STATISTICAL ANALYSIS OF FACTORS INFLUENCING THE
ADOPTION OF INTERNET BANKING IN GHANA: THE CUSTOMER
PERSPECTIVE
BY
UZERU ALIDU
(10550433)
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA,
LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR
THE AWARD OF THE MPHIL STATISTICS DEGREE
JULY, 2017
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DECLARATION
Candidate’s Declaration
I, Uzeru Alidu hereby declare that apart from references to other people’s publications,
which have been duly acknowledged, this thesis is a result of my independent ideas,
thought, deliberations and has not been submitted for the award of any degree at this
institution and other universities elsewhere.
SIGNATURE: …………………………………… DATE: ………………
UZERU ALIDU
(10550433)
Supervisors’ Declaration
We hereby certify that this thesis was prepared from the candidate’s own work and
supervised in accordance with guidelines on supervision of thesis laid down by the
University of Ghana.
SIGNATURE: …………………………………… DATE: ………………
DR. ANANI LOTSI
(PRINCIPAL SUPERVISOR)
SIGNATURE: …………………………………… DATE: ………………
DR. E. N. N. NORTEY
(CO-SUPERVISOR)
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ABSTRACT
“Internet banking” is a product offered by commercial banks to enable clients carry out
banking transaction at their convenience and at any time. It enables banks to reduce
operational cost by reducing the cost of stationary and also number of staff. However, clients
of commercial banks in Ghana still prefer conducting transaction at the branches of the banks
rather than signing on to internet banking. This is evident in the queues by clients in
commercial banks in order to carry out transaction. This study sought to find the factors
influencing the adoption of “internet banking”. The study employed “Factor Anlysis” using
the “Principal Axis” method of factoring and direct oblimin rotation. A chi-square test of
association was performed to determine whether a relationship existed between the
demographic factors and adoption of internet banking. Also, the binary logistic regression
was used to determine the chance of customer adopting “internet banking” given a factor. A
modification to the F statistic, the F ratio with 1000 permutation was used to compare the
adoption of “internet banking” local and foreign owned banks. The study found that
Trustworthiness, Usefulness, Risk, Accessibility, Ease of use, Assurance in the banks
website, Service Visibility, Awareness of benefits of internet banking and Trust in Internet
banking influenced the adoption of “internet banking”. Also, banks with “easy to use”
platforms were 3.546 more likely to influence clients to adopt “internet banking” controlling
for all other factors. There was no difference between the adoption of internet banking for
locally owned and foreign owned banks
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ACKNOWLEDGEMENT
My first acknowledgment goes to Allah for His guidance and sustenance. My Special thanks
go to Prosper Kojo Amewu, Head, Compliance Department of Bank of Africa and Laureen
Yirerong for their continuous support during the course of the programme. I would also like
to acknowledge my supervisors Dr. Anani Lotsi and Dr. E. N N. Nortey for their timeless
effort they dedicated in looking through this work. My special appreciation also goes to Dr.
K. Doku-Amponsah, the Head of Statistics Department and Dr. Louis Aseidu. Finally, a
special mention of all colleague MPHIL Statistics students of our year group especially
Emmanuel Aidoo, Joseph Amachie and Isaac Kojo Appiah.
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TABLE OF CONTENTS
DECLARATION ...................................................................................................................... i
ABSTRACT ............................................................................................................................ ii
ACKNOWLEDGEMENT ..................................................................................................... iii
TABLE OF CONTENTS ....................................................................................................... iv
LIST OF FIGURES ................................................................................................................ vi
LIST OF TABLES ................................................................................................................ vii
CHAPTER ONE ...................................................................................................................... 1
INTRODUCTION ................................................................................................................... 1
1.1 Introduction ................................................................................................................... 1
1.2 Statement of Problem .................................................................................................... 3
1.3 Objectives of the Study ................................................................................................. 6
1.4 Significance of the Study ............................................................................................... 6
1.5 Research Methodology .................................................................................................. 7
1.6 Organization of the Thesis ............................................................................................. 8
CHAPTER TWO ..................................................................................................................... 9
LITERATURE REVIEW ........................................................................................................ 9
2.1 Introduction ................................................................................................................... 9
2.2 Empirical Literature Review ......................................................................................... 9
CHAPTER THREE ............................................................................................................... 28
RESEARCH METHODOLOGY .......................................................................................... 28
3.1 Introduction ................................................................................................................. 28
3.2 Research Design .......................................................................................................... 28
3.3 Population .................................................................................................................... 29
3.4 Sampling Technique and Sample Size ........................................................................ 30
3.5 Estimation of Sample Size ........................................................................................... 32
3.6 Data Collection ............................................................................................................ 35
3.6.1 Source of Data ...................................................................................................... 35
3.6.2 Validity and Reliability of Research Instrument .................................................. 36
3.7 Method of Data Presentation and Analysis ................................................................. 36
3.7.1 Factor Analysis ..................................................................................................... 37
3.7.2 Components of Variance in Factor Analysis ........................................................ 38
3.7.3 Factor Extraction .................................................................................................. 42
3.8 The Logistic Regression Model ................................................................................... 53
3.8.1 Parameter estimation ............................................................................................ 55
3.9 Non-Parametric Multivariate Analysis of Variance .................................................... 57
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CHAPTER FOUR ................................................................................................................. 60
ANALYSIS AND RESULTS ............................................................................................... 60
4.1 Introduction ................................................................................................................. 60
4.1.1 Characteristics of sample ...................................................................................... 61
4.2 Factors Influencing the Adoption of Internet Banking ................................................ 65
4.2.1 Correlation Matrix ................................................................................................ 66
4.2.2 Number of Factors to Retain ................................................................................ 67
4.2.3 Grouping of Components into Factors ................................................................. 69
4.2.4 Reliability Analysis of the Grouped Factors ........................................................ 69
4.3 Chance of a Customer Adopting Internet Banking Given a Particular Factor ............ 72
4.4 Internet Banking Adoption of Locally and Foreign Owned Banks ............................. 76
CHAPTER FIVE ................................................................................................................... 79
CONCLUSION AND RECOMMENDATION .................................................................... 79
5.1 Introduction ................................................................................................................. 79
5.2 Summary ...................................................................................................................... 79
5.3 Conclusion ................................................................................................................... 81
5.4 Recommendations ....................................................................................................... 81
5.5 Limitation of the study ................................................................................................ 82
REFERENCES ...................................................................................................................... 84
APPENDICES ....................................................................................................................... 88
Appendix A: Questionnaire ............................................................................................... 88
Apendix B: Total Variance Explained .............................................................................. 93
Appendix C: Correlation Matrix ....................................................................................... 95
Appendix D List of Banks ............................................................................................... 106
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LIST OF FIGURES
Figure 3.1 Sampling Procedure ............................................................................................. 30
Figure 4.1: Scree Plot ............................................................................................................ 68
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LIST OF TABLES
Table 4.1: Gender Distribution .............................................................................................. 61
Table 4.2: Age Distribution ................................................................................................... 61
Table 4.3: Educational Level ................................................................................................. 62
Table 4.4: Occupation ........................................................................................................... 62
Table 4.5: Usage of Internet Banking .................................................................................... 63
Table 4.9: KMO and Bartlett's Test ....................................................................................... 66
Table 4.11 Reliability Statistics ............................................................................................. 69
Table 4.12: Table Total Variance Explained ......................................................................... 70
Table 4.13: Test of Association ............................................................................................. 71
Table 4.14: Hosmer and Lemeshow Test .............................................................................. 73
Table 4.15: Variables in the Equation ................................................................................... 74
Table 4.16: Multivariate Test for Normality ......................................................................... 77
Table 4.17: Test of average adoption for Local and Foreign Owned Banks ......................... 77
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CHAPTER ONE
INTRODUCTION
1.1 Introduction
Internet Banking according to (Kimet, 2011) is a term broadly used to describe the various
banking products and services that make use of digital, internet and mobile technology.
Internet banking started as telephone banking first time in 1980s and its usage ascended when
internet was provided at homes of individual. In some parts of Europe and United States of
America, banks and finance companies begun to work on the idea of the “home banking”.
Computer and internet were not readily available as at then, hence, it was focused to the
telephone banking. The application was first started in United States of America in 1996 and
then later, the renowned banks such as Wells Fargo and Citibank begun to provide this facility
to their client in 2001.
With the aim of refining the value of service being provided to the client of commercial banks
in Ghana, Banking has undergone a lot of changes in service delivery. In the past, Banks were
providing services to their clients through the manual system, which results in long queues in
transacting business in the banks. Additionally, companies and individuals in Ghana face the
problem of clients not accepting cheques as a payment method. Hinson (2005) attribute this
to the time and the problems involved in receiving and lodging cheques into the bank accounts
of companies.
Additionally, Hinson (2005)contends that internet enables communications on co-operative
basis with one or several people, unconstrained by space or time, in a multimedia atmosphere
with sound, image, text transmission, and at fairly low and declining costs. Commercials
Banks with Internet banking service now aid their clients to use Internet for their banking
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needs. Clients are able to access their account in order to see their account balances, transfer
funds, pay bills, manage their accounts as well as perform a lot of functions including
accessing and printing of statements of accounts.
The banking industry has advanced from old-fashioned “brick and mortar” to “internet
banking”. “Internet banking” makes use of the Internet as a remote distribution mode for
carrying out banking services according to Clemons and Hitt (2000). This includes Checking
Balances, verifying and viewing transactions on account, printing statements, monitoring
uncredited and unpaid cheques. Yang and Peterson (2004) in their studies also found that
banks are able to save 107 times of the total of its cost when “internet banking” services were
engaged. Pennathur (2001) found that “Internet banking” increase operational, legal,
reputation risks, and increase competition therefore it promotes enhanced services amongst
contending banks. “Internet banking” also allow clients to interrelate more than before with
the front office of the bank and, at the same time allow banks to consolidate back office
operations and grow their competence. It is the day and night availability that makes it so
convenient for the bank clients.
Govender and Wu (2013), states that, internet banking enables better management of funds.
58.8% of respondents strongly agreed that internet banking helped them better manage their
funds. He further states that, internet banking saves time since 100% of users agreed that
internet banking saves time. His results indicated that internet banking makes communication
between clients easier.
Like other third world countries, Ghana is not yet at par with Western countries and hence it
is not expected to have identical levels of banking services. The need to elevate services to
an internationally known degree has prompted some Ghanaian banks to offer “Internet
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banking” services. The face of banking in Ghana is fast improving and focus is now on new
supply channels. This will go a long way to advance customer service and give way to 24
hours a day access to banking services. Customers are supposed to have access to their
accounts and carry out transactions from the luxury of their homes and offices which hitherto
would have been done in the banking halls once they have internet banking access. Using a
Personal computer with an internet connection, customers can transact business on their
traditional accounts such as, settlement of utility bills, cash withdrawals, accessing and
printing of statements, Transfers from one account to the other, request for cheque books etc.
“Internet banking” is a service where access to account information and any transactions is
allowed at any time from any computer with an Internet connection. The number of people
using Internet in Ukraine has been increasing by 20-30% yearly over the last 5 years. Hence,
online banking becomes more normal for Ukrainian clients, and banks are encouraged to
propose a new and convenient way to use their services.
Customers are happy to decrease transaction costs, while banks may charge the same or even
more fees. Moreover, the details of clients transactions can be readily gotten, which makes
banking institutions to assess customers’ needs. Online services are likely to be the future of
the banking system, and the number of “Internet banking” users is likely to continue to
increase. If their behavior differs from the standard customers' one, for banks it would be
particularly interesting to know how. Thus, research results would thus also be relevant for
business.
1.2 Statement of Problem
In the quest to make banking more convenient and also eliminate long wearisome queues in
banking halls, “internet banking” is one of the recent developments that have added a new
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measurement to banking transactions. Humphrey, Mansell, Paré, and Schmitz (2004) contend
that the internet is becoming an important business tool. Clients and companies are increasing
employing the use of internet and their business and work in other to simplify task and gain
readily gain access to information.
There are however factors that make clients not get the benefit of adopting “internet banking”.
These factors are resource constraints, ability to utilize the technology and management
attitude. Lack of adequate information technology infrastructure is a critical barrier in
supporting the continual growth of online commerce, according to Chircu and Kauffman
(2000).
Small companies in Ghana can have better plans in place to make use of the internet to
increase productivity and reduce cost Hinson (2005). Again, lack of trust in the web restricts
the opportunity in technology. Min and Galle (1999), (Lee & Turban, 2001) also notes that
clients do not trust internet technology for three reasons. These are Lack of trust in service
provider, reliability in the internet service and security of the system. Concern about security
is one common factor related to unwillingness to use Internet channel for commerce. Security
breaches can lead to numerous problems such as destruction of operating systems, or
disruption of information access Min and Galle (1999). The use of the Internet in delivering
banking services is pervasive in western developed contexts. Internet banking is however just
beginning to blossom in West Africa, and Ghana in particular.
Nonetheless, there are some factors which do not encourage banking through the internet and
causes many customers to be physically present in the bank premises instead of taking
advantage of internet banking. Low broadband internet penetration, customers' preference for
traditional branches, fear of online threats/scams, lack of basic knowledge of computers and
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the high cost of internet accessibility are some of the problems that may threaten the growth
of internet banking in Ghana. Moreover, there are certain factors which fuel this new
additional way of doing business.
Viola and Jones (2004) in his study found that banks save 107 times of total cost when internet
banking is employed. Banks are able reduce operational cost such us stationary, number of
staff, electricity and water bills and so on. Internet banking users do not need to make their
way to banking halls, fill deposit slips and join queue in other to carry out a transaction.
Additionally, Drigă and Isac (2014) indicates that internet banking bring sustainable
competitive advantage. His results, based on world retail banking report 2014 for eight
European countries, US and Japan, indicates that the internet banking user paid for
transactions 34% less than an active branch user. The active branch user pays higher tariffs
than an internet user. He also spends time making his way to the banking hall and staying in
queues to be served. The internet banking user also has access to his account and can carry
out transactions at his own convenient anytime and anywhere provided he has access to the
internet.
Commercial Banks in Ghana introduce internet banking in other to enable its clients carry out
banking transaction at their own convenience without making their way to the branch and
also at any time. Additional banks are able to reduce operational cost of business once clients
adopt internet banking because of the cost of printing deposit slips other stationary for clients
who carry out branch banking. Also, the number of staff employed to serve clients in tradition
branch banking will be greatly reduced if clients roll on to internet banking. Additionally,
with clients adopting internet banking, the rate of the country depending on cash transaction
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will be reduced and help the countries drive for cash light country thereby making payment
and settlement easier.
However, the internet banking has seen low patronage by clients of commercial banks in
Ghana. Clients still prefer to go to the branches of commercial banks to carry out transactions.
This study therefore seeks to examine the key factors that affect the adoption of internet
banking in Ghana, using some selected international and local owned banks. The study will
examine the factors that impact the adoption of internet banking in Ghana using Factor
Analysis and the logistic regression. The study will also compare the study will also compare
the results of locally owned and foreign Banks.
1.3 Objectives of the Study
The main objective of the study is to examine the key factors which affect adoption of internet
banking in Ghana, with a sample of local owned banks and foreign owned banks as case
study. The following specific objectives will be accomplished.
1. To examine the factors that significantly contributes to customer’s adoption of internet
banking.
2. To determine the chance of a customer’s adoption of internet banking given some
significant factors
3. To compare internet banking adoption between international banks and local banks
owned in Ghana
1.4 Significance of the Study
Internet banking provides clients of commercial banks the opportunity to carry out banking
transactions at their convenience. The study seeks to determine the factors that influence the
choice of internet banking facility by clients of banks in Ghana. This will assist banks in
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Ghana improve on their services thereby enabling clients to enrol on the internet banking
platform.
Additionally, increased enrolment of clients of commercial banks on the internet banking
platforms will help reduce the rate at which transactions in Ghana are cash dependant. The
internet banking model enables bills and payments to be effected easily without the use of
cash. Utility bills can be paid using the internet banking facility without having to withdraw
money from your account and making payments at the offices of the utility service. Therefore
the study will enable the government identify the factors that determines the choice of internet
banking so as to formulate policies that will geared towards making Ghana a cashless society.
1.5 Research Methodology
This research will begin with a detailed literature review. Then, a qualitative approach will
be employed in order to design a questionnaire, after which a quantitative approach will be
used in order to collect data and test theories. Semi-structured interviews help to “build a
complex, holistic picture, formed with words, reporting detailed views of informants and
conducted in a natural setting” Creswell et al. (1994). The theoretical review and qualitative
study provide the foundations for measurement items and constructs included in the
integrated models. The quantitative research helps to test the theories “composed of variables,
measured with numbers, and analysed with statistical procedures” Creswell et al. (1994). This
research employs a predominantly quantitative approach in order to test the hypotheses and
to help us understand the related phenomena on internet banking adoption in Ghana by the
examining the relationships between various factors and the adoption of internet in Ghana, as
well as comparing the differences between adopters and non-adopters.
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1.6 Organization of the Thesis
Chapter one, which is the introduction, entails the background of the study, the problem
statement, the objectives, the significance of the study, the research methodology and the
organization of the study.
In Chapter two, that is the literature review; related previous researches and literature on
internet banking adoption are obtained and reviewed to enrich this study. Chapter three has
the heading methodology, which deals with the sampling technique and sample size, data
collection, data management, reliability and validity of the instrument, and also treated factor
analysis, Binary logistic regression analysis and a modification of the F statistic, F-ratio test
with permutation for comparison of the location parameter (median) in the non-parametric
analysis of variances.
Further, Chapter four deals with the presentation of results emanating from the analysis of the
data using charts, frequency tables, factor analysis, Binary logistic models and the F-ratio
test. It also contains the discussion of the study.
Finally, Chapter five comprises a summary of the findings, conclusions and some suggested
recommendations.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
The chapter seeks to review existing literature that discusses the main conceptual pillars
relevant to the study. It identifies what research has been conducted to date, and where the
current study stands in relation to that research. The main domain of the literature studied
relates to behavioural intention toward Internet Banking adoption. Furthermore, five
theoretical models related to behavioural intention toward Internet Banking adoption will be
discussed and then combined, in order to form the theoretical foundation of this study.
The first part of this chapter examines relevant theories, and reports on the empirical
applications of these theories. The second section goes on to review the general literature on
Internet Banking, in order to identify gaps in the research.
2.2 Empirical Literature Review
In the area of adoption of new technologies, several researches have been carried out and
more specifically on adoption of internet banking. These studies can generally be classified
into three groups.
