading between the quality dimension of customer value and other dimensions of
service quality (see Chapter 6, Table 6.4 and Appendix 6 table C). In terms of statistics, it is
suggested that quality dimension of customer value measures service quality better than the
other dimensions of service quality. Another alternative explanation could be that when
applying an integrative model (involving service quality and customer value at the same
time), the employment of quality dimension on customer value might become redundant.
This is because it concerns quality. However, in a separate examination in which service
quality and customer value were not investigated simultaneously, all of the five dimensions
of customer value including quality were valid and significant as measures of customer
value. Peterson and Wilson (1985) point out that cues relating to functional value such as
price, reliability and durability have been identified as being determinants of quality
dimensions. Since there were signs of cross-loading with the quality dimension and the
potential redundancy in explaining quality aspect in this thesis, the quality aspect has been
removed. As a consequence, despite acknowledging the importance of the quality
dimension in higher education, quality as a dimension of customer value is not discussed
and it is assumed that the importance of quality in higher education has been covered in the
service quality construct.
Figure 7.11 PLS Loadings for Price Dimension
B6
0.9077
Price
B5
0.8271
B7
0.8438
B8
0.7385
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7.3.1.2.5 Summary of Discussions on Customer Value
Overall, the PLS analysis has identified four dimensions as valid and reliable measures of
the customer value construct. The quality dimension was dropped as there was a sign of
cross-loading. The remaining four dimensions (reputation, social, emotion and price) were
shown to have significant positive relationships with customer value (Figure 7.7). Based on
these results, Hypothesis 6 is partly supported since there were only four valid and reliable
dimensions measuring customer value in the Indonesian higher education sector, namely:
reputation, social, emotion and price. More importantly, this research has shown the
specific findings for Indonesian higher education where social dimension is considered as
the the most important among Indonesian students, followed by emotion, reputation and
price. Table 7.2 provides a summary of the terminology of the dimensions used to measure
customer value in this thesis.
Table 7.2 Dimensions of Customer Value Dimensions Definitions
Emotion Descriptive judgment regarding the pleasure that a product or service generates.
Price The price of a service as encoded by the consumer.
Reputation The prestige or status of a product or service, as perceived by the customer, based on the image of the supplier.
Social The utility derived from the product’s ability to enhance social self-concept.
Source: Petrick (2002, p. 125) and Sweeney and Soutar (2001)
7.3.2 The Structural Model
As was discussed in Section 4.3.3.6.4.2, the PLS approach is particularly useful for this
thesis since it enables the researcher to simultaneously predict a set of dependent variables
from a large set of independent variables. PLS also places emphasis on the prediction of the
model, so it can be applied to both exploratory and confirmatory study. Following the
satisfactory results of psychometric properties (validity and reliability) in the measurement
model, the structural model examines the relationships among the key main constructs.
Before proceeding to a more detailed discussion on the empirical findings, it is important to
emphasise the point that even though the partial models were also analysed, this procedure
was conducted only for comparative purposes. However, the focus of this thesis is on the
proposed conceptual model and, consequently, discussions are focused on the findings from
223
the conceptual model (integrative model). The discussions in the conceptual model will be
divided into two main sections: the direct relationships and the indirect relationships. Both
sections are aimed at answering research questions 2 and 3.
Research question 2: “How do service quality, customer satisfaction and customer value
relate to behavioural intentions in the higher education sector in Indonesia?”
The studies of the relationships among service quality, customer satisfaction, customer
value and behavioural intentions have dominated the services marketing literature (Cronin
et al. 2000). Furthermore, the previous marketing literature has recorded inconsistencies in
the relationships among these four key constructs. Some studies have emphasised the direct
relationships while other studies emphasise the indirect relationships (see Section 3.3.2 and
3.3.4). Other issues were also concerned with the causal directions of the relationship
whether cognitive (service quality or customer value) leads to affective (satisfaction) or
vice-versa.
An issue regarding an integrative/simultaneous model relating the four key constructs has
been raised in the marketing literature. Cronin et al. (2000), Rust and Oliver (1994) and
Ostrom and Iacobucci (1995) suggest that simultaneously investigating the relationships
among all four constructs offers a more accurate and comprehensive picture of the nature of
the relationships. Cronin et al. (2000, p. 198) “believe that partial examinations of simple
bivariate links between any of the constructs and behavioural intentions may mask or
overstate their true relationship due to omitted variable bias”. In addition, Ostrom and
Iacobucci (1995) suggest simultaneously examining the consumer judgments on the four
key constructs in one study and further comparing their relative effects on subsequent
consequential variables.
In order to create a more realistic picture of the underlying relationships that exist among
these constructs, an empirical investigation of an integrative model is examined in this
thesis, particularly in the Indonesian higher education sector. Figure 7.12, which is similar
to Figure 6.4, is re-presented in this chapter to assist the discussions in the structural model.
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Figure 7.12 Structural Model Result
7.3.2.1 The Direct Relationships
7.3.2.1.1 Empirical Findings from the Direct Relationships
Based on the PLS analysis (Figure 7.12), all of the path coefficients were significant. The
positive and significant direct relationships across all of the key constructs (SQ, CS, CV
and BI) in the conceptual model were also supported by the four alternative partial models
(see Appendix 7 Figure B to E). Accordingly, these findings provide support for all of the
hypotheses relating to the direct relationships among the four key constructs, where:
Hypothesis 2: Service quality is positively associated with customer satisfaction.
Hypothesis 3: Service quality is positively associated with behavioural intentions.
Hypothesis 4: Customer satisfaction is positively associated with behavioural
intentions.
Hypothesis 7: Service quality is positively associated with customer value.
Hypothesis 8: Customer value is positively associated with satisfaction.
Hypothesis 9: Customer value is positively associated with behavioural intentions.
Tangible R2=0.669
Content R2=0.674
Attitude R2=0.458
Competence R2=0.475
Delivery R2=0.396
Reputation
R2 =0.685
Emotion
R2 =0.714
Social
R2 =0.724
Price
R2 =0.520
Behavioural Intentions R2=0.456
Satisfaction R2=0.567
Service Quality
Customer Value R2=0.474
0.688****
0.368****
0.217****
0.427****
0.451****
0.097**
0.818****
0.821****
0.677****
0.689****
0.629****
0.845****
0.828****
0.851****
0.721****
Note: ****p<0.001; ***p<0.01; **p<0.05; *p<0.1
225
Based on Figure 7.12, the strongest path coefficient was shown by the service quality -
customer value relationships (0.688) followed by customer value - customer satisfaction
(0.451), customer value - behavioural intention (0.427), service quality - customer
satisfaction (0.368), customer satisfaction - behavioural intentions (0.217) and service
quality - behavioural intention (0.097). In the evidence that there was a strong causal
pathway between service quality and customer value, this implies that the students’
perception of the quality of services they received has a direct and substantial impact on
their value perceptions. This suggests that the efforts directed specifically at improving
elements of service quality in higher education institutions might be expected to have a
greater impact on the ‘value’ aspects of students’ experience, which, in turn, appears to
have the greatest impact on student satisfaction and behavioural intentions.
It can be asserted from Figure 7.12 that customer value is somewhat superior in this
proposed conceptual model since customer value has strong path coefficients with other
constructs. This implies that the service quality construct, while theoretically and
empirically identified as being important constructs for satisfaction and behavioural
intentions, was not as important as customer value in shaping students’ customer
satisfaction and behavioural intentions. A more detailed result was presented in Section
6.4.4.3.2 relating to the relative impacts of service quality and customer value.
Managerially, there should be a focus on providing activities that will lead to enhanced
value perceptions. Managers must understand the reasons behind the sacrifices that students
have made and the benefits they expect to receive after such sacrifices. There are other
important alternative means of increasing customer satisfaction and behavioural intentions
other than focusing on the improvement of service quality. Previous studies have only
focused on the improvement of service quality while ignoring customer value aspects.
These conditions mandate that institutions should address the issue of providing customer
value and quality appropriately.
In the higher education sector, research involving the integrative model has been performed
by Alves and Raposo (2007). Their study identified the importance of reputation as the
most influential variable of the loyalty construct. Customer value also identified as a
226
variable that has positive influence on loyalty. There is also support for the service quality
relationship to both satisfaction and value. Satisfaction in higher education is influenced by
the students’ perception of value and customer value is influenced by quality. However, the
relationship between satisfaction and word-of-mouth communication is not direct but only
indirect through loyalty. Since Alves and Raposo’s (2007) model did not link service
quality and customer value directly to behavioural intentions, this thesis enriches the
empirical findings in the higher education sector relating to a more comprehensive
approach to the service quality, customer value, customer satisfaction and behavioural
intentions relationships. In addition, this thesis accommodates a broader dimension of
customer value and service quality. Support for the contribution of those four key
constructs has also been provided by Sakhtivel and Raju (2006), who found a strong
correlation between education service quality and customer value and, furthermore,
customer value with customer satisfaction.
The significance of the customer satisfaction and behavioural intentions relationship
implies that student satisfaction in the Indonesian higher education sector directly effects
students’ behavioural intentions. The positive word-of-mouth communication of satisfied
customers may attract new customers. Positive word-of-mouth communication reduces
marketing expenses and may further increase revenues once new customers are attracted
(Reicheld & Sasser 1990). Students’ satisfaction has certainly remained vital as an
antecedent of behavioural intentions, since it also provides a more tactical strategy for the
manager. The benefits from satisfied and loyal students are also significant for the
institutions’ short-term and long-term survival. Past research shows those word-of-mouth
recommendations as a major influence on the students’ college choice process (Athiyaman
2000).
Understanding factors that may trigger students’ behavioural intentions, which, in this
thesis is translated into loyalty and word-of-mouth recommendations is important. The
higher education sector is a service business involving a complex person-to-person
relationship, continuous delivery of services and a lengthy relationship; therefore, building
loyalty and positive behavioural intentions towards institutions is important. The students’
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positive behaviour can be a strategic tool for fostering differentiation and competitive
advantage. Binsardi and Ekwulugo’s (2003) study using a student sample found that the
best promotion strategies are those based on students’ networks. This underlines the
importance of the antecedents of behavioural intentions (service quality, customer
satisfaction and customer value) to build up network effects.
7.3.2.1.2 The Causal Direction of Cognitive - Affective
In response to the causal ordering between service quality (cognitive) and satisfaction
(affective), this thesis adopts the sequence proposed by Bagozzi’s (1992) and Oliver’s
(1997) approaches of causal ordering( “the appraisal → emotional response → coping
framework” or ‘cognitive response leads to affective response”) as a basis (see Section
3.3.2). There were debates regarding the causal ordering between service quality and
satisfaction. Most studies except Parasuraman et al. (1988), Bitner (1990) and Bolton and
Drew (1991), arrived at the conclusion that service quality determines customer
satisfaction, and that customer satisfaction has a significant effect on purchasing intentions.
Studies that have been conducted regarding higher education show that service quality is an
antecedent of customer satisfaction (Browne et al. 1998; Goulla 1999), except that
completed by Athiyaman’s (1997) who argues that satisfaction leads to quality. As
indicated in the results of the path analysis of the relationships proposed in the conceptual
model (integrative model), this thesis presents evidence of the causal sequence as suggested
by Bagozzi (1992) and Oliver (1997). There were positive influences by service quality and
customer value on satisfaction and further to behavioural intentions. Thus, these findings
support Bagozzi’s (1992) and Oliver (1997) suggestion that cognitive evaluations precede
emotional responses applies in the Indonesian higher education sector. The results also
provide empirical support for the Woodruff (1997) conceptualisation of customer value and
satisfaction (customer’s desired value hierarchy leads to satisfaction feeling at each level in
the hierarchy). The multi-attribute attitude model framework, i.e. cognition (service quality
and customer value) – affect (satisfaction) – conation (behavioural intentions), is robust
across national boundaries.
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7.3.2.2 The Indirect Relationships
In addition to the direct effects, this thesis also argues for the existence of indirect effects
across the four key constructs (SQ, CS, CV and BI). Therefore, four hypotheses relating to
the indirect relationships were proposed.
Hypothesis 5: Customer satisfaction mediates the relationship between service
quality and behavioural intentions.
Hypothesis 10: Customer satisfaction mediates the relationship between customer
value and behavioural intentions.
Hypothesis 11: Customer value mediates the relationship between service quality and
customer satisfaction.
Hypothesis 12: Customer value mediates the relationship between service quality and
behavioural intentions.
7.3.2.2.1 The Mediating Effects of Customer Satisfaction
The mediating effect of satisfaction between service quality and behavioural intentions and
customer value and behavioural intentions has been identified by several studies (see
Tables 3.1 and 3.4). A majority of studies have found that satisfaction only partially
mediates the relationship between customer value and behavioural intentions (e.g. Cronin et
al. 2000; Choi et al. 2004; Gill et al. 2007; Lam et al. 2004; Oh 1999). However, Patterson
and Spreng (1997) and Eggert and Ulaga (2002) found that satisfaction fully mediates the
relationship between customer value and behavioural intentions. This thesis found that
customer satisfaction partially mediates the relationship between service quality and
behavioural intentions in both the conceptual and partial models. Customer satisfaction also
mediates partially the relationships between customer value and behavioural intentions.
These findings consequently support the propositions in Hypothesis 5 and Hypothesis 10.
In higher education, the importance of the satisfaction as a mediating variable has also been
identified by Tsarenko and Mavondo (2001). This thesis suggests that student satisfaction is
central to students’ recommending the institution and should be managed effectively so as
to benefit the institutions.
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7.3.2.2.2 The Mediating Effects of Customer Value
As is the case with customer satisfaction, the mediating effect of customer value has been
identified in earlier studies (see Chapter Three Table 3.4). This thesis also found the partial
mediating effects of customer value on both the relationships between service quality and
behavioural intentions, and between service quality and satisfaction. These findings
consequently support the propositions in Hypothesis 11 and Hypothesis 12.
