FACTORS AFFECTING ADOPTION OF INTERNET BANKING.
A CASE STUDY OF DIAMOND TRUST BANK INDIVIDUAL
CUSTOMERS OF KAMPALA DISTRICT BRANCHES.
BY
SEKATAWA ISSAH
2008/HD06/12281U
A DISSERTATION SUBMITTED IN PARTIAL FULLFILLMENT OF THE
REQUIREMENTS FOR THE AWARD OF A DEGREE OF MASTERS OF ARTS
IN ECONOMICS OF MAKERERE UNIVERSITY
NOVEMBER 2011
i
DECLARATION
I hereby declare that this piece of work is my original work and has not been submitted
for a degree in any other university or institution.
………………………………………
Date: ……………………………
Issah Sekatawa
This dissertation has been submitted for examination with our approval as supervisors.
………………………………………
Date: …………………………..
Dr. James Muwanga
……………………………………...
Date: ……………………………
Dr. Gertrude Sebunya Muwanga
DEDICATION
ii
I dedicate this piece of work to my mother, Hajat Mariam Nansubuga and to my wife,
Joweria Nakidde.
iii
ACKNOWLEDGMENTS
I am indebted to my supervisors Dr. James Muwanga and Dr. Gertrude Sebunya
Muwanga for their support, guidelines, comments, and encouragement through out this
research. I also appreciate all the assistance given to me by all the lecturers at the Faculty
of Economics and Management, who laid the theoretical foundation during my studies in
all economics courses. Special thanks to Dr. Umar Kakumba for the guidance and
encouragement through the process of writing this research report.
I am also grateful for the support and assistance extended to me by the management and
staff of Diamond Trust Bank. Specifically, I thank the CEO, Mr. Varghese Thambi for
granting me four months leave to pursue my studies in Nairobi and another two months
leave to collect the necessary data for this study. The support of my work colleagues,
Denis Ssembajjo, Freda Namutebi and Sam Matekha, is highly recognized.
Appreciation also goes to my classmate, Sandra Basemera; her belief that it was possible
to complete the course kept me going. I thank all my friends who cheered me on from the
beginning especially Ambrose Ahisiibwe and Pauline Nteboheng.
Last but not least, I acknowledge the support given to me by my family; my mother, my
sisters Hafswa, Mariam and Nampebwa, and my brothers Muhammad, Hajji Sula, Elias
and Ali. Special thanks go to my wife, Joweria for her patience, tolerance, support and
encouragement throughout my entire course. Any errors in this piece of work are entirely
my responsibility.
iv
TABLE OF CONTENTS
Declaration -------------------------------------------------------------------------------------------- I
Dedication -------------------------------------------------------------------------------------------- II
Acknowledgments --------------------------------------------------------------------------------- IV
Table of Contents ----------------------------------------------------------------------------------- V
List of Tables ------------------------------------------------------------------------------------ VIII
List of Abbreviations ------------------------------------------------------------------------------ X
Abstract---------------------------------------------------------------------------------------------- XI
CHAPTER ONE: INTRODUCTION ----------------------------------------------------------- 1
1.1
Background of the Study ------------------------------------------------------------------- 1
1.2
Statement of the Problem ------------------------------------------------------------------- 3
1.3
Objectives of the Study --------------------------------------------------------------------- 4
1.4
Research Hypotheses ------------------------------------------------------------------------ 4
1.5
Significance of the Study ------------------------------------------------------------------- 4
1.6
Scope of the Study --------------------------------------------------------------------------- 5
1.7
Organization of the Report ----------------------------------------------------------------- 5
CHAPTER TWO: LITERATURE REVIEW ------------------------------------------------- 7
2.1
The Concept of Internet Banking ---------------------------------------------------------- 7
2.1.1
Defining Internet Banking --------------------------------------------------------------- 7
2.1.2
Types of Internet Banking --------------------------------------------------------------- 8
2.1.3
Benefits of Internet Banking to Banks and Customers ------------------------------ 8
v
2.1.4
History of Internet Banking ----------------------------------------------------------- 11
2.1.5
Overview of the Global Status of Internet Banking ------------------------------- 12
2.1.6
Status of Internet Banking in Uganda ----------------------------------------------- 13
2.2
Theoretical Aspects of Internet Banking ----------------------------------------------- 15
2.2.1
Theory of Consumer Behavior-------------------------------------------------------- 15
2.2.2
Consumer Perceptions and Attitudes ------------------------------------------------ 17
2.2.3
Diffusion of Innovations --------------------------------------------------------------- 19
2.3
Empirical Evidence on Factors Affecting Adoption of Internet Banking --------- 20
2.3.1
The Influence of Demographic Factors---------------------------------------------- 21
2.3.2
The Influence of Perceptions and Attitudes----------------------------------------- 24
CHAPTER THREE: METHODOLOGY ---------------------------------------------------- 31
3.1
Research Design --------------------------------------------------------------------------- 31
3.2
Target Population -------------------------------------------------------------------------- 32
3.3
Sample Size and Sampling Procedure -------------------------------------------------- 32
3.4
Data Collection Methods and Procedures ---------------------------------------------- 33
3.5
Validity and Reliability of the Instruments -------------------------------------------- 33
3.6
Data Analysis Techniques ---------------------------------------------------------------- 34
3.6.1
Frequencies and Percentages ---------------------------------------------------------- 35
3.6.2
Pearson’s Chi-square ------------------------------------------------------------------- 35
3.6.3
Independent t-test ----------------------------------------------------------------------- 35
3.6.4
Logistic Regression--------------------------------------------------------------------- 36
vi
3.7
Ethical Considerations -------------------------------------------------------------------- 39
CHAPTER FOUR: PRESENTATION AND ANALYSIS OF FINDINGS ----------- 40
4.1
Response Rate ------------------------------------------------------------------------------ 40
4.2
Differences between Adopters and Non-adopters of IB with Respect to their
Demographic Factors and Perceptions towards IB ----------------------------------- 40
4.2.1
Differences between adopters and non-adopters of IB with respect to their
demographic factors -------------------------------------------------------------------- 41
4.2.2
Differences between adopters and non-adopters of IB with respect to their
perceptions towards IB----------------------------------------------------------------- 48
4.3
Factors Influencing the Probability of Adopting Internet Banking ----------------- 61
CHAPTER FIVE: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 65
5.1
Summary of the Study -------------------------------------------------------------------- 65
5.2
Conclusions from the Study -------------------------------------------------------------- 66
5.3
Recommendations from the Study ------------------------------------------------------ 67
5.4
Areas for Future Research ---------------------------------------------------------------- 70
REFERENCES ------------------------------------------------------------------------------------ 71
APPENDICES ------------------------------------------------------------------------------------- 80
Appendix I: Questionnaire ------------------------------------------------------------------------ 80
Appendix II: Table for appropriate sample size for a given population -------------------- 83
vii
LIST OF TABLES
Table
No.
Page
No.
3.1
Results of reliability analysis ………………………………………………… 34
4.1
Response rate about adoption of Internet Banking ……………………..……. 40
4.2
Differences between adopters and non-adopters of Internet Banking with
respect to gender ……………………..………………………………………. 42
4.3
Differences between adopters and non-adopters of Internet Banking with
respect to age …………..………………………..…………………………… 42
4.4
Differences between adopters and non-adopters of Internet Banking with
respect to education ………………..………………………………………… 44
4.5
Differences between adopters and non-adopters of Internet Banking with
respect to occupation ………………………………………………………… 45
4.6
Differences between adopters and non-adopters of Internet Banking with
respect to income …………………………………………………………….. 46
4.7
Differences between adopters and non-adopters of Internet Banking with
respect to marital status ……………………………………………………… 47
4.8
Differences between adopters and non-adopters of Internet Banking with
respect to perception that IB saves time ……………………………………... 48
4.9
Differences between adopters and non-adopters of Internet Banking with
respect to perception that IB improves communication with the bank ……… 50
4.10
Differences between adopters and non-adopters of Internet Banking with
respect to perception that IB is very complex ……………………………….. 52
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4.11
Differences between adopters and non-adopters of Internet Banking with
respect to perception that IB process is simple ………………………………… 54
4.12
Differences between adopters and non-adopters of Internet Banking with
respect to perception that IB is safe and secure ………………………………... 55
4.13
Differences between adopters and non-adopters of Internet Banking with
respect to perception that customers are not afraid of disclosing their account
details on the Internet …………………………………………........................... 57
4.14
Differences between adopters and non-adopters of Internet Banking with
respect to perception that IB is very expensive ……………………………….. 59
4.15
Differences between adopters and non-adopters of Internet Banking with
respect to perception that Internet installation is very expensive …………….... 60
4.16
A logistic regression of factors affecting adoption of Interne Banking ………... 62
ix
LIST OF ABBREVIATIONS
ATM
Automated Teller Machine
AVP
Automated Voice Response
DTB
Diamond Trust Bank
GNS
Global Network Standard
IB
Internet Banking
ICT
Information and Communication Technologies
IFS
Interactive Financial Service
PC
Personal Computer
WAP
Wireless Application Protocol
x
ABSTRACT
The major objective of this study was to identify the factors affecting adoption of Internet
Banking (IB) among DTB individual customers of Kampala District Branches. The
specific objectives were: to find out the difference between adopters and non-adopters of
IB with respect to demographic factors such as age, income, education, marital status,
occupation status, and gender; and with respect to their perceptions towards IB such as
relative advantage, complexity, perceived risk, and perceived cost of IB; and to determine
the factors influencing the probability of adopting IB.
Using a cross-sectional survey approach, primary data was collected from a random
sample of 274 Diamond Trust Bank individual customers of Kampala District branches
using self-administered questionnaires.
The data was analyzed using frequencies,
percentages, chi-square tests, independent t-tests, and logistic regression analysis
techniques.
Based on the results of the chi-square test, the conclusion is that there was a significant
difference between adopters and non-adopters of IB with respect to four demographic
factors including age, income, education, and occupation; while there is no significant
difference between adopters and non-adopters of IB with respect to the demographic
factors of marital status and gender. Furthermore, based on the results of the independent
t-test, the conclusion is that there was a significant difference between adopters and nonadopters of IB with respect to their perceptions towards IB such relative advantage,
complexity, perceived risk, and perceived cost. Also, based on the logistic regression
xi
results, the conclusion is that the factors of age, income, education, relative advantage,
complexity, perceived risk, and perceived cost significantly influenced the probability of
adopting IB, with income having the biggest relative influence.
The study recommends promoting IB among customers of the relatively young age
although not at the expense of the relatively older age since some of them were found to
be using IB, investing more resources in promoting IB among the relatively high income
customers, promoting IB among the relatively more educated customers, increasing
awareness about IB through sensitizing customers about the benefits of IB, installing
simple and easy-to-use IB systems, installing modern and powerful security features to
protect sensitive customer information, and minimizing costs associated with IB through
introducing price bands.
xii
xiii
CHAPTER ONE
INTRODUCTION
1.1
Background of the Study
Internet banking (IB) is the act of conducting financial intermediation on the Internet
(VanHoose, 2003). It represents an electronic market place where customers can conduct
their financial transactions virtually (Srivastava, 2007). It is different from Electronic
banking (e-banking) with the latter being a higher level activity encompassing not only
IB, but also Telephone banking, Automated Teller Machines (ATM), Wireless
Application Protocol (WAP)-banking, and other electronic payment systems not operated
through the Internet.
In developed countries, the popularity of IB as delivery channel for banking services has
grown, replacing the branch-based model of banking and the manual service functions
provided by employees (Cheng, Lam and Yeung, 2006). IB enables the users to perform
various activities including: writing checks, paying bills, transferring funds, printing
statements, and inquiring about account balances, from any location, provided there is
Internet access (Hoppe, Newman and Mugera, 2001; Frust, Lang and Nolle, 2000).
