ADDIS ABABA UNIVERSITY

COLLEGE OF BUSINESS AND ECONOMICS

DEPARTMENT OF ACCOUNTING AND FINANCE

THE ROLE OF ELECTRONIC BANKING ON THE FINANCIAL

PERFORMANCE OF COMMERCIAL BANKS IN ETHIOPIA

A Thesis Submitted to the School of Graduate studies of Addis

Ababa University in Partial Fulfillment of the Requirements for the
Degree of Master of Science in Accounting and Finance.

By: Elias Girma

ID No. GSE/0903/09

Advisor: Abebe Yitayew (PhD)

 Statement of Declaration

I declare that the thesis entitled: The role of electronic banking on the financial performance of
commercial banks in Ethiopia, hereby submitted by me in partial fulfillment of the requirements
for the Degree of Master of Science in Accounting and Finance at Addis Ababa University, is
my original work and has not been presented for the award of any degree in any other University
or institution. I have undertaken it independently with the advice of my advisor, Abebe Yitayew
(PhD). In performing the thesis I have used different sources and material which have been
properly acknowledged.

Name: Elias Girma

Signature _______________

Date ___________________

I
 Statement of Certification

This to certify that this thesis titled “The role of electronic banking on the financial performance
of commercial banks in Ethiopia” carried out by Elias Girma Kebede. The work is original in
nature and is suitable for the submission for the Master of Science Degree in Accounting and
Finance.

Advisor:

Name: Abebe Yitayew

(PhD)
Signature
________________
Date ______

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 Addis Ababa University

School of Graduate Studies

This is to certify that the thesis prepared by Elias Girma, entitled: “The role of electronic
banking on the financial performance of commercial banks in Ethiopia” and submitted in
partial fulfillment of the requirements for the Degree of Master of Science in Accounting and
Finance complies with the regulations of the University and meets the accepted standards with
respect to originality and quality.

Approved by:

Internal examiner: ________________________ Signature _______________ Date __________

External examiner:________________________ Signature _______________ Date __________

Advisor: Abebe Yitayew (PhD)______________Signature _______________ Date __________

III
 Acknowledgment
I thank the Almighty God for seeing me through the entire research period.

I would like to express my heartfelt gratitude to the advisor of my research paper, Abebe
Yitayew (PhD) for his invaluable guidance and comments beginning from the proposal up to the
completion of the study.

I also acknowledge the support of all commercial banks‟ e-banking department and National
Bank of Ethiopia staffs, who have always been positive and supportive.

My appreciation also goes to all my friends, especially Eyob kindu and Bikila assefa for their
support and encouragement in every difficulty and for all ideas they have shared with me.

IV
 Table of
Content
Acknowledgment……………………………………………………………………………….…I
Table of Content ..................................................................................................................................................... II
List of Tables........................................................................................................................................................... V
List of Figures ........................................................................................................................................................
VI
List of Abbreviations and Acronyms ............................................................................................................. VII
CHAPTER ONE ............................................................................................................................. 1
INTRODUCTION .......................................................................................................................... 1
1.1. Background of the Study ..................................................................................................... 1
1.2 Statement of the Problem ..................................................................................................... 3
1.3 Research Objectives ........................................................................................................ 6
1.3.1 General Objective of the Study .................................................................................... 6
1.3.2 Specific Objectives ....................................................................................................... 6
1.4 Research Hypothesis .......................................................................................................... 7
1.5 Significance of the Study ..................................................................................................... 7
1.6 Scope and Limitations of the Study ..................................................................................... 8
1.7 Organization of the Paper ..................................................................................................... 9
CHAPTER TWO .......................................................................................................................... 10
LITERATURE REVIEW ............................................................................................................. 10
2.1 Theoretical Review ............................................................................................................ 10
2.1.1 Innovation Diffusion Theory ...................................................................................... 10
2.1.2 Task Technology Fit (TTF) Theory............................................................................ 11
2.1.3 Theory of Planned Behavior ....................................................................................... 12
2.1.4 Technology Acceptance Model .................................................................................. 13
2.1.5Theories of Bank Profitability ..................................................................................... 14
2.1.6 Definition of E-Banking ............................................................................................. 15
2.1.7 Overview of Banking and Banking Practice in Ethiopia ............................................ 17
2.1.7.1 Overview of E-Banking in Ethiopia ................................................................... 20
2.1.8 Types of E-Banking Service ....................................................................................... 22
2.1.8.1 Automated Teller Machine (ATM) ..................................................................... 22
2.1.8.2 Point of Sale Terminal (POS) ............................................................................. 23

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 2.1.8.3 Mobile Banking Service ..................................................................................... 23
2.1.8.4 Internet Banking Service..................................................................................... 24
2.1.8.5 Debit Card ........................................................................................................... 25
2.1.8.6 Credit Card .......................................................................................................... 25
2.1.8.7 Agent Banking .................................................................................................... 26
2.1.9 Determinants of Banks Profitability ........................................................................... 27
2.1.10 Electronic-Banking and Financial Performance ....................................................... 29
2.1.11 Performance Measurement in Banks ........................................................................ 30
2.2 Review of Empirical Studies .............................................................................................. 32
2.2.1. Related Empirical Studies in the World .................................................................... 32
2.2.2. Related Empirical Studies in Africa .......................................................................... 35
2.2.3. Related Empirical Studies in Ethiopia ....................................................................... 37
2.2.4 Gaps in Literature ...................................................................................................... 39
2.2.5 Conceptual Framework.............................................................................................. 41
CHAPTER THREE .................................................................................................................... 42
RESEARCH DESIGN AND METHODOLOGY .................................................................... 42
3.1 Research Design ................................................................................................................. 42
3.1.1 Population of the Study and Sampling Techniques .................................................... 43
3.1.2 Source and Types of Data ........................................................................................... 43
3.1.3 Data Analysis .............................................................................................................. 43
3.2 Model Specification ........................................................................................................... 44
3.3 Variables Definition and Hypothesis Development ........................................................... 46
3.3.1 Dependent Variable .................................................................................................... 46
3.3.2 Independent and Other Control Variable.................................................................... 48
3.3.2.1 Number of Automated Teller Machine Terminals (NATM) .............................. 48
3.3.2.2 Debit Card (DC) .................................................................................................. 49
3.3.2.3 Number of Mobile Banking Users (NMOBU ..................................................... 51
3.3.2.4 Value of Transaction of Automated Teller Machine (VATMT) ........................ 52
3.3.2.5 Value of Transaction of Mobile Banking (VMOBT) ......................................... 52
3.3.2.6 Bank Size (BS) .................................................................................................... 54
3.3.2.7 Inflation (INF) ..................................................................................................... 55
III
CHAPTER FOUR ......................................................................................................................... 57
DATA ANALYSIS AND INTERPRETATION .......................................................................... 57
4.1. Descriptive Statistics ......................................................................................................... 57
4.2 Correlation Analysis ........................................................................................................... 59
4.3 Classical Linear Regression Model (CLRM) Assumptions and Diagnostic Test .............. 60
4.3.1 Test for Average Value of the Error Term is Zero (E (ut) = 0) .................................. 60
4.3.2 Test for Heteroskedasticity Assumption (var(ut ) = σ2 ) ............................................ 60
4.3.3 Test for Multicollinearity............................................................................................ 61
4.3.4 Test For Normality ssumption (ut N(0, σ2) .......................................................... 63
4.3.5 Test for Choosing Random Effect (RE) Versus Fixed Effect (FE) Models ............... 64
4.4 Result of the Regression Analysis ...................................................................................... 65
4.4.1 Operational Model ...................................................................................................... 66
4.4.2 Interpretations on Regression Results and Research Hypothesis ............................... 68
4.4.2.1 Number of ATM Terminal and Return on Equity .............................................. 68
4.4.2.2 Debit Card Issued and Return on Equity ............................................................ 69
4.4.2.3 Number of Mobile Banking Users and Return on Equity................................... 69
4.4.2.4 Value of ATM Transactions and Return on Equity ............................................ 70
4.4.2.5 Value of Mobile Banking Transactions and Return on Equity ........................... 71
4.4.2.6 Bank Size and Return on Equity ......................................................................... 72
4.4.2.7 Inflation and Return on Equity ........................................................................... 73
CHAPTER FIVE .......................................................................................................................... 75
CONCLUSION AND RECOMMENDATION ............................................................................ 75
5.1 Conclusion .......................................................................................................................... 75
5.2 Recommendations .............................................................................................................. 76
5.3 Further Research Consideration ......................................................................................... 77
REFERENCE
APPENDIXES