The significance of mediating variables (customer satisfaction and customer value) in the
service quality and behavioural intentions relationships has underlined the importance of
considering direct and indirect relationships in the higher education sector. As identified by
Cronin et al. (2000), future studies need to consider indirect relationships rather than simply
examining the direct relationships. This will provide better information on the nature of the
relationships because considering the direct effects will likely only result in incomplete
assessments of the basis of these decisions. This implies that even though it is well known
that service quality has a significant impact on behavioural intentions, the impact could be
stronger if mediating variables (satisfaction or customer value) are added.
7.3.2.2.3 The Effects Ratio
The effect ratio analysis (see Section 6.4.4.3.1) provides additional insight on the important
contributions made by the mediating variables. From these results, the following
observations can be made:
1. Based on the service quality (SQ) – customer value (CV) – behavioural intentions
(BI) relationships (SQ-CV-BI), the effects ratio was less than 1 therefore, the
indirect effect was greater than the direct effect. This means that even though
previous studies have noted the importance of service quality for behavioural
intentions, the role of service quality is less effective without customer value.
2. Based on the three other proposed mediating models (SQ-CS-BI, SQ-CV-CS and
CV-CS-BI), the effects ratio was greater than 1. This means that the direct effect
was greater than the indirect effect. However, the total effects were all increased
when both the direct effect and indirect effect were added together (Table 6.10).
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This means that although the mediating variable is not as important as in the case of
SQ-CV-BI relationship, as the total effect increases, it is argued that the mediating
variables play a significant role in the relationships.
7.3.2.2.4 Does Customer Value Explain Behavioural Intentions Better?
As discussed in Chapters Two and Chapter Three, this thesis follows the suggestion of
Cronin et al. (2000), Rust and Oliver (1994) and Ostrom and Iacobucci (1995) to
empirically investigate a model simultaneously relating service quality, customer
satisfaction, customer value and behavioural intentions. Customer value is a newer
construct and less well researched compared to the other three constructs. By incorporating
customer value into the service quality, customer satisfaction and behavioural intentions
relationships, a more comprehensive model should enable researchers to understand the
broader issues relating to the factors that will influence behavioural intentions in the service
and higher education sectors, particularly in Indonesia. However, this statement raised
several questions regarding whether or not the inclusion of customer value does improve
the model. Does customer value better explain satisfaction and behavioural intentions? In
order to answer these questions, the following research question 3 is proposed.
Research question 3: What are the effects of the inclusion of customer value construct in
the relationships between service quality, customer satisfaction and behavioural intentions?
Based on the conceptual model, and all of the partial models that placed behavioural
intentions as the final outcome (see Figure 7.12 and Appendix 7, Figure A to D), the R2 of
the proposed conceptual model (integrative model) appeared to be the highest compared to
other partial models when behavioural intentions was modelled as the dependent variable
(see Section 6.5). The conceptual model has an R2= 0.456 and the other partial models were
less than 0.456. This implies that the conceptual model better explain the variance of the
behavioural intentions in the Indonesian higher education sector. Cronin et al. (2000) and
Tam (2004) findings also came to the same results supporting the notion that integrating
customer value with service quality and customer satisfaction in a single model can better
explain and predict behavioural intentions. However, it should also be noted that this thesis
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has less R2 when compared to the previous studies that have examined the integrative
model. The R2 of the past studies was higher, including Cronin et al.’s (2000) study which
produced R2
= 94% in their research model; R2=62% in Oh (1999); R
2= 72% in Choi et al.
(2004); and R2= 79% in Tam (2004).
The fact that this thesis produced R2=45.6%, which is lower than previous studies, requires
careful interpretation since testing was conducted in a different service sector and the
model proposed was slightly different from that used in previous studies. The different
conceptual model proposed leads to the different measures involved. However, this thesis
has proved that the conceptual model was more robust than the alternative partial models
examined from the same data. This provides a more meaningful interpretation that a more
comprehensive model will explain the outcomes better (behavioural intentions). In addition,
this thesis supports the robustness of the integrative model (as proposed in the conceptual
model) in the different context (higher education) and, accordingly, it also supports the
Cronin et al.’s (2000) ‘Research Model’.
7.3.2.2.5 The Impact of Customer Value versus Service Quality
In order to determine the relative impact of the customer value and service quality
constructs on customer satisfaction and behavioural intentions (see Section 6.4.4.3.2), their
direct and total effects were examined. In addition, since the relative impact between
customer value and service quality will be compared, the results are based on Figure F,
Appendix 7, where satisfaction is used as the only mediating variable (removing the path
between service quality and customer value). This was done to ensure an equal number of
paths and, therefore, an equal comparison between service quality and customer value.
From this analysis, the following observations can be made:
1. The Conceptual Model (Integrative model)
Based on the examination of the conceptual model, the findings from the total effects and
direct effects can be explained as follows:
1. The total effect of customer value on behavioural intentions was 0.526, whereas
service quality showed a total effect of 0.178 on students’ behavioural intentions.
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2. The direct effect of customer value on behavioural intentions was 0.427 and the
direct effect for service quality on behavioural intentions was 0.098.
3. The direct effect of customer value on customer satisfaction was 0.452 and the
direct effect for service quality on customer satisfaction was 0.367.
2. The Partial Models
Based on the examination of the partial models, the findings from the total effects and
direct effects of the two partial models (service quality - customer satisfaction - behavioural
intentions /SQ-CS-BI) and customer value - customer satisfaction - behavioural intentions
(CV-CS-BI) can be explained as follows:
1. The total effect of customer value on behavioural intentions (CV-CS-BI) was 0.639,
whereas service quality (SQ-CS-BI) showed the total effect of 0.539 on students’
behavioural intentions.
2. The direct effect of customer value on behavioural intentions (CV-CS-BI) was
0.450 and the direct effect of service quality on behavioural intentions (SQ-CS-BI)
was 0.264.
3. The direct effect of customer value on customer satisfaction (CV-CS-BI) was 0.730
and the direct effect of service quality on customer satisfaction (SQ-CS-BI) was
0.682.
By making a comparison between the consequences of customer value and service quality,
it can be demonstrate that customer value has a stronger influence than service quality on
customer satisfaction. Regardless of whether or not direct or indirect relationships (through
satisfaction), it appeared that customer value is also more superior to service quality in
predicting behavioural intentions. These results clearly point out that customer value is
increasingly becoming a more important antecedent to behavioural intentions and
satisfaction than service quality. However, different results were found by Choi et al.
(2004) who stated that service quality has a stronger influence on behavioural intentions
than customer value.
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Nevertheless, even though it seems that customer value is superior to service quality, it was
clearly shown that the contribution of service quality to customer value was also strong.
This implies the important role of service quality in building positive perceptions of value
for students. An alternative interpretation of this outcome could be that those five
dimensions of service quality included in the model is not the one that are most directly
impacting on higher education students’ satisfaction and behavioural intentions. Therefore,
considering the mediating variables such as customer value and satisfaction is important
when examining the service quality construct in the higher education sector.
7.3.2.2.6 Customer Satisfaction and Customer Value as Mediating Variables
As can be seen from Table 6.10, customer value shows a higher mediating effect to service
quality and behavioural intentions relationship than customer satisfaction. This implies that
customer value is not only superior to service quality, but also to customer satisfaction.
Furthermore, based on Appendix 7, the direct effects of customer value on behavioural
intentions are always higher than customer satisfaction on behavioural intentions. This
provides evidence on the important role of customer value over customer satisfaction, and
therefore, manager and administrator are suggested to include the improvement of aspects
that students perceived as providing value (benefits exceeding costs in the education
experiences).
7.3.2.3 Summary of the PLS Analysis
The discussions on the main analysis provide some insights into the structure of the
dimensions that made up the key constructs (service quality, customer satisfaction,
customer value and behavioural intentions) and into the nature of the relationships among
the key constructs. Despite acknowledging the significance of the direct impacts among the
key constructs, this thesis provides evidence of the significance of customer satisfaction
and customer value as a mediating variable. The causal direction, where cognitive response
leads to affective response, also applies in the Indonesian higher education sector. This
thesis particularly found that the inclusion of customer value increases the predictive power
in explaining behavioural intentions in the conceptual model. Furthermore, when compared
to service quality, customer value was shown to have a stronger impact on customer
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satisfaction and behavioural intentions. Overall, this thesis provides evidence of the
superior role of customer value in the Indonesian higher education sector. Therefore, it is
important to emphasis, the customer value, rather than concentrating solely on various
aspects of service quality, customer satisfaction and behavioural intentions.
7.4 CONCLUSION
This thesis was intended to examine the three research questions. A series of hypotheses
were developed around the research questions and statistical analyses were undertaken to
test them. A primary consideration in this Chapter was to discuss the outcomes of the
preliminary and main analyses using PCA and PLS, respectively. In very broad terms, the
outcomes of the research indicated that:
1. There were five dimensions of service quality (content, tangible, attitude,
competence and delivery) and four dimensions of customer value (social, emotion,
reputation and price) identified as valid and reliable scales in the Indonesian higher
education sector. These first-order dimensions identified as valid and reliable were
positively associated with their respective second-order constructs. More
specifically, content, tangible, attitude, competence and delivery were associated
with service quality, while social, emotion, reputation and price were associated
with customer value.
2. The proposed conceptual model (the integrative model) developed from services
marketing seemed to be applicable to the Indonesian higher education sector. All of
the path coefficients were positive and significant, supporting all of the direct and
indirect relationships proposed in the hypotheses.
3. The causal direction, where cognitive leads to affective, also applies in the
Indonesian higher education sector and, therefore, this thesis adds weight to the
Bagozzi’s (1992) and Oliver’s (1997) approaches. This thesis supports the notion
of causal directions which holds that the cognitive response leads to the emotional
response.
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4. The inclusion of customer value in the model does increase its predictive power to
explain behavioural intentions. Customer value was also found to be a superior
construct to service quality.
In addition to discussing the outcomes from the statistical analyses, this Chapter also
examined potential implications and suggestions for practitioners and policy makers within
the higher education sector. This has particular application to the customer-oriented
strategic decisions, since the increased competition within the Indonesian higher education
marketplace requires a stronger customer focus. A more comprehensive approach to
marketing is necessary since it will provide more opportunities to better manage the
institutions.
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CHAPTER EIGHT
CONCLUSIONS AND IMPLICATIONS
8.1 INTRODUCTION
The purpose of this chapter is to draw conclusions from the main findings, discuss
implications for marketing theories and practices, discuss the limitations of the research
and, finally, to suggest future research directions.
This chapter consists of six sections. The first section (8.2) summarises the different stages
of the research to provide a background of this research in relation to the research
questions, objectives and methodology. The second section (8.3) presents reviews of the
overall results. The third section (8.4) focuses on the contributions to marketing theories.
The fourth section centres on the implications for practitioners (8.5). The fifth section (8.6)
addresses the limitations of this research, and the final section (8.7) advances suggestions
for future research. The conclusion of the chapter (8.8) is presented thereafter.
8.2 SUMMARY OF STAGES OF THE RESEARCH
Chapter One discussed the background to, the objective of and the rationale for this
research. As discussed in Chapter Two, service quality and customer satisfaction are the
most researched constructs in the marketing field (Cronin et al. 2000; Giese & Cote 2000).
Customer value as a newer construct is less developed and less researched. In a highly
competitive environment, quality and satisfaction are no longer adequate as sources of
competitive advantage (Woodruff 1997), which is why customer value is increasingly
becoming a construct of interest.
Consistent with the global trend, higher education institutions in Indonesia are facing many
challenges such as rising competition, operational costs and rising student expectations of
service quality, increasing the need to raise satisfaction levels and the need for positive
opportunity cost. This situation dictates that higher education institutions should no longer
237
be dependent on government funding and traditional management systems. More and more
higher education institutions have applied at least one marketing approach in order to better
deal with the new conditions of the market. In response to the intense competition in this
sector, higher education institutions need to understand the factors that may heighten
students’ perceptions of quality, satisfaction and value, thereby influencing their
behavioural intentions. A comprehensive model relating to service quality, satisfaction and
customer value has been examined in general services marketing and was found to exert
significant influence over behavioural intentions. The higher education sector as a service
sector should also benefit from understanding the same framework.
Despite having the primary responsibility of providing quality education, it is necessary for
the Indonesian higher education sector to adopt a marketing approach and a customer focus
in order to survive. Statistics indicate that neighbouring countries have a significant
proportion of Indonesian students studying overseas (Ehef 2008). The Indonesian
government’s new policy (PP 60 in 1999) of allowing joint establishments to be formed
between local and international institutions increases the level of competition in the higher
education sector in Indonesia. The high level of competition that necessitates marketing
approach in the higher education provides justification for focusing this research on service
quality/SQ, customer satisfaction/CS, customer value/CV and behavioural intentions/BI.
Chapter Two focussed on reviewing literature on major research constructs of relevance to
the present study. These included the services sector, students as customers, service quality,
customer satisfaction, customer value and behavioural intentions. These research themes
were discussed in relation to the Indonesian higher education sector, concept and
dimensions, measurement and aspects related to the structural model.
Chapter Three was a logical extension of the literature review, and was designed to develop
the conceptual model which formed the basis of this thesis. This chapter discussed the
previous studies that applied the integrative models and also developed hypotheses based
on the three research questions.
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Chapter Four concentrated on the research methodology. This thesis adopts the research
process from Kumar et al. (1999). This chapter focused on the research design which
covers the research approach (exploratory/descriptive/causal & data collection method),
research tactics (measurement and questionnaire design, pre-testing, sampling plan and
statistical analysis) and ethical considerations.
Chapter Five discussed the preliminary analysis of the quantitative data collected through
the survey. This chapter was aimed at providing the fundamental underlying characteristics
of the data. This preliminary analysis served as a preparation for a more thorough
examination of the proposed conceptual model of this thesis. The discussions covered the
general demographic and descriptive analysis, data screening, reliability analysis and the
results from the Exploratory Factor Analysis (EFA) using Principal Component Analysis
(PCA). PCA was employed to test the unidimensionality of the measures since the
conceptual model involved second-order factors.