Conditions that are necessary for bank customers to start using Internet banking is the first
group investigated. Based on surveys, customers tend not to use the service according to Al-
Al-Rfou, Perozzi, and Skiena (2013) even if the service is provided. The complex nature of
its usage, privacy being low and poor quality of Internet connection are the proposed reasons
for clients not subscribing to Internet Banking in Jordan according to Al-Rfou et al. (2013).
His study considered clients of commercial banks in Jordan. Questionnaires were
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administered to clients and data gathered were analysed using Simple Regression Analysis.
The study revealed that the usage of internet banking in Jordan was weak. It also was
discovered that there is a strong association amongst the use of internet banking, security and
privacy, ease of use, and quality of internet connection.
There are many gaps identified in the study. Firstly, the study did not use the appropriate
model (Simple Regression Model). This is because, the dependent variable (Use of internet
banking) was measured in likert scale hence it was not a continuous variable and does not
meet the assumptions in other simple linear regression.
Secondly, the regression analysis was done using only one independent variable at a time.
For example, regression was done on ‘internet banking usage’ and ‘ease of usage’ without
reference to the combined effect of all the other independent variables such us, ‘security and
privacy’, ‘quality of internet connections’ etc.
Also, the research did not consider the effect of other important variables like, the ‘Risk’,
‘usefulness’ and ‘the service reliability’ of internet banking adoption.
To breach these gaps, the appropriate model which is Logistic regression coupled with Factor
Analysis were used. Factor Analysis was used to obtain the significant factor that contributes
to the adopting internet banking in Ghana and since the dependent variable is categorical with
several levels, Logistic Regression was used to find the chance of a customer adopting
internet banking given some significant factor.
Al-Rfou et al. (2013) also confirmed this evidence in his study. Awareness was added as an
important factor. Questionnaires were distributed among users of internet banking from
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different bank. The study showed that the demographic characteristics of customers also
affect the usage of internet banking as well.
Karacaoglu, Bayrakdaroglu, and San (2012) in his study on developing internet banking as
an innovative distribution channel in Turkey example also notes that consumers making use
of internet banking products have increased over the time. This has overcome the clash
between banking industry security, web infrastructure and diversifying banking products.
Hence the tendency of the use of web banking was strengthened and effectively, sped-up
development of internet banking.
Karacaoglu et al. (2012) also concludes that to heighten the use of internet banking in Turkey,
initially, you need to investigate attitude users in detail in other know them better. Due to
increasing demand for internet banking, banks do not open new branches and operational cost
staff is reduced. It therefore reduces cost generally and makes services fruitful and effective.
Usage of internet banking in the coming years is anticipated to grow and make banks to
provide creative and innovative solutions.
When compared with other countries in the world, Ali contends that Turkey is not at the
required level in the use of internet banking. He noted that banks should consider it more
important to take some precautions when factoring in this position, so as to spread electronic
services. Security problem must be solved considering each technological system. It is
therefore important that banks provide fast and effective communication to customers in the
instance that there are uncommon movements on the accounts of customers. Ali also stated
that, in Turkey there is an increasing trend in internet usage although it has less internet usage
ratios as compared to developed countries.
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His research used descriptive statistics using mean and frequency approach. He also used
trend analysis in analysing the active number of internet banking users. His study
concentrated on the increase in internet banking adoption in Turkey rather than what
accounted for the rise of internet banking adoption in Turkey. The perspective of the customer
was not examined.
Koskosas (2008), emphasise the importance of trust and stringent security control for efficient
Internet banking. His study examined the advantages and disadvantages of internet banking
adoption. The study concludes that has increased the completion among banks and also
provides convenience and save time. He also added that people have the difficulty of fearing
possibility of identity theft and the security of online transactions. Some banks take the risk
of identity theft more seriously than others even though it is of significant concern. In other
for clients to ensure that their expectations are met, they have to investigate the banks security
policies and protection before enrolling on the internet banking platform. A customer may
adopt internet banking of a bank because of the trust in that bank.
His study only looked at the benefits and the disadvantages relating to internet banking
adoption as opposed to this study which looks at the elements affecting internet banking
adoption using Factor Analysis and Logistic regression.
Also, Yee-Loong Chong, Ooi, Lin, and Tan (2010) in their research on growing the Chinese
Internet Banking Sector noted that trust is a key predictor of positive word of mouth and
generates more explanatory power than perceived risk, which reduces word of mouth of
mouth intention. Perceived justice is mediated by trust and leads to positive word of mouth
recommendation. Interestingly, perceived industry reputation has a stronger positive
influence on perceived justice than on trust; a positive industry reputation also reduces
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Chinese consumers’ risk perception. They also noted that attention must be given to resolve
consumers’ low perceived justice perception, low trust in the bank and its Internet banking
system in order to minimize unpleasant experience and negative word of mouth.
Their study developed an online survey using quantitative design. Questionnaire was
administered online to respondents in other to get data for the study. They also adopted
convenient sampling technique in carrying out the study. Their study also adopted Factor
Analysis in ascertaining factors that accounted for the growth of internet banking adoption in
china.
This study will administer questionnaire to the various customers of the banks. Commercial
banks in Ghana have been grouped into two categories; locally and foreign owned banks. In
contrast to their study, multistage sampling will be employed to get sample for the study.
Also, in addition to the Factor Analysis, Logistic Regression will be employed to determine
the chance of a customer adopting internet banking given a significant factor.
Also, Thulani and Tofara in thier study of internet adoption and use in Zimbabwe, noted that
even though there is a good adoption rate of internet banking in Zimbabwe, the usage of the
service has remained relatively low since a lot of consumers are not using the facility many
in the country. The implication he noted was that, banks in Zimbabwe needed to enhance
their efforts at marketing by making customers aware and getting them interested in internet
banking. The major use of internet banking in Zimbabwe, Tofara observed, was for balance
checks, bill payments and funds transfer.
(Thulani & Tofara) also found that the perceived merits of internet banking use in Zimbabwe
were a reduction in cost, loyalty increase and to attract new customers. He however noted
that in the process of adopting internet banking by banks in Zimbabwe there were several
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challenges such as cost of implementation, legacy systems, and security concerns
compatibility among others. He therefore suggested that the Zimbabwean Government
should, increase investments in infrastructure development and education through the
Reserve Bank of Zimbabwe, to enable more consumers and firms to adopt internet Banking.
(Thulani & Tofara)considered all commercial banks in Zimbabwe in carrying his study. To
this end questionnaires were distributed to all the commercial banks in Zimbabwe. Out of the
sixteen (16) commercial banks, twelve (12) banks responded to the questionnaire. Descriptive
statistics and simple linear regression analysis was used in analysing the data.
His methods of getting data for the study did not consider the clients of the commercial banks
but was rather limited to the banks in Zimbabwe. This did not allow for the research to
determine appropriately what accounted for the adoption of internet banking by clients of
commercial banks in Zimbabwe. The method only could only identify what commercial
banks of Zimbabwe thought were the reasons why clients adopted internet banking. To bridge
this gap, this study will solicit response of clients of commercial banks instead of the
commercial banks.
Additionally, the use of descriptive statistics and linear regression analysis for in analysing
the data was not appropriate since the dependent variable is categorical with several levels,
Logistic Regression will be used to determine the chance of a customer adopting internet
banking given some significant factor.
The second group of research assesses the aggregate effect of banking performance with
respect to internet banking. Drigă and Isac (2014) claim that, Internet banking can result in
commercial banks gaining a competitive edge in terms of shares on the market, but not in
commercial banks profit making. The results are based on a report on the World Retail
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Banking 2009 (for 8 European countries, the US and Japan). It reveals that a client who is
active in the use of internet banking paid for transactions of 34% lesser than a client who uses
the branch on average. However, they noted that these findings were influenced by the policy
of European banks' to aggressive discourage customers from visiting branches. They also
found that commercial banks were able to assist and serve customers better using new
technologies anywhere the customer is located in the world not necessarily in bank branches.
Also, customers are visiting branches of commercial banks less often and they use internet
and mobile technology for their banking needs more often as a result of the convenience
digital platforms provide. There is a fast growth in online and mobile banking while branch
importance is declining quickly. When it came to getting banking advice, he noted that clients
of commercial banks still preferred branch banking. He further stated that mobile and internet
banking have become the main medium for clients to interact with commercial banks even
though they do not completely replace the other channels. Hence, in the nearest future internet
banking will overcome traditional banking.
Their study used trend analysis in the analysis of their results and also concentrated on the
data on internet banking adoption globally. They did not take the customer into perspective
as opposed to this study.
(Bouckaert and Degryse (1995)argue about two opposite effects of remote banking services
on interest rates. Firstly, they promote depositors to add more saving accounts or keep more
funds on existing ones, which facilitates attraction additional deposits at current interest rates.
Secondly, providing remote services can decrease customer's transaction costs for other banks
that offer similar services, facilitating competition and causing increase in interest rates. Their
study also reveals that clients of commercial banks who adopt internet banking are different
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from others on characteristics such as wealth, income, age and activity before the start of
usage.
Their study considered account balances of clients and the number of transactions on clients
account as the variable of interest. They considered the factors that push customers to adopt
internet banking as endogenous and not observable but do not fluctuate. Panel data regression
was therefore used with individual fixed effects before and after internet banking adoption
which eliminates sample selection bias. This study however looks at responses from clients
concerning factors that accounts for the adoption of internet banking.
Also, their study only considered data from one Bank in Ukraine. The selected customers
were monitored over a period. This approach could lead to the selection of bias samples for
the study as problems associated with that particular bank will affect results of the study. This
study however considers samples from different banks in Ghana hence reduce bias in the
samples.
In the third group the levels of satisfaction and loyalty of clients of commercial banks with
Internet banking are measured. Fathima and Muthumani (2015), worked on how satisfaction
of customers can be influenced by the quality of online services provided. They noted that
seven factors were found to impact customer loyalty in internet banking of a bank. These
factors are, ‘Customer Satisfaction’, ‘Service Quality’, ‘Service Value’, ‘Brand Reputation’,
‘Trust’, ‘Habit’, and ‘Switching Cost’. Of these factors, four was found to be predominant in
influencing bank customer loyalty. These are, Customer Satisfaction, Trust, Habit and
reputation of the bank.
Their study in gathering the data used convenient sampling technique to collect data for study.
The questionnaire they administered was in a likert scale form of measurement. Also, their
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study employed multiple Regression analysis in other to ascertain the relationship between
customer loyalty in internet banking and the construct.
Their study did not use the appropriate model (Multiple Regression Analysis Model).This is
because, the dependent variable (Use of internet banking) was measured in likert scale hence
it was not a continuous variable and does not meet the assumptions in other multiple
regression analysis.
Also, the use of convenient sampling technique could have led to choosing bias sample for
the study thereby rendering the results to representative of the population.
To bridge these gabs, this study employed the multistage sampling technique in other to get
a more representative set of data. Additional, this study will employ Logistic Regression
analysis and the Factor Analysis models since they meet the assumptions for use in the case
of likert scale measurement.
Also, Gulati et al. (2013)studied how a client is influenced by quality of internet services.
They assessed internet banking and customer satisfaction in Pakistan. Their study showed a
positive relationship existing between tangibility, assurance, responsiveness and reliability
with customer satisfaction and internet banking usage in Pakistan. They suggested that, in
other to get the attention of both existing and new clients, management of commercial banks
offering internet banking have to focus on making the content and design of the websites
more appealing. They further have to enhance the safety and security of internet banking
platforms so that clients of their banks can maintain longer with the bank. Commercial banks
providing the internet banking service need to provide a more reliable service to their clients
to make the customers more confident and comfortable in the bank. Effective systems should
be developed by management of banks to solve the issues raised by customers quickly.
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They also used factor analysis and multiple regression analysis to establish the link between
the two variables: internet banking and customer satisfaction.
Pikkarainen, Pikkarainen, Karjaluoto, and Pahnila (2004) also found that two reasons
fundamentally accounted for the diffusion and development of internet banking. First, there
is reduction in cost incurred by banks as a result of internet banking services provided.
Research by Sathye (1999), shows that once put in place banking via the internet is the
cheapest route for delivering bank products. Second, Sathye (1999) also noted that branch
networks as well as staff strength in banks is reduced when internet banking is adopted. This
paved a way for the introduction of channels that are self-served as a good number of
customers felt that banking at the branch level required a lot of effort and time (Mattila,
Karjaluoto, & Pento, 2003). Therefore, the reasons underlying acceptance of online banking
are time and cost savings and freedom from place.
Customers gain access to their bank accounts, according to Essinger (1999), remotely
through the use of a website and then to act out some transactions on their account, while
complying with tight checks in security. (Mols, Nikolaj D. Bukh, & Flohr Nielsen) also
asserts that by using the Internet a number of banking services such as bill payment and
money management services can be offered 24 hours a day.
According to Hutchinson and Warren (2003), privacy issues are a source of concern for many
Internet users. These issues some of which include collection, being transparent, using and
disclosing personal information. Financial institutions have to take advantage of the growth
in the users of internet resulting from the mature nature of internet technology recently. There
is an increase in demand as many clients and businesses become more sophisticated. The aim
of the commercial banks is to increase their share of the market as it redefines its service
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delivery and maintain competitive in the banking industry. Commercial banks survival and
growth in the industry hinges on quality service delivery and product. Internet banking has
become a key tool in the service delivery of commercial banks. (Maniraj Singh, 2004) Moody
(2002) found that internet banking was a fast growing service that commercial banks provide
so that they can retain and gain appreciable share in the market, reduce cost of transaction,
and provide better and quicker response to market changes.
Maduku (2014), is his study found that the adoption of internet banking was determined
mostly by trust in the in the electronic-banking system. He further states banks in South Africa
should adopt strategies that are aimed at increasing customers trust in the e-banking system.
Banks need to be certain that internet banking platforms are sound technically with the
necessary security system to minimise the risk end users may exposed to. Maduku (2014)also
states that banks should lobby government to enact laws that will assist in arresting and
prosecuting people suspected of internet banking fraud. This will enable clients regain trust
in the internet banking.
Maduku (2014)also cited apathy as a reason for non-adoption of internet banking in South
Africa. Lack of effective communication in other to create awareness and demonstrate the
benefits of internet banking and cell phone banking lead to indifference on the part of clients.
He further states that banks need to device communication strategies to promote internet
banking usage by clients. The study considered customers of four main banks in South Africa
in which questionnaires were administered to. The study employed the use of descriptive
statistics, Factor Analysis and Multiple Regression Analysis.
Also, Massilamany and Nadarajan (2017) noted that trust, self-efficacy and knowledge
influenced the internet banking adoption in Malaysia. Their study used simple random
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technique to administer 200 questions to respondents. They also employed multiple
regression analysis and ANOVA to assess the factors that determine adoption of internet
banking in Indonesia.
Massilamany and Nadarajan (2017) noted a significant relationship existing between the
adoption of internet banking and self-efficacy and knowledge. According to Suzanne
Harrison, Peter Onyia, and K. Tagg (2014), a client with prior knowledge on the use of the
internet and computer and knows the benefits is likely to adopt internet banking. Clemes,
Gan, and Du (2012) also had similar findings. They found that customers who have earlier
experience of using internet have higher chance of accepting internet banking.
Massilamany and Nadarajan (2017)found that trust was part of factors that influenced internet
banking adoption. They noted that trust gets the highest attention in electronic commerce as
a result of uncertainty and high risk in internet transactions. Trust was found to be a factor
that affected client adoption in many services such as online news services by Howard Chen
and Corkindale (2008), and health websites by Fisher, Burstein, Lynch, and Lazarenko (2008)
and internet banking by Flavián, Guinaliu, and Torres (2005). Trust is divided into two; initial
trust and continuance trust. Initial trust is related to the behavior of the client in trust
development early stage. Initial trust is affected by a lot of factors. Website is the first
category of factors is related to the website. Most clients who do not have earlier experience
will depend on the perception according to Prema and Sudhakar (2009) also states that
reliability affected internet banking adoption in India. They found that managements of
commercial banks should educate clients on the benefits of internet banking products. They
also found that security and awareness should be enhanced to attract client’s attention.
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Prema and Sudhakar (2009) used three models that were thought to have effect on customer
acceptance of internet banking in the India in his study. These models are; Usefulness, Ease
of use and Reliability. Awareness was seen to have an effect though indirect on internet
banking adoption through its influence on the three.They indicated that the rate at which
clients adopt internet banking is influenced by the awareness level of the client of internet
banking platforms. Sathye (1999) also highlighted that many clients of commercial banks are
not aware of internet banking and its unique benefits.
Also, Lang and Colgate (2003) found different decisions made by clients regarding
alternatives in the market, awareness of the alternative was the reason for the clients to stay
with their bank rather than the alternative. The idea was supported by Nui Polatoglu and Ekin
(2001). They also found clients of commercial banks fail to adopt internet banking because
they do not know of the benefits of the internet banking product. Skill and additional
knowledge a client has about internet banking makes it easier for the client to use the product,
Nui Polatoglu and Ekin (2001) also noted. Therefore customers of commercial banks who
know about internet banking would see internet banking as easier to use, useful, and with
great reliability, hence influences how they adopts internet banking.
Also, Davis (1993) notes that cost effectiveness and usefulness are factored in when a client
decides to adopt new technology proving services as well as goods. Usefulness according to
Davis (1993) is the degree to which an individual perceives that using a particular technology
will improve his ability to perform. Convenience, effective management of finances and
quick services was also noted by Davis (1993) as major factors that affect adopting and using
internet banking products comparing to traditional banking services.
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Ease of use of the internet banking platform is also set to affect the adoption of the product.
Ease of use refers to the level to which a person thinks that using a particular system would
be effortless. Research done extensively over the past years gives evidence of the important
effect of the ease of use, either directly or indirectly through its influence on usefulness
(Agarwal & Prasad, 1999). Information technologies that are easy to use are less alarming to
the individual (M.-K. Kim, Park, & Jeong, 2004).
This means that ease of use is expected to influence positively in customers interaction with
internet banking systems. There is also a positive correlation between ease of use and use of
consumer technologies, such as computer software(Davis, 1993) in labelling a dimension
“ease of use” showed the effect on internet banking adoption. Therefore the easier it is for the
customer to use, the more likely is it for the customer to adopt internet banking.
Furthermore, there is no trust for internet technology for two specific reasons: Security of the
system and concerns about how reliable the internet services are. According to (Lee &
Turban, 2001). Security is one common reason that makes individuals unwilling to use
internet routes for commerce.