IV
 List of Tables
Table 3.1 Definition, notation and expected sign of the explanatory variables .................56

Table 4.1 Descriptive statistics...........................................................................................57

Table 4.2 Correlation Analysis...........................................................................................59

Table 4.3 Heteroskedasticity test....................................................................................... 61

Table 4.4 Correlation Matrix between Independent variables …………………………..62

Table 4.5 Multicollinearity Test …………………………................................................63

Table 4.6 Normality test.....................................................................................................63

Table 4.7 Hausman test for fixed and random effect model……………….…………….65

Table 4.8 Fixed effects model regression results………....................................................66

Table 4.9 Summary of Variables, Hypothesis Test and Decisions ....................................74

V
 List of Figures

Figure 2.1 Conceptual Framework or model of the study…….............................................. 41

Figure 4.1 Normality Test (Histogram) for residuals..............................................................64

VI
 List of Abbreviations and Acronyms

ATM Automated Teller Machine

CLRM Classical Linear Regression Model

DC Debit Card

E-banking Electronic banking

EFT Electronic Fund Transfer

ETB Ethiopian Birr

FEM Fixed Effect Model

ICT Information and Communication Technology

Ksh Kenyan Shilling

LM Linear Model

LOG Logarithm

MOB Mobile banking

NBE National Bank of Ethiopia

NIAT Net income after tax

NIBT Net income before tax

NIM Net Interest Margin

OLS Ordinary List Square

PAT Profit after Tax

PBT Profit before Tax

POS Points of Sale

VII
REM Random Effect Model

ROA Return on Asset

ROE Return on equity

SMS Short Message Service

SSA Sub Saharan African

TPB Theory of Planned Behavior

VIII
 Abstract
This study examines the Roles of E-banking on Financial Performance of commercial Banks in
Ethiopia using return on equity as proxy of profitability. Technological development by banks is
significant to maximize returns and pull in more customers who are becoming complex and
demanding for better and quality service. The study used secondary data and employed
purposive sampling technique to select ten commercial banks operating in Ethiopia covering the
periods from 2015 to 2018. In light of prior literature, key bank specific and macroeconomic
variables were identified to disclose their relationship and influence on financial performance of
commercial banks. These variables were number of ATM terminals, number of debit cards,
number of mobile banking users, value of ATM transactions, value of mobile banking
transactions, bank size and inflation rate. The fixed effect regression technique and correlation
analysis was used to analyze the data using the econometric package STATA version 13
software. The regression analysis showed that from bank specific variables, number of mobile
banking users and value of ATM transactions had positive and significant roles on bank’s
profitability measured by return on equity which indicated that increasing the number of mobile
banking users and the value or price of transactions executed by ATM had a positive roles on the
financial performance of commercial banks as these made basic financial services more
accessible by minimizing time and distance to the nearest bank branches as well as reduced the
bank‘s overheads and transaction- related costs and had the potential to extend the limited
nature and reach of the formal financial services to various customers thereby increasing their
profitability. On the other hand bank size had negative and significant role while inflation had a
positive significant role from macroeconomic variable on bank’s profitability. The study suggests
that focusing and enhancing on awareness creation alongside the key internal drivers could
enhance e-banking practice as well as the performance of commercial banks in Ethiopia.

Keywords: Electronic Banking; Commercial banks; ROE; Profitability and Macroeconomic.

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 CHAPTER ONE

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

Banks play significant role for economic development of nations in general and of developing
countries like Ethiopia in particular, where the financial system as a whole is bank dependent
due to poor development or even absence of the stock market. Banks play an important role as
financial intermediaries for savers and borrowers in an economy. Financial intermediaries as a
component of the financial system provide a payment mechanism, match supply and demand in
the financial markets, deal with complex financial instruments and markets, provide market
transparency, and perform risk transfer and risk management functions. All sectors of the
economy virtually depend on the banking sector for their very survival and growth. Thus,
financial performance analysis of commercial banks has been of great interest to academic
research (Elshaday, T., Kenenisa, D. and Mohammed,S. 2018).

The fast advancing global information infrastructure, information technology and computer
networks such as the Internet and Telecommunication systems, enabled the development of
electronic commerce at a global level. The nearly universal connectivity which the Internet offers
has made it an invaluable business tool. These developments have created a new type of
economy, which is called the digital economy (Shah and Clarke, 2009).Today e-banking starts a
new phase in competition because of its characteristics like speed, efficiency, diminishing the
expenses, and gaining benefit of the unique opportunities. Obviously, if the bank`s investment
rises the profitability, the e-banking usage in banking industry would be beneficial (Torki et al.,
2004).

