The Effect of E-Banking on Organizational Performance: A

Study On Selected Commercial Banks in Dire Dawa Administration

BY:

Terefelign Meles

Under the Guidance of

Mesfin M. (PhD Candidate)

A Thesis Submitted to the School of Graduate Studies of Dire Dawa

University in Partial Fulfillment of the Requirements for the Award of the
Degree of Master of Business Administration (MBA)

Dire Dawa University

MBA Program

MAY,2021
Dire Dawa, Ethiopia
 Declaration

I hereby declare that this MBA thesis is my original work and has not been presented for a
degree in any other university, and all sources of material used for this thesis / dissertation have
been duly acknowledged.

Name of Student: _______________________________________________

Signature: ______________________ Date _______________

This MBA thesis has been submitted for examination with my approval as Thesis
advisor.

Name of major advisor: ___________________________________

Signature: _____________________ Date ____________________

Place of Submission: ________________________________

Date of Submission: _________________________________

I
TABLE OF CONTENT

S
TABLE OF CONTENTS...............................................................................................................................................II
ABSTRACT.................................................................................................................................................................IV
CHAPTER ONE.............................................................................................................................................................1
1.INTRODUCTION........................................................................................................................................................1
1.1Background of the study............................................................................................................................................1
1.2. Problem Statement...................................................................................................................................................3
1.3 Objective of study.....................................................................................................................................................4
1.3.1. General objective..........................................................................................................................................4
1.3.2. Specific Objective.........................................................................................................................................4
1.4 Research Questions and Hypothesis.........................................................................................................................5
1.4.1. Research Questions.......................................................................................................................................5
1.5. Significance of the Study.........................................................................................................................................6
1.6. Scope........................................................................................................................................................................7
1.7. Limitation of the study.............................................................................................................................................7
1.8. Operational definition..............................................................................................................................................7
1.9. Organizations of the study.......................................................................................................................................8
CHAPTER TWO............................................................................................................................................................9
2.REVIEW OF RELATED LITERATURE...................................................................................................................9
2.1. Introduction..............................................................................................................................................................9
2.2. Theoretical Review..................................................................................................................................................9
2.3. Empirical Review...................................................................................................................................................11
2.4. Gap in Literature....................................................................................................................................................14
2.5. Conceptual Framework..........................................................................................................................................14
CHAPTER THREE.......................................................................................................................................................16
3. RESEARCH METHODOLOGY..............................................................................................................................16
3.1. Research design......................................................................................................................................................16
3.2. Source and Type of Data........................................................................................................................................16
3.3. Sampling Design....................................................................................................................................................16
3.4. Method of data collection, Design, and Administration........................................................................................17
3.5. Method of Data Analysis.......................................................................................................................................19
3.6. Ethical Consideration.............................................................................................................................................19

II
4. WORK PLAN AND COST BREAK DOWN..........................................................................................................20
4.1 WORK PLAN.........................................................................................................................................................20
4.2 COST BREAK DOWN..........................................................................................................................................21
REFERENCE..............................................................................................................................................................23
APPENDICES..............................................................................................................................................................26
APPENDIX A:..............................................................................................................................................................26

III
 ABSTRACT

The rapid advancement of technology has made it possible for organizations to adopt
electronic banking strategies for competitive positioning and thus organizational performance.
The general objective of this study is to examine the effect of e-banking on bank's performance of
commercial banks in Ethiopia located at Dire Dawa Administration. To achieve this objective
the study collect data through secondary source .The population of interest in this study
comprise four banks selected using a purposive sampling method for the period (i.e. 2009-2013
E.C (2016/17-2020/21 G.C)). To determine the effect, the researcher used five independent
variable to say internet banking, agent banking Mobil banking, point of sales and automated
teller machines and return on asset (ROA) used to measure banks organizational performance.
Descriptive statistics was compute interims of frequencies, percentages, means and standard
deviations were clearly showed in the form of both tables and figures. and graph. In order to
draw conclusions, inferential statistics was computed. A regression model was used to determine
the nature of the relationship between dependent and independent variables.

Result from the study concludes that E-banking influence organization performance of banks in
Ethiopia located at dire dawa positively. The adoption of E-banking by commercial banks has a
high potential of improving organization performance and hence better returns to the
shareholders.Covid 19 enhances the urgency of their adoption rate to be high among both the
banks and their customers. It could be challenging if the adoption was only with either the banks
or the purchasers.

Key Words: Electronic Banking, Organizational Performance, Commercial Banks, Dire

Dawa Administration.

IV
V
 CHAPTER ONE

1. INTRODUCTION

This chapter begins by presenting brief background of the study which is followed by
the statement of the problem. Under the statement of the problem, the study states the reasons to
carry out this study. Following the statement of the problem, the general and specific
objectives of the study are presented. The next section presents the research hypothesis.
Finally, significance of the study, scope and limitation of the study including organization of
the paper are presented.
1.1 Background of the study.

Electronic banking has become an integral part in the operations of every commercial banking
institution around the world which helps financial service providers, customers, individuals and
are able to access their accounts, do transactions and acquire latest information on financial
products and services from public or private networks, like the web . (Driga and Isac,2014).

History of E-banking in the world traced back to 1967 G.C. According to Shah and Clarke
(2009), cash teller machines (ATMs) were the primary means of providing electronic access to
retail customers, made possible through the introduction of computer networks. The rapid climb
and practice of e-banking, globally, several empirical studies exist on e- banking and bank
performance. Mabwai (2016) indicated that the number of mobile banking transactions, capital
adequacy, markets share and the size of the assets had a positive influence on the financial
performance of commercial banks. Argamo (2015) explain that agency banking enables
accessibility of banking services, low cost of service and customer transactions hence enhancing
financial performance of Chase Bank. Cheruiyot (2015) show that online client’s deposits and
banking transaction has important connection with ROA. Mutiso (2017) further indicate that
there was a positive correlation between automated teller machines and return on assets implying
a strong relationship between the two variables.

Despite the rapid advancement in technology and increased forces of competition, several
challenges confront the performance of commercial banks. This situation is affected by the ever

1
changing business environment and therefore the dynamic legal environment resulting from
formulation of various regulations and policies governing the way banks operate (Azeem, et al.,
2015). Several regulations for example fixing of the interest rates and increasing the minimum
capital requirement have been passed in the recent past time forced these institutions to come up
with innovative strategies and ways of survival. Thus, as a response to these changes, most
commercial banks have embraced e-banking strategies, most commercial banks have adopted e-
banking majorly to widen the revenue channels and grow the level of profits (Agarwal, 2|016).

It is therefore important that e-banking innovations are made by sound investigation of risks
and costs to avoid harms on the organization performance. On the other side, the bank
performance is directly associated with efficiency and effectiveness of electronic banking, but on
the opposite tight controls and standards are needed to stop losses associated with electronic
banking. The banks need to balance these two options so as to not impair its overall prosperity.
This is only possible if overall effects of electronic banking on the banks and its customers are
understood. (Aduda1 and Kingoo, 2012).

In Ethiopia until 2001, banks were only offering services through the physical branch. The
concept of e-banking implemented in Ethiopia when the largest state owned, commercial bank of
Ethiopia (CBE) introduced ATM to deliver service to the local users during late 2001 .The
country now has electronic services such as Automated Teller Machines (ATMs), SMS banking,
internet banking, agent banking.

Ethiopian commercial banks have not been left behind in the journey of e-banking as mobile
banking, internet banking and automated teller machines have highly been adopted to remain
competitive and to create digital Ethiopia by 2025. These situations are basis of the current study
that seeks to determine how adoption of e-banking strategy among these selected four Ethiopian
Commercial Banks located in dire dawa has impacted on their performance.

1.2. Problem Statement

2
 The pace at which each bank is investing and adopting innovation is appalling (Muchiri, 2017).
Most commercial banks have invested in agency banking model, internet banking, ATMs and
mobile banking. With time, the marginal benefits and costs of adoption of e-banking strategies
are likely to be neutralized if no new e-banking strategies are developed (Özataç and
Gökmenoglu,2017).The existing literature reveals that e-banking strategy has an improve
financial performance. Chemirmir (2016) looks at electronic banking as an innovative strategy
and how it influences performance of Kenyan commercial banks. The finding was that e-banking
has significant influence on performance. Njoroge and Mugambi (2018) examined e-banking and
its influence on financial performance of Kenyan commercial bank.

In some developing countries, the expected result is not seen due to infrastructure investment
could not enough and customer choose traditional based banking which leads to block the
impacts of expected cost effectiveness and profitability.(Neha,2017) .The case is also real for
online banking activities. Internet infrastructure based on relatively old technology blocks the
achievement of expected performance of banks in developing countries (Alam et al., 2007).