This study factors in “Reliability” which is explained as the degree to which internet banking
is seen to be safe and reliable” in transmitting financial transactions securely. An individual
who may potentially adopt internet banking is not likely to use internet banking if there is a
perception of it being unsafe and may create mistakes. (Cook, 2011). Sathye (1999) and
Polatoglu and Ekin(2001) assert that for consumers who used electronic banking, the security
issue was an important factor. Sathye (1999) also found that security had a positive relation
with the use of electronic banking. The present need for banks is not to simply cause a
reduction fraudulent activities relating to banking via the internet. Also it is about consumers’
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confidence retention and the reliance of customers in their bank and the bank being able to
provide channels securely to their money, but also in internet banking as an important
pathway for delivery. Therefore reliability would affect positively the adoption of internet
banking expectedly.
B.-M. Kim, Widdows, and Yilmazer (2005)in their study on the determinants of consumer
adopting internet banking found that ability and attitude of consumers and opportunity cost
of time play an important role. Their study also showed that literate and younger clients have
a greater likelihood of adopting internet banking. They noted that when the individuals’ age
related with level, the age effect varied across educational groups. The effect of age on the
probability of adopting internet banking is humped-shaped amongst people with lower
backgrounds in education. However, those with higher educational background had their
probability of adopting internet banking decreasing with age.
B.-M. Kim et al. (2005) assumed that consumption behaviour of individuals had basis on their
past and present experience (tastes, prices and income), as well as expectations in future.
Adding to this basic perspective, the Beckerian theory of consumer behaviour emphasizes
time, which cannot be augmented, to explain consumption behaviour. Becker (1971)
remodelled the consumption model using the variables commodities and time to produce a
specific good. His model explains the association between opportunity cost of time for labour
participation and consumption, using the combination of time value and price of commodities
within budget restriction. Considering time in the consumption model, effects of time saving
products could be investigated within the model.
B.-M. Kim et al. (2005)developed their hypothesis regarding demographic factors affects
benefits and cost of adopting internet banking along the following;
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There is the expectation of the existence of connections within technologies such that
diffusing any technology is not independent of diffusing another technology (Stoneman and
Kwon, 1993). “Internet banking” is one of the technologies that dependents very much on
computer networks and an advanced technology over other banking technologies. Bayus
(1987) and Norton and Bass (1987) noted that the willingness of a consumer in adopting new
technology is affected by a prior pattern of adopting related technologies. The effect of one
technology on the next generation of that innovation is expected to be positive especially
when the two technologies complement each other.
Karjaluoto et al. (2002) indicates that earlier knowledge of computer experience such as
Internet, e-mail, and e-payment significantly impacted on online banking usage, and also
technology experience, such as ATM, e-ID, teletext, and automats, was also an important
factor for attitude toward online banking among Finland bank consumers (Arndt et al., 1985;
DeLone, 1988; Igbaria et al., 1995; Karjaluoto et al., 2002; Levin & Gordon, 1989).
Lee and Lee (2001) indicated that frequent use of banking service was the most important
factor in adopting Internet banking among non-adopters as that can save time and effort
conveniently, and prior Internet purchasing behavior was also an important factor, but not as
much as the usage of related banking technologies. They employed the use of banking service
as a proxy variable indicating a need for banking service. However, it may be hard to adopt
recent banking technology when there is no prior experience of banking technologies and
even though the thought will be that internet banking is a necessity, lack of comfort and
confidence would prevent the use of it. Therefore, in investigating the link between banking
technologies, it is best if the effect of the use of related banking technologies like ATM, debit
cards and direct payments is studied instead of the use of banking service.
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Consumers may adopt Internet banking easily if they can use banking technologies and
computer software to manage money. There will therefore be an improved efficiency in their
use of Internet banking. Even though consumers with have no experience in the use of
banking technologies and computer software recognize the benefit of Internet banking, they
may be hesitant to adopt Internet banking because it will require more time and money to
learn Internet banking.
In addition, demographic factors should have an effect on the adoption of Internet banking,
Lee and Lee (2001) argued. Age has an effect on the attitude of individuals towards Internet
banking and they been able to learn how to invest. Hence they tested the hypothesis that;
In addition, higher income earners value time better than low income earners, so consumers
with high income can benefit more through the adoption of Internet banking. Also, it is
beneficial to consumers with higher levels of financial assets in terms of saving time since
they use money transactions more often. Hence the hypothesis was tested.
Bartel and Sicherman (1998) stated that individuals who have some form of education need
little training to technologically change if they are to learn new technology. Also, Gronau and
Hamermesh (2001) investigated variances in demand according to differences in the
opportunity costs of various activities. They found that individuals who are well educated
have better home productivity than individuals who are less educated because with relatively
smaller inputs and time, they can produce household goods.
Consequently, the response rate is higher in well-educated than individuals with little
education when Internet banking is presented making it advantageous in terms of time and
cost saving. Well educated individuals will have the skills to learn quickly hence may have
the desire to submit training time to learn how to use “Internet banking”. However, the effect
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of education on adopting Internet banking should also be dependent on consumer's age.
Karjaluoto et al. (2002) revealed that occupation was an important reason for adoption of
“Internet banking”. Occupation was split into two categories, white-collar workers and blue-
collar workers. White-collar workers were more likely to adopt “Internet banking” than blue-
collar workers. Highly paid trained workers had more chance of using advanced technologies
(Liu et al., 2001) because their productivity can be enhanced through using advanced
technologies within a given time.
Their study considered responses from 30 private bank customers. They also interviewed
executives of the two top banks in Finland and consultants. The study employed the use of
descriptive statistics.
The study of Kim, Widdow and Yilmazer (2001) also associates occupation with adoption of
“Internet banking” in terms of ability. They contend that consumers with more opportunities
to use computer or Internet in their job are more able to use technologies related to computer
or Internet than others. Users were put into two groups according to their occupation.
Consumers with managerial, professional, and technical jobs are put in the first group.
Generally, they possibly make use of the internet or computers regularly in their job, hence
they essentially have more skill to use computer or the Internet than those in the other
category. Users with service, labor, farming, fishing, and forestry jobs are put in the second
category. They more likely to have less opportunity to use computers or the Internet in their
job, so their capability of using computers or the Internet might be relatively lesser than the
other category.
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Their study interviewed 4,440 households in other to get responses for the study. Simple
random sampling was used to attain the samples. Also, descriptive statistics and probit
regression was used in their analysis.
Again, to bridge these gaps, this study will employ the use of multi stage sampling technique
in other to obtain the samples for the study. Logistic Regression, Factor analysis and a
modification to the F test, the F-ratio test for non-parametric analysis of variance.
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CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter explain the research methodology adopted for the study. The chapter begins with
the research design and continues with the philosophical foundation of the research,
evaluating the research methods selected, and continues by identifying the reasons for
adopting quantitative methods. This chapter includes five main section: Research design, the
population of the study, sampling technique and the sample size, data collection, method of
data presentation and analysis.
3.2 Research Design
To minimise the possibility of a waste of efforts in a study, choosing an appropriate research
design for the survey is very important Churchill Jr (1979). The study employed explanatory
research design. This was employed to examine the impact of the various factors on the
adoption of internet banking products of clients of commercial banks in Ghana. The study
examines the factors that impact the adoption of internet banking in Ghana. Clients of
commercial banks in the country were considered and questionnaire administered to them.
Commercial banks in Ghana were stratified into locally owned commercial banks and foreign
owned commercial banks. To this end, the ownership structures of all commercial banks in
Ghana were examined. Out of the thirty (30) banks in Ghana as at the end of 2016, sixteen
(16) banks were foreign owned whiles fourteen (14) were locally owned (see Appendix D)
Due to time and cost constrains, two locally owned banks and three foreign owned banks
were selected using purposive sampling to carry out the study. The banks selected were GCB
Bank Limited and UniBank Limited representing locally owned banks and Barclays Bank
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Ghana Limited, Ecobank Ghana Limited and Stanbic Bank Limited representing foreign
owned banks. Finally, accidental sampling was used to select clients to administer the
questionnaire for the study.
Also, the study considered only branches of commercial banks that were situated in Accra
because of time constrains. The number of questionnaire administered to clients of each bank
was proportionate to the share of deposit of the bank as at the end of the year 2016.To
determine the factors that impact the adoption of internet banking in Ghana, Factor Analysis
was used. Additionally, logistic regression was also be applied to the data gathered to
determine the significant factors that impacts the adoption of internet banking in Ghana.
The quality of good research depends on the selection of appropriate research methods (Baker
1994, 109; Silverman 2001, 25). (Lambert & Harrington, 1990) suggested that “the best
methods to get high response rates include advance letters or telephone calls, first-class
outgoing main and hand-stamped return envelopes, monetary incentives, assurance of
confidentiality for sensitive issues, follow-up questionnaires/letters”. Applying an organised
questionnaire in this study for the purpose of data collection, this study takes the above
recommendations: follow-up calls will be made, and two hot spring spa vouchers are provided
in order to increase the likelihood of higher response rates.
3.3 Population
The target population for study is clients of commercial banks in Ghana. Both clients of
locally owned banks and foreign owned banks in Ghana are considered in this study. The
study however limited the coverage to only commercial banks in Accra due to time
constraints. Hence only clients of commercial banks in Accra will be considered for this
study.
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3.4 Sampling Technique and Sample Size
Figure 3.1 Sampling Procedure
The researcher adopted multistage sampling technique in other to carry out the research.
Firstly, the study used stratified sampling technique to group customers of banks in Ghana
into two strata, customers of locally owned and customers of foreign owned banks. The
rationale of choosing the stratified sampling technique was based on the premise that clients
of locally owned banks experience similar services whiles clients of foreign owned banks
also experience similar services. Locally owned banks therefore formed one stratum while
foreign owned banks formed another stratum.
Secondly, purposive sampling was used to select two banks from locally owned banks and
three banks from foreign owned banks. The rationale for selecting three foreign owned banks
and two local owned banks was the market share of the various banks in terms of customer
Foreign Banks
Customers
ECOBANK
Local Banks
Customers
BARCLAYS STANBIC UniBank GCB
122 70 78 63 67
Customers
Stratification
Purposive
Sampling
Accidental
Selected Customers
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deposits for 2016. Three foreign Banks with the highest market share were chosen for the
study. These banks were Stanbic Bank, Ecobank and Barclays Bank. Also, the two locally
owned banks with the highest market share in deposit were selected. These banks are
UniBank and GCB bank.
Out of the 30 banks that were operation per the pwc report of 2015, 14 were locally owned
and 16 were foreign owned. The study therefore considered 3 foreign owned banks and 2
locally owned banks because of the relative number of each category of banks in the country
with the higher number of foreign owned banks given more weight than locally owned banks.
Thirdly, proportional allocation was used to assign the number of customers to be used for
each bank sampled in the study. The number of clients selected from each bank was
proportional to the market share of the bank in question. Banks with higher market share of
deposit was given higher questionnaire for its customers.
Finally, accidental sampling technique was used to select the clients in each bank to
administer the questionnaire. The researcher went to the selected banks at a chosen day and
administered the questionnaire to clients who came into the banks to carry out transactions
on that day. The researcher assisted clients in filling out the questionnaire.
In all, 400 customers from across the five banks were sampled which was made up of 122,
78, 70, 67 and 63 for Ecobank, Stanbic Bank, Barclays Bank respectively. This represents the
sampled clients for the foreign owned banks. Whiles GCB Bank and Unibank had 67 and 63
clients sampled respectively.
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3.5 Estimation of Sample Size
Let N be the population of customers of banks in Ghana which can be divided into L
mutually exclusive and exhaustive strata with strata sizeshN . Since a random sample is drawn
from each category of banks, the sampling scheme is known as stratified random sampling.
Let
hN be the population size of bank category h
hn be the sample size of hth bank category
Since no other information is known about the category of banks except hN , the allocation
of a given sample size n was done in proportion to hN
Hence, for each bank, the number of clients selected for the study was calculated using
hh
Nn n
N
(3.1)
The sample size n is calculated using the Yamane (1967) estimation of sample size given by
21
Nn
Ne
(3.2)
Where e is the margin of error.
The number of client base of each bank in Ghana as at the end of 2016 was difficult to
ascertain. Hence the study considered the share of deposit of the various banks in the country
as a way of determining how many samples should be allocated to each bank sampled. The
share of client’s deposit of a particular bank is an indicator of the size of the given bank.
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The population of Ghana is estimated to be 28.03 million as at the end of 2016 according to
estimates of the United Nation. Of this, it is estimated that only about 30 percent have a bank
account.
Let
N be the total number of customers of all commercial banks in Ghana.
n be the total sample size of the study
1N be the total number of clients of foreign banks in Ghana
1n be the sample size of foreign owned bank customers
2N be the total number of clients of locally owned banks in Ghana
2n be the total sample size of clients of locally owned banks
Hence the number of individual with bank account
30 28.038.409
100N million
Also, per the pwc industry analysis of the banking system in Ghana for 2015, the share of
deposits of locally owned banks in Ghana was 32.5% whiles that of foreign owned banks was
67.5%
Hence the total number of clients for foreign banks using the percentage market share of
deposit is
1
67.5 8409,0005,676,075
100N
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And also
2
32.5 84090002,732,925
100N
Using the margin of error of 0.05, we calculate the minimum sample size required for this
study as follows
2
8409000399.981 400
1 8409000 0.05n
Therefore the study sampled 400 clients.
To calculate for the minimum sample size for foreign owned banks customers we use the
proportional allocation,
11
Nn n
N
1
5676075400 270
8409000n
Hence 270 clients of foreign owned banks were selected
And the minimum sample size for locally owned banks customers are
22
Nn n
N
2
2732925400 130
8409000n
Hence 130 clients of locally owned banks were selected.
Also, to select the number of clients from the sampled banks, proportional allocation was use.
The sampled clients were selected base on the relative share of the deposit of the bank
involved.
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For Ecobank Ghana Limited, the sample size was 11.7
270 121.97 12225.9
For Stanbic Ghana Limited, the sample size was 6.7
270 78.19 7925.9
For Barclays Bank limited, The sample size was 7.5
270 69.84 7025.9
For GCB bank, the sample size was 8
130 66.67 6715.6
For Unibank Ghana limited, the Sample size 7.6
130 63.33 6415.6
3.6 Data Collection
The instrument used for the collection of data for the study was questionnaire. The
questionnaire was in two parts or section. The first part was made up of the bio-data of the
customers. The second part comprised question on the determinants of “internet banking”
adoption. Most of the question were in likert scale form or rated with the remaining few question
not rated. All questions were however closed except some few questions.
3.6.1 Source of Data
Primary data was obtained from respondents by administering a questionnaire. Clients of
commercial banks were made to answer questionnaire regarding bio data and factors that
impacts their adoption of internet banking. The questions about the factors were in a likert
scale.
Commercial Banks in Ghana were grouped into locally owned banks and foreign owned
banks. The number of locally and foreign banks to be sampled for the study was ascertained
using the numerical strength of each category of bank in Ghana. Hence, two locally owned
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banks and 3 foreign owned banks were selected for the study. These banks are Unibank and
the GCB Bank for locally owned banks while that for the foreign owned banks were Barclays
Bank, Stanbic Bank limited and Ecobank Ghana.
Due to time and cost constrain, 400 questionnaires was distributed among clients of both
locally owned and foreign banks proportional.
3.6.2 Validity and Reliability of Research Instrument
Validity and reliability should be considered by a qualitative researcher in order to judge the
quality of the study, designing the study and analysing results (Golafshani, 2003).
The data was gathered by administering questionnaires to respondents. The questionnaire
was developed taking into account existing theories and literature that are relevant to the study.
The questions focused on the purpose of the study, research questions and relevant theories in the
study area.
To ensure that each of the items were correlated a reliability testing was conducted on
multiple-items to ensure that the items could be grouped to form a construct(Bell & Bryman,
2007) . The Cronbach‘s alpha test was used. A Cronbachs alpha level of 0.7 and above means
there was an acceptable degree. It therefor confirms that the items under each construct have
higher correlation or not.
3.7 Method of Data Presentation and Analysis
The response categories gathered from clients were in a Likert scales and were ranked in
order and therefore referred as ordinal because ordinal scale of measurement is one that
conveys order (Jamieson, 2004). In this study, the research used STATA and Statistical
Product for Service Solutions (SPSS) for the analysis.
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All the qualitative data were grouped, quantified and coded to facilitate counting of
frequencies of responses that were given by respondents. The data were further edited to
ensure that the items were answered correctly to determine their accuracy, consistency,
appropriateness of the responses and also to avoid errors and biases.
The SPSS software was used to carry out reliability test (Cronbach‘s alpha‘s test for each
construct). Finally, the study will also utilise Factor Analysis technique, Logistic Regression
modelling and a modification to the F-statistic with 1000 permutation to in order to measure
the specific objectives.
3.7.1 Factor Analysis
Factor analysis method is used in this study to determine the factors that impacts the adoption
of “internet banking” in Ghana. This method can be traced back to the 1900s through the
efforts of Charles Spearman and Karl Pearson. It is a model about hypothetical component
variable that account for the linear relationship that exist between observed variables. To be
appropriately applied, “Factor analysis” requires that the factional relationship between
variables is linear and the variables to be analysed have non-zero correlation existing between
them. Since the data gathered shows that there is a linear relation between the adoption of
“internet banking” and the various factors analysed and the variables being analysed are
correlated, hence the use of Factor Analysis in the study.
Let
𝑌1, 𝑌2, … , 𝑌𝑛 be the observed adoption of internet banking in Ghana.
𝑋1, 𝑋2, … , 𝑋𝑛 be the common factors that impacts the adoption of internet banking in Ghana
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𝜆𝑖1, 𝜆𝑖2, … , 𝜆𝑖𝑛 be the factor-patten loading. This is weights assigned to a common factor
indicating how much a unit change in the common factor will produce a change in the 𝑖𝑡ℎ
observed variable that impacts the adoption of internet banking in Ghana.
𝜀1, 𝜀2, … 𝜀𝑛 be the unique variable ,
where 𝑖 = 1,2, … , 𝑛
The factor analytic model can be expressed mathematically as follows
1 11 1 1 1 1
2 21 1 2 2 2
3 31 1 3 3 3
1 1
...
...
...
. ........ .... ......... ......
. ......... .... ......... ......
. ........ .... ......... ......
...
r r
r r
r r
n n nr r n n
Y X X
Y X X
Y X X
Y X X
We also assume that the common factors and the unique factor variables have zero means and
unit variances. That is, 0jE X , 2 1jE X and 0jE , 2 1jE .
where 1,2,...,j r and 1,2,...,i n .