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 Electronic banking (e-banking) is the use of electronic and telecommunication networks to
deliver a wide range of value added products and services to bank customers (Steven, 2002). It is
transforming the banking and financial industry in terms of the nature of core products or
services and the way these are packaged, proposed, delivered and consumed. It is an invaluable
and powerful tool driving development, supporting growth, promoting innovation and enhancing
competitiveness (Gupta, 2008; Kamel, 2005). The evolution of banking technology has been
driven by changes in distribution channels as evidenced by automated teller machine (ATM),
Mobile banking, Tele-banking, PC-banking and most recently internet banking (Gallup
Consulting, 2008). ATM cards, credit cards, debit cards, smart cards, POS, mobile banking, all
these have eased up human life to an extent that today life without these happen to be hard and
loaded with inconveniences. The financial business over time has opened to noteworthy change
that can be called e-developments which is progressing quickly in all areas of financial
intermediation and financial markets, for example, e-finance, e-money, electronic banking (e-
banking), e-brokering, e-insurance, e-exchanges, and even e-supervision (Bonsu, F. 2015).

The application of information and communication technology concepts, techniques, policies and
implementation strategies to banking services has become a subject of fundamental importance
and concerns to all banks and indeed a prerequisite for local and global competitiveness banking.
The advancement in Technology has played an important role in improving service delivery
standards in the banking industry. In its simplest form, Automated Teller Machines (ATMs) and
mobile banking now allow consumers carry out banking transactions beyond banking hours.
With online banking, individuals can check their account balances and make payments without
having to go to the bank hall. This is gradually creating a cashless society where consumers no
longer have to pay for all their purchases with hard cash hence improving customer relationship
management system. For example, bank customers can pay for airline tickets by transferring the
money directly from their accounts, or pay for various goods and services by electronic transfers
of credit to the sellers account. As most people now own mobile phones, banks have also
introduced mobile banking to cater for customers who are always on the move. Mobile banking
allows individuals to check their account balances and make fund transfers using their mobile
phones (Bonsu, F. 2015).

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The practice of excellent service delivery integrated with consumer items is a powerful generator
to cater for customers' needs and also engage them. Considering the fact that numerous banks
offer undifferentiated products in a rival marketplace, banks are giving careful consideration to
service delivery in order to pick up a greater advantage over their competitors. “Banks that
deliver quality services can pick up a competitive edge in terms of higher revenue hence benefit,
customer loyalty and customer retention” (Kumar et al., 2010).

The banking industry in Ethiopia is embarking on capacity building preparations and modernize
the banking system by employing the state of the art technology being used anywhere in the
world (Gardachew, W. 2010). Same fashion, now a day Commercial Bank of Ethiopia and other
private Commercial Banks are also implementing different kinds of e-banking services. A few
from among are: Internet banking, mobile banking, ATM (Automated Teller Machine) and POS
(Point of Sale Terminal) channels.

The emergence of E-banking in Ethiopia goes back to the late 2001, when the largest state
owned commercial bank of Ethiopia (CBE) introduced ATM to deliver service to the local users.
Despite being the pioneer in introducing ATM based payment system, CBE was challenged by
other competitor banks like Dashen Bank, which worked aggressively to maintain its lead in e-
payment system (Gardachew, W. 2010).

The rapidly growing information and communication technology is knocking the front-door of
every organization in the world, where Ethiopian banks would never be exceptional. In Ethiopia,
however, cash is still the most dominant medium of exchange, and electronic payment systems
are at an embryonic stage. In the face of rapid expansion of electronic payment systems
throughout the developed and the developing world, Ethiopia‟s financial sector cannot remain an
exception in expanding the use of the system. Currently the use of cash has been replaced by
digital cash and digital wallets. It can be precisely said that this is the fourth stage of evolution
after Barter, Currency, Paper money (Checks) and now digital cash (Gardachew, W. 2010).

1.2 Statement of the Problem

Electronic banking has been recognized to play an important role in economic development on
the basis of their ability to create liquidity in the economy through financial intermediation.
Electronic banking system like mobile banking (m-banking), ATMs and internet banking has a
3
 great impact on bank performance because they increase profitability, reduce bank cost of
operations, and increase bank asset and bank efficiency (Ngango, M.A. 2015). Electronic
banking has made banking transaction to be easier by bringing services closer to its customers
hence improving banking industry performance (Josiah, A. and Nancy, k. 2012).

With the changes in the banking environment, all commercial banks in Ethiopia are investing a
significant sum of their capital towards digital banking and digitizing their operations in order to
meet up with international standards and also to gain domestic competitiveness (NBE websites),
and It has been proven that integrating e-banking in banking operations leads to increase
efficiency and speed in terms of how transactions are conducted and service delivered thus
leading to increase profitability in the long run. But at the same time significant investment will
have to be put in order to achieve a fully integrated e-banking sector, therefore the aspect of cost
also comes in here, It is therefore important that e-banking innovations are made through sound
analysis of risks and costs associated so as to avoid harms on the bank performance. On one hand
the bank performance is directly related to efficiency and effectiveness of electronic banking, but
on the other tight controls and standards are needed to prevent losses associated with electronic
banking (Josiah, A. and Nancy, k. 2012).

Pertinent to previous studies, different attributes have been reflected on the area of e-banking by
various scholars. For example, Solomon, W. (2016) conducted a research on the roles of e-
banking on financial performance of commercial banks in Ethiopia. The study used secondary
data and employed purposive sampling technique to select ten commercial banks operating in
Ethiopia covering the periods from 2013 to 2015.Using ROA as one of the most fundamental
indexes of profitability, key explanatory variables were identified to disclose their relationship
and influence on financial performance of commercial banks. These independent and other
control variables were value or price of transaction of ATM, value or price of transaction of
POS, debit card, number of automated teller machine terminals, number of point of sale terminal
and market share of banks. Result exhibited that numbers of ATM terminals, number of POS
terminals and bank market share had positive and significant role on financial performance of
commercial banks measured by return on asset. The study showed that increased number of
ATM, POS and market share had a positive role on the financial performance of commercial

4
 banks with many banking institutions indicating that increased market share allowed a
company to achieve greater scale in its operations which generally improved its profitability.

Girma, A.(2016) conducted a research on the impact of ICT (Information and Communication
Technology) on the performance of Ethiopian banking industry using secondary data over
the period 2010 – 2014. The study employed purposive sampling technique to select the
required sample of banks from commercial banks in Ethiopia. Using ROA as a measure of
performance in the study and the explanatory variables were ICT investment, ATM, POS, INF,
BRAN and GDP. The finding showed that the ICT, ATM and POS had no statistically
significant effect on return on asset on commercial banks in Ethiopia. Moreover the study result
showed that the POS, ICT and number of branches had negative effect on return on asset on
commercial banks in Ethiopia.