Chapter Six provided broad examinations of the measurement and structural model using
Structural Equation Modelling (SEM) with the Partial Least Squares (PLS) technique. The
purified measures from the previous PCA were further analysed and tested using PLS. This
chapter commenced with a brief discussion on the procedures used in PLS and was then
followed by evaluations of the measurement and structural model.
Chapter Seven discussed all the findings derived from Chapters Five and Six. A more
detailed discussion of all the dimensions of the constructs being studied and the
interpretation of their relationships was provided. The findings from the preliminary and
PLS analysis were specifically discussed in relation to the Indonesian higher education
sector.
Chapter Eight essentially provides an overview of the previous chapters, while also
drawing conclusions, discussing contributions, implications and limitations, and making
suggestions for future research in the same context.
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8.3 REVIEW OF OVERALL RESULTS
The main objectives of this research were, firstly, to examine the interrelationships among
service quality/SQ, customer satisfaction/CS, customer value/CV and behavioural
intentions/BI in the Indonesian higher education sector. Since the proposed conceptual
model simultaneously relates the four key constructs (SQ, CS, CV and BI), the model is
called “the integrative model”. The findings on the relationships among the four key
constructs in the conceptual/integrative model are re-presented in Figure 8.1. Secondly, this
research assesses the dimensions that underpin the service quality and customer value
constructs. Thirdly, this research investigates the relative impacts of customer value on the
model. To achieve these objectives, three specific research questions were developed.
Figure 8.1 The Structural Relationship of the Four Key Constructs
The three research questions addressed in this research were:
Research question 1: What constitutes valid and reliable scales for measuring service
quality and customer value in the Indonesian higher education sector?
Research question 2: How do service quality, customer satisfaction and customer value
relate to behavioural intentions in the higher education sector in Indonesia?
Research question 3: What are the effects of the inclusion of the customer value variable
in the relationships between service quality, customer satisfaction and behavioural
intentions?
Note: ****p<0.001; ***p<0.01; **p<0.05; *p<0.1
Behavioural Intentions R2=0.456
Customer Satisfaction R2=0.567
Service
Quality
Customer Value
R2=0.474
0.688****
0.368****
0.217****
0.427****
0.451****
0.097**
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The following sections explain the results of the proposed hypotheses.
Research question 1: What constitutes valid and reliable scales for measuring service
quality and customer value in the Indonesian higher education sector?
In order to answer research question 1, two hypotheses (H1 and H6) were proposed. The
summary of the status of the hypotheses are appended below.
H1 Service quality is a multidimensional construct and it can be defined in terms of tangible, competence, attitude, delivery, content, and reliability.
Partly Supported
H1a: Tangible is associated with service quality. S
H1b: Competence is associated with service quality. S
H1c: Attitude is associated with service quality. S
H1d: Delivery is associated with service quality. S
H1e: Content is associated with service quality. S
H1f: Reliability is associated with service quality. NS
(S: Supported; NS: Not Supported)
H6 Customer value is a multidimensional construct and it can be defined in terms of quality, social, price, emotion and reputation.
Partly Supported
H6a: Quality is associated with customer value. NS
H6b: Social is associated with customer value. S
H6c: Price is associated with customer value. S
H6d: Emotion is associated with customer value. S
H6e: Perception is associated with customer value. S
(S: Supported; NS: Not Supported)
Based on the PCA and PLS analysis on the two hypotheses proposed (including their
respective sub-hypotheses), the reliability dimension was not supported due to: 1) low
loadings of items that measured the dimension (<0.5 based on PCA analysis, Table 5.3); 2)
problem with interpretability, as all reliability items loaded into other dimensions of service
quality; and 3) problem related to cross loading (based on PLS analysis). As a consequence,
the proposed association between reliability and service quality was not applicable.
Similarly, the quality dimension of customer value was not valid based on PLS analysis due
to the occurrence of cross-loading with some of the service quality dimensions (see
discussion Section 6.3.2.2.2.1). As a consequence, quality dimension was dropped from the
conceptual model. Overall, H1f and H6a were not supported, and there were five
dimensions of service quality (content, tangible, attitude, competence and delivery) and
four dimensions of customer value (social, emotion, reputation and price) identified as
valid and reliable scales in the Indonesian higher education sector. More specifically, this
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research also reveals the specific findings of the Indonesian higher education sector, where
content has the highest association to service quality, followed by tangible, competence,
attitude and delivery. While social associated highest to customer value, and respectively
followed by emotion, reputation and price.
Research question 2: How do service quality, customer satisfaction and customer value
relate to behavioural intentions in the higher education sector in Indonesia?
Research question 2 was intended to examine the interrelationships among service quality,
customer satisfaction, customer value and behavioural intentions in the Indonesian higher
education sector. Two main analysis were addressed which covered the direct and the
indirect relationships.
The direct relationships S/NS
H2 Service quality is positively associated with customer satisfaction. S H3 Service quality is positively associated with behavioural intentions. S H4 Customer satisfaction is positively associated with behavioural intentions. S H7 Service quality is positively associated with customer value. S H8 Customer value is positively associated with customer satisfaction. S H9 Customer value is positively associated with behavioural intentions. S
(S: Supported; NS: Not Supported)
The indirect relationships S/NS H5 Customer satisfaction mediates the relationship between service quality and
behavioural intentions. S
H10 Customer satisfaction mediates the relationship between customer value and behavioural intentions.
S
H11 Customer value mediates the relationship between service quality and customer satisfaction.
S
H12 Customer value mediates the relationship between service quality and behavioural intentions.
S
(S: Supported; NS: Not Supported)
Based on the main analysis using the PLS technique, the results indicated that all of the
hypotheses relating to the direct and indirect relationships were supported. In addition to
acknowledging the significance of the direct impacts among the key constructs, this thesis
also provides evidence of the significance of customer satisfaction and customer value as
mediating variables. The findings also confirm the causal direction as proposed by Bagozzi
(1992) and Oliver (1997) that cognitive response leads to affective response. This means
that the cognitive construct (service quality and customer value) influences the affective
242
construct (satisfaction) and then the conative construct (behavioural intentions), applies to
Indonesian higher education.
Research question 3: What are the effects of the inclusion of the customer value variable
in the relationships between service quality, customer satisfaction and behavioural
intentions?
For research question 3, the direct effect, indirect effect, total effect and the R2 were
examined to analyse the relative contributions among the constructs being assessed.
1. Based on the R2 Examination
This thesis indicated that the R2 of behavioural intention increased when customer
value was employed in the conceptual model. The conceptual model showed R2 =
0.456, whilst without customer value, the service quality - customer satisfaction -
behavioural intention model showed R2 = 0.378 (Figure 8.2 and/or Appendix 7,
Figures A and B).
Figure 8.2 The Structural Relationship (Customer Value Excluded)
2. Based on the Direct Effect, Indirect Effect and Total Effect Examinations
Based on the findings from the conceptual model, the influence of customer value on
satisfaction and behavioural intentions were stronger than that made by service
quality. By modelling satisfaction as a mediating variable, the total effect between
customer value to behavioural intentions was also higher than service quality to
Note: ****p<0.001; ***p<0.01; **p<0.05; *p<0.1
Behavioural Intentions R2=0.378
Customer Satisfaction R2=0.466
Service
Quality
0.682****
0.403****
0.264****
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behavioural intentions (see Section 6.4.4.3.2 and 7.3.2.2.5). Customer value also
demonstrated as a better mediating variable than customer satisfaction in relation to
service quality and behavioural intentions relationship. These evidences highlight the
important role of customer value in the conceptual model and, more importantly,
higher education administrators should consider a more comprehensive approach than
focusing exclusively on service quality and customer satisfaction.
8.4 THEORETICAL CONTRIBUTIONS
Guided by the research objectives, an examination of those four key constructs of interest
has contributed to the theory as follows:
1. Filling a Gap in the Knowledge
The relationships among service quality, customer satisfaction, customer value and
behavioural intentions have been widely discussed in the literature. However, there
was a lack of studies that incorporated all of these four constructs into an integrated
model, particularly in the higher education sector. Previous studies have mostly
investigated the direct (bivariate) relationships or indirect relationships involving
three constructs (not all four constructs simultaneously). The importance of
simultaneously investigating all of the four constructs was based on the argument
advanced by Ostrom and Iacobucci (1995, p. 198) that “partial examinations of the
simple bivariate links between any of the constructs and behavioural intentions may
mask or overstate their true relationship due to omitted variable bias”. They further
recommended a simultaneous investigation to ascertain their relative impacts of
subsequent consequential variables. The conceptual model proposed in this thesis
was developed based on the Cronin et al. (2000) “Research Model”. The
simultaneous investigation of the relationships among all four constructs would
provide a more accurate and comprehensive picture of the nature of the
relationships.
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In addition to integrating all four key constructs together, this thesis also contribute
to the theory in a way that service quality and customer value are measured using
multidimensional measurement. There has been no previous empirical research
which examines the multidimensional conceptualisation of both service quality and
customer value and both constructs (including satisfaction and behavioural
intentions) are configured together as in the conceptual model proposed in this
thesis.The utilisation of the multidimensional construct helps the researchers to
explain the complex nature of many marketing constructs. In this thesis, service
quality and customer value particularly were conceptualised as second-order
constructs measured by their respective first-order constructs. By involving
multidimensional conceptualisations of service quality and customer value, this
thesis provides an extension of the earlier integrative model as proposed by Cronin
et al. (2000) in their ‘Research Model’. In short, the proposed model provides a
comprehensive picture of the relationships among the key constructs (SQ, CS, CV
and BI), and therefore allowing us to see the relative impacts of subsequent
consequential variables, as well as accommodates the complex nature of service
quality and customer value constructs (multidimensional measures).
2. Inclusion of the Customer Value Construct in the Higher Education Sector
Despite the fact that the service quality and satisfaction constructs are well-known,
the key role of both constructs as drivers of competitive advantage within the
marketing research domain has been questioned. A more comprehensive approach
than a simple focus on service quality or customer satisfaction is required, in order
to better explain what creates and sustains a competitive advantage (Vargo & Lusch
2004; Woodruff 1997). Customer value, which is conceptualised as a trade-off
between costs and benefits, is seen as a new source of competitive advantage
(Woodruff 1997) since it covers the broader aspects (than just satisfaction and
service quality) in explaining behavioural outcomes.
This thesis contributed to the understanding of the inclusion of customer value in
the service quality, customer satisfaction and behavioural intentions relationships.
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Particularly in the higher education sector, the findings showed evidence of the
dominant role of customer value on behavioural intentions, as compared to service
quality and customer satisfaction. Customer value also has a stronger influence on
customer satisfaction than service quality. The inclusion of customer value in the
conceptual model has increased the predictive power (R2) in explaining behavioural
intentions.
However, care must be carefully taken when employing multidimensional measures
of service quality and customer value together in one model. Even though care has
been taken to design a multidimensional conceptualisation of service quality and
customer value, there was one dimension in customer value quality which
overlapped or cross-loaded into the service quality construct as identified by the
PLS measurement model. When analysed in the conceptual model, quality, as one
of the customer value dimensions, was not robust as a measure for customer value.
This phenomenon was identified by Peterson and Wilson (1985). They mentioned
that cues relating to functional value have the possibility of becoming the
determinant of service quality dimensions. This suggests that there are dimensions
of service quality and customer value that measure the same element and may lead
to redundancy/overlap when utilised together. Future research should be cautious of
using quality dimension of customer value when both service quality and customer
value are simultaneously assessed in one model.
3. Partial Least Squares (PLS)
The following contribution relates to the operationalisation of the PLS technique
used to examine the conceptual model. This thesis applied the PLS technique to
examine the simultaneous multiple relationships proposed in the conceptual model.
Although previous studies (see Table 3.4) have used advanced second-generation
statistical techniques such as LISREL, AMOS and EQS in examining the
relationships among service quality, customer satisfaction, customer value and
behavioural intentions, there were limited numbers of studies using PLS. PLS was
used in this thesis because of the practicality of the PLS applications in the
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marketing area. Fornell and Bookstein (1982, p.440) argue that “marketing data
often do not satisfy the requirements of multi-normality and interval scaling or
attain the sample size required for maximum likelihood estimation”. There is a
tendency for the data to be negatively skewed in the measure of customer perception
of satisfaction and the like (Anderson & Fornell 2000). For social research, PLS has
a real strength in terms of its ability to cope well with mixed levels of measurement,
small sample size, non-normally distributed data, etc. (Abdi 2003, Pirouz 2006).
Despite its practicality, the application of PLS in this thesis is important due to its
ability to simultaneously examine multiple relationships (complex relationships).
The advantage of this simultaneity is that it allows for the assessment of the relative
importance of a variety of constructs when they are examined at the same time. In
the context of this thesis, the inclusion of customer value and the relative
importance of customer value as compared to service quality and customer
satisfaction can be assessed at once.
4. Cognitive-Affective Approach
Regarding the nature of the relationships among the four key constructs, this thesis
provides evidence that the causal directions proposed by Bagozzi (1992) and Oliver
(1997) apply in Indonesian higher education sector. This approach argues that
cognitive evaluation (service quality) leads to emotive satisfaction assessment and,
in turn, drives behavioural intentions. This indicates that the ‘cognitive response
leads to affective response’ approach is robust across different nations including
Indonesia.
5. Indonesian Context
Most of the studies involving the simultaneous assessment of the four key
constructs have been carried out in developed economies. The vast majority of past
studies on higher education sector issues have been geographically concentrated in
developed economies. Only a limited number of studies have been carried out in
247
developing economies and, more specifically, there is no evidence of the integrative
model being investigated in the Indonesian higher education sector.
This thesis makes a contribution by empirically testing the conceptual model in a
developing economy. Indonesia, as a developing country, has a variety of cultures,
languages and economic backgrounds. Thus, the findings of this thesis provide
different perspectives on the existing literature that mostly relates to, and is
established in, developed economies.
8.5 IMPLICATIONS FOR PRACTITIONERS
According to the results obtained from the PLS analysis (Figure 8.1), service quality
strongly influences customer value. This indicates that the ability to provide high quality
service is the key to achieving better student appreciation. Furthermore, satisfaction and
behavioural intentions were also strongly influenced by customer value. This implies that
customer value is important to predict students’ satisfaction and behavioural intentions.