3.7.2 Components of Variance in Factor Analysis
“Factor analysis” is concerned with explaining the total common variance found between
variables in terms of the common factors. If there is a non-zero correlation between “Ease of
use”, “trust”, “Risk” and the use of internet banking in Ghana, this suggest that there exist
some hypothetical components common to these variables. These common factors correspond
to the common variables.
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However, the lack of perfect correlation between the variable also suggest the presence of
“unique factors” associated with each variable which correspond to “unique variance”.
Additionally, the presence of random error in the measurement of a particular variable also
contributes to the unique variance. If this error is uncorrelated with other factors, it will not
contribute to the covariance between the variables.
Contained in the “unique variance” is the “true variance” which uncorrelated with other
observed variables. This is known as “specific variance”. It corresponds to some reliable part
of the variable that is found in no other part of the variable. The total variance in a factor
analysis model is given by,
Total Variance = Common Variance + Specific Variance + Error Variance
Where;
True Variance = common Variance + Specific Variance
Unique Variance = Specific Variance + Error Variance
3.7.2.1 Variance of a variable in terms of its factors
From equation 3.1, if 𝑌𝑖 is the 𝑗𝑡ℎ variable in terms its factors, the variance 𝜎2𝑗 is given by
22 2
1 1 ...j i i ir r i iE Y E X X (3.3)
This reduces to
2 2
1 1
2i k
r r
j ih ik hk i ik i
h k
(3.4)
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Since the correlation between a common factor and a unique factor is always zero, the middle
term becomes zero. Thus the variance of a variable in terms of its factors components is
given by
2 2
1 1
r r
j ih ik hk i
h k
(3.5)
Equation 3.4 is the formula for variance of a variable in terms of correlated factors. If the
common factors are uncorrelated, the variance reduces to
2 2 2
1
r
j ik i
k
(3.6)
The communality model for uncorrelated factors is given by
2 2
1
r
i ik
k
h
(3.7)
where
2
ik is the square of the 𝐾th factor loading of the 𝑖th variable.
Also,
2 2
1
r
ik i
k
(3.8)
Correspond to the “unique variance” of the 𝑖th variable.
Since we defined the variable 𝑌𝑖 to be expressed in the standard score form, it follows that
𝜎2𝑖 = 1
Consequently the following equation can be developed
2 2
11ih (3.9)
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2 21i ih (3.10)
The Unique variance contains true specific variance and random error variance unique to
specific variable. This is expressed as follows
2 2 2
i i is e (3.11)
where
𝑠2𝑖 is the specific(true) variance of the variable 𝑖
𝑒2𝑖 is the random error variance unique to variable 𝑖
It follows that the reliability of the variable 𝜌𝑖𝑖, is the sum of the two components of the of
the true variance
2 2
ii i ih s (3.12)
Hence the communality of a variable is always less than or equal to the reliability of the
variable.
3.7.2.2 Correlation between Two Variables in Terms of the Factors
For any two variable 𝑌𝑖 and 𝑌𝑚expressed in a normalised form, the correlation between the,
𝜌𝑖𝑚, is equivalent to
im i mE YY (3.13)
After substitution and expansion, the correlation between variable in terms of correlated
factors becomes
1 1
r r
im ik mh hk
h k
(3.14)
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This contains no term corresponding to the unique factors because none of the nonzero
weights for the unique factors matches with the nonzero weights of another unique factor
during derivation.
If the common factors are uncorrelated, the equation reduces to
1 1
r r
im ik mh
h k
(3.15)
3.7.3 Factor Extraction
The Principal-Axes method of factor extraction was used in extracting the factors that impacts
the adoption of “internet banking” in Ghana. This method seeks to find the most
representative score derived from the observed scores.
3.6.3.1 General Computing Algorithm for Finding Factors
Let 𝑌 be an 𝑛 × 1 vector of the adoption “internet banking” in Ghana and the observed
random variable of rate of internet banking adopted be 𝑌1, 𝑌2, … , 𝑌𝑛.
Assuming 𝐸(𝑌) = 0,
𝐸(𝑌𝑌΄) = 𝑅𝑌𝑌 is the correlation matrix with unities for the variances in its principal
diagonal.
Let 𝑋 be an 𝑟 × 1 random vector of factors impacting the adoption of internet banking in
Ghana whose variables are observed common factors 𝑋1, 𝑋2, … , 𝑋𝑟
Assuming 𝐸(𝑌) = 0 and 𝐸(𝑋𝑋΄) = 𝑅𝑥𝑥 is the correlation matrix
Let also 𝑬 be an 𝑛 × 1 random vectors whose variables are unique factors 𝜀1, 𝜀2, … , 𝜀𝑛, having
the property 𝐸(𝑬) = 𝟎 and 𝐸(𝑬𝑬΄) = 𝑰.
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Also, let 𝚲 be an 𝑛 × 𝑟 matrix of common factor-pattern coefficient and 𝜓 and 𝑛 × 𝑛 diagonal
matrix of unique factor-pattern coefficients whose diagonal elements are 𝜓1, 𝜓2, … , 𝜓𝑛
Then
Y X E (3.16)
The equation above is the fundamental equation of “factor analysis”. This indicates that the
adoption of internet banking in Ghana is weighted combination of the common factors in 𝑋
and the unique factors in 𝐸.
Substituting the equation above into ' YYE YY R we have
'
' ' ' '
' ' ' ' ' ' ' '
' ' 2
YY
xx XE EX
R E X E X E
E X E X E
E XX XE EX EE
R R R
But because the common factors are assumed not to be correlated,
0XE EXR R
Hence
2
YY xxR R (3.17)
Equation () is the fundamental theorem of “factor analysis” in matrix notation.
Because 𝛹2 is a diagonal matrix, subtracting it from 𝑅𝑌𝑌 only affects the principal diagonal
of 𝑅𝑌𝑌, leaving the off-diagonal elements unaffected. Hence the off-diagonal correlations are
due to only the common factors. The principal diagonal of 𝑅𝑌𝑌 − 𝛹2 contains the
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communalities of the variables. These are the variances of the observed variables due to just
the common factors. 𝑅𝑌𝑌 − 𝛹2 is called the reduced correlation matrix.
On the other hand if we subtract Λ𝑅𝑥𝑥Λ΄ from 𝑅𝑌𝑌
' 2
YY xxR R (3.18)
Equation 3.6 represents the partial covariance matrix among the adoption of “internet
banking” in Ghana, when the common factor holds, it is a diagonal matrix. Once all the
common-factor parts of the observed variables have been partialled from them, there remains
no correlation between the respective residual variable.
The predicted adoption of internet banking in Ghana from the common factors in 𝑋
1ˆ
x YX XXY R R X (3.19)
We can derive the variance-covariance matrix as follows
1 ' 1
1 1
'
ˆX YX XX XX XY
YX XX XX XX XY
XX
E Y E R R XX R R
R R R R R
R
(3.20)
Λ = 𝑅𝑌𝑋𝑅𝑋𝑋−1 represents the transpose of the regression weight matrix for predicting the
observed variable from the common factors. Each of the coefficients in a row of Λ represents
how much a unit change in common factors produces a change in the variable corresponding
to that row.
There are two kinds of matrices that reveal relationships between the adoption of internet
banking and factors. The first is Λ = 𝑅𝑌𝑋𝑅𝑋𝑋−1 which is referred to as the factor-pattern matrix.
The coefficients of a factor-pattern matrix are weights to be assigned to the common factors
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in deriving the adoption of internet banking as a linear combination of the common and
unique factors. The factor-pattern coefficients are equivalent to regression weights for
predicting the observed variable from the common factors.
The second matrix is known as the factor-structure. The coefficients of this matrix are the
covariances between the adoption of internet banking and factors. It is derived as follows;
𝑅𝑌𝑋 = 𝐸(𝑌𝑋΄)
= 𝐸[(Λ𝑋 + Ψ𝐸)𝑋΄]
= 𝐸(Λ𝑋𝑋΄) + 𝐸(Ψ𝐸𝑋΄)
= Λ𝑅𝑋𝑋 + Ψ𝑅𝐸𝑋
= Λ𝑅𝑋𝑋 (3.21)
Since 𝑅𝐸𝑋 = 0.
Assuming we know the score and the unique-factors. This will make it possible to use
multiple regressions to illustrate the method of extracting one factor at a time. Assuming also
that we have an 𝑛 × 1 random vector 𝑬 of unique factor variables. Let us partial the unique
factors from the 𝑛 × 1 vector 𝑌 on 𝑛 observed variables. The matrix of the covariance matrix
is among the unique factors is an identity matrix 𝐼 because it is assumed that the unique factors
have zero means and standard deviation of 1 and are mutually uncorrelated.
The matrix of covariance between the adoption of internet banking adoption, Y, and the
unique factors in 𝐸 is given by
𝑅𝑌𝐸 = 𝐸(𝑌𝐸΄)
= 𝐸[(Λ𝑋 + Ψ𝐸)𝐸΄]
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= Λ𝐸(𝑋𝐸΄) + Ψ𝐸(𝐸𝐸΄)
= Λ𝑅𝑋𝐸 + Ψ𝐼
= Ψ (3.22)
Because 𝑅𝑋𝐸 = 0
For any observed adoption of internet banking, 𝑌𝑐
Let 𝑌𝑐 = 𝑌 − Ψ𝐸
From this we obtain
𝑣𝑎𝑟(𝑌𝑐) = 𝑅𝑐
= 𝐸(𝑌𝑐𝑌𝑐΄)
= 𝐸[(𝑌 − Ψ𝐸)(𝑌 − Ψ𝐸)΄]
= 𝑅𝑌𝑌 − 𝑅𝑌𝐸Ψ − Ψ𝑅𝐸𝑌 + Ψ. Ψ
= 𝑅𝑌𝑌 − Ψ2 (3.23)
Hence 𝑌𝑐 contains variables dependent on only the common factors. We can use the 𝑌𝑐 to
derive the first factor however, there is no unique factor solution for this. Depending on the
method used, different solutions can be obtained. However each method will yield an 𝑛 × 1
vector 𝑏1 such that the first common factor 𝑓1 will be found as a linear composite of the
variable 𝑌𝑐. This is because 𝑌𝑐is assumed to be a linear combination of 𝑟 common factors and
the variables in 𝑌𝑐 and 𝑟 are in the same space.
Hence our first factor will be
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𝑓1 = 𝑏1΄ 𝑌𝑐 (3.24)
For 𝑓1 to be normalised to have a unit variance, 𝐸(𝑓12) = 1.
Suppose that 𝛽1 is an 𝑛 × 1 weight matrix which combines the variables in 𝑌𝑐 to produce a
composite corresponding to the factors.
The variance –covariance matrix of cY is given by
2
c c YYVar Y R R
2 ' 2
1 1 1YYR
Hence
1 11
1b
(3.23)
Having found 1f , we now find the first 1n column
1 of the factor pattern matrix .if we
assume we are working with uncorrelated factors, then the correlation of the variables with
1f will give us 1 . Hence
1 1E Yf
(3.24)
But
'
1 1 cf bY
Hence
'
1 1cE YY b
'
1 1E Y Y E b
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'' ' 2
1 1 1YYE YY YE b R b
(3.26)
The first column, 1 , of the factor pattern matrix, , is equal to the approximate linear
transformation applied to the correlation matrix with the unique variances partialled from it.
We then proceed to find the second factor 2f . In other that
2f be uncorrelated with 1f , we
partial out 1f from the vector
cY . The covariance matrix between cY and
1f is given by
1 1cE Y f E Y E f
1 1E Yf Ef
But because the correlation between the common factors and the unique factors is zero we
have
1 1 1cE Y f E Yf (3.28)
We define the first residual variable vector 1Y as
1 1 1cY Y f
Hence the matrix of covariance among the first residuals will be given by
' ' '
1 1 1 1 1 1c cE YY E Y f Y f
' ' ' '
1 1 1 1 1 1 1c c c cE Y Y E Y f E f Y E f f
2 ' ' '
1 1 1 1 1 1
2 '
1 1
YY
YY
R
R
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As before, finding the second factor will involve finding some weight matrix that transforms
the residual variables into the next factor. If 2b is the second 1n weight vector, then
'
2 2 1f b Y
The column vector 2b can be seen as some other vector
2 multiplied by a scaler 2
1
2 2
2
1b
Where
2 2 '
2 2 1 1 2YYR
But 1f is not correlated with
2f . That is
1 2
'
1 2 1 1 2f f E f f E f Y b
' '
1 1 1 2cE f Y f b
' '
1 1 1 1 2cE f Y E f f b
Substituting 1 1 1cE Y f E Yf , we have,
1 2
' '
1 1 2 0f f b
Hence the two factors are not correlated.
In finding the second factor, we find the second column2 of the factor pattern matrix
which for uncorrelated factors gives the correlation between Y and 2f :
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2 2E Yf
'
1 1 2E Y Y E f b
2 '
1 1 2YYR b
Generally in factor analysis, we extract one factor at a time. We find that the pth column of
the factor loading matrix p for the pth common factor pf extracted from the scores in cY
is given by
2 ' '
1 1 1 1...p YY p p pR b
Where pb is the weight vector required to find the pth factor and it is given by
1p p
p
b
(3.29)
Where
2 ' 2
1 1 1 1...p p YY p p pR
3.7.3.2 The Principal-Axes Method of Factoring
Let Y be an 1n standardized random vector whose coordinates are n variable. We wish to
find a linear composite of 1X by weighing the variables in Y with a weight of vector , that
is
'
1X Y (3.30)
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Also,
1
2 2
1( ) 'X YYE X R
Is the maximum given the constrain, ' 1 . This constrain enable us to find a linear
variable having the greatest possible variance under the restriction that the sum of the squares
of the weights used to find the composite equals 1. Without it the weights in β could be
allowed to get larger and larger without bound, allowing the variance to increase without
bound. Hence there would be no definite maximum variance.
Let us find the weight vector of β that serves as a weight vector in equation 3.28. This involves
finding the maximum of a function, we therefore resort to derivative calculus. Also, because
of the constrain placed on the solution, we have to use the Lagrangian multipliers.
The function F of the weights in β to maximise is
2 ' '
1 ' 1F E X E YY
' ' 1YYR
(3.31)
Where is a Lagrangian multiplier and YYR is an n×n correlation matrix of the variable Y.
Note that ' ' '
YY YYR tr R and ' 'I .
Taking the partial derivative of F with respect to the weights vector is β, we get,
2 2YY
FR
At maximum, 0F
hence ,
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0YYR I
The equation above represents the system of equation in n unknowns. But we cannot solve
the equation by finding the inverse of YYR I . The solution will be trivial 0 and it
will violate the constraint that ' 1
Hence we must not be able to find the inverse of the YYR I . This will imply that
YYR I is a singular matrix. To be singular, it must have a determinant of
0YYR I
That is
11 1
1
0
n
n nn
Expanding the determinant of YYR I will results in the polynomial equation in . The term
of the highest order in will come from the product of the diagonal element of the
determinant. Hence n
is the highest order term of the polynomial and YYR I is a
polynomial of degree n.
Let f be the polynomial, then
1
1 1 0... 0n n
nf b b b
(3.32)
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The equation above is the characteristic equation of the matrix YYR and f is known as
the characteristic polynomial forYYR with n roots. The roots of
1 2, ,..., n are known as the
characteristic roots or the eigenvalues of YYR .
Also, multiplying the equation 0YYR I by ' we get
' ' ' 0YYR
' '
YYR
If we take any satisfying 0YYR I and the constrain ' 1 then the above
equation becomes
' YYR (3.33)
This is an equation for the variance of the composite random variable.
3.8 The Logistic Regression Model
In other to determine the chance of a customer adopting internet banking given a particular
factor, the logistic regression model will be employed. The Logistic regression model is
generally used to model the outcomes of a categorical dependent variable. The response
variable is not measured on a ratio scale and the error terms are not normally distributed.
Since the adoption of internet banking data is categorical in nature and the error terms are not
normally distributed, the logistic regression can be used determine the factors that impact the
adoption of internet banking.
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For purposes of interpretation and in line with the binary logistic techniques, customers who
use internet banking facility were considered the reference variable and according assigned a
value of 1 and non-users were assigned zero.
Let
Z be a random variable that can take on one of two possible value, either a client uses internet
banking or not, for a data set with a total sample size of say M . Each observation is
independent. Z is considered as M binomial random variables iZ . The value of 1 is
considered a “success” of adoption of internet banking while the value of 0 is considered as
“failure”, clients who do not have internet banking.
𝑁 be the total number of population and 𝑛 be the column vector with elements 𝑛𝑖 representing
the number of observations in population 𝑖 where the total sample size is
∑ 𝑛𝑖
𝑁
𝑖=1
𝑌 be a column vector of length 𝑁 where each element 𝑌𝑖 is a random variable representing
the number of success of 𝑍 for population in i .
Also, let iy represents the observed counts of the number of successes for each population
and be a column vector of let N with elements ( 1/ )i iP Z i which is the probability
of success for any given observation in the thi population.
The linear components of the model contain the design matrix and the vector of parameters
to be estimated. The design matrix of the independent variables, X , is composed of N rows
and 1K columns, where K is the number of independent variables specified in the model.
For each row of the design matrix, the first element0 1iX . This is the intercept say . The
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parameter vector, , is a column vector of length 1K . There is one parameter
corresponding to each of the K columns of independent variables settings in X plus one 0
as intercept denoted by .
The logistic regression model equates the logit transform, the log-odds of the probability of
success, to the linear components.
0
log1
1,2,...,
Ki
ik k
ki
x
i N
(3.34)
3.8.1 Parameter estimation
The goal of the model is to estimate the 1K unknown parameters . This is done by using
the maximum likelihood estimation which entails finding the parameters for which the
probability of the observed data is greatest. The maximum likelihood equation is derived from
the probability distribution of the dependent variable. Since each iy represents a binomial
count in the thi population, the joint probability density function of Y is
1
!1
! !
i ii
Nn yyi
i i
i i i i
nf y
y n y
(3.35)
For each population, there are i
i
n
y
different ways to arrange iy successes from among
in
trials. The probability of iy success is iy
i since the probability of a success for any one of
the in trials is
i . Also, the probability of i in y failures is 1 i in y
i
Thus, the likelihood function is given by
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1
!
