august - institutional repositoryeprints.utar.edu.my/2706/1/fyp_bf_2017_lajy_-_1406608.pdf ·...

CUSTOMERS’ SATISFACTION TOWARDS ONLINE

BANKING IN MALAYSIA:

A PRIMARY DATA ANALYSIS

BY

AMANDA LEE JING YI

CHIA ANN MIN

CHOONG LI WOI

FOO KAR YAN

A research project submitted in partial fulfillment of the

requirement for the degree of

BACHELOR OF BUSINESS ADMINISTRATION (HONS)

BANKING AND FINANCE

UNIVERSITI TUNKU ABDUL RAHMAN

FACULTY OF BUSINESS AND FINANCE

DEPARTMENT OF FINANCE

AUGUST 2017

II

Copyright @ 2017

ALL RIGHTS RESERVED. No part of this paper may be reproduced, stored in a retrieval

system, or transmitted in any form or by any means, graphic, electronic, mechanical,

photocopying, recording, scanning, or otherwise, without the prior consent of the authors.

III

DECLARATION

We hereby declare that:

(1) This undergraduate research project is the end result of our own work and that due

acknowledgement has been given in the references to ALL sources of information be

they printed, electronic, or personal.

(2) No portion of this research project has been submitted in support of any application for

any other degree or qualification of this or any other university, or other institutes of

learning.

(3) Equal contribution has been made by each group member in completing the research

project.

(4) The word count of this research report is _________25758______.

Name of Student: Student ID: Signature:

1. AMANDA LEE JING YI 14ABB06608 __________________

2. CHIA ANN MIN 13ABB04734 __________________

3. CHOONG LI WOI 14ABB06374 __________________

4. FOO KAR YAN 14ABB06999 __________________

Date: 23 August 2017

IV

ACKNOWLEDGEMENTS

This study has been successfully completed by the assistance of various authorities. Therefore,

we would like to take this opportunity to thank those who have directly or indirectly help us to

complete this study. We are thankful to Universiti Tunku Abdul Rahman (UTAR) giving us the

opportunity to conduct this Final Year Project which title Customers’ Satisfaction towards

Online Banking in Malaysia: A Primary Data Analysis. We would also like to thank the

respondents of this study as they have participated in our questionnaires surveys.

Besides, we would like to express our deepest thanks and gratitude to our beloved supervisor, Dr.

Abdelhak Senadjki for his support and guidance. He always gives us supports and invested his

full effort in guiding us to understand the things that we should know while writing our research

project. His understanding in finance and economics field which guide us to overcome the

problems that we faced while completing this research project. We truly appreciate the valuable

time, guidance and advice he has given us.

Secondly, we would like to thank our second examiner, Mr. Mak Yong Sam. He has been

provided us some suggestions for our research project regarding the understanding to this study.

His advice and suggestion helped us to improve the knowledge in this study.

Lastly, we would like to express gratitude to all our group members. Million thanks for their

effort and cooperation to complete this study. Deepest thanks and appreciation to our families

and friends that have been fully support to this research project, from the beginning till the end.

V

DEDICATION

We would like to express our sincere gratitude to our supervisor, Dr. Abdelhak Senadjki for his

valuable guidance, suggestions, encouragement and help us to complete this research project.

Besides, we would like to dedicate this research project to all of our group members, Amanda

Lee Jing Yi, Chia Ann Min, Choong Li Woi, and Foo Kar Yan for co-operation, encouragement,

and fully support with each other during the proceeds of this research project.

In addition, we would like to dedicate this research project to our family members and friends to

share with our achievement in this research project as an appreciation to their encouragement and

support throughout this period of research project.

Lastly, we would like to dedicate this research project to the respondents who had participated in

our questionnaire survey and provided us valuable and supportive information to complete this

research project.

VI

TABLE OF CONTENTS

Page

Copyright Page………………………………………………………………….………ii

Declaration……………………………………………………………………………...iii

Acknowledgement……………………………………………………………………...iv

Dedication………………………………………………………………………………v

Table of Contents……………………………………………………………………….vi

List of Tables……………………………………………………………………………xi

List of Figures…………………………………………………………………………..xiii

List of Abbreviations…………………………………………………………………...xiv

List of Appendices……………………………………………………………………...xv

Preface………………………………………………………………………………….xvi

Abstract………………………………………………………………………………...xvii

CHAPTER 1 RESEARCH OVERVIEW

1.0 Introduction…………………………………………………..……………………..1

1.1 Research Background………………………………………………………………1

1.1.1 Customer Satisfaction towards Online Banking in Malaysia………….…5

1.2 Problem Statement……………………………………………................................12

1.3 Research Question……………………………………………………...………….13

1.4 Research Objective……………………………………….......................................14

1.5 Significance of the Study……………………………………..................................14

1.6 Chapter Layout…………………………………………...........................................15

VII

1.7 Conclusion………………………………………………………………………….15

CHAPTER 2 LITERATURE REVIEW

2.0 Introduction………………………………………………………………………...16

2.1 Review of Literature………………………………………………………………..16

2.1.1 Theories and Concept of Customer Satifaction…………………………..16

2.1.1.1 Dissonance Theory………………………………….…………………..16

2.1.1.2 Equity Theory…………………………………......................................17

2.1.1.3 Value-Percept Theory…………………..................................................21

2.1.1.4 Assimilation Theory………………………………………....................22

2.1.1.5 Contrast Theory……………………………………...............................23

2.1.1.6 Expectancy – Disconfirmation Theory…………………………………24

2.1.1.7 Opponent Process Theory……………………………………................27

2.1.1.8 Cognitive Dissonance Theory……………………………………..........28

2.2 Review of Previous Studies………………………………………………………...29

2.2.1 Relationship between Security & Privacy and Customers’ Satisfaction…29

2.2.2 Relationship between Customer Loyalty and Customers’ Satisfaction…..32

2.2.3 Relationship between Service Quality and Customers’ Satisfaction……..34

2.2.4 Relationship between Convenience and Customers’ Satisfaction………..37

2.3 Finding the Gaps…………………………………………………………………….39

2.4 Proposed Theoretical/Conceptual Framework……………………………………...40

2.5 Hypotheses Development………………...................................................................51

2.6 Conclusion…………..................................................................................................52

VIII

CHAPTER 3 METHODOLOGY

3.0 Introduction…………………………………………………………………………..53

3.1 Research Design…………………………………………….......................................53

3.1.1 Quantitative Research …………………………..........................................53

3.1.2 Descriptive Research…………………………............................................53

3.2 Data Collection Methods…………………………………….....................................54

3.2.1 Primary Data……………………………………………………………….54

3.3 Design of Sampling …………………………………………………………………55

3.3.1 Target Population……………………………….........................................55

3.3.2 Frame and Location of Sampling………………………………………….55

3.3.3 Elements of Sampling ……………………………………………………..55

3.3.4 Technique of Sampling ……………………………………………………56

3.3.5 Size of Sampling …………………………………………………………..56

3.4 Research Instrument…………………………………………………………………57

3.4.1 Questionnaire Design………………………………………………………57

3.4.2 Pilot Test………………………………………...........................................57

3.5 Constructs Measurement…..........................................................................................59

3.5.1 Nominal Scale…………………………………...........................................59

3.5.2 Ordinal Scale……………………………………………………………….59

3.5.3 Likert Scale……………………………………...........................................59

3.6 Data Processing……………………………………....................................................60

3.6.1 Data Checking……………………………………………………………...60

3.6.2 Data Editing……………………………………..........................................60

IX

3.6.3 Data Coding…………………………………….........................................61

3.6.4 Data Transcribing…………………………………………………………62

3.7 Data Analysis………………………………………………......................................62

3.7.1 Descriptive Analysis………………………................................................62

3.7.2 Scale Measurement – Internal Reliability Test……………………………63

3.7.3 Inferential analysis.......................................................................................64

3.7.3.1 Pearson Correlation Coefficient Analysis……………………….64

3.7.3.2 Multiple Linear Regression Model………………………….......65

3.7.3.3 Analysis of Variance (ANOVA) Test…………………………...66

3.8 Conclusion…………………………………………………......................................66

CHAPTER 4 DATA ANALYSIS

4.0 Introduction………………………………………………………………………….68

4.1 Descriptive Analysis……………………………………...........................................68

4.1.1 Demographic Profile……………………………………............................68

4.1.2 Central Tendencies Measurement of Constructs …………………………72

4.2 Scale Measurement………………………………………………………………….80

4.2.1 Internal Reliability Test………………………………...…………………80

4.3 Inferential Analyses………………………………………………………………....84

4.3.1 Pearson Correlation Coefficient…………………………………………..84

4.3.2 One-way ANOVA…………………………………...................................85

4.3.3 Multiple Regression Analysis……………………………..........................94

4.4 Conclusion…………………………………………………......................................102

X

CHAPTER 5 DISCUSSION, CONCLUSION AND IMPLICATION

5.0 Summary ……………………...…………………………………………………....104

5.1 Policy Implications ……………………………………...........................................108

5.2 Limitations……………………….............................................................................111

5.3 Recommendations……………………......................................................................112

References……………………........................................................................................113

Appendices…………………….......................................................................................127

XI

LIST OF TABLES

Page

Table 1.1: Numbers and Percentages of Online Banking Users from

year 2005 to 2016 3

Table 3.1: Reliability Test for Pilot Testing 58

Table 3.2: Rule of Thumb for Internal Reliability Test 63

Table 3.3: Rule of Thumb for Correlation Coefficient 65

Table 4.1: Demographic Information of Respondents 68

Table 4.2: Central Tendencies Measurement of Construct (Customer Satisfaction) 72

Table 4.3: Central Tendencies Measurement of Construct (Security & Privacy) 73

Table 4.4: Central Tendencies Measurement of Construct (Customer Loyalty) 75

Table 4.5: Central Tendencies Measurement of Construct (Service Quality) 77

Table 4.6: Central Tendencies Measurement of Construct (Convenience) 79

Table 4.7: Results of Cronbach’s Alpha 80

Table 4.8: Reliability Test for Substantive Study 83

Table 4.9: Pearson Correlation Analysis 84

Table 4.10: ANOVA One Way Test (Overall Model) 85

Table 4.11: ANOVA One Way Test (Security & Privacy) 86

Table 4.12: Post Hoc Test (Security & Privacy) 87

Table 4.13: ANOVA One Way Test (Customer loyalty) 88

Table 4.14: Post Hoc Test (Customer loyalty) 89

XII

Table 4.15: ANOVA One Way Test (Service Quality) 90

Table 4.16: Post Hoc Test (Service Quality) 91

Table 4.17: ANOVA One Way Test (Convenience) 92

Table 4.18: Post Hoc Test (Convenience) 93

Table 4.19: Multiple Linear Regression on Four Independent Variables and

Customers’ Satisfaction towards Online Banking (Model Summary) 94


Customers’ Satisfaction towards Online Banking (Coefficient) 95

XIII

LIST OF FIGURES

Page

Figure 1.1: Percentages of Customers Preferred Banking Method in year 2012. 3

Figure 1.2: The Number of Secure Internet Servers (per 1 million people) and

Number of Online Banking Users in Malaysia from Year 2005 to

2015. 6

Figure 1.3: Percentages of Convenience to use Manual Banking in year 2007 9

Figure 1.4: Percentage of online banking customer across Asian countries in

year 2014 10

Figure 2.1: Proposed Theoretical/ Conceptual Framework 48

XIV

LIST OF ABBREVIATIONS

AIF Asian Institute of Finance

ANOVA Analysis of Variance

ATM Automated Teller Machines

BNM Bank Negara Malaysia

DF Degree of Freedom

EDP Expectancy-Disconfirmation paradigm

EDT Expectancy Disconfirmation Theory

GIRO General Interbank Recurring Order

GPS Global Positioning System

RENTAS Real Time Electronic Transfer of Funds and Securities

SEM Structural Equation Model

SERVQUAL Service Quality

SPSS Statistical Package for Social Sciences

UTAR Universiti Tunku Abdul Rahman

XV

LIST OF APPENDICES

Page

Appendix A: Certification Letter…………………………………………………………….....127

Appendix B: Survey Questionnaire…………………………………………………………….128

Appendix C: Respondent Demographic Profile………………………………………………..135

Appendix D: Central Tendencies Measurement of Constructs…………………………………138

Appendix E: Scale Measurement - Reliability Test……………………………………………146

Appendix F: Pearson Correlation Analysis……………………………………………………..149

Appendix G: ANOVA One Way Test………………………………………………………….150

Appendix H: Multiple Regression Analysis……………………………………………………153

Charts…………………………………………………………………………………………...154

XVI

PREFACE

This research project is submitted in partial fulfillment of the requirement for the degree of

Bachelor of Business Administration (HONS) Banking and Finance at Universiti Tunku Abdul

Rahman (UTAR). This research paper is conducted under the supervision of Dr. Abdelhak

Senadjki. This study provides a detailed explanation of our topic we completed towards

accomplishing our project goals.

The title for this report is “Customers’ Satisfaction towards Online Banking in Malaysia: A

Primary Data Analysis”. The major variables included are Security & Privacy, Customer Loyalty,

Service Quality, and Convenience. Next, the general objective of this research is to identify the

factors that affect customer’s satisfaction towards online banking in Malaysia.

Firstly, this study begins by introducing the topic selected and explaining the relationship

between the independent variables and dependent variable. This study then examines the

relationship between the variables according to the theory in detailed literature review. Next,

questionnaire distribution method is used in this study through random sampling method and the

data collected is analyzed and presented in order to achieve the study’s goals. The results of the

relationship between the variables are provided and discussed. As a conclusion, this research

paper has concluded the overall test results, policy implications, limitations and

recommendations.

XVII

ABSTRACT

The advancement of new technology has caused online banking to be a famous and essential

distribution channel in banking industry for most of the countries. In Malaysia, the number of

Internet users has increased potentially. However, the adoption rates of online banking is

considered low in Malaysia as compared to other Asian countries even though the growth of

online banking in Malaysia is increasing in recent years. Due to this circumstance, several issues

have been raised and will be taken into account in this study to investigate the factors that affect

customers’ satisfaction towards online banking. Thus, the main objective of this research is to

examine the impact of independent variables namely, Security & Privacy, Customer Loyalty,

Service Quality, and Convenience towards dependent variable which is Customers’ Satisfaction

towards Online Banking in Malaysia. An in-depth reviews which were done by previous

researchers will be discussed in the part of Literature Review.

In this research, we have used the questionnaire method to collect the data from the respondents.

There are 400 sets of survey questionnaires which consist of 33 questions are constructed,

distributed and collected from the respondents who are online banking users in Malaysia. Next,

the result of statistical analyses is analyzed by using Statistical Package for the Social Sciences

(SPSS) software. In order to test the hypotheses developed in this research, there are few

analyses conducted which are Pearson’s Correlation Test, Multiple Linear Regression, and

Analysis of Variance (ANOVA) Test.

XVIII

Last but not least, there are few major findings found after the analyses were conducted. It can be

concluded that there is a positive relationship between four independent variables (Security &

Privacy, Customer Loyalty, Service Quality, and Convenience) with the dependent variables

(Customers’ Satisfaction towards Online Banking) which is consistent with the hypotheses

developed.

Lastly, this study may provide some useful contributions for bankers and future researchers. For

bankers, they may develop a better strategy in order to enhance their competitive advantage as

the findings are mainly based on customer’s point of views. For future researchers who intend to

conduct a similar study, they may be able to get a better and more reliable results after taking

into account the limitations of this study.

Customers’ Satisfaction towards Online Banking in Malaysia: A Primary Data Analysis

Undergraduate Research Project Page 1 of 156 Faculty of Business and Finance

Chapter 1: Research Overview

1.0 Introduction

This chapter consists of several sections of the study. The first section is research

background and this section is explaining the online banking system in Malaysia

such as the history and trends of online banking in this banking sector. The second

section is problem statement which describes the importance and the foundation of

study. Thirdly, this study has stated the general objective and four specific

objectives. The following section of study is research questions. These questions

indicate the determinants which influence the customer’s satisfaction towards

online banking in Malaysia. The next section is continued by significance of the

study. This chapter ends with chapter layout which outlines each chapter of the

research report while the conclusion section has summarized the content of whole

chapter.

1.1 Research Background

Nowadays, the usage of technology and internet has been expanded and

increasingly becoming important. Online banking has been well known and

accepted around the globe (Chong, Islam, Manaf, & Mustafa, 2015). This offers

new opportunities and challenges to banking institutions around the world in order

to compete and survive in the global banking market (Guru, Balachandran, &

Suganthi, 2001). Most of the banks are available with the internet facilities. With

these internet facilities, customers are able to manage their banking accounts or

transactions with a single click via the internet connection.



According to Pang (1995), the electronic revolution in the Malaysian banking sector

was started in the 1970's. However, the Automated Teller Machines (ATMs), a self-

service technology which was introduced in the early 1980s are known as the first

and most visible type of electronic innovation in the Malaysian banking industry.

This technology has replaced the brick and mortar tradition that the customers need

to perform their transactions at the bank branches. Subsequently, the banks in

Malaysia have introduced telebanking in the early 1990’s and PC-banking or

desktop banking was offered as an extension of existing delivery channel due to the

expansion in telecommunications and information technology. On 1st June 2000,

Malaysia’s central bank (BNM) implemented the legal framework by allowing the

local commercial banks to provide online banking services to their customers (Guru

et al., 2001). Maybank, one of the largest domestic banks in Malaysia was the first

bank to provide online banking services in Malaysia by creating its own portal at

www.maybank2U.com and this service is still popular and widely used by

Malaysians.

Asian Institute of Finance, AIF (2016) showed that the online banking in Malaysia

has advanced rapidly due to the growth in internet penetration. Malaysia is

experiencing increasing adoption rates but on average there is some of the

Malaysians are still reluctant to use it (Polasik & Wisniewski, 2009). According to

AIF (2016), it stated that the usage of online banking is growing among Generation

Ys and Generation Xs. Based on Table 1.1, the official portal of Bank Negara

(2016) also mentioned that only 12.0% or 3.2 million people are willing to adopt

online banking services in 2006. However, there were 66.3% or 20.5 million people

http://www.maybank2u.com/





are willing to use online banking services in 2016. This shows that online banking

services are widely accepted by Malaysians.

Table 1.1: Numbers and Percentages of Online Banking Users from year 2005 to

2016

Source : Official Portal of Bank Negara (2016)

Figure 1.1 : Percentages of Customers Preferred Banking Method in year 2012.

Source : American Bankers Association (2012)



In recent years, many overseas banks have started to offer online banking services

with the advent of the Internet. According to Munusamy, Annamalah, and Chelliah

(2012), Bank Negara Malaysia (BNM) authorized all banks to embark on online

banking and offer their online services. As the number of internet users increases

gradually from year to year, most of the people adopt online banking system and

this system is gaining popularity among the customers. Based on Figure 1.1, almost

45% of the people prefer to use online banking system instead of waiting in line at

the counter as well as the Automated Teller Machine (ATM). This situation arises

due to the basic characteristics or features of the online banking system. Online

banking can bring a lot of convenience to customers but customers must go through

a series of security steps in order to log in, including PIN number and passwords.

In addition, banks are no longer restricted by the geographical location with the

presence of the online banking system. Thus, account holders are able to access

their accounts with anybody, at anytime and anywhere in the world.

Although online banking services are designed for personal or business purposes, it

still can be found that these services have yet to meet the requirements of some

portion of customers (Singhal & Padhmanabhan, 2008). There are several existing

challenges faced by online banking namely customer resistance to change, bank

vulnerable attacked by hackers, security issues and so on (Hamid, Amin, Lada, &

Ahmad, 2007). Part of the customers are reluctant to change their behavior and this

may lead to the problem of accepting the new online services for the banking

institutions (Aliyu & Tasmin, 2012; Ayrga, 2011). Hackers may breach defense of

banking systems or network to obtain the information of customers illegally. Once

the disclosure of information happened, customers may lose confidence towards the



online bank because they no longer feel protected as the bank fails to protect their

privacy (Aliyu & Tasmin, 2012). These issues may generate the enormous impact

towards the banking sectors. Therefore, online banking systems must always be

strengthened so that the challenges no longer restrict the further improvement of it.

However, online banking has benefited human in different ways. It enabled

customers to review their banking activities at their convenience. Individual

customers and business owners can review their banking activities on a daily basis

as long as they have internet access. They can check on their deposit balance,

cleared checks and conduct other activities at any time through online banking. This

ease of access helps the company or individual customer to detect potential errors

so that they can prevent any delays or errors occurs in their transaction. Besides,

online banking also helps to reduce business operating costs. Online banking helps

business to reduce overhead and banking expenses as customers can conduct

transaction by using e-banking. With online banking, customers can withdraw funds

from ATM machines and transfer funds electronically via either RENTAS or GIRO

system at any time. In addition, online banking also enabled cross-border banking.

The speed of online banking transaction also leads to productivity gains in banking

sector. The amount of time for the payment to reach the payee varies for different

types of transaction but the transaction still can be processed within a short time. E-

banking has made banking much more easier and effective for users.

1.1.1 Customer’s Satisfaction towards Online Banking in Malaysia

According to Barquin and HV (2015), Asian customers are moving quickly into

online banking services. Nowadays, there are 670 million of online banking



customers in Asia and it is expected to increase to 1.7 billion in year 2020

(McKinsey & Company, 2014). Asian customers are getting more and more reliable

and satisfied with the online banking facilities (Barquin & HV, 2015). Furthermore,

there is nearly 20 percent of key product purchases are now completed in their

online banking services at few leading banks (McKinsey & Company, 2014). On

average, it was found that there is about 25 percent of pre-purchase decision-making

and 40 percent of post-purchase servicing is conducted through online banking

across Asian (McKinsey & Company, 2014).

Figure 1.2: The Number of Secure Internet Servers (per 1 million people) and

Number of Online Banking Users in Malaysia from Year 2005 to 2015.

Source: World Bank (2016), Official Portal of Bank Negara (2016)

Figure 1.2 illustrates the number of secure internet servers and number of online

banking users in Malaysia from year 2005 to 2015. Secure servers are web servers

using encryption technology which can protect data from unauthorized interception



in order to ensure a secure online transactions (World Bank, 2016). It is widely used

by online businesses to prevent security threats and conduct secure and private

network transactions. Based on Figure 1.2, it is observed that the number of online

banking users has increased drastically from year 2005 to 2015. The number of

secure internet servers per 1 million people in Malaysia also experienced an

increasing trend over the past 10 years. Due to the increasing demand of secure

online banking, more servers are needed to be built to accommodate all online

banking users and to strengthen the internet security. As both graphs depict the same

trend, it can be deduced that internet security in Malaysia is constantly upgraded

and maintained to meet its heavy demands. Thus, this strong correlation has further

reinforced the importance of security towards online banking in Malaysia.

Although a positive apprehension of online banking service quality might lead to

customer satisfaction, interaction between the customer and the bank is also

important for the development of customer relationship. This is due to satisfaction

towards online banking service encountered are not enough, instead, a deeper

feeling of commitment and trust are needed for the customer to develop a sense of

loyalty and conduct a loyal behaviour (Eriksson & Schuster, 2009). For example,

when a customer entered into the bank and open an e-banking account, the process

of customer retention begins. When a relationship is established, it is easier for the

customer to trust and feel committed to the bank and the bank online system

(Eriksson & Schuster, 2009).

In terms of service quality, Latimore, Watson, and Maver (2000) mentioned that

87% of online banking user expects to use different financial services from online



banking such as making payment for bills through online, check monthly statement

or even buying insurance. Thus, the study of customer service delivery has become

more important in today’s society as the improvement in products and services

quality has become the concern of most banking business (Munusamy, Chelliah, &

Hor, 2010). With online banking, banks able to provide service to customers in

other channels such as website apart from physical location. Thus, customer service

quality is very important to determine customer satisfaction especially customers in

today’s society can experience the service quality of particular website by using

smart devices.

Convenience is considered to be an influential factor in affecting satisfaction

towards online banking (Aliyu, Rosmain, & Takala, 2014). According to Singhal

and Padhmanabhan (2008), nearly 81% of the respondents agree or strongly agree

that online banking is convenient to be used. Many of respondents felt that it can

provide convenience to the users. At the same time, there is also a number of

respondents (19%) showed that they disagree and strongly disagree with the

statement above. The remaining 19% respondents do not prefer to use online

banking because there are existing of hindrances cause them less desired to use the

online banking although it benefit users. Black, Lockett, Winklhofer, and Ennew

(2001) indicated that those who do not prefer to use online banking believe that

manual banking is more convenient compared to online banking.



Figure 1.3: Percentages of Convenience to use Manual Banking in year 2007

Source: Srivastava (2007)

Figure 1.3 shows that there are around 50% of respondents felt the manual banking

is convenient to use. This could be one of the reasons why customers might not

prefer to adopt online banking. Munusamy et al. (2012) stated that non users

perceive manual banking is convenient and therefore they are unwilling to switch

from manual banking to online banking. Ong, Hong, Teh, Soh, and Tan (2014)

indicated that customers place convenience as their top priority before they choose

their banking methods. Hence, convenience is considered as a significant factor that

can lead to an enormous impact on customers’ satisfaction towards online banking

in Malaysia.