Despite the existence of significant direct relationships, this thesis also found the important
role of customer satisfaction and customer value as mediating variables. The implications
therefore cover:
1. Since service quality has a strong influence on customer value, administrators
should consider the role of service quality. It appeared in this thesis that content was
the strongest construct, followed by other dimensions. This confirms the Kwan and
Ng (1999) study which highlighted that Hong Kong and Chinese students were
intensely practical and only focus on study-related matters. This evidence might be
similar to the Indonesian case, where students placed more concern on the content
of curriculum, as this would boster their confidence in gaining better employment.
Therefore, administrators must assess, update and offer content that students regard
as valuable as part of an effort to improve quality. To improve quality, content of
curriculum could be developed in collaboration with the industry, so that there is a
better match between the knowledge and skills produced by university and
demanded by the industrial market. The other dimensions of service quality are also
equally important and administrators should be able to manage proportionally
248
which aspects of service quality best accommodates their institutions to further have
best effect on customer value.
2. The significant effects of the mediating variables (customer satisfaction and
customer value) suggest that administrators consider the role of the mediating
variables. In order to have an effect on behavioural intentions, service quality is less
effective without customer value. Similarly, customer satisfaction also served as a
significant mediating variable. Since determinantss of service quality and customer
value are important to create satisfaction, therefore, focus can be placed on their
determinants. For staff and administrators, it is important not only to consider
service quality in order to acquire positive behavioural intentions, but also to
provide customer value and customer satisfaction. For example, all staff must
ensure that a good attitude and competency delivered should be valuable and
creating satisfaction to customer. Competencies needed and offered must be seen
from customer point of views, not merely the competencies that the staff could offer
without seeking what students want.
3. The R2 = 0.456 of the behavioural intentions in the conceptual model was higher
than the other three alternative partial models (Appendix 7, Figure B - D). This
indicates that it is advisable for administrators to collectively examine service
quality, customer satisfaction and customer value to better predict students’
behavioural intention. When customer value is excluded from the conceptual model
(Figure 8.2 or Appendix 7 – Figure B), the R2 of the behavioural intentions is lower
(0.378). In addition, since SQ, CS and CV are commonly related to building
organization competitive advantage, collectively improving SQ, CS and CV will
also assist higher education to enhance institutions’competitive advantage. This
implies that administrators must deliver the service as a bundle of package that
provides quality, value and satisfaction. For example, the quality of content and
competencies must be selected based on what is valuable for students and therefore
offers better satisfaction. The quality of staff’s competencies and content which
support the skills needed for students’future employment (more practical and less
theories) might be more valuable and thus increase satisfaction.
249
4. Despite acknowledging the important role of service quality and customer
satisfaction, the dominant role of customer value in the model necessitates the
administrators to improve every elements of customer value in the higher education
sector (e.g. providing good value for money, providing better social approval,
maintaining good reputation). The social dimension was identified as a dimension
mostly influencing customer value. Therefore, administrators must respond
accordingly to the factors that may increase social aspects of studying in the higher
education. The fact that social and emotion dimensions have stronger path
coefficients than other dimensions (price and reputation) implies that the affective
aspects of education process are important in the Indonesian higher education. Even
though content and tangible appeared to have the strongest association to service
quality, administrators must be able to proportionally emphasized dimensions of
service quality which might contribute best to increasing affective aspects of
customer value.
5. More importantly, the dominant role of customer value in the findings informs
administrators on the importance of considering the benefits and costs perceived by
all stakeholders involved in the higher education sector. The strategic decisions
made by the institutions should be based on the opportunity cost of the benefits that
exceed the costs/sacrifices as perceived by customers/stakeholders. For example,
the cost spent in terms of money, time and being away from family must be worth
spending for acquiring the skills and knowledges as well as having access to
facilities provided and gaining social approval. Understanding what students
(stakeholders) perceived as providing benefits will help administrators how to
market the institutions and how to fulfill students’ best interests.
8.6 LIMITATIONS
Although this thesis is based on sound literature and methodological foundations, specific
limitations are acknowledged. The following discussions highlight some of these
limitations and suggest strategies to deal with them.
250
The first area of limitations relates to the dimensions contained within the research model.
Although the research model and the key constructs contain the dimensions that are central
to the research questions, there are a number of other possible dimensions that could also
affect the relationships that flow among the service quality, customer satisfaction, customer
value and behavioural intentions constructs. Particularly with regard to the customer value
construct, which has only recently become the cynosure among marketing scholars, there
are still a number of possible aspects of value perceptions that could be specifically
addressed to identify the specific value of education experiences.
Second, both the service quality and customer value constructs were conceptualised as
second-order constructs measured by related first-order constructs. These types of
conceptualisations limit the direct influence between each dimension that builds upon both
construct directly to satisfaction and behavioural intentions. Future studies need to test the
first-order dimensions of both constructs directly to customer satisfaction and behavioural
intentions. Although this might produce very complicated interrelationships, the PLS
technique can be used since it was designed to examine a complex model as compared to
the covariance-based model.
Third, since this thesis was conducted in the context of the higher education sector in
Yogyakarta, Indonesia, generalisation of the findings beyond the higher education industry
and the target population should be made with caution.
The fourth limitation was linked to the cross-sectional design of this thesis. The
disadvantage of such a design is that the nature of causality is difficult to infer (Bollen
1989). The cross-sectional design also ignores the dynamics of the environment and,
therefore, its impact on perceptions and the related strategies. Since students’ perceptions
may differ from time to time, the dynamics of student perception and the evolution of belief
development cannot be captured through a cross-sectional study. However, through the
effective use of extensive literature, hypothesised relationships could still be tested in the
cross-sectional study. A longitudinal study would be desirable, even though this was not
possible for cost and time reasons in this thesis.
251
The fifth limitation relates to the back translation method used to design the questionnaires.
The limitation of the back translation method is that misinterpretation of the real meaning
of each item may occur during the translation process or during completion of the
questionnaires (Nasution 2005). Even though questionnaire design issues have been
carefully considered, including the back-translation method, it is still acknowledged as a
limitation of this thesis.
8.7 SUGGESTIONS FOR FUTURE RESEARCH
In addition to the limitations identified above, the following are additional directions for
future research that may be explored.
1. As identified in Section 8.4 (the Theoretical Contributions - point three), there was
evidence of redundancy when simultaneously applying the quality dimension in the
customer value and service quality constructs. Although this model was developed
based on the sound literature review, the empirical evidence suggests that when
using multidimensional conceptualisations of service quality and customer value in
one model, the design and the choice of dimensions must be carefully considered to
avoid redundancy. Future research must, therefore, carefully consider the
functional aspects of customer value since it often reflects the service quality
dimension.
2. In referring to some dimensions that were not found to be as robust a measure in
this thesis, a more consistent issue may better explain the dimensions. This thesis
found that reliability was not robust a measure of service quality in the Indonesian
higher education context. This was somewhat surprising since other service sectors
consistently consider reliability to be the most important dimension of service
quality. As discussed in Section 7.2.1, there appeared to be different issues being
raised even though all reflect the aspect of reliability (such as the ways of handling
feedback, the credibility of degree awarded, and the security of information issues).
252
Future research should address more specific and consistent issues relating to
reliability elements than generic aspects of the construct.
3. Customer value in this thesis was treated as second-order constructs measured by
their respective first-order constructs. In the case with customer value, when
adopting the benefit and cost trade-off, some studies measure all dimensions
together as part of the customer value construct (either first-order or second-order
construct). However, as discussed in Section 2.6.8, other studies also treated
dimensions of customer value such as price, sacrifices, reputation, etc, as
antecedents of customer value. For example, sacrifice was treated separately as an
antecedent of value despite the consensus that sacrifice is part of the overall value
itself (Cronin et al. 2000; Wang & Lo 2003); price was separately modelled as an
antecedent to customer value (Oh 1999; Tam 2004); and reputation was
independently modelled as an antecedent to customer value (Alves & Raposo 2007).
Debates are still open regarding how to configure the dimensions that build
customer value. Even though this is not an issue of being right or wrong, future
research needs to emphasise the objective of the research. Researchers must be clear
about their research objective since it impacts on how to model the construct;
whether to analyse the whole concept of customer value or whether to focus more
on the specific influence of the dimensions of customer value.
4. In order to enrich the varieties of sample to increase the generalisability of the
findings, future studies would be much benefited by taking into account the other
stakeholders of the higher education sector. This is due to several objections when
only employing students as sample in the higher education sector, while there are
other stakeholders of higher education. Other stakeholders (government, family,
communities and industrial employers) also play important roles in the higher
education industry and, therefore, the future and competitiveness of higher
education. In addition, since students are not the only ones who fully make
decisions when enrolling in higher education, research targeted towards the parents’
or family perceptions would certainly assist higher education institutions to match
253
their strategies to market needs. Careful adjustments to the questionnaires are
necessary since these stakeholders (other than students) do not have day-to-day
experiences with higher education institutions and they also have different
perceptions of quality.
5. This thesis focuses on the Indonesian higher education sector. The conceptual
model and the dimensions that build the constructs have been carefully designed to
correspond with the higher education setting. Since the current study only used a
sample from Yogyakarta and focused on the Indonesian experience, future studies
could replicate the model within wider geographic locations. Extending this
research into other countries and/or countries with significant number of
international students will allow richer comparisons to be made concerning different
geographical locations as well as opportunities to address the impact of different
cultural backgrounds. Comparison in terms of demographic data, types of
institutions (public/private, university, TAFE), disciplines (engineering, economics,
art), full fee paying – scholarship sponsored funding, undergraduate – postgraduate,
etc. may also enrich the findings from the conceptual model.
8.8 CONCLUSION
This chapter presented a review of the stages of the research, conclusions on the overall
model findings, the contributions for marketing theory, implications for practice and
limitations. It then makes suggestions for future research.
This thesis investigates the “The importance of customer value to service quality, customer
satisfaction and behavioural intentions relationship in the Indonesian higher education
sector”. In so doing, this thesis expanded the application of Cronin et al.’s (2000) ‘Research
Model’ by proposing service quality and customer value as multidimensional constructs.
Guided by the three research questions, twelve hypotheses were proposed relating to the
direct and indirect relationships in the conceptual model. More specifically, the dimensions
of service quality and customer value in the higher education sectors, and the effect of the
254
inclusion of customer value were also examined. PLS technique was employed to address
the hypotheses by testing the measurement and structural model. The findings highlighted
the dominant role of customer value in the conceptual model. One of the major outcomes of
this thesis has been the incorporation of customer value in service quality and satisfaction
relationship model. This strategy of including customer value should be incorporated to
increase the competitive advantage of the higher education sector.
255
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APPENDICES
Appendix 1
Information Sheet
Australian Graduate School of Entrepreneurship Faculty of Business and Enterprise Swinburne University of Technology Date: August 2007
The Role of Customer Value within the Service Quality, Customer Satisfaction and
Behavioural Intentions Relationships: An Empirical Examination in the Indonesian
Higher Education Sector
INFORMATION SHEET SURVEY Dear Potential Participant, I am a Doctoral candidate at the Swinburne University of Technology. As part of my thesis requirements, I am conducting a research project entitled: The relationships between perceived quality, value, satisfaction and behavioural intentions in Indonesian higher education sector: An empirical study of universities in Yogyakarta. Essentially, this research will place students’ perspectives as a base to examine the relationship between quality, value, satisfaction and behavioural intentions. By examining the relationships based on students’ perspectives, it is expected that the findings of this research will be useful. It will increase the understanding of how dimensions of quality in higher education will effect on the students’ perceived value, satisfaction and behavioural intentions. Understanding students’ perception on quality and value will also provide information for higher education institutions to allocate resources as well as program designs that could be directed for better satisfaction for their students. You are cordially invited to participate in the survey. This will involve your opinions, perceptions, and suggestions on the statements provided in the questionnaire. The survey asks you questions on how you regard quality and value, as well as their effect on satisfaction and behavioural intentions. If you decide to complete this survey, it should take only about 20 to 30 minutes of your time. You are encouraged to take your time and complete the survey at your convenience. Please be aware of these following issues:
• Your participation in this survey is completely voluntary.
• There will be no disadvantage if you decide not to complete the survey.
• You can withdraw your participation at any time.
• All information collected will be treated as strictly confidential.
• Completing the questionnaire is understood as your informed consent.
291
Please fill out the attached survey and return it directly to the researcher or a deposit box provided in your department. (Information about the dates and the location of the deposit box will be added here) If you have any concerns or would like to know the outcome of this project, please contact me or Assoc Prof. Siva Muthaly, my doctoral thesis supervisor. Thank you for your interest and considering this invitation. Please feel free to retain this information for future reference.
Ratna Roostika Australia contact:
Swinburne University of Technology Ph: 61-3-9214 5974
Indonesia contact: E-mail: roostika@swin.edu.au
Assoc Prof. Siva Muthaly Head of Marketing & International business
Swinburne University of Technology Ph: 61-3-9214 5885 E-mail: smuthaly@groupwise.swin.edu.au
This project has been approved by or on behalf of Swinburne’s Human Research Ethics Committee (SUHREC) in the line with the National Statement on Ethical Conduct in Research Involving Humans. If you have any concerns or complaints about the conduct of this project, you can contact:
Research Ethic Officer, Office of Research & Graduate Studies (H68), Swinburne University of Technology, PO Box 218, HAWTHORN VIC 3122.
