statistical analysis of factors influencing the adoption

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

University of Ghana http://ugspace.ug.edu.gh

i

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)


ii

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.


iv

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


v

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


1

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


4

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


5

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


6

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


7

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.


8

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


10

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


11

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.


12

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


13

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


14

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


15

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


16

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


17

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.


18

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


19

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


20

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.


21

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.


22

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’


23

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;


24

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.


25

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


26

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.


27

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.


28

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


29

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.


30

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


31

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.


32

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.


33

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


34

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.


35

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


36

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.


37

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


38

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.


39

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)


40

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)


41

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)


42

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.


43

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


44

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


45

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΄]


46

= 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


47

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


48

'' ' 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


49

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 :


50

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)


51

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 ,


52

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)


53

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.


54

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


55

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


56

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


57

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.


58

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


59

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.


60

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.


61

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


62

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%


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


Level 50.0% 50.0% 100.0%

% within Usage 17.1% 30.8% 22.0%

Bachelor Count 102 53 155


Level 65.8% 34.2% 100.0%

% within Usage 39.7% 37.1% 38.8%

Masters Count 73 3 76


Level 96.1% 3.9% 100.0%

% within Usage 28.4% 2.1% 19.0%

PhD Count 3 2 5


Level 60.0% 40.0% 100.0%

% within Usage 1.2% 1.4% 1.3%

Professional Count 19 5 24


Level 79.2% 20.8% 100.0%

% within Usage 7.4% 3.5% 6.0%


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


66

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.


67

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


78

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.


79

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.


80

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.


81

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.


82

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


83

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.


84

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88

“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 [ ]


89

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


90

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


91

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


92

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


93

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


94

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.


95

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


96

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


97

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


98

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


99

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


100

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


101

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


102

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


103

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


104

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


105

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


106

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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statistical analysis of factors influencing the adoption

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