! !/ 1 i ii
Nn yyi
i i
i i i i
nL
y yy
n
The factorial terms are constants since they do not contain i hence they can be ignored
1
/ 11
i
i
yN
nii
i i
L y
(3.36)
From equation 3.32
0
01
K
ik k
k
K
ik k
k
x
ix
e
e
Substituting i into equation 3.8 we have,
0
0
0
0 0
1
1
/ 1
1
1
iK
iK ik k
kik k
k
K
i k
k
iK K
i ik k ik k
k k
ny x
N x
xi
n
N y x x
i
eL y e
e
e e
Taking the log, we get
0
1 0
.log 1
K
ik k
k
N K x
i ik k i
i k
l y x n e
(3.37)
Differentiating with respective , we get
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0
0
1
1. . 1
1
K
ik k
k
K
ik k
k
N x
i ik ixik k
ly x n e
e
1
N
i ik i ik ik
i
y x n x
.
The maximum likelihood for can be found by setting
0k
l
and finding the value of
each k . Note that
ik is a function of k
Thus,
1
logN
i i i
i
it x
. (3.39)
The quantity 1
i
i
odds
for the individual
ix
3.9 Non-Parametric Multivariate Analysis of Variance
In comparing the internet Banking Adoption of clients of commercial banks in Ghana, the
non-parametric multivariate analysis of variance was employed. This was used because the
multivariate test of normality was carried out using the Royston normality test which resulted
in the finding that the factors extracted were not multivariate normally distributed. The
Royston normality test assesses the hypothesis that:
0 :H The factors are multivariate normally distributed
1:H The Factors are not multivariate normally distributed
The null hypothesis was rejected, hence the factor extracted were not multivariate normally
distributed.
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Because the multivariate normality assumption of MANOVA was not met, the study used the
one way non-parametric multivariate analysis of variance. The essence of the test was to
compare the location parameter (median) between the categories of banks. Firstly, the test
statistic is constructed.
The hypothesis for the test is given by
0 :H There is no difference in the location parameter between the categories of banks
1:H There is difference in the location parameter between the categories of banks
Let
N be the total number of observations
ijd be the distance between observation 1,2,3,...,i N and 1,2,3,...,j N
The total sum of square is given by
12
1 1
1 N N
T ij
i j i
SS dN
(3.39)
Also the within bank sum of squares or residuals is given by
2
1 1
1 N N
W ij ij
i j i
SS d en
(3.40)
Where ije takes the value of 1 if i and j are in the same group or zero otherwise.
Then A T WSS SS SS , Where
ASS is the group sum of squares.
The pseudo F-ratio to test the multivariate hypothesis is given by
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1A
W
SSa
FSS
N a
(3.41)
Secondly, the p-value is ascertained,
If the null hypothesis is true, and the adoption of internet banking across the category of banks
are the same, then the observation under each category of bank can be exchanged among the
other category. Thus, the random permutation of observation of locally and foreign owned
banks is carried out. This leads to the calculation of a new value of F say *F comparing the
values computed, the p-value is then computed as
*
*
. .
. . .
No of F Fp
Total No of F
(3.42)
Note that the original F computed is a member of the distribution of *F .
The P-value obtained is a measure of confidence in the null hypothesis (Freedman and Lane
1983).
We reject the null hypothesis if the P-value is less 0.005.
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CHAPTER FOUR
ANALYSIS AND RESULTS
4.1 Introduction
Internet banking is product of commercial banks that is made available to its clients to enable
them conducts transactions without necessarily going to the banking halls. It enables clients
also conducts banking transactions at their own convenience. Banks are also able to reduce
cost in terms of stationary and even staff strength when clients adopt internet banking as
opposed to the traditional branch banking.
This study sought to examine the key factors that impact the adoption of internet banking in
Ghana. The following specific objectives were set for the study.
1. To examine the factors that significantly contributes to customer’s adoption of internet
banking.
2. To determine the chance of a customer’s adoption of internet banking given some
significant factors.
3. To compare internet banking adoption between international banks and local banks in
Ghana.
To examine the objectives above, commercial banks in Ghana were stratified into locally
owned and foreign owned banks using their ownership structure which indicates persons and
entities that own the various commercial banks in Ghana. Majority shareholders of banks that
were local was classified as locally owned banks and that of foreign was classified as foreign
owned banks. Based on the total number of each category of banks in Ghana, a sample of 3
foreign owned banks and 2 locally owned banks were selected for the study.
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A questionnaire was design to gather responses from clients of the various banks in Ghana.
400 clients were sampled for the study with a total of 130 sampled from locally owned banks
and 270 from foreign owned banks based on proportional allocation.
4.1.1 Characteristics of sample
Even though the selections of the samples were random, more males were samples than
females for the study. Out of the 400 sampled clients of commercial banks in Ghana, 61.5%
were males whiles 38.5% were females as shown in the table below.
Table 4.1: Gender Distribution
Gender Frequency(n) Percentage (%)
Male 246 61.5
Female 154 38.5
Total 400 100.0
Also, clients in the age category of between 20 and 30 years were sampled more than any
other age category with a total percentage sampled of 49.75% while clients in the age category
of 50 years and above were the least sampled for the study. A total of 4% of the sampled
clients were in the age category of 50 years and above. A sizeable number of clients in the
age category of 30 and 40 years were also sampled for the study. A total of 34.5% of the
entire sampled clients were in the age category of between 30 and 40 years. Below is the table
of the age distribution.
Table 4.2: Age Distribution
Age Frequency Percent
Less than 20 years 25 6.25
20-30 Years 199 49.75
31-40 years 138 34.50
41-50 years 22 5.50
51 years and above 16 4.0
Total 400 100.0
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The sampled clients were also comparatively more educated. 38% of the sample clients had
a minimum educational level of a Bachelor degree whilst clients with the minimum
educational level of Junior High School and Doctorate degrees were the lowest sampled with
percentages of 1 and 1.3 respectively. Below is the distribution of minimum educational level
of the sampled clients.
Table 4.3: Educational Level
Highest Educational Level Frequency Percent
JHS 4 1.0
SHS 48 12.0
Diploma 88 22.0
Bachelor 155 38.8
Masters 76 19.0
PhD 5 1.3
Professional 24 6.0
Total 400 100.0
Also, clients working in private organizations/entrepreneurs and students were sampled more
for the study. A total of 32% and 36% of the entire samples were samples from these
categories respectively.
Table 4.4: Occupation
Current Occupation Frequency Percent
Student 144 36.0
Public servant 102 25.5
Civil servant 24 6.0
Private/Entrepreneur 128 32.0
Housewife 2 0.5
Total 400 100.0
The clients sampled also consist of 64.3% of clients using at least one of the internet banking
products of commercial banks whilst 35.7 of clients do no use any of the internet banking
products.
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Table 4.5: Usage of Internet Banking
Do you use Internet
banking? Frequency Percent
Yes 257 64.3
No 143 35.7
Total 400 100.0
A cross tabulation of sex and usage of internet banking reveals that 66.7% of males sampled
for the study used internet banking whiles 33.% where non users of the service. Also, out of
the total number of females sampled for the study, 60.4% of them were users of the internet
banking platform whiles 39.6 were non users of the service.
Table 4.6 Cross tabulation of Sex and Usage of Internet Banking
Sex Do you use internet banking
Total Yes No
Male Count 164 82 246
% within Sex 66.7% 33.3% 100.0%
% within Usage 63.8% 57.3% 61.5%
Female Count 93 61 154
% within Sex 60.4% 39.6% 100.0%
% Within Usage 36.2% 42.7% 38.5%
Total Count 257 143 400
Also, a cross tabulation of age and usage of internet banking shows that the highest within
age group users of internet banking was the age group of 50 years and above recording 100%
usage within the age group. This was followed by the age group between 31 and 40 years
recording 80.4% users as against 19.6% non-users in the same age group. The lowest within
age group users where the age group less than 20 years recording 20% users against 80% non-
users.
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Table 4.7 cross Tabulation of Age and usage of internet banking
Age Do you use internet banking
Total Yes No
Less than 20 years Count 5 20 25
% within age 20.0% 80.0% 100.0%
% within Usage 1.9% 14.0% 6.3%
20-30 Years Count 108 91 199
% within age 54.3% 45.7% 100.0%
% within Usage 42.0% 63.6% 49.8%
31-40 years Count 111 27 138
% within age 80.4% 19.6% 100.0%
% within Usage 43.2% 18.9% 34.5%
41-50 years Count 17 5 22
% within age 77.3% 22.7% 100.0%
% within Usage 6.6% 3.5% 5.5%
51 years and above Count 16 0 16
% within age 100.0% 0.0% 100.0%
% within Usage 6.2% 0.0% 4.0%
Total Count 257 143 400
A cross tabulation of Educational level and usage of internet banking products reveal that
sampled individuals whose highest qualification was masters level had more users of internet
banking within that category recording 96.1% whiles non-users in that educational level was
only 3.9%. Individuals whose highest education level was Senior High School had the lowest
users of internet banking users within that educational level. This recorded 29.2 users against
70.8 non-user.
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Table 4.8 Cross Tabulation of Educational Level and Usage of Internet Banking
Educational Level Usage of internet banking Total
Yes No
JHS Count 2 2 4
% within Educational
Level 50.0% 50.0% 100.0%
% within Usage .8% 1.4% 1.0%
SHS Count 14 34 48
% within educational
Level 29.2% 70.8% 100.0%
% within Usage 5.4% 23.8% 12.0%
Diploma Count 44 44 88
% within Educational
Level 50.0% 50.0% 100.0%
% within Usage 17.1% 30.8% 22.0%
Bachelor Count 102 53 155
% within Educational
Level 65.8% 34.2% 100.0%
% within Usage 39.7% 37.1% 38.8%
Masters Count 73 3 76
% within Educational
Level 96.1% 3.9% 100.0%
% within Usage 28.4% 2.1% 19.0%
PhD Count 3 2 5
% within Educational
Level 60.0% 40.0% 100.0%
% within Usage 1.2% 1.4% 1.3%
Professional Count 19 5 24
% within Educational
Level 79.2% 20.8% 100.0%
% within Usage 7.4% 3.5% 6.0%
Total Count 257 143 400
4.2 Factors Influencing the Adoption of Internet Banking
To determine the factors that contributed to the adoption of “internet banking in Ghana”, the
exploratory factor analysis was used to the extract factors. The principal axis method of
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factoring was used. Also because of the correlation between the variables as shown in the
correlation matrix in appendix, the direct oblimin rotaion was adopted since it allows for
correlation between variables.
4.2.1 Correlation Matrix
The correlation matrix in appendix C indicates how each of the 58 items associates with each
other. From the correlation matrix, it can be seen that some of the correlation are relatively
higher whiles some are relative low even zero. Relatively high correlation suggests that there
is a relationship between variables whiles relatively low correlation on no correlation is an
indication that there is no relationship between the variables.
Also, relatively high correlation between variables is an indication that factor analysis will
probably group the variables in question into a factor. They normally would have higher
loadings on the same factor. Variables with lower correlation are however an indication that
they will have lower loadings on the same factor.
Table 4.9: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.87
Bartlett's Test of Sphericity Approx. Chi-Square 17728.418
Df 1653
Sig. 0.000
To determine whether or not enough items are predicted by each factor in the model, a
Kaiser-Meyer-OIkin (KMO) test was carried out. A KMO greater that 0.7 is an indication
that enough items are predicted by each factor in the model. Since the KMO test for the model
was greater than 0.7 (0.870>0.7), each of the factors extracted by the use of the principal axis
method of extraction was able to predict enough items in the model.
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Additionally, the Bartlett’s test of sphericity was carried out to ascertain whether the variable
were correlated enough to warrant factor analysis. It used to test that the correlation matrix
has significant correlation among the variables. The observed correlation matrix is expected
to have small off-diagonal coefficients if the variables are independent.
Hypothesis:
oH : The correlation matrix is an identity matrix
1H : The correlation matrix is not an identity matrix
The Bartlett’s test of sphericity tests the hypothesis that the correlation matrix is an identity
matrix. The hypothesis is rejected if the significance value is small (P<0.05). Since the P-
value from the Bartlett’s test of sphericity above is small yielded 17728.418 with a p-value
of 0.00<0.005, we reject the hypothesis that the correlation matrix is an identity matrix. Hence
the correlation matrix is significantly different from an identity matrix. Therefore the
variables are correlated enough to warrant factor analysis.
4.2.2 Number of Factors to Retain
To determine the number factors to retain, the scree plot was used. It is a plot of the
eigenevalues against the factors. The screen plot shows a plot of the eigenevalues verses the
components of the factors using the principal axis method of factoring. The scree plot below
shows that 12 factors were significant in determining the internet banking adoption in Ghana
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Figure 4.1: Scree Plot
However, the parallel analysis indicates that only nine factors were significant in determining
the adoption of “internet banking” in Ghana. Based on the parallel analysis conducted, nine
factors were therefore extracted using the principal axis method of factoring with the Direct
Oblimin rotation method. The Direct Oblimin method was used because it allows for
correlation among the variables as in this case. The sum of squares (SS) loadings for the
various factors is shown in the table below.
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4.2.3 Grouping of Components into Factors
The components were grouped into factors using the factor pattern matrix. The items in factor
matrix cluster into groups which had higher loadings. Nine groups of components or factors
were extracted using the factor pattern matrix and the factor analysis image below. The
grouped components were renamed as Trustworthiness of the Bank, Usefulness of internet
banking, Risk, Accessibility, Ease of use, Assurance in the banks website, Service Visibility,
Awareness of benefits of internet banking and Trust in internet banking as a solution to the
banking needs.
The Factor, ‘Trustworthiness’ in the bank was found to be the accelerating factor as it
accounted for the highest correlation among the variables in the first factor. This was followed
by ‘Usefulness’, ‘Risk’, ‘accessibility’, ‘Ease of use’, Assurance in the banks website, Service
Visibility, Awareness of benefits of internet respectively.
4.2.4 Reliability Analysis of the Grouped Factors
Table 4.11 Reliability Statistics
Factor Cronbach's Alpha
N of
Items
1 .921 15
2 0.826 9
3 0.683 9
4 0.914 7
5 0.826 3
6 0.841 4
7 0.896 4
8 0.916 4
9 0.804 3
In other to test whether grouped factors were correlated enough to be grouped together to
form a factor, a reliability analysis was conducted. Since all the Cronbachs Alpha for the
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various factors were more than 0.7, we conclude that the factors were correlated enough to
be grouped as factors.
Table 4.12: Table Total Variance Explained
Factor Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation
Sums of
Squared
Loadingsb
Total % of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
1 25.989 44.809 44.809 25.706 44.320 44.320 15.295
2 5.127 8.839 53.648 4.826 8.320 52.641 13.083
3 2.560 4.413 58.062 2.188 3.773 56.414 5.348
4 2.325 4.008 62.070 2.061 3.553 59.967 13.793
5 1.778 3.065 65.135 1.491 2.570 62.537 6.554
6 1.605 2.767 67.903 1.315 2.266 64.803 11.602
7 1.365 2.353 70.256 1.081 1.864 66.667 14.390
8 1.273 2.196 72.451 .982 1.693 68.360 4.385
9 1.139 1.964 74.415 .810 1.396 69.756 4.778
10 1.024 1.766 76.181
11 .964 1.663 77.843
Extraction Method: Principal Axis Factoring.
a. Only cases for which Do you use internet banking = Yes are used in the analysis phase.
b. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.
Table 4.9 above consist of the components, the initial eigenevalues associated with each
factor, and the extraction sum of squares loadings for the nine factors extracted. Of the 58
components analyzed, nine factors were found to be significant in the adoption of internet
banking of clients of commercial banks in Ghana. The selection criterion was based on the
Scree plot and the parallel analysis of the eigenevalues (Monte Carlos Simulation of the
eigenevalues). The Scree plot retained 12 factors while the parallel analysis found out of the
12 factors only 9 were significant in the adoption of internet banking in Ghana.
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Table 4.9 indicates, that the nine factors listed above cumulatively accounts for 74.415% of
the variability in internet banking adoption in Ghana using the sampled data set before
rotation. Trustworthiness, which is the accelerating factor, accounts for 44.809% of the
variability while Usefulness, Risk, Accessibility Ease of use, Assurance in the banks website,
Service Visibility, Awareness of benefits of internet banking and Trust in internet banking as
a solution to the banking needs accounted for 8.839, 4.413, 4.008, 3.06, 2.767, 2.353, 2.196
and 1.964% of the variability effect on internet banking adoption in Ghana respectively. The
other 49 components together accounted for a total of 25.585% of the variability in the data
set before rotation.
After rotation, the percentage of variance accounted by the nine factors cumulatively was
56.756%. This was more than the rest of the 49 components. Trustworthiness accounted for
44.32% whilst usefulness, Risk and Accessibility, Ease of use, Assurance in the banks
website, Service Visibility, Awareness of benefits of internet banking and Trust in internet
banking as a solution to the banking needs accounted for 8.32%, 3.773%, 3.553 and 2.570,
2.266, 1.864, 1.693, and 1.396 respectively.
Additionally, Chi-square test of association was used to ascertain whether there was a
relationship between the adoption of internet banking in Ghana and the various demographics
from the sampled clients. Table 4.13 below shows results of the Chi-square test of association
between the demographics and the adoption of internet banking in Ghana.
Table 4.13: Test of Association
Demographics Chi
Square
Degree
of
Freedom
P-
value
Gender 1.3629 1 0.243
Age 56.203 4 0.000
Education Q 69.847 6 0.000
Occupation 80.025 4 0.000
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There was a relationship between Age, Education qualification and occupation of a client and
adoption of internet banking. The p values of these demographic factors are less than α=0.05
hence there exist a relationship between the factors and internet banking.
However, the p value for the Chi-Square test of association between Internet banking
adoption and gender was greater than α=0.005. Therefore, no relationship exists between
internet banking adoption of clients and gender from the sampled clients of commercial banks
in Ghana.
4.3 Chance of a Customer Adopting Internet Banking Given a Particular Factor
To determine the chance of a customer adopting internet banking given a particular factor,
the binary logistic regression was used to analyse the factors extracted. The binary logistic
regression was used because the response variable was categorical and dichotomous. The
response variable takes the value of 1 with probability of success p, or 0 with the probability
of failure 1-p.
To ascertain whether the logistic regression model was fit, the Hosmer-Lemeshow test was
used. It tests the adequacy of the logistic regression model by examining the overall goodness-
of-fit test. The model fits if the difference between the observed and the fitted values are small
and there is no systematic contribution of the differences to the error structure of the model.