Tel (03) 9214 5218 or + 61 3 9214 5218 or resethics@swin.edu.au
Yours sincerely, Ratna Roostika PhD candidate
292
Appendix 2
Questionnaire
The Role of Customer Value within the Service Quality, Customer Satisfaction and
Behavioural Intentions Relationships: An Empirical Examination in the Indonesian
Higher Education Sector
Section A: Service Quality A: Below is a set of statements that refer to your perceptions or opinions about the quality dimensions of the faculty where you are currently studying. Please indicate to what extent you agree or disagree with the following statements. The scales are to be interpreted as: (1) Strongly disagree (2) Disagree (3) Somewhat disagree (4) Neither agree nor disagree (5) Somewhat agree (6) Agree (7) Strongly agree
Code Statements Strongly disagree
Strongly agree
A - 1 The academic staff has expertise in their teaching area. 1 2 3 4 5 6 7
A - 2 The academic staff is up-to-date in their subject. 1 2 3 4 5 6 7
A - 3 The academic staff has relevant theoretical knowledge in their area.
1 2 3 4 5 6 7
A - 4 The academic staff incorporates relevant practical knowledge in their area.
1 2 3 4 5 6 7
A - 5 There are sufficient number of academic staff in this faculty.
1 2 3 4 5 6 7
A - 6 The support staff (technician, receptionist, administrative, secretaries, etc) is competent.
1 2 3 4 5 6 7
A - 7 The academic staff understand their students’ academic needs.
1 2 3 4 5 6 7
A - 8 The academic staff is willing to help. 1 2 3 4 5 6 7
A - 9 The academic staff provides clear guidance and advice. 1 2 3 4 5 6 7
A - 10 The academic staff provides adequate personal attention for their students.
1 2 3 4 5 6 7
A - 11 The courses offered in this faculty are stimulating. 1 2 3 4 5 6 7
A - 12 The course materials are presented in a logical and timely manner.
1 2 3 4 5 6 7
A - 13 The exams cover course materials presented in class. 1 2 3 4 5 6 7
293
In the following sections, the scales are to be interpreted as: 1) Very low (2) Low (3) Somewhat low (4) Neither low nor high (5) Somewhat high (6) high (7) Very high
Code Statements Very low Very high
A - 14 Degree to which the programs contain basic knowledge/skills.
1 2 3 4 5 6 7
A – 15 Degree to which the programs incorporate additional content.
1 2 3 4 5 6 7
A –16 Relevance of curriculum for future jobs of students. 1 2 3 4 5 6 7
A – 17 The extent to which students learn communication skills (e.g. presentation, discussion).
1 2 3 4 5 6 7
A - 18 The extent to which students learn team working. 1 2 3 4 5 6 7
A - 19 The applicability of knowledge learnt in other fields. 1 2 3 4 5 6 7
A - 20 Credibility of the degrees awarded from this faculty. 1 2 3 4 5 6 7
A - 21 Degree to which school/department handles feedback from students.
1 2 3 4 5 6 7
A - 22 The extent to which personal (confidential) information is secure.
1 2 3 4 5 6 7
In the following sections, the scales are to be interpreted as: (1) Very poor (2) Poor (3) Somewhat low (4) Neither poor nor excellent (5) Somewhat excellent (6) Excellent (7) Very excellent
Statements Very poor
Very excellent
A – 23 Sufficiency of academic equipment (laboratories, workshops).
1 2 3 4 5 6 7
A – 24 Ease of access to the equipment. 1 2 3 4 5 6 7
A – 25 Degree to which the equipment are modern. 1 2 3 4 5 6 7
A – 26 Ease of access to information sources (books, journals, software information network, etc).
1 2 3 4 5 6 7
A – 27 Degree to which environment is visually appealing. 1 2 3 4 5 6 7
A – 28 The availability of support services (common room, sports facilities, quiet room, etc)
1 2 3 4 5 6 7
Section B: Customer Value
B: Below is a set of statements that refer to your perceptions or opinions about the value obtained from the faculty where you are currently studying. Please indicate to what extent you agree or disagree with the following statements. (1) Strongly disagree (2) Disagree (3) Somewhat disagree (4) Neither agree nor disagree (5) Somewhat agree (6) Agree (7) Strongly agree
Statements Strongly disagree
Strongly agree
B -1 This faculty has outstanding quality 1 2 3 4 5 6 7
B -2 This faculty is reliable 1 2 3 4 5 6 7
B -3 This faculty is dependable 1 2 3 4 5 6 7
B -4 This faculty has consistent quality. 1 2 3 4 5 6 7
B -5 The courses are reasonably priced. 1 2 3 4 5 6 7
294
Cont’d – Customer Value B -6 The courses offer good value for money. 1 2 3 4 5 6 7
B -7 This faculty provides good services for the price. 1 2 3 4 5 6 7
B -8 Studying in this faculty will be economical for me. 1 2 3 4 5 6 7
B -9 Studying in this faculty will improve the way I am perceived.
1 2 3 4 5 6 7
B -10 Studying in this faculty will make a good impression on other people.
1 2 3 4 5 6 7
B -11 Studying in this faculty will provides me social approval. 1 2 3 4 5 6 7
B -12 Studying in this faculty makes me feel good. 1 2 3 4 5 6 7
B -13 Studying in this faculty gives me pleasure. 1 2 3 4 5 6 7
B -14 Studying in this faculty gives me a sense of joy. 1 2 3 4 5 6 7
B -15 Studying in this faculty makes me feel delighted. 1 2 3 4 5 6 7
B -16 Studying in this faculty gives me happiness. 1 2 3 4 5 6 7
B -17 This faculty has a good reputation. 1 2 3 4 5 6 7
B -18 This faculty is well respected. 1 2 3 4 5 6 7
B -19 This faculty is well thought of. 1 2 3 4 5 6 7
B -20 This faculty has a good status. 1 2 3 4 5 6 7
B -21 This faculty is reputable. 1 2 3 4 5 6 7
Section C: Customer Satisfaction
C: In this section, we would like to know how satisfied you are with the faculty you are currently studying. Please indicate to what extent you agree or disagree with the following statements. (1) Strongly disagree (2) Disagree (3) Somewhat disagree (4) Neither agree nor disagree (5) Somewhat agree (6) Agree (7) Strongly agree
Statements Strongly disagree
Strongly agree
C - 1 I am satisfied with my decision to study at this faculty. 1 2 3 4 5 6 7
C - 2 If I had the opportunity to do otherwise, I would not enroll in this faculty.
1 2 3 4 5 6 7
C - 3 My choice to enroll in this faculty is a wise one. 1 2 3 4 5 6 7
Statements Strongly disagree
Strongly agree
C - 4 I feel bad about my decision to enroll in this faculty.* 1 2 3 4 5 6 7
C - 5 I think I did the right thing when I decided to enroll in this faculty.
1 2 3 4 5 6 7
C - 6 I am not happy that I enrolled in this faculty. 1 2 3 4 5 6 7
C - 7 This facility is exactly what is needed for this service. 1 2 3 4 5 6 7
C - 8 The services provided by this faculty meet my expectations.
1 2 3 4 5 6 7
C - 9 Considering everything, I am extremely satisfied with this faculty.
1 2 3 4 5 6 7
295
Section D: Behavioural Intentions
D: To what extent do each of the following statements express your behavioural intentions regarding the faculty at which you are currently studying. (1) Strongly disagree (2) Disagree (3) Somewhat disagree (4) Neither agree nor disagree (5) Somewhat agree (6) Agree (7) Strongly agree
Statements Strongly disagree
Strongly agree
D - 1 I like talking about this faculty to my friends. 1 2 3 4 5 6 7
D – 2 I like helping potential students by providing them with information about this faculty and its courses.
1 2 3 4 5 6 7
D – 3 When talking to people about this faculty outside the school, I say positive things.
1 2 3 4 5 6 7
D – 4 I would recommend this faculty to my employer as a place to recruit students.
1 2 3 4 5 6 7
D – 5 I would recommend this faculty as a place to get a degree.
1 2 3 4 5 6 7
D – 6 I plan to contribute money to this faculty after graduation.
1 2 3 4 5 6 7
D – 7 I will consider making non-monetary contributions to this faculty once I become a graduate (e.g. consultation, guest lecture, on-the job training).
1 2 3 4 5 6 7
D - 8 Would you recommend this faculty to a friend applying to study business?
1. Yes 2. No
Section E: Student’s general opinions on quality, value, satisfaction, and behavioral intentions
E1. For what other educational services is quality important at your faculty?
E2. Which educational services are most important for your satisfaction with this faculty?
E3. Which educational services provide the most value for money at this faculty?
E4. When you feel good about your faculty, what do you do?
E5. When you feel bad about your faculty, what do you do?
296
Section F: Student’s personal details
Please provide some personal information about yourself. All responses are confidential and will only be used for statistical purposes in this research.
1. What is your gender?
a. Female b. Male
2. What is your age?
3. Why did you choose your current faculty? (You may choose more than one)
a. Good location b. Cheap tuition fee c. Social d. Reputation e. Degree offering e. Other, specify……………………
4. How did you know about your current faculty? (you may choose more than one)
a. From family members b. From friends c. From printed materials d. From teacher/high school e. From Alumni f. From TV/radio f. Other, specify……………
Comment:
297
Appendix 3
Descriptive statistic
Table A. Descriptive Statistics
Measures N Minimum Maximum Mean Std.
Deviation
Academic staff expertise 643 1 7 5.84 1.002
Academic staff up-to-date 643 1 7 5.70 1.094
Relevant theoretical knowledge 643 1 7 5.91 .862
Relevant Practical knowledge 643 1 7 4.94 1.384
Sufficient number of staff 643 1 7 4.91 1.492
Support staff competent 643 1 7 4.78 1.348
Understand student's needs 643 1 7 4.32 1.482
Willing to help 643 1 7 5.04 1.391
Provide clear guidance-advice 643 1 7 4.75 1.447
Provide adequate personal attention 643 1 7 4.45 1.459
Course offered are stimulating 643 1 7 5.20 1.271
Presentation in logical-timely manner 643 1 7 5.56 1.068
Exams cover materials presented in class 643 1 7 5.93 .923
Degree which programs contain basic knowledge 643 1 7 5.13 1.115
Degree which programs incorporate additional content
643 1 7 4.86 1.241
Relevance curriculum for future jobs 643 1 7 5.11 1.360
Students learn communication skills 643 1 7 5.92 1.088
Students learn team working 643 1 7 5.71 1.108
Applicability of knowledge in other fields 643 1 7 5.04 1.246
Credibility of degree awarded 643 1 7 5.53 1.368
Degrees school handle feedback 643 1 7 4.18 1.617
Personal information is secure 643 1 7 5.31 1.384
Sufficiency of academic equipment 643 1 7 5.02 1.603
Ease of access of equipment 643 1 7 4.77 1.526
Equipments are modern 643 1 7 4.89 1.514
Access to information sources 643 1 7 5.00 1.515
Environment is appealing 643 1 7 4.97 1.406
Availability of support services 643 1 7 4.72 1.547
This faculty has outstanding quality 643 1 7 5.68 1.136
This faculty is reliable 643 1 7 5.44 1.176
This faculty is dependable 643 1 7 5.63 1.113
This faculty has consistent quality 643 1 7 5.47 1.209
Courses are reasonably priced 643 1 7 4.95 1.393
Courses offer good value for money 643 1 7 4.96 1.373
The faculty has good services for the price 643 1 7 4.84 1.423
Studying here is economical 643 1 7 4.90 1.484
Studying here improve the way I am perceived 643 1 7 5.37 1.205
Give me good impression to other people 643 1 7 5.42 1.189
Provides social approval 643 1 7 5.30 1.191
Makes me feel good 643 1 7 5.56 1.117
Gives me pleasure 643 1 7 5.39 1.119
Gives me a sense of joy 643 1 7 5.04 1.246
Makes me feel delighted 643 1 7 4.98 1.164
Gives me happiness 643 1 7 4.91 1.215
298
Cont’d - Table A. Descriptive Statistics Measures
N Minimum Maximum Mean Std.
Deviation
This faculty has a good reputation 643 1 7 5.77 1.035
This faculty is well respected 643 1 7 5.90 .970
This faculty is well thought of 643 1 7 5.89 .908
This faculty has a good status 643 1 7 5.99 .917
This faculty is reputable 643 1 7 6.08 .955
Satisfied with the decision to study 643 1 7 5.49 1.228
If I had the opportunity I would not enroll 643 1 7 4.85 1.649
My choice to enroll is a wise one 643 1 7 5.36 1.182
I feel bad about decision to enroll 643 1 7 5.23 1.486
I did the right thing when deciding to enroll this. 643 1 7 5.46 1.163
I am not happy enrolling this faculty 643 1 7 5.19 1.353
The facility is exactly what needed for this service 643 1 7 5.15 1.462
The services provided meet my expectations 643 1 7 4.71 1.572
Considering everything, I am satisfied 643 1 7 5.18 1.411
I like talking about this faculty 643 1 7 5.30 1.168
Helping potential students with info 643 1 7 5.20 1.278
Say positive things 643 1 7 5.47 1.247
Recommend to my employer 643 1 7 5.09 1.315
Recommend as a place to get a degree 643 1 7 5.38 1.255
Contribute money after graduation 643 1 7 4.26 1.460
Non-monetary contribution after graduation 643 1 7 4.97 1.383
Table B. Correlations Service Quality Construct
Attitude Competence Content Delivery Tangible
Spearman's rho
Attitude Correlation Coefficient 1.000 .401(**) .380(**) .416(**) .332(**)
Sig. (1-tailed) . .000 .000 .000 .000
N 643 643 643 643 643
Competence Correlation Coefficient .401(**) 1.000 .398(**) .398(**) .348(**)
Sig. (1-tailed) .000 . .000 .000 .000
N 643 643 643 643 643
Content Correlation Coefficient .380(**) .398(**) 1.000 .427(**) .591(**)
Sig. (1-tailed) .000 .000 . .000 .000
N 643 643 643 643 643
Delivery Correlation Coefficient .416(**) .398(**) .427(**) 1.000 .377(**)
Sig. (1-tailed) .000 .000 .000 . .000
N 643 643 643 643 643
Tangible Correlation Coefficient .332(**) .348(**) .591(**) .377(**) 1.000
Sig. (1-tailed) .000 .000 .000 .000 .
