Examining Perceived Value for Money, Relationship Commitment and Re-buying Intention in a Business-

To-Business Context – A Suggested Model

Yap Hock Seng

This thesis is presented for the degree of

Doctor of Business Administration at

The University of Western Australia

University of Western Australia Business School

2010

i i

Abstract

Value for money has been seen as an important construct in recent years,

but most of the research that has examined this construct has been

undertaken in business-to-consumer contexts and very little is known about

its role in business-to-business contexts. The current study developed and

tested a model that explained the influence of some suggested antecedents

(past satisfaction, perceived risk, product quality and service quality) had on

perceived value for money and their subsequent impact on relationship

commitment, customer loyalty and re-buying intention.

Two of the antecedent variables (service quality and customer loyalty) were

found to have discriminant validity problem and were excluded from a

revised model. The results suggested past satisfaction, perceived risk and

product quality all had significant influences on perceived value for money.

However, Baron and Kenny‟s (1986) mediation assessment procedure

suggested perceived value for money was not a dominant mediator between

these antecedent variables and commitment to the relationship. This led to

the development of an alternative model in which perceived value for money

was included as an exogenous construct that fitted the data better then the

initially suggested model.

The results also suggested switching costs moderated the relationship

between commitment to the relationship and intention to re-buy, as

commitment to the relationship‟s impact on re-purchase intention was

greater when switching costs were high. Switching costs also moderated the

ii ii

relationship between product quality and intention to re-buy, as, when

switching costs were high, product quality had no direct effect. However,

when switching costs were low, product quality had a strong direct positive

effect.

Product quality has a direct influence on organisational buyer‟s re-buying

intention and managers must clearly understand the customers‟

requirements of a product, and continuously improve and develop new

product that meet the customers‟ needs. Beside the monetary aspect of

value for money of a product, managers should also consider the non-

monetary aspect as this have an impact on the customer‟s perception of a

product quality. Also, conducting customer satisfaction survey regularly and

offering assurances help in reducing the customers‟ perception of risk. While,

erecting high switching costs in a product is being viewed as negative by

many customers, however, this strengthens their commitment to stay with

the existing supplier. Consequently, all of these activities help improve and

deepen the customer‟s commitment to a relationship, which has a direct

impact on buyer‟s re-buying intention.

iii iii

Acknowledgement

The journey towards the completion of this doctoral research study was long,

arduous and convoluted. This would not be possible without the valuable

support and encouragement from several key people. These special people

had made my doctoral research study an unforgettable life experience. My

heartfelt gratitude and special thanks to the following people:

First and foremost, my wonderful and caring supervisor, Professor

Geoffrey Soutar who has been very patient in providing his valuable

advices and guidance towards the completion of my thesis. At times

when I stumbled and without any complaint, he guided me and shown

me the way. He did this despite his busy schedules and other

commitments. It has been a great privilege for me to have him as my

supervisor.

My sincere thanks to the Business School of The University of

Western Australia for the approval of my research grant application

which had helped me financially to a certain extent. This was a

pleasant surprise for me and I am grateful for this financial assistance.

Above all, I would like to thank my loving wife, Linda Choo. She has

never once complains about the lack of time spent with our family. As

our only son, Marcus is still young, her care and devotion on him had

freed me to focus on my study. Linda and Marcus are my source of

energy and motivation, and they are what I am living for.

iv iv

TABLE OF CONTENTS

Page

Abstract ------------------------------------------------------------------------------ i

Acknowledgement --------------------------------------------------------------- iii

List of Figures --------------------------------------------------------------------- ix

List of Tables ----------------------------------------------------------------------- xi

Chapter One: Introduction ----------------------------------------------------- 1

1.1 Background to the Study ------------------------------------------------- 1

1.2 The Research Questions ------------------------------------------------ 5

1.3 The Present Study --------------------------------------------------------- 9

1.3.1 Suggested Antecedents to Perceived Value for Money

and Intention to Re-buy ----------------------------------------- 10

1.3.2 The Suggested Model ------------------------------------------- 12

1.3.3 Defining the Constructs ----------------------------------------- 14

1.4 Summary --------------------------------------------------------------------- 15

Chapter Two: Review of the Literature and Hypotheses ------------- 17

2.1 Introduction ------------------------------------------------------------------- 17

2.2 Organisational Buying ----------------------------------------------------- 17

2.3 The Influences on Organisational Buyers‟ Re-buying

Intention ---------------------------------------------------------------------- 24

2.3.1 The Relationships between Commitment to Relationship,

Loyalty and Intention to re-buy -------------------------------- 24

2.3.1.1 Intention to Re-buy ------------------------------------ 26

2.3.1.2 Commitment to a Relationship --------------------- 29

2.3.1.3 Customer Loyalty -------------------------------------- 32

2.3.2 The Relationship between Perceived Value for Money and

Commitment to the Relationship ------------------------------ 36

2.3.2.1 Perceived Value --------------------------------------- 36

v v

2.3.3 The Relationships between Perceived Risk, Service Quality,

Product Quality, Past Satisfaction and Perceived

Value for Money --------------------------------------------------- 43

2.3.3.1 Product Quality ----------------------------------------- 45

2.3.3.2 Service Quality ----------------------------------------- 47

2.3.3.3 Perceived Risk ----------------------------------------- 48

2.3.3.4 Past Satisfaction --------------------------------------- 51

2.3.4 The Relationship between Product Quality and Intention

to Re-buy ----------------------------------------------------------- 54

2.3.5 The Effect of Switching Costs on Product Quality,

Commitment to the Relationship and Intention

to Re-buy ----------------------------------------------------------- 54

2.3.5.1 Switching Costs ---------------------------------------- 55

2.3.6 Conceptual Model of the Hypothesised Relationships -- 57

2.4 Summary --------------------------------------------------------------------- 58

Chapter Three: Research Approach and Methodology -------------- 60

3.1 Introduction ------------------------------------------------------------------ 60

3.2 The Research Model ------------------------------------------------------ 60

3.3 The Measures Used ------------------------------------------------------- 62

3.3.1 The Perceived Risk Measure ---------------------------------- 62

3.3.2 The Product Quality Measure --------------------------------- 63

3.3.3 The Service Quality Measure ---------------------------------- 64

3.3.4 The Past Satisfaction Measure -------------------------------- 66

3.3.5 The Perceived Value for Money Measure ------------------ 67

3.3.6 The Commitment to Relationship Measure ---------------- 68

3.3.7 The Customer Loyalty Measure ------------------------------ 71

3.3.8 The Intention to Re-buy Measure ---------------------------- 71

3.3.9 The Switching Cost Measure ---------------------------------- 73

3.4 Data Collection and Sampling ------------------------------------------ 74

3.4.1 Questionnaire Design -------------------------------------------- 74

3.4.2 The Sample -------------------------------------------------------- 76

vi vi

3.4.3 The Data Collection Approach -------------------------------- 78

3.4.4 Collecting the Data ----------------------------------------------- 80

3.4.4.1 Pre-Testing ---------------------------------------------- 80

3.4.4.2 The Major Data Collection Phase ----------------- 81

3.5 Data Analysis Approach-------------------------------------------------- 81

3.6 Summary -------------------------------------------------------------------- 85

Chapter Four: Data Analysis – Part One ---------------------------------- 86

4.1 Introduction ------------------------------------------------------------------ 86

4.2 The Sample ------------------------------------------------------------------ 86

4.3 Some Descriptive Statistics ---------------------------------------------- 89

4.4 Testing for Normality ------------------------------------------------------- 91

4.5 The Scales‟ Measurement Characteristics --------------------------- 91

4.5.1 The Perceived Risk Construct --------------------------------- 94

4.5.2 The Product Quality Construct -------------------------------- 95

4.5.3 The Service Quality Construct -------------------------------- 96

4.5.4 The Past Satisfaction Construct ------------------------------ 97

4.5.5 The Perceived Value for Money Construct ----------------- 98

4.5.6 The Commitment to Relationship Construct --------------- 99

4.5.7 The Customer Loyalty Construct ----------------------------- 100

4.5.8 The Intention to Re-buy Construct --------------------------- 101

4.5.9 The Switching Cost Construct --------------------------------- 102

4.5.10 Discriminant Validity Assessment ---------------------------- 105

4.6 Exploratory Factor Analysis --------------------------------------------- 106

4.6.1 Scale Purification ------------------------------------------------- 110

4.6.2 A Re-assessment of Discriminant Validity ------------------ 119

4.7 Summary --------------------------------------------------------------------- 123

vii vii

Chapter Five: Data Analysis – Part Two ---------------------------------- 125

5.1 Introduction ------------------------------------------------------------------ 125

5.2 The Measurement Model ------------------------------------------------ 125

5.3 The Structural Model ------------------------------------------------------ 127

5.3.1 The Structural Model Fit and Path Estimates

Assessment --------------------------------------------------------- 128

5.3.2 An Examination of the Total Effects, Direct Effects and

Indirect Effects on the Models in the Revised Model ---- 131

5.4 Assessing Perceived Value for Money‟s Mediating Role -------- 133

5.4.1 The Mediation Effect of Perceived Value for Money on the

Past Satisfaction - Commitment Relationship ------------- 135

5.4.2 The Mediation Effect of Perceived Value for Money on the

Perceived Financial Risk - Commitment Relationship --- 136

5.4.3 The Mediation Effect of Perceived Value for Money on the

Product Quality - Commitment Relationship --------------- 137

5.5 An Alternative Model ------------------------------------------------------ 139

5.5.1 Assessing the Fit and Path Estimates of the

Alternative Model -------------------------------------------------- 140

5.5.2 Assessing the Mediation Effect of Commitment to the

Relationship in the Alternative Model ------------------------ 142

5.5.2.1 The Mediation Effect of Commitment to the

Relationship on the Perceived value for Money -

Intention to Re-buy Relationship ------------------- 142

5.5.2.2 The Mediation Effect of Commitment to the

Relationship on the Past Satisfaction - Intention

to re-buy Relationship -------------------------------- 143

5.5.2.3 The Mediation Effect of Commitment to the

Relationship on the Perceived Financial Risk

- Intention to Re-buy Relationship ----------------- 144

5.6 Comparing the Revised Model with the Alternative Model ------ 146

5.7 Summary --------------------------------------------------------------------- 149

viii viii

Chapter Six: Data Analysis – Part Three ---------------------------------- 150

6.1 Introduction ------------------------------------------------------------------ 150

6.2 An Analysis of the Moderating Effect of Switching Costs -------- 151

6.3 The Relationship between Product Quality and Intention to

Re-buy with Switching Costs as the Moderator -------------------- 155

6.4 Summary --------------------------------------------------------------------- 158

Chapter Seven: Conclusion, Limitation and Implication of the

Research ------------------------------------------------------ 159

7.1 Introduction ------------------------------------------------------------------ 159

7.1.1 A summary of the Present Study ----------------------------- 159

7.1.2 A review of the Research Questions ------------------------- 162

7.2 The Theoretical Implications -------------------------------------------- 165

7.3 The Managerial Implications -------------------------------------------- 167

7.4 Limitations of the Present Study --------------------------------------- 172

7.5 Implications for Future Research -------------------------------------- 174

7.6 Conclusions ----------------------------------------------------------------- 176

References -------------------------------------------------------------------------- 178

Appendix I: Questionnaire ----------------------------------------------------- 194

Appendix II: Information Letter for Questionnaire --------------------- 200

Appendix III: Descriptive Statistics for Individual Items ------------- 201

ix ix

List of Figures

Figure 1.1: The Suggested Model ---------------------------------------------- 12

Figure 2.1: Cascading the three complementary models of customer

Value Configuration ------------------------------------------------ 41

Figure 2.2: The Research Model ----------------------------------------------- 58

Figure 4.1: The Perceived Risk Construct ----------------------------------- 95

Figure 4.2: The Product Quality Construct ----------------------------------- 96

Figure 4.3: The Service Quality Construct ----------------------------------- 97

Figure 4.4: The Past Satisfaction Construct --------------------------------- 98

Figure 4.5: The Perceived Value for Money Construct ------------------- 99

Figure 4.6: The Commitment to Relationship Construct ------------------ 100

Figure 4.7: The Customer Loyalty Construct -------------------------------- 101

Figure 4.8: The Intention to Re-buy Construct ------------------------------ 102

Figure 4.9: The Switching Cost Construct ------------------------------------ 103

Figure 4.10: The Final Perceived Financial Risk Construct -------------- 113

Figure 4.11: The Perceived Operational Risk Construct ------------------ 115

Figure 4.12: The Loyalty-Commitment Construct --------------------------- 116

Figure 4.13: The Revised Model ------------------------------------------------ 121

Figure 5.1: The Revised Measurement Model for the Present Study

(Indicators excluded to improve readability) ------------------ 127

Figure 5.2: The Revised Structural Model ------------------------------------ 128

Figure 5.3: The Revised Structural Model with Standardised

Path Coefficients ---------------------------------------------------- 129

Figure 5.4: Conditions for Mediator Effect ------------------------------------ 134

Figure 5.5: Mediator Effect of Perceived Value for Money

(Past SatisfactionPerceived ValueCommitment) ------- 136

Figure 5.6: Mediator Effect of Perceived value for Money

(Perceived RiskPerceived ValueCommitment) --------- 137

Figure 5.7: Mediator Effect of Perceived Value for Money

(Perceived QualityPerceived ValueCommitment) ------ 138

Figure 5.8: Proposed Alternative Model --------------------------------------- 139

x x

Figure 5.9: The Alternative Model – Structural Model and

Path Estimates ------------------------------------------------------- 140

Figure 5.10: Mediator Effect of Commitment to Relationship

(Perceive ValueCommitmentIntention to Re-buy) ---- 143

Figure 5.11: Mediator Effect of Commitment to Relationship

(Past SatisfactionCommitmentIntention to Re-buy) -- 144

Figure 5.12: Mediator Effect of Commitment to Relationship

(Perceived Financial RiskCommitmentIntention to Re-buy) - 145

Figure 6.1: Hypothesised Moderator Effects of Switching Costs ------- 151

Figure 6.2: The Alternative Model at Low Switching Cost ---------------- 156

Figure 6.3: The Alternative Model at High Switching Cost --------------- 157

xi xi

List of Tables

Table 3.1: The Perceived Risk Measure -------------------------------------- 63

Table 3.2: The Product Quality Measure ------------------------------------- 64

Table 3.3: The Service Quality Measure -------------------------------------- 66

Table 3.4: The Past Satisfaction Measure ------------------------------------ 67

Table 3.5: The Perceived Value for Money Measure ---------------------- 68

Table 3.6: The Commitment to Relationship Measure -------------------- 70

Table 3.7: The Customer Loyalty Measure ---------------------------------- 71

Table 3.8: The Intention to Re-buy Measure -------------------------------- 73

Table 3.9: The Switching Cost Measure -------------------------------------- 74

Table 4.1: Respondent Profiles ------------------------------------------------- 88

Table 4.2: Summary and Result of the Goodness of Fit ------------------ 104

Table 4.3: Summary of Individual Reliability and Average Variance

Extracted ---------------------------------------------------------------- 105

Table 4.4: Average Variance Extracted and Square Correlations ------ 106

Table 4.5: Exploratory Factor Analysis Matrix ------------------------------- 109

Table 4.6: Final Construct Fit Indices ------------------------------------------ 118

Table 4.7: Final Construct Reliability and Average Variance

Extracted ---------------------------------------------------------------- 119

Table 4.8: Average Variance Extracted and Square Correlations

(After EFA and Purification) ---------------------------------------- 119

Table 4.9: Average Variance Extracted and Square Correlations

(After excluding Service Quality) -------------------------------- 120

Table 5.1: Direct, Indirect and Total Effects of the Revised Model ----- 132

Table 5.2: Square Multiple Correlations for the Revised Model --------- 133

Table 5.3: Direct, Indirect and Total Effects of the Alternative Model -- 141

Table 5.4: Square Multiple Correlations for the Alternative Model ------ 142

Table 5.5: The Absolute and Incremental Fit Indices for the Revised

and Alternative Models --------------------------------------------- 148

Table 5.6: The Parsimony Fit Indices for the Revised and Alternative

Models ------------------------------------------------------------------ 148

xii xii

Table 6.1: Chi-square Difference between Constrained and

Unconstrained Models ----------------------------------------------- 154

Table 6.2: Multiple Group Analysis – Switching Cost as a Moderator

Variable ------------------------------------------------------------------ 155

Table 7.1: Summary of Hypotheses Tests and Results ------------------- 161

1

Chapter One

Introduction

1.1 Background to the Study

In today‟s uncertain world with ever-changing consumer needs and

increased complexity, predicting and projecting the future can be perilous.

Firms face highly competitive and technology driven environments that have

demanding challenges. This complexity has led to “perceived risk both on

the supply and the demand side” (Ruyter, Moorman, & Lemmink, 2001, p.

272). Despite this, organisations are pressured to deliver ever improving

results. Indeed, globalisation has meant the pace of change is likely to

increase, which may lead many organisations to change their approach,

especially given the global financial crisis of recent years. Vantrappen (1992,

p.53) argued nearly twenty years ago that “customer value is emerging as

the strategic imperative” and it still seems to be the case (e.g., Chi, Yeh, &

Yang Jang, 2008; Logman, 2008). In recent years, interest in value-based

and value-focused strategies has increased dramatically. Value research

has been undertaken in three general categories as financial economists

examine shareholder value, marketers examine customer value and

stakeholder theorists examine stakeholder value. However, the source of all

value is customer value (Khalifa, 2004).

The impact perceived value has on behavioural outcomes has been an

important focus in marketing research in recent years and value is

2

increasingly gaining recognition as a source of competitive advantage

(Shank, 2002). In order to understand consumers‟ purchase behaviour, it is

necessary to look at their perception of a product‟s or a service‟s value, as

well as quality and price perceptions as all are important determinants of

people‟s willingness to buy (Choi, Lee, & Subramani, 2004). Driven by more

demanding customers and global competition, many organisations have

looked internally to improve quality management and have involved

themselves in re-engineering, downsizing and restructuring (Woodruff,

1997). However, such organisations must remain oriented to the

marketplace, understanding customers and providing superior customer

value as this ensures higher prices and better profitability (Ron, 2005),

which create values for shareholders (Vantrappen, 1992; Wind & Thomas,

1980).

While perceived value had been argued as an important construct in

predicting customers‟ behavioural outcomes (Dodds, Monroe, & Grewal,

1991; Sweeney, Soutar, & Johnson, 1999), relationship commitment also

plays an important role (Gounaris, 2005). In most business-to-business

environment, suppliers develop a long term relationship with customers to

obtain repeat purchases (Gounaris, 2005).

Different people in a decision making unit influence buying decisions and,

consequently, business-to-business marketers need to understand what

motivates these people, as well as the value such purchases have to the

organisation and to the individual (Tanner, 1990). Other idiosyncratic

3

personal, interpersonal, organisational, and environmental conditions also

impact on the business-to-business buying process (Wind & Thomas, 1980).

Organisational buying is complex and varies by product, industry and buying

situation (Wind & Thomas, 1980). Other factors, including corporate culture

and behaviour (Quinn & Rohrbaugh, 1983), inter-organisational relationships

(Wind & Thomas, 1980), organisational structure and design (Robey &

Johnston, 1977), the importance of the decision to the company and to the

buyer (Tanner, 1990), and the nature of the buyer-seller relationship

(Henthorne, LaTour, & Williams, 1992), also influence the decision making

process. However, in understanding organisational buying‟s decisions,

researchers should also take account of both cognitive and affective

variables (Eggert & Ulaga, 2002).

As noted earlier, organisational buying‟s decisions “are most often made by

a buying centre or decision making unit (DMU), which is a collection of

individuals whose input receives some consideration in the purchase

decision” (McQuiston & Dickson, 1991, p. 159). Indeed, sales people‟s

expertise, after sales support and communication effectiveness are key

elements in influencing organisational buyers in a business-to-business

context, especially in the high-technology markets (Ruyter et al, 2001).

However, the level of complexity and the inherent perceived risk involved in

business-to-business relationships leads to intricate interplays of the various

factors that may affect customers‟ intentions to stay in a relationship with an

existing supplier.

4

Why does perceived value warrant further study in organisational buying

contexts? The organisational buying context is generally assumed to be a

collective and rational process, while consumer buying context is viewed as

a more immediate, individualistic and idiosyncratic in nature (Wilson, 2000).

The differences between organisational and consumer buying are distinct

and should be examined separately. Wilson (2000, p.786) suggested

“greater emphasis should be placed on the personal and social aspects of

organisational buying process, and on the effect of pre-existing influences

such as experience, personal paradigms, cultural preferences and

habitations”.

While perceived value research has been generally undertaken in the

consumer contexts, some research has been carried out in organisational

buying (or business-to-business) contexts. Indeed, Schultz (2000, p.108)

suggested “research on customer value in business-to-business

relationships is in its infancy and has mainly been conducted at a conceptual

level.” Despite the growing body of value research, especially in consumer

and retail contexts, it is still unclear how perceived value impacts in

business-to-business contexts and, in particular, its impact on people‟s

behavioural intention is uncertain (Wilson, 2000). Given the economic

importance of business-to-business marketing this is surprising and clearly

suggests a need for research to be undertaken to examine this issue. The

inter-relationships of the various constructs surrounding perceived value are

complex. Several multidimensional customer value perspectives have been

suggested that offer insights into perceived value‟s role in organisational

5

buying contexts (e.g., Sheth, Newman, & Gross, 1991: Sweeney & Soutar,

2001; Woodruff, 1997).

1.2 The Research Questions

Value is an abstract concept and its meaning varies according to the context

in which it is being considered (Patterson & Spreng, 1997). Marketing

practitioners and researchers have become increasingly interested in the

value customers expect from products and services as value provides a way

to sustain a competitive advantage. Economic conditions, technology,

competition and the environment change over time, as do customers‟

expectations (Jones & Sasser, 1995). Value expectations vary across

customers but can also change over time (Vantrappen, 1992). This led

Parasuraman (1997, p.157) to suggest “evidence confirming that the value

assessment criteria and process differ across the different stages will have

major implications for customer value theory and measurement”. As new

buyers evolve into long-term customers, the criteria they use in assessing

value may change and it is critical to understand the importance of the

various value attributes at different stages of the buyer-seller relationship

(Parasuraman, 1997). Further, as technology changes, product quality is

less likely to be the sole driver of customer loyalty (Gounaris, 2005). There

is a need to monitor customers‟ value as this is critical to maintaining

customer satisfaction and loyalty and, ultimately, gaining a competitive

advantage.

6

Organisations derive value from interacting with other organisations

(Rajdeep, James, & Raj, 2001) and the key is to maintain mutually profitable

customer relationships that are based on more than price (Schultz & Good,

2000). By understanding the value organisational buyers attach to a

potential purchase, and the strength of the customer relationship, sellers can

design “high value” products and services buyers are more likely to

purchase. Such information provides sellers with knowledge that can help

them negotiate higher prices and develop better longer-term relationships.

Suppliers must strive to sell a solution and not a product (Thull, 2005) and,

in return, buyers develop a commitment to their supplier. Suppliers must

ensure the quality of their products and the attention given by contact

personnel, paying special attention to emotional aspects that arise from

customers' enjoyment of the product and from their buying experiences

during the decision making process.

While quality, value perceptions, relationship commitment and satisfaction

are important antecedents to intention, switching costs are likely to have a

moderating effect on these relationships (Anton, Camarero, & Carrero, 2007;

Lee, Lee, & Feick, 2001; Vasudevan, Gaur, & Shinde, 2006; Yang &

Peterson, 2004). The presence of switching cost makes it more difficult or

costly for customers to change providers (Jones, Mothersbaugh, & Beatty,

2000). While, several studies had investigated the moderating role switching

plays in business-to-consumer contexts (e.g., Anton et al., 2007; Yang &

7

Peterson, 2004), this moderating impact has not been investigated in a

business-to business context.

In a business-to-consumer context, consumers evaluate retailers or

suppliers of a product or service to determine how well they match their

expectations. This also holds true in business-to-business contexts

(Hollyoake, 2009). However, there are some differences between business-

to-business and business-to-consumer contexts, including:

(1) In a business-to-business context, buyers act on behalf of their

business and are trying to meet business goals, needs and objectives.

In a business-to-consumer context, buyers act to meet their personal

needs.

(2) Business-to-business buyers generally do not use the products they

buy. Whereas, consumers generally do.

(3) Business-to-business buyers make purchases in an attempt to add

value to their organisation, whereas consumers are more likely to be

motivated by emotion, intuition and impulse (Schmitt, 2003).

The present study examined the prior research conducted in business-to-

consumer contexts and used the various suggested interrelationships to

develop a business-to-business model that would allow business-to-

business marketers to better understand their buyers‟ behaviour and

decision making processes and to suggest how to provide the value

8

business-to-business customers need. The study attempted to answer a

number of questions within a business-to-business context, namely:

(1) What effect does product quality have on customers‟ value

perceptions?

(2) Are perceived value and product quality distinct constructs?

(3) If so, is product quality an antecedent to perceived value, as

Sweeney, Soutar and Johnson (1999) found in a business-to-

consumer context?

(4) Is product quality a better predictor of perceived value than other

suggested antecedent constructs, such as past satisfaction,

service quality and perceived risk?

(5) Is perceived value a mediator between its antecedent variables

and relationship commitment?

(6) Are customers‟ commitment to the relationship and customers‟

loyalty distinct constructs?

(7) Does commitment to the relationship impact on customer‟s

intention to re-buy from an existing supplier?

(8) If this is so, do switching costs have a moderating effect on the

relationship between commitment to the relationship and intention

to re-buy from an existing supplier?

9

(9) Do switching costs also have a moderating effect on the

relationship between product quality and intention to re-buy?

1.3 The Present Study

Very little is known about the composition and nature of influence patterns

among buying centre members. It seems a person‟s influence is affected by

such factors as personal bias, personal values and external pressure (Rice,

2006; Zeithaml, 1988), and by the stage of the decision process (i.e. search,

evaluation or choice) (McQuiston & Walters, 1989) and business-to-

business marketers need to understand what motivates buying centre

members.

As was noted earlier, there are differences between organisational and

consumer buying, and most perceived value research had been undertaken

in consumer contexts (e.g. Dodds & Monroe, 1985: Snoj, Korda, & Murnel,

2004; Sweeny & Soutar, 2001; Zeithaml, 1988). Considerably less research

has been carried out in business-to-business contexts, which means

relatively little is known about value‟s role in such a context. Therefore, the

present study used previously developed business-to-consumer value

models to develop an organisational buying (or business-to-business) model

and tested its applicability empirically in a business-to-business context.

It would have been impossible to include all of the antecedents that have

been suggested as having an impact on value perceptions, relationship

commitment and intention to re-buy. However, an examination of prior

research, which is provided in Chapter Two, suggested a small set of

10

antecedents that were likely to provide a better understanding of

organisational buyer‟s re-buying intention. Consequently, the model outlined

in the next section was examined in the present study.

1.3.1 Suggested Antecedents to Perceived Value for Money and

Intention to Re-buy

Customer perceived value is not a new concept and is a key concept in

marketing (Dodds, 1991). McLeon (2006) argued people buy items that

provide them with the greatest perceived value. Price and cost may be

major factors, but value is crucial as it is a measure of a product‟s or a

service‟s overall worth and it is the basis of exchange (Snoj et al., 2004).

The suggested model‟s relationships (between past satisfaction, customer

loyalty, re-buying intention, perceived risk, perceived quality, commitment

and perceived value) have been examined in a variety of contexts, as an

understanding of these relationships is likely to explain a great deal about

people‟s purchase decisions (Snoj et al., 2004). Product and service quality

have positive effects on perceived value, while perceived risk has a negative

effect (Snoj et al., 2004) and value is related to satisfaction (Eggert & Ulaga,

2002; Snoj, et al., 2004). It has also been suggested that product quality has

a direct positive influence on people‟s re-buying intentions (Hult, Boyer, &

Ketchen, 2007). Switching costs are suggested to have a moderating effect

on the relationship between product quality and intention, as well as on the

relationship between commitment and intention (Anton, Camarero, &

Carrero, 2007).

11

Sweeny and Soutar (2001) developed a four-dimensional (emotional value,

social value, price functional value and quality functional value) multiple-item

perceived value scale that can be used as a basis for undertaking research

in a business-to-business context. Sweeny and Soutar (2001, p. 214) found

“quality and emotional values were more important in explaining

perceptions” in a consumer context. However, in an industrial (or business-

to-business) context, the focus is mainly on operations, services and

products (Perkins, 1993). Consequently, only their price-functional value (or

value for money) dimension was included in the present study. Several

antecedents suggested by prior perceived value research were included;

namely past satisfaction, perceived risk, product quality and service quality

(Snoj et al., 2004).

Customers must feel satisfied and the service and product must be seen as

providing value if people are likely to re-buy (Hume, Mort, & Winzar, 2007).

Jones and Sasser (1995) have also suggested switching costs may impact

on people‟s re-buying intention as high switching costs discourage them

from changing to a new supplier.

The present study examined the influence these antecedents (past

satisfaction, perceived risk, service quality and product quality) had on

perceived value of money, relationship commitment, customer loyalty and

re-buying intention in a business-to-business context. The study also

examined the moderating impact switching costs had on the relationship

between product quality and intention to re-buy, as well as on the

12

relationship between commitment and intention to re-buy. Switching costs,

which include transactional switching costs and learning costs, are relevant

in many industries and in business-to-consumer contexts, as well as in

business-to-business contexts. However, the size of their impact is likely to

differ across markets and, consequently, their impact was examined in the

present business-to-business context.