Studies on banking and performance of the banks in African countries that relatively lower
level of development. For example, Abaenew et al. (2013) and Hassan et al. (2013) made studies
on Nigeria and Adua and Kingoo (2012) and Nguyen Gakur Connection (2013) made studies on
Kenya that electronic banking activities increase profitability on banks. on the other hand,
Khrawish and Al-Sa’di (2011), Al-Samadi and Al-Wabal (2011), and Gutu (2014) findings show
that the impact on the profitability of electronic banking is negative.

several studies exist in Ethiopian (Assefa 2013) focus on the assessment study and the
correlation between e-banking and customer satisfaction .Likewise,Gemechu (2014); Gardachew
(2010) evaluated the adoption of e-banking in the context of banks perception, in addition, the
closest to this areas of study (Tilahun2015) focus only ATM,debit card and POS ,which mainly
cover period until 2016.

With regard studies showed as e- banking is contributing a number of benefits to commercials

banks with respect to enhancing their performance. For example deposit mobilization, easy
reaching of customer, low transaction cost, enhancement bank reputation (cheng et al,2015).it
becomes very important to carry out a study in Ethiopian context whether e-banking has effect

3
on performance of commercial banks. It is therefore, necessary for bankers, bank regulators,
supervisors and researchers to understand e-banking effects on the performance of banks. In Dire
Dawa the effect e-banking on bank performance not done. The researchers' main purpose is to
fill this significant gap by studying the effect electronic banks on the performance of Ethiopian
commercial banks located in diredawa.

1.3 Objective of study

1.3.1. General objective

The general objective of this study was to examine the effect of e-banking on bank's
performance of select commercial banks in located at Dire Dawa Administration.

1.3.2. Specific Objective

1. To determine the effect of implementing mobile Banking on Return on Asset of selected

commercial banks in Dire Dawa Administration.

2. To determine the effect of implementing agent Banking on Return on Asset of selected

commercial banks in Dire Dawa Administration.

3. To determine the effect of adopting Point-of-Sale (POS) system on Return on Asset of

selected commercial banks in Dire Dawa Administration.

4. To determine the effect of implementing internet banking on Return on Asset of

selected commercial banks in Dire Dawa Administration.

5. To determine the effect of carrying out automated teller machines on Return on Asset of
selected commercial banks in Dire Dawa Administration.

1.4 Research Questions and Hypothesis

1.4.1. Research Questions

1, How does mobile banking affects bank's Return on Asset of commercial banks in
selected commercial banks in Dire Dawa Administration.
2, How does agent banking affects bank's Return on Asset of commercial banks in
selected commercial banks in Dire Dawa Administration.

4
 3, How does Point-of-Sale (POS) affects bank's Return on Asset of commercial banks in
selected commercial banks in Dire Dawa Administration.
4, How does internet banking affects bank's Return on Asset of commercial banks in
selected commercial banks in Dire Dawa Administration.
5, How does automated teller machines affects bank's Return on Asset of commercial
banks in selected commercial banks in Dire Dawa Administration.

Hypothesis

H01: There is a positive and significant relationship between mobile banking and return
on Asset
H02: There is a positive and significant relationship between agent banking and return
on Asset
H03: There is a positive and significant relationship between Point-of-Sale (POS) and
return on Asset

H04: There is a positive and significant relationship between internet banking and return
on Asset

H05: There is a positive and significant relationship between automated teller machines
and return on Asset

1.5. Significance of the Study

In competitive global economy, use of E-banking technology is not optional decision for bank
to ensure profitability and survival in the future (Nigussie,2015). In response to the increasing
turbulent business environment, commercial banks have resorted to e-banking strategies with the
aim of increasing their alternative channels of revenues and increased customer satisfaction
besides better response to the needs and wants of customers (Chedrawi, Harb &Saleh, 2019).

The finding of the study will be of great importance to executives of commercial banks in
Ethiopia as they will understand the effect of electronic banking on profitability of commercial
banks in Ethiopia, this will assist them in making decision on whether to adopt electronic

5
banking or not and the expected results of electronic banking adoption to their banks
profitability.

The study finding will enlighten the policy makers in the banking industry on the expected
effect of electronic banking on banks profitability; this will assist them in designing appropriate
policy for electronic banking adoption by commercial banking in Ethiopia. The study will be of
great importance to future scholars and academicians as it will form basis for future research as
well as providing literature for future studies on electronic banking.

The study will help the researcher to attain the qualification of a Master's Degree in the
Business Administration due since its one of the pre-requisites and will also help the researcher
to identify areas for recommendation for further improvement.

1.6. Scope

Conceptual scope

Conceptually, the research was carried out on two study variable that is to say E-Banking as an
independent variable with determinants of mobile Banking, agent Banking, Point-of-Sale (POS),
internet banking, automated teller machines, and Financial Performance as the dependent
variable of Return on asset (ROA).

. Geographical scope

Geographically, the research was conducted at four commercial banks; Commercial Bank of
Ethiopia (CBE), Bank of Abyssinia (BoA), Awash Bank, Dashen Bank located at Dire Dawa
Administration, Ethiopia.

Time Scope

The research was conducted with the time scale given by the university, as of the study began
at June .the researcher has not enough time to study problem .however, the variable to represent
the dependent was adequate to examine the objective.

1.7. Limitation of the study

6
 As stated the study was carried out at commercial banks located in Dire Dawa Administration,
and the data the researcher was track down are available at the annual financial report of the
bank's, so those reports indicate only the overall financial performance of the bank's not specified
as of the Dire Dawa branches because of this the researcher ought to use those data for the study.

1.8. Operational definition

E-banking represent a system through which financial service providers, customers, individuals
and businesses are able to access their accounts, do transactions and obtain latest information on
financial products and services from public or private networks, such as the internet. For
example, using intelligent devices such as personal computer, auto-mated teller machines
(ATMs) and personal digital assistant (PDA), customers access e-banking services and do their
transactions with less effort as compared to the branch based banking. The term e-banking can be
explained in different way from different perspectives. Nonetheless, researchers across the world
have made extensive efforts to provide a precise and all-inclusive concept of e-banking.

1.9. Organizations of the study

This study is organized in to five major chapters. The first chapter deals with the introduction
of the study, while the second chapter focuses on the review of the related literature. Chapter
three deals with the research approaches followed to conduct the study and discuss research
design and methodology in detail. The fourth chapter will present result and discussion. Chapter
five includes summary of findings, conclusions and recommendations.

7
 CHAPTER TWO

2. REVIEW OF RELATED LITERATURE

2.1. Introduction
This chapter reviews the existing literature on electronic banking on performance of
commercial banks. In specific the chapter reviews that theoretical review where various theories
on electronic banking are reviewed, empirical review where empirical studies done on effects of
electronic banking on banks performance are reviewed.

2.2. Theoretical Review

2.1.1 Definition of E- Banking
E-Banking is defined as using electronic devices like internet, wireless connection networks,
ATM, phone and cell phone in banking services. These services were parts of providing currency
for and economic system for the country (Makara and Monametsi, 2018).

According to Makara and Monametsi (2018) the more we are going to the higher levels of E-
Banking, the less manual works will be, the more computer systematize, the more networks
available, the less time restriction, and ultimately the more secure banking system will be.
Services are done by internet network that is popular for its security. On the other hand, E-
Banking is the use of communication in exchanging currency in banking system (Kamau and
Oluoch, 2016). In other definition E- Banking is a service producer with low costs by the help of
electronic channels. These productions and services can be: account bill, loan, deposit
management, E-payment, E-money. E- Banking is using internet and or intranet that are
accessible for the people. Definitions such as digital money, E- check, E- money, E- signature
are new phenomena that their origin returns to E-Banking (Alwan and Al-Zubi, 2016).
Accounting, exchanging, receiving bill, and paying bill are given to the clients a list with time
order. E- Banking has a lot of benefits like increasing clients, and decreasing bank costs.
8
Moreover, by using E- Banking it is easy to the bank to do their services faster and with more
security. They can also increase their shareholders (Alwan and Al-Zubi, 2016).

2.1.2. Economic Rationale of E-Banking

Innovation in Technology has distorted the traditional retail banking business model by
making it possible for banks to break their traditional value creation chain so as to allow the
production and distribution of financial services to be separated into different businesses. Thus,
for example, primarily Internet banks distribute insurance and securities as well as banking
products, but not all the products they distribute are produced by their group (Delgado et al
2012). However, the main economic argument for diffusion of adopting the Internet as a
delivery channel is based on the expected reduction in overhead expenses made possible by
reducing and ultimately eliminating physical branches and their associated costs (e.g. staff,
marketing and rent).

As stated by DeYoung (2012), and Delgado et al (2013) the Internet delivery channel may
generate scale economies in excess of those available to traditional distribution channels.
Besides them, Haq (2013) also states that bank exists because of their ability to achieve
economies of scale in minimizing asymmetry of information between savers and borrowers. The
unit costs of Internet banking fall more rapidly than those of traditional banks as output increases
as a result of balance sheet growth. In this context, DeYoung (2012) refer to the Internet banking
as a "process innovation that functions mainly as a substitute for physical branches for delivering
banking services.