N 643 643 643 643 643
** Correlation is significant at the 0.01 level (1-tailed).
299
Table C. Correlations Customer Value Construct
Emotion Price Reputation Social
Spearman's rho
Emotion Correlation Coefficient 1.000 .474(**) .495(**) .660(**)
Sig. (1-tailed) . .000 .000 .000
N 643 643 643 643
Price Correlation Coefficient .474(**) 1.000 .405(**) .470(**)
Sig. (1-tailed) .000 . .000 .000
N 643 643 643 643
Reputation Correlation Coefficient .495(**) .405(**) 1.000 .607(**)
Sig. (1-tailed) .000 .000 . .000
N 643 643 643 643
Social Correlation Coefficient .660(**) .470(**) .607(**) 1.000
Sig. (1-tailed) .000 .000 .000 .
N 643 643 643 643
** Correlation is significant at the 0.01 level (1-tailed).
300
Table D. Correlations between Customer Value Indicators
B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20 B21
B5 1 0.6762 0.5467 0.4906 0.2693 0.3126 0.3035 0.3283 0.3003 0.3387 0.3628 0.3529 0.289 0.3121 0.2987 0.2748 0.1188
B6 0.6762 1 0.7185 0.524 0.3968 0.3944 0.34973 0.4171 0.4091 0.4224 0.3951 0.4176 0.4163 0.3868 0.3701 0.3857 0.2217
B7 0.5467 0.7185 1 0.4425 0.3798 0.3856 0.33967 0.3814 0.3875 0.4099 0.4214 0.4281 0.4334 0.3421 0.3238 0.3865 0.2369
B8 0.4906 0.524 0.4425 1 0.3198 0.3203 0.31923 0.3094 0.2992 0.3176 0.3033 0.305 0.2323 0.2675 0.2652 0.2601 0.1179
B9 0.2693 0.3968 0.3798 0.3198 1 0.7922 0.67484 0.5992 0.504 0.4683 0.4623 0.4957 0.5401 0.5091 0.4672 0.5093 0.3703
B10 0.3126 0.3944 0.3856 0.3203 0.7922 1 0.75087 0.6455 0.5484 0.509 0.463 0.4941 0.5386 0.55 0.5549 0.5403 0.3766
B11 0.3035 0.3497 0.3397 0.3192 0.6748 0.7509 1 0.7027 0.5613 0.5306 0.4729 0.4912 0.4531 0.4737 0.4592 0.4717 0.3376
B12 0.3283 0.4171 0.3814 0.3094 0.5992 0.6455 0.70267 1 0.6725 0.5934 0.5203 0.555 0.5142 0.4931 0.4817 0.5178 0.3866
B13 0.3003 0.4091 0.3875 0.2992 0.504 0.5484 0.56129 0.6725 1 0.7441 0.6787 0.6621 0.473 0.4423 0.4543 0.4675 0.3568
B14 0.3387 0.4224 0.4099 0.3176 0.4683 0.509 0.53055 0.5934 0.7441 1 0.765 0.7383 0.422 0.4238 0.4094 0.4469 0.2788
B15 0.3628 0.3951 0.4214 0.3033 0.4623 0.463 0.4729 0.5203 0.6787 0.765 1 0.8596 0.41 0.3741 0.3597 0.4067 0.2173
B16 0.3529 0.4176 0.4281 0.305 0.4957 0.4941 0.49124 0.555 0.6621 0.7383 0.8596 1 0.4421 0.4044 0.3857 0.4216 0.2268
B17 0.289 0.4163 0.4334 0.2323 0.5401 0.5386 0.45309 0.5142 0.473 0.422 0.41 0.4421 1 0.7156 0.6965 0.7269 0.5108
B18 0.3121 0.3868 0.3421 0.2675 0.5091 0.55 0.47373 0.4931 0.4423 0.4238 0.3741 0.4044 0.7156 1 0.8419 0.7723 0.5245
B19 0.2987 0.3701 0.3238 0.2652 0.4672 0.5549 0.45925 0.4817 0.4543 0.4094 0.3597 0.3857 0.6965 0.8419 1 0.7942 0.5511
B20 0.2748 0.3857 0.3865 0.2601 0.5093 0.5403 0.47169 0.5178 0.4675 0.4469 0.4067 0.4216 0.7269 0.7723 0.7942 1 0.5923
B21 0.1188 0.2217 0.2369 0.1179 0.3703 0.3766 0.33758 0.3866 0.3568 0.2788 0.2173 0.2268 0.5108 0.5245 0.5511 0.5923 1
All Correlation is significant at the 0.01 level (1-tailed).
301
Appendix 4
Principal Component Analysis (PCA)
Table A. Exploratory Factor Analysis of Service Quality (28 items) Rotated Component Matrix
No Measures/Items Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Tests
Tangible KMO: 0.929 Barlett Significance: 0.000 A23 Sufficiency of academic equipment .843 .059 .196 .126 .109
A24 Ease of access of equipment .838 .101 .178 .126 .143 A26 Access to information sources .818 .116 .216 .098 .162 A25 Equipments are modern .801 .077 .289 .062 .163 A27 Environment is appealing .739 .124 .228 .150 .019 A28 Availability of support services .664 .239 .140 .083 -.040 A21 Degrees school handle feedback .466 .402 .392 .068 -.170 A22 Personal information is secure .465 .282 .240 .093 .032 A5 Sufficient number of staff .392 .316 .142 .270 .064
Cronbach Alpha: 0.902
Content
A15 Degree which programs incorporate additional content
.268 .130 .689 .097 -.098
A17 Students learn communication skills .159 -.019 .648 .046 .406
A16 Relevance curriculum for future jobs .274 .250 .643 .157 .082
A18 Students learn team working .222 .023 .621 .085 .358
A19 Applicability of knowledge in other fields .172 .182 .621 .206 -.035
A14 Degree which programs contain basic knowledge
.208 .051 .507 .192 .096
A20 Credibility of degree awarded .439 .142 .476 .180 .082
A11 Course offered are stimulating .354 .333 .370 .176 .235
Cronbach Alpha: 0.834
Attitude
A9 Provide clear guidance-advice .114 .805 .088 .095 .153
A8 Willing to help .155 .770 .013 .080 .234
A10 Provide adequate personal attention .094 .762 .157 .092 -.125
A7 Understand student's needs .193 .740 .047 .239 .099
A6 Support staff competent .151 .584 .229 .153 .138
Cronbach Alpha: 0.844
Competence
A2 Academic staff up-to-date .075 .160 .158 .764 .114
A3 Relevant theoretical knowledge .165 .134 .071 .692 .270
A1 Academic staff expertise .172 .078 .186 .688 .175
A4 Relevant Practical knowledge .119 .244 .241 .636 -.174
Cronbach Alpha: 0.734
Delivery
A13 Exams cover materials presented in class .104 .200 .113 .169 .734
A12 Presentation in logical-timely manner .201 .275 .236 .302 .541
Cronbach Alpha: 0.662
302
Table B. Exploratory Factor Analysis of Customer Value (21 items) Rotated Component matrix
No Measures Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Tests
Reputation KMO: 0.938 Barlett Significance: 0.000 B19 This faculty is well thought of .844 .178 .134 .177 .211
B18 This faculty is well respected .822 .219 .212 .157 .217 B20 This faculty has a good status .800 .227 .274 .146 .186 B17 This faculty has a good reputation .668 .172 .428 .178 .213 B21 This faculty is reputable .625 .082 .296 .029 .159
Cronbach Alpha: 0.906 Emotion B15 Makes me feel delighted .127 .849 .183 .228 .177 B16 Gives me happiness .180 .829 .162 .218 .210 B14 Gives me a sense of joy .207 .801 .240 .210 .209 B13 gives me pleasure .248 .742 .274 .158 .265
Cronbach Alpha: 0.928 Quality B1 This faculty has outstanding quality .290 .227 .812 .133 .208 B2 This faculty is reliable .294 .215 .805 .147 .188 B3 This faculty is dependable .310 .254 .746 .193 .196 B4 This faculty has consistent quality .290 .211 .720 .248 .136
Cronbach Alpha: 0.923 Price B5 Courses are reasonably priced .102 .182 .125 .817 .078 B6 Courses offer good value for money .149 .192 .358 .780 .144 B8 Studying here is economical .130 .160 -.053 .751 .202 B7 This faculty has good services for the price .144 .198 .366 .696 .106
Cronbach Alpha: 0.848
Social B10 Give me good impression to other people .292 .198 .211 .176 .806 B11 Provides social approval .203 .304 .090 .148 .803 B9 Studying here improve the way I am perceived .227 .182 .276 .169 .776 B12 Makes me feel good .285 .484 .213 .160 .560
Cronbach Alpha: 0.896
303
Table C. Component Matrix Satisfaction
Component
1
I feel bad about decision to enroll .807
I am not happy enrolling in this faculty .806
I did the right thing when deciding to enroll this
faculty. .800
Satisfied with the decision to study .793
My choice to enroll is a wise one .786
The services provided meet my expectations .750
The facility is exactly what needed for this
service .734
If I had the opportunity, I would not enroll .634
Extraction Method: Principal Component Analysis. a 1 components extracted.
Table D. Component Matrix Behavioural Intentions
Component
1
Recommend as a place to get a degree .748
Recommend to my employer .700
Non-monetary contribution after graduation .698
Helping potential students with information .693
Say positive things
.685
I like talking about this faculty .680
Contribute money after graduation .668
Extraction Method: Principal Component Analysis. a 1 components extracted.
304
Appendix 5
Partial Least Squares (PLS Graph)
Table A. Outer Model Loadings for Refined Data
Indicators Original sample estimate
Mean of subsamples
Standard error
T-statistic
Tangible A23 0.8867 0.8883 0.0095 93.6516
A24 0.8947 0.8960 0.0099 90.0145
A26 0.8896 0.8897 0.0102 87.5520
A25 0.8991 0.8998 0.0088 102.3317
A27 0.7917 0.7925 0.0170 46.7001
Attitude
A9 0.8269 0.8237 0.0141 58.5821
A8 0.8196 0.8203 0.0157 52.2934
A10 0.7432 0.7399 0.0275 27.0121
A7 0.8171 0.8161 0.0165 49.6662
A6 0.7119 0.7158 0.0211 33.6646
Content
A15 0.7283 0.7279 0.0224 32.4492
A17 0.7139 0.7110 0.0326 21.8956
A16 0.7580 0.7571 0.0214 35.4700
A18 0.7268 0.7260 0.0299 24.3126
A19 0.6748 0.6757 0.0296 22.7974
A14 0.6164 0.6082 0.0370 16.6528
Competence
A2 0.7870 0.7879 0.0189 41.5868
A3 0.7724 0.7679 0.0264 29.2464
A1 0.7631 0.7666 0.0250 30.5500
A4 0.7066 0.7081 0.0255 27.7399
Delivery
A12 0.9015 0.9007 0.0102 88.2621
A13 0.8256 0.8227 0.0198 41.5945
Reputation
B19 0.8933 0.8935 0.0127 70.2122
B18 0.9107 0.9101 0.0114 79.7172
B20 0.9050 0.9039 0.0118 76.4665
B17 0.8579 0.8587 0.0162 52.8247
B21 0.6982 0.6952 0.0348 20.0873
Emotion
B15 0.9184 0.9187 0.0094 97.9564
B16 0.9110 0.9115 0.0139 65.9564
B14 0.9145 0.9152 0.0080 114.4417
B13 0.8858 0.8860 0.0111 79.5467
Price
B5 0.8271 0.8293 0.0186 44.5207
B6 0.9077 0.9088 0.0069 132.2366
B8 0.7385 0.7388 0.0275 26.8904
B7 0.8438 0.8456 0.0187 45.0153
305
Cont’d - Table A. Outer Model Loadings for Refined Data
Indicators Original sample estimate
Mean of subsamples
Standard error
T-statistic
Social
B10 0.9054 0.9071 0.0106 85.6624
B11 0.8806 0.8806 0.0114 77.5629
B9 0.8665 0.8677 0.0137 63.0603
B12 0.8368 0.8389 0.0159 52.6875
Satisfaction
C1 0.8174 0.8182 0.0177 46.1018
C3 0.7935 0.7931 0.0211 37.5915
C4 0.7817 0.7825 0.0241 32.4768
C5 0.8140 0.8145 0.0166 48.8959
C6 0.7909 0.7935 0.1248 31.8356
C7 0.7465 0.7512 0.1211 35.4114
C8 0.7650 0.7678 0.0248 30.8696
Behaviour Inentions
D1 0.7138 0.7119 0.0261 27.344
D2 0.6914 0.6945 0.0285 24.2373
D3 0.6859 0.6834 0.0358 19.1644
D4 0.6738 0.6718 0.0339 19.8798
D5 0.7529 0.7541 0.0284 26.5068
D6 0.6609 0.6695 0.0285 23.1993
D7 0.6852 0.6912 0.0370 18.5286
Refined Data: All problematic indicators have been removed (A5, A11, A20, A21, A22, A28, C2 and indicators that measure ‘Quality’ construct – B1, B2, B3 and B4)
Table B. Outer Model Weights for Refined Data Indicators Original sample
estimate Mean of
subsamples Standard error
T-statistic
Tangible A23 0.2256 0.2248 0.0046 48.6369
A24 0.2322 0.2331 0.0046 51.0171
A26 0.2358 0.2346 0.0050 47.3423
A25 0.2361 0.2363 0.0045 52.8695
A27 0.2150 0.2146 0.0052 41.5301
Attitude
A9 0.2552 0.2527 0.0096 26.6988
A8 0.2541 0.2543 0.0108 23.4416
A10 0.2227 0.2219 0.0127 17.5767
A7 0.2761 0.2761 0.0117 23.6988
A6 0.2663 0.2702 0.0136 19.5532
Content
A15 0.2398 0.2400 0.0114 21.0435
A17 0.2206 0.2201 0.0115 19.1028
A16 0.2737 0.2743 0.0129 21.2132
A18 0.2421 0.2434 0.0118 20.5340
A19 0.2313 0.2342 0.0129 17.9434
A14 0.2082 0.2057 0.0122 17.0256
306
Cont’d - Table B. Outer Model Weights for Refined Data
Indicators Original sample estimate
Mean of subsamples
Standard error
T-statistic
Competence
A2 0.3279 0.3289 0.0148 22.1320
A3 0.3359 0.3343 0.0149 22.6116
A1 0.3376 0.3375 0.0142 23.8555
A4 0.3182 0.3173 0.0165 19.3048
Delivery
A12 0.6516 0.6534 0.0215 30.2399
A13 0.4997 0.4998 0.0200 24.9664
Reputation
B19 0.2387 0.2391 0.0050 47.5589
B18 0.2490 0.2491 0.0064 39.1213
B20 0.2480 0.2483 0.0054 46.0605
B17 0.2444 0.2452 0.0056 43.4441
B21 0.1803 0.1794 0.0096 18.7054
Emotion
B15 0.2658 0.2651 0.0038 69.1051
B16 0.2730 0.2739 0.0049 55.6338
B14 0.2804 0.2802 0.0047 60.1350
B13 0.2831 0.2826 0.0054 52.3347
Price
B5 0.2766 0.2752 0.0098 28.1938
B6 0.3416 0.3415 0.0107 31.8230
B8 0.2573 0.2548 0.0118 21.7748