Despite the importance of e-banking in explaining banking performance, research on the effect
of e-banking on banks performance is insufficient. Attempts have been made to investigate the
role of electronic banking on bank performance, although the outcomes of the research are
contradicting each other, For example, Karawish, H.A and Al-sa‟di (2011) made an attempt to
assess the impact of e-banking on profitability of the bank sectors, and results have shown that
applying the e-banking services through the internet had no significant effect on Return on
Equity (ROE), but significant in terms of Return on Assets (ROA). Three years later, similar
study conducted by Mensah, M. (2014) clearly elaborated that e-banking had significant impact
on profitability of banks both in terms of ROA and ROE. These researchers reached a conclusion
by inferring ROA and ROE, but the present research tried to encompass ROA, ROE, and NIM
which is more comprehensive than prior researches.

Although a number of earlier studies have made to add their own contribution to the role of
electronic banking service on financial performance of commercial banks and stated their own
policy implication, most of the studies were out of contexts where the cultural, geographical, and
economic conditions are different from Ethiopia. But, few studies have been conducted on the
roles of e-banking on the financial performance of commercial banks in Ethiopia. For instance,
study conducted by Solomon,W. (2016) and Girma, A.(2016), However, other research works
conducted in Ethiopia in relation to the issue of electronic banking, concentrated on adoption of
e-banking and its challenge and opportunities in Ethiopian banking industry, i.e (Ayana, G.
5
 2010) on adoption of e-banking in Ethiopia; Barriers and Drivers; Gardachew, W. (2010) on
electronic banking practices in Ethiopia: opportunities and challenges, Million, (2013) on impact
of electronic banking on customer satisfaction. This implies that prior research works did not
give an emphasis on e-banking implication on profitability.

So the theme of this research is to examine the roles of e-banking on the financial performance
of commercial banks in Ethiopia by adding variables: number of mobile banking users and value
of transaction executed by mobile banking as a proxy of e-banking which were not included in
previous study.

This study therefore intends to fill these relevant gaps in literature by studying the roles of e-
banking service on ROE as a financial performance indicator of commercial banks by replicating
the existing, in the Ethiopian context, and by providing other factors that are untouched and that
affect profitability.

1.3 Research Objectives

1.3.1 General Objective of the Study

The general objective of the study is to examine the role of electronic banking service on
financial performance of commercial banks in Ethiopia, focusing on its role on return on equity
(ROE).

1.3.2 Specific Objectives

Specifically, this study addresses the following objectives:-

 To examine the roles of ATM terminals on the financial performance of commercial

banks in Ethiopia
 To examine the roles of Debit cards on the financial performance of commercial banks in
Ethiopia

6
  To examine the roles of Mobile banking on the financial performance of commercial
banks in Ethiopia
 To examine the roles of Bank size on the financial performance of commercial banks in
Ethiopia
 To examine the roles of inflation rate on the financial performance of commercial banks
in Ethiopia

1.4 Research Hypothesis

The following research hypotheses were developed in order to address the research question.
Therefore, this study attempts to test the following hypothesis, in order to address the impact of
e-banking on the financial performance of commercial banks in Ethiopia.

Hypothesis 1: Number of ATM terminals has positive and significant role on ROE of
Commercial banks in Ethiopia
Hypothesis 2: Number of debit card users has positive and significant role on ROE of
Commercial banks in Ethiopia
Hypothesis 3: Number of mobile banking users has positive and significant role on ROE
Of commercial banks in Ethiopia
Hypothesis 4: Value of transaction of ATM has positive and significant role on ROE of
Commercial banks in Ethiopia
Hypothesis 5: Value of transaction executed by mobile banking has positive and significant role
On ROE of commercial banks in Ethiopia
Hypothesis 6: Bank size has positive/negative significant role on ROE of commercial banks
In Ethiopia
Hypothesis 7: Inflation has positive/negative significant role on ROE of commercial banks
In Ethiopia

1.5 Significance of the Study

The finding of the study will be of great importance to managers 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

7
 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 of commercial banks 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.

1.6 Scope and Limitations of the Study

The study was limited to the roles of electronic banking on the financial performance of
commercial banks in Ethiopia, where bank size was used as the control variable; other aspects
that influence performance of the banks were not considered in this study. Other factors include;
other products offered by the bank for example internet banking, POS, the different types of
accounts, loans and advances, investments for example in government securities among others,
but not relevant for this study and were cited by many other researchers.

The study was also limited to the degree of precision of the data obtained from the secondary
source. While the data was verifiable since it came from each selected banks‟ head office and
commercial banks published annual audited financial statements the data could still have some
shortcomings as to precision.

The study was also limited to 10 out of 18 commercial banks registered in Ethiopia; the findings
will be generalized to the entire banking industry. The study was based on 4 years period from
year 2015 to 2018. The short period which especially mobile banking has been in existence could
not give a long trend for analysis. Mobile banking was only introduced in Ethiopia in 2012(VOA
news). It has only been seven years since the launch which may not give a clear picture of the
relationship as not all commercial banks have adopted mobile banking in its operations. This
may have probably given a shorter time focus hence given a limited dimension to the problem.
The size of the bank has been held constant across the period.

Lastly, the study did only factor, annual inflation rate in the changes in the macroeconomic
environment and did no factor other changes in the macroeconomic environment such as real
8
 GDP and Foreign exchange rate that could have affected the financial performance of
commercial banks.

1.7 Organization of the Paper

This study consists of five chapters. Chapter one presents introduction, statement of the problem,
objective of the study, hypotheses, scope and limitations and significance of the study. Chapter
Two reviews the most significant theoretical and empirical studies. Chapter three presents
methodology of the study. Then chapter four provides data analysis and presentation of
econometric model outcomes and finally, chapter five gives summary, conclusions and
recommendations with policy implication and further research direction.

9
 CHAPTER TWO

LITERATURE REVIEW
This chapter discusses the literature related with e-banking. Accordingly, the review of the
literature provides the reader with the explanation of the theoretical rationale of the problem
being studied, types of electronic banking as well as what research has already been done and
how the findings relate to the problem at hand. The purpose of the literature review is to avoid
unnecessary intentional or accidental duplication of material already covered. This literature
review was reviewed from previous past major activities that had been undertaken to address the
issues in electronic banking.