1.3.2 The Suggested Model

The antecedents discussed in the previous section which are justified in

more detail in the review of prior research that is provided in Chapter Two,

were based on theory and past research into consumers‟ purchase

behaviour. The model suggested by the relationships between these

constructs is shown in Figure 1.1.

Figure 1.1: The Suggested Model

Commitment

to Relationship

Customer

Loyalty

Past

Satisfaction

Product

Quality

Perceived

Risk

Service

Quality

Perceived Value

for Money

Intention

to Re-buy

Switching

Cost

13

While many business-to-consumer product or service could be a business-

to-business product, few business-to-business products or services are

likely to be used by consumers. For example, dish washing detergent, a

typical business-to-consumer product, can be seen as a business-to

business product if it is bought in larger quantities by a restaurant for their

own use. However, few people will buy a milling machine for private use. To

avoid examining products that may be sold in both business-to-consumer

and business-to business contexts, it was decided to use capital equipment

in the present study. In doing so it was recognised that such purchases also

include after sales and service activities (e.g., spare parts sales, consumer

parts sales and chargeable services).

As one of the study‟s objectives was to examine the suggested model in a

business-to-business context, two products from a similar category were

included, namely:

(1) Surface Mount Technology (SMT) equipment, which is used to

assemble printed circuit boards.

(2) In-Circuit Test (ICT) equipment, which is used to test assembled

printed circuit boards

Both SMT and ICT products use mature technologies, but are the primary

ways in which electronics manufacturing is undertaken as it is cost effective

(Radio-Electronics). The price of SMT and ICT equipment ranges from

US$100,000 to US$400,000 depending on the model and configuration,

14

suggesting such equipment will be seen as a capital purchase by most

manufacturing organisations.

1.3.3 Defining the Constructs

Organisational Buying A process in which decisions “are most often

made by a buying centre or decision making

unit (DMU), which is the collection of individuals

whose input receives some consideration in the

purchase decision” (McQuiston & Dickson,

1991, p. 159).

Perceived Value for Money The ratio or trade-off between quality and price.

(Sweeney & Soutar, 2001).

Past Satisfaction An “evaluation of perceived product or service

performance based on customers‟ judgment of

the value that had been created for them” (Flint,

Woodruff, & Gardial, 1997, p. 172).

Product Quality A “consumer‟s judgement about an entity‟s

overall excellence or superiority” (Zeithaml,

1988, p. 3)

Service Quality A “global judgement, or attitude, relating to the

superiority of a service” (Parasuraman,

Zeithaml, & Berry, 1988, p. 16)

15

Perceived Risk A “subjective expectation of a loss” (Sweeney,

Soutar, & Johnson, 1999, p. 81).

Customer Loyalty A “deeply held commitment to re-buy or re-

patronise a preferred product / service

consistently in the future, thereby causing

repetitive same-brand or brand-set purchasing,

despite situational influences and marketing

efforts having the potential to cause switching

behaviour” (Oliver, 1999, p.34).

Relationship Commitment An “exchange partners‟ desire to maintain a

valued relationship” (Coote, Forrest, & Tam,

2003, p. 596).

Intention to Re-buy A “customer‟s decision to engage in future

activity with a provider” (Hume et al., 2007, p.

137).

Switching Cost “Any perceived disutility a customer would

experience from switching providers” (Chen &

Hitt, 2002, p. 258).

1.4 Summary

Chapter One discussed the rationale for the present study and outlined the

research stages. It examined the constructs that influence organisational

buyers‟ perceived value for money and re-buying intention. This led to a

16

model in which aspects of product quality, service quality, risk and past

satisfaction were seen as antecedents to perceived value of money and,

subsequently, as impacting on relationship commitment, customer loyalty

and intention to re-buy.

Chapter Two discusses past research into organisational buying in general

and the various key constructs in particular, leading to the suggested model

that was shown in Figure 1.1 and a discussion of the hypotheses implicit in

that model. Subsequent Chapters outline the research undertaken to

examine these hypotheses, report the results obtained and discuss their

academic and managerial implications.

17

Chapter Two

Review of the Literature and Hypotheses

2.1 Introduction

Chapter One introduced the current study and discussed its importance,

while also outlining the research questions, research framework and

research model. The model, which was shown in Figure 1.1, suggested a

number of constructs were necessary to explain organisational buyers‟

perceived value for money and re-buying intention. The research that led to

this model is examined and discussed in the current chapter.

The current chapter is divided into two sections: the first begins with a

review of the literature on organisational buying and discusses business-to-

business purchasing; while the second discusses the various hypotheses

that were implicit in the suggested model and shows how they were drawn

from prior research. This is followed by a review of the existing literature

relevant to the key constructs and their inter-relationships.

2.2 Organisational Buying

Organisational buying research examines business-to-business purchasing.

As was noted in Chapter One, many people are part of the decision making

units (DMUs) that are at the heart of organisational buying - all of whom may

influence the final purchase decision (McQuiston & Dickson, 1991).

Robinson, Faris, & Wind (1967) initially suggested a framework that included

18

a matrix of buy classes and buy phases. There are three common buying

situations that may be influenced, namely:

(1) A straight re-buy.

(2) A modified re-buy

(3) A new-task purchase (Robey & Johnston, 1977; Wind & Thomas,

1980; Leonidou, 2005; Robinson, Faris, & Wind, 1967)

In a straight re-buy, buyer purchases familiar products from existing

suppliers and their limited involvement of people, little information is

obtained and there is little consideration of alternative sources of supply. A

modified re-buy on the other hand requires more time, personnel and effort,

and possibly a search for new suppliers, perhaps due to dissatisfaction with

a current supplier and or problems with previously purchased products. The

most complex and difficult is new task buying, in which a buyer tries to fulfil

new need and requirements and, consequently, many people are involved,

more information is needed, more time is needed and different alternative

suppliers are compared (Leonidou, 2005).

The number of people involved, the amount of information needed, the time

required, the risk involved and the possible need to find new suppliers will

differ across these buying situations (Leonidou, 2005). Organisational

buying is a basic business process as it is an integrated activity that crosses

over an organisation‟s engineering, production, quality and cost control

19

functions, and is often influenced by longer-term strategic considerations,

rather than by short-term operational considerations (De Rose, 1992).

Many organisations, evaluate alternatives carefully, undertake onsite

evaluations when making purchase decisions and have extensive and

formalised search processes (Doyle, Woodside, & Michell, 1979; Weiss &

Heide, 1993). The nature of this process seems to be influenced by the

perceived risk of the purchase and the type of buying situations (Wind &

Thomas, 1980). Various “idiosyncratic personal, interpersonal,

organisational, and environmental conditions” (Wind & Thomas, 1980, p.

243) may also impact on the process. Indeed, although “organisational

buying behaviour involves a more cognitive approach than individual buying

behaviour, affective processes frequently occur, or sometimes even

dominate, organisational buying decisions” (Erevelles, 1998, p. 209).

The organisational buying process model is complex and varies according to

product and buying situation (Wind & Thomas, 1980). Bradley (1997)

suggested a four stage process, while Wind (1978) suggested a twelve

stage process. The buyers‟ expertise and experience are also important

elements, along with the evaluation processes that are used (Doyle et al.,

1979).

Despite a general acceptance of the nature of the organisational buying

process, very little is known about the composition and nature of the

influences among buying centre members, which may or may not be

affected by factors such as personal bias, personal values and external

20

pressure (Rice, 2006; Zeithaml, 1988), and by the stage of decision-making

process (i.e. search, evaluation and choice) (McQuiston & Walters, 1989).

However, as the present study focused on organisational buyers‟ re-buying

intentions, these factors were not considered.

Within a buying centre, one individual may avoid making a decision, while

others may fight over “territorial” rights when making a purchase decision

(Tanner, 1990). Consequently, task leaders who contributed more to the

discussion usually have higher status and more influence on the choices

that are made (Krapfel, 1982). Expert power can also have an important

impact (Wind & Thomas, 1980).

Tanner (1990, p.58) argued that, “although two organisational buyers may

identify the task and assess the reward system in the same way, their

behaviours can vary”. Consequently, business-to-business marketers need

to understand the influence and participation of all DMU members as such

information enables them to detect differences in buyers‟ views (Silk &

Kalwani, 1982). Depending on the buying situation and influence level,

organisational buyers may adopt:

(1) A defensive behaviour strategy because of higher perceived risk

and greater exposure to possible loss. In this situation, the

concern is more about the process than about the result.

(2) An offensive behaviour strategy because perceived risk and

potential loss are deemed to be low. In this situation, the concern

is more about the result than about the process (Tanner, 1990).

21

A defensive behaviour strategy can take on a number of forms, such as

finding issues with buyers, mimicking the behaviour of an evaluator,

increasing the number of people in the buying process, moving responsibility

upward or choosing well known or trusted vendors; while offensive

strategies can involve being a cooperative negotiator, seeking to create a

win-win outcome and evaluating product performance objectively (Tanner,

1990).

The information sources available to an organisation can also influence the

decision-making and the buying processes. When the complexity,

importance and perceived risk of a purchase rise, the buyer will seek more

information (Garrido-Samaniego & Jesus, 2004). “Gatekeepers” who are the

main interface between the buying centre and seller, control and channel

this information (Hansen, 2004; Lau, Razzaque, & Ong, 2003) and are likely

to have considerable influence on organisations‟ purchase decisions

(McQuiston & Dickson, 1991, p. 160). Gatekeepers include purchasing

agents and production and engineering personnel (Lau, Razzaque, & Ong,

2003). Consequently, such people were also included in the groups from

which information was obtained in the present study.

Draper (1994, p. 50) pointed out that organisations develop “social

arrangements for the controlled performance of collective goals” and he

suggested four relevant control categories, namely:

(1) Personal Control, which is oftenfound in small organisations in

which most decisions are made by the chief executive or owner.

22

(2) Bureaucratic Control, which emphasises carefully prescribed

procedures in which authoritative instructions are accepted as

rational rules.

(3) Output Control, in which objectives are set. This is thought to be the

most widely used approach.

(4) Cultural Control, which is achieved through a combination of

appropriate recruitment and the subsequent socialisation of those

recruited (Draper, 1994).

Organisational buyers operate in one of these control practices or in a

mixture of these practices and suppliers are usually aware of their buyers‟

procedures and processes and take them into account in their marketing

operations (Draper, 1994).

The significant rise in international purchasing has also meant the country of

manufacture can influence choices (Saghafi & Puig, 1997). Products

manufactured by advanced industrialised economies (e.g. the United States,

Germany or Japan) are often seen to be more reliable, more technically

advanced, and to have better performance and quality than products

manufactured in developing nations (Chinen, Jun, & Hampton, 2000;

Saghafi & Puig, 1997). Studies have shown country of manufacture

stereotyping exists in the buying process (Lawrence, Marr, & Prendergast,

1992), especially in the technological and industrial sectors. Marketers must

realise that, although buyers may not regard them favourably in regards to

23

some attributes, they may be considered more favourably in regards to other

attributes, such as country of manufacture (Lawrence et al., 1992).

The marketing paradigm is also shifting from a transactional to a relationship

approach (Ravald & Gronroos, 1996). Traditional business-to-business

sellers focused on selling and delivering a physical product, with customer

service seen as an “add on”, but this is no longer enough (Maria & Tore,

1999). It has become important for a seller to understand customers‟

requirements and engage proactively to manage their relationships and to

minimise conflict (Henthorne et al., 1992). If a buyer-seller relationship is

successfully managed, there is a higher probability sales can be realised

(Henthorne et al., 1992) and the total cost of the value chain will be reduced

for both buyer and seller (Canon & Homburg, 2001). Buyers tend to monitor

their relationship with sellers so as to reduce the risks of doing business

(Lohtia & Krapfel, 1994). An emphasis on quality and the widespread

adoption of the just-in-time inventory approach have also changed the

relationships buyers have with sellers, and have led to a significant

reduction in the number of suppliers (Thompson, Knox, & Mitchell, 1997).

Ultimately, long term relationships with key customers give suppliers a key

competitive advantage that positively impacts on profitability (Heskett,

Sasser Jr, & Hart, 1994).

24

2.3 The Influences on Organisational Buyers’ Re-buying Intention

Much importance has been attached to the impact product quality, service

quality, past satisfaction and perceived risk have on consumers‟ value

perceptions and to those perceptions‟ influences on commitment to the

relationship, customer loyalty and repurchase intentions. However, these

relationships have not been examined as closely in business-to-business

contexts as they have in business-to-consumer contexts. Given value‟s

importance to the present study, perceived value and its antecedent

relationships are examined in some detail in this review. A more selective

review of the other constructs in the suggested model and their

interrelationships is also carried out.

2.3.1 The Relationships between Commitment to Relationship, Loyalty and Intention to Re-buy

Unlike intention to re-buy, which refers to an intended behaviour, loyalty

refers to customers‟ commitment and preference for re-buying a particular

product or service over time (Oliver, 1999). Loyalty has a significant, positive

impact on customer retention and customer purchase (Hume et al., 2007).

This suggests:

Hypothesis 1: The greater an organisational buyer‟s loyalty to a

product or service, the greater will be their intention to re-buy.

Research has examined the antecedents of loyalty and commitment

primarily focusing on industrial markets and distribution channels as well as

the purchase of consumer goods (Sharma & Patterson, 1999), but very little

25

research has examined the relationship between these two constructs.

Indeed, commitment is a vital element in a successful relationship leading to

loyalty (Gounaris, 2005). Loyalty was argued to be an emotional reaction

comprising of affective elements that weaken the claims of economic

judgement (Ewin, 1993). However, commitment is an awareness or

recognition of the state of an attachment bond (Morgan & Hunt, 1994). Both

loyalty and commitment were considered as obligatory aspects of

attachment, which include emotional and cognitive factors (Ewin, 1993).

Loyalty commitment is an appropriate conception for an inter-organisational

setting, while commitment is composed of a loyalty sentiment that

contributes to the longevity of a relationship (Gilliland & Bello, 2002). Loyalty

is conceived as a commitment to a supplier which stimulates a positive

attitude that provides the motivation to maintain the relationship (Gounaris,

2005).

The daily interactions and operations between customers and suppliers

bring about fewer disputes, which lead to loyalty commitment (Ring & Van

De Ven, 1992). Both the customers and suppliers are concerned with

maintaining the relationship rather than with establishing specific

accountability or performance (Gundlach & Murphy, 1993). This steadfast

support for one another through difficult times allows a relationship to

continuously change and evolve. However, this reduces the need to adhere

strictly to the interpretation of a contractual agreement, relying more on

social means of dispute resolution (Gilliland & Bello, 2002). Loyalty

commitment is the result of shared experiences that typically revolve around

26

a similarity of business goals and values that works to justify and reinforce

the identification of a relationship (Ring & Van De Ven, 1992). Fullerton

(2003) argued customer commitment to the relationship has a positive

impact on customer loyalty, suggesting:

Hypothesis 2: The greater an organisational buyer‟s commitment to

a relationship with an existing supplier, the greater will be their loyalty

to that supplier.

Commitment also has a direct influence on intention to buy (Jones,

Reynolds & Motherbaugh, 2007), although it has an indirect effect on the

customer‟s behavioural intention in a relationship (Wetzels, Ruyter, &

Birgelen, 1998). Gill and Ramaseshan (2007) suggested the relationship

between a supplier‟s commitment and repurchase intention is positive,

suggesting:

Hypothesis 3: The greater an organisational buyer‟s commitment to

a relationship with an existing supplier, the greater will be their

intention to re-buy from that supplier.

2.3.1.1 Intention to Re-buy

A variety of customer responses, such as intention to recommend, complaint

approach, purchase intention, repurchase intention and willingness to buy,

are all specific behavioural outcomes. People act in accordance with their

intentions, and because people‟s willingness to do something is the best

predictor of behaviour (Ajzen, 2001); intention is often used as the main

27

dependent variable in customer research (Söderlund & Öhman, 2005).

Models such as the theory of reasoned action (Ajzen & Fishbein, 1970) and

the theory of planned behaviour (Ajzen, 1991) were developed to predict

intention (Söderlund & Öhman, 2005). These models add to the popularity of

using intention as a dependent variable. As intention is about the formation

of an assessment, “intention to re-buy or repurchase intention (can be)

defined as a customer‟s decision to engage in future activity with a provider”

(Hume et al., 2007, p. 137).

Intention to re-buy (or repurchase intention) high involvement products can

be complex, and consumers‟ past experiences can also have an impact.

Traditional marketing theory argues a consumer‟s repurchase intention is

driven by value, service quality and past satisfaction (Hume et al., 2007).

The interrelationships between price, perceived quality, perceived sacrifice,

perceived value, purchase intention and repurchasing intention suggest

price has objective external properties and internal subjective

representations that have meaning to customers (Dodds et al., 1991). The

“external cues of price, brand name, and store name are the three main

cues that influence perceptions of product quality and value and purchase

intention” (Dodds et al., 1991, p. 308). Higher prices lead to higher

perceived quality, and consequently higher buying intention, but on the other

hand, higher prices can also lead to higher sacrifice and reduced buying

intention (Dodds et al., 1991). However, the traditional marketing literature

suggests price is often relatively unimportant, especially in high technology

markets (Ruyter et al., 2001). A seller must be able to establish the

28

perceived benefits of a product or service so as to influence the buyer‟s

purchasing (or repurchasing) intention (Grunert & Ramus, 2005). As this

intention is influenced by the perceived risk of acquiring a product, the buyer

balances the trade-offs or sacrifices against a product‟s various attributes

(Telser & Zweifel, 2002).

While the outcome of the past interactions a buyer had with a seller impacts

on a buyer‟s purchasing responsiveness (Rossler & Hirsz, 1996), the buyer

also balances the benefits of the purchase against its cost (Grewal, Monroe,

& Krishnan, 1998). While experiences from past purchases have similarities

to customer satisfaction, past purchases experiences have no direct

influence on repurchase intention (Gardial & Clemons, 1994). Indeed,

research that examined the link between service quality, customer

satisfaction, and behavioural intention suggested satisfaction is strongly

associated with a customer‟s behavioural intention (Woodside, Frey, & Daly,

1989). Conceptually, these studies have shown customer satisfaction

influences repurchase intention (Hume et al., 2007).

However, the relationship between satisfaction and intention to re-buy is

contingent on switching costs because such costs reduce switching

tendencies (Jones et al., 2000). Consequently, switching costs have no

influence on intention to re-buy at high levels of satisfaction but do have an

influence when satisfaction is low (Jones et al., 2000).

29

2.3.1.2 Commitment to a Relationship

The importance of the relationship with customers in a business-to-business

environment is widely acknowledged and has become part of many

organisations‟ strategies (Ruyter et al., 2001). Indeed, customer

commitment has been central to relationship marketing (Fullerton, 2003),

which has emerged in recent years as an important construct in explaining

various pro-social behaviours and that has proved key to achieving valuable

outcomes (Gilliland & Bello, 2002). A successful relationship involves

fulfilling promises that benefit both parties, creating a “win-win” situation

(Patterson & Smith, 2001). The essence of marketing, especially in services,

is the development of long-term and value added relationships with

customers (Sharma & Patterson, 1999). Relationships are mainly viewed

from two perspectives: that of the firm providing the service; and one that

assumes an ongoing contact between an organisation and a customer

(Patterson & Smith, 2001).

Commitment has been a key element to relationship marketing and in

achieving valuable outcomes (Morgan & Hunt, 1994) and, since it enhances

the effectiveness, productivity, and efficiency of relationship exchanges, it is

still a fundamental prerequisite for successful business-to-business

relationships (Coote et al., 2003). Commitment is generally considered to be

a relationship-enhancing bond (Gilliland & Bello, 2002). The achievement of

commitment is an important goal for channel and relationship managers

(Coote et al., 2003). In a business-to-business context, commitment to a

30

relationship can also be an important element between sellers and buyers.

“Relationship commitment can be defined as an exchange partners‟

endurance desire to maintain a valued relationship” (Coote et al., 2003, p.

596) and this is critical in inter-organisational relationships, and for

successful long-term relationships (Sharma & Patterson, 1999).

To keep parties engaged with, and attached to, one another, commitment

can be treated as an investment (Fein & Anderson, 1997). Attitudinal

commitment is also an awareness or recognition of the state of the

attachment bond (Morgan & Hunt, 1994). While different commitment

conceptualisations add to our understanding of this complex construct, this

study focused on attitudinal commitment (O'Reilly & Chatman, 1986).

Attitudinal commitment has two different and sometimes incompatible

strains that organise business and personal relationships (i.e. calculative

commitment and loyalty commitment) (Gilliland & Bello, 2002). Calculative

commitment is a measure of a partner‟s cognitive state of attachment, which

focuses on the realisation of the benefits sacrificed and the losses incurred

(Geyskens, Steenkamp, & Scheer, 1996), while loyalty commitment is

affective in nature and is a state of attachment to a partner (Kalleberg &

Reve, 1993). The underlying motivation of calculative commitment can be

negative as it comes from a cost and benefit assessment; while the

underlying motivation of affective commitment is positive because it comes

from a sense of affiliation and identification (Gounaris, 2005).

31

In a business-to-business context, commitment is associated with trust,

perceived value, interaction quality and involvement between customers and

sellers (Lapierre, 1997; Patterson & Spreng, 1997). Sharma and Patterson

(1999) argued communication effectiveness has a direct impact on

relationship commitment, as well as on perceptions of quality and trust. They

further argued that quality and trust have the most significant impact on

relationship commitment when customers want the best possible return on

their investment at some anticipated level of risk. Trust leads customers to

focus more on their positive motivations and the sense of affiliation and

identification they have with a supplier and less on calculative reasons

(Ruyter et al., 2001). Indeed, customers who report a positive experience

and high service quality are likely to enjoy higher satisfaction and

commitment to a relationship. Sharma and Patterson (1999) argued regular

and effective communication with customers is essential in reducing

perceived risk and uncertainty, as it helps manage expectations.

Misunderstandings can be avoided and, over time, social and emotional

bonds that can act as barriers to exiting a relationship are strengthened

(Sharma & Patterson, 1999). In addition, these emotional and social bonds

lead to a sense of closeness and ease in a relationship, making it more

resistant to the occasional problems that may arise (Sharma & Patterson,

1999). A satisfactory experience strengthens a buyer‟s confidence in a

supplier (Sharma & Patterson, 1999). In the current study, trust measures

were incorporated in the past satisfaction measures, as will be pointed out in

32

Chapter Three. Consequently, trust was not included as a separate

construct in the suggested model.

Garbarino and Johnson (1999, p. 73) argued “commitment rather than

satisfaction, is a focal intermediate construct of those customers who have a

high relational or partnering orientation to a supplier”. The behavioural

intention of low relational customers is driven by satisfaction directly, while,

for high relational customers, satisfaction has no significant influence on

behavioural intention. Indeed, commitment can be a reflection of the level of

customer satisfaction (Garbarino & Johnson, 1999). The initially suggested

model in the current study was not designed to test this relationship.

However, a key objective was to test the mediating effect perceived value

for money between these two constructs. Consequently, this issue was

examined in a different way.

Ballantyne, Christopher, & Payne (2003, p.161) pointed out that “relationship

marketing has tended to highlight the ambiguities in notion of values and it

now seems on the edge of taking its next steps, into uncharted territory”.

The idea of value exchange is the foundation stone of relationship marketing

and recognises the need to reflect, learn and act within a growing network of

interdependencies (Ballantyne, Christopher, & Payne, 2003).

2.3.1.3 Customer Loyalty

What is customer loyalty and how can we measure it? In the past, loyalty

was perceived or considered as simply being represented by repeat

purchase behaviour. Since the early 1970s, however, research has shown

33

repurchase alone is not the same as loyalty because such measures

included spurious loyalty (Gounaris, 2005). Loyalty may be expressed in

many ways, such as by expressing a preference for one company over

another, by continuous repurchasing or by future commitment (Zeithaml,

Berry, & Parasuraman, 1996). Loyalty is sometimes argued as being

attitudinal and behavioural (Brandt, 2000) and other times argued as being

only behavioural (Neal, 2000), a situation that has raised important issues

on the measurement and conceptualisation of loyalty. Loyalty research has

suggested attitudinal and behavioural criteria should be evaluated (Day,

1969; Grisaffe, 2001). Although there are doubts about the effectiveness of

loyalty programmes, research has shown they can have a significant

positive impact on customer retention, service use, and share of customer

purchases (Leverin & Liljander, 2006) mainly in the business-to-consumer

context. Jones and Sasser (1995) argued there are two types of customer

loyalty:

(1) True long term loyalty.

(2) False loyalty.

Day (1969, p. 34) suggested “true brand loyal buyers are committed to the

value and price appeal of the brand by being confident that they have

judged the brand correctly, coupled with a perceived need to economise”.

Day (1969, p. 35) has also suggested “true brand loyal buyers are mainly

older and are rigid in their preferences and that true brand loyalty is never

total, and any decision that was made in the past will be reviewed again

34

when competitiveness and circumstances change”. This means when a

competitor offers a new product, a customer will re-assess or re-evaluate a

current supplier‟s existing product and compare it to the new product (Flint

et al., 1997).

There are many definitions of loyalty, most of which are process definitions

that define what consumers do to become loyal (Oliver, 1999). For example,

loyalty has been defined as frequent repeat purchasing or same brand

purchasing (Tellis, 1988) or loyal customers have been defined as those

people who re-bought a brand or who considered only that brand (Newman

& Werbel, 1973). However, both these definitions refer to repeat purchases

of the same product or brand and do not mention the factors surrounding it.

To include the act of consumption, Oliver (1999, p. 34) defined loyalty as “a

deeply held commitment to re-buy or re-patronise a preferred product or

service consistently in the future, thereby causing repetitive same-brand or

brand-set purchasing, despite situational influences and marketing efforts

having the potential to cause switching behaviour”.

As noted earlier, satisfaction and perceived value lead to customer loyalty

(Jones & Sasser, 1995; Tsao & Chen, 2005). Indeed, Yu et al.‟s (2005, p.

707) study of the Lexus automobile in Taiwan found “customer satisfaction

negatively influences customer complaints and positively influences

customer loyalty”, although “customer complaints have no negative effect on

customer loyalty”. However, an earlier similar study on Lexus car owners in

the United States had contradictory results to those obtained in Taiwan

35

(Fornell, Johnson, & Anderson, 1996). The contradiction between the two

studies suggests issues such as cultural differences, customers‟ maturity

level or complaint handling skills, require further study (Yu, Wu, & Chiao,

2005). Jones and Sasser (1995, p. 90) argued there was a “tremendous

difference in the loyalty of „merely‟ and „completely‟ satisfied customers”.

Their study into the relationship between customer satisfaction and

customer loyalty in five different market segments confirmed that merely

satisfying customers does not necessarily lead to customer loyalty and that

there is no guarantee of re-purchasing from the same supplier. In some

cases behavioural loyalty does not reflect attitudinal loyalty as other factors

prevent customers from defecting (Leverin & Liljander, 2006).

Oliver (1999) argued there are obstacles to loyalty, and suggested

consumer idiosyncrasies, such as variety seeking, multi-brand loyalty,

withdrawal and changes of needs, as examples of such obstacles. Other

obstacles include switching incentives, as competitors can provide

incentives and engage consumers through persuasive messages as they

attempt to lure them away from existing suppliers (Oliver, 1999).

In a business-to-business context, personal idiosyncrasies and variety

seeking behaviours may lead organisational buyers to instigate change

because of a desire to attain a satisfactory level of stimulation.

Organisational buyers may explore the environment and change the

stimulus field (Steenkamp & Baumgartner, 1992).

36

2.3.2 The Relationship between Perceived Value for Money and Commitment to the Relationship

Intense competition has led many organisations to focus on ensuring they

add maximum value to their products and services. However, perceived

value is not only a functional construct, as it includes social, emotional and

even epistemic value components (Patterson & Spreng, 1997; Sheth et al.,

1991; Sweeney & Soutar, 2001). However, customer commitment to a

relationship with a supplier can also improve the emotional and social bonds

that are developed through the passage of time (Sharma & Patterson, 1999).

Sharma and Patterson (1999) argued relationship commitment is a function

of communication effectiveness, perceived quality and trust, particularly as

customers stay in a relationship only when they perceive the sum of the

benefits exceeds the costs, whether they are financial or non-financial

(Sharma & Patterson, 1999). This suggests:

Hypothesis 4: The greater an organisational buyer‟s perceived value

for money, the greater will be their commitment to their relationship

with their supplier.

2.3.2.1 Perceived Value

“Value is a cognitive comparison process” (Eggert & Ulaga, 2002, p. 110)

and has been described as a “cognitive-based construct which captures any

benefit-sacrifice discrepancy in much the same way disconfirmation does for

variation between expectations and perceived performance” (Patterson &

Spreng, 1997, p. 142). Khalifa (2004) argued both monetary and non-

37

monetary factors, such as time and effort, are involved in acquiring and

using a product. The three value perspectives are:

(1) The buyer‟s perspective, which suggests value is created through

the consumption of products and services.

(2) The seller‟s perspective, which suggests value is created through

customer equity.

(3) The buyer-seller perspective, which suggests value is created

through relationships and networks (Jantrania, 2002).

Defining customer value has been difficult (Woodruff, 1997), because of its

subjectivity and ambiguity, which is further complicated by the dynamic

nature of value that evolves over time (Jaworski & Kohli, 1993). Indeed,

customer value should be determined by customers‟ perceptions and not by

suppliers‟ assumptions (Zeithaml, 1988). The major concern of most buyers

is the cost of acquiring the perceived benefits, and therefore many buyers

use the principles of cost-benefit analysis to evaluate purchases, part of a

process commonly known as perceived customer value evaluation (Khalifa,

2004). During the process of evaluation, the consumer‟s frame of reference

can influence their perception of value (Zeithaml, 1987). Several definitions

of perceived value are available, one of which is the ratio and trade-off

between quality and price, which suggests a value-for-money

conceptualisation (Sweeney & Soutar, 2001). However, Zeithaml‟s (1998,

p.14) definition of perceived value as a “consumer‟s overall assessment of

the utility of a product based on perception of what is received and what is

38

given” is generally accepted. This is mostly referred to as a comparison of a

product or service‟s “get” and “give” components (Sweeney & Soutar, 2001).