2.1.3. Technology- Organization- Environment (TOE) Framework

TOE framework was proposed by Tornatzky and Fleischer; it is designed for studying the
likelihood of adoption success of technology innovations. This framework is a comprehensive
and well received framework in the context of innovation adoption by organizations and has
been used in many studies (Salwani, et al, & Ellis 2009; Chang et al 2007, Zhu & Kraemer
2006). According to Tornatzky and Fleischer (1990), technology adoption within an organization
is influenced by factors pertaining to the technological context, the organizational context, and

9
the external environment. Typical characteristics of technology considered in technology
adoption studies are based on the assumption of Roger’s diffusion of innovation (Rogers 2003),
Which include relative advantages (perceived benefits), and relative disadvantages (perceived
risks).

While the organizational factor refers to the organization’s characteristics that influence its
ability to adopt and use of E-banking system. The environmental factor refers to the external
environment in which an organization operates and its condition for supporting the development
of E-banking services. For each context, various factors have been identified from the literature
but only those that are considered relevant for E-banking adoption are included in the
framework. Details of factors considered in this study are discussed below.

Technological Factors
It appears that there is a lack of consensus on what factors belong to this context. For example,
one study (Salwani 2009) includes technology competence covering existing technology
infrastructure and skills to utilize the technology in this context, while other studies (Ellias 2009
& Chang 2007) consider some relevant characteristics of technology. To avoid overlapping
between technology and organizational contexts, researcher chooses two basic factors related to
technology competence, which have relevant to the organizational factors, i.e perceived benefits
and perceived risks are considered in this study from the technological factors.

Perceived benefits: - Perceived benefits of E-banking cover both direct and indirect benefits for
the banking industry as well as for the consumers. Direct benefits include the savings on
operational cost, improved organizational functionality, productivity gain, improved efficiency
and increased profitability. Indirect benefits include the opportunity or intangible benefits such
as improved customer’s satisfaction through improved services, improved banking experience
and fulfillment of their changing needs and lifestyle (Lu et al. 2005; Kuan & Chau 2001 &
Iacovou 1995).

Perceived risks: - One of the important risks faced by banking institutions in offering
Ebanking services is the customers’ resistance to use the services which significantly hinder the
growth of E-banking (Zhao et al. 2008 & Laforet 2005). Issues related to security have always
been a concern when dealing with technologies related to online transactions such as E-banking

10
(Chang 2007 & Rogers 2003). Therefore, the perception of the risks regarding E-banking is
expected to influence its adoption and further growth.

Organizational Factors
Organizations are different in their preference to adopt technological innovation (Iacovou 1995
& Grover 1993) influenced by a number of factors, like firm size, top management support and
financial and human resources. In the framework for this study, researcher uses one basic
organizational factor as discussed below.

Financial and human resources: - Financial resources are an important factor in

facilitating innovation adoption for any organization and they are often correlated with the
firm size (Kuan 2001 & Iacovou 1995). Therefore, it is expected that the availability of
financial resources within the adopting firms is important for E-banking practice. These
resources enable banking institutions to obtain human related resources including the
required skills and expertise to develop and support provision of E-banking services.

Environmental Factors
Researcher identified factors related to the environmental context that play a crucial role in
technology adoption and some factors in this category are arguably more influential than others,
especially when countries under study have an authoritative government leadership. The Four
factors relevant for E-banking adoptions included in this study are: -

Legal Frameworks: - The existence and maturity of E-commerce legal frameworks

within a country influence the diffusion of online transactions including E-banking as
demonstrated in various studies (Tan & Wu 2002; Martinson &Trappey 2001).

The National ICT infrastructure: - National ICT infrastructure is a major factor that
supports the adoption of E-banking as the case for other E-commerce initiatives.
Without an adequate development level and quality of a nation’s ICT infrastructure, E-
banking adoption and use cannot do well (Efendioghu 2004 &Scupola 2003).

Competitive pressure: - Competitive pressure can strongly influence any bank to

develop and adopt E-banking initiatives and it may affect the bank’s perception towards

11
 E-banking system. As implied in previous studies (Quaddus & Hofmeyer 2007; Gibbs,
Kraemer & Dedrick 2003).

Government Support: -Government can either directly or indirectly affect the

adoption of Ebanking in terms of creating a favorable environment and impetus for
banking institutions and their customers so that the services can be diffused with the
community (Kuan 2001 & Iacovou 1995)

2.1.4. Type of Electronic Banking

Types electronic banking consist in this study are Mobile Banking, Internet Banking, Agent
Banking, Point of Sale (POS), Automated Teller Machine.

Mobile Banking

Mobile banking involves the use of mobile phone for settlement of financial transactions.
It supports person to person transfers with immediate availability of funds for the
beneficiary. Mobile payments use the card infrastructure for movement of payment
instructions as well as secure Short Message Service (SMS) messaging for confirmation of
receipt to the beneficiary. Mobile banking is meant for low value transactions where speed
of completing the transaction is a key. The services covered under this product include
account enquiry, funds transfer, recharge phones, changing of passwords and bill payment
which are offered by few institution (Sathye, 1999).

Internet Banking

Internet banking involves conducting banking transactions such as account enquiry

printing of statement of account; funds transfer payments for goods and services, etc on the
internet (WorldWide Web) using electronic tools such as the computer without visiting the
banking hall.E-commerce is greatly facilitated by internet banking and is mostly used to
effect payment. Internet banking also uses the electronic card infrastructure for executing
payment instructions and for final settlement of goods and service over the internet between
the merchant and the customer currently the most common internet payments are for
consumer bills and purchase of air ticket through the websites of the merchants (Littler,
2006).

Agent Banking

12
 These are banking services which a customer of a financial institution can assess an agent
line as a link to the financial institution’s computer center. Services rendered through agents
include account balance funds transfer, low cost of service, customer transactions, and bills
payment (James, 2009).

Point of Sale (POS)

According to Malak (2007) cited in Ayana (2014) POS system allows consumers to pay
for retail purchase with a check card, a new name for debit card. This card looks like a credit
card but with a significant difference. The money for the purchase is transferred
immediately from account to debit card holder to the store’s account.

Automated Teller Machine (ATM)

Automated Teller Machine (ATM) is a device, which offers a range of services to users
that are authorized by using a PIN-code. From a cash ATM, user is able to make payments,
withdraw money or view account information (Myllynen, 2009).

ATMs have reduced costs per transaction to almost one-fourth as compared to almost the
branches. ATMs support a variety of transactions such as cash withdrawal, cash deposits
and placement of service requests, including the request for a new cheque book. New
technology has facilitated the installation of ATMs in shopping malls or busy commercial
localities and has further reduced the transaction and operation costs for banks
(Sambamuthy et al., 2010).

The ATMs were one of the first ICT technologies to be used by banks and it has remained
one of the most successful. The ATM is a computerized telecommunication device that
provides bank customers with self-service access to their financial accounts. A prototype
was first created in 1939, a modern ATM was patented in 1966, an ATM was installed in
Barclays Bank in London in 1967 and the United States started productizing ATMs in 1968
(Bellis, 2010).

2.1.5. Bank Performance Indicators

Good Financial performance is the ultimate goal of commercial banks. All the strategies
designed and activities performed thereof are meant to realize this grand objective.

13
 However, this does not mean that commercial banks have no other goals. Commercial banks
could also have additional social and economic goals. However, the intention of this study is
related to the first objective, financial performance. Studies made on the financial
performance of commercial Banks largely used Return on Asset(ROA) and Return on
Equity(ROE) as a common measure (Murthy &Sree, 2003; Alexandru, 2008; Ezra, 2013).

As concluded by extensive Prior academic research there are different accounting based
measures for banks‟ profitability. For instance, Return on Equity (ROE) used by (Goddard, J.,
Molyneux, P. and Wilson, S. J., 2004), Return on Assets (ROA) used by (Flamini, McDonald, &
Schumacher, 2009), the Return on Equity (ROE) and Return on Assets (ROA) utilized by
(Athanasoglou, Delis, &Staikouras , 2006) and (Ommeren, 2011), ROE, ROA and Profit Earning
Ratio (PER) applied by (Moin, 2008)and among others, (Huizinga & Demirguc - Kunt, 1999)
uses the net interest margin (NIM) as proxy for banks‟ Financial performance.

Return on Asset (ROA)

ROA is major ratio that indicates the profitability of a bank. It is a ratio of Income to its total
asset (Khrawish, 2011). It measures the ability of the bank management to generate income by
utilizing company assets at their disposal. In other words, it shows how efficiently the resources
of the company are used to generate the income. It further indicates the efficiency of the
management of a company in generating net income from all the resources of the institution
(Khrawish, 2011). (Wen, 2010), state that a higher ROA shows that the company is more
efficient in using its resources and ROA takes to account leverage/debt while ROE does not.