The benefits of IB to banks and customers are many. To the banks, IB lowers operating
costs since it requires less staff and fewer physical branches; it promotes customer
loyalty, and builds bank reputation among others (Chau and Lai, 2003; Tan and Teo,
2000). To the customers, IB saves time on physically visiting a branch; it is convenient
1
since it enables one to transact without necessarily queuing or writing cheques; is
accessible twenty-four hours a day, seven days a week; and it executes transactions
almost immediately (Suganthi and Balachandran, 2001; Bradley and Stuart, 2002).
Because of its well documented benefits, banks are beginning to leverage the Internet as a
means of providing financial services. This is crucial for long-term survival of banks in
the world of electronic commerce (Burnham 1996), given that its market is projected to
grow sharply in the coming years (Duclaux 1996; Liao et al. 1999). Banks, particularly
in developed countries already invested heavily in developing IB and promoting its
adoption. In these countries, using IB is a norm rather than an exception; as such, rates of
adoption of the service are relatively high (Arunachalam and Sivasubramanian, 2007).
In most developing countries, IB is in its infancy stage. However, banks are beginning to
take advantage of the benefits it offers; hence, its availability is growing day-by-day. In
the context of Uganda, IB started in the year 2001, where CitiBank used it to mainly
serve its corporate and high-end individual customers (CitiDirect Release, 2001). Over
the years, other commercial banks have developed the service and are promoting its use
among their customers. However, its low adoption among customers is a concern. A
recent report by The Uganda Banker (2008) echoes this concern when it asserts that bank
customers in Uganda generally still prefer traditional branch-based retail banking in spite
of the availability of IB. The report further states that this challenge must be addressed if
IB in Uganda is to develop and customers reap its potential benefits.
2
Diamond Trust Bank (DTB) is among the banks promoting IB for use among its
customers in Uganda.
The bank launched IB in January 2007, which is currently
available to customers of Kampala District branches. The customers can use this service
to access latest balance, financial statements, view account details, customize, print,
download statements, and obtain a recent history statement on all their accounts
(www.diamondtrustbank.co.ug). In spite of the bank’s efforts, the level of adoption of IB
among its customers is very low. Only about 400 of its estimated 28,000 individual
customers of Kampala District branches are actively using IB, representing a low ratio of
approximately 1.4 percent (Centralized Information Department of DTB). The bank
managers need to understand the factors affecting adoption of IB in order to design
effective measures for enhancing its adoption.
1.2
Statement of the Problem
Although DTB is promoting IB for use among its customers of Kampala District
branches, the adoption of the service remains low, suggesting the service is largely
unnoticed and underutilized in spite of its availability. Only about 400 of its estimated
28,000 individual customers of Kampala District branches are actively using IB,
representing a low ratio of approximately 1.4 percent (Centralized Information
Department of DTB). Hence, there is need to identify the factors affecting adoption of
Internet Banking among Diamond Trust Bank individual customers of Kampala District
branches.
3
1.3
Objective of the Study
The major objective of this study was to examine the factors affecting adoption of
Internet Banking among Diamond Trust Bank individual customers of Kampala District
branches. The specific objectives of the study were:
i.
to find out the difference between adopters and non-adopters of IB with respect
to their demographic factors such as gender, age, education, occupation, marital
status and income; and with respect to their perceptions towards IB such as
relative advantage, complexity, perceived risk, and perceived cost of IB, and
ii.
to determine the factors influencing the probability of adopting IB.
1.4
Research Hypotheses
The study was based on the following hypotheses:
i.
there is a significant difference between adopters and non-adopters of IB with
respect to demographic factors such as gender, age, education, occupation and
income; and with respect to their perceptions and/or attitudes towards IB, such as,
relative advantage, complexity, perceived risk, and perceived cost of IB,
ii.
there is a positive relationship between education, income, relative advantage and
the probability of adopting IB, and
iii.
there is a negative relationship between age, complexity, perceived risk, perceived
cost and the probability of adopting IB.
1.5
Significance of the Study
This study is significant in two ways. First, although IB is available in Uganda, customer
adoption of the service is still low, and yet to date, there is lack of sufficient research on
4
factors affecting adoption of IB in the country. Investigating these factors may enable
banks to increase their market share by creating solutions and strategies that attract
consumers to use this type of banking. Therefore there is a need for a study of this kind.
Second, the study shall contribute to the extremely scanty literature on IB in Uganda,
especially since most of the empirical studies on the subject highlight studies largely
conducted in developed countries, while few studies have been conducted on this issue in
developing countries, and hardly any has been conducted in Uganda.
1.6
Scope of the Study
Geographically, the study was confined to Diamond Trust Bank individual customers of
Kampala District Branches. Kampala District was chosen because the potential users are
customers of these branches. Theoretically, although there are various categories of
factors mentioned in literature affecting adoption of IB, this study was confined to the
widely documented demographic factors and customer perceptions towards IB. This
study considered the time period from the year 2007 to 2009.
1.7
Organization of the Study
This paper is organized into five chapters.
Chapter one is the introduction, which
includes: background of the study, statement of the problem, objectives of the study,
research hypotheses, significance of the study, and the scope of the study. Chapter two is
the literature review. Chapter three is the methodology, which specifies the methods and
procedures used to conduct the study. Chapter four is the presentation and interpretation
5
of findings, while chapter five presents the summary of the study, the conclusions, the
recommendations, and areas for further research.
6
CHAPTER TWO
LITERATURE REVIEW
This chapter reviews literature related to Internet Banking. It covers different sections
including: concept, theoretical aspects, and empirical evidence on factors affecting
adoption of Internet Banking.
2.1
The Concept of Internet Banking
This section gives an explanation of the concept of Internet Banking. It comprises various
sub-sections including: definition, types, benefits to banks and customers, history,
overview of global and local status of IB.
2.1.1 Defining Internet Banking
Different authors have attempted to define IB differently. However, this study uses
VanHoose’s (2003) definition: IB is the act of conducting financial intermediation on the
Internet. With the exception of cash withdrawals, internet banking gives customers
access to almost any type of banking transaction at the click of a mouse (De Young,
2001).
The use of the internet as a new alternative channel for the distribution of financial
services is important for achieving a competitive advantage (Flavián, Torres and
Guinalíu, 2004). All banks using the internet as an additional delivery channel and those
using only the Internet as a delivery channel can equally compete for customers around
the world. This is why the popularity of IB as a delivery channel for financial services is
increasing in this era (Karjaluoto et al., 2002).
7
2.1.2 Types of Internet Banking
According to Molla (2002), there are different forms of Internet Banking based on: a
bank’s proprietary software, personal computers using dial-up software, on-line services,
and the World Wide Web. Internet banking based on a bank’s proprietary software uses
the bank as an “electronic gateway” to customer accounts. In this case, customers install
this software on their home computers to enable them to transfer funds and pay bills
electronically. Internet Banking based on personal computers using dial-up software
makes use of home finance software to link customers to banks for online banking.
Internet Banking based on on-line services involves banks setting up retail branches on
subscriber-based online services, as is the case with America Online. Internet Banking
based on the World Wide Web bypasses subscription based services and allows banks to
interact directly with their customers through the World Wide Web. In Uganda’s case,
mixtures of the third and fourth types of IB-based features are the commonly used types
by customers.
2.1.3 Benefits of Internet Banking to Banks and Customers
2.1.3.1 Benefits to banks
IB offers many benefits to banks. The main benefits to banks are cost savings, reaching
new segments of the population, efficiency, enhancement of the bank’s reputation and
better customer service and satisfaction (Brogdon, 1999).
The more those clients convert to IB, the greater the monetary saving will be. According
to Robinson (2000), the cost of an electronic transaction is dramatically lower than the
cost of a face-to-face branch transaction. In a study conducted by Booz-Allen and
8
Hamilton (1997), it was found that the establishment of specialized IB requires only
US$1 – 2 million, which is lower than branch-based banking set up. The traditional
bank’s running costs account for 50% - 60% of its revenues, while the bank’s running
costs of IB is established at 15% - 20% of its revenues.
Robinson (2000) adds that IB strengthens the relationship between the service provider
(e.g. bank) and the customer because it brings banking services directly to a customer’s
home, office or mobile phone. This creates customer loyalty. The author further asserts
that online services are a must for banks that have to compete with a growing number of
services from other financial institutions, investment concerns and insurance companies.
This is in light of the fact that banking is no longer tied to time and place. As a result
global competition is expected to broaden.
Sheshunoff (2000) says that the single most important driving force behind the
implementation of full-service internet banking by banks is the need to create powerful
barriers to customers exiting. The author argues that once a customer moves to full–
service internet banking, the likelihood of that customer moving to another financial
institution is significantly diminished. The main reasons for this behaviour can be found
in the consumer behaviour theory, which indicates that switching always requires much
time and effort from the individual consumer. The author concluded that the competitive
advantage of internet banking for banks is very significant.
9
Burns (2000) argues that electronic banking customers are more valuable to banks than
traditional customers.
Through electronic banking, banks can achieve better cross-
channel productivity and performance. The move towards internet banking increases the
need for a holistic approach to channel and process management, especially when
integrating new delivery channels into existing frameworks (as many traditional banks
are currently doing). Burns (2000) indicates that the Internet will not replace other
delivery channels, but will offer increased flexibility and the opportunity for improved
service.
Internet banking customers are said to be more loyal to their bank than non–internet
banking customers (Mols, 1998). Mols concluded a survey in Denmark and presented
some interesting insights about internet banking users. His results suggest that internet
banking customers: are more satisfied with their bank; have higher switching barriers;
provide more positive word-of-mouth opinions about their bank; have higher repurchase
intentions; have lower price sensitivity; have a lower propensity to exit and a higher
propensity to complain.
2.1.3.2 Benefits to customers
Bank customers can also benefit from Internet banking in a number of ways. With the
help of the Internet, banking is no longer bound to time or location. Consumers all over
the world have relatively easy access to their accounts 24 hours a day, seven days a week.
10
Second, IB avails customers with a full range of services including some services not
offered at branches. The greatest benefit of Internet banking is that it is cheap to
customers or even free. However, in one of the past studies, price seemed a significant
barrier to adoption or use of Internet banking.
Third, Internet banking also has the advantage that customers avoid traveling to and from
a bank branch.
In this way, Internet banking saves time and money, provides
convenience and accessibility, and has a positive impact on customer satisfaction
(Karjauloto, 2003). Turban et al. (2000) indicated that Internet banking is extremely
beneficial to customers because of the savings in costs, time and space it offers, its quick
response to complaints, and its delivery of improved services, all of which benefits make
for easier banking.
In summary, Internet banking offers many benefits to banks and their customers. The
major benefits accruing to banks are in terms of cost savings, reaching new segments of
the population, efficiency and enhancement of the bank’s reputation. Customers on the
hand benefit mainly through improved customer service and satisfaction.
2.1.4 History of Internet Banking
Since 1981, the banking sector has witnessed a number of innovations, beginning with
the introduction of the Automated Teller Machines (ATM). Early in the 1990s, the
Automated Voice Response (AVR) was introduced in the financial industry. This facility
gave financial institutions the opportunity and ability to launch electronic banking
services to their customers. As technology kept changing the way financial services were
11
produced and delivered, banks were able to offer services to customers who owned
personal computers (Sohail and Shanmughan, 2003).
NetBank in the United States of America was the first online bank which was formed in
1996 under the name of Atlanta Internet Bank (Gonzalez et al., 2008). Other online
financial services such as Junipercom, e-Trade.com joined the electronic banking
industry in 2001, and well established banks such as CitiBank and Wells Fargo followed
suit (Gefen and Straub, 2000). Over the years, Internet banking has experienced growth
in many countries—developed and developing--and has transformed traditional practices
in banking dramatically.