(1) Value Models

Khalifa (2004) suggested customer value can be grouped into three general

categories, which he termed esteem value or “want”, exchange value or

“worth”, and the utility value or “need”; the result of a buying decision can

include one or a combination of these elements. Esteem value involves a

buyer‟s desire to own; exchange value explains why the product interests a

buyer and the use of the product itself. Utility value, being the primary value,

describes a product‟s attributes, performance and physical characteristics

(Kuafman, 1998).

Another value components model is based on disconfirmation theory, which

is common in consumer research (Oliver, 1997; Rust & Oliver, 2000). This

model has three value components: Dissatisfiers (must be), satisfiers (more

is better) and delighters, (exciters) (Joiner, 1994; Thompson, 1998). These

components are briefly described in subsequent paragraphs:

Dissatisfiers: The characteristics and features in a product or

service that are expected, are generally taken for granted, and

are considered as basic requirements. However, if such

characteristics and features are missing in a product or service,

customers will be agitated as their absence annoys them. These

are known as the “must haves” in a product or service.

39

Satisfiers: These are features that are explicit requirements

requested by customers, typically to meet performance related

needs. If such needs are not met, or are poorly met, customers

will be disappointed. The better these needs are met, the higher a

customer‟s satisfaction. Therefore, these features are often

considered the minimum standards that need to be met in order to

stay in business.

Delighters: These are features customers do not expect but that

are able to solve their latent needs. In most cases, these are

innovative ideas that surprise customers. There is no negative

impact as they are not expected in the first place. However, when

present they have a positive effect and will delight customers.

(2) Benefit-Cost Ratio Models

Customer perceived value is also often viewed as the difference between a

customer‟s perceived benefits and perceived cost and is defined in terms of

customers‟ perceptions of acquisition, use, and maintenance, as well as

their satisfaction (Khalifa, 2004). The trade-off between the positive

consequences (benefits) or desired outcomes and negative consequences

(sacrifice) or costs are ways to generate customer value:

(1) A focus on low price (sacrifice).

(2) A focus on the wants in a product or service (benefits).

(3) A focus on quality for the price paid (trade-off).

40

(4) A focus on the total benefits received compared to the total

sacrifice incurred (Zeithaml, 1988).

Such cost-benefit models are widely used and can be found in much of the

literature on strategy, providing a basis for understanding organisations‟

strategic approaches.

(3) Mean-Ends Models

Customers acquiring and using a product or service to achieve favourable

ends provide the basis for the mean-ends model (Khalifa, 2004). Means-end

theory suggests there are links between product‟s attributes, with

consequences through consumption, and that consumers‟ perception of

value underlie the decision making process (Huber, Herrmann, & Morgan,

2001). Means are the products and services and ends are the value

perceptions a consumer considered are important (Khalifa, 2004). The

mean-ends models explain why customers attach different weights to

various benefits when evaluating alternative products or services (Khalifa,

2004). However, there has been little research attention paid to the trade-

offs a customer has to make between benefits and sacrifices (Khalifa, 2004).

Khalifa (2004) suggests the three models (i.e. the value component model,

the cost-benefit ratio model and the mean-ends model) are not intended to

be stand alone models, but are closely related and complementary to one

another. He cascaded these customer value models into a single

configuration with three overlapping circles (i.e. outer, centre and inner

circles), as can be seen in Figure 2.1. The outer circle is the value exchange

41

model that summarises all business activities that aim to create value,

through to the point of exchange that includes a supplier and buyer

relationship, transaction-based relationship and interaction-based

relationship. In order to offer customers superior value of exchange, the

centre circle, which is the value build-up model, helps a firm understand the

value their customers‟ needs, how to generate and accumulate value for

them, and the factors influencing them. The inner circle is the value dynamic

model, as, in order to build up value, it is essential to understand the

elements and components that may create or destroy value.

Figure 2.1: Cascading the three complementary models of customer value configuration (Khalifa, 2004)

Different customers develop different perceptions about a product or service

and these offerings must be seen to have competitive advantage over those

of competitors if they are to be seen to provide good value (Evans, 2002).

There was a misunderstanding that business-to-business buyers look at

price when making purchasing decisions because it is a tangible and easily

measurable factor (Woodruff, 1997). However, other factors also influence

business-to-business choices: Personal selling, for example, is a major part

Value Exchange

Model

Value Build-up

Model

Value Dynamic

Model

42

of a business-to-business relationship (Tullous & Munson, 1992). The

knowledge of customer value is central to a seller‟s key decisions, which

include product development, pricing, market segmentation and

communication (Jantrania, 2002). The most effective sales approaches are

those that focus around a “customer orientation” (McQuiston & Walters,

1989). High-performing sales professionals seem to respond better to this

challenge (Ron, 2005) and throughout the sales process they execute sales

strategies that build the perceived value of their solutions and help

customers justify paying higher prices (Ron, 2005). Indeed, it is “easier to

sell to a buyer‟s perceived need than…to create a need in the buyer‟s mind”

(Brooks, 2004, p. 30). Most customers wish to deal with sales staff who are

well trained, helpful, prompt and courteous (McLeon, 2002) and are able to

effectively promote the value of their products and services.

Conceptually, there are differences between customers‟ perceived value

and satisfaction. They are distinct constructs and are complementary to one

another. Eggert and Ulaga (2002) argued there are differences between

customers‟ perceived value and satisfaction, namely:

(1) Perceived value is a cognitive construct, while satisfaction is an

affective construct.

(2) Perceived value is measured by asking questions of both present

and potential customers, while customer satisfaction can only be

measured from present customers who have used the product or

service.

43

(3) Perceived value can be looked at from a pre-purchase or post-

purchase perspective, whereas customer satisfaction has only

post-purchase perspective.

(4) Perceived value research is typically more strategic orientated,

while customer satisfaction research has more of an operational

orientation.

While it may be true that organisational buying behaviour involves a more

cognitive approach than individual buying behaviour, there are frequent

occurrences of affective elements that may even dominate organisational

buying decisions (Erevelles, 1998).

2.3.3 The Relationships between Perceived Risk, Service Quality, Product Quality, Past Satisfaction and Perceived Value for Money

“Value is the key linkage between the cognitive elements of perceived

quality or performance, perceived monetary sacrifice and behavioural

intentions” (Patterson et al., 1997, p. 416). The buying process is influenced

by the perceived risk of a purchase (Wind & Thomas, 1980), and Sweeney

et al. (1999, p. 99) argued “perceived risk, as measured by elements of

performance and financial risk, has a more powerful, direct effect on

perceived value” than other suggested antecedents. As was mentioned in

Chapter One, only the price-functional value (or value for money) dimension

was included in the present study because the study‟s focus was on

operations, services and products, which are mainly related to this aspect of

value.

44

The relationships between past satisfaction, perceived risk, perceived

quality and perceived value have been examined in a variety of contexts that

can help explain people‟s purchase decisions (Snoj et al., 2004). Sweeney

et al. (1999, p. 81) argued that “product quality, price, service quality and

risk contribute directly and indirectly to perceived value”, although their

research was undertaken in a business-to-consumer context. Snoj et al.

(2004) too, argued product quality has both a direct effect on perceived

value and an indirect effect on it by reducing perceived risk. This suggests:

Hypothesis 5: The greater an organisational buyer‟s risk perception,

the lower will be their value for money perception.

Hypothesis 6: The greater an organisational buyer‟s perception of

product quality, the greater will be their value for money perception.

As noted earlier, a customer‟s overall assessment of service quality is

affected by their perceptions of the service performance level and the

service value (Bolton & Drew, 1991). Service value is a “trade-off between

customer‟s evaluations of benefits of using a service and its cost” (Socha,

1998, p. 2). Bolton and Drew (1991) went a step further to argue perceived

service value is analogous to the concept of perceived value. Expanding on

Zeithaml‟s (1988) model, Baker (1990) supported the idea that service

quality has a direct positive relationship on perception of value. This

suggests:

Hypothesis 7: The greater the organisational buyer‟s perception of

service quality, the greater will be their value for money perception.

45

Information about what customers‟ value, their satisfaction with suppliers

and how value perceptions are changing, are all important to customer

retention strategies (Flint et al., 1997). Value and satisfaction are “evaluative

judgments and both placed special importance on the use situation”

(Woodruff, 1997, p. 143). These are two distinct constructs (Sweeney &

Soutar, 2001) and perceived value influences preferences directly through

satisfaction (Hellier et al., 2003), suggesting:

Hypothesis 8: The higher an organisational buyer‟s satisfaction with

prior purchases, the greater will be their value for money perception.

2.3.3.1 Product Quality

Quality and value are closely related constructs and, as such, are often not

well differentiated. Additionally, value is frequently confused with price

(Dodds et al, 1991). Dodds et al. (1991) argued perceived value is a

cognitive trade-off between a customer‟s perception of quality and sacrifice.

Price is often used as a quality cue in the absence of other information

(Stokes, 1974; Zeithaml, 1988) and it impacts on quality perceptions (Dodds

& Monroe, 1985; Lambert, 1972; Shapiro, 1968; Shapiro, 1973; Zeithaml,

1988). Generally, buyers have a set of prices that are perceived as

acceptable for a considered purchase: not only will they refrain from

purchasing a highly priced product, but they may be suspicious about quality

if a price is much lower than what they consider to be acceptable (Dodds et

al., 1991)

46

Consumers make sacrifices by paying for a product and its associated

benefits. Therefore, consumers‟ price choices are affected by pre-conceived

beliefs and perceptions about a product that, in the absence of well

established brand names, are related to product quality judgements

(Lambert, 1972). Agarwal and Teas (2002) argued price has a positive

influence on perceived quality, although their results varied across the

countries included in their study. Most of the time, consumers lack the

detailed information, expertise, interest, and time necessary to evaluate

product quality based on intrinsic product attributes (Monroe, 1971).

Consequently, consumers rely on extrinsic cues, such as brand, price or

image, when assessing product quality (Dodds et al., 1991). However,

Dodds et al. (1999) have argued brand and image do not enhance price

effects. In a business-to-business context, if a buyer lacks knowledge about

the product, information search becomes critical to the buyers and this

includes looking at brand, image and word-of-mouth to make quality

assessments. Consequently, less reliance is place on price cues.

Quality can also be viewed as a cognitive mediator linking environment

appropriateness to utilitarian value perceptions. Babin, Chebat, & Michon‟s

(2004) research in an upscale metropolitan shopping mall suggested an

appropriate environmental setting can be a quality cue. Consumers also rely

on other cues when assessing the quality of a product in the absence of

price and brand information; turning, for example, to country of manufacture,

recommendations or even word of mouth (Berger, Draganska, & Simonson,

2006).

47

Favourable product knowledge also plays an important role in reducing risk

perceptions, leading to positive product quality perceptions (Sweeney et al.,

1999). In addition to general perceptions of a product‟s quality, it has a

positive impact on perceived value (Choi, Lee, & Subramani, 2004). While a

relationship has been established between perceived quality and

behavioural intention (intention to buy or re-buying intention), this may not

be a direct relationship, as perceived value has been suggested to mediate

their relationship (Dodds, 1991). However, Hult, Boyer and Ketchen (2007)

argued product quality has a direct influence on repurchase intention that is

not mediated by perceived value and this issue needs to be resolved.

2.3.3.2 Service Quality

The growing importance of service competitiveness has prompted research

into the problems of measuring and managing service quality (e.g. Bitner,

1990; Bolton & Drew, 1991; Boulding, Kalra, & Staelin, 1993; Parasuraman,

Zeithaml, & Berry, 1985). Service quality results from a comparison of

expectations and performance perceptions (Snoj et al., 2004) and, unlike

product quality, which has measurable indicators (e.g. durability and

numbers of defects), service quality is abstract and elusive in nature

(Parasuraman et al., 1988). This led Parasuraman et al. (1988, p.16) to

define “service quality as a global judgement, or attitude, relating to the

superiority of a service”. On other occasions, service quality has been

defined as the “results of an evaluation process of the expected and

experienced service” (Heinonen, 2004, p. 205).

48

Quality efforts should not focus only on tangible service aspects, but also on

intangible aspects, such as the quality of personal interactions that are not

immediately measurable in term of sales (Rust, Zahorik, & Keiningham,

1995). Two fundamental components found in service quality are technical

service quality and functional service quality (Sharma & Patterson, 1999).

Functional service quality is measured by the delivery of the service; while

technical service quality is measured as an outcome of the service

(Sweeney & Soutar, 1999). However, Sharma and Paterson (1999) argued

technical service quality relates to actual outcomes perceived by a customer,

while functional service quality is the process through which the service is

delivered. While they use different expressions in explaining technical and

functional service quality, their underlying meanings are the same. Sweeney

et al. (1999) suggest functional service quality has a significant influence on

perceived value.

2.3.3.3 Perceived Risk

Perceived risk or uncertainty influences people‟s purchase decisions

(Tullous & Munson, 1992), but people‟s risk perceptions vary (Stone &

Gronhaug, 1993). There is a general acceptance that people are risk averse

and that risk perceptions depend on a person‟s propensity to accept risk and

their past experiences (Chiam, 2006).

Sweeny and Soutar (1999, p. 81) defined “perceived risk as the subjective

expectation of a loss”, following a conceptualisation suggested by Stone and

Gronhaug (1993). Several types of risk have been identified and studied.

49

Henthorne et al. (1990) suggested three types of risks that are significantly

affected by informal interpersonal influence, namely:

(1) Performance risk, which is linked to likelihood of product or service

failure,

(2) Financial risk, which is related to the potential dollar investment loss

from the purchase of a product or service, and

(3) Social risk, which arises when the purchased product or service

does not meet the approval of an important reference group.

Others have suggested five main risk types (physical, functional, social,

psychological, and financial risks). Kaplan et al., (1974) argued using a

single overall measure of risk is inadequate and suggested it is best to

consider them separately. The various risk contributions of these overall

risks vary considerably depending on the purchase situation (Stone &

Gronhaug, 1993). Sweeney et al. (1999, p.99) argued “perceived risk, as

measured by elements of performance and financial risk, has a…powerful,

direct effect on perceived value”. This is often crucial in the business-to-

business purchases of technically complex big ticket items, as they are

inherently risky (Henthorne et al., 1990). However, Stone and Gronhaug

(1993) suggested financial and psychological risks are the predominant risk

dimensions for expensive products, which are often perceived as complex.

This is difficult to judge as “financial risk has already captured the essence

of the performance risk” (Stone & Gronhaug, 1993, p. 47).

50

Because organisational buyers wish to reduce their risk and are concerned

about satisfying different members of the decision making unit, Tullous and

Munson (1992) suggested two uncertainty categories, namely:

(1) Low need uncertainty (LNU), where an organisation is planning to

re-purchase a similar product, perhaps with some slight

modifications. Consequently, the organisation is experienced in

buying such a product.

(2) High need uncertainty (HNU), where an organisation is planning to

purchase a product with which it has no prior experience.

Price was found to be more important in a low need uncertainty situation.

However, in a high need uncertainty situation, technical service was deemed

to be more important (Hutton, 1997; Tullous & Munson, 1992). These

findings suggest buyers who are re-purchasing a product understand the

risks associated with it and, consequently, can focus more on price.

However, when buying a new product, buyers are uncertain and need

assurance as to the performance of the product. While price may be

important, it is not a central focus as customers also evaluate product quality

and service aspects.

Social risk, convenience risk, physical risk and psychological risk can be

perceived as more important factors for services than for products. However,

there is no difference in this regard for financial risk and performance risk

(Ostrom & Iacobucci, 1995; Turley & LeBlanc, 1993), as each are situational

and subject to specific purchase situation considerations, such as product

51

attribute, product reliability, product pricing and the company‟s purchase

goals (Dowling & Staelin, 1994).

All decisions are risky, but the risk varies in different contexts (Feldman,

1983; Tullous & Munson, 1992). Informal, interpersonal information and

information gathered by buyers can influence perceived risk positively or

negatively (Henthorne et al., 1990) and is one of the ways by which people

can manage risk. Just as positive informal information helps in decreasing

perceived risk, negative informal information increases risk perceptions

(Henthorne et al, 1990). In countering a negative flow of information, sales

people can affect risk perceptions and, hence, influence purchase decisions

(Sweeney et al., 1999). Consumers develop a perception of risk if they have

little or no experience of a product (Dodds, 1991) and it seems decision

makers who are moderately familiar with a product or service are more likely

to search for more information as they attempt to minimise risk (Park, Sohi,

& Marquardt, 1997).

2.3.3.4 Past Satisfaction

Measuring customer satisfaction has becoming increasingly popular over

the last twenty years and is an important source of information (Perkins,

1993). Just as price elasticity varies among companies and industries, so

does “customer satisfaction elasticity” (Fornell, 1992, p. 16). However,

monopolistic companies are less sensitive to customer satisfaction than

companies in competitive markets (Fornell, 1992). Satisfaction can be

described as “a state of mind and is only important as an indication of

52

intention of the behavioural outcomes such as willingness to buy, repeat

purchase, word-of-mouth and referral” (Khalifa, 2004, p. 645). Customer

satisfaction can also be defined as “an evaluation of perceived product or

service performance that is based on customers‟ judgment of the value

created for them” (Flint et al., 1997, p.172). The latter definition involves

satisfaction with products or services and is relevant to the current study

There has been growing recognition of the negative repercussions from

having dissatisfied customers, which explains the growing number of

companies focusing on improving customer satisfaction (Szymanski &

Henard, 2001). Achieving customer satisfaction is now a key in many

businesses and something companies must measure if it is to be managed

(Fecikova, 2004) and contributes positively to an organisation‟s financial

strength and competitiveness through customer retention (Hume et al.,

2007). Few studies have examined the relationships between perceived

value and customer satisfaction (Hellier, Geursen, & Carr, 2003). However,

there has been a wide acceptance that satisfaction is a strong predictor of

behavioural variables such as willingness to buy, repurchase intentions,

word-of-mouth and loyalty (Eggert & Ulaga, 2002; Ravald & Gronroos, 1996).

Due to the emotive nature of satisfaction, it also has a direct influence on

other behavioural intentions such as perceptions of value and relationship

commitment (Brady & Robertson, 2001).

Customer satisfaction can also be measured in relation to the size and

direction of disconfirmation, which is “the difference between an individual‟s

53

pre-purchase expectations (or some other comparison standard) and post-

purchase performance of the product or service” (Patterson, Johnson, &

Spreng, 1997, p.5). Satisfaction is a post-purchase or post-use evaluation of

a product, whereas value can be a pre-purchase expectation or a post-

purchase valuation (Sweeney & Soutar, 2001). Not surprisingly, value is

related to satisfaction (Eggert & Ulaga, 2002; Snoj et al., 2004) and

customer perceived value is seen as a complement and not as a substitute

for customer satisfaction (Eggert & Ulaga, 2002).

Most satisfaction research has been based on finding the attributes that

influence customers‟ purchase decisions (Woodruff, 1997). When competing

products have similar physical characteristics, customer satisfaction can be

influenced by non-product variables, such as service response, sales

people‟s attitudes and the handling of complaints (Perkins, 1993). Customer

satisfaction has been explained by what is termed the „disconfirmation

paradigm' theory, which suggests customers compare a product‟s perceived

performance with some expectation (Churchill & Surprenant, 1982; Flint et

al., 1997; Oliver, 1980; Trawick & Swan, 1981).

Most firms feel that quality and satisfaction are equally important; but

satisfaction should be seen as more important as it has a greater impact on

organisational performance (Fornell, 1992). Rust and Zahorik (1995, p. 60)

argued “improved service quality improves perception of quality, customer

satisfaction and perhaps reduces cost” and that this also leads to higher

customer retention and positive word-of-mouth.

54

2.3.4 The Relationship between Product Quality and Intention to Re-buy

As noted earlier, product quality is an important cue to a buyer facing a

purchase decision and this can be impacted by other factors such as price

and credit offerings (Woodside & Taylor, 1986). Gill and Ramaseshan (2007)

suggested improved product performance increases the likelihood that a

customer will repurchase a product. Hult et al. (2007) likewise argued that

product quality has a positive direct influence on customer‟s repurchase

intention, suggesting:

Hypothesis 9: The better an organisational buyer‟s perception of the

quality of a product, the greater will be their intention to re-buy from

their supplier.

2.3.5 The Effect of Switching Costs on Product Quality, Commitment to the Relationship and Intention to Re-buy

As already noted, switching costs are the sacrifice a customer associates

with switching suppliers (Burnham et al., 2003). Customers develop a

perception about the costs or barriers to exit and, so, tend to maintain their

relationship with their existing suppliers when such costs are high (Burnham

et al, 2003; Lee at al. 2001). This suggests:

Hypothesis 10: The effect commitment to the relationship has on a

buyer‟s intention to re-buy is higher when switching costs are high.

Anton et al. (2007) argued that, when switching costs are low, dissatisfaction

with the quality of service will increase the likelihood of switching suppliers.

55

In contrast, when switching costs are high, customers have a “false loyalty”

and choose to stay with their existing suppliers, even though they are

dissatisfied with quality being provided. With poor quality, customers

develop a perception that a supplier is uncommitted, underlining the

customer‟s intention to switch suppliers (Anton et al., 2007). This suggests:

Hypothesis 11: The effect product quality has on a buyer‟s intention

to re-buy is higher when switching costs are low.

2.3.5.1 Switching Costs

Building an exit barrier through switching costs that prevent customers from

terminating a relationship may sound negative. However, Roche (2005)

argued these can be healthy elements of customer relationship

management. From a psychological perspective, companies sometimes

adopt an exit barrier strategy with the intention of making switching appear

risky (Roche, 2005).

Barriers can make customer defection difficult or costly. Such barriers

include switching costs and a lack of attractiveness of alternatives (Jones et

al., 2000). As competition intensifies and the cost of attracting new

customers increases, many companies are attempting to improve customer

retention and one of the more common customer retention programs is to

ensure customers are satisfied (Fornell, 1992). Others approach the

situation by creating switching barriers through the development of strong

interpersonal positive relationships (Jones et al., 2000).

56

Switching costs can arise from a variety of factors, such as the nature of the

product or service, customers‟ characteristics or deliberate strategies

undertaken by product or service providers (Chen & Hitt, 2002; Yang &

Peterson, 2004). For example, a product technology that is incompatible

between brands can increase switching costs (Marinoso, 2001). Three types

of switching costs have been suggested, namely:

(1) Transaction switching costs, which are incurred when starting a new

relationship or when terminating an existing relationship with a

provider.

(2) Learning switching costs, which results from the effort needed to

attain the same level of comfort with a new product or new service

provider as with an old provider.

(3) Artificial or contractual switching costs, which are created to prevent

customers from defecting and include activities such as frequent

flyer programs or repeat-purchase discounts (Klemperer, 1987).

In a business-to-consumer context, monetary, behavioural, search and

learning aspects can all be elements of overall switching costs (Yang &

Peterson, 2004), which are the costs customer incur when they change

suppliers (Lee et al., 2001) or the “perceived disutility a customer would

experience from switching service providers” (Chen and Hitt, 2002, p.258). A

firm can create switching costs, but competitors may counter by offering

incentives that make it easier for potential customers to overcome such

switching cost barriers (Yang & Peterson, 2004).

57

Switching costs are not only economic (Morgan & Hunt, 1994). They can

also be emotional and psychological barriers that are sometimes termed

relational switching costs (Sharma & Patterson, 2000; Vasudevan et al.,

2006) and result from the interpersonal relationships and personal bonds

developed between a customer and a supplier (Jones et al., 2000).

Vasudedevan et al. (2006) suggested the higher the relational switching

costs, the more likely people are to stay in a relationship. He also argued

relational switching costs have a significant moderating effect on the

satisfaction and commitment relationship. Even when product or service

performance is less than satisfactory, customer-supplier personal bonds that

are built over time are likely to create a psychological exit barrier. Jones et al.

(2000) suggested switching costs of all types play important roles in the

satisfaction and repurchase intention relationship. The relationship between

satisfaction and re-buying intention was not tested in the initially suggested

model. Consequently, moderating effect switching cost had on the

relationship between satisfaction and re-buying intention was not tested in

the current study. Switching costs can moderate the relationship between

quality and intention, as well the relationship between commitment and

intention (Anton et al., 2007) and both these relationships were tested in the

current study.

2.3.6 Conceptual Model of the Hypothesised Relationships

The conceptual model showing the hypothesised relationships can be seen

in Figure 2.1. Prior research suggests perceived value for money impacts on

58

re-buying intentions through relationship commitment and customer loyalty

(as was discussed in previous sections). The antecedents to perceived

value for money and re-buying intention, as well as the impact of switching

costs on intention, are also included in the model. A total of eleven

hypotheses were suggested based on the present review of past research

that were examined in the present study.

Figure 2.2: The Research Model

2.4 Summary

The present chapter reviewed the literature surrounding organisational

buying, the constructs in the research model and their suggested

relationships. The constructs of interest and their interrelationship that were

discussed include past satisfaction, perceived risk, service quality, product

quality, perceived value (a multi-dimensional construct), commitment to the

relationship, customer loyalty, re-buying intention and the effect of switching

Commitment

to Relationship

Customer

Loyalty

Past

Satisfaction

Product

Quality

Perceived

Risk

Service

Quality

Perceived Value

for Money

Intention

to Re-buy

Switching

Cost

+

-

+

+

+ +

+

++

59

costs. The discussion suggested a number of hypothesised relationships

and a suggested research model. Chapter Three describes the research

approach and methodology used in the present study that was undertaken

to explore the suggested model and test the various hypotheses.

60

Chapter Three

Research Approach and Methodology

3.1 Introduction

In Chapter Two, past research into of the various constructs was reviewed.

The relationships between the constructs that were included in the initially

suggested research model were discussed and the implied hypotheses were

noted. In the current chapter, the research approach taken to examine these

suggested relationships are discussed and examined. The chapter starts

with a brief description of the research model and the constructs within the

model. The measures chosen to measure the constructs are then discussed,

as is the development of the questionnaire and the investigative methods

that were used.

3.2 The Research Model

The current research model was discussed in Chapter One and Chapter

Two, and the relationships in the model were shown in Figure 2.1 of Chapter

Two. The present study extended prior value research by examining the

influence perceived value for money had on customer commitment to a

relationship, customer loyalty, and customer re-buying intention in a

business-to-business context. The hypotheses argued in Chapter 2

suggested prior satisfaction, product quality and service quality positively

influence perceived value for money, while perceived risk negatively

influence perceived value for money. As was noted in Chapter Two, other

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studies have found satisfaction, product quality, service quality and

perceived risk are important antecedents to perceived value for money.

While most prior studies have suggested perceived value influences

satisfaction (Eggert & Ulaga, 2002; Hellier et al., 2003), Brady and

Robertson (2000) have suggested customer satisfaction also influences

behavioural intention, which led to the inclusion of prior satisfaction in the

research model as an antecedent to perceived value for money.

As can also be seen in Figure 2.1 and, as was noted in the previous chapter,

prior research has shown perceived value for money impacts on re-buying

intention through relationship commitment and customer loyalty. Hence,

perceived value for money was hypothesised to have a positive influence on

customer commitment to a relationship, which in turn, positively influences

customer loyalty. Customer loyalty was hypothesised to have a positive

influence on customers‟ re-buying intentions.

Marketing practitioners and researchers are increasingly interested in

understanding consumers‟ buying behaviours and what motivates them.

Most popular buying behaviour studies have been undertaken in business-

to-consumer context. Much less is known about the influences that influence

organisational buyers‟ behaviour. The research questions that were outlined

in Chapter One attempt to increase our understanding in such a context and

to estimate the strength and nature of the interrelationships between some

key constructs in a business-to-business context.

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One of the most common approaches to examining the types of research

questions that were asked in the present study is the cross-sectional design,

in which a population or sample of interest is questioned at a point in time.

Cross-sectional surveys provide a snapshot at a specific moment in time,

which means they provide less information than longitudinal designs, but

they are less costly, which explains their popularity. Such studies estimated

the significance, strength and direction of relationships between different

constructs through correlational approaches.

3.3 The Measures Used

This section discusses the scales used to measure each of the constructs

included in the suggested research model. The scales used were adapted

from existing scales to suit the business-to-business context of the present

study. In some instances, scales from multiple sources were combined. The

key objective was to consider the different aspects found in the respective

measures and to investigate their relevance in a business-to-business

context. While the scales were developed in specific contexts and for

specific purposes, care was taken to ensure scales were modified for the

present business-to-business context without losing their original meaning.

The measures used are discussed in turn in subsequent sections.

3.3.1 The Perceived Risk Measure

While a number of risk dimensions have been suggested, only, the financial

and performance risk aspects noted by Sweeney et al. (1999) were included

in the present study. Organisations from which data were obtained were

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profit oriented. Consequently, the purchase of goods and services is

generally justified in financial terms (i.e. return on investment, net profit, the

cost of owning) or in performance terms (i.e. productivity, efficiency, quality

yield), which made the two aspects included appropriate risk indicators in

the present research context. The eight financial risk and performance risk

items used in Sweeney et al.‟s (1999) study, which are shown in Table 3.1

and which were measured on a seven-point strongly disagree (1) to strongly

agree (7) scale, were used in the present study.