2.3. Empirical Review

Mobile Banking and Financial Performance

Several studies have been conducted on the effects of mobile banking on bank performance.
For instance, Mabwai (2016) conducted a study on the effects of mobile banking on the financial
performance of commercial banks in Kenya. The study adopted a descriptive research design and
descriptive statistics for analysis. The findings indicated that the number of mobile banking
transactions, capital adequacy, markets share and the size of the assets had a positive influence

14
on the financial performance of commercial banks. Hence, the conclusion that mobile banking
adoption has resulted to improved financial performance of the commercial banks. The study
recommended that commercial banks should increase their focus and investments in mobile
banking as this is the future of the banking industry in order for them to remain profitable. Mutua
(2014) carried out an investigation on the effects of mobile banking on the financial performance
of commercial banks in Kenya. The study acknowledged that mobile banking offers millions of
people a potential solution in emerging markets that have access to a cell phone, yet remain
excluded from the financial mainstream. The study employed a descriptive research design and
descriptive statistics for analysis. The findings of the study revealed that there exists a weak
positive relationship between mobile banking and the financial performance of commercial
banks in Kenya. The study recommended that policy makers keep a keen eye on the
developments of mobile banking as it is a new platform for competition among commercial
banks as the world moves into a digital age.

Internet Banking and Financial Performance

Kagendo (2015) conducted a study on E-banking strategy and performance of commercial

banks in Kenya. The findings of the study showed that the connection among e-banking
infrastructure, e-banking efficiency strategies, and e-banking quality improve the performance of
Kenya commercial banks. this research recommended that banks should improve devices for
more e-banking approaches that address the needs and wants of the customers in a timely and
manner that is important. E-banking approaches should be more than ICT infrastructures,
competencies and quality but again customers value addition through the e-banking approaches.
Asia (2015) conducted a study on electronic banking and financial performance of commercial
banks in Rwanda: A Case Study of Bank of Kigali. The findings showed that banks in Kigali has
diversities of e-banking services for their customers in order to offer effective and competence
service delivery. These include: ATM that is one of the e-banking services that is commonly
used by e-banking by the customers of bank of Kigali, pay direct that let you approve particular
deposits, phone banking that let you call your financial institution with instructions to pay certain
bills or to transfer funds between accounts after an agreement with the institution to make such
transfers and many others. The various electronic banking endorses effectiveness and

15
competence in service delivery since customers can be able to withdraw and deposit money,
approves payments and check account balance.

Agent Banking and Financial Performance

Various studies have been carried out on the effects of agent banking has on performance of
banks. For example, Argamo (2015) investigated on the effect of agency banking on the financial
performance of commercial banks in Kenya, using Chase bank a case study. The study
acknowledges that agency banking as a replica has been very flourishing in financial inclusion
thereby boosting the commercial banks’ performance in most developing states. The study used
descriptive research design and descriptive statistics for analysis. The study found that agency
banking enables accessibility of banking services, low cost of service and customer transactions
hence enhancing financial performance of Chase Bank. The study recommended that commercial
banks in Kenya should increase the number of agents in estates and in the rural areas and also
lower the charges of making transactions in agency banks. Simboley (2017) conducted a study
on the effects of agency banking on the financial performance of commercial banks in Kenya.
The study acknowledged that the cost of travelling to a bank was often higher than the cost of
making a transaction in a brick and motor institution but since 2010 bank have adopted agent
banking which has convenience banking services. The study adopted a descriptive research
design and descriptive statistics for analysis. The findings of the study indicated that there was a
significant growth in customers as a result of agency banking and that it reduced on cost that
would have been incurred building another building. The recommendation of the study was that
customer care needs should be improved as services are being relayed to customers and also
there should be development of new products and services so as to reach more customers.

Point of Sale (POS) and Financial Performance

According to Simboley (2017) POS system allows consumers to pay for retail purchase with a
check card, a new name for debit card. This card looks like a credit card but with a significant
difference. The money for the purchase is transferred immediately from account to debit card
holder to the store’s account.

Automated Teller Machine and Financial Performance

Jegede (2014) did a study on effects of automated teller machine on the performance of
Nigerian banks. The findings show that the positioning of ATMs terminals have enhanced

16
averagely the performance of Nigerian banks due to the ATM fraud that is at an alarming rate.
ATM service quality is less correlated to security and privacy of those who are using and the
providers. This study concludes that banks should strive to grow their security layers to
undermine the tricks of web scammers, reduce the amount that clients may be permitted to
withdraw at a time and offer electronic attention to client’s phone for all the transactions that
have been carried out on their bank accounts through ATMs and the provisions of extra security
layer that can prevent third party to utilize ATM card that belongs to someone else for illegal
withdrawals electronically. Murigu, (2016) conducted a study on the usage of automated teller
machines case study: Barclays Bank of Kenya. The findings showed a devastating preference for
the ATM against the choice of using bank cashier to withdraw cash. The findings further showed
that the factors that influence the ATM usage comprise of the presence of a guard at ATM
location, the preference for ATMs that are located at the branch of the bank, procedures that are
taken to make sure there is sufficient security at the location of ATM, the dependability of ATM
to offer services, the length of the queue at the ATM, the cleanliness of the ATM location,
sufficient lighting at the ATM location, the choice of the ATM location aligned to customer
preferences, the surveillance camera deployed at ATM locations and ATM located within a
lobby are preferred. Banks and Financial Institution need to ensure they implement the factors
that affect the usage of ATMs by reviewing each ATM location before and after installation, to
ensure they realize the maximum benefit from their investments.

2.4. Gap in Literature

Using a panel data of fifteen Jordanian banks for the period of 2000–2010, Al-Smadi and Al
Wabel (2011) studied the impact of e-banking on the performance of Jordanian banks. In their
study, performance of banks was measured by ROE and two sets of control variables were used.
Using pooled OLS regression technique they found significant negative impact of e-banking on
financial performance of banks. The major limitation of this study is that authors did not look at
ROA after a specific year of adoption of e-banking. Since adoption of e-banking technology
involves cost, this might take time to recover cost and experience profits. On the other hand, in
Indian context Malhotra and Singh (2019) studied the effects of internet banking on performance
and risk of Indian commercial banks and found no significant relationship between the
profitability and offering of internet banking. Rahman (2007) studied impact of innovative

17
technology on the profitability of the banks operating in Bangladesh and found that technology
adopted banks experienced improved performance as they gain maturity. The limitation of this
study is that author only showed the performance changes overtime but did not explained
whether such changes are significant or not.

2.5. Conceptual Framework

In the research mobile banking, point of sale (POS), internet banking, agent banking, and
automated teller machines have been taken as key dimensions to represent the overall e-banking
practice of commercial banks, and their impact on organizational performance for those banks.
Return on asset (ROA) will be used to measure banks organizational performance.

Fig. 1. Model of Effective Workforce Diversity Management and Employees’ Performance.

E-Banking Organizational Performance

Mobile Banking
Mobile Banking

Internet Banking
Internet Banking
Return on Asset
Agent Banking
(ROA)

Point
PointofofSale
Sale
(POS)(POS)

AutomatedTeller
Automated
Teller Machines
Machines

Source: Researcher computation based on

reviled literatures (2021).

18
 CHAPTER THREE

3. RESEARCH METHODOLOGY

3.1. Research design

Considering the research problem and objective along with the philosophy of the different
research approaches, the quantitative nature of the data collected, quantitative research approach
is found to be appropriate for this study. According to Creswell (2009) Quantitative research is a
means for testing objective theories by examining the relationship among variables. These
variables in turn, can be measured, typically on instruments so that numbered data can be
analyzed using statistical procedures. Explanatory research approach helps to identify and
evaluate causal relationship b/n difference variable under consideration.Marczyk et..,(2005)

The researcher employed quantitative research approach to see the regression result analysis
with respective empirical literatures on the effect of e-banking on commercial banks
performance. This quantitative study gathered information from the Financial Statements of the
bank. The years that were considered were 2009 to 2013 E.C, solicit information concerning the
E banking and bank performance. A panel data is analyzed using SPSS Version 22.

3.2. Source and Type of Data

The research utilizes secondary data collection. Bank specific data were collected from
financial statements (i.e. Balance Sheet and Profit & Loss Statement) of each selected
commercial banks included in the sample and NBE. The research analyzes the panel data for the
previous five years (i.e. 2009-2013 E.C (2016/17-2020/21 G.C)) of those selected commercial
banks. Return on Asset for those banks are calculated from the division of net income with
average asset for each period. Both net income and average asset of those banks can be gain from
the annual financial statements of the banks. The determinants of e-banking selected in this study
was the user of mobile banking, number of POS, user of internet banking, number of agent bank,
and number of ATM. All of these data were collected from annual report of each sampled
commercial banks.

