the role of customer value within the service quality

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.


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.


Delivery The manner in which the product or service is being delivered and presented.


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.


Social The utility derived from the product’s ability to enhance social self-concept.


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




Service Quality




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


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.

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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.

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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.


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).

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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

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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.

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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.


Functional value (installations, professionalism & quality), emotional value and social value.

Price.

Whittaker et al. (2007) – business services.


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

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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

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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)

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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

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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

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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.

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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

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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.

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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

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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

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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

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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.


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


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.


3.3.2.3 Hypothesis Development: Service Quality, Customer Satisfaction and


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.


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


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:

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H11: Customer value mediates the relationship between service quality and customer

satisfaction.

H12: Customer value mediates the relationship between service quality and


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).

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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).

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Figure 3.6 Conceptual Model

Tangibles

Competence

Delivery

Quality

Content

Reliability

Attitude

Emotion

Reputation

Social

Price

Service

Quality

Customer

Value

Customer

Satisfaction


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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


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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


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


Competence

A2 Academic staff up-to-date. .764

A3 Relevant theoretical knowledge. .692

A1 Academic staff expertise. .688

A4 Relevant Practical knowledge. .636


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


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


Quality B1 Institution has outstanding quality. .812 B2 Institution is reliable. .805 B3 Institution is dependable. .310 .746 B4 Institution has consistent quality. .720


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


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


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 0.7130 No problem


A11 0.6766 No problem

A17 0.648 Cross loading A17 0.6722 No problem


Delivery


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.




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


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)


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

188

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

189

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


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


192

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



role of satisfaction as a mediating variable increased the total effect to 0.538.

2) SQ-CV-BI


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



195

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


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


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

204

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

208

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

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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

217

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.

224

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


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’

227

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.

228

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


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’


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 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


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.



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


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


The indirect relationships S/NS H5 Customer satisfaction mediates the relationship between service quality and


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


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



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: [email protected]

Assoc Prof. Siva Muthaly Head of Marketing & International business

Swinburne University of Technology Ph: 61-3-9214 5885 E-mail: [email protected]

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 [email protected]

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


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


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


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


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


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


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


Delivery

A13 Exams cover materials presented in class .104 .200 .113 .169 .734

A12 Presentation in logical-timely manner .201 .275 .236 .302 .541


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


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


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


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


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)


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)


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

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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 ====================================================================

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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


R2=0.351

0.593****

Customer

Value


R2=0.419

0.647****

Customer Value


0.707****

Service

Quality

Customer Value

R2=0.470

0.686****

Service Quality


0.538****

Service

Quality


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


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


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.

the role of customer value within the service quality

Documents