Table 3.1: The Perceived Risk Measure

There is little risk in purchasing equipment from our supplier

We are unlikely to lose money on the products we buy from our supplier because of operational problems

There is little chance the equipment we buy from our supplier will not work properly

Products we buy from our supplier usually perform as expected

There is little chance there will be anything wrong with the products we buy from our supplier

We are unlikely to lose money on products we buy from our supplier because maintenance costs turn out higher than was expected

There is little potential for loss in purchasing equipment from our supplier

The long term costs of operating products we buy from our supplier are usually in line with what is expected

3.3.2 The Product Quality Measure

Much of the research that has examined product quality has used a single-

item scale but, even when multi-item measures have been used, reliability

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has often not been assessed (Stone-Romero & Stone, 1997), although

Sweeney, Soutar and Johnson (1999) are an exception and their research

suggested product quality is unidimensional. The items included in Sweeney

et al.‟s (1999) study, which were based on items developed by Dodds et al.

(1991), were used to examine durable goods. Consequently, the items were

easily rephrased with the meanings intact to suit the present capital goods

business-to-business context. The six product quality items, which are

shown in Table 3.2 and which were measured on a seven-point strongly

disagree (1) to strongly agree (7) scale, were used to measure product

quality in the present study.

Table 3.2: The Product Quality Measure

Our supplier‟s product are reliable

Our supplier produces good quality products

Our supplier's products perform well consistently

Our supplier's products are dependable

Our supplier's products meet our needs

Our supplier's products are well made

3.3.3 The Service Quality Measure

While it is important to understand the perceived quality of a product, it is

equally important to understand the quality of the service provided. As

previously noted, research has suggested service quality is multidimensional

(e.g. Parasuraman, Zeithaml, & Berry, 1988). Parasuraman et al. (1988)

developed the SERVQUAL scale with tangibles, reliability, responsiveness,

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assurance and empathy dimensions, while Grönroos (1984) has argued

there are processes and outcomes aspects that need to be considered

when measuring service quality. The outcomes aspects relate to what is

received from provided services, while processes aspects relate to the way

in which services are delivered. Grönroos (1984) argued outcomes aspects

are a necessary, but not sufficient, and that process aspects are often more

important than outcomes aspects.

Given the complexity of the suggested perceived quality of service model

that are dependent on two variables (expected service and perceived

service) (Grönroos, 1984, 1990), it was decided to adopt only the processes

aspect of Grönroos‟s (1984, 1990) approach. Consequently, the six items

used in Sweeney et al.‟s (1999) study together with four items used in

Sharma and Patterson‟s (1999) study were used to measure functional

service quality. The ten items included in the present study, which were

measured on a seven-point disagree (1) to strongly agree (7) scale, are

shown in Table 3.3.

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Table 3.3: The Service Quality Measure

Our supplier's after sales and technical support staff give us individual attention

Our supplier has helped us achieve our goals

Our supplier has performed well in its interactions with us

Our supplier provides courteous and friendly service to our firm

Our supplier responds promptly to our requests

Our supplier has made good recommendations to us

Our supplier shows a genuine care and interest in our firm

Our supplier's after sales and technical support staff provide good quality service

Our supplier's after sales and technical support staff are willing to help

Our supplier's after sales and technical support staff give us personal attention

3.3.4 The Past Satisfaction Measure

Satisfaction is an evaluative and affective response (Patterson & Spreng,

1997). Oliver (1981) has suggested there are four central concepts in

measuring customer satisfaction (which he termed expectations,

disconfirmation, satisfaction, and attitude), while Szymanski and Henard

(2001) focused on modelling the effects expectations, disconfirmation,

performance, affect, and equity had on satisfaction. However, the studies on

which they were based were conducted in consumer and retail settings.

Consequently, their applicability in business-to-business is unclear.

Further, in a business-to-business environment, marketers are faced with

the added complexities created by multiple buyers, complex product (and

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service) attributes and a diverse customer base (Rossomme, 2003).

Consequently, it was decided to use Perkin‟s (1993) industrial customer

satisfaction approach that measures satisfaction in three key areas

(operations, service and product). Thus, the twelve items from this scale,

which are shown in Table 3.4 and which were measured on a seven-point

not at all satisfied (1) to extremely satisfied (7) scale, were used in the study.

Table 3.4: The Past Satisfaction Measure

the availability of the service support provided by your supplier

the delivery and installation of your supplier‟s product

the prices your supplier charges

the financing arrangements offered by your supplier

your supplier‟s sales and service support

your supplier‟s service support

your supplier‟s technical support

your supplier‟s product range

the technical quality of your supplier‟s products

the reliability of your supplier‟s products

your supplier‟s products‟ designs and specifications

overall with your supplier

3.3.5 The Perceived Value for Money Measure

Perceived value is often linked with behavioural intention (Bolton & Drew,

1991). As was noted in Chapter Two, perceived value is not only a

functional construct as it includes social, emotional and even epistemic

value components (Patterson & Spreng, 1997; Sheth et al., 1991; Sweeney

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& Soutar, 2001). The growing interest in the perceived value construct has

led to the development of a number of perceived value scales, notably by

Sweeny and Soutar‟s (2001) four-dimensional PERVAL scale. As the

present study was undertaken in a business-to-business context it was

decided to only use the perceived value for money dimension as it was likely

to be the major value driver in such contexts and enabled the estimation of a

simpler model. Consequently, the four value for money items from the

PERVAL scale, which are shown in Table 3.5 and which were measured on

a seven-point strongly disagree (1) to strongly agree (7) scale, were used in

the present study.

Table 3.5: The Perceived Value for Money Measure

Our supplier's products are good products for the price charged

Our supplier's products are reasonably priced

Our supplier‟s products are economical choice

Our supplier's products are good value for money

3.3.6 The Commitment to Relationship Measure

Although attitudinal commitment has been measured as a single-item

construct (Gilliland & Bello, 2002), it has also been measured as a multiple-

item construct (Kim and Frazier, 1997). There are two primary reasons for

adopting a multiple-item construct when measuring attitudinal commitment,

namely:

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(1) Multiple items add specificity to the attitudinal commitment.

(2) A better analysis of the consequences of commitment can be

undertaken (Gilliland & Bello, 2002).

The customer commitment to relationship construct is thought to have

multiple facets, including customer loyalty, willingness to make short-term

sacrifices, a long term orientation and an intention to invest in the

relationship (Walter, Muller & Helfert, 2003). It should also measure the

extent to which a customer values their relationship with their supplier and to

which the relationship generates the desired outcomes (Coote et al., 2003).

“Identification” occurs when a customer wants to establish a relationship

with their supplier, while internalisation occurs because the supplier‟s

behaviour is congruent to the customer‟s value system” (Brown, Lusch, &

Nicholson, 1995, p. 366). Research has also found word of mouth, purchase

intention and price sensitivity are key determinants of affective commitment

(Bloemer & Odekerken-Schroder, 2003). The present study focused on

attitudinal commitment, using four items from Walter et al.‟s (2003) study,

three items from Coote et al.‟s (2003) study, two items from Gounaris‟s

(2005) study, six items from Brown et al.‟s (1995) study and two items from

Bloemer and Oderkenken-Schroder‟s (2003) study. These eighteen items,

which are shown in Table 3.6 and which were measured on a seven-point

strongly disagree (1) to strongly disagree (7) scale, were included in the

present study.

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Table 3.6: The Commitment to Relationship Measure

We are likely to expand our business with our supplier in the future

Our attachment to our supplier is mainly based on the similarity of our values

We talk about our supplier as a great organisation to be connected with

We focus on long term goals in our relationship with our supplier

Our supplier‟s value and philosophy are important to us

We prefer our supplier because of what it stands for, its values

We are very committed to our relationship with our supplier

If our suppliers‟ values were different, we would not be as attached to them

We expect to continue working with our supplier for a long time

We defend our supplier if it is criticized by people outside our company

We feel our supplier views us as an important "team member", rather than just another buyer

Our supplier‟s operating philosophy is a match for our operating philosophy

We are willing to invest time and other resources in our relationship with our supplier

We remain customers because we enjoy working with our supplier

Our relationship with our supplier has been a profitable one for our firm

We put long-term cooperation with our supplier before our short-term profit

We have a comfortable relationship with our supplier

We are very happy to be a customer of our supplier

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3.3.7 The Customer Loyalty Measure

Customer loyalty is a deeply believed held commitment to re-buy a preferred

product consistently in the future, despite situational influences (Oliver,

1999). Loyalty is an emotional reaction that includes the obligatory aspects

of the attachment and suggests an emotional bond (e.g., voice expression,

preference, willingness) (Gilliland & Bello, 2002). This led Gilliland and Bello

(2002, p. 29) to suggest loyalty “is an appropriate conception for an inter-

firm setting”. The customer loyalty construct used in this study was

measured as self-expressed loyalty using well-established items. The loyalty

items, which are shown in Table 3.7 and which were measured on seven-

point strongly disagree (1) to strongly agree (7) scale, included three items

suggested by Leverin and Liljander‟s (2006) loyalty scale and an item

suggested by Gilliland and Bello‟s (2002) scale.

Table 3.7: The Customer Loyalty Measure

We never seriously consider changing suppliers

Our firm is a loyal customer of our supplier

We would strongly recommend our supplier to other firms

We are proud to tell others we are associated with our supplier

3.3.8 The Intention to Re-buy Measure

As was noted previously, intention is an important behavioural outcome. As

already discussed in Chapter Two, three types of intention constructs have

been suggested, which have been termed intentions-as-expectation,

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intention-as-plan and intentions-as-wants, each of which has a different level

of volition (Söderlund & Öhman, 2005).

Both multiple-item measures and single-item measures have been used to

measure intention (Fong, 2007). Some researchers have argued multiple-

item measures lead to confounded measurement (Sutton, 1998) and that,

because buying intention is a concrete attribute, only a single item is needed

(Rossiter, 2002). Despite these arguments, the reliability of single-item

constructs cannot be measured and a lack of reliability can attenuate a

construct‟s relationships with other constructs, which mean significant

relationships could be missed (Peter, 1979). Consequently, a multiple-item

intention measure was used in the present study.

In the present study, the focus was on the intention to re-buy construct and,

therefore, three items adapted from Fong‟s (2007) re-patronising intention

model were used. To understand customers‟ future intentions better and to

reflect intention to re-buy, four items from the Bharadwaj and Matsuno‟s

(2006) future intention scale were also included. All of these items, which

were measured on a seven-point strongly disagree (1) to strongly agree (7)

scale, are shown in Table 3.8.

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Table 3.8: The Intention to Re-buy Measure

We expect our relationship with our supplier to last a long time

We would like to continue using our supplier in the future

It is highly probable we will be doing business with our supplier a year from now

We are likely to continue using our supplier in the future

Purchases from our supplier are virtually automatic

There is little chance we will terminate our relationship with our supplier in the next two years

We plan to use our supplier in the future

3.3.9 The Switching Cost Measure

As was noted in Chapter Two, switching costs are often economic (Morgan

& Hunt, 1994), but can also have emotional and psychological aspects

(Sharma & Patterson, 2000; Vasudevan et al., 2006). A customer‟s

perception of the time consumed, the money and effort associated with

changing suppliers is sometime used to measure switching cost (Jones et

al., 2000). Gilliland and Bello (2002, p.31) saw such costs as a “calculative

commitment which represents continuance cognitively experienced as an

appraisal of forgone benefits and incurred losses should the relationship

end”. The switching cost items used in the present study, which are shown

in Table 3.9 and which were measured on seven-point strongly disagree (1)

to strongly agree (7) scale, included three items from Gilliland and Bello‟s

(2002) study and three items from Jones et. al.‟s (2000) study.

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Table 3.9: The Switching Cost Measure

It would take us a great deal of time and effort to get used to a new supplier.

Even if we wanted to change suppliers, we would not as the cost would be too great

It would be a real hassle to switch to another supplier

It would cost us too much to switch to another supplier

We continue to use our usual supplier as changing suppliers would be too disruptive

We continue using our supplier, as leaving would cause us real problems

3.4 Data Collection and Sampling

3.4.1 Questionnaire Design

Having chosen items to measure the various constructs, a questionnaire

was designed to collect the needed data. The questionnaire was written in

English and, where necessary, the basic wordings of the various questions

were changed to suit the Surface Mount Technology equipment and In-

Circuit Test equipment business-to-business context in which the present

study was undertaken. A pilot study of 30 respondents was undertaken to

examine the applicability of the questionnaire in the present context. The

wordings of some of the scale items were revised because of the pilot study.

The pre-testing ensured the measures were appropriate and allowed fine-

tuning before the main data collection activity was undertaken in which

questionnaires were sent to business-to-business organisational buyers, as

is outlined in the subsequent section.

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As was noted in the previous sections, Likert-type scales, which are the

most common scales used in measuring opinions, beliefs, and attitudes

(O'Connor, 2004), were used in the present study. Chiam (2006) pointed out

a number of issues that needed to be considered when using of Likert-type

scales, including:

Whether to use an odd or even number of scale responses.

Category response options.

Whether to use a forced choice option.

Whether to use a balanced response option.

Whether to have only positive responses.

The argument for using an odd number of responses is to allow neutrality

and indecisiveness in responses (Chiam, 2006). Consequently the present

questionnaire had an odd number of responses. While some of the scales

used different scale types in their original studies, seven-point Likert-type

scales were used throughout the questionnaire to ensure consistency and

reliability (DeCoster, 2000). The questionnaire used a forced choice option

as respondents were not provided with a “non applicable” or “don‟t know”

option (Chiam, 2006). Except for the perceived risk scale, which used

negative worded items to prevent any misinterpretation of the questions, all

of the scales used positively worded items as negatively worded items are

often less meaningful to respondents, and can reduce reliability

(Parasuraman, Berry, & Zeithaml, 1991).

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The questionnaire, which is shown in Appendix 1, had four sections and an

introductory page. The introductory page outlined the study‟s objectives and

included three screening questions that ensured participants were from the

targeted group. Following the introductory page, section one included sixty-

five questions that measured the eight constructs included in the research

model (perceived risk, product quality, service quality, perceived value for

money, commitment to the relationship, customer loyalty, intention to re-buy

and switching costs). Section two included the twelve questions that

measured prior satisfaction, while section three gathered information about

each respondent‟s company and section four gathered information about

respondents themselves. A cover letter, which was available if a respondent

requested it, is provided in Appendix II.

3.4.2 The Sample

The present study used a cross-sectional survey to obtain the needed data.

When using Structural Equation Modelling (SEM), which was needed to

estimate the suggested model, Hair et al. (2006) recommended a sample of

at least 200 respondents to ensure a stable Maximum Likelihood Estimation

(MLE) solution. Consequently, the data collection was undertaken with this

objective in mind. The data used to test the suggested model were obtained

from a sample of organisational buyers employed in electronics

manufacturing companies in two countries (Singapore and Malaysia). To

ensure variation across the components analysed, data were collected from

local companies, Japanese, American and European based Small-to-

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Medium Enterprises (SMEs) and Multinational Companies (MNCs). Lists of

potential respondents were obtained from the researcher‟s personal

contacts, a survey firm‟s list of such firms and a company‟s customer

database to which the researcher had access.

The data were collected from different organisations across the two

countries. Companies have their own unique culture and there are national

cultural differences that may influence employees (Fu, Liu, & Zapata, 2005).

A person‟s values are learned at a young age and are maintained

throughout life (Hofstede, 1991). Hofstede‟s (1991) four dimensions are

often used to sort countries and cultures. These dimensions are:

(1) Small and large power distance countries.

(2) Collectivist and individualist countries.

(3) Weak and strong uncertainty avoidance countries.

(4) Feminine and masculine countries (Hofstede, 1980).

Hofstede's (1980) four cultural values dimensions have dominated the

literature in the past two decades. The dimensions represent a lifetime of

work surrounding the complexity of culture. Hofstede and Bond (1988)

identified a fifth dimension, which they termed Confucian Dynamism, which

is unique and interesting as it focuses on time orientation and Confucian

values. Hofstede and Bond (1988, p. 16) noted "the values on the left select

those teachings of Confucius that are more oriented toward the future

(especially perseverance and thrift), whereas those on the right select

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Confucian values oriented toward the past and present". However,

Confucian Dynamism dimension has received little attention since its initial

analysis.

Therefore, the present study took the level of uncertainty avoidance in each

country into account and limited the problems associated with examining the

purchases of different types of products and categories (e.g. cost and risk

differences) by asking about two products from a similar category (i.e.

Surface Mount Technology (SMT) equipment which is used to assemble

printed circuit boards, and the In-Circuit Test (ICT) equipment, which is used

to test assembled printed circuit boards). The prices ranges from

US$100,000 to US$400,000 depending on the model and the equipment‟s

configuration, suggesting such equipment would be seen as capital

purchases by most manufacturing organisations.

3.4.3 The Data Collection Approach

As the study targeted busy executives from electronics manufacturing

companies, professional help was needed to find relevant respondents to

participate in the survey. Three survey companies were short listed and they

were assessed on their network, manpower, contact lists of potential

respondents and prior experience in such survey. Telephone interviews

were preferred to mail survey as these allowed respondents an opportunity

to clarify any doubts that they may have. Further, mail surveys typically have

much lower response rates. Consequently, the data were collected by a

survey company that was appointed for this purpose and responses were

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obtained through telephone interviews. The survey company was briefed on

the questionnaire and was advised to exercise extreme care and be

sensitive to respondents during data collection.

Permission was obtained from the University of Western Australia‟s Ethics

Committee prior to commencing the study and conducting the survey.

Potential respondents were invited to participate voluntarily and had the right

to withdraw at any point of time. Anonymity was assured and all of the data

has been kept confidentially and in a secure environment.

Key informants were contacted by telephone to seek their cooperation on a

voluntary basis. If they agree to participate, the interviewer highlighted the

survey‟s objectives and asked the screening questions that ensured the

company surveyed used SMT or ICT equipment in their manufacturing

operations and that the respondent was a member of the relevant decision-

making unit or was a person whose input receives consideration in the

decision making process. Depending on the respondent‟s preferences, an

explanatory cover letter was sent to them by email.

Key informant‟s preferences have been found to be similar to that of the

organisation he or she represents (Hansen, 2004) and such people behave

as gatekeepers (Lau, Razzaque, & Ong, 2003). Key informants are the main

interface between seller and buyers (Lau et al., 2003) and are channels for

information flow between selling and buying organisations. They also play

an important role for the seller, as they understand a buyer‟s needs,

decision making processes and the composition of the buying organisation‟s

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decision-making unit (Hansen, 2004; Lau et al., 2003). To ensure people

were “key informants”, potential respondents had to be:

(1) A senior manager,

(2) An engineering staff member,

(3) A purchasing and procurement staff member,

(4) A finance staff member, or

(5) A production or operation staff member.

3.4.4 Collecting the Data

3.4.4.1 Pre-Testing

As was mentioned earlier, a pre-test was conducted after the initial

questionnaire had been completed in order to:

See whether there were ambiguous or biased questions.

Estimate the time it took to complete survey

Arrange a logical sequence within the questionnaire

Ensure the instructions were easy to follow.

Determine an appropriate questionnaire layout (Chiam, 2006)

A pre-test of 30 telephone interviews was undertaken by the research firm

participating in the study. Respondents were asked for feedback, clarity,

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sequencing and their understanding of the questionnaire. This led to minor

changes, including re-arranging the sequence of the questions and re-

phrasing some wording, although the meaning of the changed items

remained the same. The modified questionnaire was used to collect the

main data set.

3.4.4.2 The Major Data Collection Phase

Potential respondents who worked in appropriate roles in Singapore and

Malaysia were selected from the various sources available to the researcher

and the survey firm. Potential respondents were contacted by telephone and

invited to participate in the survey on a voluntary basis. Two hundred of the

six hundred sixty four managers who were approached provided usable

responses potential, providing a response rate of thirty percent, which is

reasonable for this type of survey (e.g., Gao, 1998; Perkins, 1993).

During the pilot survey, there was some confusion and misinterpretation of a

few questions, which were rectified. However, during the main data

collection phase, different problems occurred, including respondents‟

unavailability and unwillingness to participate because of busy schedules.

Consequently, it took three months to obtain the two hundred usable

responses that were needed to estimate the suggested research model.

3.5 Data Analysis Approach

The study used a quantitative approach to test the study‟s various

hypotheses and, as has already been noted, the constructs were measured

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using existing scales. The proposed model has four exogenous constructs

(perceived risk, product quality, service quality and past satisfaction), three

mediating constructs (perceived value for money, commitment to

relationship and customer loyalty), one moderating variable (switching cost)

and one dependent variable (intention to re-buy).

Several methods have been suggested to estimate models with interaction

terms in the presence of measurement error, such as Structural Equation

Modelling (SEM), Two Stage Least Square (TSLS), Ordinary Least Square

(OLS), Partial Least Square (PLS) and Regression on Factor Scores (FSR).

OLS is useful for predictive purposes, but not for theory testing. PLS and

FSR are appropriate for small samples, but do not correct measurement

errors bias. TSLS methods eliminate measurement error bias and can be

used for path analysis, but are limited to models with a single dependent

variable. The other drawback of the TSLS method is that its estimates are

based on one portion of the model at a time, which means it is a partial

informational technique. SEM methods, on the other hand, eliminate

measurement bias and are full informational techniques because estimates

take the entire model into account. Given the nature of the dependence

relationships that needed to be estimated, Structural Equation Modelling

(SEM) was the major analysis method used.

The analysis in this case used the four SEM stages recommended by Hair

et al. (2006), which are discussed in subsequent sections. The final stage of

the data analysis used multiple-group analysis (Homburg & Giering, 2001) to

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examine the moderating effects switching costs had on the relationship

between commitment and intention to re-buy and the relationship between

product quality and intention to re-buy. This stage is discussed in a later

section of the present chapter.

Stage 1: Pre-analysis Data-Examination

A preliminary analysis of the data was initially undertaken to compute a

number of relevant descriptive statistics so as to examine the nature of the

distributions within the data set as this provides useful information and an

indication of the scales‟ applicability and usefulness. Following the

preliminary data examination, the data were further examined to identify

outliers, see if there were departures from normality and to assess the

importance and impact of missing data.

Stage 2: Validation of the Measures

Confirmatory Factor Analysis (CFA) was used to evaluate the measurement

properties of the various constructs in the present business-to-business

context as many of the scales had been developed in business-to-consumer

contexts. CFA was used to examine the unidimensionality of the constructs

and to determine their measurement properties. In this data analysis phase,

model fit was examined using a variety of fit measures (the chi-square

statistic, the Goodness of Fit Index (GFI), the Comparative Fit Index (CFI),

the Normal Fit Index (NFI), the Standardised Root Mean Square of the

Residuals (SRMR) and the Root Mean Square Error of Approximation

(RMSEA)). Construct validity was determined using the approach suggested

84

by Fornell and Larcker (1981), which involves computing construct reliability

(CR) and average variance extracted (AVE) scores and examining

parameter estimates. Discriminant validity was examined using the Fornell

and Larcker (1981) approach, which involves comparing the AVE scores

with the square of the correlation between the relevant constructs. If the

square of the correlation is less than the minimum AVE score, discriminant

validity can be assumed and, if not, constructs need to be removed or

refined (Fornell & Larcker, 1981).

Stage 3: Examining the Measurement Model

The measurement model was estimated by specifying a confirmatory factor

analysis with all of the model‟s constructs allowed to intercorrelate. Model fit

was assessed using the chi-square statistic, the normed chi-square statistic,

the Goodness of Fit Index (GFI), the Comparative Fit Index (CFI), the

Normal Fit Index (NFI), the Standardised Root Mean Square of the

Residuals (SRMR) and the Root Mean Square Error of Approximation

(RMSEA) (Hair, Black, & Babin, 2006).

Stage 4: Assessing the Structural Model and the Path Estimates

SEM procedures were used to estimate the various relationships in the

proposed model (Hair et al., 2006). Following the CFA phase, and after the

measurement model was accepted, the structural model was estimated and

its fit was examined. If revisions were needed to improve model fit, they

were made if they could be justified on the basis of theory or prior research

85

and, after a good fit had been found, the various hypothesised relationships

were examined.

Stage 5: Assessing the Moderator Effects of Switching Costs

Finally, multiple-group analysis was used to examine the moderator effects

of switching costs. The sample was divided into two groups (high and low

switching costs perceptions) through a median split on the switching costs

construct. Multiple-group analyses compared the two sub-samples by

estimating an unconstrained path model and a model in which the structural

paths were constrained to be equal and computing their respective chi-

square statistics. The statistical significance of the difference between the

two chi-square statistics, which is also distributed as a chi-square statistic,

provided an indication of the moderating effects switching costs had on the

model‟s various relationships.

3.6 Summary

The present chapter began with a description the suggested research model

and its constructs. The scales used to measure the various constructs were

then discussed, explained and the rationale behind the selection of the

various scales was outlined. This chapter also discussed the way in which

the questionnaire was developed and how the study‟s sample size was

determined. The data collection approach used was then outlined and the

obtained response rate reported. Finally, the data analysis approach was

described and the different stages of the analysis were discussed. Chapter

Four present the results of the first of these analysis phases.

86

Chapter Four

Data Analysis – Part One

4.1 Introduction

In Chapter Three, the measures that were used, the data collection

approach undertaken, the sample obtained and the data analysis approach

were discussed. The data analysis stages included a pre-analysis

examination of the data in the form of descriptive statistics for the various

scales (stage 1), a validation of the measures (stage 2), an assessment of

the measurement model (stage 3), the estimation of the suggested research

model (stage 4) and, finally, an assessment of the moderator effects of

switching costs (stage 5). The present chapter starts by describing the

sample‟s profile, followed by a presentation of the results of stage one and

stage two of the data analyses. The results obtained from stage three, stage

four and stage five of the data analysis are presented in subsequent

chapters. The SPSS 16.0 and AMOS 16.0 computer programs were used to

undertake all of the analyses in the present study. The following sections

describe the results of the initial data analysis phases.

4.2 The Sample

As was discussed in the previous Chapter, a sample of two hundred

different respondents who uses either ICT and SMT or both was obtained

from organisational buyers employed in electronics manufacturing

companies in Singapore and Malaysia. An examination of the data found no

87

missing values and, therefore, the two hundred responses were used in the

various data analysis stages. Of the two hundred respondents who were

either members of the decision making unit or whose input received

consideration in the decision making process, eighty-four percent used ICT

equipment in their manufacturing facilities and ninety-six percent used SMT

equipment in their manufacturing facilities. For those who used ICT

equipment, twenty-nine percent were usually directly involved in such

decisions, forty-six percent were indirectly involved and twenty-five percent

were sometime involved or were asked for a recommendation. For those

who used SMT equipment, forty-four percent were usually directly involved

in such decisions, sixteen percent were indirectly involved and forty percent

were sometime involved or were asked for recommendation.

Table 4.1 shows the respondents‟ backgrounds and companies‟ profiles. As

can be seen from the table, more than half of the respondents (55%) were

between thirty-one and forty years of age, while the second largest group

were between forty-one and fifty years of age. A large majority of the

respondents were male (94%). This was expected as males dominate the

engineering segment of the industry within which the study was undertaken.

As expected, more than half (57%) of the respondents were engineers,

while twenty-four percent had management roles and fourteen percent had

production and operations roles. Forty-six percent of the respondents were

employed in Singapore and fifty-four percent were employed in Malaysia.

88

Table 4.1: Respondent Profiles

Background Variables N %

Age Group 21 - 30 years 17 9

31 – 40 years 110 55

41 – 50 years 63 31

51 – 60 years 10 5

Gender Male 187 94

Female 13 6

Functional Role Management 47 23

Engineering 113 57

Purchasing / Procurement 6 3

Finance 2 1

Production / Operation 28 14

Others 4 2

Company Location Singapore 91 45

Malaysia 109 55

Number of Employees Less than 100 16 8

100 – 199 15 7

200 – 299 10 5

300 – 399 7 3

400 – 499 8 4

500 or more 144 72

Headquarter Location USA 34 17

Japan 18 9

Europe 30 15

Asia (excl. Japan) 117 58

Others 1 1

Industry Segment Automotive Electronics 19 10

Telecommunications 15 7

IT / Computing 8 4

Consumer Electronics 24 12

Medical Electronics 5 2

Industrial Electronics 31 16

EMS / Sub-Contracting 94 47

Others 4 2

89

More than two thirds (72%) of the respondents‟ companies employed five

hundred or more people, while a majority (59%) of the companies‟

headquarters were situated in Asia (excluding Japan), followed by the

United States (17%) and Europe (15%). Slightly less than half of the

respondents (47%) operated in the Electronics Manufacturing Services

(EMS) industry, often acting as sub-contracting agents for major

organisations. As many companies outsource much of their manufacturing it

is not surprising EMS companies made up the largest segment. The second

largest industry group was the Industrial Electronics segment (16%).

4.3 Some Descriptive Statistics

Stage 1: Pre-analysis Data-Examination

The pre-analysis data examination included testing for normality, as is

outlined in a subsequent section. Seventy-five items were used to estimate

the nine constructs that were included in the suggested model. These

constructs were intention to re-buy, customer loyalty, commitment to

relationship, perceived value for money, past satisfaction, service quality,

product quality, perceived risk and switching costs. As was noted in Chapter

Three, all of the items were measured on a seven-point Likert-type scale

that ranged from strongly disagree (1) to strongly agree (7), except for the

past satisfaction scale that ranged from not at all satisfied (1) to extremely

satisfied (7). As was also mentioned in Chapter Three, the items in the

perceived risk scale were negatively worded, but these items were recoded

before being included in the analysis.

90

The descriptive statistics for the individual items are shown in Appendices

III-a, III-b and III-c. The means ranged from 4.36 (an item in the commitment

to relationship construct – “We defended our supplier if it is criticised by

people outside our company”) to 5.84 (an item in the intention to re-buy

construct – “We expect our relationship with our supplier to last a long time”).