2.1 Theoretical Review

There are numerous theoretical foundations that serve as basis to formulate a model to practice a
research. For instance, in determining the performance and profitability of the bank service
employing high technology devices and machines there are four significant theories. These are
innovation diffusion theory; task technology fit theory, theory of planned behavior, and
technology acceptance model. According to (Ajzen, I. 1991), a theoretical framework guides
research, determining what variables to measure, and what statistical relationships to look for in
the context of the problems under study. Thus, the theoretical literature helps the researcher to
identify clearly the variables of the study; provides a general framework for data analysis; and
helps in the selection of applicable research design.

2.1.1 Innovation Diffusion Theory

This theory was officially introduced by Bradley and Stewart in the year 2002 and it affirms that
firms engage in the diffusion of innovation in order to gain competitive advantage, reduce costs
and protect their strategic positions. The innovation diffusion theory put forward by Rogers
in 1962 is a well -known theory that explains how an innovation is diffused among users over
time (Liu, Y. & Li, H. 2009).It also helps to understand customers‟ behavior in the adoption or
non-adoption of an innovation (Vaugh, J., Schavione, F. 2010; Lee and others, 2003). The theory
depicts that the adopters of any innovation follow a bell shaped distribution curve which
may be divided into five parts to categorize users in terms of innovativeness (Liu, Y. & Li,
10
H. 2009). Rogers classified users as innovators, early adopters, early majority, late majority and
laggards (Liu, Y. & Li, H. 2009).

The adoption and use of mobile banking has the potential to extend the limited nature and reach
of the formal financial sector to the poor and rural population in Africa. Most of the existing
literature is from the developmental practitioners‟ arena with a few scholarly studies emerging
(Mas, I. and Morawczynski, O. 2009).

Although most of the studies from the practitioners are not peer reviewed, they provide valuable
information on actual usage and contextual information on the development and use of the
phenomenal. For example, Ivatury, G. and Pickens, M. (2006) provided valuable insight into the
characteristics of the early adopters of WIZZIT, one of the first major initiatives dedicated to
offering mobile banking to the poor in South Africa.

By applying the traditional technology acceptance models and frameworks to the adoption of
transformational mobile banking services, this study aims to bring the discussion to the
mainstream information systems literature. This theory was used to study how various new
mobile banking products affects financial performance of commercial banks.

2.1.2 Task Technology Fit (TTF) Theory

Task technology fit (TTF) theory contends that information technology (IT) is more likely to
have a positive impact on individual performance and be used if the capabilities of the IT
match the tasks that the user must perform (Goodhue, D., & Thompson, R. 1995). Further,
Goodhue, D. & Thompson, R. (1995, P.141) mentioned the factors that measure task-technology
fit as; quality, floatability, authorization, and compatibility, eases of use/training, production
timeliness, systems reliability and relationship with users”. Their model is useful in the analysis
of various context of a diverse range of information systems including electronic commerce
systems and combined with or used as an extension of other models related to information
systems outcomes.

According to TTF theory the success of an information system have a strong correlation between
task and technology, hence success has been related to individual performance (Goodhue, D. &
Thompson, R.1995) and to group performance (Zigurs, I. & Buckland, B. 1998). For group

11
 support systems, a specific theory of TTF was developed by Zigurs, I. & Buckland, B. (1998)
and later tested by Wilson et al. (1999) and detailed the requirements of group support systems to
fit group tasks. For mobile information systems, TTF has been shown to be generally relevant,
but more specific questions regarding the applicability of task technology fit to mobile
information systems remain unanswered (Gebauer, J. & Shaw, M. 2004).

The theory of task-technology fit maintains that a match between business tasks and information
technology is important to explain and predict the success of information systems
(Goodhue, D. & Thompson, R.1995; Zigurs, I. & Buckland, B. 1998). For various scenarios of
task and technology, statistical significance has been established of a positive association
between task-technology fit and information system success measures, such as Dishaw, M. &
Strong, D. 1999, and impact on individual performance Goodhue, D. & Thompson, R. 1995,
and on group performance (Zigurs et.al 1999). The concept of task technology fit promises to
help identify aspects that are critical to support a given business task, and can thus, contribute to
the success of technology innovations (Junglas, I. & Watson, R. 2006). One such innovation is
represented by mobile technology to support an increasingly mobile workforce (Barnes, S.J.
2003).

2.1.3 Theory of Planned Behavior

The theory of planned behavior (TPB) was developed by Ajzen, I. 1988. The theory posits that
individual behavior is driven by behavior intentions, where behavior intentions are a function of
three determinants: an individual‟s attitude toward behavior, subjective norms and perceived
behavioral control. Attitude refers to the degree to which a person has positive or negative
feelings of the behavior of interest. Behavioral intention represents a person's motivation in the
sense of her or his conscious plan or decision to perform certain behavior (Conner, M. &
Armitage, C.J. 1998). Subjective norms perceived are a person‟s own estimate of the social
pressure to perform the target behavior. Subjective norms are assumed to have two components
which work in interaction: beliefs about how other people, who may be in some way important
to the person, would like them to behave (normative beliefs). Perceived behavioral control is the
extent to which a person feels able to enact the behavior. It has two aspects: how much a person
has control over the behavior and how confident a person feels about being able to perform or
not perform the behavior (Conner, M. & Armitage, C.J. 1998).
12
 The theory of planned behavior predicts behavior, because behavior is planned. This theory has
been widely applied and extended to studies on individual behavior, especially in the prediction
of individual‟s intention to behave and the actual behavior. It is generally expected that the more
favorable the attitude and subjective norm with respect to a behavior, and the greater the
perceived behavioral control, the stronger should be an individual‟s intention to perform the
behavior (Chen, S. & Li, S. 2010).

2.1.4 Technology Acceptance Model

Technology acceptance model (TAM) was originally proposed by Davies in 1986. This model
was designed to forecast the user‟s acceptance of information technology and usage in an
organizational setting. David, C. (2004) posits that firms are adopting technology to cope with
the dynamics of the external environment. This model has been tailored in a manner that can
accommodate changes for improved costs reduction and efficiency. Technology Acceptance
Model deals with perceptions as opposed to real usage, the model suggest that users , the key
factors that influence their decision on how, where and when they will use it (Davis, F.D 1989).