2.1.5 Overview of the Global Status of Internet Banking
According to Stegman (1999) cited by Ongkasuwan (2002), IB in the United States has
tremendously developed, reduced costs in the banking industry and improved service
quality for their existing and potential new customers in the country. The demand for
online banking via the Internet increased from 4.8 million customers in 1997 to about 7.8
million customers in 1998 (Ongkasuwan, 2002), and was estimated at 20.2 million in the
year 2007. Most of the forecasts for online banking predicted that this growth rate would
continue beyond the year 2007, with more than 34 million customers using online
banking services via the Internet during the year 2001.
According to Birth and Young (1997) cited by Ongkasuwan (2002), UK IB services
encountered an increasing demand for cross-border payment transactions involving small
amounts. Many UK banks continue to develop and launch new banking services on the
12
Internet in order to satisfy and meet their Internet-based customer requirements in terms
of time, ease of use, security and privacy. By June 2005, the U.K. and eight other
western European countries, namely, France, Spain, Portugal, Germany, Switzerland,
Holland, Luxembourg and Scandinavia had become leading nations in providing internet
banking services in Europe.
According to Tang (2004), China decided to take advantage of the financial restructuring
process and Internet revolution in Asia. China’s Central Bank initiated and encouraged
the development of IB services since 31 May, 2000. This new Internet banking system
provides 24 hours access to financial transactions, personal financial consulting and
utility fee payment.
According to Ongkasuwan (2002), in Asia and the Pacific, many banks, lending
organizations, credit companies such as VISA, and computer vendors such as IBM have
formed alliances in order to develop IB service standards for their customers. Banks in
Singapore, Australia, Indonesia, Korea, Hong Kong, Taiwan, and Thailand formed an
organization called Interactive Financial Services (IFS). Through IBM’s Global Network
Standard (GNS), members are able to provide and exchange their IB services to their
alliance customers. This will eventually allow seamless, interactive banking and other ebusiness services across these banks around the world.
2.1.6 Status of Internet Banking in Uganda
The banking industry in Uganda is undergoing rapid growth following various financial
sector reforms by Bank of Uganda on behalf of the government, and a positive economic
13
environment. Banks in Uganda are competing through many commercials and a range of
products and services, for customers. Products and services such as business accounts,
mortgage loans, childrens’ accounts, VISA and ATM cards among others, which used to
be offered by a few banks, are now offered by most of the banks.
Many products and services are now a matter of competitive necessity rather than a
competitive advantage. With many banks offering similar products and services, the
focus of competition is now moving towards speed, customization of products and
opening up of more branches to add value to the core banking products and services
(BOU, 2007).
The competitive landscape in Uganda’s banking sector has become highly dynamic with
relatively newer banks wrestling banking awards from traditional banks in the recent
banking awards (BOU, 2008). Digital technology is becoming an important tool in
designing bank strategy, with Wide Area Networks (WAN) being incorporated by almost
all banks to create business value (BOU, 2008). Door-to-door sale of bank products has
become a norm. Yet, according to The Uganda Banker (2008) the banking customer in
Uganda is far from being satisfied.
The Internet is now emerging as a new market place in Uganda a number of banks
already hitting the airwaves in many commercial and business interviews, promising
optimal customer value in their new Internet delivery channel. Uganda, notwithstanding,
is one of the African countries with the lowest Internet patronage, with only 1.8% of the
14
country’s population accessing the Internet (Internet World Stats, 2009). The full impact
of the Internet has not been felt in Uganda, especially in e-commerce and banking. The
level of e-readiness of the Uganda community is low, yet, research on particularly IB, is
scanty. Understanding IB issues, particularly its adoption was the major purpose of this
study.
2.2 Theoretical Aspects of Internet Banking
This section presents an overview of the theoretical aspects related to adoption of IB.
Specifically; the theoretical aspects are related to consumer behavior, consumer
perceptions and attitudes, and diffusion of innovations. These are briefly explained in the
following sub-sections.
2.2.1 Theory of Consumer Behavior
The basis for understanding consumer behavior is learning about how consumers will
accept or reject product offerings, as well as the factors that shape these decisions. For
this reason, this section starts with a theory of consumer behavior, which provides a
foundation on which the rest of the study stands. The purpose of this literature review is
to understand the theory behind the problem, which is to investigate the factors
influencing the adoption of IB.
Lamb, et al. (2000) define consumer behavior as the acts of decision-making units
(families as well as individuals) directly involved in obtaining and using goods and
services that satisfy their needs, and this also includes the decision-making process that
precedes and determines these acts. These acts refer to activities like traveling to and
15
from the stores, evaluation of both goods and services available in the market and the
actual purchasing of goods.
When referring to consumers, Rice (1997) explains that consumers are people who use
services and products, and pay for them. Consumer behavior is about learning about
consumers and their buying behavior. Schiffman and Kanuk (2000) explain that a
“consumer” is used to describe two kinds of consumers, i.e. personal and business or
organizational consumers.
Personal consumers are consumers who buy goods and
services for their own use, and business consumers are those buying products, equipment
and services in order to run a business. Block and Roering (1979) define consumer
behavior as the acts of individuals directly involved in obtaining and using economic
goods and services. This includes the decision making processes that consumers go
through when buying goods.
With a better understanding of consumer behavior, banks will be able to identify
customer profiles. Beckett, et al. (2000) suggests that the type of financial product being
purchased influences consumer purchasing behavior. Secondly, the emphasis on trust
and having a relationship is also highly pertinent to the strategies of banks and other
financial providers.
Thirdly the ability to retain customers and increase customer
profitability is very important (Karjaluoto, et al., 2002).
Individual differences in
consumer behavior have been theorized and found to be associated with the acceptance of
new information technology, such as internet banking (Nelson, 1990).
16
According to Wang (2002), the emergence of IB has created highly competitive market
conditions, which have had a critical impact upon consumer behavior. Internet banking
providers must therefore attempt to better understand the factors affecting consumer
acceptance of internet banking. If they succeed, banks will be able to influence and even
determine consumer behavior, which will become a major issue in creating competitive
advantage in the future.
2.2.2 Consumer Perceptions and Attitudes
According to Lamb, et al. (2000), perception is the process whereby an individual selects,
organizes and integrates stimuli into a meaningful and overall picture.
Perception
involves all the senses (seeing, feeling, tasting, smelling and hearing), and these sensory
stimuli play a role in causing certain sensations which influence consumers in deciding
whether to purchase or not.
According to Lussier (2000), perception has defense mechanisms that are used to protect
consumers against undesirable stimuli from the environment. According to Reekie and
Brits (1997) different consumers will perceive a product offering differently, depending
on their needs. Consumer perception towards a product and service can play a role to
influence their buying behavior. Consumers’ acceptance of technological innovations
such as IB may be influenced not only by their socioeconomic and demographic
characteristics, but also by their perceptions of specific technologies and by the
characteristics of different products and services (Davis, 1989).
17
Attitude is a positive or negative feeling or mental state of readiness, learned and
organized through experience that exerts specific influences on a person’s response to
people, objects and situations (Gibson, et al., 2000). Consumer attitude refers to the
feeling of liking or disliking that consumers have towards products, stores, brands and
other marketing stimuli. The attitude of consumers is important to marketers because
they show consumers’ intentions and behaviors towards the marketing variables of
product, price, place and promotions (Foxall and Goldsmith, 1994). Attitudes are learned
and those which result in purchase behavior are formed as a result of direct experience
with the product, information acquired from others, and exposure to mass media
(Hawkins, et al., 1989).
According to Guo (1999), attitudes are often viewed as
determinants of meanings, because they provide a context for the interpretation of new
information, and help individuals to evaluate each other’s opinions and organize and
select facts.
The attitude theory suggests that the more a person has a favorable attitude towards a
given product/service, the more likely that person is to buy or use that product/service.
The overall attitude towards an object is expected to relate to behaviours towards the
object (Ajzen and Fishbein, 1980). The measure and understanding of attitudes allow
and help marketers in the development of products that consumers want and promote
them effectively and in evaluating their efforts at promoting the products (Foxall and
Goldsmith, 1994).
18
According to Lussier (2000), attitude is an overall perception about an object. Attitudes
both affect and are affected by behavior. Hence attitude refers to the overall evaluation
of an object. Attitudes are personal feelings that influence a person’s tendency to act in a
particular way. In this study, attitude describes a person’s perception towards IB.
Attitude motivates consumers towards a particular behavior. According to Mink (2001),
of the ten countries studied, 3% of consumers had no interest in IB; customer service is
what really matters and they receive that at a traditional bank.
An innovation presents potential adopters with a new means of solving problems and
exploiting opportunities. According to Rogers (1983) and Ching and Ellis (2004), an
individual first forms an attitude towards the innovation leading to a decision to adopt or
reject the innovation. If the innovation is perceived to be better than the existing system
(a measure of is relative advantage), is consistent with the needs of the potential adopter
(a measure of its compatibility), and is easy to understand and use (a measure of its
complexity), it is more likely that a favorable attitude towards the innovation will be
formed. Therefore, if a person has a positive attitude toward IB, he or she is more likely
to become a user of internet banking (Du, 2002). Thus, this study sought to provide
greater insights into how attitudes towards IB in general affect adoption of IB among
DTB individual customers of Kampala District branches.
2.2.3 Diffusion of Innovations
Diffusion of innovation theory attempts to identify patterns and rates of adoption of
innovation. This is especially significant in consumer markets in terms of attempting to
forecast demand and market growth (Valente, 1993). According to Kotler (2000), an
19
innovation refers to any goods, service, or idea that is perceived by someone as new. The
idea may have a long history, but it is an innovation to the person who sees it as new.
Rogers (1995) defines an innovation as any idea, practice, or object that is perceived as
being new by an individual or other unit of adoption. Almost all of the new ideas are
technological innovations, and innovation and technology are often used as synonyms. A
technology is a design for instrumental action that reduces the uncertainty in the causeeffect relationships involved in achieving a desired outcome.
According to Kotler (2000), adoption is the decision of an individual to become a regular
user of a product. IB is potentially the most radical innovation, especially in the context
of banks dominated by the branch as the means to provide service to customers. Only a
few studies have investigated diffusion of innovation within the retail banking sector
(Bradley and Stewart, 2003). Senior bank management have an interest in studies which
have investigated the adoption of IB as the results can shed light on how to better market
their IB services and thus accelerate the rate of adoption.
If the service can more quickly reach a critical mass of customers, then the respective
bank’s investment in IB could be recouped more quickly (Gerrard and Cunningham,
2003). Thus, this study sought to gain an understanding of the diffusion of IB among
DTB individual customers of Kampala District Branches.
2.3 Empirical Evidence on Factors Affecting Adoption of Internet Banking
This study concentrated on two main categories of factors which affect the adoption of
IB.
These are: consumer demographic characteristics, which demonstrate how age,
20
education level, income and occupation affect adoption of IB; and consumer perception
and attitude, which is analyzed under the sub-headings of relative advantage, complexity,
perceived cost and perceived risk. With a greater understanding of how these factors
affect consumer adoption of new products, DTB and generally other banks in Uganda
will be able to create new internet banking solutions which are more acceptable to
potential consumers.
2.3.1 The Influence of Demographic Factors
Literature identifies a number of demographic factors that influence adoption of IB
including: age, gender, income, and occupation. Within the Information Systems area, a
number of studies have found evidence that explains the significant, direct and
moderating affect of age on the behavioral intention, adoption and usage behaviors
(Harris et al., 1996; Morris and Venkatesh, 2000; Venkatesh et al., 2003). A study by
Venkatesh et al. (2003) suggests that the age group in the United States of America which
mostly adopts computers is 15-17 years, which is then followed by the age group of 2635 years. Similarly, Lee and Choudrie, (2002), found in South Korea that the group that
increased the adoption of broadband via the PC was also the younger age group. In turn,
the younger generation’s usage of broadband in South Korea exerted a substantial
influence on parents’ decisions for subscribing to broadband, since parents considered
broadband as imperative for educational and entertainment purposes.