The differences in the means and the size of the standard deviations

suggested respondents‟ views were different over the items included in the

questionnaire. Respondents were relatively positive about repurchasing

from the same supplier, as the mean for intention to re-buy was high (the

intention to re-buy scale mean was 5.19 on the seven-point scale).

Respondents were also generally satisfied with their suppliers, as the

satisfaction scale mean was 5.15. However, customer loyalty was lower (the

scale mean was 4.88), despite the relatively high intention to re-buy. The

switching costs had a scale mean of 4.66 and may have had an effect on

people‟s re-buying intention. Most respondents felt they had a stronger than

average commitment to their supplier (the commitment to relationship scale

mean was 5.14). As can seen from the tables in the appendices, product

and service quality were seen as good, as the product and service quality

scale means were both high (5.30 and 5.23 respectively). However,

perceived value for money was lower (the scale mean score was 4.76),

which may due to the dampening effect of perceived risk, which had a scale

mean of 3.08.

91

4.4 Testing for Normality

SEM makes a number of assumptions about the data and normality is an

important one. Hair et al. (2006) suggest a lack of multivariate normality

inflates the chi-square goodness of fit statistic, reducing the likelihood of

finding an acceptable fit. As the present study used SEM as the main

analytical technique, the normality of the data was examined. Most of the

items in the present study had skewness and kurtosis values that ranged

between -1 and +1, suggesting the data were sufficiently normal for use in

multivariate analysis (Hair et al., 2006). However, sixteen items had kurtosis

values that were greater then +1 (ranging from 1.05 to 2.50), which are

moderately non-normal. Given the violations of normality were not serious in

the current data set, transformations were not used and the data were

treated as multivariate normal.

4.5 The Scales’ Measurement Characteristics

Stage 2: Validation of the Measures

The structural model is meaningful only if the constructs have good

measurement properties (Anderson & Gerbing, 1988). Consequently, the

constructs were assessed prior to estimating the structural model. As was

discussed in the previous chapter, confirmatory factor analysis (CFA) was

used to do this. Following Anderson and Gerbing‟s (1988) suggestion, the

measurement properties of each of the nine constructs were examined prior

to examining their structural interrelationships. The constructs were

examined to assess:

92

1. Goodness of Fit, which implies the suggested model fits the

obtained data. Fit was assessed by examining a number of absolute

fit measures and a number of incremental fit measures. Hair et al.

(2006) suggested three to four acceptable fit indices provide evidence

of model fit and that at least one absolute index and one incremental

index should be reported, in addition to the chi-square statistic and its

associated degrees of freedom.

Absolute Fit Measures include the Chi-Square Statistic, the Normed

Chi-Square Statistic, the Goodness of Fit Index (GFI), the Adjusted

Goodness of Fit Index (AGFI), the Root Mean Square Error of

Approximation (RMSEA) and the Standardised Root Mean Squared

Residual (SRMR). Hair et al. (2006) noted that a chi-square statistic

with a significance level greater than 0.05 suggests a good fit.

However, the chi-square statistic is affected by sample size.

Consequently, with a reasonable sample, as in the present case, the

normed chi-square statistic, which is the ratio of chi-square statistic

divided by its degrees of freedom, is often used. A value of less than

3.00 is said to suggest a good fit (Kline, 2005).

The GFI and AGFI are both fit measures that range from zero to one,

with values greater than 0.90 suggesting a good fit (Byrne, 2001).

The RMSEA takes account of the error of approximation in the

population and determines how well a model with unknown, but

optimally chosen, parameter values would fit the population variance

93

matrix if it were available (Byrne, 2001). An RMSEA of less than 0.08

suggests a good fit (Byrne, 2001). The SRMR represents the average

value across all of the standardised residuals and Byrne (2001)

suggests a well fitting model has an SRMR value of 0.05 or less.

Incremental Fit Indices include the Normed Fit Index (NFI), the

Comparative Fit Index (CFI) and the Tucker Lewis Index (TLI). The

NFI is the ratio of the difference in the chi square value for a fitted

model and a null model divided by the chi square value for the null

model and can range from zero to one, with a value greater than 0.90

suggesting a good fit (Hair et al., 2006). For the NFI measure, the CFI

measure and the TLI measure, a value greater than 0.90 suggests a

good fit. The TLI is not normed and, consequently, values can fall

below zero or above one. Values close to one imply a good fit (Hair et

al., 2006).

2. Unidimensionality can be assumed if items are strongly related to

their relevant construct. Unidimensionality can be assumed if the

relevant CFA fits the data and the standardised estimates (factor

loadings) are greater than an absolute value of 0.60 (Bagozzi & Yi,

1989).

3. Construct Reliability suggests the construct is internally consistent

and comparatively free of measurement error. Hair et al. (2006)

suggest construct reliability should be greater than 0.70 and this

convention was accepted in the present study.

94

4. Convergent Validity is implied if there is more information than noise

in a latent construct. Fornell and Larcker (1981) suggested this can

be assumed if the construct‟s AVE score is 0.50 or greater.

The measurement properties of the nine constructs were examined in turn

and the results obtained are discussed in subsequent sections.

4.5.1 The Perceived Risk Construct

A CFA of the eight items used to measure the perceived risk construct was

undertaken. However, the model did not fit the data well (χ2 = 104.33, df =

20, p < 0.001). Consequently, revisions were made to obtain a better fit. In

this phase, the focus was on the constructs‟ measurement properties.

Consequently items with standardised coefficients that were less than 0.60

(Baggozi & Foxall, 1996) or items with errors that correlated with other items

that had smaller absolute loadings were removed, as such items can cause

problems in the final estimation of the structural model. Such items were

removed iteratively, starting with items with the lowest loadings, before

correlations between error terms were examined. Through this process,

three items were retained in this present case, as can be seen in Figure 4.1.

As there are only three items, there are no degrees of freedom and model fit

cannot be assessed. However, an examination of the error variances

suggested two were very similar. Consequently, they were constrained to be

equal, which added a degree of freedom to the estimation, allowing the

constructs‟ fit and other measurement properties to be assessed. In this

case there was a good fit (χ2 =2.56, df = 1, p > 0.10) and two of the three

95

standardised loadings exceeded 0.60, with the third only marginally below

that standard (0.57). All of the other fit indices were also acceptable (GFI =

0.99; AGFI = 0.95; RMSEA = 0.08; SRMR = 0.03; NFI = 0.98; CFI = 0.99

and TFI = 0.97). Construct reliability in this case was 0.75, which was

acceptable, as was the AVE score (0.51), suggesting the perceived risk

construct had internal consistency and convergent validity. The three-item

perceived risk construct can be used with confidence in subsequent analysis.

Perceived Risk

Q48. Products w e buy from our

supplier usually perform as expectede6

Q43. The long term costs of operating

products w e buy from our supplier

are usually in line w ith w hat is expected

e4

Q30. There is little potential for

loss in purchasing equipment

from our supplier

e2

.59

.80

.73

Figure 4.1: The Perceived Risk Construct

4.5.2 The Product Quality Construct

A CFA of the six items used to measure product quality was undertaken.

However, the model did not fit well (χ2 = 26.52, df = 9, p < 0.002) and the

same revision process as was used for the perceived risk construct was

undertaken. In this case, the five items shown in Figure 4.2 had an

acceptable fit (χ2 = 3.89, df = 5, p > 0.50), and the other fit indices were all

acceptable (GFI = 0.99; AGFI = 0.98; RMSEA = 0.00; SRMR = 0.02; NFI =

0.99; CFI = 1.00 and TFI = 1.00). The items‟ loadings ranged from 0.62 to

96

0.77, while construct reliability was 0.84 and the AVE score was 0.51. The

five-item product quality construct can be used with confidence in

subsequent analysis.

Product Quality

Q38. Our supplier's products are

w ell madee6

Q14. Our supplier's products

meet our needse4

Q12. Our supplier's products perform

w ell consistentlye3

Q10. Our supplier produces good

quality productse2

Q1. Our supplier's product are reliablee1

.62

.72

.70

.77

.76

Figure 4.2: The Product Quality Construct

4.5.3 The Service Quality Construct

A CFA of the ten items used to measure service quality was undertaken.

However, the model did not fit well (χ2 = 84.49, df = 35, p < 0.001). In this

case, the revision process led to the six items shown in Figure 4.3 being

retained. This model had an acceptable fit (χ2 = 13.02, df = 9, p > 0.10), and

the other fit indices were all acceptable (GFI = 0.98; AGFI = 0.95; RMSEA =

0.05; SRMR = 0.02; NFI = 0.98; CFI = 0.99 and TLI = 0.99). The items‟

loadings ranged from 0.71 to 0.85, while construct reliability was 0.88 and

the AVE score was 0.56. The six-item service quality construct can be used

with confidence in subsequent analysis.

97

Service Quality

Q39. Our supplier's after sales and

technical support staff are w illing to helpe9

Q19. Our supplier's after sales and

technical support staff provide

good quality service

e8

Q55. Our supplier has made

good recommendations to use6

Q51. Our supplier responds promptly to

our requestse5

Q34. Our supplier provides courteous

and friendly service to our f irme4

Q29. Our supplier has performed w ell

in its interactions w ith use3

.75

.74

.74

.69

.71

.85

Figure 4.3: The Service Quality Construct

4.5.4 The Past Satisfaction Construct

A CFA of the twelve items used to measure past satisfaction was

undertaken. However, the model did not fit well (χ2 = 174.99, df = 54,

p < 0.001). In this case, the revision process led to the five items shown in

Figure 4.4 being retained. This model had an acceptable fit (χ2 = 7.78, df = 5,

p > 0.15) and the other fit indices were all acceptable (GFI = 0.99; AGFI =

0.96; RMSEA = 0.05; SRMR = 0.02; NFI = 0.99, CFI = 0.99 and TLI = 0.99).

The items‟ loadings ranged from 0.76 to 0.85, while construct reliability was

0.90 and the AVE score was 0.64. The five-item past satisfaction construct

can be used with confidence in subsequent analysis.

98

Past

Satisfaction

Q77. overall w ith your suppliere12

Q74. the technical quality of

your supplier's productse9

Q71. your supplier's service supporte6

Q67. the delivery and installation

of your supplier's producte2

Q66. the availability of the service

support provided by your suppliere1

.85

.82

.74

.76

.83

Figure 4.4: The Past Satisfaction Construct

4.5.5 The Perceived Money for Value Construct

A CFA of the four items used to measure perceived value for money was

undertaken. However, the model did not fit well (χ2 = 12.46, df = 2, p < 0.03).

As with the perceived risk construct, a reduction to three items left no

degrees of freedom and model fit could not be assessed. However, an

examination of the error variances again suggested two were very similar.

Consequently, these error variances were constrained to be equal, which

added a degree of freedom to the estimation, allowing the constructs‟ fit and

other measurement properties to be assessed. In this case, the three items

shown in Figure 4.5 had an acceptable fit (χ2 = 1.43, df = 1, p > 0.20) and

the three standardised loadings exceeded 0.60. All of the other fit indices

were also acceptable (GFI = 0.99; AGFI = 0.97; RMSEA = 0.05; SRMR =

0.01; NFI = 0.99; CFI = 0.99 and TFI = 0.99). Construct reliability in this

case was 0.83, which was acceptable, as was the AVE score (0.62),

99

suggesting the three-item perceived value for money construct can be used

with confidence in subsequent analysis.

Perceived Value

for Money

Q49. Our supplier's products

are economical choicese3

Q23. Our supplier's products

are reasonably pricede2

Q13. Our supplier's products are

good products for the price chargede1

.89

.79

.66

Figure 4.5: The Perceived Value for Money Construct

4.5.6 The Commitment to Relationship Construct

A CFA of the eighteen items used to measure commitment to relationship

was undertaken. However, the model did not fit the data well (χ2 = 321.79, df

= 135, p < 0.001). In this case, the seven items shown in Figure 4.6 had an

acceptable fit (χ2 = 11.60, df = 14, p > 0.60), and the other fit indices were all

acceptable (GFI = 0.98; AGFI = 0.96; RMSEA = 0.00; SRMR = 0.02; NFI =

0.98; CFI = 1.00 and TLI = 1.00). The items‟ loadings ranged from 0.62 to

0.81, while construct reliability was 0.88 and the AVE score was 0.52. The

seven-item commitment to relationship construct can be used with

confidence in subsequent analysis.

100

Commitment to

Relationship

Q62. We remain customers because

w e enjoy w orking w ith our suppliere14

Q53. We feel our supplier view s

us as an important "team member",

rather than just another buyer

e11

Q47. We expect to continue w orking

w ith our supplier for a long timee9

Q40. We are very committed to

our relationship w ith our suppliere7

Q25. We prefer our supplier

because of w hat it stands for, its valuese6

Q9. We talk about our supplier

as a great organization

to be connected w ith

e3

.62

Q32. We are very happy to

be a customer of our suppliere18

.63

.71

.80

.74

.69

.81

Figure 4.6: The Commitment to Relationship Construct

4.5.7 The Customer Loyalty Construct

A CFA of the four items used to measure customer loyalty was undertaken.

While the fit was acceptable (χ2 = 1.96, df = 2, p > 0.30), one of the items

(we never seriously consider changing suppliers) had a very low loading

(0.42) and was excluded. The reduction to three items left no degrees of

freedom and model fit could not be assessed. However, an examination of

the error variances again suggested two were very similar. Consequently,

these error variances were constrained to be equal, which added a degree

of freedom to the estimation, allowing the constructs‟ fit and other

measurement properties to be assessed. In this case, the three items shown

in Figure 4.7 had an acceptable fit (χ2 =1.33, df = 1, p > 0.20) and two of the

three standardised loadings exceeded 0.60, with the third only marginally

below that standard (0.56). All of the other fit indices were also acceptable

101

(GFI = 0.99; AGFI = 0.97; RMSEA = 0.04; SRMR = 0.02; NFI = 0.99;

CFI = 0.99 and TFI = 0.99). Construct reliability in this case was 0.75, which

was acceptable, as was the AVE score (0.50), suggesting the three-item

customer loyalty construct can be used with confidence in subsequent

analysis.

Customer Loyalty

Q36. We are proud to tell others

w e are associated w ith our suppliere4

Q31. We w ould strongly recommend

our supplier to other f irmse3

Q28. Our f irm is a loyal

customer of our suppliere2

.56

.82

.73

Figure 4.7: The Customer Loyalty Construct

4.5.8 The Intention to Re-buy Construct

A CFA of the six items used to measure intention to re-buy was undertaken.

However, the model did not fit the data well (χ2 = 34.34, df = 14, p < 0.01). In

this case, the four items shown in Figure 4.8 had an acceptable fit (χ2 = 1.73,

df = 2, p > 0.40), although one item‟s loading (it is highly probable we will be

doing business with our supplier a year from now) was marginally below the

0.60 standard (0.56). All of the other fit indices were all acceptable (GFI =

0.99; AGFI = 0.98; RMSEA = 0.00; SRMR = 0.02; NFI = 0.99, CFI = 1.00

and TLI = 1.00). The items‟ loadings ranged from 0.56 to 0.81, while

102

construct reliability was 0.80 and the AVE score was 0.51. The four-item

intention to re-buy construct can be used with confidence in subsequent

analysis.

Intention

to Re-buy

Q60. We plan to use our

supplier in the futuree7

Q27. We are likely to continue

using our supplier in the futuree4

Q22. It is highly probable w e w ill be

doing business w ith our supplier

a year from now

e3

Q8. We w ould like to continue

using our supplier in the futuree2

.55

.69

.81

.77

Figure 4.8: The Intention to Re-buy Construct

4.5.9 The Switching Cost Construct

A CFA of the six items used to measure switching costs was undertaken.

While the fit was acceptable (χ2 = 9.02, df = 9, p > 0.40), three of the items

had low loadings (ranging from 0.39 to 0.55) that led to an AVE score below

0.50. The reduction to three items left no degrees of freedom and model fit

could not be assessed. However, an examination of the error variances

again suggested two were very similar. Consequently, these error variances

were constrained to be equal, which added a degree of freedom to the

estimation, allowing the constructs‟ fit and other measurement properties to

be assessed. In this case, the three items shown in Figure 4.9 had an

acceptable fit (χ2 =0.00, df = 1, p > 0.90). All of the other fit indices were

acceptable (GFI = 1.00; AGFI = 1.00; RMSEA = 0.00; SRMR = 0.00; NFI =

103

1.00; CFI = 1.00 and TFI = 1.00). Construct reliability in this case was 0.76,

which was acceptable, as was the AVE score (0.52), suggesting the three-

item switching cost construct can be used with confidence in subsequent

analysis.

Switching Cost

Q45. We continue using our supplier,

as leaving w ould cause us

real problems

e6

Q35. We continue to use our usual

supplier as changing suppliers w ould

be too disruptive

e5

Q24. It w ould cost us too much to

sw itch to another suppliere4

.61

.67

.86

Figure 4.9: The Switching Cost Construct

Table 4.2 summarises the goodness of fit of the various confirmatory factor

analyses, while Table 4.3 summarises the construct reliability and

convergent validity of the various construct that were included in the

research model. A composite reliability value exceeding 0.70 suggest a

reliable measure and an AVE value of at least 0.50 is sufficient to establish

convergent validity (Fornell & Larcker, 1981). All of the revised constructs

met the measurement requirements and, as previously suggested, they can

all be used in the subsequent analysis.

104

Table 4.2: Summary and Result of the Goodness of Fit

Construct No. of Items

Absolute Fit Measures Incremental Fit

Indices

Chi-Square Statistic

Normed Chi-

Square GFI AGFI RMSEA SRMR NFI CFI TLI

Perceived Risk 3 2.56

(df=1; p>0.10) 2.56 0.99 0.95 0.08 0.03 0.98 0.99 0.97

Product Quality 5 3.89

(df=4; p>0.50) 0.78 0.99 0.98 0.00 0.02 0.99 1.00 1.00

Service Quality 6 11.03

(df=9; p>0.25) 1.22 0.98 0.96 0.03 0.02 0.98 0.99 0.99

Satisfaction 5 7.78

(df=5; p>0.15) 1.55 0.99 0.96 0.05 0.02 0.99 0.99 0.99

Perceived Value for Money

3 1.43

(df=1; p>0.20) 1.43 0.99 0.97 0.05 0.01 0.99 0.99 0.99

Commitment to Relationship

7 11.60

(df=14; p=0.60) 0.83 0.98 0.96 0.00 0.02 0.98 1.00 1.00

Customer Loyalty 3 1.33

(df=1, p>0.20) 1.33 0.99 0.97 0.04 0.02 0.99 0.99 0.99

Intention to Re-Buy 4 1.73

(df=2; p>0.40) 0.87 0.99 0.98 0.00 0.02 0.99 1.00 1.00

Switching Cost 3 0

(df=1; p>0.90) 0.00 1.00 1.00 0.00 0.00 1.00 1.00 1.00

105

Table 4.3: Summary of Individual Construct Reliability and Average Variance Extracted

Construct Reliability Average Variance

Extracted

Perceived Risk 0.75 0.51

Product Quality 0.84 0.51

Service Quality 0.88 0.56

Satisfaction 0.90 0.64

Perceived Value for Money 0.83 0.62

Commitment to Relationship 0.88 0.52

Customer Loyalty 0.75 0.50

Intention to Re-Buy 0.80 0.58

Switching Cost 0.76 0.52

4.5.10 Discriminant Validity Assessment

Following the procedure suggested by Fornell and Larker (1981), the

discriminant validity of the various pairs of constructs was assessed. The

procedure involved a comparison between the squared correlation between

a construct pair and the two measures‟ AVE scores. Fornell and Larker

(1981) suggested discriminant validity between constructs could be

assumed if the minimum AVE score for a construct pair exceeded the

square of the correlation between them.

As can be seen in Table 4.4, the AVE of twenty out of thirty-six construct

pairs exceeded the square correlation between them. This suggests many of

the constructs were too highly correlated, despite the scales having good

measurement properties otherwise. Therefore, discriminant validity cannot

be assumed for all the constructs and they cannot be used in a SEM

analysis in their present form. This also suggests respondents may have

106

had a general impression or “halo” about their suppliers that impacted on

their responses to the questionnaire (Bacon, 1997).

The other possibility for the multicollinearity found could be a “common

method factor” as respondents were asked for multiple answers in the same

context (Bacon, 1997). To reduce multicollinearity, the data was re-

examined using Exploratory Factor Analysis (EFA) (Bacon, 1997) and the

results obtained were further revised and examined through the same type

of confirmatory factor analysis as was used initially. The results obtained are

discussed in the subsequent sections.

Table 4.4: Average Variance Extracted and Square Correlations

Constructs Average Variance Extracted

Square Correlations

IRB CL CR PVM PR PQ SQ SAT

Intention to Re-Buy (IRB) 0.51 1

Customer Loyalty (CL) 0.50 0.72* 1

Commitment to Relationship (CR) 0.52 0.74* 0.88* 1

Perceived Value for Money (PVM) 0.62 0.45 0.41 0.46 1

Perceived Risk (PR) 0.51 0.72* 0.62* 0.85* 0.71* 1

Product Quality (PQ) 0.51 0.69* 0.56* 0.77* 0.62* 0.92* 1

Service Quality (SQ) 0.56 0.61* 0.55* 0.79* 0.50 0.94* 0.88* 1

Satisfaction (SAT) 0.64 0.42 0.45 0.56 0.46 0.67* 0.62* 0.69* 1

Switching Cost (SC) 0.52 0.31 0.48 0.38 0.10 0.25 0.22 0.20 0.18

* Square correlation of the construct pair is higher than the individual construct Average Variance Extracted

4.6 Exploratory Factory Analysis

As was explained in the previous section, the CFA results suggested all of

the constructs have good measurement properties. However, discriminant

validity could not be assumed due to the presence of multicollinearity in the

data. Bacon (1997) suggested using Exploratory Factor Analysis (EFA) to

107

correct for such multicollinearity as it transforms correlated explanatory

variables into a small number of relatively uncorrelated factors. The two

commonly used EFA extraction techniques are principal component analysis

and the common factor analysis. Principal component analysis attempts to

explain as much item variance as possible, while common factor analysis

attempts to identify interpretable factors that explain the observed

correlations among constructs.

The two hundred responses were examined using common factor analysis

as the main objective was to identify underlying factors. All the variables with

the exception of intention to re-buy, which was the dependent variable, and

switching costs, which had been identified as a potential moderating variable,

were included in this phase of the analysis. While the sample was small, the

obtained factors were subsequently, examined through CFA procedures

before being used to estimated the suggested model, ensuring they had

good measurement properties. Three commonly applied decision rule were

used to derive a stable factor structure, namely:

(1) Retain factors that had eigenvalue one or greater.

(2) Remove items with loadings that were less than an absolute value of

0.40.

(3) Remove items that had loadings of an absolute value of 0.40 or greater

on two or more factors (Hair et al., 2006).

108

Forty-three items were eliminated in this way, after which the remaining

thirty-four items suggested eight underlying factors, as can be seen in

Table 4.5.

An examination of the factor loadings obtained after a varimax rotation was

used to derive a simple structure, which are shown in Table 4.5, suggested

the factors could be labelled as past satisfaction, service quality,

commitment to relationship, product quality, perceived financial risk, loyalty-

commitment and perceived operational risk.

109

Table 4.5: The Exploratory Factor Analysis Matrix

Proposed Factor /

Construct Name

Rotated Factor Matrixa

Items

Factor

1 2 3 4 5 6 7 8

PAST SATISFACTION

your supplier's service support 0.81

your supplier's sales and service support 0.81

your supplier's technical support 0.78

74. the technical quality of your supplier's products 0.73

77. overall with your supplier 0.68

75. the reliability of your supplier's products 0.66

66. the availability of the service support provided by your supplier

0.64

67. the delivery and installation of your supplier's product

0.62

76. your supplier's products' designs and

specifications 0.60

19. Our supplier's after sales and technical support staff provide good quality service

0.51

SERVICE QUALITY

57. We have a comfortable relationship with our supplier

0.71

47. We expect to continue working with our supplier for a long time

0.56

51. Our supplier responds promptly to our requests 0.56

42. Our supplier's products are dependable 0.54

63. Our relationship with our supplier has been a profitable one for our firm

0.54

55. Our supplier has made good recommendations to us

0.52

29. Our supplier has performed well in its interactions with us

0.52

COMIITTMENT to

RELATIONSHIP

61. We are willing to invest time and other resources in our relationship with our supplier

0.61

65. Our supplier's after sales and technical support staff give us personal attention

0.60

31. We would strongly recommend our supplier to other firms

0.57

52. We defend our supplier if it is criticized by people outside our company

0.54

PERCEIVED VALUR FOR

MONEY

23. Our supplier's products are reasonably priced 0.68

49. Our supplier's products are economical choices 0.65

68. the prices your supplier charges 0.57

13. Our supplier's products are good products for the price charged

0.55

PRODUCT QUALITY

12. Our supplier's products perform well consistently

0.68

11. Our supplier has helped us achieve our goals 0.63

10. Our supplier produces good quality products 0.54

PERCEIVED FINANCIAL

RISK

58. We are unlikely to lose money on products we buy from our supplier because maintenance costs turn out higher than was expected

0.66

37. We are unlikely to lose money on the products we buy from our supplier because of operational

problems

0.64

LOYALTY-COMIITTMENT

4. We are likely to expand our business with our supplier in the future

0.62

41. If our suppliers' values were different, we would not be as attached to them

0.51

PERCEIVED OPERATIONAL

RISK

3. There is little risk in purchasing equipment from our supplier

0.56

50. There is little chance there will be anything wrong with the products we buy from our supplier

0.53

110

4.6.1 Scale Purification

These factors were examined in the same way as the original scales had

been examined as confirmatory factor analysis was used to determine their

internal consistency and convergent validity. As assessment of their

discriminant validity was also undertaken.

The Final Past Satisfaction Construct

A CFA of the ten past satisfaction items did not fit well (χ2 = 123.48, df = 14,

p < 0.01) and the scale was refined using the same process as was used in

the initial analysis. In this case, six items had an acceptable fit (χ2 = 8.03, df

= 9, p > 0.50), and the other fit indices were all acceptable (GFI = 0.99;

AGFI = 0.97; RMSEA = 0.00; SRMR = 0.02; NFI = 0.99, CFI = 1.00 and TLI

= 1.00). The items‟ loadings ranged from 0.64 to 0.91, the construct

reliability was 0.92 and the AVE score was 0.65. The six-item past

satisfaction revised construct can be used with confidence in subsequent

analysis.

The Final Service Quality Construct

A CFA of the seven service quality items did not fit the data well (χ2 = 26.26,

df = 14, p < 0.02). In this case, the five items had an acceptable fit (χ2 = 7.54,

df = 5, p > 0.10), and the other fit indices were all acceptable (GFI = 0.98;

AGFI = 0.96; RMSEA = 0.05; SRMR = 0.02; NFI = 0.98; CPI = 0.99 and TLI

= 0.99). The items‟ loadings ranged from 0.70 to 0.82, the construct

reliability was 0.88 and the AVE score was 0.59. The five-item revised

111

service quality construct can be used with confidence in subsequent

analysis.

The Final Commitment to Relationship Construct

A CFA of the four commitment to relationship items had an acceptable fit (χ2

= 2.65, df = 2, p > 0.20), and the other fit indices were all acceptable (GFI =

0.99; AGFI = 0.99; RMSEA = 0.00; SRMR = 0.01; NFI = 0.99; CFI = 1.00

and TLI = 1.00). The items‟ loadings ranged from 0.65 to 0.82, the construct

reliability was 0.82 and the AVE score was 0.53. The four-item revised

commitment to relationship construct was retained and can be used with

confidence in subsequent analysis.

The Final Perceived Value for Money Construct

A CFA of the four perceived value for money items did not fit well (χ2 =

10.15, df = 2, p < 0.005). The reduction to three items left no degrees of

freedom and model fit could not be assessed. However, an examination of

the error variances suggested two of them were very similar. Consequently,

these error variances were constrained to be equal, which added a degree

of freedom to the estimation, allowing the constructs‟ fit and other

measurement properties to be assessed. In this case, the three items had

an acceptable fit to the data (χ2 =1.43, df = 1, p > 0.20). All of the other fit

indices were also acceptable (GFI = 0.99; AGFI = 0.97; RMSEA = 0.05;

SRMR = 0.01; NFI = 0.99; CFI = 0.99 and TFI = 0.99). Construct reliability in

this case was 0.83, which was acceptable, as was the AVE score (0.62),

112

suggesting the three-item revised perceived value for money construct can

be used with confidence in subsequent analysis.

The Final Product Quality Construct

There were only three product quality items, which left no degrees of

freedom and model fit could not be assessed. However, an examination of

the error variances again suggested two of them were very similar.

Consequently, these error variances were constrained to be equal, which

added a degree of freedom to the estimation, allowing the constructs‟ fit and

other measurement properties to be assessed. The three items had an

acceptable fit to the data (χ2 =0.07, df = 1, p > 0.70). All of the other fit

indices were also acceptable (GFI = 1.00; AGFI = 0.99; RMSEA = 0.00;

SRMR = 0.00; NFI = 1.00; CFI = 1.00 and TFI = 1.00). The items‟ loadings

ranged from 0.75 to 0.79, the construct reliability was 0.82 and the AVE

score was 0.60. The three-item revised product quality construct was

retained and can be used with confidence in subsequent analysis.

The Final Perceived Financial Risk Construct

The perceived financial risk scale had only two items, which meant the

model was not identified and its measurement properties could not be

assessed. However, in order to achieve identification for a latent variable

that was measured by two indicators, Bollen‟s (1989) “two indicator rule”

was adopted. As he pointed out, identification of a two-item construct can be

achieved if:

113

(1) The scale with two indicators is correlated with at least one other

latent variable which has at least two indicators.