The hypothesis of the Hosmer-Lemeshow goodness of fit test is given by
0 :H The model fits the data
1 :H The model does not fit the data
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The null hypothesis is rejected if the p-value is less than 0.05 and conclude that the model is
not fit otherwise we fail to reject the null hypothesis and conclude that the model is fit for the
observed data.
Table 4.14: Hosmer and Lemeshow Test
Step Chi-
square df Sig.
1 11.702 8 0.165
Since the p-value in our test is greater than 0.05, we fail to reject the null hypothesis that the
model is fit. Hence, we conclude that the logistic model is fit for the observed data. The
factors impacting the adoption of internet banking is not significantly different from those
used in the model.
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Table 4.15: Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Age .254 .336 .570 1 .450 1.289 .667 2.488
Educational Level .644 .221 8.484 1 .004 1.905 1.235 2.938
Occupation .053 .179 .088 1 .767 1.055 .743 1.498
Trustworthiness -1.322 .309 18.348 1 .000 .267 .146 .488
Usefulness .207 .264 .613 1 .434 1.230 .733 2.064
Risk .351 .327 1.154 1 .283 1.421 .749 2.696
Accessibility .950 .313 9.244 1 .002 2.586 1.402 4.772
Ease of Use 1.266 .250 25.634 1 .000 3.546 2.172 5.789
Assurance of
Website
-.869 .313 7.718 1 .005 .419 .227 .774
Service Visibility .952 .299 10.128 1 .001 2.590 1.441 4.653
Knowledge of the
product
1.123 .252 19.940 1 .000 3.075 1.878 5.036
Tust in the product -.267 .270 .985 1 .321 .765 .451 1.298
Constant .971 1.018 .909 1 .340 2.640
The coefficients of the model predictors are tested via the hypothesis
0
1
: 0
: 0
j
j
H
H
From Table 4.15, Educational Level, Trustworthiness, Accessibility, Ease of use, Assurance
of Website, Service visibility, and knowledge of the product are significant at 0.05 with
their respective significant values of 0.004, 0.000, 0.002, 0.000, 0.005, 0.001 and 0.000.
Hence we reject the null hypothesis and conclude that they are each significant in predicting
the adoption of internet banking using the binary logistic regression.
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However, Age, Occupation, Usefulness, Risk, and Trust in the product were not significant
at 0.05 with respective significance level of 0.45, 0.767, 0.434, 0.351, and 0.321, hence
we fail to reject the null hypothesis and conclude that they are each not significant in
predicting the adoption of internet banking adoption using the binary logistic regression.
The logistic regression model obtained from the model is follows
log 1 0.646 1.322 0.950
1.266 0.869 0.952 1.123
it p y Educationallevel Trustwothiness Accessibility
Easeofuse Assurance ServiceVissibility nowledgeofPropduck t
Also, from the table 4.15, the strongest factor in adopting internet banking is “Ease of Use”.
This indicates how user friendly the internet banking platforms are. It had the odds ratio of
3.546(95% C.I=2.172-5.789). This indicates that banks with internet banking application that
easy to use are 3.546 times more likely to influence the adoption of internet banking than
those with application that are not easy to use controlling for all other factors in the model.
The next highest was “knowledge of Product” with the odds ratio given by 3.075(95%
C.I=1.878-5.036). This implead clients with the knowledge of the benefits of internet banking
are 3.075 times more likely to adopt internet banking than those who did not have knowledge
of the product controlling for all other factors in the model.
“Service Visibility” had the next highest odds ratio given by 2.590 (95% C.I=1.441-4.653).
This implies that banks with whose internet banking products are highlighted or visible have
2.59 times chance of influencing clients to adopt internet banking than those whose internet
banking products are not controlling for all other factors in the model.
The odds ratio of “Accessibility” of the internet banking applications was the next highest
with 2.586 (95% C.I= 1.402-4.772). This indicates that banks with internet banking
application that are accessible have 2.586 times chance of influencing clients to adopt their
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internet banking platforms than those whose applications are not easily accessible controlling
for all other factors in the model.
It can also be seen from Table 4.13 that the odds ratio of Education Level was 1.905 (95%
C.I=1.235-2.938). This indicates that clients whose educational level are higher have 1.905
chance of adopting internet banking compared to clients with lower educational background
controlling for all other factors in the model.
The odds ratio for “Assurance” in the internet website was 0.419(95%C.I=0.227-0.774). This
indicates that there were 41.9% decrease in adoption of internet banking adoption if there was
lack of structural assurance in the internet banking website of the bank controlling for other
variables in the model.
Finally, the odds ratio of “Trustworthiness” was 0.267 (95% C.I=0.146-0.488). This indicates
that there were 26.7% decrease in adoption of internet banking adoption if there was lack of
Trustworthiness in the bank offering the internet banking controlling for all other factors in
the model. For a unit increase in lack of trustworthiness, internet banking adoption will
decrease by 0.927 controlling for all other factors in the model.
4.4 Internet Banking Adoption of Locally and Foreign Owned Banks
To compare the adoption of internet banking of clients of locally owned banks and that of
foreign owned banks, the factors where tested for multivariate normality using the Royston
normality test.
The hypothesis tested was
0 :H The factors are multivariate normally distributed
1:H The Factors are not multivariate normally distributed
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We reject the null hypothesis if the p-value is less than 0.05.
Table 4.16: Multivariate Test for Normality
H-test 4319.481
P-value 0.000
Since the p-value is less than 0.05, we reject the null hypothesis and conclude that the factors
are not multivariate normally distributed. Since the assumption of multivariate normality was
not met, the non-parametric multivariate analysis of variance was used to compare the means
of the factors extracted.
A modification to the F test statistic was used to compare the means of the factors influencing
the adoption of internet banking. A distribution of the test statistic is created under the null
hypothesis using permutation of the observation as suggested by Edgignton (1995). A 1000
permutation of the observations were carried out and the result of the F statistic generated.
The hypothesis for the statistic is
0 :H Internet Banking Adoption for Local and Foreign Banks are the same.
1 :H Internet Banking Adoption for Local and Foreign Banks are not the same.
We reject the null hypothesis if the p value is less than 0.05
Table 4.17: Test of average adoption for Local and Foreign Owned Banks
Test Statistic df1 df2 P-value
Permutation
Test
P-
value
F 1.4665 7 392 0.178 0.173
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Since the P-value of the F statistic is greater than 0.05 we fail to reject the null hypothesis and
conclude that there is no enough evidence to say that the means of factors affecting adoption
internet banking for locally and foreign owned banks are not the same.
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CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
5.1 Introduction
The study sought to analyse the factors affecting the “internet banking adoption” in Ghana
from the customer’s perspective. Response of clients of commercial banks in Ghana were
analysed using the factor analysis and the binary logistic regression.
The summary results, conclusion and recommendation of the study are presented in this
Chapter.
5.2 Summary
The factors influencing the adoption of internet banking in Ghana were analysed using
Principal Axis method of Factor Analysis and Oblimin rotation. It was realized that nine
factor were significant.
These factors are “Trustworthiness”, “Usefulness”, “Risk”, “Accessibility”, “Ease of Use”
“Assurance in Banks Website”, “Service Visibility”, “Awareness of the Benefits of Internet
Banking” and “Trust in Internet Banking as a solution to the banking needs of clients.
Additionally, chi square test of association showed a relationship between age, educational
level and occupation clients of commercial banks.
To determine the chance of a customer adopting internet banking given a significant factor,
the binary logistic regression was used. The binary logistic regression indicated that
Educational level, Trustworthiness, Accessibility, Ease of use, Assurance Service visibility
and knowledge of the product were significant in predicting internet banking adoption.
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Whiles age, Occupation, Usefulness, Risk and Trust in the Product were not significant in
predicting internet banking adoption.
It was noted that “Ease of Use” had the highest chance of influencing clients of commercial
banks to adopt internet banking with 3.546 times more likely to influence clients at 95%
confidence level controlling for all other factors in the model.
This was followed closely by “Knowledge of Product” with 3.075 times more likely to
influence clients of commercial banks to adopt internet banking controlling for all other
factors in the model.
“Service Visibility” and “Accessibility” were 2.59 and 2.586 times more likely to influence
clients of commercial banks to adopt internet banking respectively controlling for all other
factors in the model.
Also, “Educational Level” was found to be 1.905 times more likely to influence clients of
commercial banks controlling for all other factors in the model.
However, lack of “Assurance” in the internet banking platform and lack of “Trustworthiness”
respectively, were found to decrease internet banking adoption by 41.90% and 26.7%
controlling for all other factors in the model.
Also, to compare the adoption of internet banking of clients of locally owned and foreign
owned banks, a modification of the F test as suggested by Edgignton (1995) was used to
compare the mean adoption internet banking of clients of locally and foreign owned banks in
Ghana. The test suggested there was no difference between the adoptions of internet banking
of client of locally and foreign owned banks.
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5.3 Conclusion
The objective of this study was to investigate the factors that influence the adoption of internet
banking in Ghana, determine the chance of a customer adopting internet banking given a
particular factor and compare the internet banking adoption of clients of locally and foreign
owned banks.
The study found that nine factors were significant in influencing clients of commercial banks
in Ghana to adopt internet banking using the exploratory Factor Analysis. These are
Trustworthiness, Accessibility, Ease of Use, Assurance, Service Visibility, and Product
knowledge, Usefulness, Risk and Trust in the product.
The study also found that Ease of Use, Product Knowledge, Service Visibility, Accessibility,
Educational level respectively were 3.546, 3.075, 2.59, 2.586 and 1.905 more likely to
influence internet banking adoption of clients of commercial banks in Ghana controlling for
all other factors.
Additionally, the study found that lack of Trustworthiness and Assurance decrease the
adoption of internet banking by 26.7% and 41.9% respectively controlling for all other
factors.
Finally, the study found that there was not difference in the adoption of internet banking of
clients of locally and foreign owned commercial banks in Ghana.
5.4 Recommendations
We recommend that commercial banks in Ghana should take a keen interest in making the
internet banking platforms easy to use or user friendly since this impacts more on client’s
adoption of the product.
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Additionally the more clients are aware of the internet banking products and its benefits the
more they are likely to adopt this product as suggested by this study. Hence banks should
make it a point of educating its clients and the general public about the internet banking
product and its benefits.
Also, banks should make their internet banking platforms readily available as frequently
breakdown of platforms can influence the adoption of the internet banking products of the
bank.
5.5 Limitation of the study
In carrying out the study there were some limitations that are likely to have impact on the
study. The study could not get the total number of clients of each bank in order to estimate
the number clients of each bank to administer the questionnaire. The share of deposits of the
respective banks where used in allocating the number of clients of the banks to administer
questionnaire to. In cases where the share of deposits of banks are not proportional to the
number of clients of the banks, this assumption could be flawed.
Also, the use of purposive sampling techniques in the study could influence the study as a
result of the personal bias of the investigator in selecting the banks for the study. There is no
possibility of knowing extent of accuracy achieved when this method is used and also the
sampling error cannot be estimated. Additionally, the perspective of clients of other banks
that were not sampled, on internet banking as a result of the choice purposive sampling will
not be captured on this study.
Also, the use of accidental sampling could also lead to bias in sampling the clients. Cooperate
clients and other clients who do not come to the banking hall for transaction would
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automatically be left out in the study. Additionally, clients who make use of internet banking
properly would be left out as they do not need to come to the banking hall at all.
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REFERENCES
“Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of
new information technologies? Decision sciences, 30(2), 361-391.
Al-Rfou, R., Perozzi, B., & Skiena, S. (2013). Polyglot: Distributed word representations for
multilingual nlp. arXiv preprint arXiv:1307.1662.
Bell, E., & Bryman, A. (2007). The ethics of management research: an exploratory content
analysis. British Journal of Management, 18(1), 63-77.
Bouckaert, J., & Degryse, H. (1995). Phonebanking. European Economic Review, 39(2), 229-
244.
Bulmer, M. G. (2003). Francis Galton: pioneer of heredity and biometry: JHU Press.
Chircu, A. M., & Kauffman, R. J. (2000). Limits to value in electronic commerce-related IT
investments. Journal of Management Information Systems, 17(2), 59-80.
Churchill Jr., G. A. (1979). A paradigm for developing better measures of marketing
constructs. Journal of marketing research, 64-73.
Clemes, M. D., Gan, C., & Du, J. (2012). The factors impacting on customers’ decisions to
adopt Internet banking. Banks and Bank Systems, 7(3), 33-50.
Clemons, E. K., & Hitt, L. M. (2000). The Internet and the future of financial services:
Transparency, differential pricing and disintermediation.
Cook, D. (2011). Comments on ‘Financial and Monetary Cooperation in Asia: Challenges
after the Global Financial Crisis’ by Soyoung Kim and Doo Yong Yang. International
Economic Journal, 25(4), 589-591.
Creswell, L. L., Moulton, M. J., Wyers, S. G., Pirolo, J. S., Fishman, D. S., Perman, W. H., .
. . Szabo, B. A. (1994). An experimental method for evaluating constitutive models
of myocardium in in vivo hearts. American Journal of Physiology-Heart and
Circulatory Physiology, 267(2), H853-H863.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user
perceptions and behavioral impacts. International journal of man-machine studies,
38(3), 475-487.
Drigă, I., & Isac, C. (2014). E-banking services-features, challenges and benefits. Annals of
the University of Petroşani, Economics, 14(1), 41-50.
Essinger, J. (1999). The virtual banking revolution: the customer, the bank and the future:
Internat. Thomson Business Press.
University of Ghana http://ugspace.ug.edu.gh
85
Fathima, Y. A., & Muthumani, S. (2015). USER ACCEPTANCE OF BANKING
TECHNOLOGY WITH SPECIAL REFERENCE TO INTERNET BANKING.
Journal of Theoretical & Applied Information Technology, 73(1).
Fisher, J., Burstein, F., Lynch, K., & Lazarenko, K. (2008). “Usability+ usefulness= trust”:
an exploratory study of Australian health web sites. Internet research, 18(5), 477-498.
Flavián, C., Guinaliu, M., & Torres, E. (2005). The influence of corporate image on consumer
trust: A comparative analysis in traditional versus internet banking. Internet research,
15(4), 447-470.
Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The
qualitative report, 8(4), 597-606.
Govender, J. P., & Wu, J. (2013). The adoption of Internet banking in a developing economy.
Journal of Economics and Behavioral Studies, 5(8), 496.
Gulati, A., Jabbour, A., Ismail, T. F., Guha, K., Khwaja, J., Raza, S., . . . Dweck, M. R. (2013).
Association of fibrosis with mortality and sudden cardiac death in patients with
nonischemic dilated cardiomyopathy. Jama, 309(9), 896-908.
Hinson, R. E. (2005). Internet adoption among Ghana's SME non-traditional exporters:
expectations, realities and barriers to use. Africa insight, 35(1), 20-27.
Howard Chen, Y.-H., & Corkindale, D. (2008). Towards an understanding of the behavioral
intention to use online news services: An exploratory study. Internet research, 18(3),
286-312.
Humphrey, J., Mansell, R., Paré, D., & Schmitz, H. (2004). E‐commerce for Developing Countries: Expectations and Reality. IDS bulletin, 35(1), 31-39.
Hutchinson, D., & Warren, M. (2003). Security for internet banking: a framework. Logistics
information management, 16(1), 64-73.
Jamieson, S. (2004). Likert scales: how to (ab) use them. Medical education, 38(12), 1217-
1218.
Karacaoglu, K., Bayrakdaroglu, A., & San, F. B. (2012). The impact of corporate
entrepreneurship on firms’ financial performance: Evidence from Istanbul stock
exchange firms. International Business Research, 6(1), 163.
Kim, B.-M., Widdows, R., & Yilmazer, T. (2005). The determinants of consumers’ adoption
of Internet banking. Retrieved June, 20, 2009.
Kim, M.-K., Park, M.-C., & Jeong, D.-H. (2004). The effects of customer satisfaction and
switching barrier on customer loyalty in Korean mobile telecommunication services.
Telecommunications policy, 28(2), 145-159.
University of Ghana http://ugspace.ug.edu.gh
86
Koskosas, I. V. (2008). Trust and risk communication in setting Internet banking security
goals. Risk Management, 10(1), 56-75.
Lambert, D. M., & Harrington, T. C. (1990). Measuring nonresponse bias in customer service
mail surveys. Journal of Business Logistics, 11(2), 5.
Lang, B., & Colgate, M. (2003). Relationship quality, on-line banking and the information
technology gap. International Journal of Bank Marketing, 21(1), 29-37.
Lee, M. K., & Turban, E. (2001). A trust model for consumer internet shopping. International
Journal of electronic commerce, 6(1), 75-91.
Maduku, D. K. (2014). Customers’ adoption and use of e-banking services: the South African
perspective. Banks and Bank Systems, 9(2), 78-88.
Maniraj Singh, A. (2004). Trends in South African internet banking. Paper presented at the
Aslib Proceedings.
Massilamany, M., & Nadarajan, D. (2017). Factors That Influencing Adoption of Internet
Banking in Malaysia. International Journal of Business and Management, 12(3), 126.
Mattila, M., Karjaluoto, H., & Pento, T. (2003). Internet banking adoption among mature
customers: early majority or laggards? Journal of services marketing, 17(5), 514-528.
Min, H., & Galle, W. P. (1999). Electronic commerce usage in business-to-business
purchasing. International Journal of Operations & Production Management, 19(9),
909-921.
Mols, N. P., Nikolaj D. Bukh, P., & Flohr Nielsen, J. (1999). Distribution channel strategies
in Danish retail banking. International Journal of Retail & Distribution Management,
27(1), 37-47.
Nui Polatoglu, V., & Ekin, S. (2001). An empirical investigation of the Turkish consumers’
acceptance of Internet banking services. International Journal of Bank Marketing,
19(4), 156-165.
Pennathur, A. K. (2001). “Clicks and bricks”:: e-Risk Management for banks in the age of
the Internet. Journal of banking & finance, 25(11), 2103-2123.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance
of online banking: an extension of the technology acceptance model. Internet
research, 14(3), 224-235.
Prema, C., & Sudhakar, J. (2009). Internet Banking Acceptance–An Extended Technology
Acceptance Model. RVS Journal of Management, 2(1), 110-118.
Sathye, M. (1999). Adoption of Internet banking by Australian consumers: an empirical
investigation. International Journal of Bank Marketing, 17(7), 324-334.
University of Ghana http://ugspace.ug.edu.gh
87
Suzanne Harrison, T., Peter Onyia, O., & K. Tagg, S. (2014). Towards a universal model of
internet banking adoption: initial conceptualization. International Journal of Bank
Marketing, 32(7), 647-687.