19
3.3. Sampling Design

The research was conducted using purposive sampling method .Purposive Sampling also
known as judgmental, selective, or subjective sampling, is a form of non-probability sampling in
which researchers rely on their own judgment when choosing members of the population to
participate in their surveys. The reason decides this sampling is enable the researcher to select
based on experience banking industry for the last 9 years and the bank were top perform in
investing to e-banking, so as an expert in the field I try to recognize that which bank is work
more focused on E-Banking exercises, thus those four banks has started earlier than other banks
operated in the country, and they always tries to run up works related with E-Banking service.

Sample and Target Population

The target population for this research is of four commercial bank i.e. Commercial Bank of
Ethiopia, Bank of Abyssinia, Dashen Bank, and Awash Bank located at Dire Dawa that were
registered by national bank of Ethiopia (NBE) and banks that are top performer of commercial
bank (bank that have net profit greater than one billion in 2020/2021).

Table 3.1 List of banks selected for this research (Target population)
Year of Profit after
Name of the Bank Establishment Tax
1 Commercial Bank of Ethiopia 1964 (PBT)17.6 Billion
2 Awash International Bank 1995 5.56Billion
3 Dashen Bank 1996 2.4. billon
4 Bank of Abyssinia 1997 2.87 billion

Source: National Bank of Ethiopia (2021).

3.4. Method of data collection, Design, and Administration

Method of data collection

The study used secondary data collected from the annual report of commercial banks and
National Bank of Ethiopia and, published and unpublished reports obtained from E- banking
departments of sampled banks. Secondary data refers to that statistical material which is not
originated by the investigator himself, but which he obtains from others records. (Gupta, 2004).

20
The data was collected using data collection sheet which was edited, coded and cleaned. Data was
mainly obtained covering the period from 2016 to 2020. Additional data were obtained by
examining various documents, including, research reports, books and journal articles.

Method of Administration and Quality Criteria

In this research a multiple regression of secondary data was conducted, the F statistic and the
corresponding F prop p-value (Note: This is not the p value you get individually for every
dependent variable) for the model will tell about the reliability of the research model. The
probability - p value for F test is associated with the model as a whole, for example, if this F prob
is lower than 0.05 it can say that the independent variables predict the dependent variable -
reliably, as there is a chance of lover then 0.05% that it is by chance your independent variables
predict the dependent variable. In other words it will tell if the independent variables as a
group predict the dependent variable reliably.

3.5. Method of Data Analysis

Collected data had a code before being captured in statistical software, SPSS statistics software
version 22 is used for descriptive analysis, and STATA version 14.2 was used for inferential
statistic. The research was conducted both descriptive and inferential statistical analysis. In the
descriptive analysis the mean was computed to analyze the central tendency of each variable
from the panel data. Maximum and minimum was computed to analyze the largest and smallest
values of those variables from the data. The final descriptive analysis that was conduct in this
research is standard deviation to measure how spread out the data are about the mean for each
variable from the panel data.

The inferential statics was analyzed to answer the research question, objective (the general and
specific objectives), and to test the hypotheses by conducting panel regression analysis.
Basically, there are three types of regression for panel data i.e. Pooled OLS, Fixed-Effects (FE)
Model, and Random-Effects (RE) Model, to decide which model is used in this research the
researcher will conduct Hausman-Test.

3.5.1, How to decide which Model is appropriate?

21
 Choosing between Pooled OLS, Fixed-Effects (FE), and Random-Effects (RE): Basically,
there are six assumptions for simple linear regression models that must be fulfilled. Two of them
can help us in choosing between Pooled OLS, Fixed-Effects (FE) ,and Random-Effects (RE).
These assumptions are:

 Linearity
 Exogeneity
 Homoskedasticity
 Non-autocorrelation
 Independent variables are not Stochastic
 No Multicolinearity

If the second or third (or both) assumptions are violated, then FE or RE might be more suitable.

Choosing between FE and RE: Answering this question depends on our assumption, if the
individual, unobserved heterogeneity is a constant or random effect. But this question can also be
answered performing the Hausman-Test.

The selection of appropriate model between the pooled OLS and fixed effect is based on the
joint significance of differing group means, which is used to test null hypothesis that the pooled
OLS model is adequate. A low p-value means that fixed effects model is more appropriate then
the pooled OLS model.

 Breusch-Pagan: test is often used for the selection between the pooled OLS and random
effects model. A low p-value means that random effects model is more appropriate then
the pooled OLS model. The selection of appropriate model between random effect and
fixed effect is based on the Hausman test which is used to test null hypothesis that the
random effect model is adequate and more appropriate then the fixed effect.

 Hausman-Test: In simple terms, the Hausman-Test is a test of endogeneity. By running

the Hausman-Test, the null hypothesis is that the covariance between IV(s) and alpha is
zero. If this is the case, then RE is preferred over FE. If the null hypothesis is not true, we
must go with the FE-model.

22
3.6. Empirical Model

Performance refers to the degree of success in attaining stated objective (Sathye, 2005). The
major objective of banks as other financial institutions is maximizing shareholders' wealth.
Following the literature, return on Asset (ROA) is the common measure of performance.

Data analysis was done using SPSS version 22 whereby multiple regression models was
employed. To test the effects of electronic banking on profitability of commercial banks in
Ethiopia, located at Dire Dawa Administration a multiple regression model was used:

Yjt = C + αXjt + … + εjt ------------------- (1)

j refers to the commercial bank; t refers to year; Yjt is the dependent variable and refers to the
return on asset (ROA) of bank j in a particular year t; C is the intercept; X represents the
independent variables, whereas α are co-efficient and εjt represent the error term. The
significance of the regression model will be determined at 95% confidence interval and 5% level
of significance. The empirical model to be used in the study to test the effect of electronic
banking on profitability of commercial banks in Ethiopia is presented as follows:

ROAjt = C + β1MB jt + β2 POS jt + β3IB jt + β4AB jt + β5ATM jt + εjt ------ (2)

Where:

ROA jt: return on Asset for bank j in year t

MB jt: is the mobile banking which was measured by number of user.

POS jt: is the point of sale which was measured by number of POS
terminal.

IB jt: is the internet banking which was measured by number of user.

AB jt: is the agent banking which was measured by number of user.

ATM jt: is the Automatic Teller Machine which was measured by number
of ATM.

23
 εjt: represent the error term

3.7, Model Assumptions

The following diagnostic tests were carried out to ensure that the data suits the basic
assumptions of classical linear regression model.

Normality: To check the normality, descriptive statistics was used. A normal distribution is not
skewed and is defined to have a coefficient of kurtosis of (Brooks, 2008). One of the most
commonly applied tests for normality; the Bera-Jarque formalizes these ideas by testing whether
the coefficient of Skewness and the coefficient of excess kurtosis are zero and three respectively.
(Brooks, 2008) Also states that, if the residuals are normally distributed, the histogram should be
bell-shaped and the Bera-Jarque statistic would not be significant at 5% significant level.

Normality is defined as the "shape of the data distribution or an individual metric variable and
its correspondence to the normal distribution, which is the benchmark for statistical methods"
(Hair et al., 2016). Violation of normality might affect the estimation processor the interpretation
of results especially in Ordinary Least squared analysis.

Multicollinearity: There were different arguments towards the multicollinearity problem.

multicollinearity problems exist when the correlation coefficient among variables greater than
0.75 (Gujarati D. N., 2004). According to Cooper and Schendlar (2003) Suggested that a
correlation above 0.8 between explanatory variables can cause multicollinearity problem. Lastly,
(Hair JF, 2006) argued that also correlation coefficient below 0.9 may not cause serious
multicollinearity problem. In contrary to this, (Kennedy, 2008) argued that as any correlation
coefficient above 0.7 could cause a serious multi collinearity problem leading to inefficient
estimation and less reliable results. A correlation matrix used to ensure the correlation.

Heteroscedasticity: According to Brooks (2008), Heteroscedasticity means that error terms do

not have a constant variance. If heteroscedasticity occur, the estimators of the ordinary least
square method are inefficient and hypothesis testing is no longer reliable or valid as it will
underestimate the variances and standard errors. There are several tests to detect the

24
Heteroscedasticity problem, which are Park Test, Glesjer Test, Breusch-Pagan-Goldfrey Test,
White‟s Test and Autoregressive Conditional Heteroscedasticity (ARCH) test. In this case, this
study chooses to use ARCH test to detect Heteroscedasticity.

H0: There is no Heteroscedasticity problem in the model.

H1: There is Heteroscedasticity problem in the model.

Decision Rule: Reject H0 if p-value greater than significance level. Otherwise, do not reject
H0.

Tests for Autocorrelation: Assumption that is made of the CLRM‟s disturbance terms is that
the covariance between the error terms over time (or cross-sectional, for that type of data) is
zero. In other words, it is assumed that the errors are uncorrelated with one another. If the errors
are not uncorrelated with one another, it would be stated that they are “auto correlated” or that
they are “serially correlated”. A test of this assumption is therefore required. To test the presence
of autocorrelation, to test for autocorrelation the researcher applies Breusch-Godfrey Serial
Correlation LM test.