Carveth and Kretchmer (2002), found that in many West European countries, the older
demographic groups are less likely to use the internet compared to the younger groups.
According to their findings, in the UK, 75 percent of those aged 16-24 had internet access
21
compared to just 15 percent in the 65-74 age range, 6 percent over the age of 75 years,
and 4 percent in the 25-63 age range. A study by Anderson et al. (2002) also suggests
that the demography of dial-up users is different to that of broadband users. Therefore,
significant age differences are expected in terms of the broadband adopters and nonadopters. The younger and middle aged consumers are expected to be more apathetic to
adoption, whilst the older age consumers is expected to be more relevant to the nonadopters.
With respect to gender, a number of studies have investigated the role of gender in the
adoption and usage of ICTs (Harris et al., 1996; Gefen and Straub, 1997; Morris and
Venkatesh, 2000; Venkatesh and Morris, 2000; Venkatesh et al., 2000; Leonard and
Cronan, 2005, Venkatesh et al., 2003; Choudrie and Lee, 2004, Haines and Leonard,
2007). The findings of the previous studies revealed that gender has an important role
when considering technology adoption and usage in both the organizational and
household contexts. The study by Morris and Venkatesh (2000) illustrated that male
users used a computer more than females, and suggested the male gender to be one of the
most important variables when examining PC adoption in the household. Choudrie and
Lee (2004) also found that differences in gender were not important in determining
adoption of broadband. A study by Carveth and Kretchmer (2002), however, shows
results similar to those by Choudrie and Lee (2004) for internet users in the USA,
suggesting that in the USA, there are approximately equal numbers of men and women
using the internet.
22
Past research on the influence of education on technology (PC) adoption suggests a
positive correlation between the level of education, technology ownership and usage
(Morris and Venkatesh, 2000). Morris and Venkatesh found that people with higher
educational qualifications used computers more than less educated people. Education is
widely reported to be one of the most important drivers of broadband adoption in South
Korea (Choudrie and Lee, 2004; Choudrie and Papazafeiropoulou, 2006). Choudrie and
Dwivedi (2005) and Anderson et al., (2002) suggest that household consumers with
secondary or tertiary education are more likely to have internet access. The above
evidence from theory and empirical research suggests that education can be considered as
a factor in adoption of Internet banking, and a basis for establishing differences between
adopters and non-adopters. This is because Internet banking is considered to be useful
for educational purposes and performing banking related tasks. Therefore, it is expected
that consumers with higher educational attainment or working towards higher educational
attainment, i.e. degrees or postgraduate students, are more likely to adopt Internet
banking.
With respect to income, the adaptive structuration theory argues that Information
Technology has the potential to increase the resources of both those who had resources
prior to its adoption and those who possessed few resources prior to its adoption (Mason
and Hacker, 2003). The findings of a longitudinal study using the USA census data
found a positive correlation between income and computer ownership (Venkatesh et al.,
2000). Further, this study suggested that a considerable gap persists between the lower
and higher income groups.
23
A study by Choudrie and Dwivedi (2005) also confirmed that income and occupation
drive the general pattern of IB ownership and usage. Similarly, Carveth and Kretchmer
(2002) suggested that in the USA, the higher the household income, the more likely the
members of the household will own a computer and use the internet. A similar pattern
was suggested for Western European countries and the UK. This study suggested that
only 23 percent of lower income groups in comparison to 68 percent of the higher income
groups in the UK used the internet (Carveth and Kretchmer, 2002). A recent study
focusing upon the determinants of the global digital divide also confirmed the importance
of per capita income in explaining the gap in computer and internet use (Chinn and
Fairlie, 2004). These theoretical arguments and empirical evidence support the inclusion
of both income and occupation as factors that affect adoption of Internet banking, and
which can provide a basis for establishing the difference between Internet banking
adopters and non-adopters.
2.3.2 The Influence of Perceptions and Attitudes
According to Rogers (1983), there are three characteristics of innovations: relative
advantage, compatibility, and complexity. He asserts that adopters have invariably been
found to have different perceptions about these characteristics in comparison with nonadopters. Rogers further argues that if the innovation is perceived to be better than the
existing system, is compatible with the needs of the potential adopter, and is easy to
understand and use, then it is more likely that this innovation will generate a positive
attitude on the side of the adopter leading tom its adoption. Thong (1999) found that the
perceived relative advantage and complexity of the innovation played a key role in the
24
adoption of internet banking. Therefore, this section reviews empirical evidence on how
these characteristics of innovation influence the adoption of internet banking.
2.3.2.1 Relative advantage
Relative advantage describes the degree to which an innovation is perceived as being
better than its precursor (Rogers, 1983). He points out that there are a number of subdimensions of relative advantage such as the degree of economic profitability; decrease in
discomfort; time saving; and effort.
Gerrard and Cunningham (2003) identify a
perceived relative advantage as being a significant factor driving the adoption of IB. This
construct is similar to the perceived usefulness in the Technology Acceptance Model,
defined as the degree to which a person believes that a particular information technology
would enhance his or her job performance. It has been revealed to be a factor towards the
adoption of internet banking (Leaderer, et al., 2000).
Agarwal and Prasad (1998) found that relative usefulness of an innovation is positively
related to its rate of adoption. Therefore it is possible to suggest that the way that people
perceive the usefulness of IB could affect its rate of adoption. In another survey, a large
proportion of consumers said that twenty-four hour availability was the most important
factor in their use of computer banking (Lockett and Littler, 1997). A study of 220
consumers found that shoppers appreciated the ability to visit virtual stores at any hour
(www.studioarchetype.com).
25
Consumers may be motivated to use some electronic banking technologies because of the
time saving. Time saving equates to a customer being able to bank without physically
visiting a branch.
In one survey of computer banking users, 79% indicated that
convenience was very important in their decision to use computer banking and 71% said
that saving time was very important (Fox, 2002). Further still, a survey conducted in
Finland (Karjaluoto, et al., 2002) shows that IB users do not hunger for traditional
banking. Usually, visiting bank branches is considered time-consuming due to long
queues, and yet, IB users are not eager to queue at branches. It is therefore possible to
suggest that the advantages that IB offers over and above regular banking methods could
influence its rate of adoption.
2.3.2.2 Complexity
Complexity measures the degree to which an innovation is perceived to be easy to
understand and use. Adoption will be less likely if the innovation is perceived as being
complex or difficult to use (Rogers, 1983). Complexity can be considered as the exact
opposite of ease of use in the Technology Acceptance model, which has been found to
directly impact the adoption of the Internet (Leaderer, et al., 1999). Consumers will
reject an innovation if it is very complex and not user friendly. In this context, Cooper
and Zmud (1997) report that ease of use of innovative products or services as one of the
three important characteristics for adoption from the customer's perspective.
Research by Davis (1989) has found that perceived complexity is associated with the
adoption of electronic technologies. Research conducted in Estonia (Kerem, 2001) states
that the most important factors in starting to use internet banking are first and foremost
26
better access to the services (convenience), better prices and a high-level of privacy.
Better service (i.e. preferring self-service over office-service) was also considered to be
above average in terms of importance. Therefore the adoption of internet banking is
likely to be increased when customers consider using internet banking processes to be
easy. An individual is far less likely to adopt a new technology if this requires a high
level of technical skills. Conversely the adoption of internet banking is far more likely to
occur if the internet banking processes are simplified and are user friendly.
2.3.2.3 Perceived risk
Perceived risk reflects the extent to which consumers are uncertain about the
consequences of buying, using or disposing of an offering. Risk or uncertainty regarding
the most appropriate purchase decision or the consequences of the decision is a
significant variable influencing the total amount of information gathered by consumers
(Loudon and Bitta, 1993).
According to Loudon and Bitta (1993) certain situations influence the consumer’s
perception of uncertainty or consequences and, thus, the perception of risk. These are:
uncertainty regarding buying goals; uncertainty regarding which alternatives (such as
product, brand, or model) will best match or satisfy the purchase goals; and the perceived
possible undesirable consequences if the purchase is made (or not made) and the result
fails to satisfy buying goals. If the consumer senses any of these situations, then he or
she is said to perceive risk in the situation.
27
Research conducted in Turkey (Polatoglu and Ekin, 2001) states that risk includes
financial, physical, or social risks associated with trying an innovation. It is known that
security risks are one of the major barriers to the adoption of online banking. With the
introduction of internet banking services by a few large, well-known, and trusted banks in
Turkey, customers perceive the security risk to have decreased considerably.
According to Liu and Arneet (1999) the need for secure transactions is important not only
for internet banking but that of any e-commerce related to website. Consequently the
lower the perception of risk in using internet banking the more likely an individual would
be prepared to use it. Hartman, et al. (2000) point out that security is a major concern
wherever online transactions take place.
They suggest that Internet-based service
providers must implement access control, authentication procedures, encryption,
firewalls, audit trails and virus protection to secure their online services.
Another survey conducted by Cranor and Laurie (1999) found that 81% of Internet users
are concerned about threats to their privacy while online. An empirical study found that
consumers are often reluctant to share personal information for fear that their financial
life will become an open book to the Internet universe (Bestavros, 2000). Lain (2000)
conducted an Internet survey and found that South Africans are just as concerned about
security as US consumers were a year previously. Security has been widely recognized
as one of the main obstacles to the adoption of internet banking. Many studies suggest
that banks must first convince their customers that internet banking and transactions are
secure before customers will show a willingness to use internet banking. Consequently
28
the adoption of internet banking is likely to increase when the risk of using internet
banking is low.
2.3.2.4 Perceived cost
According to Ching and Ellis (2004), adoption will be driven by the perceived costs and
benefits inherent in the particular innovation. The cost of an innovation comprises many
components: initial investment costs, operational costs, and utilization costs. Rothwell
and Gardiner (1984) observe that there are two fundamental sets of factors affecting user
needs, namely price factors and non-price factors. To this extent Gupta (1988) identifies
price as a major factor in brand switching. If consumers are to use new technologies, the
technologies must be reasonably priced relative to alternatives.
Otherwise, the
acceptance of the new technology may not be viable from the viewpoint of the consumer.
According to the Comptroller’s Handbook (1999), cost is another factor that would stand
in the way of consumer adoption of internet banking. In internet banking, two types of
costs are involved.
First the normal costs associated with Internet access fees and
connection charges. Secondly, the bank fees and charges. Bradley and Stewart (2003)
found high initial set up costs; cost reductions and the costs incurred during
implementation are considered as the greatest inhibitors of the diffusion of internet
banking. Another study indicates that consumers will not adopt a new financial product
unless it reduces their costs and does not require them to change their behavior when
using it (Bareczal and Ellen, 1997). From a customer retention perspective, Goosen, et
al. (1999) point out that the introduction of internet banking, the existing lower switching
29
costs and the easy accessibility to the internet, imply that customers who are dissatisfied
with the services or products offered by their banks are more likely to withdraw their
loyalty if their requirements are not provided for.
Clearly cost perception is a factor which continues to inhibit the adoption of internet
banking in many areas. To overcome this barrier, banks should be at pains to prove to
consumers that internet banking is a cost effective and beneficial form of banking and
actively take measures to dispel any misperceptions that consumers may have about
online banking costs.
2.4
Summary of the Literature Review
In summary, this chapter has reviewed a number of issues related to Internet banking,
such as: definition, types, benefits, advantages and disadvantages, history, theoretical
aspects, and the empirical aspects related to IB. From the theoretical and empirical
evidence reviewed, it emerges that a number of factors affect adoption of IB including:
demographic factors such as age, marital status, education, and income; and attitudes and
perceptions towards IB with respect to relative advantage, complexity, perceived cost and
perceived risk. This study attempted to investigate whether these particular factors affect
adoption of IB among DTB individual customers of Kampala District Branches.
30
CHAPTER THREE
METHODOLOGY
This chapter presents the methods and procedures used to conduct this study.