Even though the number of internet users has increased gradually from year to year,

the adoption rate of online banking still considered low in Malaysia compared to

some of the Asian countries such as South Korea, Hong Kong, Singapore, Taiwan

and so on (McKinsey & Company, 2014).



Figure 1.4: Percentage of online banking customer across Asian countries in year

2014

Source: McKinsey Asia Personal Financial Services Survey (2014)

Chong et al. (2015) mentioned that statistic on online banking subscriber from year

2006 until 2014 in Malaysia is relatively low. This indicates that online banking

system is not fully utilized by Malaysian even there are increasing trends of internet

users. There are still a high percentage of customers in Malaysia who prefer to

conduct transaction at the counter. Chong et al. (2015) also mentioned that the user

satisfaction and acceptance of online banking in Malaysia is considered as low and

unfavorable especially the elder generation.

AIF (2016) reveals that online banking in Malaysia has grown rapidly due to

advance in communication technology and expansion of various smart devices. The

research shows that consumers nowadays are increasingly using mobile phones to

conduct online transaction while computer is still the most common and popular

channel to conduct online transaction. However, many customers still think that



security issue is their main reason for not using online banking (Chong et al., 2015).

This statement supported by the study of AIF (2016) whereby majority (58%) of

the surveyed customer said that potential lack of privacy is their biggest concern,

followed by the possibility of disclosure of personal data (53%). According to

Chong et al. (2015), most of the previous studies found that the most relevant factors

that influence the level of customer’s satisfaction on online banking in Malaysia are

usefulness and the ease to access.

High degree of customer satisfaction is a key element for all successful businesses

as it can affect the profit and reputation of a business. Satisfaction is commonly

defined as a person’s feelings and it can lead to positive or negative feelings towards

a product or service received according to his or her expectation. However,

customer satisfaction is slightly more important for online companies as it is easier

for those unsatisfied customers to switch to other online companies if they are

unsatisfied with the current products or services received (Kadir, Rahmani, &

Masinaei, 2011). Hence, it is crucial for those online companies to outline the

customer’s minimum requirements for satisfactory level such as web design,

response time and speed of web page.

For those banking institutions, customer satisfaction is critical as it can help to retain

and attract customers at the same time. A strong relationship between customer and

bank can be established if the customer has higher customer satisfaction towards a

particular bank. This is because customer is more likely to become a satisfied and

loyal customer if the bank offers better quality products or services to them. So,

customers’ satisfactions play a critical role for bank in order to stay competitive in



the banking sector. In terms of online banking, there are few factors which

contribute towards better customers’ satisfactions such as web design and content,

service quality, security and privacy, convenience, speed and so on (Goh, Yeo, Lim,

& Tan, 2016).

In online banking industries, protection of security and privacy of account holders

should always be ensured in order to prevent leakage of customers’ personal

information to the third party (Hamid et al., 2007). Othman (2015) stated that

consumer loyalty is a very important factor and become a critical issue because of

positive impact on long-term profitability. By maintaining their customer loyalty,

the bank can retain their customers. So, they are able to compete with other banks

and possibly gain huge income in the future. Besides, banking customers are more

likely to be satisfied to the bank if the online banking greatly beneficial to

customers, such as provides them maximum convenience and comfort as well as

able to perform what they expected (Singhal & Padhmanabhan, 2008). This study

aims to gain insights into the level of customers’ satisfaction towards online

banking in Malaysia by studying the factors namely, security and privacy, customer

loyalty, service quality and convenience.

1.2 Problem Statement

Customers’ satisfaction is one of the main factors for banks in order to stay

competitive in this banking sector. This is because high customers’ satisfaction

helps the bank to retain its existing customers and attract new customers at once.

However, low customer’s satisfaction often lead to business failure. This

circumstance arises may generate an enormous impact on the reputation and image



of a bank. It is not easy to fulfill the requirements or needs of majority customers

because there are some problems exist to restrict the improvement of banking

sectors. Thus, the banking institutions should always consider customer’s

perspective before implementing any new plans.

From year 2005 to 2015, the total number of internet users has increased from 48.63

to 71.06 per 100 people (World Bank, 2016). Theoretically, the higher the number

of internet users, the number of online banking users tend to raise potentially (Yeoh

& Chan, 2011). Although the growth of online banking in Malaysia has shown an

increasing trend in recent years, the adoption rates of online banking is considered

low in Malaysia as compared to other Asian countries. This phenomena has raised

several issues which will be investigated in this study. Does the lack of trust in

online banking security among Malaysians deter them from using online banking

services? Do the majority of Malaysians find it difficult to deal with the user

interface of online banking websites? Is the quality of online banking services in

Malaysia so poor that it discourages people from using them? All the

aforementioned issues will be taken into account in this study to investigate the

factors that affect customers’ satisfaction towards online banking.

1.3 Research Questions

Q1: How security and privacy affect the customer satisfaction level towards

online banking?

Q2: How customer loyalty affects the customer satisfaction level towards online

banking?



Q3: How service quality affects the customer satisfaction level towards online

banking?

Q4: How convenience affects the customer satisfaction level towards online

banking?

1.4 Research Objectives

The general objective of this study is to identify the factors that affect customer’s

satisfaction towards online banking service in Malaysia. The specific objectives are:

1. To examine the impact of security and privacy on customer satisfaction level

towards online banking service.

2. To examine the impact of customer loyalty on customer satisfaction level

towards online banking.

3. To examine the impact of service quality of online banking facilities on

customer satisfaction level towards online banking.

4. To examine the impact of convenience on customer satisfaction level


1.5 Significance of the study

The importance of this study is to provide banks insights regarding the factors

influence the customer’s satisfaction towards online banking in Malaysia. This

study helps banking institutions to develop and enhance their online banking

services. The study assists the banks to be able to carry out further improvement

based on the study’s recommendations. The findings is very useful for future

decision making as banks are able to identify and understand what customers’

needs.



1.6 Chapter layout

This chapter has clearly explained the purpose of this research and the significance

of the study. In addition, this study also explained problem statement, research

questions and the research background. The coming chapter discusses the relevant

literature review and develop theoretical framework as well as the hypothesis.

Chapter 3 describes how the research is carried out in terms of research design, data

collection methods, sampling design, research instrument, constructs measurement,

and methods of data analysis. Chapter 4 interprets and presents the data collected.

Besides, the major findings of the study are discussed. Chapter 5 provides a

summary description of the statistical analyses and major findings. Furthermore,

implication, limitation and recommendation of the study are discussed.

1.7 Conclusion

This study has discussed the background, problem statement, objective, research

questions, and significance of the study. Moreover, the study also provides an

overview of the study by describing all chapters in the research report. Other

relevant information and variables are discussed further in the following chapters.



Chapter 2: Literature Review

2.0 Introduction

This chapter reviews and examines the related studies on customer satisfaction

towards online banking system. Firstly, this chapter is divided into two main

subtopics, namely theories and concepts of customer satisfaction and variables

affecting customer satisfaction towards online banking. The research gaps also have

been addressed. Next, to clearly illustrate the stated hypotheses, an adopted and

proposed theoretical framework is developed.

2.1 Review of the Literature

2.1.1 Theories and Concepts of Customer Satisfaction

Customer’s satisfaction or dissatisfaction refers to people’s ability of learning from

their past purchasing experiences. According to Isac and Rusu (2014), these

experiences not only lead to evaluation of the degree of satisfaction, it also can

influence the perceptions and attitudes of customer towards a certain product or

service. The following part is the critical review of several theories related to

customer satisfaction which is significant for the development of this research.

2.1.1.1 Dissonance Theory

Dissonance Theory mentions that a customer who seeked for a high quality product

but received a low quality product would compare the performance and experience

a cognitive dissonance (Cardozzo, 1965). In other words, customers may make

some cognitive comparison between their expectation on the product and the



product’s actual performance. When a customer psychologically invested in a

product or service and experienced lower performance than expected, he or she

would minimize the discrepancy mentally (Cardozzo, 1965). The dissonance

always appears if there is a discrepancy between the customer’s expectations and

the product’s performance (Isac & Rusu, 2014). Consumers try to avoid dissonance

by lowering their expectations or positively increase their perception on the product

or service’s performance in the case of subjective disconfirmation (Anderson,

1973). Therefore, this theory suggests that customer can reduce tension or

dissatisfaction arises from their experience with the service or product provided by

adjusting their expectations to minimize the relative importance of their

experience’s disconfirmation in order to increase satisfaction level (Olson &

Dover, 1979). By applying the concept of Cognitive dissonance theory to

affirmation and disconfirmation of expectation, customers may try to increase their

satisfaction level by removing the dissonance experiences after they used or

consume a product or service. When expectation on a product or service

performance are close to the initial expectation or norm, customer satisfaction may

arise. However, when the performance is vary greatly from this norm, customer’s

dissatisfaction may arise (Yuksel & Yuksel, 2008).

2.1.1.2 Equity Theory

Equity theory is built based on the argument that “one’s rewards in exchange with

others should be proportional to his or her investments” (Swan & Oliver, 1989).

Equity commonly defined as perceived “fairness” as this theory suggests that

customer’s satisfaction arises if customer experience fair output or input ratio (Swan

& Oliver, 1989). This theory also focuses on the exchange because it proposes that



after the exchange, customer will begin evaluation on the equity of the exchange

(Oliver & Desarbo, 1988). This evaluation will result in certain degree of

customer’s satisfaction or dissatisfaction (Oliver & Desarbo, 1988). In other words,

the equity theory conceptualized that the outcomes to inputs ratios in a trade should

be constant among the participants in order for the exchange parties to be satisfied

(Oliver & Desarbo, 1988). Equity model of customer satisfaction is different from

others as it evaluates satisfaction relative to other parties in an exchange and taken

into consideration the outcome of all parties sharing the same experience (Yuksel

& Yuksel, 2008). By applying this concept to the research of customer satisfaction,

customers are satisfied if they believed that their ratio of outcomes to inputs is

proportionate to that of the other exchange person. Equity theory is important in

the study of satisfaction although it did not generate the same level of interest in the

research of customer satisfaction (Oliver, 1993).

Based on the above discussion, Equity Theory is adopted to explain the variables

which are security & privacy, customer loyalty, service quality and convenience.

Firstly, security & privacy. Au, Ngai, and Cheng (2008) constructed an effort /

benefit ratio which is originated from the input / outcome ratio of equity theory.

This ratio focuses at exploring psychological processes that produce different types

of satisfaction and dissatisfaction (Au et al., 2008). Besides, it was found that users

will not recognize information security practices as restrictions when using

information system (Albrechtsen, 2007). Albrechtsen (2007) also mentioned that

the awareness of the importance of information security is growing among the users

from time to time. It is suggested by Aytes and Connolly (2004) that when it

concerns information security, the way the user perceives the benefits of safe



behavior and the consequences of not engaging it affects the user's behavior.

Montesdioca and Macada (2015) also believed that the benefit of information

security is known by the users although some of them do not adopt security

practices. Internet users tend to go for high security authentication than low security

authentication as users will compare the qualities and benefit obtained from the

purchases or services (Lee, Rao, Nass, Forssell, & John, 2012).

Secondly, customer loyalty can be examined by using equity theory. According to

Messick and Cook (1983), equity theory stated that the creating relationships with

customers able to enhance customer loyalty. Through this theory, retaining

customer loyalty can create a result on long-term financial performance. The

customer should feel satisfy and favorable to the service and quality offered in order

to build customer loyalty, however, the relationship will dissolve if mutual benefits

between the customer and bank are not met. According to Cahill (2007), equity

theory also useful in conceptualizing fairness which is consider as a determinant of

customer loyalty. Furthermore, equity theory can be used for better understanding

on how to create customer loyalty by building relationship. However, there are

marketing literature that has overlooked the role of equity in developing customer

loyalty.

Apart from studying customer expectations, equity theory also proposed that there

are other bases of comparison in determining customer satisfaction (Hussein, 2016).

In service context, the output or input ratio may compared to the benefit gain by

others who experience the same service (Meyer & Westerbarkey, 1996). This theory

compared the qualities and benefit obtained from the purchases or services. Fisk



and Coney (1982) also stated that customer is less satisfied if they realized that other

consumers get a more favourable price or service. Customers may incur certain

price or costs in exchange for a service with certain level of quality. Customer feel

satisfied if they knew that the performance or quality of the service is worth the

price that he or she paid (Hussein, 2016). In this situation, the customers feel that

they are equitably treated as the price paid for the service is proportionate to the

service quality. According to Hussein (2016), the consumer may further compare

the input-output ratio of the service offered by one company to the input-output

ratio of another company. The company also should take a look at other firms’

input-output ratio because it is only reasonable for a company with superior quality

service to charge higher. This concept indicates that bank should provide online

banking service with superior and better quality than other banks in order to remain

competitive and enhance customer satisfaction.

According to Olsen and Johnson (2003), equity theory has been widely used in

services marketing literature to investigate the service fairness which is related to

convenience. Since the service fairness is important to mediate the relationship

between service convenience and customer satisfaction, thus it can be said that

service convenience has a significant positive relationship with service fairness

(Roy, Lassar, & Shekhar, 2016). Anderson and Srinivasan (2003) noted that

different customers have different views towards their convenience orientation. For

example, some of the customers are driven by information gathering and money,

whereas the rest are driven by convenience. Lapierre (2000) argued that customers

value convenience, namely saving time and effort costs, are not only when they

making purchase, but it also while accessing, accepting and completing a service.



According to equity theory, this means that customers will compare the

convenience and outcome which provided by the product or service. The customer

will be satisfied if they think that the ratio of their outcomes to input is fair (DeSarbo

& Oliver, 1988). Fisk and Coney (1982) also mentioned that customer will be less

satisfied if they found that other customers get a better service than them.

2.1.1.3 Value-Percept Theory

Value-Percept Theory which is proposed by Westbrook and Reilly (1983) suggests

that the expectation of a product may not be in correspondence with what is valued

and desired in the product. Therefore, as opposed to Expectancy-Disconfirmation

paradigm (EDP) which uses expectations to analyze consumer satisfaction,

Westbrook and Reilly (1983) argue that Value-Percept Theory provides a better

framework which uses values as the comparative standards. EDP might not be the

perfect model to describe satisfaction of a customer because the level of customer

satisfaction tends to be affected by comparative standards apart from expectations

(Westbrook & Reilly, 1983). This theory states that satisfaction is an emotional

reaction that is caused by cognitive evaluative process in which the perceptions of

an offer are compared to one's values, needs, wants or desires (Westbrook & Reilly,

1983). Yüksel and Yüksel (2008) stated that when discrepancy between one’s

perceptions and one’s values is increasing, the level of dissatisfaction will be

increased. A comparison between the value-percept disparity model and

expectation-confirmation model was also done by Westbrook and Reilly (1983).

The value-disparity refers to the discrepancies of the actual features and

performance characteristics of the product with its needs and desires with an

assessment scale ranging from “less than my needs” to “exactly my needs”.



Westbrook and Reilly (1983) observed that the customers’ satisfaction is greatly

affected by the deviation in expectations as compared to values, which opposes the

hypothesis they put forward previously. However, it is suggested that both

parameters are crucial in determining customers’ satisfaction as neither of them is

sufficient to support the findings. This suggestion was also supported by recent

studies which found that integrating expectations and desires into one framework

would generate better results as they are strongly related to customers’ satisfaction

(Spreng, Mackenzie, & Olshavsky, 1996).

2.1.1.4 Assimilation Theory

According to Anderson (1973), assimilation theory mentioned that there is some

kind of cognitive comparison can be made by the customers in between their

expectations about the product and the performance of perceived product. As

mentioned by Isac and Rusu (2014), this assimilation theory can be supported and

related to the theory of dissonance because dissonance theory form basis for the

theory of assimilation. If there is existing of discrepancy between expectations and

perceived product performance then dissonance or negative disconfirmation arises

(Clinton, Aigbavboa, & Thwala, 2013). The assimilation theory arises after

discovering the evaluations of products after the customer using them. If a customer

is able to adjust his or her perceptions towards a given product, the customer able

to reduce the dissonance. Thus, the product will become more and more matching

with their expectations and requirements (Anderson, 1973). Based on this theory,

customers can reduce the tension resulting from poor product performance

(Anderson, 1973). They may distort their perceptions towards the product so that

their expectations able to accord with actual performance of perceived product.



Olson and Dover (1979) said that the customers can also raising the satisfaction’s

level by minimizing the relative importance of the disconfirmation experienced.

Further criticism based on the assimilation theory’s stated that customers have

sufficient motivation to adjust their perceptions towards the performance of a

product (Peyton, Pitts, & Kamery, 2003). Besides, the dissatisfaction among the

customer could never appear unless the customers have begun with negative

customer expectations in evaluation process. In order to achieving higher level of

customer satisfaction, customers are suggested to change their perceptions towards

actual performance of a product so that can match nearer to their expectations (Isac

& Rusu, 2014). This means that a product still can satisfied the customers if they

willing to make changes in their perceptions towards the product. Although the

actual performance of a product still has yet to reach their requirements but the level

of customer’s satisfaction is gaining nearer to be fulfilled. The statement above had

supported by Peyton et al. (2003), they stated that there is a connection between

expectations and customers’ satisfaction.

2.1.1.5 Contrast Theory

According to Danijela, Jasminka, and Srecko (2015), customer satisfaction can be

explained by using contrast theory. Contrast theory indicated that the contrast

among the expectation and outcome will cause the customer to exaggerate the

disparity when actual product or service performance unable to meet the customer’s

expectations and requirements (Danijela, Jasminka, & Srecko, 2015). In order

words, this theory of customer satisfaction predicts customer reaction instead of

reducing dissonance. Contrast theory stated that customers who receive a product

or service less valuable than what they actually expected, will magnify the



difference between the product received and the product expected (Yi, 1990;

DeSarbo & Oliver, 1988). This theory predicts that product performance which is

lower than expectations will be rated under than its actual level. Once customers

are dissatisfied with the service of a bank, the probability to retain potential

customers is low (Spreng et al., 1996). In order to achieve the higher level of

customer satisfaction, Oliver (1997) maintains that all service encounters offer an

opportunity to provide superior service quality and distinguish the firm from its

competitors. Oliver (1997) also stated that the poor performance would be rated

worse than simply poor; while good performance would be better than a rating of

good would suggest. This circumstance may cause the dropping of the level of

customers’ satisfaction in long run because customers think that they only can

acquire the poor product which was not match with their expectations. On the other

hand, the product performance exceeds customer’s expectation or satisfaction will

be rated higher than its actual rate (DeSarbo & Oliver, 1988). This circumstance

may cause the increasing of customer satisfaction’s level because customers think

that they may obtain a product which able to meet further with their needs and

requirements.

2.1.1.6 Expectation Disconfirmation Theory

Expectation Disconfirmation Theory relates the negative relationship between

trusting expectations and disconfirmation. Lankton, McKnight, and Thatcher

(2014) mentioned that it is not easy for performance to accommodate the high

expectations. Thus, higher expectation will often lead to negative disconfirmation

when performance perceived is lower than expected. Oliver (1980) assumed

satisfaction as a direct function of disconfirmation, specified that as the discrepancy



between customer’s perceived performance and pre-adoption expectations.

According to Kemunto (2015), the researcher had explained the customer

satisfaction process by using disconfirmation theory. Disconfirmation theory was

championed by Oliver (1980). Disconfirmation theory is the most popular theory

among the other customer satisfaction theories (Mattila & O’Neill, 2003).

Disconfirmation theory stated that customer satisfaction is formed as a result of the

discrepancy between the perceived performance of a product or service and the

customers’ expectation (Mattila & O’Neill, 2003; Kemunto, 2015). Mattila and

O’Neill (2003) showed that the method delivered by the service is more important

than the outcome of the service process. This is because customers seek service with

some expectation and compared their expectation with the perceived performance

of the service. If the outcome generated is better than the expectation, positive

confirmation will occur. In contrast, if there is a difference between the expectation

and the outcome, negative confirmation will occur. If the expectation is equal to the

outcome, the assessment is neutral or zero disconfirmation. Therefore, positive

disconfirmation will lead to customer satisfaction; negative disconfirmation will

lead to customer dissatisfaction; zero disconfirmation will lead to no effect on

satisfaction (Aigbavboa & Thwala, 2013). According to Oliver (1980), results of

customer expectations are formed based on the past experience, statements made

by friends and associates. Nowadays, customers are becoming value-sensitive

(Munusamy, Annamalah, & Chelliah, 2012). If customers are satisfied, they will

share their positive purchase experiences to few people, however dissatisfied

customers will discourage more persons to deal with the banks or organization

(Waligora & Waligora, 2007). Hence, banks not only need to constantly innovate

and improve their services to match customers’ expectation but also need to provide



convenient, reliable, and expedient services to retain customers’ satisfaction

(Munusamy, Annamalah, & Chelliah, 2012).

Based on the above discussion, Expectation Disconfirmation Theory is adopted to

explain the variables which are customer loyalty and service quality. Several

researches (Oliver, 1980; Bakri & Elkhani, 2012) proposed that Expectancy

Disconfirmation Theory (EDT) is an important theory to measure customer

satisfaction from perceived quality of services or products in e-commerce.

According to Bakri and Elkhani (2012), positive disconfirmation will arise when

customer perceived the product or service quality is higher than their expectation.

However, when customer perceives that the performance or quality of the product

or service is worse than their expectation, negative disconfirmation may arise. This

positive and negative disconfirmation will lead to customer’s satisfaction and

dissatisfaction respectively as negative disconfirmation means that the perceived

quality of products or services unable to attract the customer satisfaction (Yi, 1990).

In addition, Oliver (1980); Bakri and Elkhani (2012) describe that EDT has the

capacity to fulfill the responsibility of measuring and evaluating customer’s

satisfaction from the website’s quality, product and services quality. If customer

realized that perceived quality of product or service can satisfy their initial

expectations, the positive disconfirmation leads to their satisfaction. On the

contrary, the perceived quality of products or services does not fit with their initial

expectation, negative disconfirmation may arise and lead to customer’s

dissatisfaction (Alkhani & Bakri, 2012).



According to Lin, Tsai, and Chiu (2009), the expectation disconfirmation theory

can be used to relate customer loyalty and customer satisfaction. Expectation

disconfirmation theory can predict customer loyalty through the customer

satisfaction (Taylor & Baker, 1994). This theory shows that the level of customer

satisfaction is determined by customer expectations and disagreement based on

initial expectation (Thong, 2006). Anic and Radas (2006) stated that customer

satisfaction due to past purchase experience outcome in terms of rewards and costs,

which to indicate the degree of meets or exceeds customer expectations. A positive

customer perception of bank attributes will increase satisfaction and lead to positive

loyalty intentions (Anic & Radas, 2006).

2.1.1.7 Opponent Process Theory

Opponent process theory was a theory of motivation reformulated by Solomon and

Corbit (1974). This theory has been adapted from the basic physiological

phenomena known as homeostasis (Solomon & Corbit, 1974). Homeostasis is to

know how a person under conflicting stresses and motivations can maintain a stable

psychological condition (Cannon, 1930). A good example to describe homeostasis

is the law of supply and demand (Cannon, 1930). This is because the interaction

between supply and demand can either maintain or affect the stability of market

prices. Besides that, the opponent process is an internal drive which can make

adjustment to the satisfaction or dissatisfaction towards online banking to an

original or new level (Solomon & Corbit, 1974). In addition, according to Rust and

Oliver (2000) research showed that satisfied or dissatisfied emotion is temporary.

If the satisfied or dissatisfied emotion maintains for a long period of time will lead

to stress (Solomon, 1980). This means that when the customers have felt satisfied



on online banking for a long period of time, they actually depend on their memory

(Levine, 1997). However, these memories are fallible because difficult to measure

the memory will remain how long in customer memory (Souca, 2014). In order to

retain the customers and their memory, a strong online banking services is an

important driver for bank’s performance and customer service delivery (Thulani,

Njanike, Manomano, & Chiriseri, 2011). The quality of online banking services has

become a major area of attention among researchers and bank managers due to its

strong impact on business performance, profitability and customer service delivery

(Aliyu & Takala, 2013). According to Waligora and Waligora (2007), customer

satisfaction is a prerequisite for customer loyalty and retention. If customers are

satisfied with the product or services, they probably will be loyal to the bank and

the loyalty will be translated into repeated purchases. Repeated purchases will

increase financial results for the bank (Waligora & Waligora, 2007).

2.1.1.8 Cognitive Dissonance Theory

According to Festinger (1957), cognitive dissonance theory states that people tends

to reduce dissonance by changing their attitudes, beliefs, and behaviours, or by

justifying or rationalizing them. If there are appearance of multiple conflictive ideas

in simultaneously, it will bring customer an uncomfortable feeling. Thomas and

Monica (2010) mentioned that the phenomenon of cognitive dissonance has been

quickly adopted by customer behaviour research. It is also believed to exert high

exploratory power in explaining the state of discomfort buyers are often in after

they made a purchase (Thomas & Monica, 2010). Although cognitive dissonance is

quite famous among the customer behavior researches, it still can be found that

dissonance is always temporary and difficulties in collecting data and measuring



the cognitive dissonance. Hence, applications are oppositely scarce in researches of

current marketing (Thomas & Monica, 2010).