B7 0.3213 0.3228 0.0103 31.3422
Social
B10 0.2913 0.2910 0.0057 51.2166
B11 0.2741 0.2738 0.0063 43.4305
B9 0.2789 0.2791 0.0065 42.7958
B12 0.3026 0.3014 0.0077 39.4523
Satisfaction
C1 0.2040 0.2020 0.0085 24.0966
C3 0.1892 0.1876 0.0082 23.0292
C4 0.1524 0.1532 0.0077 19.9035
C5 0.1877 0.1875 0.0085 21.9662
C6 0.1728 0.1730 0.0085 20.3863
C7 0.1826 0.1825 0.0092 19.8627
C8 0.1808 0.1803 0.0086 20.9960
Behaviour Inentions
D1 0.2538 0.2516 0.0133 19.0236
D2 0.1961 0.1966 0.0132 14.9080
D3 0.2001 0.1991 0.1129 15.5140
D4 0.1669 0.1635 0.0117 14.2863
D5 0.2373 0.2354 0.0154 15.4145
D6 0.1916 0.1924 0.0119 16.0718
D7 0.1871 0.1890 0.0161 11.6005
Refined Data: All problematic indicators have been removed (A5, A11, A20, A21, A22, A28, C2 and indicators that measure ‘Quality’ construct – B1, B2, B3 and B4)
307
Appendix 6
Cross Loadings Matrix
Table A. Cross Loadings after Problematic Items Dropped (A5, A11, A20, A21, A22, A28 and C2) (PLS Graph)
Competence Attitude Delivery Content Tangible Quality Price Social Emotion Reputation satisfaction BI
A1 0.726414 0.267847 0.333951 0.333244 0.291729 0.336857 0.267445 0.31718 0.303809 0.308477 0.317496 0.290131
A2 0.787693 0.311421 0.34015 0.29683 0.241398 0.307907 0.23204 0.242869 0.221194 0.219211 0.230566 0.246452
A3 0.741891 0.309544 0.357337 0.289218 0.300051 0.356624 0.245918 0.238844 0.247169 0.271732 0.302018 0.258363
A4 0.732238 0.368223 0.256933 0.339588 0.252178 0.210359 0.204002 0.236024 0.225676 0.156361 0.240491 0.240872
A6 0.300496 0.71536 0.334186 0.364809 0.294577 0.282679 0.255441 0.244431 0.314197 0.233012 0.286786 0.326538
A7 0.376062 0.817365 0.339718 0.302111 0.306764 0.311648 0.284466 0.22449 0.297123 0.175408 0.285208 0.231691
A8 0.283288 0.785275 0.382577 0.245849 0.254619 0.271888 0.243803 0.190218 0.290479 0.173568 0.24152 0.229968
A9 0.288508 0.794476 0.352938 0.274345 0.205794 0.257748 0.245292 0.198518 0.294574 0.159658 0.20155 0.245464
A10 0.275093 0.726283 0.210503 0.273438 0.187322 0.248865 0.234157 0.175268 0.223584 0.097326 0.177797 0.177252
A12 0.376926 0.386249 0.924381 0.413549 0.370144 0.400982 0.358866 0.387594 0.374877 0.278166 0.374719 0.345924
A13 0.308114 0.332353 0.782121 0.326444 0.255395 0.256857 0.292378 0.276918 0.261123 0.256541 0.311296 0.284906
A14 0.283728 0.253339 0.305906 0.625503 0.380957 0.351128 0.277108 0.257489 0.285452 0.323963 0.330556 0.313966
A15 0.251734 0.263422 0.246531 0.720697 0.447778 0.444457 0.331887 0.342437 0.360319 0.324684 0.41895 0.390499
A16 0.352653 0.365947 0.395809 0.743032 0.437691 0.480025 0.39405 0.441313 0.430607 0.381408 0.410708 0.408275
A17 0.246819 0.188556 0.298379 0.675072 0.389512 0.377828 0.294347 0.327647 0.304593 0.428468 0.311266 0.331621
A18 0.308445 0.235924 0.332423 0.737297 0.436785 0.448733 0.28495 0.321527 0.289796 0.39164 0.335242 0.284389
A19 0.306726 0.286404 0.323377 0.650179 0.344275 0.365573 0.324805 0.337199 0.309154 0.278499 0.345245 0.351537
A23 0.305826 0.259458 0.295459 0.492355 0.842955 0.548412 0.372776 0.320969 0.331771 0.438229 0.604363 0.339008
308
Cont’d – Table A. Cross Loadings after Problematic Items Dropped (A5, A11, A20, A21, A22, A28 and C2) (PLS Graph)
Competence Attitude Delivery Content Tangible Quality Price Social Emotion Reputation satisfaction BI
A24 0.315032 0.310024 0.340033 0.504233 0.883352 0.561172 0.387899 0.322482 0.386507 0.390842 0.598269 0.374455
A25 0.27347 0.286732 0.325352 0.557557 0.89779 0.590516 0.404725 0.356489 0.373761 0.445888 0.609535 0.34709
A26 0.298803 0.303496 0.36107 0.537186 0.883732 0.619306 0.418979 0.372227 0.406778 0.447214 0.619989 0.399766
A27 0.298039 0.267288 0.305435 0.476414 0.788028 0.549395 0.441771 0.44374 0.49742 0.414824 0.579274 0.35138
B1 0.354975 0.307977 0.34722 0.539794 0.635733 0.884605 0.465785 0.487431 0.468254 0.61013 0.621017 0.436775
B2 0.356267 0.331177 0.376154 0.542483 0.601805 0.904153 0.474469 0.474172 0.470271 0.596479 0.591825 0.435809
B3 0.297067 0.317971 0.341748 0.533665 0.578458 0.890193 0.500886 0.501879 0.494218 0.60492 0.604437 0.440446
B4 0.334542 0.308173 0.343771 0.50278 0.565658 0.849239 0.502366 0.470607 0.470676 0.58216 0.550763 0.395822
B5 0.208909 0.251921 0.248946 0.301024 0.229093 0.341877 0.71106 0.33892 0.370642 0.292306 0.262718 0.297935
B6 0.269152 0.306569 0.364605 0.423791 0.4572 0.530421 0.927511 0.442117 0.446275 0.406903 0.49659 0.383347
B7 0.284181 0.322248 0.343306 0.411857 0.488973 0.503785 0.885894 0.4215 0.448394 0.394494 0.498978 0.36408
B8 0.126833 0.191673 0.219916 0.240187 0.125418 0.239712 0.632042 0.354773 0.333542 0.255792 0.214167 0.317049
B9 0.261929 0.21971 0.316066 0.432947 0.383523 0.500007 0.424107 0.849409 0.532444 0.549729 0.499091 0.450955
B10 0.274404 0.235054 0.331281 0.431994 0.374925 0.485674 0.42998 0.883723 0.558969 0.591297 0.461912 0.451477
B11 0.280564 0.228382 0.330096 0.397579 0.344674 0.411672 0.386074 0.877963 0.570269 0.498807 0.425167 0.476774
B12 0.274082 0.238244 0.33653 0.436055 0.373522 0.503307 0.43538 0.85318 0.648062 0.5459 0.504006 0.487152
B13 0.245479 0.317295 0.334688 0.434168 0.41243 0.512812 0.425853 0.646248 0.856684 0.490776 0.58554 0.495119
B14 0.283313 0.35912 0.370215 0.467335 0.461765 0.502734 0.453196 0.606047 0.910889 0.459098 0.553855 0.531601
B15 0.243396 0.341072 0.309686 0.390957 0.371454 0.448945 0.438084 0.547041 0.904336 0.403947 0.531058 0.520349
309
Cont’d – Table A. Cross Loadings after Problematic Items Dropped (A5, A11, A20, A21, A22, A28 and C2) (PLS Graph)
Competence Attitude Delivery Content Tangible Quality Price Social Emotion Reputation satisfaction BI
B16 0.279813 0.321604 0.318467 0.398132 0.390233 0.453551 0.451881 0.582523 0.895619 0.439465 0.539348 0.515807
B17 0.287004 0.222418 0.293201 0.444205 0.52384 0.64354 0.442954 0.565326 0.478941 0.857214 0.56298 0.473093
B18 0.250617 0.199881 0.301549 0.431217 0.41902 0.561793 0.39411 0.560703 0.452505 0.877105 0.499025 0.430762
B19 0.25794 0.170381 0.286043 0.38941 0.366849 0.538602 0.376897 0.543052 0.444241 0.890568 0.466972 0.410072
B20 0.287796 0.207203 0.31805 0.459255 0.453092 0.604165 0.413829 0.562914 0.483515 0.877997 0.548593 0.481138
B21 0.164361 0.086963 0.164918 0.337986 0.361689 0.447685 0.233458 0.406366 0.297596 0.698264 0.419564 0.305692
C1 0.252125 0.23119 0.28615 0.405959 0.493404 0.532756 0.4498 0.475381 0.578078 0.488481 0.791793 0.500367
C3 0.240601 0.190289 0.27916 0.393472 0.416968 0.481108 0.420913 0.489213 0.514131 0.489648 0.761503 0.47599
C4 0.216631 0.204634 0.307928 0.367286 0.508631 0.479544 0.367035 0.385425 0.446637 0.430959 0.820625 0.439853
C5 0.268878 0.21361 0.304956 0.38867 0.412119 0.494911 0.416072 0.48369 0.54298 0.470842 0.786247 0.499581
C6 0.320207 0.258936 0.318203 0.427218 0.53264 0.529938 0.370861 0.446496 0.543005 0.454843 0.824876 0.482604
C7 0.31525 0.269296 0.385067 0.454959 0.732559 0.551792 0.433386 0.394282 0.412953 0.499505 0.745218 0.400063
C8 0.324582 0.312722 0.309554 0.447659 0.730086 0.568932 0.430875 0.382295 0.417101 0.464831 0.773296 0.379193
D1 0.259865 0.246298 0.294676 0.383548 0.361798 0.42299 0.33011 0.406764 0.474349 0.388754 0.492263 0.668111
D2 0.238018 0.164474 0.242008 0.295807 0.309044 0.309559 0.257026 0.343519 0.350491 0.316571 0.418717 0.691546
D3 0.233544 0.244272 0.265527 0.301535 0.231921 0.267815 0.335203 0.325591 0.342514 0.296684 0.347018 0.653398
D4 0.159857 0.149922 0.181647 0.319493 0.214921 0.271624 0.214203 0.325015 0.325064 0.306715 0.311097 0.672244
D5 0.225331 0.221884 0.261385 0.388965 0.368244 0.415788 0.304667 0.477421 0.470141 0.42906 0.477444 0.752562
D6 0.246707 0.276422 0.247802 0.265049 0.23707 0.270025 0.333604 0.353049 0.404915 0.287792 0.305192 0.642036
D7 0.167578 0.212348 0.181268 0.320222 0.24543 0.278649 0.203966 0.336319 0.404764 0.308807 0.361957 0.666719
310
Table B. Cross Loading after ‘Quality’ Dropped (PLS Graph) Competence Attitude Delivery Content Tangible Price Social Emotion Reputation Satisfaction BI
A1 0.726414 0.267835 0.333951 0.333409 0.291729 0.251104 0.31691 0.304013 0.308506 0.317394 0.290567
A2 0.787693 0.3114 0.34015 0.296915 0.241398 0.227441 0.24289 0.220079 0.21909 0.230256 0.24643
A3 0.741891 0.309532 0.357337 0.28939 0.300051 0.240422 0.238788 0.247814 0.271328 0.302014 0.25863
A4 0.732238 0.368204 0.256933 0.339532 0.252178 0.191914 0.235879 0.223297 0.156165 0.24045 0.241344
A6 0.300496 0.71535 0.334186 0.364807 0.294577 0.247578 0.244315 0.314665 0.232851 0.286854 0.327017
A7 0.376062 0.817368 0.339718 0.302233 0.306764 0.273626 0.224418 0.295819 0.174973 0.28507 0.232232
A8 0.283288 0.785289 0.382577 0.245873 0.254619 0.244153 0.190233 0.292139 0.173141 0.241452 0.230226
A9 0.288508 0.794482 0.352938 0.274277 0.205794 0.245342 0.198381 0.293408 0.159137 0.201452 0.246017
A10 0.275093 0.726275 0.210503 0.27343 0.187322 0.231761 0.175238 0.225018 0.096816 0.177703 0.177746
A12 0.376926 0.386243 0.924381 0.413607 0.370144 0.347708 0.387627 0.373346 0.277913 0.374545 0.346822
A13 0.308114 0.332336 0.782121 0.326538 0.255395 0.286479 0.276953 0.260806 0.25651 0.311369 0.285067
A14 0.283728 0.253333 0.305906 0.625503 0.380957 0.270732 0.257177 0.285064 0.323426 0.330389 0.313813
A15 0.251734 0.263422 0.246531 0.720093 0.447778 0.312184 0.342265 0.358283 0.324213 0.418895 0.390416
A16 0.352653 0.365948 0.395809 0.743347 0.437691 0.37002 0.441218 0.429059 0.38097 0.410632 0.408227
A17 0.246819 0.188556 0.298379 0.675035 0.389512 0.282458 0.327522 0.301472 0.428423 0.311299 0.331073
A18 0.308445 0.235926 0.332423 0.737334 0.436785 0.266674 0.321455 0.286025 0.391503 0.335323 0.284015
A19 0.306726 0.2864 0.323377 0.650571 0.344275 0.323428 0.337251 0.307463 0.278139 0.345145 0.351491
A23 0.305826 0.259459 0.295459 0.492374 0.842955 0.317659 0.320799 0.329455 0.437725 0.604065 0.338233
A24 0.315032 0.310033 0.340033 0.504125 0.883352 0.343586 0.32244 0.383094 0.390306 0.597869 0.373896
A25 0.27347 0.286726 0.325352 0.557391 0.89779 0.353794 0.356501 0.370735 0.445554 0.609257 0.346241
A26 0.298803 0.303506 0.36107 0.537042 0.883732 0.380945 0.372031 0.404146 0.446935 0.619713 0.39915
A27 0.298039 0.267297 0.305435 0.476254 0.788028 0.412682 0.443918 0.493894 0.414523 0.578867 0.351033
311
Cont’d – Table B. Cross Loading after ‘Quality’ Dropped (PLS Graph) Competence Attitude Delivery Content Tangible Price Social Emotion Reputation Satisfaction BI
B5 0.208909 0.25193 0.248946 0.301047 0.229093 0.792156 0.339082 0.370377 0.292539 0.262662 0.298307
B6 0.269152 0.306572 0.364605 0.423711 0.4572 0.89804 0.441878 0.443657 0.406821 0.496525 0.383725
B7 0.284181 0.32226 0.343306 0.411767 0.488973 0.845206 0.421616 0.446822 0.393944 0.498816 0.364277
B8 0.126833 0.191673 0.219916 0.240185 0.125418 0.703636 0.354673 0.331645 0.25596 0.214166 0.317592
B9 0.261929 0.219695 0.316066 0.43297 0.383523 0.413234 0.848641 0.531297 0.549618 0.499119 0.450272
B10 0.274404 0.235043 0.331281 0.431996 0.374925 0.424397 0.883968 0.556941 0.591296 0.461922 0.450894
B11 0.280564 0.228369 0.330096 0.397536 0.344674 0.389326 0.878582 0.568024 0.498835 0.42515 0.476162
B12 0.274082 0.238236 0.33653 0.436223 0.373522 0.430636 0.853249 0.646734 0.545901 0.504074 0.486646
B13 0.245479 0.317283 0.334688 0.4342 0.41243 0.411051 0.646414 0.856684 0.490758 0.585472 0.494702