Specifically, the sections covered in this chapter include: research design, target
population, sample size and sampling procedure, measurement of variables, data
collection methods and procedures, data analysis techniques, and ethical considerations.
3.1
Research Design
A research design specifies the purpose of the study, the approach of the study and the
strategy used for the study (Saunders, Lewis & Thornhill, 2000). Given that there is
hardly any empirical study on adoption of Internet banking in Uganda, this study sought
to explore and identify the factors affecting adoption of IB in Uganda using Diamond
Trust Bank individual customers of Kampala District branches. This study adopted a
cross-sectional survey design involving the use of questionnaires to collect data on a wide
range of variables at a given point in time. A sample of DTB individual customers of
Kampala District Branches were selected to participate in the study and asked to provide
relevant information concerning issues related to adoption of IB as specified in the
questionnaire. This study also adopted a quantitative approach to analyze the relevant
data.
The quantitative research approach involved numerical representation and
manipulation of the data for the purpose of describing and explaining the phenomenon of
adoption of IB by DTB individual customers of Kampala District Branches.
31
3.2
Target Population
The target population of this study comprised all Diamond Trust Bank individual
customers of Kampala District branches including: Main Branch, Old Kampala Branch,
Kikubo Branch, Wandegeya Branch, Kitintale Branch, Ntinda Branch, Industrial Area
Branch, Ndeeba Branch, and Equatorial Hotel Branch. Kampala District was chosen for
this study because of two reasons. First, Diamond Trust Bank has most of its branches
located in Kampala District. Second, IB is currently promoted among Kampala District
Branches, and as such, most of its potential users are attached to Kampala District
Branches. According to the Centralized Department of Diamond Trust Bank, there are an
estimated 28,000 individual customers attached to Kampala District branches including
both users and non-users of Internet banking, and these constituted the target population
of this study.
3.3
Sample Size and Sampling Procedure
Given a population of 28,000 DTB individual customers of Kampala District branches, a
sample of 378 customers was chosen for the study. This sample size was determined
using Krejcie and Morgan (1970) table showing appropriate sample size for a given
population. A copy of the table can be located in appendix II for review.
The sample was selected using stratified random sampling method. Stratified random
sampling involves a process of stratification, followed by random selection of subjects
from each stratum.
In this study, DTB individual customers of Kampala District
branches were stratified into nine strata according to the nine Kampala District branches
32
outlined in section 3.4, and 42 customers were targeted to be randomly selected from
each branch, giving a total sample size of 378 customers.
3.4
Data Collection Methods and Procedures
Primary data was collected using a self-administered questionnaire which comprised of
both close-ended and open-ended questions. The choice of the questionnaire was based
on the facts that: it is a quick method to collect data, it is less time consuming, it is able to
cover the entire sample within the proposed time frame, and it offers greater assurance of
anonymity. The questionnaire consisted of three parts. Part I sought customer responses
on Internet banking-related issues. Part II sought information perceptions and attitudes of
respondents toward using Internet Banking services. All responses to items on
perceptions/attitudes towards IB were measured on a Likert scale ranging from
1=strongly disagree to 5=strongly agree. Part III obtained demographic information such
as age, marital status, education, income and occupation. Prior to the exercise of data
collection, permission was sought and secured from the relevant Diamond Trust Bank
authorities to conduct the study among its customers. In addition, two research assistants
were employed and trained in data collection techniques to enable them effectively seek
audience from respondents.
3.5
Validity and Reliability of the Instruments
Validity and reliability are critical features of effective research. Validity refers to the
extent to which questions in an instrument accurately measure the variables therein (Hair
et al., 2003), while reliability refers to the degree to which a set of variables are
consistent with what they are intended to measure (Amin, 2005). The validity of the
33
instrument was established through pilot-testing it using 20 customers of a different
bank—Stanbic Bank (Uganda) Limited, Lugogo Branch--which also offers IB, while the
reliability of the items in the instrument was established using Cronbach’s Alpha. The
Cronbach’s Alpha was computed using the following formula:
k    k2 

1  2  ………………………………………………………… (3.1)
k 1 
 
where

2
k
is the sum of the variance of k parts (usually items) of the test and  is the
standard deviation of the test. A minimum Cronbach’s Alpha value of 0.6 was used to
indicate reliability of the constructs.
Table 3.1: Results of reliability analysis
Variable
Number of items
Cronbach’s coefficient
Adoption of IB
1
0.73
Relative advantage
2
0.68
Complexity
2
0.81
Perceived risk
2
0.89
Perceived cost
2
0.76
The results of reliability analysis reflected a Cronbach’s Alpha coefficient ranging
between 0.68 and 0.89, implying that all the items used in the data collection instrument
were reliable and could be used to collect the relevant information.
3.6
Data Analysis Techniques
34
A number of data analysis techniques were used in this study including: frequencies,
percentages, Pearson’s chi-square, and logistic regression. These are briefly explained in
the following sub-sections.
3.6.1 Frequencies and Percentages
A frequency distribution shows the number of times a score or a response occurs. A
percentage is defined as a proportion of a subgroup to the total group or sample and it
ranges from 0% to 100%. These two concepts were useful in comparing groups that
differed in size.
3.6.2 Pearson’s Chi-square
Chi-square is a statistical technique which attempts to establish a relationship between
two variables both of which are categorical in nature. The technique compares the
proportion observed in each category with what would be expected under the assumption
of independence between the two variables. If the observed frequency greatly departs
from what is expected, then we reject the null hypothesis that the two variables are
independent of each other. We would then conclude that one variable is related to the
other. This is usually done by establishing the significance level of the test before hand.
3.6.3 Independent t-test
An independent t-test was used to determine whether there was a significant difference
between the means of two independent samples, adopters and non-adopters of IB. If the
observed means greatly depart from what is expected, then we reject the null hypothesis
that the two groups are not significantly different from each other. We would then
35
conclude that one group is significantly different from the other. This is usually done by
establishing the significance level of the test before hand.
3.6.4 Logistic Regression
The second specific objective of this study was to determine the factors influencing
adoption of IB among DTB individual customers of Kampala District Branches. A
logistic regression model was specified and estimated and used to predict adoption of IB
among DTB individual customers. Logistic regression is useful for situations where
there is need to predict the presence or absence of a characteristic or outcome based on
values of a set of predictor variables. Since the dependent variable, adoption of IB has
two possible outcomes, that is, a customer can adopt IB or not, the logistic regression
analysis technique was best suited for this study.
The model exhibits a binomial
distribution with a probability lying between 0 and 1. The probability that a customer
adopts IB was expressed as:
Pi 
1
…………………………………………………………………. (4.1)
1  e  Ai
Where:
Pi  Probability that a customer adopts IB
n
A  0    j X j
n 1
X j are independent variables in the model.
Equation 4.1 represents the cumulative logistic distribution function. Ai ranges from
negative infinity to positive infinity, Pi ranges between 0 and 1 and that Pi is n on-linearly
36
related Ai. If Pi, the probability of adopting IB, is given by equation 4.1, then (1 - Pi), the
probability of not adopting IB, is
1  Pi 
1
………………………………………………………………(4.2)
1  e Ai
Combining equations 4.1 and 4.2, we can write
Pi
1  e Ai

 e Ai ………………………………………………………. (4.3)
 Ai
1  Pi 1  e
Pi/(1-Pi) is simply the odds ratio in favor of adopting IB—the ratio of the probability of
adopting IB to the probability of not adopting IB. Taking the natural log of equation 4.3,
we obtain
 P
Li  In i
 1  Pi

  Ai  1   2 X i ………………………………………….. (4.4)

That is, L, the log of odds ratio, is not only linear in X, but also linear in parameters. L is
called the logit, and hence the name logit model. The interpretation of the logit model is
as follows: β2, the slope, measures the change in L for a unit change in Xi, that is, it tells
how the log-odds in favor of adopting IB changes as Xi change by a unit. In this study,
the estimated logit model was as follows:
 P 
In  i   1   2 AGE   3 INC   4 EDU   5 PRK   6 RAD   7 COM   8 PCT  
1  Pi 
37
Where;
AGE = age which is a continuous variable measured as the number of years
INC = income which is a continuous variable measured as average monthly
income of a customer
EDU = education which is a dummy variable measured as 1 if the customer has
university/tertiary education and 0 otherwise
PRK = perceived risk which is a dummy variable measured as 1 if IB is perceived
as risky and 0 otherwise
RAD = relative advantage which is a dummy variable measured as 1 if IB is
perceived as advantageous and 0 otherwise
COM = complexity which is a dummy variable measured as 1 if IB is perceived
as complex and 0 otherwise
PCT = perceived cost which is a dummy variable measured as 1 if IB is perceived
as costly and 0 otherwise
ε = error term
38
In this model, the coefficients of age, perceived risk, complexity, and perceived cost are
expected to be negative, while the coefficients of income, education, and relative
advantage are expected to be positive.
3.7
Ethical Considerations
The goal of ethics in research is to ensure that no one is harmed or suffers adverse
consequences from research activities (Cooper and Schindler, 2001). The following were
done to ensure that the respondents’ rights are protected:
i.
informed consent was sought and appropriate documentation was kept,
ii.
questionnaires were coded guarantee anonymity as one of the respondents was
named at any time during the research or in the subsequent study, and
iii.
respondents were selected for their willingness to participate without compulsion,
and no risks to the respondents could be identified at any stage during the research.
39
CHAPTER FOUR
PRESENTATION AND ANALYSIS OF FINDINGS
This chapter presents the findings of the study, followed by their interpretations. The
findings are presented in different sections including: response rate, differences between
adopters and non-adopters of IB with respect to their demographic factors and
perceptions towards IB.
4.1
Response Rate
A total of three hundred and seventy eight (378) questionnaires were distributed during
the survey. However, 289 questionnaires were returned, of which 274 questionnaires
were considered valid because they were fully filled. This represented a response rate of
72.4%. Of the 274 respondents, 20 percent were categorized as adopters of IB, while 80
percent were categorized as non-adopters of IB as indicated in table 4.1.
Table 4.1: Response rate about adoption of IB among respondents
Frequency
Percent
Adopters of IB
55
20
Non-adopters of IB
219
80
Total
274
100
4.2
Differences between Adopters and Non-adopters of IB with Respect to their
Demographic Factors and Perceptions towards IB
One of the specific objectives of this study was to find out the difference between
adopters and non-adopters of IB with respect to their demographic factors such as age,
education, occupation and income; and with respect to their perceptions and/or attitudes
40
towards IB such as relative advantage, complexity, perceived risk, and perceived cost of
IB. The findings in this regard are presented in two subsections; differences between
adopters and non-adopters of IB with respect to their demographic factors; and the other
with respect to their perceptions towards IB.
4.2.1 Differences between adopters and non-adopters of IB with respect to their
demographic factors
A Chi-square test was used to test whether there were significant differences between
adopters and non-adopters with respect to their demographic factors such as gender, age,
education, income, and occupation. The results regarding this objective are presented in
the following sub-sections.
4.2.1.1 Gender
The null hypothesis was that there is no difference between adopters and non-adopters of
IB with respect to gender, against the alternative that there is a significant difference
between adopters and non-adopters of IB with respect to gender. Table 4.2 presents a
summary of the findings regarding this variable.
The table shows that the biggest
percentage of the adopters (56 percent) and non-adopters (51 percent) were male. The
chi-square test value of 40.265 had a probability of 0.146 which is greater than 0.05.
This means that the null hypothesis that there is no significant difference between
adopters and non-adopters of IB with respect to gender was not rejected, and it was
concluded that there is no significant difference between adopters and non-adopters of IB
with respect to gender.