By applying this concept to the customer satisfaction, satisfaction is assumed to be

existed once the customer believes that the service marketers can reduce the

dissonances to create positive brand image for the organization (Bose & Sarker,

2012). Service industry such as bank which provides a wide range of products and

services to their customers should put more attention in order to ensure that their

services are free from creating cognitive dissonance (Bose & Sarker, 2012). Once

the firm able to find out the key factors that create dissonance and tried to reduce it,

this will help it to gain the positive customer’s attitude or perception (Bose &

Sarker, 2012). Xu, Goedegebuure, and Heijden (2007) indicated that customer

perception has a positive influence on customer satisfaction. If a customer has

positive perception towards the product, they will be more satisfied with the

product. In contrast, they may not only dissatisfied with the product, but may also

influence others’ perceptions towards the product if negative perception is exist.

2.2 Review of Previous Studies

2.2.1 Relationship between Security & Privacy and Customers’ Satisfaction

According to Dixit and Datta (2010), security can be explained as a form of

protection to secure the safety of customers and avoid hackers’ violation on

customers’ privacy. On the other hand, privacy is also an important concern for the

customers as they always seek for protection on their personal and financial

information when conducting financial transactions via online banking (Goh et al.,

2016). Besides, Jalil, Talukder, and Rahman (2014) stated that the security of online



banking system is always the main interest for customers and serious destruction

can be incurred in the banking sector if the security provided for online transfer of

funds is not convincing enough. Thus, secure financial transaction matters to

majority of the online banking customers as their main concern is to protect their

money in the banks (Ndubisi & Sinti, 2006; Fatima, 2011). According to Singhal

and Padhmanabhan (2008), one of the crucial elements to online banking users is

the security of information. Security risks arise when there is an unauthorized access

into the online banking users’ bank accounts.

A well-planned security can increase customers’ satisfaction in using online

banking transactions due to the powerful influence of trust on customer's

willingness to adopt in online transactions of money and personal sensitive

information (Friedman, Kahn, & Howe, 2000; Wang, Wang, Lin, & Tang, 2003).

According to Heikkinen and Iivarinen (2011), an important aspect regarding the

security of customers’ transactions was to construct a relevant security against

fraudulent transactions to avoid dissonance from happening. Saleh (2011)

mentioned that if the three main tools namely, password, encryption, and firewalls

or server security are established in an online banking system, the security of the

system is said to be strong enough to protect the security of the customers.

Nowadays, encryption technology, a technology converting all information into a

series of unrealizable numbers before they are exchanged over the internet is widely

adopted by banks to safeguard the security of customers’ information. The security

is strengthened by adding few unique identifiers such as a password, mother's

maiden name, a favourite colour, or a few minutes of inactivity automatically logs

users off the account (Ahmad & Al-Zu’bi, 2011).



Based on the previous empirical studies, many researches have concluded that

security and privacy is statistically significant in affecting adoption of online

banking. For example, Lee (2008) stated that security risk was the most relevant

factor towards the usage of online banking as the higher security risks tended to

restrict the adoption rates of online banking. This is due to the fact that most of the

online users fear about fraud and identity theft during online banking transaction

(Lee, 2008). In this case, the level of customers’ satisfaction will be greatly affected

if the customers are not satisfied with the online security provided by banks. In

addition, customers’ satisfaction will be hugely reduced if there is a possibility of

losing confidential data (Lichtenstein & Williamson, 2006). This is because

Lichtenstein and Williamson (2006) stated that data corruption which caused by

viruses, noise, hacking, and system crash is always the main concern for internet

users as they are worrying about losing their confidentiality. Lu, Cao, Wang, and

Yang (2011) also mentioned that security and information disclosure in the online

medium have always been the main focus for customers when it comes to online

banking. Besides, it was found that a higher security authentication is more

preferred for most of the Internet users (Lee, Rao, Nass, Forssell, & John, 2012).

Furthermore, Ahmad and Al-Zu’bi (2011) pointed out that security and privacy

have a positive significant influence on customer satisfaction. They mentioned that

user’s willingness in engagement of online exchanges of money and confidential

information is highly dependent on trust. So, a long run relationship between

customer and bank can be established if the customers trust the bank’s privacy

protection. However, Goh et al. (2016) found that security and privacy does not

have a significant relationship on customer’s satisfaction towards online banking.



This is because the online banking users assume that those online banking providers

play an important role in protecting the consumers’ privacy as it was their

obligations to provide a secured online banking platform for the customers (Goh et

al., 2016).

Therefore, in this study, it is hypothesized that security and privacy have a positive

impact on customers’ satisfaction towards online banking. This is because there are

more evidence had shown that security and privacy are important in fulfilling

customers’ satisfaction towards online banking.

2.2.2 Relationship between Customer Loyalty and Customers’ Satisfaction

Customer satisfaction is one of the most important keystones when creating

customer loyalty in the bank sector (Ribbink, Riel, Liljander, & Streukens, 2004;

Leverin & Liljander, 2006; Methlie & Nysveen, 1999). According to Dowling and

Uncles (1997); Osman, Mohammad, & Mohammad (2015) showed that customer

loyalty is a very important factor and become a critical issue because of positive

impact on long-term profitability. Customer loyalty is a deeply held commitment to

rebuy or repatronize a preferred product or service consistently in the future (Oliver,

1997). According to Methlie and Nysveen (1999) showed that customer satisfaction

on customer loyalty can be differentiated into two stage which are affective loyalty

and conative loyalty, whereas according to Kuo, Lai, Chang, & Cheng (2011)

showed that customer satisfaction on customer loyalty can be differentiated into

three stage which are cognitive loyalty, affective loyalty and conative loyalty.

Cognitive loyalty is the customer based on past or current experience to determine

their satisfaction towards their preferred bank. Affective loyalty is how much the



customer satisfied their preferred bank and their attitude towards the bank. Conative

loyalty is the customer satisfied to the services by the bank and intends to use their

preferred bank in the future. According to Ioanna (2002) showed that product

differentiation in banking industry is almost impossible as the banks offered similar

services and core products to customer. Therefore, Ioanna (2002) stated that banks

should differentiate themselves from their competitors by improving the quality of

services delivered in order to retain their customer’s loyalty. According to Myftaraj

and Nexhipi (2014) showed that attracting new customers is an expensive process,

but it will become profitable when the customers have relationship with the bank.

This relationship will become less costly when a customer becomes loyal to the

bank.

The relationship between customer satisfaction toward online banking and

customer loyalty has been found to be significant in numerous studies (Anderson &

Srinivasan, 2003; Park & Kim, 2003; Rodgers et al., 2005). Many studies on the

online buyers also showed strong relationship between customer satisfaction and

customer loyalty (Osman, Mohamad, & Mohamad, 2015). According to Koupai,

Alipourdarvish, and Sardar (2015), there is a positive relationship between

customer loyalty and customer satisfaction towards online banking. When the level

of customer satisfaction is high, the possibility of the customer becoming loyal will

increase (Bei & Chiao, 2001). This means that customer desires to have a long

lasting relationship with the bank if they have strong loyalty towards the banks

(Palmatier, Dant, & Grewal, 2006). Loyal customers who are satisfied with the

online banking services may have some benefits to the marketing effort. This is

because they are willing to buy additional products through online banking and



spread positive word of mouth as well as their reliability as a source of continuous

revenues to the banks (Zeithaml, Berry, & Parasuraman, 1996). However, there are

negative relationship between customer loyalty and customer satisfaction.

According to Zeithaml (2000); Afsar, Rehman, Qureshi, and Shahjehan (2010)

showed that customer satisfaction has very low impact and not significant towards

customer loyalty.

Based on previous studies, although there are some researchers proved customer

loyalty and customer satisfaction have negative relationship, majority of the

researchers found that customer loyalty has positive relationship with customer

satisfaction. Therefore, it is hypothesized that customer loyalty is positively related

with customer satisfaction.

2.2.3 Relationship between Service Quality and Customers’ Satisfaction

Customer satisfaction is critical for the success and survival of both traditional and

online businesses (Ho & Wu, 1999). Customer satisfaction is especially important

in internet firms as customer can easily move from one site to another site and leave

the bank or firms that dissatisfy them (Kadir, Rahmani, & Masinaei, 2011). Since

customer always demands for high quality product or services, service quality might

be important in enhancing customer’s satisfaction. Service quality of online

banking related to the number of clicks needed to find out what customer wants,

amount of information provided, responding time, speed of web page, and just-in-

time delivery of service (Kadir et al., 2011). The experience of using the service of

online banking also may affect the customer’s perception towards the services

provided. Customer will have some sort of expectation for the service performance



or quality and if the service quality does not fit with the customer’s expectation,

dissatisfaction or dissonance may arise. Discrepancy between actual service quality

and customer’s expectation may reduce the customer’s satisfaction.

According to Pitt, Watson, and Kavan (1995), the quality of service is the key for

measuring customer’s satisfaction. SERVQUAL model developed by Parasuraman,

Zeithaml, and Berry (1988) is most common to be used in assessing customer

satisfaction. Traditional SERVQUAL model evaluates and measures the

performance of companies that run their business without using online facility.

Therefore, E-SERVQUAL model is then developed by Zeithaml, Parasuraman, and

Malhotra (2002) to study how customer judge online service quality and to measure

the quality of online service included the internet banking service. Due to highly

competitive in internet banking industry, banks seek to provide high quality services

in order to survive in this industry. High quality service such as quick response of

online banking improves information sharing between the bank and customer. The

probability to retain customers will increase once customers satisfied with the

services of the bank (Spreng et al., 1996). Therefore, higher level of customer

satisfaction may arise from high service quality that can differentiate the firm or

bank from its competitors.

There are a number of previous studies from Parasuraman, Zeithaml, and Berry

(1985; 1988); Gronroos (1984) have tried to identify whether customer’s perception

towards service quality will affect customer’s satisfaction. The previous studies

conducted by Oliver (1980) also proposed that the improvement of service quality

is the key factor that affects the customer satisfaction and the purchase intention of



the customers. Cronin and Taylor (1992); Spreng and Mackoy (1996) also

suggested that service quality is one of the main factors contributing to customer

satisfaction judgments. These studies indicate that a higher quality of online

banking service would greatly affect the level of customer satisfaction and it may

lower the customer’s intention to leave the bank. Furthermore, findings from

Jayawardhena and Foley (2000) studies also supported that the feature of service

quality on online banking such as web sites, number of clicks to reach what

customer wants, amount of information provided are important for enhancing their

satisfaction level. According to Selvakumar (2015), service quality is the most

important determinant in banking sector especially in evaluating the satisfaction of

customers in order to retain and maintain average customer’s retention rate. Poor

service quality would probably lead to customer dissatisfaction. Therefore,

customer service that exceed the expectation of customers is most advantageous for

a bank (Selvakumar, 2015). On the other hand, the research from Goh et al. (2016)

provides a contradict view where they argued that service quality variable has no

relationship with customer satisfaction.

However, the importance of service quality should not be ignored because many

prior studies had shown that these variable is important in affecting customer

satisfaction. In our study, it is therefore being hypothesized that service quality is

positively related to the customers’ satisfaction on online banking.



2.2.4 Relationship between Convenience and Customers’ Satisfaction

Nowadays, customer satisfaction becomes the main factor for bank institutions to

stay competitive in this banking sector. Customer satisfaction is based on the

balance between expectations and experiences of customers with the products and

services (Khazaei, Manjiri, Samiey, & Najafi, 2014). According to Chang and

Polonsky (2012), convenience is the ability to reduce customer non-monetary costs,

namely time, energy and effort when goods and services are purchased and used by

the customers. Convenience has significant relationship with customer satisfaction

because convenience of the services provided can affect customer satisfaction

directly (Khazaei et al., 2014). Chang and Polonsky (2012) stated that only benefit

and post-benefit convenience are associated with improved behavioral intentions.

Aliyu et al. (2014) highlighted that convenience was a significant factor which was

more likely to influence the overall level of customer’s satisfaction. Ong et al.

(2014) indicated that customers always seek for convenience and place it as their

top priority when they are choosing their banking methods. In other words,

dissatisfied customers are more likely to switch than satisfied customers if products

or services received are not matched with their expectation (Kadir, Rahmani, &

Masinaei, 2011; Khazaei et al., 2014). The discrepancy between convenience and

customer’s expectation may cause dropping of the level of customer satisfaction. In

contrast, high customer satisfaction can be achieved if they feel convenient for using

online banking services which able to fit their expectations. Structural equation

model (SEM) which is invented by Sewall Wright is widely used to study the effect

of service convenience on customer satisfaction towards online banking (Khazaei

et al., 2014). According to the Khazaei et al. (2014); Ahmad and Al-Zu’bi (2011),



many researchers highlighted that convenience has a direct relationship with

customer satisfaction in the banking industry. This statement fitted to the finding of

Ahmad and Al-Zu’bi (2011), he mentioned that convenience is a competitive tool

which enables bank institutions to stay competitive in this banking sector.

There are numerous researchers tried to determine the relationship between

customer’s perception towards the convenience of online banking and customer

satisfaction (Khazaei et al., 2014; Ahmad & Al-Zu’bi, 2011). Convenience is one

of an influential factors for the adoption of online banking (Aliyu, Rosmain, &

Takala, 2014). According to Khazaei et al. (2014); Ahmad and Al-Zu’bi (2011),

prior research has empirically found a positive relationship between convenience

and customer satisfaction towards online banking. Khazaei et al. (2014) confirm

that convenience is an important service attribute that influences customers’

evaluation of service encounters. Since customers always seek for convenience and

place it as their top priority when choosing their banking method, hence

convenience is very important to satisfy the customers especially in the banking

sector (Ong et al., 2014). According to Ahmad and Al-Zu’bi (2011), they found that

higher degree of convenience provided by online banking enables customers to

access internet bank at all time and place. In addition, higher degree of convenience

able to enhance the level of customer’s satisfaction and cause them more willing to

adopt the online banking. Moreover, the researchers found that convenience was a

critical consideration that influencing a customer’s decision to adopt online banking

(Mols, Bukh, & Nielsen, 1999; Kolodinsky, Hogarth, & Hilgert, 2004). However,

Williamson (2006) stated that there is no significant relationship between

convenience and customer’s satisfaction towards online banking. Although the



online banking provides customers a convenient way to manage their money, there

are some reasons that make them less desired to use these services. For example,

customers losing trust in ability of a bank to protect their online banking accounts

(Williamson, 2006).

Since there are more evidence had mentioned that convenience is one of a main

factors that affecting customers’ satisfaction towards online banking. Thus, it is

hypothesized that convenience has a positive relationship with customers’

satisfaction towards online banking.

2.3 Finding the Gaps

Based on previous discussion, there are some gaps that needed to be highlighted.

One of the gaps is that there are only few studies available which study about the

external factors that discourage customer to patronize online banking have been

conducted in Malaysia (Aliyu, Rosmain, & Takala, 2014). More empirical studies

such as basic factors that discourage online banking adoption in Malaysia needed

to be conducted so that a more precise study of assessing significant factors that

affect customer satisfaction in Malaysia’s banking system can be carried out.

Most of the studies concerned with the information and data of online banking in

developed and developing countries, but there are relatively less research has been

carried out to determine and identify the factors that affecting customer satisfaction

on online banking in Least Developed Countries such as Bangladesh, Cambodia

and Nepal. Many studies do not include the information of online banking service

and its relation with customer satisfaction in Least Developed Countries even



though internet banking services are available in these countries. The accuracy of

data or statistic of online banking found from the website might be affected as most

studies have ignored the service of online banking in Least Developed Countries.

2.4 Proposed Theoretical/ Conceptual Framework

Based on the theories of Dissonance Theory, Equity Theory, Expectation

Disconfirmation Theory, and Value-Percept Theory, there are several main factors

which affect the customer’s satisfaction towards online banking namely design,

transaction speed, information quality, cost, security and privacy, customer loyalty,

service quality, and convenience.

Design refers to the process of organizing information to cope with the user’s needs

(Jankowski, 2011). According to Jankowski (2011), dissonance theory confirm that

the perception of attached message or design of the website is one of the elements

indicating effectiveness of a website and there is a conceptual website analysis

model in which cognition and emotion are combined to aid in understanding the

behavior of customers in online environment. Customer willing to remove

dissonance by lowering their expectations and increasing their perceptions towards

the service or product performance in order to increase their level of satisfaction

(Anderson, 1973). Palmer (2002) suggested that design plays an important role to

human-computer interaction (HCI) because it will influence customer satisfaction

and task performance when using a computer. This is because a good website design

will lead to the success of the website since the usability of the website is enhanced

(Yoon, 2010). According to Benbunan-Fich (2001), usability is defined as the

ability of the interface to facilitate the communication between the user and the



system as well as eliminating misunderstanding between them. Besides, the interest

of an online customer will be attracted, sustained, and retained through a website’s

design (Ranganathan & Ganapathy, 2002). So, the design of the online banking web

site may also have a positive impact on customer satisfaction.

According to Westbrook and Reilly (1983), value-percept theory states that

satisfaction is an emotional reaction that is caused by cognitive evaluative process

in which the perceptions of an offer are compared to one's values, needs, wants or

desires. According to Goh et al. (2016), transaction speed can influence customer

satisfaction towards online banking. This is because transaction speed is considered

as time saving features. Customer can use self-service technologies such as

Automated Teller Machines (ATMs) or mobile phone to make any transaction.

According to Yoon (2010), customers are sensitive to the speed of service delivery.

If the transaction speed is in slow response time after online transaction, the

customer do not know whether the transaction is completed or not and lead to delay

of delivery service to customer. At the same time, customer satisfied or dissatisfied

towards online banking services occur due to transaction speed (Jun & Cai, 2001).

Therefore, transaction speed is considered important to achieve the successful for

online banking and customer satisfaction.

McKinney, Yoon, and Zahedi (2002) mentioned that there are few elements which

affect the web-customers' satisfaction towards web site's information quality which

are customers’ prior expectation which is formed by their prior experiences and

exposure to vendors' marketing efforts, possible discrepancies such as

disconfirmation between such expectations, and the perceived performance of the

Web site. According to McKinney et al. (2002), Expectancy Disconfirmation theory



which stated that customer satisfaction has three main components: expectation,

disconfirmation, and perceived performance has been captured in this concept. Thus,

the authors suggested that Web-Information Quality satisfaction can be divided into

Information Quality-expectation, Information Quality-disconfirmation, and

Information Quality-perceived performance when applied to web-customer

satisfaction. Information quality refers to the information provided on the web site

(Yoon, 2010). Nowadays, many online banking web sites provide a variety of

information such as investment, real estate, and personal financial planning

information so that it can meet customer’s different requirements apart from

purchasing and making payment online. Yoon (2010) also found that information

content had a significant influence on customer satisfaction towards online banking.

However, Chong et al. (2015) research shows a contradictory result whereby

information content is not significant in affecting customer satisfaction towards

online banking. The reason behind is because there were many scam e-mails or

fraudulent banking websites design to trap users (Chong et al., 2015). Most users

will not satisfy even the website provide attractive design and a variety of

information. Moreover, people nowadays are more emphasized on better service

and hence, businesses and banks are becoming more customer-oriented. Service

quality tends to be more important in affecting customer satisfaction as compared

to information quality or content.

Dissonance theory has been used to investigate the cost of transaction (Seta, Hundt,

& Seta, 1995). This theory stated that customers are willing to remove dissonance

by lowering their expectations and increasing their perceptions towards the service

performance in order to achieve higher level of satisfaction (Anderson, 1973).



According to Ong et al. (2014), they found that there is a significant correlation

between the cost of transaction and customer satisfaction. Aliyu and Tasmin (2012)

mentioned that if customers decided to use a new technology, the technology must

reasonably priced relative to the provided value-added features. If not, the adoption

of new technology may not be viable. Some of the customers might not willing to

use these services once additional costs are required to access online banking (Ong

et al., 2014). Based on empirical result of Ong et al. (2014), the majority of

customers are impatient when waiting in line at the counter especially during peak

hours. Meanwhile, customers always place convenience as their top priority instead

of the cost of transaction (Ong et al., 2014). This means that they are willing to

spend the cost rather than need to wait for a longer time. This circumstance proved

that cost of transaction has no great impact on customer satisfaction as compared to

other variables such as convenience. Based on the cognitive-dissonance theory,

customers may avoid dissonant-evoking situations by considering the outcome they

receive for a costly experience to be very valuable (Seta, Hundt, & Seta, 1995). In

other words, the customer would minimize the discrepancy mentally if he or she

experiences the performance below the expectation (Cardozzo, 1965). Although the

cost is often very important to the customer's first purchase but it usually has a

somewhat smaller impact on customer satisfaction (Angelova & Zekiri, 2011).

Security & Privacy. The security of online banking system is always the main

concern for customers and serious destruction can be incurred in the banking sector

if the security provided for online transfer of funds is not convincing enough (Jalil

et al., 2014). Majority of the online banking customers concern about secure

financial transactions because their main purpose is to ensure their money in the

banks is protected safely (Ndubisi & Sinti, 2006; Fatima, 2011). In addition, this



factor can be explained by adopting Equity Theory which suggested that customer’s

satisfaction arises if customer experience fair output or input ratio. Au et al. (2008)

constructed an effort / benefit ratio which is originated from the input / outcome

ratio of equity theory and this ratio focuses at exploring psychological processes

that produce different types of satisfaction and dissatisfaction. Montesdioca and

Macada (2015) proposed that the effort/benefit ratio is positively related to user

satisfaction with information security practices.

Customer loyalty is a very important factor for customer satisfaction towards online

banking because of positive impact on long-term profitability (Osman, Mohammad,

& Mohammad, 2015). Loyal customers who are satisfied with the online banking

services may have some benefits to the marketing effort. This is because they are

willing to buy additional products through online banking and spread positive word

of mouth as well as their reliability as a source of continuous revenues to the banks

(Zeithaml, Berry, & Parasuraman, 1996). Besides that, customer loyalty can be

examined by using equity theory. According to Messick and Cook (1983), equity

theory stated that the creating relationships with customers able to enhance

customer loyalty. Through this theory, retaining customer loyalty can create a result

on long-term financial performance. The customer should feel satisfy and favorable

to the service and quality offered in order to build customer loyalty.

Service quality. The quality of service provided is the key factor contributing to the

failure and success of a business (Thompson, Green, & Bokma, 2000). Many banks

included companies have set up a website that provide quality service to customers

in order to get a better return and enhance customer satisfaction. A higher quality

of online banking service would greatly affect the level of customer satisfaction and



it may lower the customer’s intention to leave the bank (Cronin & Taylor, 1992).

Furthermore, Equity theory stated that the output or input ratio may compare to the

benefit gain by others who experience the same service (Meyer & Westerbarkey,

1996). Customers will be less satisfied if they realized that other consumers get a

more favorable price or service. They will incur certain costs in exchange for a

service and if the quality of the service is worth the price paid, customer feel

satisfied (Hussein, 2016).

Convenience. According to Khazaei et al. (2014); Ahmad and Al-Zu’bi (2011),

convenience is one of the influential factors for the customer satisfaction. Banking

customers are more likely to be satisfied to the bank if the services are greatly

beneficial to customers, such as providing them the maximum convenience as well

as offer their expected services (Singhal & Padhmanabhan, 2008). A service with

the higher degree of convenience provided by online banking company manages to

reduce customer non-monetary costs (Chang & Polonsky, 2012). Moreover, equity

theory has been used to investigate the service convenience (Olsen & Johnson,

2003). Based on the Equity theory, customers will make a comparison between the

convenience and outcome which provided by product or service. If the ratio of their

outcome and input is fair, the customers will be satisfied (DeSarbo & Oliver, 1988).

Based on the above discussion, it is shown that there are four important independent

variables (security and privacy, customer loyalty, service quality, and convenience)

which greatly affect customer’s satisfaction towards online banking since there are

more empirical evidence to support these four variables. For Security & Privacy,

Ahmad and Al-Zu’bi (2011) mentioned that security and privacy have a positive

significant influence on customer satisfaction. This is because user’s willingness in



engagement of online exchanges of money and confidential information is highly

dependent on trust. Thus, a long run relationship between customer and bank can

be established if the customers trust the bank’s privacy protection. According to

Koupai, Alipourdarvish, and Sandar (2015) stated customer loyalty is a very

important factor because of positive impact on customer satisfaction towards online

banking. This means that customer have a strong loyalty towards banks when the

customer desires to have a long lasting relationship with banks (Palmatier, Dant, &

Grewal, 2006). The previous studies conducted by Oliver (1980); Cronin and Taylor

(1992) also proposed that the improvement of service quality is the key factor that

affects the customer satisfaction and the purchase intention of the customers.

Service quality on online banking such as web sites, number of clicks to reach what

customer wants, amount of information provided are important for enhancing their

satisfaction level (Selvakumar, 2015). Since banking industry ie especially

customer-oriented and always in intensive competition, high service quality is very

important for banks to ensure customer satisfaction. For Convenience, Khazaei et

al. (2014) indicated that convenience has a positive significant influence on

customer satisfaction. This is because convenience is a competitive tool which

allows banking institutions to stay competitive and survive in the global banking

market (Ahmad & Al-Zu’bi, 2011). Therefore, a strong relationship between

customers and bank can be generated if the services with higher degree of

convenience are able to enhance the level of customers’ satisfaction. Besides, the

four independent variables are found to be interrelated with Equity Theory. Apart

from “fairness”, Equity Theory also focused on the exchange which each and every

customer would probably experience when they purchase a product or service.