Table 3.1 Summary of variables used and their specification

Variable Measurement Legend Expected sign

Return on Asset Net = profit(before taxes)/total asset ROA +

Mobile Banking user MB +

Number of POS POS +

Internet Banking user IB +

Agent Banking user AB +

Number of ATM ATM +

25
 CHAPTER FOUR

RESULTS and DISCUSSIONS

4.1, Introduction

This chapter presents the data findings to determine the effect of electronic banking on
performance of commercial banks in Ethiopia. These data were collected from the selected
commercial banks in Ethiopia located at Dire Dawa Administration. Regression analysis was
done for the periods to determine the effects of electronic banking on performance of
commercial banks in Ethiopia. The study covered a period of 5 Years from the 2009-2013 E.C
(2016/17-2020/21 G.C).

4.2, Descriptive Statistics

Descriptive statistics is the term given to the analysis of data that helps describe, show or
summarize data in a meaningful way. Descriptive statistics are very important because if we
simply presented the data it would be hard to visualize what the data was showing, especially if
there was a lot of it. Descriptive statistics therefore enables us to present the data in a more
meaningful way, which allows simpler interpretation of the data.

The research statistics of each variables of the study have been discussed here under. The
variables included the dependent and independent variables. The dependent variable used in this
study in order to measure the sample commercial banks performance is ROA whereas the
explanatory variables are mobile banking, point of sale (POS), internet banking, agent banking,
and automated teller machines. Accordingly, the summary statistics for all variables are
presented below in table 4.1.The descriptive table included mean, maximum, minimum, standard
deviation and observations of both of dependent and independent variables of the study.
Basically, a small standard deviation means that the values in a statistical data set are close to the

26
mean of the data set, on average, and a large standard deviation means that the values in the data
set are farther away from the mean, on average. The standard deviation measures how
concentrated the data are around the mean; the more concentrated, the smaller the standard
deviation. The general rule stated that the higher value of standard deviation implies greater
spread of data, smaller the standard deviation shows the data is concentrated around the mean.

Table 4.1 Summary Descriptive Statistics of Dependent and Independent Variables

Descriptive Statistics

Std.
N Minimum Maximum Mean Deviation

Return on Asset 5 .019 .029 .02400 .004690

Mobile Banking 5 550478 702846 631056.20 66156.422

Number of POS 5 4749 52789 27401.00 18676.942

Internet
5 8756 17497 12006.80 3636.370
Banking

Agent Banking 5 2 4 2.60 .894

ATM 5 649 1278 941.20 236.987

Valid N
5
(listwise)

Source: Researcher computation based on Descriptive Statistics

As depicted on the above table 4.1, the mean, maximum, minimum, and standard deviation
values of variables from dataset of 5 year provides the basis for descriptive analysis. This study

27
has used five variables for the analysis and interpretation, including one dependent variable,
ROA. The mean value of bank ROA was around 2.4 percent for sampled commercial banks.
Furthermore, the bank ROA growth fluctuates between 1.9 and 2.9 percent. This means,
commercial banks were achieved 2.4 percent average return on asset from the period of 2009-
2013 E.C (2016/17-2020/21 G.C) from this, it can apparently see that, with the introduction of
electronic banking in last few years, ROA of banks have been improved.

For mobile banking the mean, maximum and minimum were 631056.20, 702846, and 550478
respectively, in additions the standard deviation was 66156.422 shows large standard deviation
means that the values in the data set are farther away from the mean, on average, means the
effort of banks on distribution of mobile banking is high.

About, number of POS terminal the mean, maximum and minimum observations were
27401.00 52789, and 4749 respectively. In additions that the values in the data set are farther
away from the mean called standard deviation were 18676.942. It shows large standard deviation
means that the values in the data set are farther away from the mean, on average, means the
effort of banks on distribution of number of POS terminal is high.

For internet banking practiced by banks the mean, maximum and minimum observations were
12006.80, 17497, and 8756 respectively. In additions that the values in the data set are farther
away from the mean called standard deviation were 3636.370. It shows large standard deviation
means that the values in the data set are farther away from the mean, on average, means the
effort of banks on distribution of internet banking is high.

For agent banking practiced by banks the mean, maximum and minimum observations were
2.60, 4 and 2 respectively. In additions that the values in the data set are farther away from the
mean called standard deviation were .894. It shows very small standard deviation means that the
values in the data set are not farther away from the mean, on average, means the effort of banks
on distribution of agent banking is low.

Concerning, number of ATM installed by Banks the mean, maximum and minimum
observations were 941.20, 1278, and 649 respectively. In additions that the values in the data set
are farther away from the mean called standard deviation were 236.987. It shows large standard

28
deviation means that the values in the data set are farther away from the mean, on average,
means the effort of banks on distribution of number of ATM is high.

Figure 4.1 Bar graph results variables through the years

29
30
31
Source: Researcher computation

32
Choosing Model for Panel Data

Basically, there are three types of regression for panel data i.e. Pooled OLS, Fixed-Effects (FE)
Model, and Random-Effects (RE) Model, to decide which model is used in this research the
researcher will conduct Hausman-Test.

Choosing between pooled OLS, fixed-effects (FE), and random-effects (RE): basically there
are five assumptions for panel regression models that must be fulfilled, assumptions 2 & 3 can
help in choosing between Pooled OLS, Fixed-Effects (FE) , and Random-Effects (RE) these
assumptions are:

Linearity

Linearity test aims to determine the relationship between independent variables and
the dependent variable is linear or not. The linearity test is a requirement in the
correlation and linear regression analysis.

Decision-making process in the Linearity Test

1. If the value sig. Deviation from Linearity> 0.05, then the relationship
between the independent variables are linearly dependent.

2. If the value sig. Deviation from Linearity <0.05, then the relationship
between independent variables with the dependent is not linear.

33
 Table 4.2 Linearity result of the research

Sum of df Mean F Sig

Squares Square

Return on Asset * Betwe Combined .000 2 .000 .571 0.636

mobile banking, point en
Linearity .000 1 .000 .011 .936
of sale (POS), internet Group
banking, agent banking, s Deviation .000 1 .000 1.13 .24
and automated teller from 2
machines linearity

Within Groups .000 2 .000

Total .000 4

Source: Researcher computation

Based on the above figure, value sig. Deviation from Linearity of 0.24 > 0.05, it can be
concluded that there is a linear relationship between the variables of Return on Asset with those
E-Banking determinants.

Homoscedasticity

In regression analysis, homoscedasticity means a situation in which the variance of

the dependent variable is the same for all the data. Homoscedasticity is facilitates
analysis because most methods are based on the assumption of equal variance.

34
Figure 4.2 Scatter plot result of the variables

Source: Researcher computation

35
Since the data is a very tight distribution to the left of the plot, and a very wide distribution to the
right of the plot, so the data are not homoscedastic.

Since the third assumptions are violated, then FE or RE might be more suitable, so now the
researcher trying to conduct Hausman-Test, the null hypothesis is that the covariance between
IV(s) and alpha is zero. It basically tests whether the unique errors (ui) are correlated with the
regressors, the null hypothesis is they are not. Run a fixed effects model and save the estimates,
then run a random model and save the estimates, then perform the test. If the p-value is
significant (for example <0.05) then use fixed effects, if not use random effects.

Table 4.3 Regression Result for the decision of FE-model & RE-Model

Coefficients a

Unstandardized Std. Error Standardized t Sig

Coefficients Coefficients

B B

(Constant) -0.047 .000 3.254 0.256

Mobile 0.028 .000 4.912 0.18

Banking

Internet -3.015E-6 .000 -2.337 0.67

Banking

PoS -.005 .000 -.895 0.23

Agent -4.247E-5 .000 -2.146 0.35

Banking

ATM -2.287E-6 .000 -2.146 0.42

From the above figure the researcher sees that the p-values for all variables are greater than
0.05, thus the null hypothesis can be accepted, which indicate the covariance between IV(s) and
36
alpha is zero. So now Random-Effects (RE) Model regression for panel data will be carried out
to see the relation between the variables.

Testing the Random-Effects (RE) Model regression Assumptions

Linearity

Linearity test aims to determine the relationship between independent variables and
the dependent variable is linear or not. The linearity test is a requirement in the
correlation and linear regression analysis.

Decision-making process in the Linearity Test

1. If the value sig. Deviation from Linearity> 0.05, then the relationship between the
independent variables are linearly dependent.

2. If the value sig. Deviation from Linearity <0.05, then the relationship
between independent variables with the dependent is not linear.