Taylor, S. A., & Baker, T. L. (1994). An assessment of the relationship between service
quality and customer satisfaction in the formation of consumers' purchase intentions.
Journal of retailing, 70(2), 163-178.
Thulani, D. T., Tofara, C. C., & Longton, R.(2009): Adoption and use of Internet Banking in
Zimbabwe: An Exploratory study. Journal of Internet Banking and Commerce, 14(1),
1-13.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of
computer vision, 57(2), 137-154.
Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The
role of switching costs. Psychology & Marketing, 21(10), 799-822.
Yee-Loong Chong, A., Ooi, K.-B., Lin, B., & Tan, B.-I. (2010). Online banking adoption: an
empirical analysis. International Journal of Bank Marketing, 28(4), 267-287."
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“APPENDICES
Appendix A: Questionnaire
UNIVERSITY OF GHANA
DEPARTMENT OF STATISTICS
“Topic: Statistical Analysis of Factors Influencing Internet Banking Adoption in Ghana:
The Customer Perspective
This Questionnaire is designed to seek empirical data for the conduct of the above topic
which is purely an academic exercise. This will be submitted for the partial fulfilment of
Masters of Philosophy (MPhil) in Statistics. Your support and co-operation is very much
anticipated and your responses will be treated with maximum confidentiality.
INSTRUCTION: Please tick/mark [√] the appropriate response to each question
NOTE: For the purpose of this questionnaire, “Internet banking” describes the various
banking products that make use of the internet. This includes checking balances,
paying bills, transferring fund.
SECTION A
1. Sex
a. Male [ ] b. Female [ ]
2. What is your age?
a. Less than 20 Years [ ] b. 20-30 Years [ ] c. 31-40 Years [ ] d. 41-50 Years [ ]
e. 51 years and above [ ]
3. What is your highest educational qualification?
a. JHS [ ] b. SHS [ ] c. Diploma [ ] d. Bachelor [ ] e. Masters [ ] f. PhD [ ]
g. Professional [ ]
4. What is your current occupation?
a. Student [ ] b. Public servant [ ] c. Civil servant [ ] d. Private/Entrepreneur [ ] e.
Housewife [ ] f. Pensioner [ ]
g. Other ……………………………….
5. Name of Bank ……….
6. Do you use internet banking?
a. Yes [ ] b. No [ ]
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SECTION B
1. How long have you used Internet banking?
a. Less than one year [ ] b. 1-2 years [ ] c. More than 2 years year [ ]
2. Which of the following mode of transactions do you use frequently?
1. Branch banking [ ] 2. Cash Machine [ ] 3. Phone Banking [ ] 4. Internet Banking [ ]
3. Which banking services do you always use through Internet banking? (Please check all
that apply)
a. Basic account information [ ] b. Making online bill payments [ ] c. Accounting check
balance [ ] d. Bank transfer [ ] e. Inter-account transfer [ ] f. Stock trading [ ]
h. Applying for cheque books [ ] i. Other ……………………………………
Please Tick the number which best reflects your level of agreement or disagreement with
the following statements. [Where 1 = Strongly Disagree; 2 = Disagree; 3 = Not sure; 4 =
Agree; 5 = Strongly Agree]
1 2 3 4 5
1 “Internet banking” enables clients to conduct banking
transactions more quickly.
2 “Internet banking” enables clients to conduct banking
transactions anytime.
3 “Internet banking” makes it easier for clients to conduct
banking transactions
4 “Internet banking” enables me to manage my bank account
(s) more effectively.
5 “Internet banking” is very useful in conducting clients
banking transactions.
6 “Internet banking” is easier to learn and become skilful.
7 It easy for client to learn how to use Internet banking to
conduct banking transactions.
8 Internet banking is very easy to use.
9 Using internet banking does not demand more mental
effort.
10
Using “internet banking” website is understandable and
clearer
11 I intend to use the service for doing some of my banking
transactions
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12 I intend to use the service for most of my banking transactions
13 The service will be more useful for some of my banking
transactions.
14 The service will be more useful for most of my banking
transactions.
1 2 3 4 5
15 My bank can be trusted as an internet service provider
16 My decision on the use of internet banking is because it is a
trusted medium of financial transactions.
17 Overall, I trust Internet banking to perform my banking
transactions.
18 The decision towards the use of Internet banking to
conduct banking transactions is significant risky
19 The decision towards the use of the service to conduct
banking transactions can lead to potential for loss
20 The decision towards the use of the service to conduct
banking transactions is a not a good decision
21 The legal structures of my bank guard client from
problems.
22 Technological structures of my Bank enable clients to carry
out transactions safely.
23 The platform used by my bank for the service is adequately
protected to enable me carry out transaction.
24 The platform used by my bank for the service is robust and
safe
25 The bank is competent in providing excellent service using
the product.
26 My bank has the capability to meet its Internet banking
customers’ needs.
27 The bank knows how to provide excellent Internet banking
services
28 My bank is one of the best banks in proving the service.
29 The bank is honest to its clients enrolled to “internet
Banking”.
30 In dealing with the service you can trust my bank
31 My bank keeps their word when it come to the service
32 The bank is acting in my best interest.
33 When it comes to the service, my bank is always ready to
assist
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34 When it comes to the service, my bank has good intention to assist their clients.
35 My bank keeps adequate information about service
transactions.
36 Right levels of information regarding the service
transaction are kept.
37 My bank maintains accurate information relating to my
bank needs.
38 Details of my accounts always are accurate anywhere
39 Details of my accounts are current.
40 The information about my accounts maintained by my
bank is enough.
41 My banks makes locating the service easy
42 The service is provides me details of the products offered
by my bank.
43 The service provides exact meaning of the products.
44 The processes for conducting each of the service is made
clear
45 The services is displayed in an obvious form
46 I like the form the service is provided
47 The service is provided in a form that makes it easy to be
used.
48 The process of carrying out the service is confusing
49 Denials from the system when carrying out the transaction
is frequent.
50 I do rely on the system to come up when needed
51 I believe that if I required assistance in accessing a banking
service, my bank would assist me on that.
52 My banks assist me when I encounter a challenge in using
the service.
53 When I experience fraud, my bank assist me
54 With the service, I get quicker and easier service
55 The service is always available
56 It is easy for me to get access any banking service that I
need to conduct
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57 Specific transactions that require a phone call for completion (such as beneficiary identification) are not time
consuming.
58 Overall task technology of the bank fits properly
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Appendix B: Total Variance Explained
Factor Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation
Sums of
Squared
Loadingsb
Total % of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
1 25.989 44.809 44.809 25.706 44.320 44.320 15.295
2 5.127 8.839 53.648 4.826 8.320 52.641 13.083
3 2.560 4.413 58.062 2.188 3.773 56.414 5.348
4 2.325 4.008 62.070 2.061 3.553 59.967 13.793
5 1.778 3.065 65.135 1.491 2.570 62.537 6.554
6 1.605 2.767 67.903 1.315 2.266 64.803 11.602
7 1.365 2.353 70.256 1.081 1.864 66.667 14.390
8 1.273 2.196 72.451 .982 1.693 68.360 4.385
9 1.139 1.964 74.415 .810 1.396 69.756 4.778
10 1.024 1.766 76.181
11 .964 1.663 77.843
12 .913 1.574 79.418
13 .782 1.348 80.766
14 .742 1.280 82.046
15 .702 1.210 83.256
16 .687 1.185 84.441
17 .608 1.048 85.488
18 .580 .999 86.488
19 .569 .982 87.470
20 .518 .894 88.363
21 .506 .873 89.236
22 .464 .799 90.035
23 .433 .746 90.781
24 .426 .735 91.516
25 .365 .630 92.146
26 .355 .612 92.758
27 .319 .550 93.308
28 .305 .526 93.834
29 .287 .495 94.329
30 .267 .461 94.790
31 .258 .445 95.235
32 .243 .420 95.654
33 .219 .378 96.032
34 .207 .356 96.388
35 .185 .320 96.708
36 .182 .313 97.021
37 .166 .286 97.308
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38 .164 .282 97.590
39 .151 .260 97.849
40 .137 .236 98.086
41 .126 .217 98.303
42 .119 .205 98.507
43 .108 .186 98.693
44 .102 .177 98.870
45 .081 .140 99.010
46 .070 .120 99.130
47 .067 .116 99.246
48 .065 .113 99.359
49 .061 .105 99.464
50 .056 .097 99.561
51 .048 .083 99.644
52 .045 .078 99.722
53 .040 .069 99.791
54 .033 .057 99.848
55 .031 .054 99.901
56 .022 .038 99.939
57 .019 .033 99.972
58 .016 .028 100.000
Extraction Method: Principal Axis Factoring.
a. Only cases for which Do you use internet banking = Yes are used in the analysis phase.
b. When factors are correlated, sums of squared loadings cannot be added to obtain a total
variance.
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Appendix C: Correlation Matrix
Variable 1 2 3 4 5 6 7
Correlation 1 1.000 .674 .657 .579 .690 .593 .668
2 .674 1.000 .606 .582 .648 .471 .530
3 .657 .606 1.000 .544 .658 .496 .511
4 .579 .582 .544 1.000 .666 .515 .610
5 .690 .648 .658 .666 1.000 .583 .563
6 .593 .471 .496 .515 .583 1.000 .755
7 .668 .530 .511 .610 .563 .755 1.000
8 .559 .597 .509 .598 .571 .619 .769
9 .567 .524 .435 .553 .560 .632 .728
10 .500 .470 .441 .478 .481 .640 .690
11 .622 .541 .446 .568 .569 .550 .632
12 .685 .595 .524 .483 .643 .529 .611
13 .543 .506 .446 .470 .550 .461 .553
14 .626 .621 .557 .492 .616 .511 .593
15 .547 .521 .467 .473 .577 .510 .625
16 .577 .582 .433 .493 .618 .555 .540
17 .505 .419 .335 .372 .503 .475 .515
18 .333 .156 .162 .181 .233 .370 .354
19 .234 .146 .138 .095 .188 .264 .230
20 .087 -.005 .041 .047 .009 .148 .086
21 .297 .384 .123 .248 .287 .342 .358
22 .356 .405 .276 .380 .329 .377 .430
23 .427 .362 .253 .290 .332 .387 .450
24 .329 .425 .204 .360 .355 .294 .404
25 .456 .421 .408 .348 .394 .453 .509
26 .567 .447 .460 .431 .447 .409 .591
27 .448 .357 .380 .337 .408 .411 .476
28 .483 .441 .447 .353 .475 .431 .510
29 .539 .550 .402 .513 .489 .478 .595
30 .509 .436 .462 .464 .562 .607 .568
31 .591 .453 .466 .447 .475 .477 .598
32 .487 .467 .529 .330 .510 .373 .463
33 .530 .433 .385 .408 .547 .474 .553
34 .455 .403 .344 .322 .388 .404 .472
35 .505 .398 .467 .319 .516 .471 .538
36 .400 .392 .369 .352 .469 .441 .491
37 .424 .332 .362 .276 .382 .427 .393
38 .497 .421 .489 .462 .488 .365 .405
39 .511 .529 .461 .332 .419 .257 .385
40 .584 .595 .539 .353 .523 .362 .469
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8 9 10 11 12 13 14 15
1 .559 .567 .500 .622 .685 .543 .626 .547
2 .597 .524 .470 .541 .595 .506 .621 .521
3 .509 .435 .441 .446 .524 .446 .557 .467
4 .598 .553 .478 .568 .483 .470 .492 .473
5 .571 .560 .481 .569 .643 .550 .616 .577
6 .619 .632 .640 .550 .529 .461 .511 .510
7 .769 .728 .690 .632 .611 .553 .593 .625
8 1.000 .704 .697 .622 .622 .562 .626 .540
9 .704 1.000 .705 .635 .577 .579 .562 .464
10 .697 .705 1.000 .522 .576 .538 .570 .481
11 .622 .635 .522 1.000 .723 .765 .635 .445
12 .622 .577 .576 .723 1.000 .662 .791 .628
13 .562 .579 .538 .765 .662 1.000 .560 .526
14 .626 .562 .570 .635 .791 .560 1.000 .470
15 .540 .464 .481 .445 .628 .526 .470 1.000
16 .435 .543 .447 .511 .553 .477 .503 .659
17 .472 .543 .511 .467 .568 .400 .484 .625
18 .216 .543 .221 .261 .248 .346 .127 .345
19 .076 .543 .134 .069 .148 .215 .151 .320
20 -.083 .543 -.008 -.013 .011 .046 -.036 .131
21 .288 .543 .218 .408 .345 .312 .336 .356
22 .325 .543 .315 .384 .408 .275 .400 .469
23 .387 .543 .400 .366 .405 .276 .434 .489
24 .295 .543 .311 .319 .393 .303 .389 .464
25 .470 .543 .525 .505 .533 .356 .544 .406
26 .470 .543 .577 .485 .514 .389 .559 .457
27 .432 .543 .456 .331 .441 .320 .449 .490
28 .500 .543 .501 .351 .476 .336 .520 .438
29 .615 .543 .626 .600 .586 .474 .576 .397
30 .537 .543 .609 .552 .547 .516 .522 .522
31 .455 .543 .558 .521 .517 .408 .520 .423
32 .403 .543 .476 .368 .394 .322 .379 .472
33 .528 .543 .572 .599 .614 .587 .569 .453
34 .383 .543 .494 .443 .494 .383 .494 .300
35 .469 .543 .570 .431 .530 .487 .454 .490
36 .416 .543 .476 .385 .492 .452 .532 .468
37 .361 .543 .474 .361 .440 .344 .475 .380
38 .394 .543 .336 .495 .525 .422 .487 .535
39 .419 .543 .277 .393 .533 .484 .518 .571
40 .511 .543 .343 .426 .499 .476 .532 .573
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16 17 18 19 20 21 22 23
1 .577 .505 .333 .234 .087 .297 .356 .427
2 .582 .419 .156 .146 -.005 .384 .405 .362
3 .433 .335 .162 .138 .041 .123 .276 .253
4 .493 .372 .181 .095 .047 .248 .380 .290
5 .618 .503 .233 .188 .009 .287 .329 .332
6 .555 .475 .370 .264 .148 .342 .377 .387
7 .540 .515 .354 .230 .086 .358 .430 .450
8 .435 .472 .216 .076 -.083 .288 .325 .387
9 .543 .466 .243 .159 .105 .393 .395 .471
10 .447 .511 .221 .134 -.008 .218 .315 .400
11 .511 .467 .261 .069 -.013 .408 .384 .366
12 .553 .568 .248 .148 .011 .345 .408 .405
13 .477 .400 .346 .215 .046 .312 .275 .276
14 .503 .484 .127 .151 -.036 .336 .400 .434
15 .659 .625 .345 .320 .131 .356 .469 .489
16 1.000 .614 .211 .283 .263 .455 .539 .486
17 .614 1.000 .455 .315 .123 .329 .423 .537
18 .211 .455 1.000 .715 .359 .227 .098 .137
19 .283 .315 .715 1.000 .432 .266 .280 .265
20 .263 .123 .359 .432 1.000 .402 .200 .210
21 .455 .329 .227 .266 .402 1.000 .559 .531
22 .539 .423 .098 .280 .200 .559 1.000 .754
23 .486 .537 .137 .265 .210 .531 .754 1.000
24 .570 .484 .176 .278 .286 .589 .724 .774
25 .453 .546 .155 .165 .082 .435 .644 .691
26 .516 .513 .215 .186 .185 .389 .513 .584
27 .487 .493 .157 .142 .207 .340 .523 .591
28 .574 .481 .211 .255 .206 .333 .534 .546
29 .489 .484 .165 .154 .051 .492 .523 .601
30 .508 .469 .235 .314 .085 .385 .562 .574
31 .484 .459 .172 .222 .181 .405 .612 .626
32 .480 .478 .188 .250 .220 .286 .475 .553
33 .443 .463 .267 .287 .068 .354 .433 .499
34 .477 .437 .116 .213 .061 .327 .558 .561
35 .451 .468 .203 .277 .197 .359 .512 .630
36 .492 .443 .154 .277 .131 .380 .504 .586
37 .397 .422 .082 .199 .112 .326 .546 .649
38 .422 .389 .087 .179 .090 .367 .566 .640
39 .405 .361 .173 .267 .143 .272 .419 .504