41
Table 4.2: Differences between adopters and non-adopters of IB with respect to
gender
Gender
Respondents
IB Adopters
Frequency Percent
IB Non-adopters
Frequency
Percent
Frequency
Percent
Male
143
52
31
56
112
51
Female
131
48
24
44
107
49
Total
274
100
55
100
219
100
Chi-square = 40.265; pr = 0.146
4.2.1.2 Age
The null hypothesis was that there is no significant difference between adopters and nonadopters of IB with respect to age, against the alternative that there is a significant
difference between adopters and non-adopters of IB with respect to age. Table 4.3
presents a summary of the findings regarding this variable.
Table 4.3: Differences between adopters and non-adopters of IB with respect to age
Age (years)
Respondents
IB Adopters
IB Non-adopters
Frequency Percent Frequency Percent Frequency
Percent
21 – 29
73
27
35
64
38
17
30 – 39
128
46
11
20
117
54
40 – 49
43
16
8
15
35
16
50 and above
30
11
1
1
29
13
Total
274
100
55
100
219
100
Chi-square = 74.908; pr = 0.000
42
Table 4.3 shows that a bigger percentage of the adopters (64 percent) were in the 21 - 29
age bracket while the biggest percentage of non-adopters (54 percent) were in the 30 – 39
age bracket. The chi-square test value of 74.908 had a probability of 0.000 which is less
than 0.05. This means that the null hypothesis that there is no significant difference
between adopters and non-adopters of IB with respect to age was rejected, and it was
concluded that there is a significant difference between adopters and non-adopters of IB
with respect to age. Generally, most adopters of IB among the sample of DTB individual
customers of Kampala District branches were younger compared to non-adopters.
Therefore, this finding is line with Morris and Vankatesh (2000), Vankatesh et al. (2003)
and Lee and Chaudrie (2002), who found that IB adopters tended to be generally
younger, that is, they were generally below 35 years.
4.2.1.3 Education
The null hypothesis was that there is no significant difference between adopters and nonadopters of IB with respect to education, against the alternative that there is a significant
difference between adopters and non-adopters of IB with respect to education. Table 4.4
presents a summary of the findings regarding this variable. The table shows that a bigger
percentage of the adopters (75 percent) compared to non-adopters (39 percent) had
attained university/tertiary level education. The chi-square test value of 45.881 had a
probability of 0.041 which is less than 0.05. This means that the null hypothesis that
there is no significant difference between adopters and non-adopters of IB with respect to
education was rejected, and it was concluded that there is a significant difference between
adopters and non-adopters of IB with respect to education.
43
Table 4.4: Differences between adopters and non-adopters of IB with respect to
education
Education
Respondents
IB Adopters
IB Non-adopters
Frequency
Percent
127
46
41
75
86
39
Secondary
81
30
11
20
70
32
Primary
54
20
1
2
53
25
Other
12
4
2
3
10
5
Total
274
100
55
100
219
100
University
Frequency Percent Frequency
Percent
/tertiary
Chi-square = 45.881; pr = 0.041
Generally, most adopters of IB among the sample of DTB individual customers of
Kampala District branches were more educated compared to non-adopters. This finding is
in line with the earlier studies by Morris and Vankatesh (2000), Anderson et al. (2002),
and Choudrie and Papazafeiropolou (2006), who found that adopters of IB were
relatively more educated.
4.2.1.4 Occupation status
The null hypothesis was that there is no significant difference between adopters and nonadopters of IB with respect to occupation status, against the alternative that there is a
significant difference between adopters and non-adopters of IB with respect to
occupation status. Table 4.5 presents a summary of the findings regarding this variable.
44
Table 4.5: Differences between adopters and non-adopters of IB with respect to
occupation status
Occupation
Respondents
IB Adopters
Frequency Percent Frequency
IB Non-adopters
Percent
Frequency
Percent
Employed
161
59
48
87
113
51
Unemployed
82
30
4
7
78
36
Pensioner
31
11
3
6
28
13
Total
274
100
55
100
219
100
Chi-square = 65.866; pr = 0.062
The table shows that a bigger percentage of the adopters (87 percent) and non-adopters
(51 percent) were employed. The chi-square test value of 65.866 had a probability of
0.062 which is significant at the 10% level of significance. This means that the null
hypothesis that there is no significant difference between adopters and non-adopters of IB
with respect to occupation status was rejected, and it was concluded that there is a
significant difference between adopters and non-adopters of IB with respect to
occupation status.
This finding is consistent with earlier findings by Choudrie and
Dwivedi (2005) who found that in addition to age, occupation was also a significant
driver the general pattern of IB ownership and usage.
4.2.1.5 Income
The null hypothesis was that there is no significant difference between adopters and nonadopters of IB with respect to income, against the alternative that there is a significant
difference between adopters and non-adopters of IB with respect to income. Table 4.6
45
presents a summary of the findings regarding this variable. The table shows that 44
percent of the adopters were in the income bracket 1 – 1.5 million shillings while 36
percent of the non-adopters were in the income bracket 0.5 – 1 million shillings.
Table 4.6: Differences between adopters and non-adopters of IB with respect to
mean monthly income
Monthly
income
Respondents
Frequency
IB Adopters
IB Non-adopters
Percent Frequency Percent Frequency
Percent
range
(million shs)
> 1.5 M
45
16
11
20
34
16
1 – 1.5 M
57
21
24
44
33
15
0.5 – 1 M
93
34
15
27
78
36
< 0.5 M
79
29
5
9
74
33
Total
274
100
55
100
219
100
Chi-square = 36.921; pr = 0.047
The chi-square test value of 36.921 had a probability of 0.047 which is less than 0.05.
This means that the null hypothesis that there is no significant difference between
adopters and non-adopters of IB with respect to income was rejected, and it was
concluded that there is a significant difference between adopters and non-adopters of IB
with respect to income. In general, most adopters of IB among the sample of DTB
individual customers of Kampala District branches had higher incomes compared to nonadopters. This finding was consistent with earlier studies by Choudrie and Dwivedi
(2005) and Carveth and Kretchmer (2002), who found that adopters of IB were generally
the high income group.
46
4.2.1.6 Marital status
The null hypothesis was that there is no significant difference between adopters and nonadopters of IB with respect to marital status, against the alternative that there is a
significant difference between adopters and non-adopters of IB with respect to marital
status. Table 4.7 presents a summary of the findings regarding this variable.
Table 4.7: Differences between adopters and non-adopters of IB with respect to
marital status
Marital
status
Respondents
IB Adopters
IB Non-adopters
Frequency
Percent
Married
145
53
37
67
108
50
Single
106
39
12
23
94
43
Divorced
19
7
3
5
16
7
Widowed
4
1
3
5
1
0.4
274
100
55
100
219
100
Total
Frequency Percent Frequency
Percent
Chi-square = 57.074; pr = 0.144
Table 4.7 shows that a bigger percentage of the adopters (67 percent) and non-adopters
(50 percent) were married. The chi-square test value of 57.074 had a probability of 0.144
which is greater than 0.05. This means that the null hypothesis that there is no significant
difference between adopters and non-adopters of IB with respect to marital status was not
rejected, and it was concluded that there is no significant difference between adopters and
non-adopters of IB with respect to marital status.
47
4.2.2 Differences between adopters and non-adopters of IB with respect to their
perceptions towards IB
An independent t-test was used to test whether there were significant differences between
the mean responses of adopters and non-adopters with respect to their perceptions
towards IB such as relative advantage, complexity, perceived risk and perceived cost.
Customer responses were rated on a likert scale with 1=strongly disagree, 2=disagree,
3=neutral, 4=agree and 5=strongly agree. A mean response score below 3 was used to
imply customers’ disagreement with the particular statements about perceptions towards
IB, while a mean response score above 3 was used to indicate customers’ agreement with
the statements. The results are presented in the following sub-sections.
4.2.2.1 Relative advantage
Respondents were asked to indicate their level of agreement with respect to two
statements measuring relative advantage: “IB saves time” and “IB eases communication
with the bank”.
In terms of IB saving time, the null hypothesis was that there is no significant difference
between the mean responses of adopters and non-adopters of IB with respect to
perception that IB saves time, against the alternative that there is a significant difference
between the mean responses of adopters and non-adopters of IB with respect to
perception that IB saves time. Table 4.8 presents a summary of the findings regarding
this statement. The table shows that the biggest percentage of adopters (95 percent) and
non-adopters (51 percent) agreed that IB saves time.
48
The mean response score of
adopters was 3.94 while that of non-adopters was 2.52. Since the mean response score
for adopters was greater than 3 and that of non-adopters was less than 3, it implies that,
on average, adopters agreed that IB saves time while non-adopters disagreed that IB
saves time.
Table 4.8: Differences between adopters and non-adopters of IB with respect to
perception that IB saves time
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
2
1
0
0
2
1
Disagree
74
27
3
6
71
32
Neither
35
13
0
0
35
16
Agree
94
34
25
45
69
32
Strongly agree
69
25
27
49
42
19
Total
274
100
55
100
219
100
disagree
Independent t-test = 8.302; pr = 0.000; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
The independent t-test value of 8.302 had a probability of 0.000 which is less than 0.05.
This means that the null hypothesis that there is no significant difference between the
mean responses of adopters and non-adopters of IB with respect to the perception that IB
saves time was rejected, and it was concluded that there is a significant difference
between the mean responses of adopters and non-adopters of IB with respect to the
perception that IB saves time. This finding is in line with earlier studies by Fox (2002)
49
and Karjaluoto et al. (2002), who found a positive association between adoption of IB
and its beneficial features such as the ability to save time.
In terms of IB easing communication with the bank, the null hypothesis was that there is
no significant difference between the mean responses of adopters and non-adopters of IB
with respect to perception that IB eases communication with the bank, against the
alternative that there is a significant difference between the mean responses of adopters
and non-adopters of IB with respect to perception that IB eases communication with the
bank. Table 4.9 presents a summary of the findings regarding this statement.
Table 4.9: Differences between adopters and non-adopters of IB with respect to
perception that IB eases communication with the bank
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
5
2
1
2
4
2
Disagree
84
31
3
5
81
37
Neither
69
25
0
0
69
31
Agree
77
28
25
46
52
24
Strongly agree
39
14
26
47
13
6
Total
274
100
55
100
219
100
disagree
Independent t-test = 9.827; pr = 0.001; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
50
Table 4.9 shows that the biggest percentage of adopters (94 percent) agreed that IB eases
communication with the bank, while 39 percent of non-adopters disagreed and 30 percent
agreed with the statement. The mean response score for adopters was 3.58 while that of
non-adopters was 2.30. Since the mean response score for adopters was greater than 3
and that of non-adopters was less than 3, it implies that, on average, adopters agreed that
IB eases communication with the bank while non-adopters disagreed that IB eases
communication with the bank. The independent t-test value of 9.827 had a probability of
0.001, which is less than 0.05. This means that the null hypothesis that there is no
significant difference between the mean responses of adopters and non-adopters of IB
with respect to the perception that IB eases communication with the bank was rejected,
and it was concluded that there is a significant difference between the mean responses of
adopters and non-adopters of IB with respect to the perception that IB eases
communication with the bank. This finding is consistent with earlier study by Leaderer
et al. (2000), who found that adoption of IB was higher among individuals who believed
that it improves communication with the bank.
4.2.2.2 Complexity
Respondents were asked to indicate their level of agreement with two statements
measuring perceived complexity of IB: “using IB is complex” and “using the IB process
is simple”.
In terms of the complexity of using IB, the null hypothesis was that there is no significant
difference between the mean responses of adopters and non-adopters of IB with respect
51
to perception that using IB is complex, against the alternative that there is a significant
difference between the mean responses of adopters and non-adopters of IB with respect
to perception that using IB is complex. Table 4.10 presents a summary of the findings
regarding this statement. The table shows that the biggest percentage of adopters (75
percent) disagreed that using IB is complex; while 44 percent of non-adopters agreed and
14 percent disagreed with the statement.