B14 0.283313 0.359119 0.370215 0.467399 0.461765 0.448523 0.606276 0.903066 0.459009 0.553831 0.531626
B15 0.243396 0.341073 0.309686 0.39102 0.371454 0.436657 0.547176 0.911461 0.403817 0.531119 0.520267
B16 0.279813 0.3216 0.318467 0.398191 0.390233 0.445172 0.582606 0.896853 0.43953 0.539417 0.51589
B17 0.287004 0.22241 0.293201 0.444231 0.52384 0.416252 0.565145 0.47876 0.856452 0.562983 0.472669
B18 0.250617 0.199861 0.301549 0.431293 0.41902 0.386934 0.560598 0.451265 0.878059 0.499242 0.430466
B19 0.25794 0.170359 0.286043 0.389513 0.366849 0.366613 0.543156 0.443147 0.891638 0.467132 0.409822
B20 0.287796 0.207184 0.31805 0.45925 0.453092 0.391011 0.562851 0.482102 0.877174 0.548681 0.48105
B21 0.164361 0.086957 0.164918 0.338041 0.361689 0.212299 0.406254 0.297056 0.697951 0.419489 0.305077
C1 0.252125 0.231192 0.28615 0.405991 0.493404 0.416221 0.4753 0.577542 0.488495 0.791873 0.499734
C3 0.240601 0.190291 0.27916 0.393506 0.416968 0.399107 0.48915 0.515134 0.490201 0.761864 0.475422
C4 0.216631 0.204642 0.307928 0.367383 0.508631 0.32858 0.385468 0.446835 0.430925 0.820758 0.439329
C5 0.268878 0.21361 0.304956 0.388636 0.412119 0.390125 0.483735 0.544293 0.471088 0.786689 0.49948
C6 0.320207 0.258941 0.318203 0.427361 0.53264 0.332962 0.446696 0.543064 0.454614 0.824976 0.482182
C7 0.31525 0.269287 0.385067 0.45487 0.732559 0.386197 0.394206 0.410718 0.498897 0.744859 0.399547
C8 0.324582 0.31272 0.309554 0.447566 0.730086 0.381732 0.38207 0.414139 0.464209 0.772736 0.378747
D1 0.259865 0.246309 0.294676 0.383623 0.361798 0.315978 0.406884 0.476442 0.388572 0.492012 0.666186
D2 0.238018 0.164467 0.242008 0.295605 0.309044 0.242253 0.343708 0.351928 0.316368 0.418718 0.689709
D3 0.233544 0.244271 0.265527 0.301518 0.231921 0.331843 0.325796 0.342349 0.296689 0.346965 0.654006
D4 0.159857 0.149926 0.181647 0.319458 0.214921 0.213179 0.32519 0.326789 0.306746 0.311215 0.672765
D5 0.225331 0.221884 0.261385 0.388943 0.368244 0.291992 0.477224 0.47178 0.429163 0.477471 0.751404
D6 0.246707 0.276422 0.247802 0.264988 0.23707 0.337036 0.353261 0.404823 0.287423 0.305151 0.645235
D7 0.167578 0.212346 0.181268 0.320245 0.24543 0.205515 0.336403 0.403593 0.308922 0.362034 0.667983
312
Table C. Construct Cross Loading before Quality Dropped Constructs SQ Value
Attitude 0.676 0.409
Competence 0.688 0.447
Content 0.821 0.655
Delivery 0.628 0.477
Tangible 0.819 0.637
Emotion 0.576 0.820
Price 0.534 0.711
Quality 0.720 0.848
Reputation 0.581 0.836
Social 0.550 0.824
313
Table D Table (t-statistic) as provided by PLS Graph. Path Coefficients Table (T-Statistic) ==================================================================== Tangible Content Attitude Competen Delivery Reputati Emotion Price Social SQ Value Satisfac BI Tangible 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 58.1155 0.0000 0.0000 0.0000 Content 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 47.6450 0.0000 0.0000 0.0000 Attitude 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 23.1963 0.0000 0.0000 0.0000 Competen 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 20.1596 0.0000 0.0000 0.0000 Delivery 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 19.0746 0.0000 0.0000 0.0000 Reputati 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 44.3000 0.0000 0.0000 Emotion 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 63.0593 0.0000 0.0000 Price 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 34.3526 0.0000 0.0000 Social 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 54.2746 0.0000 0.0000 SQ 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 24.8009 0.0000 0.0000 0.0000 Satisfac 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 9.2727 11.9516 0.0000 0.0000 BI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 2.0776 9.3043 3.8954 0.0000 ====================================================================
314
Appendix 7
PLS Graphic Output
Figure A. The Structural Model: PLS graph
Figure B. Partial Model SQ – CS - BI
0.685
0.463
315
Figure C. Partial Model SQ – CV - BI
Figure D. Partial Model CV – CS - BI
0.705
0.655
316
Figure E. Partial Model SQ – CV - CS
0.698
317
Figure F. Structural Model – Satisfaction as Mediating Variable
SQ-BI: Direct effect= 0.098, indirect effect= 0.080, Total effect= 0.178 Val-BI: Direct effect= 0.427, indirect effect= 0.098, Total effect= 0.525
0.684
318
Figure G. Bivariate Relationship Model SQ – CS
Figure H. Bivariate Relationship Model SQ - BI
Figure I. Bivariate Relationship Model SQ - CV
Figure J. Bivariate Relationship Model CV- CS
Figure K. Bivariate Relationship Model CV- BI
Figure L. Bivariate Relationship Model CS - BI
Satisfaction
Behavioural Intentions
R2=0.351
0.593****
Customer
Value
Behavioural Intentions
R2=0.419
0.647****
Customer Value
Customer Satisfaction R2=0.500
0.707****
Service
Quality
Customer Value
R2=0.470
0.686****
Service Quality
Behavioural Intentions R2=0.289
0.538****
Service
Quality
Customer Satisfaction
R2=0.480
0.693****
Note: p<0.001; ***p<0.010; **p<0.050; *p<0.100
319
Appendix 8
Owlia and Aspinwall’s (1996) Dimensions of
Higher Education Service Quality
Table A. Garvin’s (1987) Dimensions of Quality Garvin’s Dimensions Definition in higher education
Performance Primary knowledge/skills required for graduates
Features Secondary/supplementary knowledge and skills
Reliability The extent to which knowledge/skills learned are correct, accurate and up to date
Conformance The degree to which an institution/programme/course meets established standards, plans and promises
Durability Depth of learning
Serviceability How well an institution handles customers’ complaints
Aesthetics
Perceived quality
Source: Owlia & Aspinwall 1996
Table B. Software Quality Factors (Watts 1987) Software Dimensions Definition in higher education
Correctness The extent to which a programme/course complies with the specified requirements
Reliability The extent to which knowledge/skills learned are correct, accurate and up to date
Efficiency The extent to which knowledge/skills learned are applicable to the future career of graduates
Integrity (security) The extent to which personal information is secure from an authorized access
Usability The ease of learning and the degree of communicativeness in the classroom
Maintainability How well an institution handles customers complaints
Testability How fair examinations represent a subject of study
Expandability Flexibility (generality)
Portability The degree to which knowledge/skills learned are applicable to other field
Re-usability
Interoperability
Source: Owlia & Aspinwall 1996
320
Table C. Service Quality Factors (Parasuraman et al. 1985) SQ Dimensions Definition in higher education
Reliability The extent to which education is correct, accurate and up to date
How well an institution keeps its promises
The degree of consistency in educational processes (teaching)
Responsiveness Willingness and readiness of (academic) staff to help students
Understanding customers
Understanding students and their needs
Access The extent to which staff is available for guidance and advice
Competence The theoretical and practical knowledge of staff as well as other presentation skills
Courtesy Emotive and positive attitude towards students
Communication How well lecturers and students communicate in the classroom
Credibility The degree of trustworthiness of the institution
Security Confidentiality of information
Tangibles States, sufficiency and availability of equipment and facilities
Performance Primary knowledge/skills required for students
Completeness Supplementary knowledges/skills, use of computer
Flexibility The degree to which knowledge/skills learned are applicable to other fields
Redress How well an institution handles customers complaints and solves problems
Source: Owlia & Aspinwall 1996
321
Appendix 9
Ethics Clearance
Subject: SUHREC Project 0607/203 Ethics Clearance
Received: 5 July 2007 – 5.24 pm
To: Assoc Prof Siva Muthaly/Ms Ratna Roostika, FBE
Dear Siva and Ratna
SUHREC Project 0607/203 The relationship between perceived quality, value, satisfaction
and behavioural intentions in Indonesian higher education sector: An empirical study of
universities in Yogyakarta
Assoc Prof Siva Muthaly FBE Ms Ratna Roostika
Approved duration: 25/07/2007 To 25/10/2007
I refer to the ethical review of the above project protocols conducted on behalf of
Swinburne's Human Research Ethics Committee (SUHREC) by a Subcommittee (SHESC4)
on Friday 1 June 2007. Your responses to the review as emailed today (with revised
documentation) addresses clearly the queries relating to further or revised Swinburne and
researcher contact details being put on the consent instruments given the international
context of the project. Ethics clearance can therefore be deemed given in line with the
following standard conditions.
- All human research activity undertaken under Swinburne auspices must conform to
Swinburne and external regulatory standards, including the current National Statement on
Ethical Conduct in Research Involving Humans and with respect to secure data use,
retention and disposal.
- The named Swinburne Chief Investigator/Supervisor remains responsible for any
personnel appointed to or associated with the project being made aware of ethics clearance
conditions, including research and consent procedures or instruments approved. Any
change in chief investigator/supervisor requires timely notification and SUHREC
endorsement.
- The above project has been approved as submitted for ethical review by or on behalf of
SUHREC. Amendments to approved procedures or instruments ordinarily require prior
ethical appraisal/ clearance. SUHREC must be notified immediately or as soon as possible
thereafter of (a) any serious or unexpected adverse effects on participants and any redress
measures; (b) proposed changes in protocols; and (c) unforeseen events which might affect
continued ethical acceptability of the project.
322
- At a minimum, an annual report on the progress of the project is required as well as at the
conclusion (or abandonment) of the project.
- A duly authorised external or internal audit of the project may be undertaken at any time.
Please contact me if you have any queries about on-going ethics clearance. The SUHREC
project number should be quoted in communication.
Best wishes for the project.
Yours sincerely
Keith Wilkins
Secretary, SHESC4
*******************************************
Keith Wilkins
Research Ethics Officer
Office of Research and Graduate Studies (Mail H68)
Swinburne University of Technology
P O Box 218
HAWTHORN VIC 3122
Tel: 9214 5218
323
Appendix 9
Published Supporting Papers
Refereed Conference Paper
1. Roostika, R & Mutahly, S 2008, A conceptual model of service quality and
customer value in higher education context: A students’ perspective, Proceedings of
the 14th
Euro-Asia Conference and the 3rd
International Conference on Business
Management Research (ICBMR), Bali, Indonesia, 27-29 August.
2. Roostika, R & Muthaly, S 2008, A formative approach to customer value in the
Indonesian higher education sector: a partial least squares model, Proceedings of
ANZMAC 2008 conference (Australia & New Zealand Marketing Academy),
Sydney, Australia, 1-3 December.
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