Table 4.4 Linearity result of the research

Sum of df Mean F Sig

Squares Square

Return on Asset * Betwe Combined .000 2 .000 .571 0.636

mobile banking, point en
Linearity .000 1 .000 .011 .936
of sale (POS), internet Group
banking, agent banking, s Deviation .000 1 .000 1.13 .24
and automated teller from 2
machines linearity

Within Groups .000 2 .000

Total .000 4

Source: Researcher computation

37
 Based on the above figure, value sig. Deviation from Linearity of 0.24 > 0.05, it can be
concluded that there is a linear relationship between the variables of Return on Asset with those
E-Banking determinants.

No perfect collinearity
When the correlation between two or more independent variables is (too) high, the problem of
multicollinearity occurs (Wooldridge, 2000). The problem of multicollinearity may lead to less
accurate results in the analyses; the coefficients may have very high standard errors and perhaps
even incorrect signs or implausibly large magnitudes. Multicollinearity can be detected by
calculating the variance inflation factors (VIF) for each independent variable. Multicollinearity is
present when VIF values are larger than 10. Furthermore, the critical value can be calculated by
1/VIF. If this value is below 0.1, this would mean that more than 90% of the variation in the
variable is explained by the other variables. The variable(s) with VIF values larger than 10 or
1/VIF values below 0.1 should be excluded from the analyses Brooks (2008).

Multicollinearity problems exist when the correlation coefficient among variables greater than
0.75. Brooks (2008) suggested that a correlation above 0.8 between explanatory variables should
be corrected for. A correlation matrix was used in this study to ensure the correlation between
explanatory variables. Then balanced panel data models are applied to control for
multicollinearity.

Table 4.5 Correlation Matrix between Explanatory Variables

mb pos ib ab atm

mb 1.0000

pos 0.0518 1.0000

ib 0.0742 0.0731 1.0000

ab 0.0519 0.0195 0.0194 1.0000

atm 0.0219 0.0391 0.0631 0.0496 1.0000

38
 Source: Researcher computation

No autocorrelation

Covariance between the error terms overtime (or cross sectional, for the type of data) is zero; it
is assumed that the errors are uncorrelated with one another. In other words, it is assumed that
the errors are uncorrelated with one another. If the errors are not uncorrelated with one another, it
would be stated that they are ‘auto correlated’ or that they are ‘serially correlated’. The study
uses Durbin-Watson test (DW test) to test autocorrelation. The null hypothesis for this test is the
error at the current time and the error at previous time is independent of one another (there is no
autocorrelation) and the alternative hypothesis is that the error at the current time is dependent on
the error of the previous time (there is evidence for the presence of autocorrelation). Therefore, if
the null hypothesis is rejected then it is said that there is an evidence for the presence of
autocorrelation.

According to Brooks (2008), the DW test does not follow a standard statistical distribution
such as a t, F, or χ2. DW has 2 critical values: an upper critical value (dU) and a lower critical
value (dL), and there is also an intermediate region where the null hypothesis of no
autocorrelation can neither be rejected nor not rejected. The rejection, non-rejection, and
inconclusive regions are shown on the number line in figure 4.1 below.

Figure 4.3 Rejection and non-rejection regions for DW test

Source: Researcher computation

39
 The null hypothesis is rejected and the existence of positive autocorrelation presumed if DW is
less than the lower critical value (dL); the null hypothesis is rejected and the existence of
negative autocorrelation presumed if DW is greater than 4 minus the lower critical value (4-dL);
the null hypothesis is not rejected and no significant residual autocorrelation is presumed if
DWis between the upper critical value (dU) and 4 minus the upper critical limits (4-dU) (Brooks
2008).

The study has five explanatories variables (k) and 5 years’ period of time. So it has total of 150
observations and as per the DW table in Appendix-III for 30 observations with five explanatory
variables at level of Significance α = .05, the dL and dU values are 1.07 and 1.83, respectively
accordingly, the value of 4-dL and 4-dU are 2.93 and 2.17, respectively. The DW value of this
study is 1.910907, (Appendix-III) which lies in the no evidence of autocorrelation region where
the null hypothesis of no autocorrelation do not be rejected. Therefore, given these result it can
be concluded that there is no evidence for the existence of autocorrelation.

4.6, Results of Regression Analysis

Regression analysis is a technique used in statistics for investigating and modeling the
relationship between variables (Douglas et.al., 2012). If there is more than one repressor, it is
called multiple linear regressions. Regression results of e banking and bank performance the case
study on commercial banks of Ethiopia, located at Dire Dawa Administration. This regression
analysis is based on the data collected from National Bank of Ethiopia, and from those selected
commercial banks. The relationship between one dependent variable and five independent
variables is regressed using statistic software called SPSS version 22. Thus, the model used to
examine statistically significant effect of e-banking and bank performance;

ROAjt = C + β1 MB jt + β2POS jt + β3IB + β4AB jt+ β5ATM jt

Table 4.3 Effect of E-banking on Return on Asset

Variable Coefficient Std. Erorr t-Statistic Prob.

Mobile Banking 0.011352 0.000121 2.916356 0.0485

Number of POS 0.000001 0.037492 2.051043 0.0098

Internet Banking 0.031981 0.000515 1.904995 0.0013

40
 Agent Banking 0.293300 0.073697 3.979802 0.0000

Number of ATM 0.257900 0.065797 3.015792 0.0000

Cons 2.027027 0.858730 2.360495 0.0285

Effects Specification

Period fixed (dummy variables)

R-squared 0.715831 Mean dependent var 0.420357

Adjusted R- 0.618455 S.D. dependent var 0.275250

squared
Akaike info criterion
S.E. of regression - 0.146050 0.748532
Schwarz criterion
Sum squared resid 0.426613 -0.281466
Hannan-Quinn criter.
Log likelihood 21.22798 -0.599114
Durbin-Watson stat
F-statistic 9.222562 1.014442

Prob(F-statistic) 0.000021

Source: Researcher computation

Accordingly, the estimation result of panel regression model used in this study is presented in
table 4.3 related to ROA. The R-squared statistics and the Adjusted-R squared statistics of the
model were 71.5 % and 61.8 % respectively. The adjusted- R2 of this study indicates that, 61.8 %
of the variation on the dependent variable ROA was explained by the changes in the independent
variables. Thus it can be concluded that, all the independent variables used in this study
collectively, were good explanatory variables of commercial banks performance.

As it shown on above table 4.4, describes the names of the explanatory variables, their (Beta)
estimate (β), the standard errors of the estimates (S.E.), and the (C.R.) t statistic of each
coefficient, which is simply the ratio of estimate divided by its standard error, and the p value, or
the exact level of significance of the C.R. statistic. For each estimate, the null hypothesis is that

41
the population value of that estimate is zero, that is, the particular regressor has no influence on
the regress and, after holding the other regressor values constant. Moreover, the smaller the p
value, the greater the evidence against the null hypothesis.

Hypothesis Testing

Hypotheses H1 – mobile banking has a positive relationship with return on asset. As can be
observed from table above mobile banking and ROA (return on assets) have a positive
β=0.011352 and t-value 2.916356 with a P value of 0.0485 therefore the null hypothesis is
rejected.

Hypotheses H2 – POS has a positive relationship with return on asset. As can be observed from
table above POS and ROA (return on assets) have a positive β=0.000001 and t-value
2.051043with a P value of 0.0098 therefore the null hypothesis is rejected. 1.904995

Hypotheses H3 – internet banking has a positive relationship with return on asset. As can be
observed from table above internet banking and ROA (return on assets) have a positive
β=0.031981 and t-value 1.904995with a P value of 0.0013therefore the null hypothesis is
rejected.

Hypotheses H4 – agent banking has a positive relationship with return on asset. As can be
observed from table above agent and ROA (return on assets) have a positive β=0.293300 and t-
value 3.979802with a P value of 0.0000 therefore the null hypothesis is rejected.

Hypotheses H5 – ATM has a positive relationship with return on asset. As can be observed
from table above ATM and ROA (return on assets) have a positive β=0.257900 and t-value
3.015792 with a P value of 0.0000 therefore the null hypothesis is rejected.

Table 4.5. Summary of Hypothesis Testing on ROA

Variable Coefficient P-value Observation Decision

Mobile Banking 0.011352 0.0122 p-value>0.05 Reject null

POS 0.000001 0.0109 p-value>0.05 Reject null

42
 Internet 0.031981 0.0125 p-value>0.05 Reject null
Banking

Agent Banking 0.293300 0.0046 p-value>0.05 Reject null

ATM 0.257900 0.0005 p-value>0.05 Reject null

Source: Researcher computation

43
 Chapter Five

Conclusion and Recommendation

The basic intent of this chapter is to present the overall overviews of the research by summing
up the main findings of the analysis part and give future research directions. Accordingly, the
chapter starts with its discussion by briefly sum up the overviews of the study and its main
findings. In section two based on the study finding the researcher highlight some
recommendations for the target populations the study pivoting on.

5.1. Conclusion

The study empirically analyzed the effect of E-Banking in commercial banks performance in
Ethiopia by constructing an econometric model to study the effect of various factors such as
mobile banking, automated teller machine and point of sale, internet banking, agent banking.