(2) Each of the indicators loads onto only one factor

(3) The observed variables measurement errors are not correlated

with other observed variable measurement error.

Consequently, perceived financial risk was correlated with product quality in

the present stage of CFA, as can be seen in Figure 4.10.

Product

Quality

10. Our supplier produces good

quality productse5

11. Our supplier has helped us achieve

our goalse4

12. Our supplier's products perform

w ell consistentlye3

.77

.81

.73

Perceived Risk -

Financial58. We are unlikely to lose money on

products w e buy from our supplier

because maintenance costs turn

out higher than w as expected

e2

37. We are unlikely to lose money on

the products w e buy from our supplier

because of operational problems

e1

.51

.99

-.34

Figure 4.10: The Final Perceived Financial Risk Construct

The two perceived financial risk items were correlated with the three product

quality items to access its measurement properties. The chi-square statistic

was significant (χ2 =10.64, df = 4, p = 0.03). However, the normed chi-

square was 2.66 which was below 3.0, which suggested good fit. Other fit

indices were all acceptable except the RMSEA which was marginally higher

114

(GFI = 0.98; AGFI = 0.92; RMSEA = 0.09; SRMR = 0.01; NFI = 0.96; CFI =

0.98 and TLI = 0.94). One of the perceived financial risk items‟ standardized

loadings exceeded 0.60, with one item which was marginally below that

standard (0.51). Construct reliability was 0.75 and the AVE score was 0.62.

The two-item revised perceived financial risk construct was therefore

retained.

The Perceived Operational Risk Construct

The perceived financial operational risk scale also had only two items,

which meant the model was not identified and its measurement properties

could not be assessed. As with the perceived financial risk construct, the

two items were correlated with the three product quality items, as can be

seen in Figure 4.11, to assess the scale‟s measurement properties.

115

Perceived Risk -

Operational

50. There is little chance there w ill

be anything w rong w ith the products

w e buy from our supplier

e2

3. There is little risk in

purchasing equipment from

our supplier

e1.61

.69

Product

Quality

10. Our supplier produces

good quality productse5

12. Our supplier's products

perform w ell consistentlye3 .82

.73

-.57

11. Our supplier has helped

us achieve our goalse4

.76

Figure 4.11: The Perceived Operational Risk Construct

In this case, the chi-square statistic was not significant (χ2 =4.59, df = 4,

p > 0.30) and the normed chi-square was 1.15 which was below 3.0,

suggesting a good fit and the other fit indices were acceptable (GFI = 0.99;

AGFI = 0.96; RMSEA = 0.03; SRMR = 0.01; NFI = 0.98; CFI = 0.99;

TLI = 0.99). The items‟ loadings on perceived operational risk ranged from

0.61 to 0.69. However construct reliability was low (0.59) and the AVE score

was also low (0.42). Despite acceptable fit and loadings, the construct

reliability and AVE were low. Consequently, the two-item revised perceived

operational risk construct had poor measurement properties in the present

context and it was excluded from the subsequent analysis.

The Loyalty-Commitment Construct

The loyalty-commitment scale had only two items, which meant the model

was not identified and its measurement properties could not be assessed.

116

As was the case with perceived risk constructs, the two items were

correlated with the three product quality items to access its measurement

properties, as can be seen in Figure 4.12.

Loyalty

Commitment

41. If our suppliers' values

w ere different, w e w ould

not be as attached to them

e2

4. We are likely to expand our

business w ith our supplier

in the future

e1.77

.52

Product

Quality

10. Our supplier produces

good quality productse5

12. Our supplier's products

perform w ell consistentlye3 .77

.73

.58

11. Our supplier has helped

us achieve our goalse4

.81

Figure 4.12: The Loyalty-Commitment Construct

The chi-square statistic was significant (χ2 =11.82, df = 4, p < 0.02).

However, the normed chi-square was 2.95, which was marginally below 3.0.

Other fit indices also suggested the construct fitted the data well (GFI = 0.98;

AGFI = 0.91; RMSEA = 0.09; SRMR = 0.01; NFI = 0.96; CFI = 0.97 and TLI

= 0.93), although the RMSEA score was marginally above the 0.08 standard.

The items‟ loadings on loyalty commitment ranged from 0.77 to 0.52,

suggesting acceptable loadings, although one item marginally meeting the

0.60 standard. Construct reliability was low (0.59) and the AVE score was

also low (0.43). Consequently, the two-item revised loyalty-commitment

construct had poor measurement properties in the present context and it

was excluded from the subsequent analysis.

117

Table 4.6 shows the fit indices for all of the constructs retained as a result of

the exploratory factor analysis, while Table 4.7 shows the reliability and AVE

score for all of the constructs that were included in the revised analysis,

including intention to re-buy and switching costs construct. As has already

noted, perceived operational risk and loyalty-commitment were excluded

from the subsequent analysis as they had unacceptable measurement

properties.

118

Table 4.6: Final Construct Fit Indices

Construct No. of Items

Absolute Fit Measures Incremental Fit

Indices

Chi-Square Statistic

Normed Chi-

Square GFI AGFI RMSEA SRMR NFI CFI TLI

Intention to Re-buy

3 0.36

(df=1;p>0.50) 0.36 0.99 0.99 0.00 0.01 0.99 1.00 1.00

Commitment to Relationship

4 2.65

(df=2;p>0.20) 1.32 0.99 0.97 0.04 0.02 0.99 0.99 0.99

Perceived Value for Money

3 1.43

(df=1;p>0.20) 1.43 0.99 0.97 0.05 0.01 0.99 0.99 0.99

Product Quality 3 0.07

(df=1;p>0.70) 0.07 1.00 0.99 0.00 0.00 1.00 1.00 1.00

Service quality 5 7.54

(df=5;p>0.10) 1.51 0.98 0.96 0.05 0.02 0.98 0.99 0.99

Perceived Risk - Financial

2 10.64

(df=4;p=0.03) 2.66 0.98 0.92 0.09 0.01 0.96 0.98 0.94

Satisfaction 6 8.03

(df=9;p>0.50) 0.89 0.99 0.97 0.00 0.02 0.99 1.00 1.00

Switching Cost 3 0.00

(df=1;p>0.90) 0.00 1.00 1.00 0.00 0.00 1.00 1.00 1.00

119 119

Table 4.7: Final Construct Reliability and Average Variance Extracted

Construct Reliability Average Variance Extracted

Intention to Re-Buy 0.80 0.58

Commitment to Relationship 0.82 0.53

Perceived Value for Money 0.83 0.62

Product Quality 0.82 0.60

Service quality 0.88 0.59

Perceived Risk - Financial 0.67 0.50

Satisfaction 0.92 0.65

Switching Cost 0.76 0.52

4.6.2 A Re-assessment of Discriminant Validity

Fornell and Larker‟s (1981) procedure was again used to assess the

discriminant validity between the retained constructs. As can be seen in

Table 4.8, all the squared correlations between the construct pairs but one

(i.e. Service Quality and Intention to Re-buy) were less than the AVE scores

between them.

Table 4.8: Average Variance Extracted and Square Correlations (After EFA & Purification)

Constructs Average Variance Extracted

Square Correlations

IRB CRA PVM PQ SQ PR SAT

Intention to Re-buy (IRB) 0.58 1.00

Commitment to Relationship (CR)

0.53 0.50 1.00

Perceived Value for Money (PVM)

0.62 0.42 0.40 1.00

Product Quality (PQ) 0.60 0.52 0.34 0.50 1.00

Service quality (SQ) 0.59 0.86* 0.56 0.52 0.58 1.00

Perceived Risk - Financial (PRF)

0.50 0.18 0.24 0.16 0.14 0.22 1.00

Past Satisfaction (SAT) 0.65 0.44 0.38 0.48 0.45 0.56 0.12 1.00

Switching Cost (SC) 0.52 0.22 0.38 0.09 0.18 0.27 0.24 0.12

* Square correlation of the construct pair is higher than the individual construct average variance extracted

120 120

This suggests discriminant validity can be assumed for all but this construct

pair. As intention to re-buy is the dependent variable, the service quality

construct was removed from the subsequent analysis, after which

discriminant validity can be assumed for all of the constructs in the model

that was to be estimated, as can be seen in Table 4.9. As a result of these

analyses, the model that was estimated was revised in the ways shown in

Figure 4.13.

Table 4.9: Average Variance Extracted and Square Correlations (After excluding Service Quality)

Constructs Average

Variance

Extracted

Square Correlations

IRB CRA PVM PQ PR SAT

Intention to Re-Buy (IRB) 0.58 1.00

Commitment to Relationship (CR) 0.53 0.50 1.00

Perceived Value for Money (PVM) 0.62 0.41 0.38 1.00

Product Quality (PQ) 0.60 0.55 0.35 0.52 1.00

Perceived Risk - Financial (PRF) 0.50 0.22 0.27 0.18 0.14 1.00

Past Satisfaction (SAT) 0.65 0.44 0.37 0.46 0.46 0.12 1.00

Switching Cost (SC) 0.52 0.22 0.37 0.08 0.18 0.23 0.12

121 121

Figure 4.13: The Revised Model

The model‟s revision led to changes in the hypotheses that could be tested.

In particular, the hypotheses tested were outlined in Chapter Two:

Hypothesis 1: The greater an organisational buyer‟s loyalty to a

product or service, the greater will be their intention to re-buy.

Hypothesis 2: The greater an organisational buyer‟s commitment to

a relationship with an existing supplier, the greater will be their loyalty

to that supplier.

Hypothesis 3: The greater an organisational buyer‟s commitment to

a relationship with an existing supplier, the greater will be their

intention to re-buy from that supplier.

Commitment

to RelationshipIntention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

Switching

Cost

122 122

Hypothesis 4: The greater an organisational buyer‟s perceived value

for money, the greater will be their commitment to their relationship

with their supplier.

Hypothesis 5: The greater an organisational buyer‟s risk perception,

the lower will be their value for money perception.

Hypothesis 6: The greater an organisational buyer‟s perception of

product quality, the greater will be their value for money perception.

Hypothesis 7: The greater an organisational buyer‟s perception of

service quality, the greater will be their value for money perception.

Hypothesis 8: The higher an organisational buyer‟s satisfaction with

prior purchases, the greater will be their value for money perception.

Hypothesis 9: The better an organisational buyer‟s perception of the

quality of a product, the greater will be their intention to re-buy from

their supplier.

Hypothesis 10: The effect commitment to the relationship has on a

buyer‟s intention to re-buy is higher when switching costs are high.

Hypothesis 11: The effect product quality has on a buyer‟s intention

to re-buy is higher when switching costs are low.

123 123

4.7 Summary

The present chapter discussed the measurement characteristics of the

constructs that were included in the initial model. The results suggested all

of the constructs had good measurement properties, although three

constructs (perceived risk, customer loyalty and intention to re-buy) had a

single item with a factor loading marginally below 0.60. The nine constructs

had good construct reliability (as all were above 0.70) and their average

variance extracted scores were all at least 0.50.

However, an assessment of their discriminant validity suggested many of

the constructs were highly correlated, and were not distinct. In order to

correct this multicollinearity, an exploratory factor analysis was undertaken

on the original data set, excluding the dependable variable (Intention to re-

buy) and the potential moderating construct (switching costs). The obtained

factors were further analysed by undertaking a series of CFAs to ensure

they had acceptable measurement properties.

Six such factors were found that had good measurement properties,

although perceived financial risk had a construct reliability that was

marginally below the suggested standard of 0.70 (0.67). This was accepted

as the construct‟s AVE was 0.50. The six retained constructs and intention

to re-buy and switching cost constructs were assessed for discriminant

validity, which led to a decision to exclude service quality from the revised

model and the model was revised accordingly.

124 124

The next chapter discussed the structural relationships between the

constructs, which were examined through an assessment of measurement

model and an estimation of the revised structural model.

125 125

Chapter Five

Data Analysis – Part Two

5.1 Introduction

Chapter Four discussed the assessment of the constructs‟ measurement

properties, the correction of the multicollinearity that was found between

some of the constructs, the discriminant validity assessment of the

constructs and the model revision that resulted, which led to the revised

model that was shown in Figure 4.13. The present chapter reports the

results of estimating the measurement model, the estimation of the

structural model and a discussion of the estimated path estimates. The

results of an examination of the effect, direct and indirect effects of the

exogenous constructs on all the endogenous constructs are reported and,

finally, the results obtained when the suggested model was compared to an

alternative model are discussed.

5.2 The Measurement Model

Stage 3: Examining the Measurement Model

Anderson and Gerbing (1988) recommended that the measurement model

should be assessed before the structural model is estimated. SEM

techniques need large samples to ensure an appropriate ratio of

observations to parameter estimates (Hair et al., 2006). As already noted,

two hundred responses were obtained. Given the number of latent

126 126

constructs in the model, the number of parameters estimated needed to be

reduced to ensure stability. This was done by using the partial

disaggregation approach suggested by Bagozzi and Heatherton (1994). In

the partial disaggregation method, the items used to measure a construct

are randomly grouped into two indicators, which ensure there are enough

degrees of freedom to estimate the structural model, but reduces the

number of parameters that are estimated and provides estimates that are

less sensitive to measurement error (Bagozzi & Heatherton, 1994). This

allows the possibility to “retain the multiple measures approach to structural

equation modelling” (Sweeney & Soutar, p. 218), increasing the chance of

estimating meaningful structural estimates.

The revised measurement model was estimated by allowing all of the

structural constructs to intercorrelate, as is shown in Figure 5.1. As was

noted in Chapter Three, Anderson and Gerbing (1988) argued measurement

error in such a model can be attributed a lack of model fit. As was also noted

in Chapter Three, Hair et al. (2006) have recommended that the

measurement model‟s fit should be assessed by using the normed chi-

square index, the Goodness of Fix Index (GFI), the Normed Fit Index (NFI),

the Comparative Fit Index (CFI), the Tucker-Lewis index (TLI), the

Standardised Root Mean Square Residual (SRMR) and the Root Mean

Square Error of Approximation (RMSEA). The indices were used in the

present study.

127 127

The chi-square statistic for the measurement model was 85.67, which was

significant (p < 0.01). However, the normed chi-square was 2.19, which was

well below 3, which is the value that has been suggested as showing a good

fit (Kline, 2005) and all of the other fit indexes were acceptable (GFI = 0.93;

RMSEA = 0.07; SRMR = 0.04; NFI= 0.94; CFI = 0.96 and TFI = 0.94).

Consequently, it was accepted that the measurement model fitted the

obtained data and that the suggested research model should be estimated.

The results of this estimation are discussed in subsequent sections.

Commitment

to RelationshipIntention

to Re-buy

Past

SatisfactionProduct

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

0.74

-0.47

0.64

0.72

0.66

0.68

0.68

0.61

0.58

0.63

0.72

-0.42-0.39

-0.36

-0.52

Figure 5.1: The Revised Measurement Model for the Present Study

(Indicators excluded to improve readability)

5.3 The Structural Model

Stage 4: Assessing the Structural Model and the Path Estimates

As was the case with the measurement model, the structural model was

estimated using a partial dissagregation approach. Past research provided

support for the hypothesised relationships, as was explained in Chapter Two.

While the originally suggested model had a relationship between service

128 128

quality and perceived value for money, it was not possible to test this in the

present model as service quality was excluded due to it not having

discriminant validity with intention to re-buy. The same problem meant the

commitment to relationship and customer loyalty relationship could not be

tested as customer loyalty was excluded. The revised structural model,

which is shown in Figure 5.2, excluding the two composite indicators, that

was used to estimate the structural model was estimated and its fit and path

estimates are discussed in subsequent sections.

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

Figure 5.2: The Revised Structural Model

5.3.1 The Structural Model Fit and Path Estimates Assessment

The revised structural model had a significant chi-square statistic (x2 =

107.51, df = 45, p < 0.001). However, the normed chi-square was 2.39,

which is below the recommended 3.0 level, indicating a good fit. Further, the

other fit indices also suggested the model fitted the data well (GFI = 0.92;

RMSEA = 0.08; SRMR = 0.06; NFI = 0.92; CFI = 0.95 and TFI = 0.93). It

seems the revised model fitted the obtained data.

129 129

Following the fit assessment, the structural path coefficients of the revised

model, which are shown in Figure 5.3, were examined. All of the path

coefficients were significant at least at the one percent level (p < 0.01)

except for the path from perceived financial risk to perceived value for

money (p = 0.03), which was significant at the five percent level, which

meant all of the paths should be examined (Byrne, 2001)

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

+0.37 (p = 0.00)

+0.52 (p = 0.00)

-0.3

5 (

p =

0.0

02

)

+0.7

0 (

p =

0.0

0)

+0.43

(p =

0.0

0)

-0.3

9 (

p =

0.0

0)

-0.16 (p = 0.03) +0.43 (p = 0.00)+0.70 (p = 0.00)

Figure 5.3: The Revised Model with Standardised Path Coefficients

The various hypothesised relationships in the revised model, which were

outlined in section 4.6.2, were examined to see whether they were

supported. As the service quality and customer loyalty constructs were

excluded, three hypotheses (Hypotheses 1, 2 and 7) could not be tested,

while Hypothesis 10 and Hypothesis 11 are tested in Chapter Six.

Hypothesis 3 suggested the greater an organisational buyer‟s commitment

to a relationship with an existing supplier, the greater will be their intention to

re-buy from that supplier. This hypothesis was supported in the present

130 130

study as the impact of commitment to relationship on intention to re-buy was

significant well beyond the one percent level.

Hypothesis 4 suggested the greater an organisational buyer‟s perceived

value for money, the greater will be their commitment to their relationship

with their supplier. The results also supported this hypothesis, as the impact

perceived value for money had on commitment to the relationship was

significant well beyond the one percent level.

Hypothesis 5 suggested the greater an organisational buyer‟s risk

perception, the lower will be their value for money perception. Perceived

financial risk did have a negative influence on perceived value for money

that was significant beyond the five percent level. Consequently, this

hypothesis was supported.

Hypothesis 6 suggested the greater an organisational buyer‟s perception of

product quality, the greater will be their value for money perception. The

results obtained supported this hypothesis as product quality has a positive

impact on perceived value for money that was significant well beyond the

one percent level.

Hypothesis 8 suggested the higher an organisational buyer‟s satisfaction

with prior purchases, the greater will be their value for money perception.

This hypothesis was supported as satisfaction has a positive impact on

perceived value for money that was significant well beyond the one percent

level.

131 131

Hypothesis 9 suggested the better an organisational buyer‟s perception of

the quality of a product, the greater will be their intention to re-buy from their

supplier. The results obtained suggested product quality has positive impact

on customer re-buying intention that was significant well beyond the one

percent level.

All of the hypotheses suggested by the revised model were supported.

Consequently, their relative impact was assessed. This was done initially by

examining the various paths‟ standardised regression coefficients. As can

be seen in Figure 5.3, product quality had a greater impact on perceived

value for money (0.43) than did perceived financial risk (-0.16) or prior

satisfaction (0.37). Further, perceived value for money had a positive impact

on commitment to the relationship (0.70). Commitment to the relationship

had a strong positive influence on intention to re-buy (0.43), as did product

quality (0.52).

5.3.2 An Examination of the Total Effects, Direct Effects and Indirect Effects of the Models in the Revised Model

However, a full assessment of the relative impact the various constructs had

on intention to re-buy can only be made by looking at both the direct and the

indirect effects. The results obtained in this case are shown in Table 5.1.

132 132

Table 5.1: Direct, Indirect and Total Effects of the Revised Model.

Effect of =>

Past Satisfaction

Perceived Risk -

Financial

Product Quality

Perceived Value for Money

Commitment to

Relationship - Affective

on

=>

Perceived Value for Money

0.37 -0.16 0.43 - -

- - - - -

0.37 -0.16 0.43 - -

Commitment to Relationship

- - - 0.70 -

0.26 -0.11 0.30 - -

0.26 -0.11 0.30 0.70 -

Intention to Re-buy

- - 0.52 - 0.43

0.11 -0.05 0.13 0.30 -

0.11 -0.05 0.65 0.30 0.43

Notes: 1st row shows the value for direct effect

2nd row shows the value for indirect effect

3rd row shows the value for total effect

Of the three exogenous constructs, product quality (0.65) had a greater total

effect on intention to re-buy than did satisfaction (0.11) or perceived financial

risk (-0.05). Perceived value for money affected commitment to the

relationship (0.70) directly, but also had an indirect effect on intention to re-

buy (0.30), while commitment to the relationship had a direct effect on

intention to re-buy (0.43). It is clear product quality and commitment to the

relationship are the crucial predictors of future intentions and that managers

need to think strategically about these constructs. Past satisfaction and

perceived financial risk were also significant predictors, but their impacts

were considerable lower and, therefore, they are less important levers.

The square multiple correlations, which show how much of the variation in

an endogenous constructs is explained by the estimated model, are shown

in Table 5.2. The results suggested sixty-seven percent of the variance in

perceived value for money was explained by past satisfaction, perceived

133 133

financial risk and product quality, while forty-nine percent of the variance in

commitment to the relationship was explained. Sixty-nine percent of the

variation in intention to re-buy was explained by its antecedents in the

revised model, suggesting the model was a very good predictor of

organisational buyers‟ re-buying intention.

Table 5.2: Square Multiple Correlations for the Revised Model

Construct Square Multiple Correlation

Estimate

Perceive Value for Money 0.67

Commitment to Relationship 0.49

Intention to Re-buy 0.69

Further tests were undertaken to determine the mediating role perceived

value for money played. The results obtained in this analysis are discussed

in the next section.

5.4 Assessing Perceived Value for Money’s Mediating Role

The conditions suggested by Baron and Kenny (1986) were used to formally

assess perceived value for money‟s role as a mediator in the relationships

between the antecedent variables (i.e. past satisfaction, perceived financial

risk and product quality) and commitment to the relationship, which can be

seen in Figure 5.4. A variable is a mediator when:

(1) Variations in the independent variable significantly account for

variations in the dependent variable (path c in Figure 5.4).

(2) Variations in the independent variable significantly account for

variation in the presumed mediator (path a in Figure 5.4).

134 134

(3) Variations in the presumed mediator significantly account for

variation in the dependent variable (path b in Figure 5.4).

(4) When path a and path b are controlled, a previously significant

relation between the independent variable and dependent

variables (path c) is no longer significant. The strongest

demonstration of mediation is when path c’ in Figure 5.4 is zero.

Mediator

Variable

Dependent

VariableIndependent

Variable

a b

Independent

Variable

Dependent

Variable

c'

c

Figure 5.4: Conditions for Mediator Effect

As can be seen from the revised model (Figure 5.3), perceived value for

money was modelled as a mediator between its antecedent variables (past

satisfaction, perceive financial risk and product quality) and commitment to

the relationship. These relationships suggest:

(1) Past Satisfaction Perceived Value for Money Commitment to

Relationship

135 135

(2) Perceived Financial Risk Perceived Value for Money

Commitment to Relationship

(3) Product Quality Perceived Value for Money Commitment to

Relationship

The mediator effect perceived value for money had in each of these

suggested relationships was tested. The results obtained are discussed in

subsequent sections.

5.4.1 The Mediation Effect of Perceived Value for Money on the Past Satisfaction - Commitment Relationship

The results obtained, which are shown in Figure 5.5, suggest past

satisfaction had a significant positive influence on perceived value for money

(path a) with a standardised beta coefficient of 0.64 (p = 0.00). Perceived

value for money also had a positive impact on commitment to the

relationship (path b) with a standardised coefficient of 0.65 (p = 0.00). The

impact past satisfaction had on commitment to the relationship (path c)

excluding the mediator was examined next. The results suggested past

satisfaction had a positive influence on commitment to the relationship, with

a standardised beta coefficient of 0.62 (p = 0.00).

The impact past satisfaction had on commitment to the relationship when

the mediator was included (path c‟) was still significant (p = 0.00). However,

the beta coefficient was reduced by approximately half to 0.35. This

suggests perceived value for money had a mediating effect, although

perceived value for money was not a dominant mediator in the relationship

136 136

between past satisfaction and commitment to relationship in the present

study.

Perceived Value

for Money

Commitment

to RelationshipPast

Satisfaction

a: +

0.64

(P =

0.0

0)

b: +0.65 (P = 0.00)

Past

Satisfaction

Commitment

to Relationship -

Affective

c': +0.35 (P = 0.00)

c: +0.62 (p = 0.00)

Figure 5.5: Mediator Effect of Perceived Value for Money (Past Satisfaction Perceived value Commitment)

5.4.2 The Mediation Effect of Perceived Value for Money on the Perceived Financial Risk - Commitment Relationship

As can be seen in Figure 5.6, perceived financial risk had a negative

influence on perceived value for money (path a) with a standardised

coefficient of -0.30 (p = 0.00). Path b was also significant (p = 0.00), as

perceived value for money had a positive impact on commitment to the

relationship (0.65). Perceived financial risk had a direct negative influence

on commitment to the relationship (-0.54) (p = 0.00).

When path a and path b were controlled, perceived financial risk still had a

significant (p = 0.00) negative impact on commitment to relationship (path c’),

although the standardised coefficient was reduced to -0.33. This again

137 137

suggests perceived value for money had a mediating effect but, once again,

it was not a dominant mediator.

Perceived Value

for Money

Commitment

to RelationshipPerceived Risk -

Financial

a: -0

.30

(P =

0.0

0)

b: +0.65 (P = 0.00)

Perceived Risk -

Financial

Commitment

to Relationship -

Affective

c': -0.33 (P = 0.00)

c: -0.54 (p = 0.00)

Figure 5.6: Mediator Effect of Perceived Value for Money (Perceived Risk Perceived Value Commitment)

5.4.3 The Mediation Effect of Perceived Value for Money on the Product Quality - Commitment Relationship

As can be seen in Figure 5.7, product quality had a positive influence on

perceived value for money (path a = 0.67) (p = 0.00). Product quality also

had a positive impact on commitment to the relationship (path b = 0.65) (p =

0.00). Product quality also had a direct impact on commitment to the

relationship (path c = 0.59) (p = 0.00). When path a and path b were

controlled, the relationship was still significant (path c’ = 0.26) (p = 0.02).

This suggests perceived value for money had a mediating effect but

perceived value for money was not a dominant mediator in this relationship

either.

138 138

Perceived Value

for Money

Commitment

to RelationshipProduct

Quality

a: +

0.67

(P =

0.0

0)

b: +0.65 (P = 0.00)

Product

Quality

Commitment

to Relationship -

Affective

c': +0.26 (P = 0.02)

c: +0.59 (p = 0.00)

Figure 5.7: Mediator Effect of Perceived Value for Money (Product Quality Perceived Value Commitment)

While Sobel‟s (1982) test could have been used to further assess these

mediating relationships, the test is very conservative (MacKinnon, Warsi, &

Dwyer, 1995). Consequently, bootstrapping has been suggested as an

alternative approach (Shrout & Bolger, 2002). The AMOS software provides

bootstrapping procedures that can be used to assess mediation (Arbuckle,

1999). The procedures enable the significance of the indirect effects to be

examined as they estimate bootstrapped standard errors of the indirect

effects. In the present study, these procedures were used and, in each case,

the results obtained supported all of the outcomes that were suggested by

Baron and Kenny‟s (1986) approach.

139 139

5.5 An Alternative Model

The results described in the previous sections implied perceived value for

money did not play a dominant mediating role in the various relationships

between its antecedent variables and commitment to the relationship,

suggesting further improvement could be made to the revised model as

perceived value for money could be modelled as an antecedent variable, as

can be seen in Figure 5.8.

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

Figure 5.8: Proposed Alternative Model

The fit of the proposed alternative model was examined. Following

Anderson and Gerbing‟s (1988) recommendation, the measurement model

was assessed before the structural model was estimated, as is outlined in

subsequent sections.

140 140

5.5.1 Assessing the Fit and Path Estimates of the Alternative Model

All of the constructs remained the same in the alternative model.

Consequently, all of the fit indices of the measurement model were the

same and the measurement model was seen to be a reasonable fit to the

data. Consequently, the alternative model was estimated.

The structural path estimates obtained in this case can be seen in Figure

5.9. The model had a significant chi-square statistics (x2 = 88.54, df = 42, p

= 0.00) indicating a lack of fit. However, the normed chi-square was 2.11,

which was well below the recommended level of 3.0, suggesting a good fit.

An examination of the other fit indices (GFI = 0.93; RMSEA = 0.07; SRMR =

0.04; NFI = 0.94; CFI = 0.96 and TFI = 0.94) also suggested the data fitted

the model well. All of the path coefficients were significant at least at the five

percent level, except for the path between product quality and commitment

to the relationship (p = 0.44).

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

+0.28 (p = 0.007)

+0.25 (p = 0.04)

-0.28 (p = 0.001)

+0.43 (p = 0.00)

+0.50 (p = 0.00)

+0.0

9 (p

= 0

.44)

Figure 5.9: The Alternative Model – Structural Model and Path Estimates

141 141

Product quality and commitment to the relationship both had positive

influences on intention to re-buy (0.50 and 0.43), while product quality had a

greater impact on intention to re-buy than did commitment to the relationship.

Past satisfaction and perceived value for money had positive influences on

commitment to the relationship (their regression weights were 0.28 and 0.25

respectively). While, perceived financial risk negatively influenced

commitment to the relationship (-0.28), it had about the same impact on

commitment to the relationship as did past satisfaction or perceived value

for money. As can be seen in Table 5.3, the large total effect product quality

had on intention to re-buy (0.54) suggests this construct had a greater

impact on intention to re-buy than did satisfaction (0.12), perceived value for

money (0.11) or perceived financial risk (-0.12).