Therefore, this theory can be considered as the most appropriate theory in



explaining customer satisfaction. Since there are more evidences had mentioned

that equity theory related variables (security & privacy, customer loyalty, service

quality, and convenience) have positive relationship with customer satisfaction

towards online banking. Thus, the variables are considered as the main factors that

affecting customers’ satisfaction towards online banking. A proposed theoretical

framework is developed according to equity theory and the four independent

variables.

Figure 2.1 below shows the proposed theoretical/ conceptual framework that

developed based on equity theory and this framework acts as a foundation for the

research project. This figure clearly illustrates the determinants that affect the

customer satisfaction towards online banking. Independent variables which are

going to be investigated in this study are security & privacy, customer loyalty,

service quality and convenience. It is proposed that customers’ satisfaction is

positively related with the stated factors which are security & privacy, customer




Figure 2.1: Proposed Theoretical/ Conceptual Framework

There are some relevant rationales for us to adopt these independent variables in

this study. Firstly, security and privacy are chosen as one of the important variables

in this study because customers do not have to face the bank directly during online

banking transactions. So, people tend to be more cautious in online banking

transaction especially when monetary transactions over the Internet are the main

role of online banking (Yoon, 2010). Besides, the sensitive nature of banking

information has caused privacy to be one of the greatest concerns to customers

(Vaithilingam, Nair, & Guru, 2013). In addition, security plays an important role

for e-commerce applications as e-commerce is based on the Internet which is an

open network (Yoon, 2010). The security issue related to online banking is still the

main concern for customers although there are various technical advancements in

Internet security (Ranganathan & Ganapathy, 2002).

Secondly, customer loyalty is chosen to be one of the variables in this study. This

is due to Myftaraj and Nexhipi (2014) showed that customer loyalty is the most



important factors in economic development. Therefore, building long-term

relationship with customers may reduce costs and increase revenue of the bank.

Furthermore, customer loyalty is growing rapidly because bank and organization

realized that the benefit of customer loyalty in the marketing field (Myftaraj &

Nexhipi, 2014). The loyal customer not only feel satisfied with the bank attitudes

but also may repurchase and recommend to their family members and friends.

Service quality is also chosen to be one of the variables because customer always

demands for high quality services, hence service quality of online banking plays a

vital role in maintaining and enhancing customer satisfaction (Almotairi, Meshall,

& Alam, 2013). As there are increasing numbers of customers adopting online

banking as a channel for conducting their transactions, it is important for bank

management to be innovative in meeting customer requirement and enhancing

customer satisfaction. The revolution of internet-oriented services has large impact

to all sectors in today’s economy especially the banking sector since banking

industry is more customers oriented and highly competitive (Almotairi et al., 2013).

Therefore, service quality could be a very important factor affecting customer’s

satisfaction in modern economy. According to Thompson, Green, and Bokma

(2000), good customer service quality is important to determine the failure and

success of a business. Many companies set up a website that provide quality service

to customers in order to satisfy them and get a better return (Slu & Mou, 2003).

Therefore, the banks that provide good online services quality to their customers is

thought to be more competitive and profitable in the long run. In addition,

customers’ feedback on service quality of online banking could be useful for bank

management in improving online service quality.



Lastly, convenience is known as one of the dominant variables in our research

because most of the people are placing convenience as their top priority when

making their choices. Nowadays, customers are always seeking for convenience

when they are managing their banking account and financial transaction (Ong et al.,

2014). Especially the young generation who born into a technological, electronic,

and wireless society, they are more likely to adopt these services (Robinson &

Moore, 2009). The customers always want the financial management tools to be

more user-friendly and more prefer to products or services which could not offer

by the traditional retail bank. The online banking is increasingly becoming popular

because of its convenience and flexibility (Singhal & Padhmanabhan, 2008).

Seiders et al. (2000) mentioned that time-poor customers are seeking for providers

offering value that is convenient in terms of usage. Munusamy et al. (2012) stated

that customers are willing to switch from one banking method to another banking

method which is more able to provide them the higher degree of convenience. With

the existing of the online banking, financial transaction could be settled online at

everywhere and anytime and this system can cause customers’ life become easier

than couple of decades ago. According to Khazaei et al., (2014); Ahmad and Al-

Zu’bi (2011), they mentioned that convenience has a direct relationship with

customer satisfaction in the banking industry. Therefore, convenience is important

and it is known as a main issue that cannot be ignored by the banking institutions.

Lee (2008) stated that the positive relationship between customer satisfaction and

security & privacy can be explained when low security risk tends to encourage the

growing of usage rate of online banking. Jalil et al. (2014) said that if security of

the system is strong enough to protect the privacy of the customers, they will



become more satisfied with the online banking. While Ioanna (2002) indicated that

there is a positive relationship between customer satisfaction and customer loyalty.

If customers more satisfied with the well-provided services, the bank more able to

retain their customer’s loyalty (Ioanna, 2002). According to Spreng et al. (1996),

they mentioned that service quality positively related to customer satisfaction as

high quality services can effectively connect the bank and its customers, such as

improve the information sharing between bank and its customers. The level of

customers’ satisfaction will be increased if there is an improvement of service

quality as this situation can make the bank to stand out from its competitors (Oliver,

1999). Besides, convenience of online banking also able to build up a positive

relationship with its customer because customers are always take convenience of

services into their considerations when they are choosing their banking method

(Ong et al., 2014). Mols et al. (1999) indicated that higher degree of convenience

able to fulfill the higher level of customer’s satisfaction and encourage more

customers choose to adopt the online banking.

2.5 Hypotheses Development

Hypothesis 1: Security and privacy have positive impact on customer’s

satisfaction.

Hypothesis 2: Customer loyalty has positive impact on customer’s

satisfaction.

Hypothesis 3: Service quality has positive impact on customer’s satisfaction.

Hypothesis 4: Convenience has positive impact on customer’s satisfaction.



2.6 Conclusion

This chapter focuses on the researches successfully carried out by previous

researchers. These findings provided generate a better insight regarding online

banking. The hypotheses were developed from the theories and literature review.

The methodologies used in this study are discussed in the following chapter.



Chapter 3: Methodology

3.0 Introduction

In this chapter, the components about the methods implemented for the study are

discussed. Furthermore, 400 of survey questionnaires are constructed, distributed

and collected from the respondents. This chapter clearly explains how this research

is carried out.

3.1 Research Design

3.1.1 Quantitative Research

Quantitative research is the collection of data to define the impact between

dependent variable and independent variables. Quantitative research can be

quantified and subjected to statistical treatment in order to support or refute the

information of relationship (Creswell, 2003). In addition, quantitative research have

design issues, measurement issues, and analysis issues. Design issues is the causal

inference in sampling and different types of designs. Measurement issues is the

measurement of reliability and validity. Analysis issues is the analysis techniques

that used to quantify the relationship between dependent variable and independent

variables.

3.1.2 Descriptive Research

Descriptive research involves gathering data through questionnaire that describe the

factors or characteristics and then organizes, tabulates, depicts, and describes the

data collection (Glass & Hopkins, 1984). Descriptive research can aid the reader to



understand the data distribution by using visual aids such as graphs and charts. This

is because human mind cannot extract the full import of a large mass of raw data.

Hence, descriptive statistics are very important in reducing the data to manageable

form (Knupfer & McLellan, 2001). In this research, descriptive research is used to

discover the factors that affects the customer satisfaction towards online banking in

Perak, Malaysia.

3.2 Data Collection Method

To investigate the research objective in this study, data is collected through survey

and hence, primary data are used in this study.

3.2.1 Primary Data

According to Cooper and Schindler (2003), primary data are first source data and it

is directly related to the project at hand. They also further explain that the primary

data or information is raw data without interpretation and these information not

available to public. According to Church (2001), the individuals who collect the

data for primary data analysis also analyze it. Church (2001) also mentioned that

primary data analysis permits the researcher to design an experiment that is best fit

to the specific research hypothesis (Church , 2001). This type of data could be

collected from different methods such as questionnaire, interview, survey and

experiment. In this study, the questionnaire was distributed from 10 May 2017 to 5

June 2017 by using random sampling method. This process consume time and it is

difficult to reach all respondent from different area in Perak. However, the survey

questionnaires have been successfully distributed to respondents with different

monthly income, gender, age and qualification. There are 400 questionnaires have



been received from the respondents. The primary data collected are transformed to

statistic by using statistic software in order to generate an overview of the result.

3.3 Design of Sampling

3.3.1 Target Population

Target population is a group of people that completed the questionnaire in order to

help the researcher to make conclusions. Target population for the research is the

Perak residents that used the online banking services by Malaysia commercial

banks. Department of Statistics Malaysia stated Perak has 2.48 million of residents

in year 2016.

3.3.2 Frame and Location of Sampling

Sampling frame is a set of elements used by the researcher to select a sample of

target population while sampling location is a place to carry out the research.

Sampling frame in our research is Kampar residents as well as location of sampling

at Kampar, Perak.

3.3.3 Elements of Sampling

Elements of sampling is chosen from population that take part in the research.

Respondents who take part in this research study must have a bank account in

commercial and lives at Kampar. Through these questionnaires, researcher can

discover more clearly and accurately about the relationship between 4 independent

variables which are security and privacy, customer loyalty, service quality and

convenience that serve as the factors to influence the customer satisfaction towards

online banking.



3.3.4 Technique of Sampling

Technique of sampling that used in this research is questionnaires distribution

technique. Questionnaires distribution technique is the effective tool for collecting

information from the respondents. This is due to questionnaires distribution

technique may provide an efficient and accurate result by less costly method among

the others. Furthermore, questionnaires distribution technique makes us easy to

assemble the sample as well as give us unbiased method to select the sample. Hence,

the researcher may get an efficient and accurate result through questionnaires

distribution technique.

3.3.5 Size of Sampling

An appropriate calculation of sample size depends upon a number of unique factors

to each survey. The three most important factors are level of confidence, level of

precision, and degree of variability (Cochran, 1963). According to Henry (1990)

stated that sampling size is one of the most efficient methods to achieve estimation

by providing reliable and precise result. Krejcie and Mogan (1970) was

recommended a formula for calculating the sampling size. The formula as follow:

S = 𝑋2𝑁𝑃(1−𝑃)

𝑑2(𝑁−1) + 𝑋2𝑃(1−𝑃)

S = required sample size

X2 = the table value of chi-square for one degree of freedom at the desired

confidence level ( X2 = 3.841)

N = the population size

P = the population proportion (assumed to be 0.50 since this would provide

the maximum sample size)

d = the degree of accuracy expressed as a proportion (0.05)



According to this formula, 384 questionnaires have to be distributed in order to get

precise and reliable results.

3.4 Research Instrument

In this study, the most useful tool that can be adopted by the researchers for

collection of data from the respondents is questionnaire. It is a list of questions

which is useful for collecting statistically useful or personal information from the

targeted respondents. It benefits the researchers in this research because it is able

to assist the researchers to carry out the research more effectively and efficiently as

well as cheap as compared to other methods.

3.4.1 Questionnaire Design

There are thirty-three questions in the questionnaire constructed. Questionnaire is

divided into 3 sections which were Section A, Section B and Section C. The

demographic profile of respondents are placed at Section A, which including race,

age, highest education level, race, monthly income and so on. Section B involved

the details information about the respondents on the customer satisfaction towards

online banking in Malaysia. The last section, Section C is contain some questions

about the independent variables, which namely security and privacy, customer


3.4.2 Pilot Test

According to Ballan (2012), Pilot test is a small scale of exploratory research

technique. It helps to collect sample without any rigorous standards. This test is

widely accepted to estimate the validity, efficiency and data measurements (Ballan,



2012). Furthermore , pilot test is the initiative step that should be carried out in order

to determine whether issues exist before conducting an actual survey to the

respondents (Lavrakas, 2008). There are thirty people from Universiti Tunku Abdul

Rahman (UTAR) in the area of Kampar are chosen as the respondents of Pilot test.

The sets of survey forms are distributed to each of the students during 28 April

2017. Some of the respondents have been provided some recommendations for our

questionnaire when the pilot test is undergoing.

Table 3.1 Reliability Test for Pilot Testing

Variable Construct Cronbach’s Alpha No.of item

DV Customer Satisfaction 0.819 3

IV 1 Security & Privacy 0.752 5

IV 2 Customer Loyalty 0.892 5

IV 3 Service Quality 0.861 5

IV 4 Convenience 0.814 5

Based on Table 3.1, the overall range of Cronbach’s Alpha is fall within the value

of 0.752 and 0.892 which is generated by SPSS, version 20.0. Most of the variables

(customer satisfaction, customer loyalty, service quality and convenience) is

categorized as the level of excellent good reliability because the Cronbach’s Alpha

are range between the value of 0.8 to 0.9.However, there is only the Cronbach’s

Alpha for security & privacy is range between the value of 0.7 to 0.8 which is

categorized as the level of good reliability (George & Mallery, 2003; Zikmund,

Barry, Jon, & Mitch, 2010).



3.5 Constructs Measurement

According to Simon and Goes (2013), data comes in four types and four scales of

measurement. The levels of measurement that commonly used in research study are

ordinal scale, nominal scale, interval scale and ratio scale. In this study, ordinal

scale, nominal scale, and Likert scale are used to construct the questionnaire.

3.5.1 Nominal Scale

Nominal scale measures in term of name of designation or discrete units or

categories (Simon & Goes, 2013). For Section A, most of the questions are

developed based on nominal scale. For instance, the respondents’ frequently used

conventional bank have been categorized into several banks such as CIMB Bank

Berhad, Hong Leong Bank Berhad, Maybank Berhad, Public Bank Berhad and

Others.

3.5.2 Ordinal Scale

Ordinal scale consists of a group that is arises from the operation of rank ordering

(Steven, 1946). It measure in term of valuing the rate or rank without specify the

size of the interval (Simon & Goes, 2013). Ordinal scale have been used in Section

A for constructing some questions.

3.5.3 Likert Scale

Likert scale is used to quantify results as it usually separate the categories of

response from strongly disagree to strongly agree (Simon & Goes, 2013). For this

scale of measurement, respondents express their degree of agreement or

disagreement with the five-point scale whereby 1 represents strongly disagree, 2



represents disagree, 3 represents neutral, 4 represents agree and 5 represents

strongly agree. In this research, Likert scale is used for Section B and Section C.

3.6 Data Processing

In this study, data processing is carried out in order to ensure the accuracy and

precise of the data that to be used in data analysis. There are four steps of data

processing involving in this study namely data checking, data editing, data coding

and data transcribing.

3.6.1 Data Checking

The first step of data processing is data checking. In order to identify whether the

collected data is accurate and precise, data checking is one of the main processes

that needs to be carried out. The questionnaires which distributed to the respondents

will be collected back after they have answered all of the selected questions. In

addition, the collected data are checked thoroughly for identification of error once

the respondents are submitted the questionnaire. For example, the omitting answer

as well as the incomplete answer obtained from the questionnaires. It may cause the

data collected from questionnaires become invalid to be adopted in the data

analysis. Therefore, data checking is a critical step that is necessary to be carried

out because researchers can detect and handle the problem before they continue to

carry on the next steps.

3.6.2 Data Editing

Data editing is the following step of data processing. It is one type of the manual

process, thus this processing process is costly and time-consuming. This process



required someone to do the data checking and the correcting is needed if necessary.

In addition, data editing very useful for checking of the consistency, completeness

and legibility of data. But from now on, another method which called selective

editing is existed. This method stated that it only need to point out those errors that

has substantial effect on publication figures and it is unnecessary to correct all of

the errors. According to Waal (2013), he mentioned that the primary aim of data

editing is to do the correction to the errors. The process of data editing is carried out

after the completion of data checking. If there is incomplete of the questionnaire,

the issues lead to error and the collected data may become imprecise to be used.

3.6.3 Data Coding

The third step of data processing is data coding. Data coding is the process of

translating collected data from one format into another format. The selective

answers in all sections are assigned with ascending order which initiated with the

value of 1. For example, race, Malay is assigned with the value of 1, Chinese is 2,

Indian is 3 and Others is 4 in section A. Moreover, there are numerous and different

monthly salary groups contained in the questionnaire. Thus, respondents who has

less than RM1,000 of the monthly income is assigned with the value of 1, RM1,000

to RM2,500 is 2, RM2,501 to RM5,000 is 3, whereas more than RM5,000 is 4.

Furthermore, the ‘Likert’ scale in section B and C are categorized with the value of

1 for strongly disagree and followed by ascending value until 5 for strongly agree

(Simon & Goes, 2013). The five ‘Likert’ scale are recoded into new code which

started with the value of 1 for disagree until the value of 3 for agree.



3.6.4 Data Transcribing

Data transcribing is the last step of data processing. It is a process that downloading

the collected data and transforming it into the format of Microsoft Excel

spreadsheet. After the selection are coded with various values in Microsoft Excel,

the collected data is posted into the SPSS software, version 20.0 and run for data

analysis.

3.7 Data Analysis

In this study, a computer software program, Statistical Package for Social Sciences

(SPSS) 20.0 is used for the purpose of evaluating the research questions and data

analysis. Subsequently, the results generated are used to support the four hypotheses

of this research. In this study, descriptive analysis, scale measurement and

inferential analysis are involved.

3.7.1 Descriptive Analysis

According to Zikmund (2003), the basic features of a population or a sample in a

study can be described by using descriptive analysis. This analysis provides simple

summaries about the sample as well as the measures as it transforms the unanalyzed

data into a simpler pattern which is easier for interpretation and understanding

purposes.

Besides, nominal and ordinal scales are used in Section A of Questionnaire to

measure the responses. The cumulative percentages for every variables and

frequency counts are recorded. On the other hand, Likert scale is used in section B

and C in order to identify the mean.



3.7.2 Scale Measurement – Internal Reliability Test

This study applies an Internal Reliability Test to ensure that all respondents in this

study are at ease to answer the questions that allow them to choose the best answers

that describe their positions. Reliability means the quality of the measurements is

error-free, thus consistent result is collected (Sekaran, 2003).

In this research, Cronbach’s Alpha test is selected to test the reliability of

coefficients that estimate the consistency of the measuring scale. The Cronbach’s

Alpha reliability coefficients is rated as poor reliability if it is less than 0.6, fair

reliability if it ranges from 0.60 to 0.70, good reliability if it ranges from 0.7 to 0.8

and it is considered as excellent good reliability if it is more than 0.8 (George &

Mallery, 2003; Zikmund, Barry, Jon, & Mitch, 2010). The minimum alpha value

for each independent variable should be at least 0.70 which means that the proposed

independent variables are reliable and acceptable (Zikmund et al., 2010).

Table 3.2: Rule of Thumb for Internal Reliability Test

Cronbach’s Alpha Coefficient, α Level of Reliability

α ≥ 0.80 Excellent Good Reliability

0.70 ≤ α < 0.80 Good Reliability

0.60 ≤ α < 0.70 Fair Reliability

α < 0.60 Poor Reliability

Source: Zikmund et al. (2010)

In order to conduct Cronbach’s Alpha reliability test, a pilot test is conducted

through a participation of thirty respondents. All 33 questions in the questionnaire

are compulsory to be answered by the respondents. Afterwards, the data is gathered

and analyzed.



3.7.3 Inferential Analysis

Judgmental analysis about a population from a sample can be made through

inferential analysis. The researchers can make a conclusion to conclude if there is

any relationship between the population variables by depending on the sample data

(Hair, Money, Samouel, & Page, 2007).

In order to develop a conclusion for the hypotheses and research questions that

proposed in the previous chapter, Pearson’s Correlation Coefficient Analysis and

Multiple Linear Regression Analysis are selected and used.

3.7.3.1 Pearson Correlation Coefficient Analysis

Pearson’s Correlation Coefficient Analysis measures the covariance between the

dependent variable, Customer Satisfaction and independent variables namely,

Security & Privacy, Customer Loyalty, Service Quality, and Convenience. The

correlation coefficient or commonly known as ‘r’ demonstrates the direction and

degree of relationship among the variables. The correlation coefficient, r ranges

from -1.00 to +1.00. There is a strong positive or negative relationship when r

approaches +1.00 or -1.00 respectively. On the other hand, there is no linear

correlation between the two variables when r equals to 0.



Table 3.3: Rule of Thumb for Correlation Coefficient

Range of Coefficient Strength of Association

±0.91 - ±1.00 Very Strong

±0.71 - ±0.90 Strong

±0.41 - ±0.70 Moderate

±0.21 - ±0.40 Weak

±0.00 - ±0.20 Very Weak

Source: Hair et al. (2007)

3.7.3.2 Multiple Linear Regression Analysis

Multiple Linear Regression model consists of one dependent variable and at least

two independent variables. The objective to adopt Multiple Linear Regression is to

investigate the relationship between few independent variables and a dependent

variable. Through this model, the most influential independent variable towards

dependent variable will be ranked out. In this study, Multiple Linear Regression is

chosen to identify which independent variable (Security & Privacy, Customer

Loyalty, Service Quality, and Convenience) has the strongest impact in influencing

customers’ satisfaction towards online banking in Malaysia. Besides, this model

can explain the relationship between each independent variable and dependent

variable as the coefficient indicates the average value change in dependent variable

when there is a unit change in the independent variable (Hair et al., 2007).

The R-squared or commonly known as the coefficient of determination indicates

the proportion of the variation of the dependent variable that can be explained by

the variation of the independent variables. Next, the adjusted R-squared. It is similar

with the R-squared, but adjusted R-square takes into account the degree of freedom

while R-squared does not take into account the degree of freedom. To test whether

the overall regression model is significant, F-test will be used. Hair et al. (2007)



mentioned that a conclusion of the overall model is significant can be made if the

p-value from F-test is less than the alpha value of 0.05.

In this study, the multiple regression equation is presented as:

Y = b 0 + b 1X1 + b 2X2 + b 3X3 + b 4X4 + e

where Y indicates Customer Satisfaction towards Online Banking in Malaysia;

X1 indicates Security & Privacy;

X2 indicates Customer Loyalty;

X3 indicates Service Quality;

X4 indicates Convenience;

b 0 = intercept;

b 1, b 2, b 3, b 4 = slope of coefficient;

e = error term

3.7.3.3 Analysis of Variance (ANOVA) Test

Analysis of variance (ANOVA) is used to detect the difference among group means.

There are three assumptions needed in order to conduct ANOVA test. Firstly, the

sample must be independence which means the sample should not have relationship

with one another. Secondly, the distribution of population must be normally

distributed. Thirdly, the population variance have to be identical (Chalmer, 1986).

3.8 Conclusion

This chapter mainly focuses on the methodology used in this research. An

appropriate research methodology plays a vital role in a research as it can affect the

accuracy of the results of the study. Primary data is collected and analyzed in order



to develop a general idea about online banking. Analysis about the results of the

data collected through the questionnaire is discussed in the following chapter.



Chapter 4: Data Analysis

4.0 Introduction

This chapter presents the patterns of the results and analyses of the results which

are related to the hypotheses stated. Data is collected from 400 respondents and

analyzed by using Statistical Package for the Social Sciences (SPSS) in order to test

the validity of hypotheses. The presentation of results are in table form. In this

chapter, there are 3 main sections which are descriptive analysis, scale

measurement, and inferential analysis.

4.1 Descriptive Analysis

Descriptive analysis describes the sample characteristics of the respondents and

demonstrated the general pattern of responses which provided by the respondents.

4.1.1 Demographic Profile

There are a few questions of demographic profile by the respondents. The questions

consists of gender, age, race, highest education level, monthly income level, online

banking usage by the respondents, which conventional bank is frequently used by

the respondents, and satisfaction level towards the online banking services.

Table 4.1: Demographic Information of Respondents

Frequency Percentage (%)

Gender

Male 180 45.00

Female 220 55.00

Age

Below 20 12 3.00

21-30 327 81.75



31-40 30 7.50

41-50 18 4.50

51-60 9 2.25

Above 60 4 1.00

Race

Malay 30 7.50

Chinese 341 85.25

Indian 29 7.25

Highest Education Level

PMR 3 0.75

SPM 22 5.50

Diploma/Advanced

Diploma/STPM

68 17.00

Bachelor’s Degree 283 70.75

Master’s Degree 24 6.00

Monthly Income

Less than RM1,000 209 52.25

RM1,000 – RM2,500 64 16.00

RM2,501 – RM5,000 81 20.25

More than RM5,000 46 11.50

Online Banking Usage

1 – 5 times 294 73.50

6 – 10 times 63 15.75

More than 10 times 43 10.75

Conventional Bank

Affin Bank Berhad 4 1.00

AmBank Group Berhad 10 2.50

CIMB Bank Berhad 78 19.5

Hong Leong Bank Berhad 29 7.25

Maybank Berhad 114 28.50

Public Bank Berhad 145 36.25

RHB Bank Berhad 7 1.75

Others 13 3.25

Satisfaction Level Dissatisfied 10 2.50



Neutral 81 20.25

Satisfied 309 77.25

400 respondents have been participated in this research. According to the result,

female respondents more than male respondents. There are 220 female respondents

(55%) and 180 male respondents (45%).