Table 4.10: Differences between adopters and non-adopters of IB with respect to
perception that using IB is complex
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
39
14
33
60
6
3
Disagree
33
12
8
15
25
11
Neither
100
36
9
16
91
42
Agree
92
34
3
5
90
41
Strongly agree
10
4
2
4
7
3
Total
274
100
55
100
219
100
disagree
Independent t-test = 14.454; pr = 0.000: scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
The mean response score of adopters was 2.68 while that of non-adopters was 3.58.
Since the mean response score for adopters was less than 3 and that of non-adopters was
greater than 3, it implies that, on average, adopters disagreed that using IB is complex
while non-adopters agreed that using IB is complex. The independent t-test value of
52
14.454 had a probability of 0.000, which is less than 0.05. This means that the null
hypothesis that there is no significant difference between the mean responses of adopters
and non-adopters of IB with respect to the perception that using IB is complex was
rejected, and it was concluded that there is a significant difference between the mean
responses of adopters and non-adopters of IB with respect to the perception that using IB
is complex. This finding also collaborates with earlier study findings by Davis (1989),
who found that complexity of an innovation determines its adoption by potential users.
In terms of the simplicity of using the IB process, the null hypothesis was that there is no
significant difference between the mean responses of adopters and non-adopters of IB
with respect to perception that using the IB process is simple, against the alternative that
there is a significant difference between the mean responses of adopters and non-adopters
of IB with respect to perception that using the IB process is simple. Table 4.11 presents a
summary of the findings regarding this statement. The table shows that the biggest
percentage of adopters (84 percent) agreed that using the IB process is simple while 47
percent of the non-adopter disagreed and only 5 percent of them agreed with the
statement. The mean response score of adopters was 3.66 while that of non-adopters was
2.77. Since the mean response score for adopters was greater than 3 and that of nonadopters was less than 3, it implies that, on average, adopters agreed that using the IB
process is simple while non-adopters disagreed that using the IB process is simple.
53
Table 4.11: Differences between adopters and non-adopters of IB with respect to
perception that using IB process is simple
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
17
6
0
0
17
7
Disagree
89
33
1
2
88
40
Neither
113
41
8
15
104
48
Agree
32
12
25
45
8
4
Strongly agree
23
8
21
38
2
1
Total
274
100
55
100
219
100
disagree
Independent t-test = 16.356; pr = 0.000; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
The independent t-test value of 16.356 had a probability of 0.000, which is less than 0.05.
This means that the null hypothesis that there is no significant difference between the
mean responses of adopters and non-adopters of IB with respect to the perception that
using the IB process is simple was rejected, and it was concluded that there is a
significant difference between the mean responses of adopters and non-adopters of IB
with respect to the perception that using the IB process is simple. The finding is also
consistent with an earlier finding by Korem (2001), who found that the adoption of IB is
likely to be increased when customers consider using the IB process to be easy.
54
4.2.2.3 Perceived risk
Respondents were asked to indicate their level of agreement with two statements
measuring perceived risk of IB: “IB is safe and secure” and “I’m not afraid of disclosing
my account details on the Internet”.
In terms of the safety and security of IB, the null hypothesis was that there is no
significant difference between the mean responses of adopters and non-adopters of IB
with respect to perception that IB is safe and secure, against the alternative that there is a
significant difference between the mean responses of adopters and non-adopters of IB
with respect to perception that IB is safe and secure. Table 4.12 presents a summary of
the findings regarding this statement.
Table 4.12: Differences between adopters and non-adopters of IB with respect to
perception that IB is safe and secure
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
18
7
0
0
18
8
Disagree
80
29
2
4
79
36
Neither
88
32
4
7
84
38
Agree
67
24
31
56
36
16
Strongly agree
21
8
18
33
3
2
Total
274
100
55
100
219
100
disagree
Independent t-test = 14.975; pr = 0.000; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
55
The table shows that the biggest percentage of adopters (89 percent) agreed that IB is safe
and secure while 44 percent of non-adopters disagreed and only 18 percent of them
agreed with the statement. The mean response score for adopters was 3.48 while that of
non-adopters was 2.45. Since the mean response score for adopters was greater than 3
and that of non-adopters was less than 3, it implies that, on average, adopters agreed that
IB is safe and secure while non-adopters disagreed that IB is safe and secure. The
independent t-test value of 14.975 had a probability of 0.000, which is less than 0.05.
This means that the null hypothesis that there is no significant difference between the
mean responses of adopters and non-adopters of IB with respect to the perception that IB
is safe and secure was rejected, and it was concluded that there is a significant difference
between the mean responses of adopters and non-adopters of IB with respect to the
perception that IB is safe and secure. The finding is consistent with earlier findings by
Hartman et al. (2000), who found that perceived security of IB services determines the
likelihood of using such services.
In terms of customers not being afraid of disclosing their account details on the Internet,
the null hypothesis was that there is no significant difference between the mean responses
of adopters and non-adopters of IB with respect to perception that they are not afraid of
disclosing their account details on the Internet, against the alternative that there is a
significant difference between the mean responses of adopters and non-adopters of IB
with respect to perception that they are not afraid of disclosing their account details on
the Internet. Table 4.13 presents a summary of the findings regarding this statement.
56
Table 4.13: Differences between adopters and non-adopters of IB with respect to
perception that they are not afraid of disclosing their account details on
the Internet
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
48
18
2
4
46
21
Disagree
103
38
4
7
99
45
Neither
34
12
1
2
33
15
Agree
43
15
25
45
18
8
Strongly agree
46
17
23
42
23
11
Total
274
100
55
100
219
100
disagree
Independent t-test = 4.216; pr = 0.000; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
The table shows that the biggest percentage of adopters (87 percent) agreed that they are
not afraid of disclosing their account details on the Internet while the biggest percentage
of non-adopters (66 percent) disagreed with the statement. The mean score response of
adopters was 3.68 while that of non-adopters was 2.89. Since the mean response score
for adopters was greater than 3 and that of non-adopters was less than 3, it implies that,
on average, adopters agreed that they are not afraid of disclosing their account details on
the Internet IB while non-adopters disagreed that they are not afraid of disclosing their
account details on the Internet. The independent t-test value of 4.216 had a probability of
0.000, which is less than 0.05. This means that the null hypothesis that there is no
57
significant difference between the mean responses of adopters and non-adopters of IB
with respect to the perception that they are not afraid of disclosing their account details
on the Internet was rejected, and it was concluded that there is a significant difference
between the mean responses of adopters and non-adopters of IB with respect to the
perception that they are no afraid of disclosing their account details on the Internet. The
finding is also consistent with earlier findings by Lain (2000) and Bestavros (2000), who
found that potential customers of IB are often reluctant to share personal information for
fear that their financial life will become an open book to the Internet universe.
4.2.2.4 Perceived cost
Respondents were asked to indicate their level of agreement with two statements
measuring perceived cost of IB: “IB is expensive” and “Internet installation is
expensive”.
In terms of IB being expensive, the null hypothesis was that there is no significant
difference between the mean responses of adopters and non-adopters of IB with respect
to perception that IB is expensive, against the alternative that there is a significant
difference between the mean responses of adopters and non-adopters of IB with respect
to perception that IB is expensive. Table 4.14 presents a summary of the findings
regarding this statement.
58
Table 4.14: Differences between adopters and non-adopters of IB with respect to
perception that IB is expensive
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
5
2
2
4
3
1
Disagree
35
12
32
58
3
1
Neither
20
8
1
2
19
9
Agree
145
53
16
29
129
59
Strongly agree
69
25
4
7
65
30
Total
274
100
55
100
219
100
disagree
Independent t-test = 15.462; pr = 0.000; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
The table shows that the biggest percentage of adopters (62 percent) disagreed with the
statement that IB is expensive while the biggest percentage of non-adopters (89 percent)
agreed with the statement. The mean response score of adopters was 2.89 while that of
non-adopters was 3.92. Since the mean response score for adopters was less than 3 and
that of non-adopters was greater than 3, it implies that, on average, adopters disagreed
that IB is expensive while non-adopters agreed that IB is expensive. The independent ttest value of 15.462 had a probability of 0.000, which is less than 0.05. This means that
the null hypothesis that there is no significant difference between the mean responses of
adopters and non-adopters of IB with respect to the perception that IB is expensive was
rejected, and it was concluded that there is a significant difference between the mean
59
responses of adopters and non-adopters of IB with respect to the perception that IB is
expensive. This finding is in line with earlier finding by Bradley and Stewart (2003),
who found that high subsequent costs incurred during use of IB were considered one of
the inhibitors of the diffusion of Internet Banking.
In terms of installation of Internet being expensive, the null hypothesis was that there is
no significant difference between the mean responses of adopters and non-adopters of IB
with respect to the perception that Internet installation is very expensive, against the
alternative that there is a significant difference between the mean responses of adopters
and non-adopters of IB with respect to perception that Internet installation is very
expensive. Table 4.15 presents a summary of the findings regarding this statement.
Table 4.15: Differences between adopters and non-adopters of IB with respect to
perception that Internet installation is very expensive
Option
Respondents
IB Adopters
Frequency Percent Frequency
Strongly
IB Non-adopters
Percent
Frequency
Percent
6
2
5
9
1
1
Disagree
35
13
30
54
5
2
Neither
42
15
2
4
40
18
Agree
137
50
7
12
125
57
Strongly agree
54
20
11
21
48
22
Total
274
100
55
100
219
100
disagree
Independent t-test = 11.765; pr = 0.000; scales: 1=strongly disagree, 2=disagree,
3=neutral, 4=agree, 5=strongly agree
60
Table 4.15 shows that the biggest percentage of adopters (63 percent) disagreed with the
statement that Internet installation is very expensive while the biggest percentage of nonadopters (79 percent) agreed with the statement. The mean score of adopters was 2.76
while that of non-adopters was 3.87. Since the mean response score for adopters was less
than 3 and that of non-adopters was greater than 3, it implies that, on average, adopters
disagreed that Internet installation is expensive while non-adopters agreed that Internet
installation is expensive. The independent t-test value of 11.765 had a probability of
0.000, which is less than 0.05. This means that the null hypothesis that there is no
significant difference between the mean responses of adopters and non-adopters of IB
with respect to the perception that Internet installation is very expensive was rejected, and
it was concluded that there is a significant difference between the mean responses of
adopters and non-adopters of IB with respect to the perception that Internet installation is
very expensive. This is in line with the findings Bradley and Stewart (2003), who found
that high initial set up costs of Internet were considered the greatest inhibitors of the
diffusion of Internet Banking.
4.3
Factors Influencing the Probability of Adopting Internet Banking
The second specific objective of this study was to determine the factors influencing the
probability of adopting IB among DTB individual customers of Kampala District
Branches. A logistic regression model was estimated and the results are summarized in
table 4.16.
Only variables that were found significant at the 5 percent level of
significance in the earlier analysis involving chi-square test and independent t-test were
included in the regression model, and these include: age, income, education, relative
advantage, complexity, perceived risk and perceived cost.
61
Table 4.16: A logistic regression of factors affecting adoption of IB
Variable
Beta
Std.
Wald
df
Sig
Error
Exp
Marginal
( )
Effect
Age
-0.209
0.083
6.355
1
0.012**
0.811
-0.096
Income
0.309
0.156
3.917
1
0.008***
1.362
0.049
Education
0.170
0.164
1.075
1
0.014**
1.185
0.388
Relative
0.169
0.137
1.523
1
0.097*
1.184
0.336
Complexity
-0.035
0.120
0.084
1
0.037**
0.966
-0.001
Perceived risk
-0.229
0.103
4.952
1
0.026**
0.795
-0.097
Perceived cost
-0.095
0.152
0.393
1
0.032**
0.909
-0.072
Constant
3.216
2.782
1.337
1
0.248
24.928
advantage
Pearson’s chi-square = 21.58**
Overall cases correctly predicted = 62.04%
Correctly predicted adopters = 56%
Correctly predicted non-adopters = 67%
N = 274
*significant at 10% level, **significant at 5%, ***significant at 1%
Table 4.16 shows that the estimated coefficient of age was negative and significant at the
5 percent level of significance, implying that the probability of adopting IB decreases
with increase in age. The marginal effect result shows that, holding the other factors
62
constant, the probability of adopting Internet banking increases by 9.6 percent when the
bank customer is of a lower age (below 40 years).