Table 5.3: Direct, Indirect and Total Effects of the Alternative Model

Effect of => Past

Satisfaction

Perceived Value for Money

Perceived Risk -

Financial

Product Quality

Commitment to

Relationship - Affective

on

=>

Commitment to Relationship

0.28 0.25 -0.28 0.09 -

- - - - -

0.28 0.25 -0.28 0.09 -

Intention to Re-buy

- - - 0.50 0.43

0.12 0.11 -0.12 0.04 -

0.12 0.11 -0.12 0.54 0.43

Notes: 1st row shows the value for direct effect

2nd row shows the value for indirect effect

3rd row shows the value for total effect

As can be seen in Table 5.4, the square multiple correlations in the

alternative model suggested fifty-three percent of the variance in

commitment to relationship was explained by perceived financial risk,

product quality and past satisfaction. Intention to re-buy had a square

142 142

multiple correlation of 0.69, suggesting approximately a little more than two-

third of intention to re-buy‟s variance was explained by its antecedents.

Table 5.4: Square Multiple Correlations for the Alternative Model

Construct Square Multiple Correlation

Estimate

Commitment to Relationship 0.53

Intention to Re-buy 0.69

5.5.2 Assessing the Mediation Effect of Commitment to the Relationship in the Alternative Model

Using the same mediation assessment conditions suggested by Baron and

Kenny (1986), commitment to the relationship‟s role as a mediator in the

relationships between the antecedent variables (past satisfaction, perceived

value for money and perceived financial risk) except product quality and

intention to re-buy were assessed. As can be seen from the estimated

model (Figure 5.9):

1. Perceived Value for Money Commitment Intention to Re-buy

2. Past Satisfaction Commitment Intention to Re-buy

3. Perceived Financial Risk Commitment Intention to Re-buy

5.5.2.1 The Mediation Effect of Commitment to the Relationship on the Perceived Value for Money - Intention to Re-buy Relationship

As can be seen in Figure 5.10, perceived for money had a positive influence

on commitment to the relationship (path a), with a standardised coefficient of

0.65 (p = 0.00). Commitment to the relationship had a positive influence on

intention to re-buy (path b), with a standardised coefficient of 0.70 (p = 0.00).

143 143

Perceived value for money had a direct positive influence on intention to re-

buy (path c), with a standardised coefficient of 0.62 (p = 0.00). The impact

perceived value for money had on intention to re-buy when the mediation

path was included was still significant (path c‟ = 0.27) (p = 0.006), although

the standardised coefficient was reduced by slightly less than half. This

suggests commitment to the relationship had a mediating effect, but was not

a dominant mediator in the relationship between perceived value for money

and intention to re-buy.

Commitment

to Relationship

Intention to

Re-buyPerceived Value

for Money

a: +

0.65

(P =

0.0

0)

b: +0.70 (P = 0.00)

Perceived Value

for Money

Intention to

Re-buy

c': +0.27 (P = 0.006)

c: +0.62 (p = 0.00)

Figure 5.10: Mediator Effect of Commitment to Relationship (Perceived Value Commitment Intention to Re-buy)

5.5.2.2 The Mediation Effect of Commitment to the Relationship on the Past Satisfaction - Intention to Re-buy Relationship

As can be seen in Figure 5.11, past satisfaction had a positive influence on

commitment to the relationship (path a), with a standardised coefficient of

0.62 (p = 0.00). Commitment to relationship had a positive influence on

intention to re-buy (path b), with a standardised coefficient of 0.70 (p = 0.00).

144 144

Past satisfaction had a direct positive impact on intention to re-buy (path c),

with a standardised coefficient of 0.67 (p = 0.00).

The impact past satisfaction had on intention to re-buy when the mediation

path was included was still significant (path c’ = 0.36) (p = 0.00), although

the standardised coefficient was reduced by approximately half. This

suggests commitment to the relationship had a mediating effect but was not

a dominant mediator in the relationship between past satisfaction and

intention to re-buy.

Commitment

to Relationship

Intention to

Re-buyPast

Satisfaction

a: +

0.62

(P =

0.0

0)

b: +0.70 (P = 0.00)

Past

Satisfaction

Intention to

Re-buy

c': +0.36 (P = 0.00)

c: +0.67 (p = 0.00)

Figure 5.11: Mediator Effect of Commitment to Relationship (Past Satisfaction Commitment Intention to Re-buy)

5.5.2.3 The Mediation Effect of Commitment to the Relationship on the Perceived Financial Risk - Intention to Re-buy Relationship

As can be seen in Figure 5.12, perceived financial risk had a negative

influence on commitment to the relationship (path a), with a standardised

coefficient of -0.54 (p = 0.00). Commitment to the relationship had a positive

145 145

influence on intention to re-buy (path b), with a standardised coefficient of

0.70 (p = 0.00). Perceived financial risk had a direct negative impact on

intention to re-buy (path c), with a standardised coefficient of -0.46 (p =

0.00).

The impact perceived financial risk had on intention to re-buy when the

mediation path was included was not significant (path c’ = -0.14) (p = 0.16).

This suggests commitment to the relationship had a dominant mediating

effect on the relationship between past perceived financial risk and intention

to re-buy.

Commitment

to Relationship

Intention to

Re-buyPerceived Risk-

Financial

a: -0

.54

(P =

0.0

0)

b: +0.70 (P = 0.00)

Perceived Risk -

Financial

Intention to

Re-buy

c': -0.14 (P = 0.16)

c: -0.46 (p = 0.00)

Figure 5.12: Mediator Effect of Commitment to Relationship (Perceived Financial Risk Commitment Intention to Re-buy)

These results suggest commitment to the relationship had a mediating effect

in all of the relationships and that it was a dominant mediator to the

146 146

relationship between perceived financial risk and intention to re-buy in the

alternative model. Consequently, its role as a mediator was retained.

Once again, the AMOS program‟s bootstrapping procedures were used to

further examine these mediating relationships. In each case, the results

obtained supported the outcomes suggested by Baron and Kenny‟s (1986)

approach.

5.6 Comparing the Revised Model with the Alternative Model

As was noted in the previous section, the revised model had an acceptable

fit, as does the alternative model. Favourable fit statistics are highly

desirable, but do not prove which is the better model. The revised and

alternative models are not nested models, but they are competing or

equivalent models. Following the competing model process suggested by

Hair et al. (2006), the models‟ absolute, incremental and parsimony fit

indices were compared to determine which was the better model.

The absolute fit indices (Chi-Square Statistics, Normed Chi-Square

Statistics, GFI, AGFI, RMSEA and SRMR) and the incremental fit indices

(NFI, CFI and TLI), which were described in Chapter Four, were used to

compare the models. In addition, three parsimony fit indices, which are

generally used for model comparison, were also examined (Hair et. al.,

2006). These were:

147 147

1) The Parsimony Normed Fit Index (PNFI), which adjusts the normed fit

index (NFI) by multiplying it by the parsimony ratio. It takes on some

of the added characteristics of an incremental fit index relative to

absolute fit indices and favours less complex models. The index

values range from 0 to 1 and can be used to compare one model to

another. A model with a higher PNFI value has a relatively better fit.

2) Akaike‟s Information Criterion (AIC), which adjusts model chi-square

to penalise for model complexity and over-parameterisation. The

absolute value of AIC has no meaning, but competing models‟ AIC

values can be compared, with a lower AIC value suggesting a better-

fitting model.

3) Browne-Cudeck‟s Criterion (BCC), which was developed specifically

to analyse moment structure, penalises model complexity and lack of

parsimony more than the AIC. Like the AIC, absolute values have no

meaning and a lower BCC value suggests a better-fitting model.

An examination of the models‟ absolute and incremental fit indices, which

can be seen in Table 5.5, suggests the alternative model had a better fit

than the revised model. However, as can also be seen in Table 5.6, the

revised model had a better PNFI (0.62), suggesting it was better-fitting

model than the alternative model (PNFI = 0.59). Two of the three parsimony

indexes (i.e. the AIC and the BCC) favoured the alternative model.

Consequently, taking account of the absolute and incremental fit indices, it

148 148

seems the alternative model was the better-fitting model and it was

accepted and used in the subsequent discussion.

Table 5.5: The Absolute and Incremental Fit Indices for the Revised and Alternative Models

Construct

Absolute Fit Measures Incremental Fit

Indices

Chi-Square Statistic

Normed Chi-

Square GFI RMSEA SRMR NFI CFI TLI

Revised Model 107.50

(df=45; p=0.00) 2.39 0.92 0.08 0.06 0.92 0.95 0.93

Alternative Model 88.54

(df=42; p=0.00) 2.11 0.93 0.75 0.04 0.94 0.97 0.95

Table 5.6: The Parsimony Fit Indices for the Revised Model and Alternative Models

Construct

Parsimony Fit Indices (comparing models)

PNFI AIC BCC

Revised Model 0.62 173.50 178.10

Alternative Model 0.59 160.50 165.50

As the alternative model was accepted, it seems perceived financial risk,

perceived value for money, past satisfaction and product quality should be

modelled as antecedents to commitment to the relationship, with product

quality also having a direct influence on intention to re-buy. However, as

was noted earlier, commitment to relationship was not a dominant mediator,

except in the relationship between perceived financial risk and intention to

re-buy.

149 149

5.7 Summary

The six hypotheses that were tested were supported, although three of the

originally suggested hypotheses could not be tested due to the exclusion of

the loyalty and service quality constructs from the analysis because of

problems with their measurement properties.

Following Baron and Kenny‟s (1986) mediation assessment procedure,

perceived value for money was found not to be a dominant mediator. This

led to the development of an alternative model in which perceived value for

money was modelled as an antecedent to commitment to the relationship.

The path coefficient between product quality and commitment to relationship

was not significant in this model. Commitment to the relationship was a

dominant mediator between perceived financial risk and intention to re-buy.

Based on its better fit, the alternative model, in which perceived value of

money was modelled as an antecedent and commitment to relationship was

retained as a mediator, was accepted.

The next chapter reports the results obtained in testing hypothesis 10, which

suggested the effect commitment to the relationship has on a buyer‟s

intention to re-buy is higher when switching costs are high and hypothesis

11, which suggested effect product quality has on a buyer‟s intention to re-

buy is higher when switching costs are low.

150 150

Chapter 6

Data Analysis – Part Three

6.1 Introduction

In Chapter Four, it was noted that most of the constructs had good

measurement properties. However, discriminant validity could not be

established between some of the constructs. Consequently, exploratory

factor analysis was used to correct this problem. As a result, six factors with

good measurement properties and discriminant validity were found.

In Chapter Five, the results of estimating the revised measurement model

were reported. The measurement model was found to be a reasonable fit to

the data and the structural model was estimated. The revised model fitted

the data well and all of the estimated path estimates were significant at least

at the five percent level. The mediating effect of perceived value for money

and commitment to the relationship were then examined and the results

obtained suggested perceived value for money was not a dominant mediator,

but commitment to the relationship was a dominant mediator for at least one

of the relationships in the model.

The analysis resulted in the development of an alternative model that

included past satisfaction, perceived value for money, perceived financial

risk and product quality as antecedents to commitment to the relationship.

The alternative model was estimated and compared to the revised model.

The difference in the two models‟ chi-square statistics suggested the

151 151

alternative model was a superior model, which was used in the final stage of

the analysis, in which the moderating effect of switching costs was

examined. The results of this analysis are discussed in subsequent sections.

6.2 An Analysis of the Moderating Effect of Switching Costs

Stage 5: Assessing the Moderating Effect of Switching Costs

It was suggested in Chapter One and Chapter Two that switching costs

might moderate the relationships between product quality and intention to

re-buy, as well as the relationship between commitment to relationship and

intention to re-buy. These moderating effects are shown in Figure 6.1 and

the test of this effect was the focus of two hypotheses (Hypothesis 10 and

Hypothesis 11), as was outlined in Chapter Two.

Figure 6.1: Hypothesised Moderator Effects of Switching Costs

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

Product

Quality

152 152

The hypotheses were:

Hypothesis 10: The effect commitment to the relationship has on a

buyer‟s intention to re-buy is higher when switching costs are high.

Hypothesis 11: The effect product quality has on a buyer‟s intention

to re-buy is higher when switching costs are low.

The measurement properties of the switching costs construct were good,

which allowed the construct to be used in the present stage of the analysis.

Multiple-group analysis, which takes account of the relationship between

latent constructs (Homburg & Giering, 2001), was used to test the two

hypotheses. A median split of the switching costs construct was used to

divide the sample into two sub-groups. Respondents who scored less than

the median were categorised as perceiving low switching costs, while

respondents who scored above the median were categorised as perceiving

high switching costs. The process used to examine the moderating effects of

switching costs involved a number of steps (Jöreskog & Sörbom, 1993),

namely:

1. Estimating a similar path model for the two sub-groups in which the

paths were not constrained to be equal across the two sub-groups.

2. Estimating a path model for each of the sub-groups in which the

paths were constrained to be equal across the two sub-groups.

3. Computing a chi-square difference statistic by subtracting the

constrained model‟s chi-square statistic from the unconstrained

153 153

model‟s chi-square statistic. As was the case, when mediation was

being tested, the chi-square difference statistic is distributed as a chi-

square statistic with degrees of freedom equal to the difference

between the degrees of freedom in the unconstrained and

constrained models. If the chi-square difference statistic is

significantly different to zero, a moderating effect can be assumed.

4. Computing the t-statistics of the unconstrained path coefficients in the

two sub-groups if this is the case. If the t-statistic is greater than

some critical value (generally 1.96, which is the five percent

significance score for a two-tailed difference test, or 1.66, which is the

five percent score for a one-tailed difference test), a moderating effect

can be assumed for that relationship.

5. Examining the path coefficients to see which is greater to determine

the nature of the moderating effect.

Table 6.1 shows the chi-square difference between the constrained and

unconstrained models. As can be seen in the table, the chi-square

difference between the unconstrained model and a model in which the

measurement weights were constrained to be equal was 11.92 (Δdf = 6),

which was not significant (p = 0.06). This suggests there was no significant

difference between the unconstrained and constrained measurement

weights and that the multiple-group analysis structural model can be

estimated as the latent constructs are being viewed in the same way in both

subgroups.

154 154

Assuming the measurement weights were the same across the two-groups,

the chi-square difference between the unconstrained and the a model in

which the path coefficients were constrained to be equal was 18.29 (Δdf = 6;

p < 0.01), which suggested there was a significance structural difference

between the two sub-groups and that they relate to the latent variables

differently.

Table 6.1: Chi-Square Difference between Constrained and Unconstrained Models

Assuming model Unconstrained to be correct:

Model Δdf Δx2 p

Measurement weights 6 11.92 0.06

Structural weights 12 30.22 0.03

Assuming model Measurement weights to be correct:

Model Δdf Δx2 p

Structural weights 6 18.29 0.01

Table 6.2 shows the sub-groups‟ path coefficients and the t-values of their

differences. The critical value accepted was 1.96 as a more stringent two-

tailed test was undertaken. As can be seen in Table 6.2, both of the t-values

of interest were well above the 1.96 value. This suggests switching costs

had a moderating effect on the relationship between commitment and

intention to re-buy and on the relationship between product quality and

intention to re-buy.

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Table 6.2: Multiple Group Analysis – Switching Cost as a Moderator Variable

Path

Path Coefficient

(sub-groups) t-value

Degree of Freedom

Critical Value

Low High

Commitment to Relationship -----> Intention to Re-buy

0.20 0.90 2.76

84 1.66 Product Quality -----> Intention to Re-buy

0.69 -0.03 -4.79

The impact commitment to the relationship had on intention to re-buy was

higher when switching costs were high (0.90), than when switching costs

were low (0.20). Hypothesis 10 had suggested the effect commitment to the

relationship has on a buyer‟s intention to re-buy is higher when switching

costs are high. Consequently, the hypothesis was supported in the present

context.

Product quality did not have a significant impact on intention to re-buy (-0.03)

(p = 0.88) when switching costs were high, but had a very significant impact

(0.69) (p = 0.00) when switching costs were low. However, when switching

costs were low, product quality did impact on customer‟s re-buying

intentions. Hypothesis 11 had suggested the effect product quality has on a

buyer‟s intention to re-buy is higher when switching costs are low.

Consequently, this hypothesis was also supported.

6.3 The Relationship between Product Quality and Intention to Re-buy with Switching Costs as the Moderator

As was shown in Figure 5.9, product quality had a significant direct positive

impact on re-buying intention, but when commitment to relationship was

modelled as a mediator, its overall impact on intention to re-buy was less.

156 156

The direct and indirect influence product quality had on re-buying intention

has been widely discussed (e.g., Dodds et al., 1991; Hellier et al., 2003; Hult

et al., 2007). However, the moderating role switching costs play has not

been widely discussed.

The introduction of switching costs as a moderator provides a different

perspective on the relationship between product quality and re-buying

intention. As can be seen in Figure 6.2, product quality had a strong direct

impact on intention to re-buy when switching costs were low but its

relationship to commitment to the relationship was not significant in this

situation. Of the other antecedent variables, perceived value for money had

a greater impact on intention to re-buy than did past satisfaction or

perceived financial risk. In a low switching cost situation, emphasis should

be placed on product quality as it has a large direct impact on buyers‟

intention to re-buy.

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Risk -

Financial

0.04

0.4

-0.28

0.2

0.69

0.02

Figure 6.2 – The Alternative Model at Low Switching Costs

157 157

As can be seen in Figure 6.3, when switching costs were high, product

quality did not have a direct effect on intention to re-buy. Product quality did,

however, have an indirect impact on intention to re-buy through commitment

to the relationship. Perceived value for money also indirectly impacted on

intention to re-buy, although its impact was less. Perceived financial risk had

very little impact on commitment to the relationship and its indirect influence

on intention to re-buy was low. In a high switching cost situation, product

quality was not the single factor that most influenced re-buying intention. In

this situation, emphasis should be placed on perceived value for money and

product quality. However, given the significant role past satisfaction plays,

attempts should be made to improve current satisfaction as this will improve

future buying intentions.

Commitment

to Relationship

Intention

to Re-buy

Past

Satisfaction

Product

Quality

Perceived

Value

for Money

Perceived

Financial

Risk

0.36

0.19

-0.05

0.90

-0.03

0.36

Figure 6.3 – The Alternative Model at High Switching Costs

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6.4 Summary

Multiple-group analysis was used to assess the moderating effect switching

costs had in the estimated model, following the steps suggested by

Jöreskog and Sörbom, (1993). The results suggested switching costs

moderated the relationship between commitment to the relationship and

intention to re-buy, as commitment to the relationship‟s impact was greater

when switching costs were high. Switching costs also moderated the

relationship between product quality and intention to re-buy, as, when

switching costs was high, product quality had no direct effect. However,

when switching costs were low, product quality had a strong direct positive

effect. Consequently, Hypothesis 10 and Hypothesis 11 were supported in

the present study. Chapter Seven summarises the present study and

discusses the limitations and implications of the present study, as well as

suggesting some future research.

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Chapter Seven

Conclusion, Limitation and Implication of the Research

7.1 Introduction

Customer perceived value has gained prominence in marketing in recent

years, although mainly in business-to-consumer contexts. Considerably less

value research has been undertaken in business-to-business contexts. As a

result, very little is known about the interrelationships between customer

perceived value and other variables that may influence business-to-business

re-buying intentions. Chapter Seven summarises the present study that was

undertaken to fill some of this gap and discusses the implications of the

results that were obtained. The Chapter also discusses the study‟s

theoretical and managerial implications, as well as suggesting some future

research opportunities.

7.1.1 A summary of the Present Study

The present study was designed to develop a conceptual model that

explained how perceived risk, past satisfaction, product and service quality

influenced value for money and value for money‟s subsequent impact on

buyers‟ commitment to their relationship with their supplier, loyalty and

intention to re-buy. The moderating effects switching costs had on the

various relationships in the model were also examined. The suggested

model originally had nine constructs. However, despite having good

measurement properties otherwise, there was a discriminant validity

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problem as some of the exogenous constructs could not be differentiated.

The data were re-examined by undertaken an exploratory factor analysis to

better understand the problem. This analysis reduced the number of

constructs, leading to a revised model, which was shown in Figure 5.3. As

the constructs all had good measurement properties and discriminant

validity, the model was estimated and the model was found to be a good fit

to the obtained data. An examination of the structural fit and path estimates

supported hypotheses 3, 4, 5, 6, 8 and 9. Hypotheses 1, 2 and 7 could not

be tested as the service quality and customer loyalty constructs were

removed from the model because of the discriminant validity problems

mentioned earlier.

The mediating effects of perceived value for money and commitment to the

relationship were examined, which led to the development of an alternative

model as commitment to the relationship was a dominant mediator, but

value for money was not. The alternative model, which was shown in Figure

5.9, fitted the data better then the initially estimated model. Consequently, it

seems product quality, perceived financial risk, value for money and past

satisfaction should be modelled as antecedents to commitment to the

relationship, with product quality also having a direct influence on intention

to re-buy. It was also found that switching costs moderated the relationship

between commitment and intention to re-buy and the relationship between

product quality and intention to re-buy, supporting hypothesis 10 and

hypothesis 11. The hypotheses tests and the results obtained are

summarised in Table 7.1.

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Table 7.1: Summary of Hypotheses Tests and Results

Hypothesis Description Result

Hypothesis 1 The greater the organisational buyer‟s loyalty of the product and service, the higher would be their intention to re-buy

Not Tested

Hypothesis 2 The greater the organisational buyer‟s commitment to the relationship of an existing supplier, the higher would be their intention to re-buy.

Not Tested

Hypothesis 3 The greater an organisational buyer‟s commitment to the relationship with an existing supplier, the higher would be their intention to re-buy from that supplier.

Supported

Hypothesis 4 The greater an organisational buyer‟s perceived value for money, the greater would be their commitment to the relationship.

Supported

Hypothesis 5 The greater an organisational buyer‟s risk perception, the lower their value for money perception would be.

Supported

Hypothesis 6 The greater an organisational buyer‟s perception of product quality, the greater would be their value for money perception.

Supported

Hypothesis 7 The greater the organisational buyer‟s perception of service quality, the greater would be their value for money perception

Not Tested

Hypothesis 8 The organisation buyer‟s prior satisfaction would have a positive influence on their value for money perception.

Supported

Hypothesis 9 The better an organisational buyer‟s perception of the quality of a product, the greater would be their intention to re-buy from the supplier.

Supported

Hypothesis 10 The effect of commitment to relationship on buyer‟s intention to re-buy is higher at a high level of switching costs than at a low level of switching costs.

Supported

Hypothesis 11 The effect of product quality on buyer‟s intention to re-buy is higher at a low level of switching costs that at a high level of switching costs.

Supported

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7.1.2 A review of the Research Questions

As was noted in Chapter One, the present study sought to explain and

answer nine research questions, which are reproduced for easy reference,

namely:

(1) What effect does product quality have on customers‟ value

perceptions?

(2) Are perceived value and product quality distinct constructs?

(3) If so, is product quality an antecedent to perceived value, as

Sweeney, Soutar and Johnson (1999) found in a business-to-

consumer context?

(4) Is product quality a better predictor of perceived value than other

suggested antecedent constructs, such as past satisfaction,

service quality and perceived risk?

(5) Is perceived value a mediator between its antecedent variables

and relationship commitment?

(6) Are customers‟ commitment to the relationship and customers‟

loyalty distinct constructs?

(7) Does commitment to the relationship impact on customer‟s

intention to re-buy from an existing supplier?

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(8) If this is so, do switching costs have a moderating effect on the

relationship between commitment to the relationship and

intention to re-buy from an existing supplier?

(9) Do switching costs also have a moderating effect on the

relationship between product quality and intention to re-buy?

Questions one to four sought to examine the relationship between product

quality and value for money. Product quality and value for money were

correlated, but sufficiently different to be seen as different constructs.

However, while Sweeney et al. (1999) modelled product quality as an

antecedent to value for money in their retail context, the present study in a

business-to-business context suggested value for money was better

modelled as an exogenous variable and as an antecedent to relationship

commitment. Product quality was also suggested to be better modelled as

an antecedent to relationship commitment. Consequently, research question

four was not relevant.

Question five asked about perceived value‟s role as a mediator between its

antecedents and commitment to the relationship. However, as was noted

earlier, the present study suggested value for money was better modelled as

an exogenous variable. Consequently, value for money was not seen as a

mediating construct in the present study.

Questions six and seven sought to see whether the commitment to the

relationship construct was different to the customer loyalty construct and to

estimate the impact commitment to the relationship had on intention to re-

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buy. While Fullerton (2003) suggested customers‟ commitment to the

relationship had a positive impact on loyalty, Gilliland and Bello (2002) have

argued commitment and loyalty cannot be differentiated, as has Ewin (1993).

In the present study, loyalty and commitment could not be differentiated and

the loyalty construct was one of those removed from the initially estimated

model. Consequently, research question six was answered in the negative.

However, as commitment to the relationship was retained in the model, it

was possible to answer research question seven. In this case the research

question was answered positively, as commitment to the relationship was

positively related to intention to re-buy.

Question eight examined the moderating effect switching costs had on the

relationship between commitment and intention to re-buy. While

commitment to the relationship had a positive direct influence on intention to

re-buy, this relationship changed when switching costs were included in the

analysis. The relationship between commitment and intention to re-buy was

significantly stronger when switching costs were high. In this case, the

impact commitment to the relationship had on intention to re-buy was about

four times lower when switching costs were low. Clearly, research question

eight was answered positively.

Question nine examined the moderating effect switching costs had on the

relationship between product quality and intention to re-buy. When switching

costs were high, product quality did not impact significantly on intention to

re-buy. On the other hand, when switching costs were low, product quality

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had a significant positive impact on intention to re-buy. Clearly, switching

costs do moderate the product quality – intention to re-buy relationship and

research question nine was also answered positively. Given these

moderating effects, it is important that managers understand customers‟

switching cost perceptions as different constructs drive re-purchase

intentions. Product quality is crucial when switching costs are seen as low,

but relationship commitment is crucial when switching costs are high.

7.2 The Theoretical Implications

The present study attempted to understand the factors that influenced

organisational buyers‟ repurchase intentions and the results obtained make

several theoretical contributions, not only directly to the body of knowledge

about the relationships between perceived value for money, perceived risk,

service quality, product quality, past satisfaction, commitment to the

relationship, loyalty and repurchase intentions, but also indirectly to

business-to-business marketing in general.

First, the estimated model expanded prior repurchase intentions models by

combining past models that have been used in business-to-consumer

contexts, thereby adding several new constructs to the model. The

augmented model was then tested in an industrial (or business-to-business)

context, rather than in a business-to consumer context.

Second, the present study led to revisions that suggested a new model in

which an organisation‟s commitment to their relationship with a supplier

mediates the relationships between other drivers (i.e. past satisfaction,

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perceived risk, value for money and product quality) and repurchase

intention. Most previous studies suggested perceived value mediates the

relationships between a set of antecedents and buying intention (e.g.,

Dodds & Monroe, 1985; Sweeney et al., 1999), at least in a business-to-

consumer context. However, the present business-to-business study

provided a new perspective as perceived value for money was better

modelled as an additional driver of repurchase intention than as a mediator.

The study‟s suggestion of a link between commitment to a relationship and

other key constructs added to Ravald and Gronroos‟s (1996) argument that

managers need to understand relationship benefits and relationship costs.

Third, most prior models suggested the relationship between product quality

and intention was mediated by other constructs, at least in a business-to-

consumer context (e.g., Agarwal & Teas, 2002; Dodds, 1991; Snoj et al.,

2004; Sweeney et al., 1999; Zeithaml, 1988). The current study, however,

found product quality impacted directly on organisational buyers‟ repurchase

intention and that this relationship was not mediated by perceived value or

by commitment to the relationship. Indeed, product quality had a greater

impact on repurchase intention than the other antecedent variables included

in the estimated model (i.e. past satisfaction, perceived risk and perceived

value for money).

Finally, very little research has investigated the moderating effect switching

costs have on the model‟s relationships, especially those relationships

between commitment and repurchase intention, as well as the relationship

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between product quality and repurchase intention. Most past switching cost

research has focused on its moderating effect on the satisfaction - intention

relationship (e.g., Vasudevan et al., 2006; Burnham et al., 2003; Jones et al.,

2000; Jones, 1998). The present study found these other relationships were

also influenced by switching costs, as buyers who perceived there were high

switching costs were not influenced by product quality directly but indirectly,

mediated by commitment to the relationship. However, buyers who

perceived there were low switching costs were very influenced by product

quality directly. On the other hand, when switching costs were high,

commitment to the relationship‟s impact on repurchase intentions was four

times higher than it was when switching costs were low. Clearly, we need to

understand buyers‟ switching costs perceptions, as well as the other direct

and indirect influences on intention to re-buy, as these perceptions

determine which influence is the crucial repurchase driver.

7.3 The Management Implications

Results from this study present significant implications for industrial

marketers and sellers. With the advent of the new century, the industrial

revolution had seen the advance of new technology, globalisation,

increasing demand for better quality of products and services, and all these

led to attitudinal changes of organisational buying behaviours. While

perceived value is still a relevant and present topic today, the impact of

buying behaviour varies depending on the environment and industry. Seven

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managerial implications can be drawn from the present study relevant to the

industrial buying (or business-to-business) context.