As for age group in this research, the highest frequency and percentage are age of

respondents between 21–30 years old which consist of 327 respondents (81.75%).

The second highest frequency of age group comes from the age group between 31–

40 years old, which shows a total of 30 respondents (7.50%). Next is the age group

of 41-50 years old (18 respondents, 4.50%), age group of below 20 years old (12

respondents, 3.00%), and age group of 51-60 years old (9 respondents, 2.25%). Age

of respondents above 60 years old shows the lowest frequency and percentage

which only consists of 4 respondents (1.00%).

Meanwhile, the majority of race that using online banking services are Chinese

which consist of 341 respondents (85.25%). For Malay respondents, there are 30

respondents (7.50%). Indian respondents show the least respondents which only

consist of 29 respondents (7.25%).

Based on educational level attained, it stated that the highest frequency of education

level using online banking is Bachelor’s Degree holder consist of 283 respondents

(70.75%). Followed by Diploma/Advance Diploma/STPM holder consist of 68

respondents (17.00%). Next, Master’s Degree holder which is 24 respondents

(6.00%), while SPM certificate holder which is 22 respondents (5.50%). However,



the lowest frequency and percentage using online banking is PMR certificate holder

which only consist of 3 respondents (0.75%).

As for monthly income, it shows that the majority respondents with less than

RM1,000 per month have 209 respondents (52.25%). For the respondents with

salary between RM1,000 – RM2,500 per month and between RM2,501 – RM5,000

per month which are 64 respondents (16.00%) and 81 respondents (20.25%)

respectively. The minority respondents with salary more than RM5,000 per month

have 46 respondents (11.50%).

Based on the frequency of online banking usage, the research clearly showed that

the majority respondents used the online banking 1 – 5 times per month occupied

73.50% which is 294 respondents. However, for the respondents who used the

online banking 6 – 10 times and more than 10 times per month are 63 respondents

(15.75%) and 43 respondents (10.75%) respectively.

According to the research on frequently used conventional banks, the highest

frequency is Public Bank Berhad which is 145 respondents (36.25%), followed by

Maybank Berhad (114 respondents, 28.50%) and CIMB Bank Berhad (78

respondents, 19.50%). Hong Leong Bank Berhad, AmBank Group, RHB Bank

Berhad, and Affin Bank Berhad show an average frequency of 29 respondents

(7.25%), 10 respondents (2.50%), 7 respondents (1.75%), and 4 respondents

(1.00%) respectively. There are 13 respondents (3.25%) chose other conventional

banks such as Bank Islam Malaysia Berhad, Bank Mualamat Malaysia Berhad,



Bank Simpanan Nasional, HSBC Bank Malaysia Berhad, and OCBC Bank

Malaysia Berhad.

Based on the level of satisfaction towards online banking services, most of the

respondents are satisfied with online banking services provided by conventional

banks. There are 245 respondents (61.25%) are satisfied towards online banking

services. For strongly satisfied towards online banking services are 64 respondents

(16.00%). There are 81 respondents (20.25%) are neither satisfied nor dissatisfied

and 8 respondents (2.50%) are dissatisfied with online banking services. However,

only 2 respondents (0.50%) are strongly dissatisfied with online banking services.

4.1.2 Central Tendencies Measurement of Constructs

Central tendencies measurement of construct is very useful to evaluate each

variables by using the mean values as well as ranking. Table 4.2 shows the central

tendency measurement of the dependent variables (customer satisfaction) of the

research. There are three statements are included in customer satisfaction.

Table 4.2: Customer Satisfaction

Statement Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Standard

Deviation

Variance Ranking

Q10. 0.0 1.5 15.5 62.3 20.8 4.02 0.650 0.423 2

Q11. 0.5 3.3 23.8 56.0 16.5 3.85 0.745 0.556 3

Q12. 0.5 1.3 15.3 54.0 29.0 4.10 0.728 0.529 1

Based on Table 4.2, the first ranking of the customer satisfaction’s statement is

‘Continue using my current online banking services’ and the mean value is 4.10. It

has 54.0% of respondents rated as agreed, 29.0% of respondents strongly agreed,



15.3% of neutral respondents as well as 1.3% of respondents rated as disagreed. On

the other hand, there is only 0.5% rated as strongly disagreed.

It is followed by the statement of ‘Current online banking services provided are

useful for customers’, it has the mean value of 4.02. ‘Current online banking

services provided are useful for customers’ has 62.3% of respondents rated as

agreed, 20.8% of respondents strongly agreed and 15.5% of neutral respondents.

On the other hand, there is only 1.5% respondent were strongly disagreed.

The lowest ranking of the statement is ‘Services offered by online banking company

meet my requirement’ and it has the mean value of 3.85 which consist of 56.0% of

respondents who agreed with the statement, 23.8% of neutral respondents, 16.5%

of respondents strongly agreed and 3.3% of the respondents disagreed. There are

only 0.5% of the respondents rated as strongly disagreed.

Table 4.3 shows the central tendencies measurement of the independent variable

(security & privacy) in the study. There are five statements are included in security

and privacy.

Table 4.3: Security & Privacy

Statement Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Standard

Deviation

Variance Ranking

Q13. 1.3 7.3 33.5 45.8 12.3 3.61 0.840 0.706 5

Q14. 0.3 1.5 11.3 38.8 48.3 4.33 0.754 0.568 2

Q15. 0.3 1.5 12.0 38.8 47.5 4.32 0.760 0.578 3

Q16. 0.0 2.8 25.8 46.3 25.3 3.94 0.786 0.618 4

Q17. 0.0 1.0 8.0 31.8 59.3 4.49 0.686 0.471 1



Based on the Table 4.3, the first ranking of the statement of security & privacy is ‘I

prefer higher security authentication when using online banking transaction’ with

the mean value of 4.49. It has 59.3% of respondents who were strongly agreed with

the statement, 31.8% of respondents rated as agreed, following by 8.0% of neutral

respondents. On the other hand, there is only 1.0% of the respondent who rated as

strongly disagreed.

It is followed by the second ranking statement of ‘Online banking company should

provide a latest encryption technology to secure online transaction’ which has the

mean value of 4.33. This statement has 48.3% of respondents rated as strongly

agreed, 38.8% of respondents agreed with this statement, 11.3% of neutral

respondents as well as 1.5% of respondents rated as disagreed. Furthermore, there

are only 0.3% respondents were strongly disagreed.

The following ranking is the statement of ‘I need a secure communication access

through Internet banking website’. It has the mean value of 4.32 which consist of

47.5% of respondents who strongly agreed with the statement, 38.8% of

respondents who agreed, 12.0% of neutral respondents and 1.5% of the respondents

who disagreed. On the other hand, there is only 0.3% of the respondents are strongly

disagreed with this statement.

The following ranking statement is ‘Internet banking company concerns about

customers’ privacy and security of any transactions’ with the mean value of 3.94.

There are 46.3% of respondents who agreed, 25.8% of neutral respondents, 25.3%



of respondents who rated as strongly agreed. However, there are only 2.8% of the

respondents are disagreed.

‘I think that the websites in Malaysia have to ensure the safe transmission of its

users’ information’ was placed at the last ranking. It has the mean value of 3.61

which consist of 45.8% of respondents who rated as agreed, 33.5% of respondents

who rated as neutral, 12.3% of respondents who rated as strongly agreed and 7.3%

of the respondents disagreed with this statement. However, there is only 1.3% of

the respondents are strongly disagreed.


(customer loyalty) in the study. There are five statements are included in customer

loyalty.

Table 4.4: Customer Loyalty

Statement Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Standard

Deviation

Variance Ranking

Q18. 0.8 6.5 27.0 46.5 19.3 3.77 0.860 0.739 4

Q19. 1.0 1.5 23.0 54.8 19.8 3.91 0.755 0.570 1

Q20. 0.3 4.0 27.5 52.8 15.5 3.79 0.756 0.571 3

Q21. 0.3 3.3 27.3 53.5 15.8 3.81 0.741 0.549 2

Q22. 0.8 3.8 32.0 45.5 18.0 3.76 0.814 0.663 5

Based on the Table 4.4, the first ranking of the statement of customer loyalty is ‘I

would like to remain as a customer by continue using the online banking service of

my current bank’. It has the mean value of 3.91 and consist of 54.8% of respondents

who agreed, 23.0% of respondents who rated as neutral, 19.8% strongly agreed with



this statement, 1.5% of respondents disagreed with this statement. However, there

are only 1.0% respondent who rated as strongly disagreed.

‘I have positive thoughts about the online banking services provided by my current

bank’ has the mean value of 3.81. It has 53.5% of respondents agreed, 27.3% of

neutral respondents, 15.8% of respondents strongly agreed and 3.3% of respondents

who disagreed. Furthermore, there is only 0.3% respondents who were strongly

disagreed.

The statement of ‘I am satisfied with the responsiveness of the online banking

services by my current bank’ is placed at the third ranking which has the mean value

of 3.79. It consists of 52.8% of respondents who agreed with this statement, 27.5%

of neutral respondents, 15.5% of respondents who rated as strongly agreed, 4.0% of

the respondents disagreed with this statement. However, there are only 0.3% of the

respondents were strongly disagreed.

The following ranking statement is ‘I consider myself to be loyal to my current bank

online banking services’ with mean value of 3.77. There are 46.5% of respondents

who agreed with this statement, 27.0% of neutral respondents, 19.3% of

respondents strongly agreed, 6.5% of respondents who rated as disagreed. On the

other hand, there is only 0.8% of the respondents who strongly disagreed.

The statement of ‘I would recommend the online banking service provided by my

current bank to the others’ was placed at the lowest ranking. It has the mean value

of 3.76 which consist of 45.5% of respondents who agreed with this statement,



32.0% of neutral respondents, 18.0% of respondents who rated as strongly agreed

and 3.8% of the respondents disagreed with this statement. However, there are only

0.8% of the respondents rated as strongly disagreed.


(service quality) in the study. There are five statements are included in service

quality.

Table 4.5: Service Quality

Statement Strongly

Disagree

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Standard

Deviation

Variance Ranking

Q23. 0.0 1.8 21.5 58.5 18.3 3.93 0.681 0.464 1

Q24. 1.0 6.5 30.3 47.3 15.0 3.69 0.841 0.707 4

Q25. 1.3 8.3 33.3 45.0 12.3 3.59 0.854 0.729 5

Q26. 0.5 4.3 29.0 51.0 15.3 3.76 0.776 0.603 3

Q27. 0.3 3.0 29.8 54.0 13.0 3.77 0.718 0.516 2

Based on the Table 4.5, the first ranking of the statement of service quality is

‘Online banking services are efficient in processing transactions and other

requirements’. It has the mean value of 3.93. This statement has 58.5% of

respondents rated as agreed, followed by 21.5% of respondents who rated as neutral,

18.3% strongly agreed with this statement. However, there are only 1.8%

respondent who rated as disagreed.

It is followed by the second ranking statement of ‘The delivery of online service is

just-in-time’ which has the mean value of 3.77. It has 54.0% of respondents who

agreed with this statement, 9.8% of neutral respondents, 13.0% of respondents

strongly agreed and 3.0% of respondents disagreed. Furthermore, there is only 0.3%

respondents who were strongly disagreed.



The third ranking of the statement is ‘The information provided on the bank’s

website is adequate and accurate’ which has the mean value of 3.76. This statement

consist of 51.0% of respondents agreed, 29.0% of neutral respondents, 15.3% of

respondents who rated as strongly agreed, 4.3% of the respondents disagreed with

this statement. However, there are only 0.5% of the respondents are strongly

disagreed with this statement.

The following ranking statement is ‘The speed of logging into account is fast’ with

mean value of 3.69. It has 47.3% of respondents rated as agreed, 30.3% of

respondents who rated as neutral, 15.0% of respondents who rated as strongly

agreed, 6.5% of respondents who rated as disagreed. However, there are only 1.0%

of the respondents are strongly disagreed.

The statement of ‘It is easy to find all important information from the bank’s

website’ was placed at the lowest ranking. It has the mean value of 3.59 which

consist of 45.0% of respondents agreed, 33.3% of neutral respondents, 12.3% of

respondents who rated as strongly agreed, 8.3% of the respondents disagreed with

this statement. However, there are only 1.3% of the respondents rated as strongly

disagreed.


(convenience) in the study. There are five statements are included in convenience.



Table 4.6: Convenience

Statement Strongly

Disagre

e

(%)

Disagree

(%)

Neutral

(%)

Agree

(%)

Strongly

Agree

(%)

Mean Standard

Deviation

Variance Ranking

Q28. 0.8 2.3 19.8 48.0 29.3 4.03 0.805 0.648 3

Q29. 0.3 1.8 16.5 51.8 29.8 4.09 0.740 0.548 2

Q30. 0.3 1.0 13.8 46.5 38.5 4.22 0.733 0.528 1

Q31. 0.0 0.8 20.8 54.3 24.3 4.02 0.693 0.481 4

Q32. 2.5 9.3 36.0 36.3 16.0 3.54 0.952 0.906 5

Based on the Table 4.6, the first ranking of convenience’s statement is ‘Online

banking services can help to reduce non-monetary costs including time, energy and

effort’ with the mean value of 4.22. In fact, this statement has 46.5% of respondents

who agreed with this statement, 38.5% of respondents who strongly agreed, 13.8%

of respondents strongly agreed and 1.0% of respondents who disagreed. However,

there are only 0.3% respondent who rated as strongly disagreed.

It is followed by the second ranking statement of ‘Online banking services allow

me to access my account and conduct transaction with anybody, at any time and

everywhere in the world’ which has the mean value of 4.09. This statement has

51.8% of respondents who agreed, 29.8% of respondents strongly agreed, 16.5% of

neutral respondents, and 1.8% of respondents disagreed. On the other hand, there is

only 0.3% respondents who rated as strongly disagreed.

The statement of ‘I place convenience as my top priority when i choose my online

banking services’ was placed at the third ranking which has the mean value of 4.03.

It consist of 48.0% of respondents rated as agreed, following by 29.3% of

respondents who rated as strongly agreed, 19.8% of respondents who rated as



neutral and 2.3% of the respondents disagreed with this statement. However, there

are only 0.8% of the respondents are strongly disagreed with this statement.

The following ranking statement is ‘Online banking is an user-friendly financial

management tool’ with mean value of 4.02. It has 54.3% of respondents rated as

agreed, 24.3% of respondents who rated as strongly agreed, 20.8% of respondents

who rated as neutral. However, there are only 0.8% of the respondents are

disagreed.

The statement of ‘I will switch if the another bank is more capable to provide me a

higher degree of convenience’ was placed at the lowest ranking. It has the mean

value of 3.54 which consist of 36.3% of respondents rated as agreed, 36.0% of

neutral respondents, 16.0% of respondents who rated as strongly agreed and 9.3%

of the respondents disagreed with this statement. However, there are only 2.5% of

the respondents rated as strongly disagreed.

4.2 Scale Measurement

4.2.1 Internal Reliability Test

Table 4.7: Results of Cronbach’s Alpha

Variables Statements Cronbach’s Alpha

Customer

Satisfaction

The current online banking services provided

are useful for customers.

0.905

The online banking services provided meet

my expectation and requirement.

0.905

I will continue using my current online

banking services.

0.905



Security & Privacy

I think that the online banking websites in

Malaysia have to ensure the safe transmission

of its users’ information.

0.909

Online banking company should provide a

latest encryption technology to secure online

transaction.

0.911

I need a secure communication access through

online banking website.

0.911

Internet banking company concerns about

customers’ privacy and security of any

transactions.

0.908

I prefer higher security authentication when

using online banking transaction.

0.910

Customer Loyalty

I consider myself to be loyal to my current

bank online banking services.

0.908

I would like to remain as a customer by

continue using the online banking service of

my current bank.

0.907

I am satisfied with the responsiveness of the

online banking services by my current bank.

0.905

I have positive thoughts about the online

banking services provided by my current

bank.

0.904

I would recommend the online banking

service provided by my current bank to the

others.

0.906

Service Quality

Online banking services are efficient in

processing transactions and other

requirements.

0.906

The speed of logging into account is fast. 0.908

It is easy to find all important information

from the bank’s website.

0.909

The information provided on the bank’s

website is adequate and accurate.

0.906

The delivery of online service is just-in-time. 0.906

I place convenience as my top priority when I

choose my online banking services.

0.908



Convenience

Online banking services allow me to access

my account and conduct transaction with

anybody at anytime and everywhere in the

world.

0.907

Online banking services can help to reduce

non-monetary costs including time, energy

and effort.

0.907

Online banking is an user-friendly financial

management tool.

0.907

I will switch to another bank which is more

capable to provide me a higher degree of

convenience.

0.917

Table 4.7 shows the result of Cronbach’s Alpha for customer satisfaction, security

& privacy, customer loyalty, service quality and convenience. Based on table above,

all statements of customer satisfaction acquired the identical value of Cronbach’s

Alpha at 0.905. Customer satisfaction is categorized as having an excellent

reliability as their values have exceeded 0.8.

Security & privacy obtained the highest value of Cronbach’s Alpha at 0.911 and the

lowest value of Cronbach’s Alpha at 0.908. Customer loyalty obtained the highest

value of Cronbach’s Alpha at 0.908 and the lowest value of Cronbach’s Alpha at

0.904. Besides, service quality obtained the highest value of Cronbach’s Alpha at

0.909 and the lowest value of Cronbach’s Alpha at 0.906. Furthermore, convenience

obtained the highest value of Cronbach’s Alpha at 0.917 and the lowest value of

Cronbach’s Alpha at 0.906. The results of Cronbach’s Alpha for all variables fall

within the range from 0.904 to 0.917, which exceed 0.8. Therefore, all variables are

categorized as having an excellent reliability as their values have exceeded 0.8.



Table 4.8: Reliability Test for Substantive Study

Variables Construct Cronbach’s Alpha Number of Items

DV Customer Satisfaction 0.824 3

IV 1 Security & Privacy 0.726 5

IV 2 Customer Loyalty 0.871 5

IV 3 Service Quality 0.820 5

IV 4 Convenience 0.700 5

Table 4.8 shows the result of Cronbach’s Alpha for dependent and independent

variables namely customer satisfaction, security & privacy, customer loyalty,

service quality and convenience. Reliability was tested by measuring the 23 items

in this research. Based on table above, customer loyalty consist the highest value

which is valued at 0.871. On the other hand, convenience obtained at 0.700 which

is the lowest value of Cronbach’s Alpha. Customer satisfaction (α = 0.824) and

service quality (α = 0.820) are categorized as having an excellent reliability as their

values have exceed 0.8. In addition, security & privacy (α = 0.726) and convenience

(α = 0.700) are categorized as having a good reliability as their values have more

than 0.7. The results of Cronbach’s Alpha are range between 0.700 and 0.871. It

have exceeded the lowest limit value which were widely accepted by numerous of

the study. Thus, the proposed dependent and independent variables are reliable and

consistent.



4.3 Inferential Analyses

4.3.1 Pearson Correlation Coefficient

Pearson’s Correlation Coefficient Analysis is used to measure the covariance

between the dependent variable, Customer Satisfaction and independent variables

namely, Security & Privacy, Customer Loyalty, Service Quality, and Convenience.

In this study, instead of 2-tailed test, 1-tailed test is used to conduct this Pearson

Correlation Analysis. This is because the hypotheses stated previously in this study

are all in 1 direction which is positive relationship. 2-tailed test will only be used

when the hypotheses are stated in 2 directions (positive or negative).

Table 4.9 : Pearson Correlation Analysis

Security &

Privacy

Customer Loyalty Service Quality Convenience

Pearson

Correlation

0.545 ***

(0.000)

0.616***

(0.000)

0.595***

(0.000)

0.551***

(0.000)

*** Correlation is significant at the 0.01 level (1-tailed).

*, **, *** indicate correlation is significant at 10%, 5% and 1% respectively (1-

tailed).

Based on Table 4.9, it is shown that there are positive relationships between

Customers’ Satisfaction towards Online Banking and all of the independent

variables namely, Security & Privacy, Customer Loyalty, Service Quality, and

Convenience since all of the values of Pearson Correlation coefficient are positive.

In addition, the strength of association between Customers’ Satisfaction towards

Online Banking and all of the independent variables are moderate since all of the

values of Pearson Correlation range from 0.545 to 0.616 which fall within the range

of 0.41 to 0.70. However, all of the p-values are 0.000 which is lesser than the

significance level of 0.01. So, it can be concluded that there are positive



relationships between all of the independent variables and customers’ satisfaction

towards online banking. This is consistent with the hypotheses stated in previous

chapter.

4.3.2 One-way ANOVA

ANOVA one-way test is being used to determine the relationship between the

independents variables and the dependent variable used in this research.

Table 4.10 : ANOVA One Way Test (Overall Model)

Model Sum of Squares Df Mean

Square

F Sig.

Regression 745.155 4 186.289 124.841 0.000***

Residual 589.423 395 1.492

Total 1334.577 399

Dependent variable: Customer satisfaction

Independent variables: (Constant),Security & privacy, Customer loyalty, Service

quality, Convenience.

*, **, *** indicate 10%, 5% and 1% respectively.

H0: The respondents who are strongly disagree, disagree, neutral, agree or strongly

agree have no different perceptions towards customer satisfaction towards online

banking.

H1: At least one of the respondents who are strongly disagree, disagree, neutral,

agree or strongly agree have different perceptions towards customer satisfaction


Table 4.10 shows that the ANOVA p-value of 0.000 is smaller than the significant

level of 0.01. F value of 124.841 is significant indicates that this model is fitted and

good in describing the perception of respondents on security and privacy, customer

loyalty, service quality and convenience towards customer satisfaction on online



banking in Malaysia. Null hypothesis is rejected and there is sufficient evidence to

conclude that there are at least one of the respondents have different perception

towards customer satisfaction on online banking.

Table 4.11 ANOVA One Way Test

Sum of

Squares

df Mean Square F Sig.

Between

Groups 320.822 3 106.941 41.774 .000***

Within Groups 1013.756 396 2.560

Total 1334.577 399


H0: There is no significant difference in perceptions between Groups on Customers’

Satisfaction towards Online Banking after amending the group category.

H1: There is a significant difference in perceptions between Groups on Customers’


Since the p-value = 0.000 ≤ 0.05, the null hypothesis is rejected. Thus, there is

enough evidence to conclude that there is significant difference in perceptions

between Groups on Customers’ Satisfaction towards Online Banking after

amending the group category at α = 0.05 significant level. Next, the Post Hoc Test

is carried out.



Table 4.12 Post Hoc Test

(I)

Security_and_privacy2

(J)

Security_and_privacy2

Mean

Difference (I-J)

Std. Error Sig.

Disagree

Neutral -2.15385 1.16002 .249

Agree -3.78516*** 1.13578 .005

Strongly agree -5.18447*** 1.14230 .000

Neutral

Disagree 2.15385 1.16002 .249

Agree -1.63131*** .27503 .000

Strongly agree -3.03062*** .30082 .000

Agree

Disagree 3.78516*** 1.13578 .005

Neutral 1.63131*** .27503 .000

Strongly agree -1.39931*** .18669 .000

Strongly agree

Disagree 5.18447*** 1.14230 .000

Neutral 3.03062*** .30082 .000

Agree 1.39931*** .18669 .000

*, **, *** indicate the mean difference is significant at 10%, 5% and 1%

respectively.

A Tukey Post Hoc test reveals that there is no mean difference in customer

satisfaction between those who disagree and those who neutral with the statement

of security & privacy. The mean difference of customer satisfaction score between

those who disagree with the statements of security & privacy are 3.78516 times less

satisfied compare with those who agree with the statement of security & privacy.

The mean difference of customer satisfaction score between those who disagree

with the statement of security & privacy are 5.18447 times less satisfied compare

with those who strongly agree with the statements of security & privacy.

Furthermore, the mean difference of customer satisfaction score between those who

neutral with the statements of security & privacy are 1.63131 times less satisfied

compare with those who agree with the statements of security & privacy. Besides,

the mean difference score between those who neutral with the statements of security

& privacy are 3.03062 times less satisfied compare with those who strongly agree

with the statements of security & privacy. Lastly, the mean difference of customer



satisfaction score between those who agree with the statements of security &

privacy are 1.39931 times less satisfied compare with those who strongly agree with

the statements of security & privacy.


Sum of

Squares


Between

Groups 439.685 3 146.562 66.530 .000***

Within Groups 870.155 395 2.203

Total 1309.840 398







enough evidence to conclude that there is significant difference in perceptions



is carried out.



Table 4.14: Post Hoc Test

(I) Customer_loyalty2 (J) Customer_loyalty2 Mean Difference

(I-J)

Std. Error Sig.

Disagree

Neutral -.79817 .49041 .364

Agree -2.10455*** .47990 .000

Strongly agree -4.00000*** .50696 .000

Neutral

Disagree .79817 .49041 .364

Agree -1.30638*** .17385 .000

Strongly agree -3.20183*** .23859 .000

Agree

Disagree 2.10455*** .47990 .000

Neutral 1.30638*** .17385 .000

Strongly agree -1.89545*** .21617 .000

Strongly agree

Disagree 4.00000*** .50696 .000

Neutral 3.20183*** .23859 .000

Agree 1.89545*** .21617 .000


respectively.