Accordingly, the effects of E-Banking on return on asset in commercial banks in Ethiopia were
carefully analyzed using the OLS technique. The findings indicate that almost all the banking
services under consideration affect the profitability.

Based on the findings of the study, it can be concluded that E-banking influence financial
performance of commercial banks in Ethiopia positively. The adoption of E-banking by
commercial banks has a high potential of improving financial performance and hence better
returns to the shareholders. The versatility of E-banking has made their adoption rate to be high
among both the banks and their customers. It could have been challenging if the adoption was
only with either the banks or the customers. Banks in Ethiopia have continued to perform well
even when other sectors of the economy show lagged performance.

5.2. Recommendations
44
 The study recommends to the management of banks which are slow in innovation adoption, to
move in and adopt various innovations in their operations in order to shore up their profitability.
This recommendation is well supported by the fact that in Ethiopia, the leading banks in terms of
profitability are mostly the fast movers in adoption of new technologies. The study also
recommends to the management of banks, can save on money by not paying for tellers or for
managing branches. Plus, it's cheaper to make transactions over e banking and improve financial
performance. Profitability is crucial to shareholders and the market is also keen on the
profitability of organizations. Any ethical and responsible attempts to improve profitability of a
company will be appreciated by the shareholders. Commercial banks should therefore continue
to adopt new technologies which will improve their margins and hence their profitability in order
to maximize shareholder wealth. Since the findings of this research concluded that e-banking
affect bank performance positively, The National Bank of Ethiopia should prepare various
capacity building activities for banks regarding e-banking operation and provide incentives for
banks to invest rigorously on ICT and use of e-banking by banks and customers.

Government policy makers should also review policies related to promotion of innovation
adoption and transfer of technology. Government should encourage adoption of innovations that
will improve profitability of organizations because it will convert to better tax revenues for the
government.

5.3 Further Research

This research is an important contribution to the literature due to the findings of the study
which will help policy makers to formulate policy. This research examined the effects of e
banking service, such as, Mobile banking, ATM, POS, agent banking, and internet banking on
performance of commercial banks in Ethiopia.

However, there are other variables (e-banking services and control variables) that were not
included in this study. Therefore, future researchers may be interested in validating the
consistency of the result and provide supplementary results for this study by including other
macro-economic variables example GDP and Inflation.

45
 Despite that, there is a feeling that views banks should discharge their social responsibility by
employing human tellers instead of installing more automatic teller machines and other e
banking services that reduce employees and government employee’s tax. Because of this fact the
profitability of the banks vis-à-vis with their social responsibility should be studied to find
optimal solution for banks and society in general.

REFERENCE

Aduda, J., Kiragu, P. and Ndwiga, J. M. (2014).The relationship between agency banking and
financial performance of commercial banks in Kenya. Journal of Finance and Investment
Analysis, 2(4), 97-117.
Agolla, J. E., Makara, T. and Monametsi, G. (2018). Impact of banking innovations on customer
attraction, satisfaction and retention: the case of commercial banks in Botswana.
International Journal of Electronic Banking, 1(2), 150-170.
Alwan, H. A. and Al-Zubi, A. I. (2016). Determinants of internet banking adoption among
customers of commercial banks: an empirical study in the Jordanian banking sector.
international journal of business and management, 11(3), 95.
Argamo, H. H. (2015). The Effect of Agency Banking on The Financial Performance of
Commercial Banks in Kenya in 2014: A Case Study of Chase Bank (Doctoral dissertation,
United States International University-Africa).
Asia, N. M. (2015). Electronic Banking and Financial Performance of Commercial Banks in
Rwanda: A Case Study of Bank of Kigali. Research Project Report submitted to the
Department of Business Administration in the School of Business in partial fulfillment of the
requirement for the award of Master Degree in Business Administration (Finance Option) of
Jomo Kenyatta University of Agriculture and Technology.
Chedrawi, C., Harb, B. and Saleh, M. (2019).The E-Banking and the Adoption of Innovations
from the Perspective of the Transactions Cost Theory: Case of the Largest Commercial
Banks in Lebanon. In ICT for a Better Life and a Better World(pp. 149-164).Springer, Cham.
Creswell, J. W. (2018). Qualitative inquiry and research design: Choosing among five
approaches (3rd ed.). Thousand Oaks, CA: Sage.

46
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319-34.
Fiona, M. (2020).Reliability vs validity: what’s the difference? International validity and
reliability association, 12, 143-152. (https://www.scribbr.com/author/fionamiddleton).
Accessed on June 25, 2021.
Jegede, C. A. (2014). Effects of automated teller machine on the performance of Nigerian banks.
American Journal of Applied Mathematics and Statistics, 2(1), 40-46.
Kamau, D. M. and Oluoch, J. (2016). Relationship between financial innovation and commercial
bank performance in Kenya. International Journal of Social Sciences and Information
Technology, 2(4), 34-47.
Kagendo, F. K. (2015). E-banking strategy and performance of commercial banks in
Kenya.Unpublished MBA project, School of Business, University of Nairobi.
Kemunto, E. R. and Kagiri, A. (2018). Effect Of Implementation Of FINTECH Strategies On
Competitiveness In The Banking Sector In Kenya: A Case Of KCB Bank Kenya. European
Journal of Business and Strategic Management, 3(3), 29-40.
Khrawish, H. A. and Al-Sa’di, N. M. (2011).The impact of e-banking on bank profitability:
Evidence from Jordan. Middle Eastern Finance &Economics, Issue 13, 142-158.
Mabwai, F. (2016). Effects of mobile banking on the financial performance of commercial banks
in Kenya.Published MBA project.
Mutua, R. W. (2014). Effects of mobile banking on the financial performance of commercial
banks in Kenya.Published MBA Thesis, University of Nairobi.
Malhotra, P., & Singh, B. (2009).The impact of Internet banking on bank performance and risk:
The Indian experience. Eurasian Journal ofBusiness& Economics, 2(4), 43-62.
Okibo, B. W.; Wario, A. Y. (2014). Effects of e-banking on growth of customer base in Kenyan
banks. International Journal of Research in Management & Business Studies 1(1):78–84.
Onay, C.; Ozsoz, A. P. D. E.; Helvacıoğlu, A. P. D. A. D. (2008).The impact of internet-banking
on bank profitability-the case of Turkey, in Oxford Business &Economics Conference
Program, 22–24 June, 2008, Oxford, UK.
Puschel, 1., Mazzon, 1. A. and Hernandez, 1. M. C (2010). Mobile banking: Proposition of an
integrated adoption intention framework. International Journal of Bank Marketing, Vol. 28,
No.5: 389-409.

47
Raharjo, P. G.; Hakim, D. B.; Manurung, A. H. andMaulana, T. N. (2014).The determinant of
commercial banks’ interest margin in Indonesia: an analysis of fixed effect panel regression.
International Journal of Economics and Financial Issues 4(2): 295–308.
Rogers, E.M. (1962). Diffusion of Innovations. Glencoe: Free Press.
Sumra, S. H.,Manzoor, M. K., Sumra, H. H. and Abbas, M. (2011). The impact of e-banking on
the profitability of banks: A study of Pakistanibanks. Journal of Public Administration &
Governance, 1(1), 31-38.
Santu, T. V. C., Mawanza, W. andMuredzi, V. (2017).An Evaluation of the Agency Banking
Model Adopted by Zimbabwean Commercial Banks. Journal of Finance, 5(2), 58-66.
Silas, M. E. (2016). Evaluation of e-banking strategies on organization performance: case of
Kenya Commercial Bank (Doctoral dissertation, Mount Kenya University).
Simboley, B. C. (2017). Effects of Agency Banking on the Financial Performance of Commercial
Banks in Kenya (Doctoral dissertation, United States International University-Africa).
Simpson, J. (2002). The impact of the internet in banking: observations and evidence from
developed and emerging markets, Telematics and Informatics 19(4): 315–330.
Sokolov, D. (2007). E-banking: risk management practices of the Estonian banks, Working.
Institute of Economics at Tallinn University of Technology, 12(7), 75-89.
Victor, O. I., Obinozie, H. E. and Echekoba, F. N. (2017).The Effect Of Information
Communication Technology And Financial Innovation On Performance On Nigerian
Commercial Banks (2001–2013). International Journal of Contemporary Applied
Sciences,8(4), 43-56.
Wruuck, P., Speyer, B., AG, D. B. and Hoffmann, R. (2013). Pricing in retail banking.Scope for
boosting customer satisfaction.Frankfurt am Main: Deutsche Bank AG, 1-20
Yue, L. (2016). How to Determine the Validity and Reliability of an Instrument?
(https://blogs.miamioh.edu/discovery-center/2016/11/how-to-determine-the-validityand-
reliability-of-an-instrument).Accessed on July 18, 2021

48
49