The factors to consider are: Perceived usefulness (PU). According to Davis, it is the degree to
which a person believes that using a particular system will lead to improved performance
(Britton, D.B & McGonegal, S. 2007). Perceived ease-of-use (PEOU) is explained as the degree
to which a person believes that using a particular system would results to improved productivity.
The TAM was proposed by Davis, F.D. (1989), this model expounds on the attitude behind the
objective to use technology or a services. This theory is relevant to this study since it explains
user„s acceptance of information technology and usage in an organizational context. cceptance
is the first process in technology use and has a bipolar implication. First of all acceptance is a
precursor to adoption and hence this theory complements the preceding theories. Secondly,
acceptance dictates the attitude and perception of the users which eventually affects efficiency of
use and hence performance. Strategic adoption as well as operational efficiency and hence
productivity of systems are a function of acceptance of the technology. It is thus plausible to
conclude that without acceptance, the rest of the theories would be redundant and invalid.
Though acceptance is an initial phase, it is also an attitude shaping facet that influences adoption
and effectiveness of use Davis, F.D. (1989).

13
 2.1.5Theories of Bank Profitability

According to literatures, bank performance studies have been started in the late 1980s and/or
early 1990s. These studies revolve on different theories. For Instance, the signaling theory,
which elaborates the relationship between capital and profitability, suggests that higher capital is
a positive signal to the market of the value of bank. (Berger, A.N. 1995) By the same token, a
lower leverage indicates that banks perform better than their competitors who can‟t raise their
equity without further deteriorating the profitability (Ommeren, S. 2011).

Bankruptcy cost hypothesis on the other hand, argues that in case where bankruptcy costs are
unexpectedly high , a bank holds more equity to avoid period of distress (Berger, 1995). Hence,
both the signaling theory and bankruptcy cost hypothesis support the existence of a positive
relationship between capital and profitability. However, the risk-return hypothesis suggests that
increasing risks, by increasing leverage of the firm, leads to higher expected return (profitability)
on one hand and it will definitely reduce the equity to asset ratio (represented by capital) on the
other hand. Thus, risk-return hypothesis predicts a negative relationship between capital and
profitability. (Obamuyi, T.M. 2013)

Contrary to the above argument, Modigliani - Miller theorem conclude that no relationship exists
between the capital structure (debt or equity financing) and the market value of the bank
(Modigliani and Miller, 1958). In other words, no relationship exists between equity to asset
ratio and funding costs or profitability under perfect market. However, when the concept of
Money Market‟s perfect market is scrutinized there is no such a thing in the real world owing to
agency problem, information asymmetry problem, existence of transaction costs, etc. Thus, when
the perfect market does not hold there could be a possible negative relationship between capital
and profitability. (Ommeren, S. 2011)

Olweny and Shipho (2011) argued that the Market Power theory (MP) assumes bank profitability
is a function of external market factors, while the Efficiency Structure (ES) theories and the
balanced portfolio theory largely assume that bank performance is influenced by internal
efficiencies and managerial decisions. Despite the existence of several models to deal with bank
specific aspects, none of the models are believed to be sufficient to express all bank specific
behaviors in a holistic manner.

14
 2.1.6 Definition of E-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.

E-banking means 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, automated 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”
refers to a method of banking through which customers are able to carry out their banking
transactions electronically without visiting a bank branch (Simpson, J. 2002).

Electronic banking or e-banking refers to an umbrella term for the process by which a customer
may perform banking transactions electronically without visiting a brick-and-mortar institution
(Ombati et al, 2011). E-banking is also the use of electronic means to deliver banking services,
mainly through the Internet. The term is also used to refer to ATMs, telephone banking, use of
plastic money, mobile phone banking and electronic funds transfers.

The concept of e-banking is a delivery channel for banking services. Banks have used electronic
channels for years to communicate and transact business with both domestic and international
corporate customers. With the development of the Internet and the World Wide Web (WWW) in
the latter half of the 1990s, banks are increasingly using electronic channels for receiving
instructions and delivering their products and services to their customers. This form of banking is
generally referred to as e-banking or Internet banking, although the range of products and
services provided by banks over the electronic channel vary widely in content, capability and
sophistication.

Daniel, E. (1999), defines E-banking is as the automated delivery of new and traditional banking
products and services directly to customers through electronic, interactive communication
channels.

15
 Salehi, M. and Zhila, A. (2008), describes e-banking as an electronic connection between bank
and customer in order to prepare, manage and control financial transactions. Electronic banking
can also be defined as a variety of following platforms: (i) Internet banking (or online banking),
(ii) telephone banking, (iii) TV-based banking, (iv) mobile phone banking, and e-banking (or
offline banking).

According to Shan, T.C. (2004), Electronic banking (e-banking) can be defined as the automated,
smooth and efficient delivery of modern and traditional banking services through electronic and
communicative channels. It includes the systems that customers use to access accounts, transact
businesses and obtain information through networks, including the internet. Electronic banking
is, therefore, a general term describing the whole process of performing such transactions
without the need to physically visit the financial institution. All of the following terms refer to
different forms of electronic banking; personal computer (PC) banking, online banking, home
banking, mobile banking and virtual banking. Virtual banking is the situation where banks do all
their transactions online by the use of mobile, emails and Automated Teller Machines without
having a physical location while online banking involves the bank having a physical location but
offering services online. Internet banking is called transactional online banking, because it
involves provision of facilities such as accessing accounts, funds transfer and buying financial
products or services online.

According to Arunachalam, L and Sivasubramanian, M. (2007), Internet (electronic) banking is

where a customer can access his or her bank account via the Internet using personal computer
(PC) or mobile phone and web-browser. In addition, Ongkasuwan, M and Tantichattanon, W.
(2002) further defines Internet (electronic) banking service as banking service that allows
customers to access and perform financial transactions on their bank accounts from their web
enabled computers with Internet connection to banks' web sites any time they wish. Internet
banking service also enables bank customers to perform transactions such as transfer and
payments, access of latest balance, statement viewing, account detail viewing, customization,
print, downloading of statements and obtaining a history statement on all accounts linked to the
bank‟s customers‟ uto Bank ( TMs).

According to Khan, S.K (2007), Internet (electronic) banking includes the system that enables
financial institution customers, individuals or businesses, access accounts, transact business, or
16
 obtain information on financial products and services on public or private network including
Internet. Internet (electronic) banking is the act of conducting financial intermediation on the
Internet (Kim et al., 2006). It is that process whereby the customer is able to access, control and
use his/her account over the Internet. It should be noted, however, that the terms used to describe
the various types of electronic banking are often used interchangeably.

2.1.7 Overview of Banking and Banking Practice in Ethiopia

It was in 1905 that the first bank, the -Bank of byssinia‖, was established based on the
agreement signed between the Ethiopian Government and the National Bank of Egypt, which
was owned by the British. According to the agreement, the bank was allowed to engage in
commercial banking (selling shares, accepting deposits and effecting payments in cheques) and
to issue currency notes. The agreement prevented the establishment of any other bank in
Ethiopia, thus giving monopoly right to the Bank of Abyssinia. Apart from serving foreigners
residing in Ethiopia, and holding government accounts, it could not attract deposits from
Ethiopian nationals who were not familiar with banking services (Fasil, A. & Merhatibeb, T.
2009).