A Tukey Post Hoc test shows that there is no mean difference between those who

disagree with the Customer Loyalty’s statements and those who neutral with the

Customer Loyalty’s statements. Next, the mean difference of customer satisfaction

score between those who disagree with the Customer Loyalty’s statements is

2.10455 times less satisfied compare with those who agree with the Customer

Loyalty’s statements. The mean difference of customer satisfaction score between

those who disagree with the Customer Loyalty’s statements is 4.00000 times less

satisfied compare with those who strongly agree with the Customer Loyalty’s

statements. The mean difference of customer satisfaction score between those who

neutral with the Customer Loyalty’s statements is 1.30638 times less satisfied

compare with those who agree with the Customer Loyalty’s statements. Besides,

the mean difference of customer satisfaction score between those who neutral with

the Customer Loyalty’s statements is 3.20183 times less satisfied compare with

those who strongly agree with the Customer Loyalty’s statements. Lastly, the mean

difference of customer satisfaction score between those who agree with the



Customer Loyalty’s statements is 1.89545 times less satisfied compare with those

who strongly agree with the Customer Loyalty’s statements.

Table 4.15: ANOVA One Way Test

Sum of

Squares


Between

Groups 397.743 3 132.581 56.042 .000***

Within Groups 936.835 396 2.366

Total 1334.578 399


H0: There is no significant difference in perception between Groups on customer

satisfaction towards online banking after amending the group category.

H1: There is significant difference in perception between Groups on customer

satisfaction towards online banking after amending the group category.

Since the p-value = 0.000 ≤ 0.05, we reject the null hypothesis. There is enough

evidence to conclude that there is significant difference in perception between

Groups on customer satisfaction towards online banking after amending the group

category at α = 0.05 significant level. Next, Post Hoc Test is carried out to determine

the mean difference between the respondent’s perception.




(I) Service_quality2 (J) Service_quality2 Mean Difference

(I-J)

Std. Error Sig.

Disagree

Neutral -1.47491*** .53098 .029

Agree -2.82947*** .52263 .000

Strongly agree -4.74474*** .57166 .000

Neutral

Disagree 1.47491*** .53098 .029

Agree -1.35456*** .17136 .000

Strongly agree -3.26983*** .28813 .000

Agree

Disagree 2.82947*** .52263 .000

Neutral 1.35456*** .17136 .000

Strongly agree -1.91528*** .27244 .000

Strongly agree

Disagree 4.74474*** .57166 .000

Neutral 3.26983*** .28813 .000

Agree 1.91528*** .27244 .000


respectively.

The Post hoc test shows that the mean difference of customer satisfaction score

between those who disagree with the service quality’s statements is 1.47491 times

less satisfied compare with those who neutral with the service quality’s statements.

The mean difference of customer satisfaction score between those who disagree

with the service quality’s statements is 2.82947 times less satisfied compare with

those who agree with the service quality’s statements. On the other hand, the mean

difference of customer satisfaction score between those who disagree with the

service quality’s statements is 4.74474 times less satisfied compare with those who

strongly agree with the service quality’s statements. Moreover, the mean difference

of customer satisfaction score between those who agree with the service quality’s

statements is 1.35456 times more satisfied compare with those who neutral with the

service quality’s statements. The mean difference of customer satisfaction score

between those who strongly agree with the service quality’s statements is 3.26983

times more satisfied compare with those who felt neutral with the service quality’s

statements. The mean difference of customer satisfaction score between those who



strongly agree with the service quality’s statements is 1.91528 times more satisfied

compare with those who agree with the service quality’s statements. The overall

picture shows that the perception of respondents are more towards strongly agreed

and agreed to the statement of service quality.


Sum of

Squares


Between

Groups 294.133 2 147.067 57.338 .000***

Within Groups 1015.706 396 2.565

Total 1309.840 398







sufficient evidence to conclude that there is significant difference in perceptions



is carried out.




(I)

Convenience_2

(J)

Convenience_2

Mean Difference

(I-J)

Std. Error Sig.

Neutral Agree -1.71762*** .22666 .000

Strongly agree -3.06948*** .28768 .000

Agree Neutral 1.71762*** .22666 .000

Strongly agree -1.35186*** .22370 .000

Strongly agree Neutral 3.06948*** .28768 .000

Agree 1.35186*** .22370 .000


respectively.

A Tukey Post Hoc test shows that the mean difference of customer satisfaction score

between those who neutral with the Convenience’s statements is 1.71762 times less

satisfied compare with those who agree with the Convenience’s statements.

Besides, the mean difference of customer satisfaction score between those who

agree with the Convenience’s statements is 1.35186 times less satisfied compare

with those who strongly agree with the Convenience’s statements. Lastly, the mean

difference of customer satisfaction score between those who strongly agree with the

Convenience’s statements is 3.06948 times more satisfied compare with those who

neutral with the Convenience’s statements. The overall figure shows that the

perception of respondents are more towards strongly agreed and agreed to the

statement of convenience.



4.3.3 Multiple Regression Analysis

Table 4.19 : Multiple Linear Regression on Four Independent Variables and

Customers’ Satisfaction towards Online Banking (Model Summary)

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .747a .558 .554 1.22156

a. Predictors: (Constant), Convenience, Customer Loyalty, Security and Privacy,

Service Quality

b. Dependent Variable: Customer Satisfaction

R, the coefficient of correlation measures the degree of association between

dependent variable and all the independent variables jointly. Based on Table 4.12,

R equals to 0.747 which indicates that the four independent variables (Security &

Privacy, Customer Loyalty, Service Quality, and Convenience) are strongly

correlated to Customers’ Satisfaction towards Online Banking. The coefficient of

determination, R Square of 0.558 shows that 55.8% of the variation in Customers’

Satisfaction towards Online Banking can be explained by the variation in the four

independent variables. However, there is 44.2% of the variation in Customers’

Satisfaction towards Online Banking is left unexplained. This means that there are

other important determinants that influencing Customers’ Satisfaction towards

Online Banking are not included in this study.




Customers’ Satisfaction towards Online Banking (Coefficient)

Model Unstandardized

Coefficients

Standardized

Coefficients

T Sig.

B Std. Error Beta

1 (Constant) .017 .563 .030 .976

Security and Privacy .176*** .028 .255 6.308 .000

Customer Loyalty .182*** .024 .319 7.479 .000

Service_Quality .141*** .027 .229 5.201 .000

Convenience .110*** .030 .157 3.631 .000


According to Table 4.20, it is shown that Customer Loyalty contributes the most to

the variation of Customers’ Satisfaction towards Online Banking among the four

independent variables as Customer Loyalty has the highest unstandardized

coefficient B value and standardized coefficient Beta value. The second highest

contribution goes to Security & Privacy, followed by Service Quality. The least

contribution comes from Convenience because it has the lowest magnitude of

coefficients.

Firstly, Customer Loyalty. According to the value of unstandardized coefficient B

value, on average, an increase in 1 score in customer loyalty will lead to an increase

in the level of customers’ satisfaction towards online banking by 0.182, holding all

other variables constant. At the same time, the standardized coefficient Beta value

is interpreted as for 1 standard deviation increase in customer loyalty lead to an

increase in 0.319 of standard deviation in customers’ satisfaction towards online

banking, holding all other variables constant.



According to Table 4.20, it shows that the p-value of customer loyalty is 0.000.

The p-value of 0.000 less than the significant level of 0.05 indicates customer

loyalty is significant in explaining the variation in customers’ satisfaction towards

online banking. There is a positive relationship between customer loyalty and

customer satisfaction towards online banking. This is due to Pearson’s correlation

test showed that the p-value of 0.000 which is less than the significance level of

0.01. This result is consistent with the hypothesis developed in Chapter 2 that stated

customer loyalty has a positive relationship with customers’ satisfaction towards

online banking. The result is also consistent with the previous studies. According

to Koupai, Alipourdarvish, and Sardar (2015) showed that there is a positive

relationship between customer loyalty and customer satisfaction towards online

banking. When the level of customer satisfaction is high, the possibility of the

customer becoming loyal will increase (Bei & Chiao, 2001). This means that

customer desires to have a long lasting relationship with the bank if they have strong

loyalty towards the banks (Palmatier et al., 2006). In addition, the findings of this

research is matched with the concepts of Equity Theory. According to Messick and

Cook (1983), equity theory stated that the creating relationships with customers able

to enhance customer loyalty. Through this theory, retaining customer loyalty can

create a result on long-term financial performance. In addition, equity theory also

useful in conceptualizing fairness which is consider as a determinant of customer

loyalty (Cahill, 2007). Due to the consistency with the previous study, this research

has a strong indication to state that a higher level of customer loyalty towards online

banking has a significant impact on customer satisfaction.



Secondly, Security & Privacy. According to the value of unstandardized coefficient

B value, on average, an increase in 1 score in security and privacy will lead the

score of customers’ satisfaction towards online banking to increase by 0.176,

holding other variables constant. Furthermore, the standardized coefficient Beta

value is interpreted as for 1 standard deviation increase in security and privacy, it

will result in a 0.255 standard deviation increase in customers’ satisfaction towards

online banking, holding other variables constant.

In addition, based on the Table 4.20, the p-value of Security & Privacy is 0.000

which is smaller than the alpha value of 0.05. Hence, it can be concluded that

Security & Privacy is significant in explaining the variation in customers’

satisfaction towards online banking. This supports the hypothesis developed in the

previous chapter, which stated that Security & Privacy have a positive impact on

customers’ satisfaction towards online banking. This is because most of the

respondents strongly agreed that they would choose to use online banking

transaction which has higher security authentication. This is mostly because

customers worry about the possibility of losing confidential information. Thus,

security and privacy are important in fulfilling customers’ satisfaction towards

online banking. On top of that, the result is consistent with previous studies.

According to Jalil, Talukder, and Rahman (2014), the security of online banking

system is always the main concern for customers. This is because serious

destruction can be happened to customers and banking sector if the security

provided for online transfer of funds is not strong enough. Hence, secure financial

transaction plays an important role to majority of the online banking customers as

their main concern is to protect their money in the banks (Ndubisi & Sinti, 2006;



Fatima, 2011). Besides, the results show an anticipated view regarding the

relationship between security & privacy and customers’ satisfaction towards online

banking. Ahmad and Al-Zu’bi (2011) stated that security and privacy have a

significant positive influence on customer satisfaction towards online banking.

They mentioned that an important factor for banks to survive and grow is to have a

mutual interest with their customers. So, the safety of customer’s confidential

information and bank’s privacy protection are crucial in increasing the customers’

satisfaction in long term. Yoon (2010) also mentioned that security has positive

impact on customers’ satisfaction because customers always tend to emphasize

more on information system attributes than service attributes. In addition, the result

is consistent with the concepts of Equity Theory as Aytes and Connolly (2004)

suggested that the Internet user’s behavior is influenced by the way the user views

the advantages of information security and outcome of not using it. In contrast, Goh

et al. (2016) had provided different findings regarding the impact of security &

privacy on customer satisfaction towards online banking. They concluded that

online banking users think that online banking providers have the responsibility in

safeguarding their online banking transactions. Thus, online banking users will not

view security and privacy as one of the factors in influencing their satisfaction

towards online banking. As a conclusion, security & privacy significantly positively

influence customers’ satisfaction towards online banking.

Thirdly, Service Quality. According to the value of unstandardized coefficient B

value, on average, an increase in 1 score in service quality will lead to an increase

in the level of customers’ satisfaction towards online banking by 0.141, holding all

other variables constant. On the other hand, the standardized coefficient Beta value



shows that 1 standard deviation increase in service quality would lead to an increase

in 0.229 of standard deviation in customers’ satisfaction towards online banking,

holding other variables constant.

Based on Table 4.20, service quality with p-value of 0.000 that is less than the

significant level of 0.05 indicates that service quality is significant in explaining the

variation in customers’ satisfaction towards online banking. This shown that there

is a positive relationship between service quality and customer satisfaction towards

online banking and it is supported by the result in the Pearson’s correlation test.

This result is consistent with the “Hypothesis 3” developed in the chapter 2 which

states that service quality has a positive impact on customers’ satisfaction towards

online banking. Most of the respondents agreed that they were satisfied by the

efficiency of online banking in conducting transaction and other requirements.

Therefore, higher service quality can trigger higher level of customer satisfaction

provided the services can satisfy the customers. The probability of retaining

customers also will increase if they are satisfied to the banking service (Spreng et

al., 1996). The study of Selvakumar (2015) also stated that service quality is the

most important criteria for a bank to evaluate whether the customers are satisfied so

that they can retain and maintain average retention rate of customer. Customer

service that exceeds the expectation of customer is most advantageous to a bank as

it can satisfy customer by meeting their needs (Selvakumar, 2015). According to

Thwaites and Vere (1995), customers will select a bank by assessing on the range

and quality of the bank’s service. Furthermore, Oliver (1980) also suggests that

quality for service sector is the key factor that affects the customer satisfaction and

their intention to purchase. Therefore, the findings of this research are consistent



with the previous studies and this is a strong indication that a higher level of service

quality of online banking has a great effect on customer satisfaction. Moreover, the

finding also consistent to the concept of Equity theory that compared the qualities

and benefit obtained from the purchases or services. Customer is less satisfied if

they know that other consumers receive a more favourable price or superior service.

(Fisk & Coney, 1982). A firm or a bank that can provide better service at a

reasonable price over the others able gain advantageous in retaining and attracting

existing and potential customers compare to its competitors. However, the research

of Goh et al. (2016) had provided a contradict view where they found that service

quality do not have a relationship with customer satisfaction. This may because in

their research there are several aspects of service quality such as efficiency of online

banking, reliability, responsiveness of staff, assurance and empathy while not all of

these aspects are important or have a positive relationship towards customer

satisfaction. Therefore, Goh et al. (2016) had provided a result that are different

from others similar research. In spite of this, most of the prior research as mentioned

above have showed that the service quality of online banking is positively related

to customer satisfaction. In our study, the several aspect of service quality such as

efficiency of online banking, speed of logging in and logging out, the availability

of service, degree of ease of navigating through and using the website are important

and most of the respondents have agreed to these statements. Therefore, the

importance of service quality cannot be ignored by internet banking provider in

order to become competitive and survive in banking industry.

Lastly, Convenience. Based on the value of unstandardized coefficient B value, on

average, an increase in 1 score in convenience will lead the score of customers’



satisfaction towards online banking to increase by 0.110, holding all other variables

constant. However, the standardized coefficient Beta value shows that 1 standard

deviation increase in convenience would lead to an increase in 0.157 of standard

deviation in customers’ satisfaction towards online banking, holding other variables

constant.

Based on Table 4.20, the p-value of Convenience is 0.000 which is smaller than the

alpha value of 0.05. Thus, it can be indicated that Convenience is significant in

explaining the variation in customers’ satisfaction towards online banking. This

provides supporting evidence to prove the ‘hypothesis 4’ that developed in the

chapter 2, which stated that Convenience has a positive impact on customers’

satisfaction towards online banking. This is because of the majority of respondents

agreed that online banking services can help them to reduce non-monetary costs

when goods and services are purchased and used by the customers. Therefore,

Convenience is important in fulfilling customer’s satisfaction and expectation

especially the time-poor customers. They are seeking for providers offering value

that is convenient in terms of usage. The result fits to the previous studies.

According to Kolodinsky et al. (2004), convenience was a critical consideration that

influencing a customer’s decision to adopt online banking. If the higher degree of

convenience provided by online banking company can reduce the time, energy and

effort, customers will be more satisfied with the services provided because they able

to conduct financial transactions smoothly and efficiently at all time and place.

Lapierre (2000) argued that customers value convenience, namely saving time and

effort costs, are not only when they making purchase, but it also while accessing,



accepting and completing a service. This research has a strong indication to indicate

that a higher level of convenience towards online banking has a significant impact

on customer satisfaction. In addition, the findings of this research consistent with

the concepts of Equity Theory. According to equity theory, this means that

customers will compare the convenience and outcome which provided by the

product or service. Fisk and Coney (1982) also mentioned that customer will be less

satisfied if they found that other customers get a better service than them. Ahmad

and Al-Zu’bi (2011) mentioned that convenience is a competitive tool which

enables bank institutions to stay competitive in this banking sector. An online

banking company should provide product and service at a higher level of

convenience over the others in order to compete with its competitor. In contrast,

Williamson (2006) had provided opposite views regarding the impact of

convenience on customer satisfaction towards online banking. He found that there

are some reasons that can make customers felt less desired to use these services

even though online banking services had provided them a convenient way to

manage their accounts. Since there are more evidence had showed that convenience

significantly positively influence customers’ satisfaction towards online banking, it

can be concluded that convenience has a positive relationship with customer’s

satisfaction towards online banking. Hence, convenience is a significant factor that

enhances the level of customers’ satisfaction because customers always seek for

convenience when they are making decisions (Ong et al., 2014).

4.4 Conclusion

In this chapter, all the hypotheses developed in Chapter 2 are tested through Pearson

Correlation Analysis and Multiple Regression Analysis for Inferential Analysis.

Internal Reliability test is carried out in order to ensure the authenticity of



instruments. The results of all analyses depict that all of the four independent

variables (Security & Privacy, Customer Loyalty, Service Quality, and

Convenience) are positively related with dependent variable (Customers’

Satisfaction towards Online Banking). In next chapter, a summary of overall

analysis, discussions, limitations, and recommendations are discussed.



Chapter 5: Discussion, Conclusion and Implications

5.0 Summary

This study focuses on the effect of security & privacy, customer loyalty, service

quality and convenience on customer satisfaction towards online banking in

Malaysia. The statistics conducted by World Bank (2016) shows that the total

number of internet users has increased from year 2005 to 2015. The growth of

online banking in Malaysia also shows an increasing trend however the adoption

rates of online banking is still low in Malaysia compared to other Asian countries.

Several issues such as trust issue, poor service quality, convenience or difficulty in

using online banking services have been investigated in this study.

In the research, equity theory was adopted to explain the variables (security &

privacy, customer loyalty, service quality and convenience). According to Swan

and Olivier (1989), this theory indicated that customer’s satisfaction exists when

customer perceive their output or input ratio are fair. Equity Theory for security &

privacy stated that Internet users prefer high security authentication than low

security authentication as users will compare the qualities and benefit obtained from

the purchases or services. Customer loyalty in Equity Theory stated that create

relationships with customers able to enhance customer loyalty in order to have long-

term financial performance. For service quality, this concept stated that bank should

provide online banking services with higher quality than other banks in order to stay

competitive in banking industries. Furthermore, equity theory also explained the

convenience of services. It mentioned that customers will make a comparison



between convenience and outcome which provided by the services. Based on the

evidences stated above, equity theory is proposed to the comparison in determining

customer satisfaction towards online banking.

According to One-way ANOVA test, a conclusion about at least one of the

respondents who are strongly disagree, disagree, neutral, agree or strongly agree

have different perceptions towards customer satisfaction towards online banking

can be made. Besides, it can be deduced that the proposed model is fit and good in

explaining the perceptions of targeted respondents on security & privacy, customer

loyalty, service quality and convenience towards customer satisfaction towards

online banking in Malaysia.

The results show that there is a positive relationship between customer loyalty and

customer satisfaction towards online banking since the value of r is positive.

Positive value of r means that majority of the respondents consider customer loyalty

are positively related with customer’s satisfaction towards online banking. ANOVA

one-way test shows there is a significant difference in perceptions between

customer loyalty and customers’ satisfaction towards online banking after

amending the group category. Tukey Post Hoc test states that the mean difference

between those who strongly agree and disagree with the customer loyalty’s

statements scores the highest mean difference which is 4.0000. Therefore, Tukey

Post Hoc test proves that most of the respondents are strongly agree with the

positive impact of customer loyalty on customer’s satisfaction towards online

banking. In addition, Multiple Linear Regression shows that customer loyalty has

the highest unstandardized coefficient B value which is 0.182 and standardized



coefficient Beta value which is 0.319. Through this result, customer loyalty is

significant in explaining the variation in customers’ satisfaction towards online

banking. This finding is consistent with the hypothesis developed previously.

Hence, it can be concluded that customer loyalty is positively related with

customers’ satisfaction towards online banking in Malaysia.

The results show that there is a positive relationship between security & privacy

and customer satisfaction towards online banking since the value of r is positive.

This means that majority of the respondents consider security and privacy are

positively related with customer’s satisfaction towards online banking. Based on

ANOVA one-way test, there is a significant difference in perceptions between

security & privacy and customers’ satisfaction towards online banking after

amending the group category. A Tukey Post Hoc test also reveals that the mean

difference between those who disagree and strongly agree with the security &

privacy’s statements scores the highest mean difference (5.18447). Hence, it can be

said that most of the respondents are strongly agree with the positive impact of

security & privacy on customer’s satisfaction towards online banking. Based on

Multiple Linear Regression, security & privacy has the second highest

unstandardized coefficient B value (0.176) and standardized coefficient Beta value

(0.255). The results also show that security & privacy is significant in explaining

the variation in customers’ satisfaction towards online banking. This finding is

consistent with the hypothesis developed previously. Hence, it can be concluded

that security & privacy is positively related with customers’ satisfaction towards

online banking in Malaysia.

There is a positive relationship between service quality and customer satisfaction



toward online banking since there is a positive r value. Majority of the respondent

perceive that service quality of online banking able to affects customer’s

satisfaction. The ANOVA one-way test shows that there is a significant difference

in perceptions between service quality and customers’ satisfaction towards online

banking after amending the group category. In addition, the Tukey Post Hoc test

also reveals that the mean difference between those who disagree and strongly agree

with the service quality’s statements scores the mean difference of 4.74474. This

shows that most of the respondents are strongly agree with the positive impact of

service quality on customer’s satisfaction. For Multiple Linear Regression model,

service quality rank the third highest unstandardized coefficient B value (0.141) and

standardized coefficient Beta value (0.229). In short, service quality is significant

in explaining the variation in customers’ satisfaction towards online banking and

this is consistent with the hypothesis developed in previous chapter.

The results show that there is a positive relationship between convenience and

customer satisfaction towards online banking since the value of r is positive. It

means that majority of the respondents consider convenience is positively related

to customer’s satisfaction towards online banking. According to ANOVA one-way

test, there is a significant difference in perceptions between convenience and

customers’ satisfaction towards online banking after amending the group category.

A Tukey Post Hoc test indicates that the mean difference between those who

strongly agree and neutral with the convenience’s statements scores the highest

mean difference at 3.06948. Hence, it can be said that most of the respondents are

strongly agree with the positive impact of convenience on customer’s satisfaction

towards online banking. Based on Multiple Linear Regression, convenience has the



lowest unstandardized coefficient B value at 0.110 and standardized coefficient

Beta value at 0.157. The results also show that convenience is significant in

explaining the variation in customers’ satisfaction towards online banking. This

finding is consistent with the hypothesis developed previously. Hence, it can be

concluded that convenience is positively related with customers’ satisfaction

towards online banking in Malaysia.

5.1 Policy Implications

There are some policies that can be recommended to bank and policy maker. Banks

are advised to create and capture customer attention to internet banking services by

developing better marketing and advertising program. For example, Banks could

provide more advertisement about the features, advantages and benefits of online

banking on social media such as Facebook or Youtube in order to promote E-

banking to public or potential customer. This can effectively reduce public’s

hesitation or confusion towards online banking especially the type of service, its

convenience and the security & privacy system which become the concern of most

customers. In addition, banks can provide information and knowledge of online

banking usage through seminar, workshops, or even face-to-face explanation to

customer especially for those who are computer illiterate. Bank’s employee should

take initiative to introduce online banking system to customer who do not have an

online banking account. By doing so, bank could increase customer confidence

level towards online banking.

Besides, Cyber security agency in Malaysia has issued a warning for a massive

global cyberattack of ransomware known as “WanaCrypt0r 2.0”. “WanaCrypt0r



2.0” ransomware is a type of malicious software and also known as malware which

is designed to attack computer system in order to gain money from victims.

Therefore, biometrics technologies should be improved on the security & privacy

issues for online banking services. Banks should invest more on hardware and

software system that able to provide the latest encryption of security for customer.

For example, Bank could invest in GPS or IP Tracker system to prevent fraud

transaction. When the owner of the online banking account conduct transaction at

unusual location or trying to access the account with different IP address, this GPS

or IP Tracker system enable the bank to detect suspicious IP or unusual location. In

this case, Bank could send E-mail or a message to inform the customer and confirm

the authentication of the transaction before the transaction has occur instead of

informing the customer after the transaction has been conducted. This can minimize

the risk of using online banking and prevent fraud cases.

In order to improve service quality of online banking, bank should consistently

measure and update its online banking customer experience. Bank can do a simple

survey or a pop-up question that appears on its online banking website once the

customer has completed the transaction via online banking. This pop-up question

might be design based on the type of service that the customer has used. For

example, bank could ask about the timing for a transaction to be conducted or the

speed of log in to investigate the area that need to be improve. Bank could improve

the weakness of its online banking services and enhance its service quality based

on customer’s feedbacks. Services that make customer’s lives easier would increase

customer satisfaction and retention rate. A bank might lose customers if a bank’s

information about clients’ needs is outdated. Therefore, banks are strongly



suggested to measure and update customers experience constantly.