First, product quality was found to have a positive direct influence on

organisational buyer‟s intention to re-buy a product from their existing

supplier. In this scenario, there is a high likelihood customers will switch to

competitive products when offered higher quality, notwithstanding the

presence of switching costs. Increasingly, managers are acknowledging the

importance of quality. However, many still define and measure it from their

company‟s perspective (Zeithaml, 1988). It is important that managers

engage customers objectively to close the gap between what customers

need and what they think the customer needs. Such an engagement may

improve existing products‟ performance or lead to the development of new

products that meet customers‟ quality expectations. This can be done by

carefully looking at the situational factors surrounding the purchase and the

use to which product are put. In some instances, engaging the customer

may not be sufficient. Managers should also consider engaging the end

customers of organisational buyers. This would enhance the understanding

of quality that is needed in a product and provide a value add to

organisational buyers. All of these activities would help managers keep

abreast of changes and ensure they funded research and development

activities appropriately in order to maintain a competitive edge in today‟s

competitive marketplaces.

169 169

Second, commitment to a relationship has a positive influence on

customers‟ intention to re-buy from the existing suppliers. In this aspect,

industrial marketers and sellers need to find ways to increase customers‟

relationship commitment. As commitment to the relationship suggested an

affective bond, salespeople need to regularly communicate with the buyers

to improve their understanding and trust. Salespeople should also educate

customers about the benefits of maintaining or deepening their commitment

to the relationship, perhaps by suggesting potential improvements in

efficiency and productivity that lower the cost of doing business.

Third, perceived financial risk has a negative influence on commitment to a

relationship, lowering customers‟ re-buying intentions. As noted earlier,

commitment to the relationship mediates the relationship between perceived

financial risk and intention to re-buy. Buyers are concerned about the

monetary risk they may incur before committing further into this relationship.

However, salespeople can play an important role in alleviating buyers‟

concerns by providing assurances about product performance and support.

This can be done by signing a formal contract with the buyer addressing the

points that worry them or by extending a product‟s warranty period. This can

also be done by assuring the availability of service support and spare parts.

Establishing regular meeting sessions between salespeople and customers

is another way of reducing customers‟ risk perceptions as outstanding

issues and problems can be addressed, which increases customers‟

confidence and reduces their risk perception.

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Fourth, perceived value for money has a positive impact on commitment to a

relationship. As relationship commitment has a positive influence on

customers‟ re-buying intention, it is managers‟ interest to improve

customers‟ value for money perceptions. It is important that managers

understand how customers react to price. Monetary aspects include factors

such as efficiency, productivity and maintenance costs, while non-monetary

factors include warranty periods, reliability, quality and time to repair.

Managers must understand both monetary and non-monetary aspects and

promote the value a product or service can offer.

Fifth, past satisfaction has a positive impact on commitment to a relationship

as well as on perceived value for money. Managers need to conduct

surveys regularly to determine customers‟ satisfaction as part of a

continuing effort to improve operations, product quality and service quality.

At the same time, managers should engage customers regularly to find out if

there are changes in their value perspectives caused by changes in

technology, processes or even personnel. As customer relationship

management has become a norm in many business-to-business

organisations, it is becoming increasingly difficult to realise competitive

advantage through its implementation. Managers should also look into

different satisfaction factors that drive customers and develop strategies to

meet their‟ needs. With the advances of Information Technology (IT),

managers in business-to-business organisations need to ensure customers‟

satisfaction with their experiences are “hard wired” into their monitoring and

that corrective action is taken promptly. Despite the technology revolution,

171 171

personal contact is still important in business-to-business context. Managers

should consider building a contract framework that operates as a multitude

of levels across the different functions between the buyer‟s and seller‟s

organisations, and not just between a seller and a buyer. The key is to

ensure positive customers‟ experience and not so much the end result.

Sixth, switching costs moderate the effect product quality has on customers‟

intention to re-buy. When switching costs are low, product quality has a

greater impact on customers‟ repurchase intention than when switching

costs are high. This suggests customers are likely to search more for

alternatives when switching costs are low. Managers need to develop

barriers that make switching expensive. For example, such barriers might

include ensuring fixture or operating software or program compatibility as

such standardisation makes it more difficult to change suppliers. However,

competitors will search for ways to break down such barriers, which means

existing suppliers will need to keep a close watch on their competitors‟

activities. Regular training can also be used as a barrier as it ensures

buyers‟ employees can use a supplier‟s products or services efficiently and

that there is a retraining cost and short-term reductions in productivity

should a buyer decide to change suppliers.

Finally, when there are high switching costs, product quality has no direct

influence on intention to re-buy but has an indirect influence that is mediated

by commitment to the relationship. While product quality has a significant

impact in this context as well, the key driver influencing re-buying intention

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when switching costs are high is commitment to the relationship. However,

the three key drivers of commitment to the relationship in the current study

are past satisfaction, product quality and perceived value for money. Both

past satisfaction and product quality had a bigger influence on commitment

to the relationship than did perceived value for money. Managers need to

continuously improve customers‟ perception of product quality and their

satisfaction, as was discussed in previous sections. Simultaneously,

managers need to constantly improve customers‟ value for money

perceptions. The three drivers that influence customers‟ commitment to the

relationship have an indirect influence on customer‟s re-buying intention.

However, this is only true when customer perceive switching costs are high.

Managers need to clearly understand a customer‟s switching cost

perception before deciding on an appropriate strategy. If switching costs are

low and cannot be increased, product quality is the key driver and efforts

need to be made to ensure offerings meet customers‟ requirements better

than competitors. If switching costs are high, relationships are key and

efforts need to be made to create the affective bonds that typify a strong and

enduring relationship.

7.4 Limitations of the Present Study

Like all studies, the current study has several limitations that need to be

considered. First, a cross-sectional survey was used, which limits its

generalisability as the relationships were examined at a single point in time.

Better result may have been obtained if a longitudinal study had been

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undertaken, but cost and time constraints meant this approach was not

possible. Cost and time constraints also mean the sample was drawn from

only two business-to-business contexts (SMT and ICT). The results may not

be generalisable to other business-to-business contexts. This can only be

determined through further research in other business-to-business contexts,

but such data collection and analysis was outside the scope of the present

study.

The current study was also limited to data obtained from two countries

(Singapore and Malaysia). The proximity between Singapore and Malaysia

and the similarity of their cultures and buying behaviours meant the data

collected from the two countries were aggregated. However, the data may

not be representative of other countries that were not included in the current

study. To determine the generalisability of the present results, the research

should be extended to other countries to allow greater cross-country

validation.

The fact that the current study investigated organisational buyers‟ re-buying

intention, commitment to their relationship with a supplier, perceived value of

money, perceived risk, past satisfaction, product quality, service quality and

customer loyalty and not their behaviour is also a limitation. It is possible the

variables that were measured in the present study might be different to such

behaviour. Future research might usefully examine buyers‟ real choices.

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The current study included five antecedent variables (perceived risk, service

quality, product quality, value for money and past satisfaction). Other

antecedent variables, such as brand image, organisational reputation,

ambience, promotional activity and perceived sacrifice were not included in

the current study due to concerns about questionnaire length. Lastly, there

is a possibility of self reporting bias as the same respondents provided the

data for all of the constructs (Podsakoff, MacKenzie, & Podsakoff, 2003).

However, cost considerations meant this was the only feasible approach to

data collection in this case.

7.5 Implications for Future Research

The service quality constructs that were removed from the model need to be

re-examined and perhaps revised so the discriminant validity issues found in

the present study can be overcome. Moreover, as was noted earlier, the

current study tested the suggested model using SMT and ICT products.

Other product types targeting the electronics manufacturing industry, such

as semiconductor devices and bare printed circuit boards, could be included

in future research to better determine the generalisability of the results

across a wider range of products. Product from other business-to-business

contexts with different types of risk and different industry segments, such as

x-ray equipment to hospitals or aerobridges to airports, could also be

studied to see if there were differences in the various relationships to those

found in the present study.

175 175

As was also noted earlier, the suggested model was only tested in

Singapore and Malaysia. As culture may influence organisational buying

behaviour, further research should be undertaken to test the model

elsewhere. Respondents from different cultures may perceived value

differently and may place a higher weight on some of the variables that they

deemed as more important to them, and this may alter the relationships in

the suggested model.

The current study used multiple indicators in measuring customer

satisfaction that were derived from three key areas suggested by Perkin

(1993). Future research might use other well accepted satisfaction scale,

such as the “SERVQUAL” scale developed by Parasuraman et al. (1988) to

further examine the model suggested in the present study.

The current study suggested relationship commitment influences customers‟

re-buying intention. Future research could extend the scope of the study by

separating the types of commitment to the relationship (e.g., affective

commitment, loyalty commitment and calculative commitment) and examine

their respective influences on customers‟ re-purchase behaviour.

It could be useful to explore the differences and effects of the relational and

financial switching cost that were tested in the model. The switching cost

items included both relational and financial switching costs. Relational and

financial switching costs could be separated, and it would be interesting to

see whether relational and financial switching costs moderate the

relationships differently.

176 176

It might also be useful to examine the difference in the model resulting from

the different decision making unit roles that were identified. This would

provide further insight into the behaviours and characteristics of people in

different roles and increase our understanding of the impacts of the key

players within decision making units.

Finally, the current study focused on the monetary aspect of value (i.e. value

for money). Future research could extend the scope of value to include the

non-monetary aspects, such as social value, emotional value as well as the

quality functional value suggested by Sweeny and Soutar (2001) in

business-to-business context.

7.6 Conclusions

The final chapter of this thesis discussed the implications of the results that

were outlined in previous chapters. Although the current study addressed

and explained many research issues relevant to organisational buyers‟ re-

buying intentions, several limitations were evident. The limitations relate to

the generalisability of the current study to other industries or products in

other business-to-business context, as well as to countries outside

Singapore and Malaysia, which is where the present research was

undertaken. It was recommended that the research should be expanded to

other product segment in other business-to-business contexts and to

examine business-to-business markets beyond the borders of Singapore

and Malaysia.

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The current research used past models that had generally been developed

in business-to-consumer contexts to better understand the relationships

between perceived value and other variables surrounding it that impact on

organisational buyers‟ repurchase intention. The suggested model that was

developed built on this past research and investigated the interrelationship

between perceived risk, past satisfaction, service quality, product quality,

perceived value for money, commitment to the relationship and re-buying

intention. Although perceived value has been recognised widely as a source

of competitive advantage, the present study suggested commitment to the

relationship and product quality both impacted on customer‟s re-buying

intentions, rather than perceived value for money, as was suggested by the

models developed in business-to-business contexts. The current study also

found high switching costs moderated the relationship between relationship

commitment and re-buying intention, while low switching costs moderated

the relationship between product quality and re-buying intention.

178 178

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Szymanski, D., M., & Henard, D., H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Academy of Marketing Science. Journal, 29(1), 16.

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192 192

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194 194

Appendix I

Dear Respondent I am a DBA Student at the University of Western Australia‟s Business School, is currently undertaking a major research project as a part of my doctoral program. The purpose of my research is to investigate the value buyers look for in a business-to-business context and the impact value has on buying intentions. I would greatly appreciate your taking time to participate in the survey as your views will provide invaluable assistance to our understanding these issues. It is expected that the study will provide useful information to B2B marketers, ensuring they have a better understanding of organisational buyers‟ and users‟ decision processes, enabling them to provide more valuable services to their customers. Screener: S1. May I know if you are more than 21 years of age?

1. Yes (Go to S2) 2. No (Terminate survey)

S2. May I know if you are using any SMT or ICT equipment in your manufacturing operations?

1. Yes (Go to S3) 2. No (Terminate survey)

S3. Are you a member of the unit that makes decisions about SMT or ICT equipment or if your input receives consideration in the SMT or ICT decision making process?

1. Yes (Go to Section 1) 2. No (Terminate survey)

195 195

Please answer the following questions based on your experiences and feelings about the firm that usually supplies In-Circuit Tester (ICT) or Surface Mount Technology (SMT) equipment to your firm. This usual supplier is termed “our supplier” in the following set of questions.

Section 1:

Strongly Disagree

Strongly Agree

1 Our supplier‟s product are reliable 1 2 3 4 5 6 7

2 It would take us a great deal of time and effort to get used to a new supplier.

1 2 3 4 5 6 7

3 There is little risk in purchasing equipment from our supplier 1 2 3 4 5 6 7

4 We are likely to expand our business with our supplier in the future

1 2 3 4 5 6 7

5 Our supplier's after sales and technical support staff give us individual attention

1 2 3 4 5 6 7

6 We expect our relationship with our supplier to last a long time 1 2 3 4 5 6 7

7 Our attachment to our supplier is mainly based on the similarity of our values

1 2 3 4 5 6 7

8 We would like to continue using our supplier in the future 1 2 3 4 5 6 7

9 We talk about our supplier as a great organization to be connected with

1 2 3 4 5 6 7

10 Our supplier produces good quality products 1 2 3 4 5 6 7

11 Our supplier has helped us achieve our goals 1 2 3 4 5 6 7

12 Our supplier's products perform well consistently 1 2 3 4 5 6 7

13 Our supplier's products are good products for the price charged

1 2 3 4 5 6 7

14 Our supplier's products meet our needs 1 2 3 4 5 6 7

15 We never seriously consider changing suppliers 1 2 3 4 5 6 7

16 Even if we wanted to change suppliers, we would not as the cost would be too great

1 2 3 4 5 6 7

17 Our supplier shows a genuine care and interest in our firm 1 2 3 4 5 6 7

18 We focus on long term goals in our relationship with our supplier

1 2 3 4 5 6 7

19 Our supplier's after sales and technical support staff provide good quality service

1 2 3 4 5 6 7

20 Our supplier‟s value and philosophy are important to us 1 2 3 4 5 6 7

21 It would be a real hassle to switch to another supplier 1 2 3 4 5 6 7

22 It is highly probable we will be doing business with our supplier a year from now

1 2 3 4 5 6 7

23 Our supplier's products are reasonably priced 1 2 3 4 5 6 7

24 It would cost us too much to switch to another supplier 1 2 3 4 5 6 7

196 196

Strongly Disagree

Strongly Agree

25 We prefer our supplier because of what it stands for, its values 1 2 3 4 5 6 7

26 We are likely to continue using our supplier in the future 1 2 3 4 5 6 7

27 Our firm is a loyal customer of our supplier 1 2 3 4 5 6 7

28 Our supplier has performed well in its interactions with us 1 2 3 4 5 6 7

29 There is little potential for loss in purchasing equipment from our supplier

1 2 3 4 5 6 7

30 We would strongly recommend our supplier to other firms 1 2 3 4 5 6 7

31 We are very happy to be a customer of our supplier 1 2 3 4 5 6 7

32 Purchases from our supplier are virtually automatic 1 2 3 4 5 6 7

33 Our supplier provides courteous and friendly service to our firm

1 2 3 4 5 6 7

34 We continue to use our usual supplier as changing suppliers would be too disruptive

1 2 3 4 5 6 7

35 We are proud to tell others we are associated with our supplier 1 2 3 4 5 6 7

36 We are unlikely to lose money on the products we buy from our supplier because of operational problems

1 2 3 4 5 6 7

37 Our supplier's products are well made 1 2 3 4 5 6 7

38 Our supplier's after sales and technical support staff are willing to help

1 2 3 4 5 6 7

39 We are very committed to our relationship with our supplier 1 2 3 4 5 6 7

40 If our suppliers‟ values were different, we would not be as attached to them

1 2 3 4 5 6 7

41 Our supplier's products are dependable 1 2 3 4 5 6 7

42 The long term costs of operating products we buy from our

supplier are usually in line with what is expected 1 2 3 4 5 6 7

43 There is little chance the equipment we buy from our supplier will not work properly

1 2 3 4 5 6 7

44 We continue using our supplier, as leaving would cause us real problems

1 2 3 4 5 6 7

45 We expect to continue working with our supplier for a long time 1 2 3 4 5 6 7

46 Products we buy from our supplier usually perform as expected

1 2 3 4 5 6 7

47 Our supplier's products are economical choices 1 2 3 4 5 6 7

48 There is little chance there will be anything wrong with the products we buy from our supplier

1 2 3 4 5 6 7

49 Our supplier responds promptly to our requests 1 2 3 4 5 6 7

50 We defend our supplier if it is criticized by people outside our company

1 2 3 4 5 6 7

197 197

Strongly Disagree

Strongly Agree

51 We feel our supplier views us as an important "team member", rather than just another buyer

1 2 3 4 5 6 7

52 There is little chance we will terminate our relationship with our supplier in the next two years

1 2 3 4 5 6 7

53 Our supplier has made good recommendations to us 1 2 3 4 5 6 7

54 Our supplier's products are good value for money 1 2 3 4 5 6 7

55 We have a comfortable relationship with our supplier 1 2 3 4 5 6 7

56 We are unlikely to lose money on products we buy from our supplier because maintenance costs turn out higher than was expected

1 2 3 4 5 6 7

57 Our supplier‟s operating philosophy is a match for our operating philosophy

1 2 3 4 5 6 7

58 We plan to use our supplier in the future 1 2 3 4 5 6 7

59 We are willing to invest time and other resources in our relationship with our supplier

1 2 3 4 5 6 7

60 We remain customers because we enjoy working with our supplier

1 2 3 4 5 6 7

61 Our relationship with our supplier has been a profitable one for our firm

1 2 3 4 5 6 7

62 We put long-term cooperation with our supplier before our short-term profit

1 2 3 4 5 6 7

63 Our supplier's after sales and technical support staff give us personal attention

1 2 3 4 5 6 7

Section 2:

How satisfied are you with: Not At All Satisfied

Extremely Satisfied

64 the availability of the service support provided by your supplier 1 2 3 4 5 6 7

65 the delivery and installation of your supplier‟s product 1 2 3 4 5 6 7

66 the prices your supplier charges 1 2 3 4 5 6 7

67 the financing arrangements offered by your supplier 1 2 3 4 5 6 7

68 your supplier‟s sales and service support 1 2 3 4 5 6 7

69 your supplier‟s service support 1 2 3 4 5 6 7

70 your supplier‟s technical support 1 2 3 4 5 6 7

71 your supplier‟s product range 1 2 3 4 5 6 7

72 the technical quality of your supplier‟s products 1 2 3 4 5 6 7

73 the reliability of your supplier‟s products 1 2 3 4 5 6 7

74 your supplier‟s products‟ designs and specifications 1 2 3 4 5 6 7

75 overall with your supplier 1 2 3 4 5 6 7

198 198

Please answer the following questions for classification purposes. Kindly circle the appropriate response:

Section 3: About Your Company

76. In which industry segment(s) does your company operate? Please circle appropriate segments.

Automotive Electronics 1 Industrial Electronics 6

Telecommunications 2 Building Automation 7

IT / Computing 3 EMS / Sub-Contracting 8

Consumer Electronics 4 Others 9

Medical Electronics 5

77. In which country is your company located?

Singapore 1 Malaysia 2

78. In which region is your company‟s headquarter located?

USA 1

Japan 2

Europe 3

Asia (excl. Japan) 4

Others 5

79. How many employees are there in your company?

Less than 100 1 300 – 399 4 100 – 199 2 400 – 499 5 200 – 299 3 500 or more 6

80. Does your company use the following equipment? Please circle both if your company uses both.

In-Circuit Tester (ICT) 1 Surface Mount Technology (SMT) 2

199 199

Section 4: About Yourself 81. What is your gender?

Male 1 Female 2

82. To which age group do you belong?

20 years or younger 1 21 – 30 years 2 31 – 40 years 3 41 – 50 years 4 51 – 60 years 5 61 years or older 6

83. What is your functional role in your organization?

Management 1 Engineering 2 Purchasing / Procurement 3 Finance 4 Production / Operation 5 Others 6

84. How are you involved in the selection or purchase of ICT or SMT equipment?

ICT SMT Usually directly involved 1 1 Usually involved, but only Indirectly 2 2 Sometime involved 3 3 Not involved, but sometime asked for a recommendation 4 4 Not involved at all 5 5

Thank you for the time and effort you have taken to participate in this survey.

200 200

Appendix II

Information Letter for Questionnaire Dear Respondent Mr YAP Hock Seng, a DBA Student at the University of Western Australia‟s Business School, is undertaking a major research project as a part of his doctoral program. The purpose of his research is to investigate the value buyers look for in a business-to-business context and the impact value has on buying intentions. We would greatly appreciate your taking time to participate in the survey as your views will provide invaluable assistance to our understanding these issues. It is expected that the study will provide useful information to B2B marketers, ensuring they have a better understanding of organisational buyers‟ and users‟ decision processes, enabling them to provide more valuable services to their customers. You have been recruited to participate in the study because you: 1. Are more than 21 years of age. 2. Use SMT or ICT equipment in your manufacturing operations. 3. Are a member of the unit that makes decisions about SMT or ICT equipment or

are a person whose input receives consideration in the SMT or ICT decision making process.

The survey should take about 15 to 20 minutes of your time. As we are only interested in aggregate results, your confidentiality and anonymity is assured and only group results will be reported. By participating, you are consenting to participate in this study on a voluntary basis. You are free at any time to withdraw consent without prejudice in any way. You need give no reason, nor justification, for such a decision. In such cases, any records of the survey will be destroyed, unless you agree otherwise. Should you have any query or if you need any interpretation of the questionnaire, you may contact Yap at yaphs@singnet.com.sg or telephone number +65 9662 5251; or should you have any issues about the survey, you may me at geoff.soutar@uwa.edu.au (+61 8 6488 7885) or call directly (+61 8 6488 3703) at the University. Thank you very much for your participation. Yours truly,

Professor Geoff Soutar YAP Hock Seng

The Human Research Ethics Committee at the University of Western Australia requires that all participants are informed that, if they have any complaint regarding the manner, in which a research project is conducted, it may be given to the researcher or, alternatively to the Secretary, Human Research Ethics Committee, Registrar’s Office, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (telephone number +61 8 6488-3703). All study participants will be provided with a copy of the Information Sheet and Consent Form for their personal records.

201 201

Appendix III-a: Descriptive Statistics for Individual Items

S

urv

ey

Qu

esti

on

No

.

Statement

Me

an

Std

.

Devia

tio

n

Skew

ness

Ku

rto

sis

Perc

eiv

ed

Ris

k

3 There is little risk in purchasing equipment from our supplier

3.24 1.332 0.457 -0.016

29 There is little potential for loss in purchasing equipment from our supplier

3.11 1.046 0.444 0.508

36 We are unlikely to lose money on the products we buy from our supplier because of operational problems

3.30 1.202 0.764 1.063

42 The long term costs of operating products we buy from our supplier are usually in line with what is expected

2.86 0.960 0.343 0.072

43 There is little chance the equipment we buy from our supplier will not work properly

3.07 1.219 0.763 0.359

46 Products we buy from our supplier usually perform as expected

2.60 0.814 0.221 0.212

48 There is little chance there will be anything wrong with the products we buy from our supplier

3.13 1.106 0.563 -0.028

56 We are unlikely to lose money on products we buy from our supplier because maintenance costs turn out higher than was expected

3.38 1.218 0.553 0.358

Scale Mean 3.08

Pro

du

ct

Qu

ality

1 Our supplier‟s product are reliable 5.44 0.949 -0.179 -0.346

10 Our supplier produces good quality products 5.32 0.923 -0.215 -0.089

12 Our supplier's products perform well consistently 5.22 0.826 -0.365 0.802

14 Our supplier's products meet our needs 5.42 0.876 -0.387 0.843

37 Our supplier's products are well made 5.23 0.889 -0.556 0.698

41 Our supplier's products are dependable 5.14 0.906 -0.557 0.376

Scale Mean 5.30

Serv

ice Q

ua

lity

5 Our supplier's after sales and technical support staff give us individual attention

5.20 1.097 -0.764 1.202

11 Our supplier has helped us achieve our goals 5.36 0.952 -0.472 0.412

17 Our supplier shows a genuine care and interest in our firm

5.17 0.908 -0.344 0.299

19 Our supplier's after sales and technical support staff provide good quality service

5.37 0.926 -0.227 -0.350

28 Our supplier has performed well in its interactions with us

5.26 0.864 -0.010 0.197

33 Our supplier provides courteous and friendly service to our firm

5.24 0.902 -0.151 -0.268

38 Our supplier's after sales and technical support staff are willing to help

5.42 0.853 -0.239 -0.249

49 Our supplier responds promptly to our requests 5.16 0.962 -0.658 0.689

53 Our supplier has made good recommendations to us 5.12 0.913 -0.571 0.742

63 Our supplier's after sales and technical support staff give us personal attention

5.04 1.062 -1.157 2.449

Scale Mean 5.23

202 202

Appendix III-b: Descriptive Statistics for Individual Items

S

urv

ey

Qu

esti

on

No

.

Statement

Me

an

Std

.

Devia

tio

n

Skew

ness

Ku

rto

sis

Sati

sfa

cti

on

64 the availability of the service support provided by your supplier

5.26 0.853 -0.539 0.691

65 the delivery and installation of your supplier‟s product 5.28 0.977 -0.642 0.409

66 the prices your supplier charges 4.39 1.036 -0.291 -0.025

67 the financing arrangements offered by your supplier 4.64 1.079 -0.441 1.197

68 your supplier‟s sales and service support 5.30 0.851 -0.322 -0.104

69 your supplier‟s service support 5.32 0.849 -0.318 0.031

70 your supplier‟s technical support 5.37 0.852 -0.300 0.089

73 your supplier‟s product range 5.06 0.906 -0.078 0.148

72 the technical quality of your supplier‟s products 5.28 0.822 -0.341 -0.235

73 the reliability of your supplier‟s products 5.30 0.821 -0.165 -0.263

74 your supplier‟s products‟ designs and specifications 5.28 0.815 -0.389 1.160

75 overall with your supplier 5.28 0.765 -0.047 0.177

Scale Mean 5.15

Co

mm

itm

en

t to

Rela

tio

ns

hip

4 We are likely to expand our business with our supplier in the future

5.16 1.184 -0.571 0.247

7 Our attachment to our supplier is mainly based on the similarity of our values

5.11 0.929 -0.830 1.992

9 We talk about our supplier as a great organization to be connected with

5.18 0.984 -0.422 0.410

18 We focus on long term goals in our relationship with our supplier

5.63 0.881 0.624 1.060

20 Our supplier's values and philosophy are important to us 5.28 1.102 -0.791 1.048

25 We prefer our supplier because of what it stands for, its values

5.17 0.936 -0.346 0.006

31 We are very happy to be a customer of our supplier 5.23 0.837 -0.248 0.115

39 We are very committed to our relationship with our supplier

5.36 0.857 -0.429 0.640

40 If our suppliers‟ values were different, we would not be as attached to them

4.92 1.046 -0.567 1.073

45 We expect to continue working with our supplier for a long time

5.38 0.953 -0.779 1.618

50 We defend our supplier if it is criticized by people outside our company

4.36 1.135 -0.734 0.959

51 We feel our supplier views us as an important "team member", rather than just another buyer

5.08 0.960 -0.171 0.139

55 We have a comfortable relationship with our supplier 5.38 0.853 -0.707 2.667

57 Our supplier‟s operating philosophy is a match for our operating philosophy

4.98 0.932 -0.722 1.382

59 We are willing to invest time and other resources in our relationship with our supplier

5.04 0.937 -0.736 1.618

60 We remain customers because we enjoy working with our supplier

5.12 0.854 -0.331 -0.135

61 Our relationship with our supplier has been a profitable one for our firm

5.08 0.820 -0.140 0.147

62 We put long-term cooperation with our supplier before our short-term profit

4.98 0.956 -0.343 0.207

Scale Mean 5.14

203 203

Appendix III-c: Descriptive Statistics for Individual Items

S

urv

ey

Qu

esti

on

No

.

Statement

Me

an

Std

. D

evia

tio

n

Skew

ness

Ku

rto

sis

Cu

sto

mer

Lo

yalt

y

15 We never seriously consider changing suppliers 4.42 1.300 -0.185 -0.014

27 Our firm is a loyal customer of our supplier 5.03 1.111 -0.614 0.500

30 We would strongly recommend our supplier to other firms

5.07 1.025 -0.905 2.388

35 We are proud to tell others we are associated with our supplier

4.98 0.969 -0.505 0.271

Scale Mean 4.88

Perc

eiv

ed

Valu

e

for

Mo

ne

y

13 Our supplier's products are good products for the price charged

4.76 1.009 -0.156 0.042

23 Our supplier's products are reasonably priced 4.64 1.066 -0.321 -0.705

47 Our supplier's products are economical choices 4.66 1.109 -0.334 0.423

54 Our supplier's products are good value for money 4.98 0.969 -0.385 0.052

Scale Mean 4.76

Inte

nti

on

to

Re

-Bu

y

6 We expect our relationship with our supplier to last a long time

5.84 0.976 -0.810 0.550

8 We would like to continue using our supplier in the future 5.38 0.986 -0.391 0.335

22 It is highly probable we will be doing business with our supplier a year from now

5.02 1.116 -0.653 1.031

26 We are likely to continue using our supplier in the future 5.38 0.921 -0.737 1.131

32 Purchases from our supplier are virtually automatic 4.60 1.264 -0.491 0.075

52 There is little chance we will terminate our relationship with our supplier in the next two years

4.86 1.145 -0.463 -0.017

58 We plan to use our supplier in the future 5.25 0.971 -0.454 0.143

Scale Mean 5.19

Sw

itch

ing

Co

st

2 It would take us a great deal of time and effort to get used to a new supplier.

4.84 1.309 -0.379 -0.175

16 Even if we wanted to change suppliers, we would not as the cost would be too great

4.82 1.264 -0.475 0.187

21 It would be a real hassle to switch to another supplier 4.61 1.155 -0.459 0.791

24 It would cost us too much to switch to another supplier 4.57 1.250 0.003 -0.175

34 We continue to use our usual supplier as changing suppliers would be too disruptive

4.72 1.075 -0.322 0.591

44 We continue using our supplier, as leaving would cause us real problems

4.42 1.180 -0.524 0.278

Scale Mean 4.66