The estimated coefficient of income was positive and significant at 1 percent level of
significance, implying that the probability of adopting IB increases with increase in
income. The marginal effect result shows that, holding the other factors constant, the
probability of adopting Internet banking increases by 4.9 percent when the bank customer
is of a high income (above 1 million shillings).
The estimated coefficient of education was positive and significant at the 5 percent level
of significance, implying that the probability of adopting IB increases with increase in
education. The marginal effect result shows that, holding the other factors constant, the
probability of adopting Internet banking increases by 38.8 percent when the bank
customer has university/tertiary education.
The estimated coefficient of relative advantage was positive and significant at the 10
percent level of significance, implying that the probability of adopting IB increases with
increase in perceived relative advantage of IB. The marginal effect result shows that,
holding the other factors constant, the probability of adopting Internet banking increases
by 33.6 percent when the bank customer perceives Internet banking to be advantageous.
The estimated coefficient of complexity was negative and significant at the 5 percent
level of significance, implying that the probability of adopting IB decreases with increase
63
in perceived complexity of IB. The marginal effect result shows that the probability of
adopting Internet banking decreases by 1 percent when the bank customer perceives
Internet banking to be complex.
The estimated coefficient of perceived risk was negative and significant at the 5 percent
level of significance, implying that the probability of adopting IB decreases with increase
in perceived risk of IB. The marginal effect result shows that the probability of adopting
Internet banking decreases by 9.7 percent when the bank customer perceives Internet
banking to be risky.
The estimated coefficient of perceived cost was negative and significant at the 5 percent
level of significance, implying that the probability of adopting IB decreases with increase
in perceived cost of IB. The marginal effect result shows that the probability of adopting
Internet banking decreases by 7.2 percent when the bank customer perceives Internet
banking to be costly.
The table further shows that 62% of the total variation in the sample was explained by the
variables in the logistic model. Figures for correctly predicted adopters and non-adopters
were 56% and 67% respectively. The Chi-square in table 4.16 shows that the parameters
included in the model were significantly different from zero at the 5% level of
significance.
64
CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1
Summary of the Study
Adoption of Internet Banking among Diamond Trust Bank individual customers of
Kampala District Branches is low in spite of the bank’s efforts to avail the service. The
major objective of this study was to identify the factors affecting adoption of IB among
DTB individual customers of Kampala District Branches. The specific objectives
included: finding out the difference between adopters and non-adopters of IB with respect
to demographic factors such as age, income, education, marital status, occupation status,
and gender; and with respect to their perceptions towards IB such as relative advantage,
complexity, perceived risk, and perceived cost of IB, and determining the factors
influencing the probability of adopting IB.
Using a cross-sectional survey method, primary data was collected using selfadministered questionnaires from a random sample of 274 DTB individual customers
including both adopters and non-adopters of IB. Frequencies and percentages were used
to analyze the proportion of adopters and non-adopters in terms of the demographic
factors and the customer perceptions towards IB. The Chi-square test was used to
analyze the differences between adopters and non-adopters of IB with respect to the
demographic factors, while the independent t-test was used to analyze the differences
between the means of adopters and non-adopters of IB with respect to perceptions toward
IB. A logistic regression model was used to determine the factors influencing the
probability of adopting IB.
65
The results of the chi-square test showed that age, education, and income were significant
at 5 percent level of significance. Occupational status was significant at 10 percent level
of significance while gender and marital status were not significant. The results of the
independent t-test showed that relative advantage, complexity, perceived risk and
perceived cost were significant at 5 percent level of significance. The results of the logit
regression showed that income was significant at 1 percent level of significance. Age,
education, perceived risk, and perceived cost were significant at 5 percent level of
significance, while relative advantage was significant at 10 percent level of significance.
5.2
Conclusions from the Study
Based on the results of the chi-square test, the conclusion is that there was a significant
difference between adopters and non-adopters of IB with respect to four demographic
factors including age, income, education, and occupation; while there is no significant
difference between adopters and non-adopters of IB with respect to the demographic
factors of marital status and gender. Furthermore, based on the results of the independent
t-test, the conclusion is that there was a significant difference between adopters and nonadopters of IB with respect to their perceptions towards IB such relative advantage,
complexity, perceived risk, and perceived cost. Also, based on the logistic regression
results, the conclusion is that the factors of age, income, education, relative advantage,
complexity, perceived risk, and perceived cost significantly influenced the probability of
adopting IB, with income having the biggest relative influence.
66
5.3
Recommendations from the Study
Internet banking is important for the banking industry and its role is likely to continue
growing in future. Efforts need to be taken by the bank managers to improve adoption of
IB by its customers.
Drawing from the conclusions of this study the following
recommendations can be made.
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to age, with adopters generally being the young population.
This factor also significantly influenced the probability of adopting IB. Therefore, the
bank should put more emphasis on promoting IB among the young population by raising
awareness about benefits of IB to them. However, this should not be done at the expense
of other customers in relatively older age brackets because they too can adopt IB,
especially if they are made aware of its existence and potential benefits.
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to income, with adopters generally being the relatively high
income group. This factor also significantly influenced the probability of adopting IB.
Therefore, the bank should invest more time, effort and money on promoting IB services
among the relatively high income group who are more likely to use them. This is based
on the fact that the whole process of using Internet banking requires sacrificing some
amount of resources, which may not be affordable to low income people who are highly
constrained.
67
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to education, with adopters generally being relatively of
higher education. This factor also significantly influenced the probability of adopting IB.
The bank should therefore focus promoting IB on the relatively more educated
customers. This is because the use of IB requires skills for using complementary IB
gadgets such as computers and IB software.
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to the perceived relative advantage of IB. Adopters of IB
generally had the perceptions that IB saves time and that it improves communication with
their bank, while non-adopters generally did not believe that IB saves time and it
improves communication with their bank. Therefore the bank should increase awareness
about IB through sensitizing its customers, particularly the non-adopters, about the
various benefits of IB so as to encourage its adoption.
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to the perceived complexity of IB. Adopters of IB generally
had the perceptions that IB is not expensive and that using the IB process is simple, while
non-adopters generally had the perceptions that IB is expensive and that using the IB
process is not simple. This factor also significantly influenced the probability of adopting
IB. The bank should therefore aim to make its IB services as simple and easy to use as
possible so that customers do not perceive them as being complicated or difficult to use.
Websites should be user-friendly with clear instructions for users. To further alleviate
68
perceptions of complexity associated with IB services, demonstrations via video
presentations could be made at the bank’s branches to showcase the user-friendliness of
such services.
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to the perceived risk of IB. Adopters of IB generally had the
perceptions that IB is safe and secure and that they were not afraid of disclosing their
account details over the Internet, while non-adopters generally had the perceptions that
IB is not safe and secure and that they were afraid of disclosing their account details on
the Internet. This factor also significantly influenced the probability of adopting IB. The
bank should therefore install security features such as encryption devices, which
safeguard sensitive customer information. The bank also needs to look into equipping
their systems with more powerful and advanced computer technologies.
The study showed that there was a significant difference between adopters and nonadopters of IB with respect to the perceived cost of IB. Adopters of IB generally had the
perceptions that IB is not expensive and that Internet installation is not expensive, while
non-adopters generally had the perceptions that IB is expensive and that installing it is
expensive. This factor also significantly influenced the probability of adopting IB.
Therefore, the bank should endeavor to minimize the costs associated with IB. It should
not charge fees for similar banking services that are free-of-charge in the physical world
(for example, at bank branches and/or ATMs). However, certain transactions, such as
cheque cancellations and wire transfers, would still require administrative charges. Also,
69
the bank could consider introducing price bands where customers who process large
volumes of transactions online, receive a discount on transaction charges.
5.4
Areas for Future Research
The following are areas that could be considered for future research:
i.
The study on the adoption of IB services among DTB individual customers of
Kampala District branches can be extended to corporate customers, and
comparison can then be made between individual customers and corporate
customers in terms of the factors influencing their adoption decisions, the criteria
for selecting an internet banking service, and the types of products and services
perceived to be useful.
ii.
The study was limited to DTB customers; however, the number of respondents
interviewed could be increased in a study of the whole banking sector in Uganda
in order to extrapolate the conclusions to incorporate the general population.
iii.
When the number of customers using IB in DTB and the banking sector overall
reaches a critical mass, future studies may examine the factors that contributed to
this increase in usage.
70
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79
APPENDICES
Appendix I: Questionnaire
Dear respondent,
This questionnaire has been designed to study the factors influencing adoption of Internet
banking among Diamond Trust Bank individual customers of Kampala District branches
as a requirement for the partial fulfillment for the award of a Master’s of Arts degree in
Economics of Makerere University. The information you provide will help us understand
the reasons why adoption of Internet banking is low. I request you to respond to the
questions frankly and honestly.
Thank you very much for your time and cooperation.
1. INTERNET BANKING
1.1 Have you ever used or are you currently using Internet banking?
Yes
No
1.2 If yes, where did you learn about Internet banking?
Television/Radio
Bank leaflets/Advertisements
Newspapers/Magazines
Words-of-mouth
Others, please specify__________________________________________
1.3 How often do you use Internet banking?
Daily
Weekly
Monthly
Quarterly
80
1.4 If you have not used Internet banking, state the reasons why:___________________
_______________________________________________________________________
_______________________________________________________________________
2. PERCEPTIONS TOWARDS INTERNET BANKING
Please read each statement and put a tick in a box which best represents your level of
agreement or disagreement with a particular statement.
SDA DA N A SA
Relative advantage
Internet banking saves my time
Internet banking improves my communication with the bank
Complexity
Using Internet banking is complex
Using the Internet banking process is simple
Perceived cost
Internet banking services are expensive
Internet installation is expensive
Perceived risk
Internet banking is safe and secure
I am not afraid of disclosing my account details on the Internet
3. DEMOGRAPHIC FACTORS
Please put a tick in a box that best represents your opinion on the following demographic
factor
3.1 Your gender:
Male
Female
3.2 What is your age? _________________
81
3.3 Your marital status:
Married
Single
Divorced
Widowed
3.4 Your employment status:
Employed
Unemployed
Pensioner
3.5 Your education level:
University/tertiary
Secondary
Primary
Other, please specify_____________________________
3.6 What is your average monthly income? ______________________
Thank you for your time and cooperation.
82
Appendix II: Table for appropriate sample size for a given population
N
S
N
S
N
10
10
220
140
1200
15
14
230
144
1300
20
19
240
148
1400
25
24
250
152
1500
30
28
260
155
1600
35
32
270
159
1700
40
36
280
162
1800
45
40
290
165
1900
50
44
300
169
2000
55
48
320
175
2200
60
52
340
181
2400
65
56
360
186
2600
70
59
380
191
2800
75
63
400
196
3000
80
66
420
201
3500
85
70
440
205
4000
90
73
460
210
4500
95
76
480
214
5000
100
80
500
217
6000
110
86
550
226
7000
120
92
600
234
8000
130
97
650
242
9000
140
103
700
248
10000
150
108
750
254
15000
160
113
800
260
20000
170
118
850
265
30000
180
123
900
269
40000
190
127
950
274
50000
200
132
1000
278
75000
210
136
1100
285
1000000
Source: Krejcie and Morgan (1970); N = Population and S = Sample
83
S
291
297
302
306
310
313
317
320
322
327
331
335
338
341
346
351
354
357
361
364
367
368
370
375
377
379
380
381
382
384