Banks and businesses today have put more concern on customer loyalty. In order to

enhance customer loyalty towards online banking services, banks can create a

management team for online banking that helps to solve customer problems during

holidays and non-business hours. For example, when customer facing online

banking transaction problems during holidays or non-business hours, customer can

seek for help on internet customer services. If the operation or action of online

customer services management team is effective and efficient, it will increase

customer confidence towards online banking services and hence, increase in

customer loyalty.

In addition, some customers are not interested with which banks offer free checking

or which banks are nearby. They are more interested in banks with best online

banking experience, high interest rate and real time update in various channels.

Hence, banks have to improve their technical system to big data and analytics to

understand the customer needs and offer customers expected services for their

convenience.



5.2 Limitations

There are few limitations can be identified in the research. One of the limitations

that can be found in the research is the study mainly focuses on younger generation

which is around 21-30 years old because majority of the respondents are the

students who come from Universiti Tunku Abdul Rahman (UTAR). Young

generations who are more exposed to latest technology as well as Internet trend are

more likely to adopt these online banking services. However, the majority of the

elder people are less likely to adopt the services. This circumstance arises due to the

reason of they may not familiar and do not understand the procedures to adopt the

online banking services even though these services can provide a lot of advantages

to the users. There might be different results if there are more respondents from the

elderly group are involved in the study.

In addition, the another limitation is omitting of important determinants. The

determinants used which namely service quality, customer loyalty, service quality

and convenience selected in this research may not cover all possible determinants

that have a critical influence on customer satisfaction towards online banking in

Malaysia. In the other words, there are some others important determinants that will

influence the level of customer satisfaction had ignored in the study.



5.3 Recommendations

In order to rectify the research’s limitations, there are several recommendations for

future researches. Firstly, the targeted respondents should not be just young

generations, the views or opinions from elderly group are also important to ensure

a more generalized and accurate result. Therefore, future researchers should target

respondents from different generation to ensure an all rounded result. Through this

method, a comparison can be made by depending on the gap between the different

generations. This can help to build in-depth understanding in customers’

satisfaction towards online banking. Besides, the research should not just

particularly focus on one area as different respondents from different places may

exhibit different personalities, which in turn, will affect the accuracy of result.

Next, more significant independent variables should be included in order to

overcome the problem of factor constraint. Independent variables such as speed of

webpage and webpage design should be discussed to have a more extensive

understanding regarding customers’ satisfaction towards online banking. Hence,

future researches should be encompassed with more reliable independent variables

in order to generate a more detailed result.



REFERENCES

Afsar, B., Rehman, Z. U., Qureshi J.A., & Shahjehan A. (2010). Determinants of

customer loyalty in the banking sector: The case of Pakistan. African

Journal of Business Management, 4(6), 1040-1047.

Ahmad, A. E. M. K., & Al-Zu’bi, H. A. (2011). E-banking functionality and

outcomes of customer satisfaction: An empirical investigation.

International Journal of Marketing Studies, 3(1), 50-65.

Aigbavboa, C., & Thwala, W. (2013). A theoritical framework of users’

satisfaction/dissatisfaction theories and models. Behavioral Sciences and

Economics Issues, 2, 17-18.

Albrechtsen, E. (2007). A qualitative study of users’ view on information security.

Computers & Security, 26, 276-289.

Aliyu, A. A., Rosmain, T., & Takala, J. (2014). Online banking and customer

service delivery in Malaysia: Data screening and preliminary findings.

Procedia – Social and Behavioral Sciences, 129, 562-570.

Aliyu, A. A., & Tasmin, R. (2012). The impact of information and communication

technology on bank's performance and customer service delivery in the

banking industry. International Journal of Latest Trends Finance and

Economy Sciences, 2(1), 80-90.

Anderson, R. E. (1973). Consumer dissatisfaction: The effect of disconfirmed

expectancy on perceived product performance. Journal of Marketing

Research, 10(2), 38-44.

Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A

contingency framework. Psychology & Marketing, 20(2), 123-138.

Angelova, B., & Zekiri, J. (2011). Measuring customer satisfaction with service

quality using American customer satisfaction model (ACSI model).

International Journal of Academic Research in Business and Social

Sciences, 1(3), 232-258.



Anic, I.D., & Radas, S. (2006). The role of satisfaction and demographic factors in

building store loyalty. Economic Trends and Economic Policy, 108(19),

67-86.

Asian Institute of Finance. (2016). Malaysia embraces a shift to digital

banking. [Press release]. Retrieved from

http://www.aif.org.my/symposium2016.

Au, N., Ngai, E., & Cheng, T. (2008). Extending the understanding of end user

information systems satisfaction formation: An equitable needs fulfillment

model approach. MIS Quarterly, 32(1), 43-66.

Ayrga, A. (2011). Is Mauritius ready to e-Bank? From a customer and banking

perspective. Journal of Internet Banking and Commerce, 16(1), 1-16.

Aytes, K., & Connolly, T. (2004). Computer security and risky computing

practices: A rational choice perspective. Journal of Organizational and

End User Computing, 16(3), 22-40.

Bakri, A., & Elkhani, N. (2012). Review on expectancy disconfirmation theory

(EDT) model in B2C e-commerce. Journal of Information Systems

Research and Innovation (JISRI), 2, 1-13. ISSN 2289-1358.

Ballan, M. (2012). A pilot study: Designing and testing the task parameters.

Journal of Student Research, 2(2), 97-106.

Bank Negara Malaysia Website. Available at: http://www.bnm.gov.my

Barquin, S., & HV, V. (2015). Digital banking in Asia: What do consumers really

want? Asia Banking Practice, 1-12.

Black, N. J., Lockett, A., Winklhofer, H., & Ennew, C. (2001). The adoption of

internet financial services: A qualitative study. International Journal of

Retail and Distribution Management, 29(8), 390-398.

Bei, L.T., & Chiao, Y.C. (2001). An integrated model for the effects of perceived

product, perceived service quality, and perceived price fairness on

consumer satisfaction and loyalty. Journal of Consumer Research, 14,

125-140.

http://www.aif.org.my/symposium2016

http://www.bnm.gov.my/



Benbunan-Fich, R. (2001). Using protocol analysis to evaluate the usability of a

commercial website. Information & Management, 39(2), 151–163.

Bose, T. K., & Sarker, S. (2012). Cognitive dissonance affecting consumer buying

decision making: A study based on Khulna Metropolitan area. Journal of

Management Research, 4(3), 191-221.

Cahill, D. L. (2007). Customer loyalty in third party logistics relationships:

Findings from studies in Germany and the USA. New York: Springer.

Cardozo, R. (1965). An experimental study of consumer effort, expectations and

satisfaction. Journal of Marketing Research, 2(8), 244-249.

Chalmer, B. J. (1986). Understanding Statistics. New York: Marcel Dekker, Inc.

Chang, Y. W., & Polonsky, M. J. (2012). The influence of multiple types of

service convenience on behavioral intentions: The mediating role of

consumer satisfaction in Taiwanese leisure setting. International Journal

of Hospitality Management, 31(2012), 107-118.

Chong, H. L., Islam, M. A., Manaf, A. H. A., & Mustafa, W. M. W. (2015). Users

satisfaction towards online banking in Malaysia. International Business

Management, 9(1), 15-27.

Church, R. M. (2001). The effective use of secondary data. Learning and

Motivation, 33, 32-45. Doi:10.1006/lmot.2001.1098.\

Clinton., Aigbavboa., & Thwala, W. (2013). A theoritical framework of user's

satisfaction/dissatisfaction theories and models. 2nd International

Conference on Arts, Behavioral Sciences and Economics Issues, 48-53.

Cochran, W. G. (1963). Sampling Techniques, (2nd ed.), New York: John Wiley

and Sons, Inc.



Creswell, J. W. (2003). Research design: Qualitative, quantitative and mixed

methods approaches (2nd ed.). Thousand Oaks, CA: SAGE Publications.

Cronin, J.J., & Taylor, S.A., (1992). Measuring service quality: a reexamination

and extension. Journal of Marketing, 56(3), 55-68.

Danijela, V., Jasminka, D., & Srecko, R. (2015). Customer satisfaction impact on

banking services and relationship management innovation. International

Review, 1(2), 83-93.

Dixit, N., & Datta, S. K. (2010). Acceptance of e-banking among adult customers:

An empirical investigation in India. Journal of Internet Banking and

Commerce, 15(2), 1-17.

Dowling, G. R., & Uncles, M. (1997). Do customer loyalty programs really

work?. Sloan Management Review, 38(4), 71-82.

Eriksson, M., & Schuster, C. (2009). Customer loyalty in internet banking.

International Business and Economic Program (Bachelor dissertation).

Retrieved from

https://pdfs.semanticscholar.org/b80e/c00595e88cef0a1ea78fd6336e93789

602ad.pdf.

Fatima, A. (2011). E-banking security issues: Is there a solution in biometrics?

Journal of Internet Banking and Commerce, 16(2), 1-10.

Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford

University Press, California. Retrieved February 2, 2011 from

www.books.google.com

Fisk, R. P., & Coney, A. K., (1982). Post choice evaluation: An equity theory

analysis of consumer satisfaction and dissatisfaction with service choices

In Hunt, H.K., and Day, L. R. (Eds). Conceptual and Empirical

Contributions to Consumer Satisfaction and Dissatisfaction and

Complaining Behavior, Bloomington, IN: Indiana University School of

Business, 9-16.

Friedman, B., Kahn, P.H. Jr., & Howe, D.C. (2000). Trust online.

Communications of the ACM, 43(12), 34-40.

http://www.books.google.com/



Glass, C. V., & Hopkins, K. D. (1984). Statistical methods in education and

psychology. Englewood Cliffs, NJ: Prentice Hall.

George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide

and reference. 11.0 update (4th ed.). Boston: Allyn & Bacon.

Goh, M. L., Yeo, S. F., Lim, K. B., & Tan, S. H. (2016). Understanding customer

satisfaction of Internet banking: A case study in Malacca. Procedia

Economics and Finance, 37, 80-85.

Gronroos, C., (1984). A service quality model and its market implication.

European Journal of Marketing, 18(4), 36-44.

Guru, B., Balachandran, & Suganthi. (2011). Internet banking patronage: An

empirical investigation of Malaysia. Journal of Internet Banking and

Commerce, 1(1), 1-11.

Hair, J. F. J., Money, A. H., Samouel, P., & Page, M. (2007). Research methods

for business. The UK: John Wiley & Son Ltd.

Hamid, M. R., Amin, H., Lada, S., & Ahmad, N. (2007). A comparative analysis

of internet banking in Malaysia and Thailand. Journal of Internet Business,

4, 1-19.

Heikkinen, P., & Iivarinen, T. (2011). Ensuring trust in electronic payment media.

Journal of Payments Strategy & Systems, 5(2), 161–168.

Henry, G. T. (1990). Practical sampling. London: Sage Publications, Inc.

Ho, C. F., & Wu, W. H., (1999). Antecedent of customer satisfaction on the

internet: an empirical study of online shopping. Proceeding of the 32nd

Hawaii International Conference on System Science.

Ioanna, P. D. (2002). The role of employee development in customer relations:

The case of UK Retail Banks. Corporate Communication: An

International Journal, 7(1), 62-77.



Isac, F. L., & Rusu, S., (2014). Theories of consumer’s satisfaction and the

operationalization of the Expectation Disconfirmation Paradigm.

Academica Brancusi Publisher, 2, 82-88.

Jalil, M. A., Talukder, M., & Rahman, M. K. (2014). Factors affecting customer’s

perceptions towards online banking transactions in Malaysia. Journal of

Business and Management, 20(1), 25-44.

Jankowski, J., (2011). Integration of collective knowledge in Fuzzy models

supporing web design process. Third International Conference, ICCCI

2011 Gdynia, Poland, Proceeding Part II, 395-404.

Jayawardhena, C., & Foley, P., (2000). Changes in the banking sector: the case of

internet banking in UK. Internet Research: Electronic Networking

Application and Policy, 10(1), 19-30.

Jun, M., & Cai, S. (2001). The key determinants of internet banking service

quality: A content analysis. International Journal of Bank Marketing,

19(7), 276-291.

Kadir, H. A., Rahmani, N., & Masinaei, R. (2011). Impacts of service quality on

customer satisfaction: Study of online banking and ATM services in

Malaysia. International Journal of Trade, Economics and Finance, 2(1),

1-9.

Kemunto, M. P. (2015). Online banking service quality and customer satisfaction:

A case study of Barclays Bank Kenya limited (Degree’s thesis). University

Of Nairobi, Nairobi, Kenya.

Khazaei, A., Manjiri, H., Samiey, E. & Najafi, H. (2014). The effect of service

convenience on customer satisfaction and behavioral responses in banking

industry. International Journal of Basic Sciences & Applied Research,

3(1), 16-23.

Knupfer, N. N., & McLellan, H. (2001). Descriptive research methodologies. The

Handbook of Research for Educational Communications. Retrieved from

http://www.aect.org/edtech/ed1/41/



Kolodinsky, J. M., Hogarth, J. M., & Hilgert, M. A. (2004). The adoption of

electronic banking technologies by US consumers. International Journal

of Bank Marketing, 22(4), 238-259.

Koupai, M. R., Alipourdarvish, Z. & Sardar, S. (2015). Factors affecting loyalty

of internet bank customers (Case study: Keshavarzi (Agricultural) internet

bank customers in Tehran). Advanced Social Humanities and

Management, 2(1), 52-59.

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research

activities. Educational and Psychological Measurement, 30, 607-610.

Kuo, N. T., Chang, K. C., Cheng, Y. S., & Lai, C. H. (2013). Investigating the

effect of service quality on customer loyalty in the hotel industry: The

mediating role of customer satisfaction and the moderating roles of service

recovery and perceived value. Journal of China Tourism Research, 9,

257–276.

Lankton, N., McKnight, D. H., & Thatcher, J. B. (2014). Incorporating trust-in-

technology into Expectation Disconfirmation Theory. The Journal of

Strategic Information Systems, 23(2), 128-145.

Lapierre, J. (2000). Customer-perceived value in industrial contexts. Journal of

Business & Industrial Marketing, 15(2/3), 122-140.

Latimore, D., Watson, I. & Maver, C., (2000). The customer speaks: 3300 Internet

users tells us what they want from internet financial service, 28 May 2001.

Available at:

http://www.mainspring.com/research/document/view/1,2099,1215,00.html

Lavrakas, P. J. (2008). Encyclopedia of survey research methods, New York: Sage

Publications.

Lee, J. E. R., Rao, S., Nass, C., Forssell, K., & John, J. M. (2012). When do online

shoppers appreciate security enhancement efforts? Effects of financial risk

and security level on evaluations of customer authentication. Int. J.

Human-Computer Studies, 70, 364-376.

http://www.mainspring.com/research/document/view/1,2099,1215,00.html



Lee, M. C. (2008). Factors influencing the adoption of internet banking: An

integration of TAM and TPB with perceived risk and perceived benefit.

Electronic Commerce Research and Applications, 8, 130–141.

Leverin, A., & Liljander, V. (2006). Does relationship marketing improve

customer relationship satisfaction and loyalty?, International Journal of

Bank Marketing, 24(4), 232-251.

Levine, L.J. (1997). Reconstructing memory for emotions. Journal of

Experimental Psychology, 126(2), 165-177.

Lichtenstein, S., & Williamson, K. (2006). Understanding consumer adoption of

internet banking: An interpretive study in the Australian banking context.

Journal of Electronic Commerce Research, 7(2), 50-66.

Lin, C. P., Tsai, Y., & Chiu, C. K. (2009). Modeling customer loyalty from an

integrative perspective of self-determination theory and expectation–

confirmation theory. Journal of Business and Psychology, 24(3), 315-326.

Lu, Y., Cao, Y., Wang, B., & Yang, S. (2011). A study on factors that affect

users’ behavioral intention to transfer usage from the offline to the online

channel. Computers in Human Behavior, 27, 355-364.

Mattila, A., & O’Neill, J.W. (2003). Relationships between hotel room pricing,

occupancy, and guest satisfaction: A longitudinal case of a midscale hotel

in the united states. Journal of Hospitality & Tourism Research, 27(3),

328-341.

McKinney, V., Yoon, K., & Zahedi, F.M. (2002). The measurement of web-

customer satisfaction: An expectation and disconfirmation approach.

Information System Research, 13(3), 296-315.

McKinsey & Company. (2014). Retrieved from

http://www.mckinsey.com/singapore/our-insights/digital-banking-in-asia-

winning-approaches-in-a-new-generation-of-financial-services

Messick, D. M., & Cook, K.S. (1983). Equity theory, psychological and

sociological perspectives. New York: Praeger.

http://www.mckinsey.com/singapore/our-insights/digital-banking-in-asia-winning-

http://www.mckinsey.com/singapore/our-insights/digital-banking-in-asia-winning-



Methlie, L., & Nysveen, H. (1999). Loyalty of on-line bank customers. Journal of

Information Technology, 14, 376-386.

Meyer, A., & Westerbarkey, P. (1996). Measuring and managing hotel guest

satisfaction., in Olsen, D. M., Teare, R. & Gummesson, E. (Eds.) Service

Quality in Hospitality Organisations, Cassell, New York, NY, 185-204.

Mols, N. P., Bukh., D., & Nielsen, J. F. (1999). Distribution channel strategies in

danish retail banking. International journal of Retail and Distribution

Management,27(1), 37-47.

Montesdioca, G. P. Z., & Macada, A. C. G. (2015). Measuring user satisfaction

with information security practices. Computer & Security, 48, 267-280.

Munusamy, J., Annamalah, S., & Chelliah, S. (2012). A study of users and non-

users of internet banking in Malaysia. International Journal of Innovation,

Management and Technology, 3(4), 452-458.

Munusamy, J., Chelliah, S., & Hor, W. M. (2010). Service quality delivery and its

impact on customer satisfaction in the banking sector in Malaysia.

International Journal of Innovation, Management and Technology, 1(4),

398- 404.

Myftaraj, E., & Nexhipi, O. (2014). The importance of customers loyalty in

relationship marketing in the online and offline market: The case of the

Albanian financial sector. Interdisplinary Journal of Research and

Development, 1(2), 1-6.

Ndubisi, N. O., & Sinti, Q. (2006). Consumer attitudes, system’s characteristics

and internet banking adoption in Malaysia. Management Research News,

29(1), 16–27.

Oliver, R. l., & Swan, E. J. (1989). Consumer perceptions of interpersonal equity

and satisfaction in transactions: A field survey approach, Journal of

Marketing, 53(2), 21-35.

Oliver. R. L. (1980). A cognitive model of the antecedents of satisfaction

decisions, Journal of Marketing Research, 17(4), 460-469.

Oliver, R. L. (1997). Satisfaction: A behavioral perspective on the consumer, The

McGraw-Hill Companies, Inc. New York.



Oliver, R. L. (1999). Whence consumer loyalty? The Journal of Marketing, 63,

33-44.

Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction

judgement. Journal of Consumer Research, 14(4), 495-507.

Olsen, L. L., & Johnson, M. D. (2003). Service equity, satisfaction and loyalty:

From transaction-specific to cummulative evaluations. Journal of Service

Research, 5(3), 184-195.

Olson, J., & Dover, P. (1979). Disconfirmation of consumer expectations through

product trial. Journal of Applied Psychology, 64(2), 179-189.

Ong, T. S., Hong, Y. H., Teh, B. H., Soh, P. C. H., & Tan, C. P. (2014). Factors

that affect the adoption of internet banking in Malaysia. International

Business Management, 8(2), 55-63.

Osman, Z., Mohamad, L., & Mohamad, R. K. (2015). An empirical study of direct

relationship of service quality, customer satisfaction and bank image on

customer loyalty in Malaysian Commercial Banking Industry. American

Journal of Economics, 5(2). 168-176, DOI:

10.5923/c.economics.201501.20

Palmatier, R. W., Dant, R.P., Grewal,D., & Evans, K. R. (2006). Factors

influencing the effectiveness of relationship marketing: A Meta- Analysis.

Journal of Marketing, 70, 136-153.

Palmer, J. W. (2002). Web site usability, design, and performance metrics.

Information Systems Research, 13(2), 151–167.

Pang, J. (1995). Banking and Finance in Malaysia. Selangor: Federal Publications

Sdn Bhd.

Park, C. H., & Kim, Y. G. (2003). Identifying key factors affecting consumer

purchase behavior in an online shopping context. International Journal of

Retail & Distribution Management, 31(1), 16–29.



Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of

service quality and its implications for future research. Journal of

Marketing, 49(4), 41-50.

Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1988), SERVEQUAL: A

multiple-item scale for measuring consumer perception of service quality.

Journal of Retailing, 64(1), 12-40.

Peyton, R. M., Pitts. S., & Kamery, R. H. (2003). Consumer satisfaction /

dissatisfaction: A review of the literature prior to the 1990s, Proceedings

of the Academy of Organizational Culture, Communications and Conflicts,

7(2), 41-46.

Pitt, L.F., Watson, R.T., & Kavan, V.B. (1995). Service quality: A measure of

information system effectiveness. MIS Quarterly. 19(2), 173-187.

Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-

consumer web sites. Information & Management, 39(6), 457–465.

Reichheld, F. F. (1993). Loyalty-based management. Harvard business review,

71(2), 64-73.

Ribbink, D., van Riel, A. C. R., Liljander, V. M., & Streukens, A. C. P. (2004).

Comfort your online customer: quality, trust, and loyalty on the

internet. Managing Service Quality, 14(6), 446-456.

Robinson, J., & Moore, W. (2009). Attitudes and preferrences in relation to

internet banking in the Caribbean, 1-35.

Rodgers, W., Negash, S., & Suk, K. (2005). The moderating effect of online

experience on the antecedents and consequences of on-line satisfaction.

Psychology & Marketing, 22(4), 313–331.

Roy, S. K., Lassar, W. M., & Shekhar, V. (2016). Convenience and satisfaction:

Mediation of fairness and quality. The Service Industries Journal, 36(5-6),

239-260.

Saleh, Z. I. (2011). Improving security of online banking using RFID. Academy of

Banking Studies Journal, 10(2), 1-8.

https://cris.maastrichtuniversity.nl/portal/en/journals/managing-service-quality(629754e6-01fa-46ff-9d06-a415877417b0).html



Seiders, K., Berry, L., & Gresham, L. G. (2000). Attention retailers! How

convenient is your convenience strategy?. Sloan Management Review,

49(3), 79-90.

Sekaran, U. (2003). Research methods for business (4th ed.). Hoboken, NJ: John

Wiley & Sons.

Selvakumar, J.J. (2015). Impact of service quality on customer satisfaction in

public sector and private sector banks. PSG Institute of Management.

Seta, J. J., Hundt, G. M., & Seta, C. E. (1995). Cost’s influence on attitudes and

value: Beyond dissonance theory. Basic and Applied Social Psychology,

17(1-2), 267-283.

Simon, M.K., & Goes, J. (2013). Dissertation and scholarly research: recipe for

success (2013 ed.). Seattle, WA, Dissertation Success, LLC.

Singhal, D., & Padhmanabhan, V. (2008). A study on customer perception

towards internet banking: Identifying major contributing factors. The

Journal of Nepalese Business Studies, 5(1), 101-111.

Solomon, R. L., & Corbit, J. D. (1978). An opponent-process theory of

motivation. The American Economic Review, 68, 12-24.

Solomon, R. L. (1980). The opponent–process theory of acquired motivation.

American Psychologist, 35(8), 691-712.

Spreng, R. A., & Jr., T. J. P. (2003). A test of alternative measures of

disconfirmation. Decision Sciences, 34(1), 31-62. DOI: 10.1111/1540-

5915.02214.

Spreng, R. A., Mackenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of

the determinants of consumer satisfaction. Journal of Marketing, 60, 15-

32.

Spreng, R. A., & Mackoy, R. D. (1996). An empirical examination of the

antecedents of perceived service quality and satisfaction. Journal of

Retailing, 72(2), 201–214.

http://dx.doi.org/10.1111/1540-5915.02214

http://dx.doi.org/10.1111/1540-5915.02214



Srivastava, R. K. (2007). Customer's perception on usage of internet banking.

Innovative Marketing, 3(4), 67-73.

Steven, S. S. (1946). On the theory of scales of measurement. Science, New

Series, 103, 677-680.

Tasmin, R., Aliyu, A. A., Norazlin, H., & Takala, J. (2013, May). The impact of

online banking on customer service delivery in the malaysian banking

industsy: Kano’s Model approach. Proceeding of 2013 International

Conference On Technology Information And Industrial Management, 175-

188.

Thulani, D., Njanike, K., Manomano, C., & Chiriseri, L. (2011). Adoption and use

of SMS/mobile banking services in Zimbabwe: An exploratory study.

Journal of Internet Banking and Commerce, 16(2), 149-167.

Thomas, S., & Monika, K. (2010). Investigating the impact of cognitive

dissonance and customer satisfaction on loyalty and complaint behaviour.

Revista Brasileira de Marketing, 9(1), 5-16.

Vaithilingam, S., Nair, M. S., & Guru, B. (2013). Do trust and security matter for

the development of M-banking? Evidence from a developing country.

Journal of Asia-Pacific Business, 14(1), 4-24.