40 .520 .458 .197 .254 .092 .305 .425 .487
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24 25 26 27 28 29 30 31
1 .329 .456 .567 .448 .483 .539 .509 .591
2 .425 .421 .447 .357 .441 .550 .436 .453
3 .204 .408 .460 .380 .447 .402 .462 .466
4 .360 .348 .431 .337 .353 .513 .464 .447
5 .355 .394 .447 .408 .475 .489 .562 .475
6 .294 .453 .409 .411 .431 .478 .607 .477
7 .404 .509 .591 .476 .510 .595 .568 .598
8 .295 .470 .470 .432 .500 .615 .537 .455
9 .465 .519 .598 .365 .557 .733 .650 .633
10 .311 .525 .577 .456 .501 .626 .609 .558
11 .319 .505 .485 .331 .351 .600 .552 .521
12 .393 .533 .514 .441 .476 .586 .547 .517
13 .303 .356 .389 .320 .336 .474 .516 .408
14 .389 .544 .559 .449 .520 .576 .522 .520
15 .464 .406 .457 .490 .438 .397 .522 .423
16 .570 .453 .516 .487 .574 .489 .508 .484
17 .484 .546 .513 .493 .481 .484 .469 .459
18 .176 .155 .215 .157 .211 .165 .235 .172
19 .278 .165 .186 .142 .255 .154 .314 .222
20 .286 .082 .185 .207 .206 .051 .085 .181
21 .589 .435 .389 .340 .333 .492 .385 .405
22 .724 .644 .513 .523 .534 .523 .562 .612
23 .774 .691 .584 .591 .546 .601 .574 .626
24 1.000 .631 .570 .536 .555 .565 .530 .642
25 .631 1.000 .784 .748 .732 .734 .655 .749
26 .570 .784 1.000 .798 .791 .755 .658 .763
27 .536 .748 .798 1.000 .748 .546 .548 .597
28 .555 .732 .791 .748 1.000 .701 .644 .709
29 .565 .734 .755 .546 .701 1.000 .775 .751
30 .530 .655 .658 .548 .644 .775 1.000 .727
31 .642 .749 .763 .597 .709 .751 .727 1.000
32 .475 .678 .672 .633 .685 .603 .613 .697
33 .479 .606 .597 .433 .535 .710 .720 .736
34 .561 .694 .700 .530 .714 .756 .717 .753
35 .530 .626 .656 .559 .614 .694 .700 .685
36 .504 .599 .595 .523 .663 .672 .679 .650
37 .524 .651 .584 .512 .583 .705 .715 .731
38 .470 .590 .556 .496 .473 .617 .689 .567
39 .368 .440 .538 .509 .493 .478 .491 .443
40 .402 .460 .558 .489 .580 .552 .495 .485
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32 33 34 35 36 37 38 39
1 .487 .530 .455 .505 .400 .424 .497 .511
2 .467 .433 .403 .398 .392 .332 .421 .529
3 .529 .385 .344 .467 .369 .362 .489 .461
4 .330 .408 .322 .319 .352 .276 .462 .332
5 .510 .547 .388 .516 .469 .382 .488 .419
6 .373 .474 .404 .471 .441 .427 .365 .257
7 .463 .553 .472 .538 .491 .393 .405 .385
8 .403 .528 .383 .469 .416 .361 .394 .419
9 .452 .610 .577 .590 .530 .536 .425 .408
10 .476 .572 .494 .570 .476 .474 .336 .277
11 .368 .599 .443 .431 .385 .361 .495 .393
12 .394 .614 .494 .530 .492 .440 .525 .533
13 .322 .587 .383 .487 .452 .344 .422 .484
14 .379 .569 .494 .454 .532 .475 .487 .518
15 .472 .453 .300 .490 .468 .380 .535 .571
16 .480 .443 .477 .451 .492 .397 .422 .405
17 .478 .463 .437 .468 .443 .422 .389 .361
18 .188 .267 .116 .203 .154 .082 .087 .173
19 .250 .287 .213 .277 .277 .199 .179 .267
20 .220 .068 .061 .197 .131 .112 .090 .143
21 .286 .354 .327 .359 .380 .326 .367 .272
22 .475 .433 .558 .512 .504 .546 .566 .419
23 .553 .499 .561 .630 .586 .649 .640 .504
24 .475 .479 .561 .530 .504 .524 .470 .368
25 .678 .606 .694 .626 .599 .651 .590 .440
26 .672 .597 .700 .656 .595 .584 .556 .538
27 .633 .433 .530 .559 .523 .512 .496 .509
28 .685 .535 .714 .614 .663 .583 .473 .493
29 .603 .710 .756 .694 .672 .705 .617 .478
30 .613 .720 .717 .700 .679 .715 .689 .491
31 .697 .736 .753 .685 .650 .731 .567 .443
32 1.000 .524 .626 .619 .526 .535 .519 .447
33 .524 1.000 .671 .714 .698 .640 .548 .407
34 .626 .671 1.000 .648 .669 .743 .548 .447
35 .619 .714 .648 1.000 .735 .721 .628 .573
36 .526 .698 .669 .735 1.000 .746 .660 .464
37 .535 .640 .743 .721 .746 1.000 .695 .486
38 .519 .548 .548 .628 .660 .695 1.000 .687
39 .447 .407 .447 .573 .464 .486 .687 1.000
40 .543 .456 .444 .582 .514 .489 .553 .807
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40 41 42 43 44 45 46 47
1 .584 .439 .262 .389 .249 .282 .284 .358
2 .595 .431 .211 .285 .168 .178 .226 .322
3 .539 .252 .222 .179 .169 .233 .211 .252
4 .353 .313 .054 .319 .168 .204 .168 .220
5 .523 .300 .171 .344 .255 .268 .221 .357
6 .362 .341 .172 .395 .245 .237 .193 .360
7 .469 .511 .259 .456 .364 .350 .276 .399
8 .511 .459 .246 .364 .326 .317 .342 .282
9 .504 .545 .331 .397 .391 .393 .392 .473
10 .343 .373 .272 .388 .345 .275 .383 .288
11 .426 .335 .233 .410 .311 .243 .284 .423
12 .499 .371 .213 .437 .295 .231 .319 .353
13 .476 .355 .177 .285 .243 .185 .225 .348
14 .532 .351 .233 .324 .292 .325 .387 .414
15 .573 .415 .137 .496 .336 .282 .229 .348
16 .520 .398 .236 .467 .277 .397 .304 .600
17 .458 .376 .269 .468 .405 .327 .357 .425
18 .197 .228 -.009 .170 .171 .091 .038 .125
19 .254 .169 .114 .197 .219 .238 .110 .239
20 .092 .222 .052 .074 .189 .175 .148 .290
21 .305 .275 .214 .294 .250 .254 .257 .396
22 .425 .372 .428 .623 .440 .546 .412 .544
23 .487 .474 .484 .491 .503 .475 .495 .477
24 .402 .447 .421 .437 .414 .415 .388 .495
25 .460 .453 .567 .579 .590 .511 .604 .533
26 .558 .536 .436 .511 .508 .462 .584 .543
27 .489 .461 .390 .536 .538 .483 .539 .529
28 .580 .563 .451 .540 .542 .557 .558 .549
29 .552 .505 .501 .536 .458 .430 .577 .497
30 .495 .363 .433 .469 .467 .416 .463 .521
31 .485 .479 .469 .574 .570 .567 .547 .540
32 .543 .479 .432 .515 .536 .539 .528 .485
33 .456 .377 .379 .491 .492 .421 .475 .469
34 .444 .444 .525 .521 .438 .465 .540 .481
35 .582 .453 .494 .487 .490 .395 .485 .457
36 .514 .410 .413 .449 .468 .487 .515 .515
37 .489 .393 .587 .490 .502 .475 .558 .460
38 .553 .400 .447 .413 .397 .390 .513 .392
39 .807 .514 .291 .318 .322 .343 .395 .346
40 1.000 .565 .390 .391 .413 .526 .469 .472
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48 49 50 51 52 53 54 55 56 57 58
1 .287 .276 .269 .242 .327 .304 .390 .365 .374 .116 .382
2 .279 .207 .298 .221 .187 .276 .371 .366 .309 .240 .448
3 .229 .153 .257 .137 .301 .199 .311 .285 .251 -.007 .368
4 .374 .279 .221 .253 .228 .199 .271 .213 .220 .238 .301
5 .287 .179 .263 .233 .228 .241 .310 .311 .250 .138 .414
6 .365 .242 .252 .205 .253 .210 .187 .284 .249 .130 .298
7 .464 .269 .364 .362 .353 .291 .355 .436 .418 .287 .453
8 .349 .207 .446 .289 .324 .267 .323 .413 .392 .226 .443
9 .551 .233 .458 .384 .416 .332 .370 .427 .396 .306 .452
10 .417 .103 .408 .300 .284 .203 .348 .421 .409 .225 .434
11 .408 .274 .317 .234 .232 .193 .277 .235 .210 .153 .306
12 .355 .214 .285 .225 .225 .246 .306 .332 .285 .144 .404
13 .334 .213 .330 .195 .218 .200 .309 .285 .218 .178 .355
14 .288 .161 .261 .283 .305 .214 .347 .415 .332 .270 .451
15 .335 .233 .350 .246 .198 .364 .373 .365 .339 .139 .384
16 .431 .278 .327 .333 .292 .316 .381 .326 .355 .288 .470
17 .498 .236 .389 .305 .262 .283 .390 .400 .388 .232 .425
18 .318 .348 .124 .073 .120 .055 .148 .219 .164 .115 .025
19 .240 .290 .166 .126 .168 .129 .219 .228 .149 .231 .119
20 .202 .436 .161 .066 .162 .174 .181 .168 .229 .198 .155
21 .384 .307 .294 .213 .147 .285 .265 .209 .274 .389 .387
22 .493 .345 .512 .527 .341 .398 .385 .364 .380 .532 .514
23 .440 .217 .533 .421 .382 .414 .475 .497 .446 .462 .528
24 .472 .265 .398 .393 .305 .306 .386 .360 .344 .494 .501
25 .469 .191 .532 .537 .605 .465 .467 .517 .457 .374 .605
26 .460 .263 .485 .525 .562 .495 .567 .638 .582 .339 .579
27 .384 .261 .462 .461 .556 .516 .501 .575 .567 .296 .583
28 .424 .245 .467 .605 .625 .505 .575 .610 .580 .383 .606
29 .522 .235 .469 .497 .479 .438 .486 .509 .461 .435 .559
30 .469 .120 .490 .436 .457 .393 .360 .457 .394 .300 .505
31 .461 .278 .463 .546 .492 .441 .443 .450 .450 .406 .501
32 .362 .164 .531 .584 .524 .521 .552 .521 .489 .287 .565
33 .350 .192 .476 .316 .320 .292 .305 .391 .274 .271 .387
34 .471 .196 .436 .613 .518 .413 .471 .475 .430 .480 .523
35 .424 .203 .569 .386 .465 .505 .510 .607 .536 .298 .601
36 .348 .118 .412 .452 .458 .464 .422 .519 .423 .360 .545
37 .356 .131 .462 .486 .481 .402 .362 .445 .416 .405 .512
38 .325 .192 .434 .393 .411 .461 .404 .403 .335 .283 .456
39 .324 .298 .429 .349 .376 .487 .507 .556 .465 .302 .494
40 .342 .274 .481 .379 .459 .518 .639 .591 .500 .332 .593
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1 2 3 4 5 6 7
41 .439 .431 .252 .313 .300 .341 .511
42 .262 .211 .222 .054 .171 .172 .259
43 .389 .285 .179 .319 .344 .395 .456
44 .249 .168 .169 .168 .255 .245 .364
45 .282 .178 .233 .204 .268 .237 .350
46 .284 .226 .211 .168 .221 .193 .276
47 .358 .322 .252 .220 .357 .360 .399
48 .287 .279 .229 .374 .287 .365 .464
49 .276 .207 .153 .279 .179 .242 .269
50 .269 .298 .257 .221 .263 .252 .364
51 .242 .221 .137 .253 .233 .205 .362
52 .327 .187 .301 .228 .228 .253 .353
53 .304 .276 .199 .199 .241 .210 .291
54 .390 .371 .311 .271 .310 .187 .355
55 .365 .366 .285 .213 .311 .284 .436
56 .374 .309 .251 .220 .250 .249 .418
57 .116 .240 -.007 .238 .138 .130 .287
58 .382 .448 .368 .301 .414 .298 .453
8 9 10 11 12 13 14 15
41 .459 .543 .373 .335 .371 .355 .351 .415
42 .246 .543 .272 .233 .213 .177 .233 .137
43 .364 .543 .388 .410 .437 .285 .324 .496
44 .326 .543 .345 .311 .295 .243 .292 .336
45 .317 .543 .275 .243 .231 .185 .325 .282
46 .342 .543 .383 .284 .319 .225 .387 .229
47 .282 .543 .288 .423 .353 .348 .414 .348
48 .349 .543 .417 .408 .355 .334 .288 .335
49 .207 .543 .103 .274 .214 .213 .161 .233
50 .446 .543 .408 .317 .285 .330 .261 .350
51 .289 .543 .300 .234 .225 .195 .283 .246
52 .324 .543 .284 .232 .225 .218 .305 .198
53 .267 .543 .203 .193 .246 .200 .214 .364
54 .323 .543 .348 .277 .306 .309 .347 .373
55 .413 .543 .421 .235 .332 .285 .415 .365
56 .392 .543 .409 .210 .285 .218 .332 .339
57 .226 .543 .225 .153 .144 .178 .270 .139
58 .443 .543 .434 .306 .404 .355 .451 .384
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16 17 18 19 20 21 22 23
41 .398 .376 .228 .169 .222 .275 .372 .474
42 .236 .269 -.009 .114 .052 .214 .428 .484
43 .467 .468 .170 .197 .074 .294 .623 .491
44 .277 .405 .171 .219 .189 .250 .440 .503
45 .397 .327 .091 .238 .175 .254 .546 .475
46 .304 .357 .038 .110 .148 .257 .412 .495
47 .600 .425 .125 .239 .290 .396 .544 .477
48 .431 .498 .318 .240 .202 .384 .493 .440
49 .278 .236 .348 .290 .436 .307 .345 .217
50 .327 .389 .124 .166 .161 .294 .512 .533
51 .333 .305 .073 .126 .066 .213 .527 .421
52 .292 .262 .120 .168 .162 .147 .341 .382
53 .316 .283 .055 .129 .174 .285 .398 .414
54 .381 .390 .148 .219 .181 .265 .385 .475
55 .326 .400 .219 .228 .168 .209 .364 .497
56 .355 .388 .164 .149 .229 .274 .380 .446
57 .288 .232 .115 .231 .198 .389 .532 .462
58 .470 .425 .025 .119 .155 .387 .514 .528
41 24 25 26 27 28 29 30 31
42 .447 .453 .536 .461 .563 .505 .363 .479
43 .421 .567 .436 .390 .451 .501 .433 .469
44 .437 .579 .511 .536 .540 .536 .469 .574
45 .414 .590 .508 .538 .542 .458 .467 .570
46 .415 .511 .462 .483 .557 .430 .416 .567
47 .388 .604 .584 .539 .558 .577 .463 .547
48 .495 .533 .543 .529 .549 .497 .521 .540
49 .472 .469 .460 .384 .424 .522 .469 .461
50 .265 .191 .263 .261 .245 .235 .120 .278
51 .398 .532 .485 .462 .467 .469 .490 .463
52 .393 .537 .525 .461 .605 .497 .436 .546
53 .305 .605 .562 .556 .625 .479 .457 .492
54 .306 .465 .495 .516 .505 .438 .393 .441
55 .386 .467 .567 .501 .575 .486 .360 .443
56 .360 .517 .638 .575 .610 .509 .457 .450
57 .344 .457 .582 .567 .580 .461 .394 .450
58 .494 .374 .339 .296 .383 .435 .300 .406
59 .501 .605 .579 .583 .606 .559 .505 .501
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32 33 34 35 36 37 38 39
41 .479 .377 .444 .453 .410 .393 .400 .514
42 .432 .379 .525 .494 .413 .587 .447 .291
43 .515 .491 .521 .487 .449 .490 .413 .318
44 .536 .492 .438 .490 .468 .502 .397 .322
45 .539 .421 .465 .395 .487 .475 .390 .343
46 .528 .475 .540 .485 .515 .558 .513 .395
47 .485 .469 .481 .457 .515 .460 .392 .346
48 .362 .350 .471 .424 .348 .356 .325 .324
49 .164 .192 .196 .203 .118 .131 .192 .298
50 .531 .476 .436 .569 .412 .462 .434 .429
51 .584 .316 .613 .386 .452 .486 .393 .349
52 .524 .320 .518 .465 .458 .481 .411 .376
53 .521 .292 .413 .505 .464 .402 .461 .487
54 .552 .305 .471 .510 .422 .362 .404 .507
55 .521 .391 .475 .607 .519 .445 .403 .556
56 .489 .274 .430 .536 .423 .416 .335 .465
57 .287 .271 .480 .298 .360 .405 .283 .302
58 .565 .387 .523 .601 .545 .512 .456 .494
40 41 42 43 44 45 46 47
41 .565 1.000 .522 .492 .547 .504 .502 .505
42 .390 .522 1.000 .516 .619 .576 .619 .500
43 .391 .492 .516 1.000 .666 .654 .574 .546
44 .413 .547 .619 .666 1.000 .720 .725 .693
45 .526 .504 .576 .654 .720 1.000 .750 .704
46 .469 .502 .619 .574 .725 .750 1.000 .630
47 .472 .505 .500 .546 .693 .704 .630 1.000
48 .342 .455 .336 .447 .514 .504 .417 .658
49 .274 .405 .208 .416 .263 .290 .286 .291
50 .481 .402 .422 .408 .499 .460 .488 .444
51 .379 .562 .506 .596 .531 .660 .597 .485
52 .459 .537 .558 .434 .656 .681 .702 .581
53 .518 .575 .421 .538 .568 .581 .604 .502
54 .639 .569 .493 .421 .579 .599 .628 .519
55 .591 .559 .458 .397 .610 .499 .545 .521
56 .500 .547 .399 .409 .556 .495 .492 .488
57 .332 .422 .364 .358 .387 .528 .437 .463
58 .593 .583 .588 .436 .598 .563 .591 .628
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48 49 50 51 52 53 54 55 56 57 58
41 .455 .405 .402 .562 .537 .575 .569 .559 .547 .422 .583
42 .336 .208 .422 .506 .558 .421 .493 .458 .399 .364 .588
43 .447 .416 .408 .596 .434 .538 .421 .397 .409 .358 .436
44 .514 .263 .499 .531 .656 .568 .579 .610 .556 .387 .598
45 .504 .290 .460 .660 .681 .581 .599 .499 .495 .528 .563
46 .417 .286 .488 .597 .702 .604 .628 .545 .492 .437 .591
47 .658 .291 .444 .485 .581 .502 .519 .521 .488 .463 .628
48 1.000 .246 .366 .441 .440 .353 .448 .452 .441 .537 .515
49 .246 1.000 .194 .290 .211 .344 .281 .179 .269 .273 .207
50 .366 .194 1.000 .439 .467 .551 .485 .627 .579 .383 .607
51 .441 .290 .439 1.000 .707 .591 .515 .521 .567 .635 .625
52 .440 .211 .467 .707 1.000 .707 .669 .670 .642 .473 .665
53 .353 .344 .551 .591 .707 1.000 .705 .672 .681 .377 .598
54 .448 .281 .485 .515 .669 .705 1.000 .765 .636 .462 .670
55 .452 .179 .627 .521 .670 .672 .765 1.000 .831 .515 .775
56 .441 .269 .579 .567 .642 .681 .636 .831 1.000 .578 .770
57 .537 .273 .383 .635 .473 .377 .462 .515 .578 1.000 .658
58 .515 .207 .607 .625 .665 .598 .670 .775 .770 .658 1.000
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Appendix D List of Banks
No Foreign Local
1 Access Bank ADB Bank
2 BSIC Ghana Ltd CAL Bank
3 Sovereign Bank Fidelity Bank
4 Standared Chartered Bank First Atlantic Bank
5 Barclays Bank GCB Bank
6 Soceite General GN Bank
7 Stanbic Bank HFC Bank
8 Bank of Africa National Investment Bank
9 FBN Bank Prudential Bank
10 Ecobank The Royal Bank
11 First National Bank(FNB) Unibank
12 Bank of Baroda Universal Merchant Bank
13 Energy Bank UT Bank
14 Guaranty Trust Bank Capital Bank
15 United Bank for Africa
16 Zenith Bank
“
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