The Ethiopian Government, under Emperor Haile-Sellassie, closed the Bank of Abyssinia and
established the Bank of Ethiopia which was fully owned by Ethiopians. The Bank started
operation in 1932. The majority shareholders of the Bank of Ethiopia were the Emperor and the
political elites of the time. The Bank was authorized to combine the functions of central banking
(issuing currency notes and coins) and commercial banking. With the Italian occupation (1936-
1941), the operation of the Bank of Ethiopia came to a halt, but a number of Italian financial
institutions were working in the country. These were Banco Di Roma, Banco Di Napoli and
Banca Nazionale del Lavora. It should also be mentioned that Barclays Bank had opened a
branch and operated in Ethiopia during 1942-43. With the departure of the Italians and the
restoration of Emperor Haile Selassie„s government, the State Bank of Ethiopia was established
in 1943. In 1963, the State Bank of Ethiopia split into the National Bank of Ethiopia and the
Commercial Bank of Ethiopia S.C. with the purpose of segregating the functions of central
banking from those of commercial banking. The new banks started operation in 1964 (Fasil, A.
& Merhatibeb, T. 2009).

17
 The first privately owned company in banking business was the Addis Ababa Bank S.C.,
established in 1964. The Bank carried out typical commercial banking business. Banco Di Roma
and Banco Di Napoli also continued to operate. Thus, until the end of 1974, there were state
owned, foreign owned and Ethiopian owned banks in Ethiopia. The banks were established for
different purposes: central banking, commercial banking, development banking and investment
banking. Such diversification of functions, lack of widespread banking habit among the wider
population, the uneven and thinly spread branch network, and the asymmetrical capacity of
banks, made the issue of competition among banks almost irrelevant (Fasil, A. & Merhatibeb, T.
2009).

Following the 1974 Revolution, on January 1, 1975 all private banks and 13 insurance
companies were nationalized and along with state owned banks, placed under the coordination,
supervision and control of the National Bank of Ethiopia. The three private banks, Banco Di
Roman, Banco Di Napoli and the Addis Ababa Bank S.C. were merged to form ― ddis Bank.

Eventually in 1980 this bank was itself merged with the Commercial Bank of Ethiopia S.C. to
form the ―Commercial Bank of Ethiopia, thereby creating a monopoly of commercial banking
services in Ethiopia. In 1976, the Ethiopian Investment and Savings S.C. was merged with the
Ethiopian government saving and Mortgage Company to form the Housing and Savings Bank.

The Agricultural and Industrial Development Bank continued under the same name until 1994
when it was renamed as the Development Bank of Ethiopia. Thus, from 1975 to 1994 there were
four state owned banks and one state owned insurance company, i.e., the National Bank of
Ethiopia (The Central Bank), the Commercial Bank of Ethiopia, the Housing and Savings Bank,
the Development Bank of Ethiopia and the Ethiopian Insurance Corporation. After the overthrow
of the Dergue regime by the EPRDF, the Transitional Government of Ethiopia was established
and the New Economic Policy for the period of transition was issued. This new economic policy
replaced centrally planned economic system with a market-oriented system and ushered in the
private sector. Several private companies were formed during the early 1990s, one of which is
Oda S.C. which conceived the idea of establishing a private bank and private insurance company

18
 in anticipation of a law which will open up the financial sector to private investors (Fasil, A. &
Merhatibeb, T. 2009).

Currently, the industry comprises one state-owned development bank and 17 commercial banks,
one of which is state-owned, which is the dominant Commercial Bank of Ethiopia (CBE). After
the merger of Construction and Business bank, with CBE that make the composition of the sector
to two state owned banks; Commercial bank of Ethiopia and Development bank of Ethiopia and
16 private commercial banks. The private commercial banks currently operating in Ethiopia
alphabetically: Abay bank, Addis International Bank, Awash International bank, Bank of
Abyssinia, Birhan International bank, Bunna International bank, Cooperative bank of Oromia,
Dashen bank, Debub global bank, Enat bank, Lion International bank, Nib international bank,
Oromia International bank, united bank, Wegagen bank, and Zemen bank (www.nbe.gov.et).

Studies made regarding the financial sectors in Ethiopia witness its infancy and dominancy by
the state owned Commercial bank. Keatinge (2014) strengthen this claim declaring, State owned
CBE dominate the sector with assets accounting for approximately 70 percent of the industry‟s
total holdings. The dominance of public sector banking certainly restricts financial
intermediation and economic growth. It contrasts with regional and international peer countries
where banking industries have a much higher share of private sector and foreign participation.
(Keating, 2014) Literatures revel, compared to most countries, Ethiopia has taken a cautious
approach toward the liberalization of its banking industry. For all intents and purposes, its
industry is closed and generally less developed than its regional peers (Keating 2014 and
Harvey1996).

The Ethiopian financial sector is dominated by the banking sector. Banks are the important
component of any financial system. They play important role of channeling the savings of
surplus sectors to deficit sectors. The efficiency and competitiveness of banking system defines
the strength of any economy. Like other developing countries in Ethiopia banks plays a vital role
in the process of economic growth and development. Despite a rapid increase in the number of
financial institutions since financial liberalization, the Ethiopian banking system is still
underdeveloped compared to the rest of the world. Cash is still the most dominant medium of
exchange. The use of checks is mostly limited to government institutions, NGOs and some
private businesses (Garedachew, W. 2010).
19
 Commercial banks in Ethiopia provide the same services with the same operational style that
they used to offer before decades. The common banking functions provided by public and
private banks in Ethiopia are deposit mobilization, credit allocation, money transfer and safe
custody. Banks in Ethiopia are unable to improve customer service, design flexible and
customized products, and differentiate themselves in a market where product features are easily
cloned. Ethiopian banking is unable to come from long way of being sleepy to a high proactive
and dynamic entity. The customers of Ethiopian commercial banks have missed to enjoy with the
technological advancement in banking sector which has been entertained elsewhere in Africa and
the rest of the world. The modern banking methods like ATMs, Debit cards, Credit cards, Tele
banking, Internet banking, Mobile banking and others are new to the Ethiopian banking sector.

E-banking which refers to the use of modern technology that allows customers to access banking
services electronically whether it is to withdraw cash, transfer funds, to pay bills, or to obtain
commercial information and advices are not known in Ethiopia. In Ethiopia it is impossible to
withdraw money without presenting the pass book and money transfer is allowed only in
between branches of the same bank. However, from the public and the economy there is a strong
need for strengthening linkages among banks in order to allow healthy flow of financial
resources among financial institutions and optimize the contributions of the entire financial
system to the development processes as a whole (Garedachew, W. 2010).