Waal, T. D. (2013). Selective editing: A quest for efficiency and data quality.

Journal of Official Statistics, 29(4), 473-488.

Waligora, J., & Waligora, R. (2007). Measuring customer satisfaction and loyalty

in the automotive industry: A case of premium brand of passenger cars.

Aarhus School of Business, Denmark.

Wang, Y.S., Wang, Y.M., Lin, H.H., & Tang, T.I. (2003). Determinants of user

acceptance of Internet banking: An empirical study. International Journal

of Service Industry Management, 14(5), 501-519.

Westbrook, R. A., & Reilly, M. D. (1983). Value-percept disparity: An alternative

to the disconfirmation of expectations theory of consumer

satisfaction. Advances In Consumer Research, 10(1), 256-261.



Williamson, G. D. (2006). Enhanced authentication in online banking. Journal of

Economic Crime Management, 4(2), 1-42.

World Bank. (2016). Retrieved from:

http://data.worldbank.org/indicator/IT.NET.SECR.P6

Xu, Y., Goedegebuure, R., & Heijden, B, V, D. (2007). Customer perception,

customer satisfaction, and customer loyalty within chinese securities

business. Journal of Relationship Marketing, 5(4), 79-104.

Yeoh, S. F., & Chan, B. Y. F. (2011). Internet banking adoption in Kuala Lumpur:

An application of UTAUT model. International Journal of Business and

Management, 6(4), 161-167.

Yi, Y. (1990). A critical review of customer satisfaction, in V. A. Zeithaml (Ed.).

Review of Marketing, Chicago: American Marketing Association, 4, 68-

123.

Yoon, C. (2010). Antecedents of customer satisfaction with online banking in

China: The effects of experience. Computers in Human Behavior, 26,

1296-1304.

Yuksel, A., & Yuksel, F. (2008). Consumer satisfaction theories: a critical review.

Tourist Satisfaction and Complaining Behavior: Measurement, and

Management Issues in the Tourism and Hospitality Industry, 95-132.

Zeithaml, V.A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery

through web sites: a critical review of extant knowledge. Journal of the

Academy of Marketing Science, 30(4), 362-375.

Zikmund, W.G. (2003). Business research methods (7th ed.). Ohio: Thompson

South-Western.

Zikmund, W.G., Barry, J.B., Jon, C.C., & Mitch, C. (2010). Business research

methods (8th ed.). USA: South Western Cengage Learning.

http://data.worldbank.org/indicator/IT.NET.SECR.P6



APPENDICES

APPENDIX A: Permission to Conduct Survey



Appendix B: Survey Questionnaire



Appendix C: Respondent Demographic Profile

Frequencies

Gender

Frequency Percent Valid Percent Cumulative Percent

Valid

Female 220 55.0 55.0 55.0

Male 180 45.0 45.0 100.0

Total 400 100.0 100.0

Age


Valid

18 1 .3 .3 .3

19 3 .8 .8 1.0

20 8 2.0 2.0 3.0

21 16 4.0 4.0 7.0

22 39 9.8 9.8 16.8

23 154 38.5 38.5 55.3

24 58 14.5 14.5 69.8

25 24 6.0 6.0 75.8

26 11 2.8 2.8 78.5

27 10 2.5 2.5 81.0

28 5 1.3 1.3 82.3

29 4 1.0 1.0 83.3

30 6 1.5 1.5 84.8

31 4 1.0 1.0 85.8

32 5 1.3 1.3 87.0

34 3 .8 .8 87.8

35 4 1.0 1.0 88.8

36 3 .8 .8 89.5

37 1 .3 .3 89.8

38 6 1.5 1.5 91.3

39 2 .5 .5 91.8

40 2 .5 .5 92.3

41 2 .5 .5 92.8



42 1 .3 .3 93.0

44 2 .5 .5 93.5

45 4 1.0 1.0 94.5

46 2 .5 .5 95.0

47 2 .5 .5 95.5

48 2 .5 .5 96.0

49 2 .5 .5 96.5

50 1 .3 .3 96.8

52 3 .8 .8 97.5

54 2 .5 .5 98.0

55 1 .3 .3 98.3

57 2 .5 .5 98.8

59 1 .3 .3 99.0

61 1 .3 .3 99.3

62 3 .8 .8 100.0

Total 400 100.0 100.0

Race


Valid

Chinese 341 85.3 85.3 85.3

Indian 29 7.3 7.3 92.5

Malay 30 7.5 7.5 100.0

Total 400 100.0 100.0

Highest_Education_Level


Valid

Bachelor's Degree 283 70.8 70.8 70.8

Diploma/ Advanced

Diploma/ STPM 68 17.0 17.0 87.8

Master's Degree 24 6.0 6.0 93.8

PMR 3 .8 .8 94.5

SPM 22 5.5 5.5 100.0

Total 400 100.0 100.0



Monthly_Income


Valid

< RM1,000 209 52.3 52.3 52.3

> RM5,000 46 11.5 11.5 63.8

RM1,000 - RM2,500 64 16.0 16.0 79.8

RM2,501 - RM5,000 81 20.3 20.3 100.0

Total 400 100.0 100.0

Frequency_of_online_banking_usage


Valid

1 - 5 times 294 73.5 73.5 73.5

6 - 10 times 63 15.8 15.8 89.3

More than 10 times 43 10.8 10.8 100.0

Total 400 100.0 100.0

Frequently_used_conventional_bank


Valid

Affin Bank Berhad 4 1.0 1.0 1.0

AmBank Group Berhad 10 2.5 2.5 3.5

Bank Islam Malaysia

Berhad 3 .8 .8 4.3

Bank Muamalat Malaysia

Berhad 1 .3 .3 4.5

Bank Simpanan Nasional 2 .5 .5 5.0

CIMB Bank Berhad 78 19.5 19.5 24.5

HLB Bank Berhad 1 .3 .3 24.8

Hong Leong Bank Berhad 28 7.0 7.0 31.8

HSBC Bank Malaysia

Berhad 3 .8 .8 32.5

Maybank Berhad 114 28.5 28.5 61.0

OCBC Bank Malaysia

Berhad 4 1.0 1.0 62.0

Public Bank Berhad 145 36.3 36.3 98.3

RHB Bank Berhad 7 1.8 1.8 100.0

Total 400 100.0 100.0



Satisfaction_towards_online_banking_services


Valid

Strong Dissatisfied 2 .5 .5 .5

Dissatisfied 8 2.0 2.0 2.5

Neutral 81 20.3 20.3 22.8

Satisfied 245 61.3 61.3 84.0

Strong Satisfied 64 16.0 16.0 100.0

Total 400 100.0 100.0

Appendix D: Central Tendencies Measurement of Constructs

Customer Satisfaction

Usefulness_of_onli

ne_banking_servic

es

Level_of_expectati

ons_and_requirem

ents

Continue_using_cu

rrent_online_banki

ng_services

N Valid 400 400 400

Missing 0 0 0

Mean 4.02 3.85 4.10

Median 4.00 4.00 4.00

Mode 4 4 4

Std. Deviation .650 .745 .728

Variance .423 .556 .529

Range 3 4 4

Minimum 2 1 1

Maximum 5 5 5

Percentiles

25 4.00 3.00 4.00

50 4.00 4.00 4.00

75 4.00 4.00 5.00

Usefulness_of_online_banking_services


Valid

Disagree 6 1.5 1.5 1.5

Neutral 62 15.5 15.5 17.0

Agree 249 62.3 62.3 79.3

Strongly Agree 83 20.8 20.8 100.0

Total 400 100.0 100.0



Level_of_expectations_and_requirements


Valid

Strongly Disagree 2 .5 .5 .5

Disagree 13 3.3 3.3 3.8

Neutral 95 23.8 23.8 27.5

Agree 224 56.0 56.0 83.5

Strongly Agree 66 16.5 16.5 100.0

Total 400 100.0 100.0

Continue_using_current_online_banking_services


Valid

Strong disagree 2 .5 .5 .5

Disagree 5 1.3 1.3 1.8

Neutral 61 15.3 15.3 17.0

Agree 216 54.0 54.0 71.0

Strongly Agree 116 29.0 29.0 100.0

Total 400 100.0 100.0

Security & Privacy

Statistics

Appropriate_m

echanisms_for

_safe_transmis

sion

Latest_encrytio

n_technology_t

o_secure_trans

action

Secure_comm

unication_acce

ss

Internet_bankin

g_company_co

ncerns_about_

privacy_n_secu

rity

Prefer_high_se

curity_authenti

cation

N Valid 400 400 400 400 400

Missing 0 0 0 0 0

Mean 3.61 3.61 4.32 3.94 4.49

Median 4.00 4.00 4.00 4.00 5.00

Mode 4 4 5 4 5

Std. Deviation .840 .840 .760 .786 .686

Variance .706 .706 .578 .618 .471

Range 4 4 4 3 3

Minimum 1 1 1 2 2

Maximum 5 5 5 5 5

Percentiles 25 3.00 4.00 4.00 3.00 4.00



50 4.00 4.00 4.00 4.00 5.00

75 4.00 5.00 5.00 5.00 5.00

Appropriate_mechanisms_for_safe_transmission

Frequenc

y

Percent Valid

Percent

Cumulative Percent

Valid

Strongly

Disagree 5 1.3 1.3 1.3

Disagree 29 7.3 7.3 8.5

Neutral 134 33.5 33.5 42.0

Agree 183 45.8 45.8 87.8

Strongly Agree 49 12.3 12.3 100.0

Total 400 100.0 100.0

Latest_encrytion_technology_to_secure_transaction


Valid

Strongly Disagree 1 .3 .3 .3

Disagree 6 1.5 1.5 1.8

Neutral 45 11.3 11.3 13.0

Agree 155 38.8 38.8 51.8

Strongly Agree 193 48.3 48.3 100.0

Total 400 100.0 100.0

Secure_communication_access


Valid

Strongly disagree 1 .3 .3 .3

Disagree 6 1.5 1.5 1.8

Neutral 48 12.0 12.0 13.8

Agree 155 38.8 38.8 52.5

Strongly Agree 190 47.5 47.5 100.0

Total 400 100.0 100.0

Internet_banking_company_concerns_about_privacy_n_security


Valid Disagree 11 2.8 2.8 2.8

Neutral 103 25.8 25.8 28.5



Agree 185 46.3 46.3 74.8

Strongly agree 101 25.3 25.3 100.0

Total 400 100.0 100.0

Prefer_high_security_authentication


Valid

Disagree 4 1.0 1.0 1.0

Neutral 32 8.0 8.0 9.0

Agree 127 31.8 31.8 40.8

Strongly agree 237 59.3 59.3 100.0

Total 400 100.0 100.0

Customer Loyalty

Statistics

Consider_mys

elf_a_loyal_cus

tomer

Continue_rema

in_as_a_custo

mer

Satisfied_with_

the_responsive

ness

Positive_thoug

hts_about_onli

ne_banking_se

rvices

Recommend_c

urrent_bank_to

_others

N Valid 400 400 400 400 400

Missing 0 0 0 0 0

Mean 3.77 3.91 3.79 3.81 3.76

Median 4.00 4.00 4.00 4.00 4.00

Mode 4 4 4 4 4

Std. Deviation .860 .755 .756 .741 .814

Variance .739 .570 .571 .549 .663

Range 4 4 4 4 4

Minimum 1 1 1 1 1

Maximum 5 5 5 5 5

Percentiles

25 3.00 3.00 3.00 3.00 3.00

50 4.00 4.00 4.00 4.00 4.00

75 4.00 4.00 4.00 4.00 4.00

Consider_myself_a_loyal_customer


Valid


Disagree 26 6.5 6.5 7.3

Neutral 108 27.0 27.0 34.3

Agree 186 46.5 46.5 80.8



Strongly agree 77 19.3 19.3 100.0

Total 400 100.0 100.0

Continue_remain_as_a_customer


Valid

Strongly disagree 4 1.0 1.0 1.0

Disagree 6 1.5 1.5 2.5

Neutral 92 23.0 23.0 25.5

Agree 219 54.8 54.8 80.3

Strongly agree 79 19.8 19.8 100.0

Total 400 100.0 100.0

Satisfied_with_the_responsiveness


Valid


Disagree 16 4.0 4.0 4.3

Neutral 110 27.5 27.5 31.8

Agree 211 52.8 52.8 84.5

Strongly agree 62 15.5 15.5 100.0

Total 400 100.0 100.0

Positive_thoughts_about_online_banking_services


Valid


Disagree 13 3.3 3.3 3.5

Neutral 109 27.3 27.3 30.8

Agree 214 53.5 53.5 84.3

Strongly agree 63 15.8 15.8 100.0

Total 400 100.0 100.0

Recommend_current_bank_to_others


Valid


Disagree 15 3.8 3.8 4.5

Neutral 128 32.0 32.0 36.5

Agree 182 45.5 45.5 82.0



Strongly Agree 72 18.0 18.0 100.0

Total 400 100.0 100.0

Service Quality

Efficiency_of_o

nline_banking_

services

Log_in_speed_

is_fast

Easy_to_find_i

nformation

Adequate_and

_accurate_info

rmation

Delivery_of_on

line_services_i

s_just_in_time

N Valid 400 400 400 400 400

Missing 0 0 0 0 0

Mean 3.93 3.69 3.59 3.76 3.77

Median 4.00 4.00 4.00 4.00 4.00

Mode 4 4 4 4 4

Std. Deviation .681 .841 .854 .776 .718

Variance .464 .707 .729 .603 .516

Range 3 4 4 4 4

Minimum 2 1 1 1 1

Maximum 5 5 5 5 5

Percentiles

25 4.00 3.00 3.00 3.00 3.00

50 4.00 4.00 4.00 4.00 4.00

75 4.00 4.00 4.00 4.00 4.00

Efficiency_of_online_banking_services


Valid

Disagree 7 1.8 1.8 1.8

Neutral 86 21.5 21.5 23.3

Agree 234 58.5 58.5 81.8

Strongly agree 73 18.3 18.3 100.0

Total 400 100.0 100.0

Log_in_speed_is_fast


Valid


Disagree 26 6.5 6.5 7.5

Neutral 121 30.3 30.3 37.8

Agree 189 47.3 47.3 85.0

Strongly agree 60 15.0 15.0 100.0

Total 400 100.0 100.0



Easy_to_find_information


Valid


Disagree 33 8.3 8.3 9.5

Neutral 133 33.3 33.3 42.8

Agree 180 45.0 45.0 87.8

Strongly agree 49 12.3 12.3 100.0

Total 400 100.0 100.0

Adequate_and_accurate_information


Valid

Strong disagree 2 .5 .5 .5

Disagree 17 4.3 4.3 4.8

Neutral 116 29.0 29.0 33.8

Agree 204 51.0 51.0 84.8

Strongly agree 61 15.3 15.3 100.0

Total 400 100.0 100.0

Delivery_of_online_services_is_just_in_time


Valid


Disagree 12 3.0 3.0 3.3

Neutral 119 29.8 29.8 33.0

Agree 216 54.0 54.0 87.0

strongly agree 52 13.0 13.0 100.0

Total 400 100.0 100.0

Convenience

Convenience_

as_top_priority

Conduct_trans

action_anytime

_anywhere

Reduce_non_

monetary_cost

s

User_friendly_fi

nancial_manag

ement_tool

Intention_of_s

witching_anoth

er_bank_which

_is_more_conv

enient

N Valid 400 400 400 400 400

Missing 0 0 0 0 0

Mean 4.03 4.09 4.22 4.02 3.54

Median 4.00 4.00 4.00 4.00 4.00

Mode 4 4 4 4 4



Std. Deviation .805 .740 .733 .693 .952

Variance .648 .548 .538 .481 .906

Range 4 4 4 3 4

Minimum 1 1 1 2 1

Maximum 5 5 5 5 5

Percentiles

25 4.00 4.00 4.00 4.00 3.00

50 4.00 4.00 4.00 4.00 4.00

75 5.00 5.00 5.00 4.00 4.00

Convenience_as_top_priority


Valid

strongly disagree 3 .8 .8 .8

Disagree 9 2.3 2.3 3.0

Neutral 79 19.8 19.8 22.8

Agree 192 48.0 48.0 70.8

strongly agree 117 29.3 29.3 100.0

Total 400 100.0 100.0

Conduct_transaction_anytime_anywhere

Frequency Percent Valid Percent Cumulative

Percent

Valid


disagree 7 1.8 1.8 2.0

neutral 66 16.5 16.5 18.5

Agree 207 51.8 51.8 70.3

strongly agree 119 29.8 29.8 100.0

Total 400 100.0 100.0

Reduce_non_monetary_costs


Percent

Valid


disagree 4 1.0 1.0 1.3

neutral 55 13.8 13.8 15.0

Agree 186 46.5 46.5 61.5

strongly agree 154 38.5 38.5 100.0

Total 400 100.0 100.0



User_friendly_financial_management_tool


Percent

Valid

disagree 3 .8 .8 .8

neutral 83 20.8 20.8 21.5

Agree 217 54.3 54.3 75.8

strongly agree 97 24.3 24.3 100.0

Total 400 100.0 100.0

Intention_of_switching_another_bank_which_is_more_convenient


Percent

Valid

strongly disagree 10 2.5 2.5 2.5

disagree 37 9.3 9.3 11.8

neutral 144 36.0 36.0 47.8

agree 145 36.3 36.3 84.0

strongly agree 64 16.0 16.0 100.0

Total 400 100.0 100.0

Appendix E: Scale Measurement - Reliability Test

Cronbach's Alpha

if Item Deleted

Usefulness_of_online_banking_services .905

Level_of_expectations_and_requirements .905

Continue_using_current_online_banking_services .905

Appropriate_mechanisms_for_safe_transmission .909

Latest_encrytion_technology_to_secure_transaction .911

Secure_communication_access .911



Internet_banking_company_concerns_about_privacy_n_security .908

Prefer_high_security_authentication .910

Consider_myself_a_loyal_customer .908

Continue_remain_as_a_customer .907

Satisfied_with_the_responsiveness .905

Positive_thoughts_about_online_banking_services .904

Recommend_current_bank_to_others .906

Efficiency_of_online_banking_services .906

Log_in_speed_is_fast .908

Easy_to_find_information .909

Adequate_and_accurate_information .906

Delivery_of_online_services_is_just_in_time .906

Convenience_as_top_priority .908

Conduct_transaction_anytime_anywhere .907

Reduce_non_monetary_costs .907

User_friendly_financial_management_tool .907

Intention_of_switching_another_bank_which_is_more_convenient .917



Customer Satisfaction

Reliability Statistics

Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.824 .824 3

Security & Privacy


Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.726 .733 5

Customer Loyalty


Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.871 .874 5

Service Quality


Cronbach's Alpha Cronbach's Alpha Based

on Standardized Items

N of Items

.820 .822 5

Convenience


Cronbach's Alpha Cronbach's Alpha Based

on Standardized Items

N of Items

.682 .710 5



Appendix F: Pearson Correlation Analysis

Correlations

Customer_satisfac

tion

Security_and_Privacy Customer_Loyalty

Customer_satisfactio

n

Pearson

Correlation 1 .545** .616**

Sig. (1-tailed) .000 .000

N 400 400 400

Security_and_Privac

y

Pearson

Correlation .545** 1 .360**

Sig. (1-tailed) .000 .000

N 400 400 400

Customer_Loyalty

Pearson

Correlation .616** .360** 1

Sig. (1-tailed) .000 .000

N 400 400 400

Service_Quality

Pearson

Correlation .595** .393** .590**

Sig. (1-tailed) .000 .000 .000

N 400 400 400

Convenience

Pearson

Correlation .551** .540** .446**

Sig. (1-tailed) .000 .000 .000

N 400 400 400

Correlations

Service_Quality Convenience

Customer_satisfaction

Pearson Correlation .595 .551**

Sig. (1-tailed) .000 .000

N 400 400

Security_and_Privacy

Pearson Correlation .393** .540

Sig. (1-tailed) .000 .000

N 400 400

Customer_Loyalty

Pearson Correlation .590** .446**

Sig. (1-tailed) .000 .000

N 400 400

Service_Quality

Pearson Correlation 1** .496**

Sig. (1-tailed) .000

N 400 400



Convenience

Pearson Correlation .496** 1**

Sig. (1-tailed) .000

N 400 400

**. Correlation is significant at the 0.01 level (1-tailed).

Appendix G: ANOVA One Way Test

ANOVAa

Model Sum of

Squares

df Mean

Square

F Sig.

1

Regression 745.155 4 186.289 124.841 .000b

Residual 589.423 395 1.492

Total 1334.577 399

a. Dependent Variable: Customer_satisfaction

b. Predictors: (Constant), Convenience, Customer_Loyalty, Security_and_Privacy, Service_Quality

Security & Privacy

ANOVA


Sum of Squares df Mean Square F Sig.

Between Groups 320.822 3 106.941 41.774 .000

Within Groups 1013.756 396 2.560

Total 1334.577 399

Post Hoc Tests

Multiple Comparisons

Dependent Variable: Customer_satisfaction

Tukey HSD

(I) Security_and_privacy2 (J) Security_and_privacy2 Mean Difference

(I-J)

Std. Error Sig.

Disagree

Neutral -2.15385 1.16002 .249

Agree -3.78516* 1.13578 .005

Strongly agree -5.18447* 1.14230 .000

Neutral

Disagree 2.15385 1.16002 .249

Agree -1.63131* .27503 .000

Strongly agree -3.03062* .30082 .000

Agree Disagree 3.78516* 1.13578 .005



Neutral 1.63131* .27503 .000

Strongly agree -1.39931* .18669 .000

Strongly agree

Disagree 5.18447* 1.14230 .000

Neutral 3.03062* .30082 .000

Agree 1.39931* .18669 .000

Customer Loyalty

ANOVA

Sum of Squares Df Mean Square F Sig.

Between Groups 439.685 3 146.562 66.530 .000

Within Groups 870.155 395 2.203

Total 1309.840 398

Post Hoc Tests



Tukey HSD

(I) Customer_loyalty2 (J) Customer_loyalty2 Mean Difference

(I-J)

Std. Error Sig. 95%

Confidence

Interval

Lower Bound

Disagree

Neutral -.79817 .49041 .364 -2.0634

Agree -2.10455* .47990 .000 -3.3427

Strongly agree -4.00000* .50696 .000 -5.3080

Neutral

Disagree .79817 .49041 .364 -.4671

Agree -1.30638* .17385 .000 -1.7549

Strongly agree -3.20183* .23859 .000 -3.8174

Agree

Disagree 2.10455* .47990 .000 .8664

Neutral 1.30638* .17385 .000 .8578

Strongly agree -1.89545* .21617 .000 -2.4532

Strongly agree

Disagree 4.00000* .50696 .000 2.6920

Neutral 3.20183* .23859 .000 2.5863

Agree 1.89545* .21617 .000 1.3377



Service Quality

ANOVA



Between Groups 397.743 3 132.581 56.042 .000

Within Groups 936.835 396 2.366

Total 1334.578 399

Post Hoc Tests



Tukey HSD

(I) Service_quality2 (J) Service_quality2 Mean Difference

(I-J)

Std. Error Sig. 95%

Confidence

Interval

Lower Bound

Disagree

Neutral -1.47491* .53098 .029 -2.8448

Agree -2.82947* .52263 .000 -4.1779

Strongly agree -4.74474* .57166 .000 -6.2196

Neutral

Disagree 1.47491* .53098 .029 .1050

Agree -1.35456* .17136 .000 -1.7967

Strongly agree -3.26983* .28813 .000 -4.0132

Agree

Disagree 2.82947* .52263 .000 1.4811

Neutral 1.35456* .17136 .000 .9125

Strongly agree -1.91528* .27244 .000 -2.6182

Strongly agree

Disagree 4.74474* .57166 .000 3.2699

Neutral 3.26983* .28813 .000 2.5265

Agree 1.91528* .27244 .000 1.2124

Convenience

ANOVA


Between Groups 294.133 2 147.067 57.338 .000

Within Groups 1015.706 396 2.565

Total 1309.840 398



Post Hoc Tests



Tukey HSD

(I) Convenience_2 (J) Convenience_2 Mean Difference

(I-J)

Std. Error Sig. 95%

Confidence

Interval

Lower Bound

Neutral Agree -1.71762* .22666 .000 -2.2508

Strongly agree -3.06948* .28768 .000 -3.7463

Agree Neutral 1.71762* .22666 .000 1.1844

Strongly agree -1.35186* .22370 .000 -1.8781

Strongly agree Neutral 3.06948* .28768 .000 2.3927

Agree 1.35186* .22370 .000 .8256

Appendix H: Multiple Regression Analysis

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .747a .558 .554 1.22156

a. Predictors: (Constant), Convenience, Customer_Loyalty, Security_and_Privacy, Service_Quality

b. Dependent Variable: Customer_satisfaction

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .017 .563 .030 .976

Security_and_Priva

cy .176 .028 .255 6.308 .000

Customer_Loyalty .182 .024 .319 7.479 .000

Service_Quality .141 .027 .229 5.201 .000

Convenience .110 .030 .157 3.631 .000

a. Dependent Variable: Customer_satisfaction



Charts

august - institutional repositoryeprints.utar.edu.my/2706/1/fyp_bf_2017_lajy_-_1406608.pdf ·...

Documents