2.1.7.1 Overview of E-Banking in Ethiopia

The appearance of E-banking in Ethiopia goes back to the late 2001, when the largest state
owned, commercial bank of Ethiopia (CBE) introduced ATM to deliver service to the local
users. In addition to eight ATM Located in Addis Ababa, CBE has had Visa membership since
November 14, 2005. But, due to lack of appropriate infrastructure it failed to reap the fruit of its
membership. Despite being the pioneer in introducing ATM based payment system and acquired
visa membership, CBE Lagged behind Dashen bank, which worked aggressively to maintain its
lead in E-payment system. Dashen bank, a forerunner in introducing e-banking in Ethiopia, has
installed ATMs at convenient locations for its own cardholders (Garedachew, W. 2010).
20
By the end of 2008 Wegagen Bank has signed an agreement with Technology Associates (TA), a
Kenyan based information technology (IT) firm, for the development of the solutions for the
payment system and installation of a network of ATMs on December 30, 2008 (Asrat, S. 2010).

Zemen Bank, the only Ethiopian bank anchored in the idea of single branch banking, by
launching full-blown internet banking, a service which is new to Ethiopian banking industry in
the year 2010. The bank tested the venture through its first phase of the online service, and now
it is already started the full-fledged version, which enable customers to make online money
transfer freely. Previously, the online banking service, delivered by the bank, only gave access to
bank statements and exchange rate information. The new and never-been-tried service proposed
by the bank is to include free account money transfer, corporate payroll uploading system where
employers could upload payroll to the system and make payments to individual worker‟s
accounts online and online utility bill settlement system, when utility companies are ready
(Asrat, S. 2010).

The number of banks which deliver E-banking service is increase gradually up to 2011 and
reaches 4. Surprisingly, on June 2012, 3 banks enter in to the market with consortium which
makes the provider of E-banking service to 7. And at the end of 2013, Berhan international bank
joined group and makes the provider of E-banking service in to 8.now all commercial banks start
e-banking service for their customers using et-switch solution (NBE website and Et-switch
website).

Certainly the banking industry in Ethiopia is underdeveloped and therefore, there is an all
immediate need to embark on capacity building arrangements and modernize the banking system
by employing the state of the art technology being used anywhere in the world. With a growing
number of import-export businesses, and increased international trades and international
relations, the current banking system is short of providing efficient and dependable services and
therefore all banks operating in Ethiopia should recognize the need for introducing electronic
banking system to satisfy their customers and meet the requirements of rapidly expanding
domestic and international trades, and increasing international banking services (Garedachew,
W. 2010).

21
 The agreement signed by three private commercial banks to launch an Automated Teller
Machine (ATM) and Point of Sale terminal (POS) network, in February 2009 is welcoming
strategy to improve electronic card payment system in Ethiopia. Three private commercial
banks: Awash International Bank S.C., Nib International Bank S.C and United Bank S.C. have
agreed in principle to establish an ATM network called Fettan ATM network. If everything goes
as planned, Fettan ATM will install over 140 ATM machines and over 340 POSs across
Ethiopia. There will be one ATM at every branch of the consortium banks, all domestic airports
serviced by commercial service, shopping complexes and merchants. The agreement is the first
significant cooperation between competing banks in Ethiopia, which others should be
encouraged to follow as there is no single bank in Ethiopia that can afford to provide extensive
geographical coverage and access (Binyam, T. 2009).

2.1.8 Types of E-Banking Service

2.1.8.1 Automated Teller Machine (ATM)

The basic form of non-branch bank is the ATM (Automated teller machine) a type of banking
where customers can access with their card and pin and check their balances, withdraw money,
and make payment. This type of banking is a small machine that can be found in banks, and all
around the city depending from the usage rate. Kaplan, R. and Norton, D. (2002), postulate that
ATM allows a bank customer to conduct his/her banking transactions from almost every other
ATM machine in the world. However, the spread of the machines has been generating a lot of
heat, as customers face a splurge of frustration in using it; either the machines will not dispense
cash, or debit transactions when cash is not dispensed or cards get stuck in them. The
proliferation of the machines is giving more concern. As with every other technological
breakthrough the ATMs have generated astronomical challenges and problems for the
beneficiaries of financial services in most countries. Most users of ATM have encountered the
problem of scam.

The relationship between banking efficiency and the use of ATM (Automated Teller Machine) is
a complex one. This is because the overall levels of efficiency and productivity do influence the
organization overall success. This explains why most modern banking sectors develop ways of
increasing organization and workers‟ efficiency. Some of these ways include goal setting, job

22
enrichment, adoption information technology, globalization, training and development (Karen, F.
2010). All these represent several practical ways of increasing banking sector‟s performance,
which could also be a reflection of institutions efficiency.

2.1.8.2 Point of Sale Terminal (POS)

Point of sale is the place where a customer completes a transaction, such as a checkout counter
and these point-of-sale transactions can be processed using a wide variety of tools including cash
registers, electronic card readers and barcode scanners (Investopedia LLC, 2017). A POS is a
device that installed in sale centers to remove the need to transfer the physical money and to
deduct money from buyer account and to add it to seller account. This activity is done by a POS
connected to central computer in the bank. It is provided by the bank for the seller and has
modem and printer. Sale center and department stores are where POS is used. A POS perform
functions like; exchanging currency from buyer account to seller account, that is very secure,
printing the account on paper and bill paying availability (Meihami et al, 2013).

2.1.8.3 Mobile Banking Service

Mobile banking is a term used for performing balance checks, account transactions, payments,
credit applications and other banking transactions through a mobile device such as a mobile
phone or Personal Digital Assistant (PDA). Mobile banking is also known as M-Banking or m-
banking. M-banking is defined as “a form of banking transaction carried out via a mobile
phone”. Moreover, it is defined as a “type of execution of financial services in the course of
which - within an electronic procedure- the customer uses mobile communication techniques in
conjunction with mobile devices” ( Johnson, A.M. 2013).

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. It can make basic
financial services more accessible by minimizing time and distance to the nearest retail bank
branches (CG P, 2006) as well as reducing the bank‟s own overheads and transaction- related
costs. Mobile banking presents an opportunity for financial institutions to extend banking
services to new customers thereby increasing their market (Lee, Lee and Kim, 2007).Banking
was driven by income generated from fees for services rendered, interest earned deposits and
interest received from loans. The move from traditional banking to agency banking and currently

23
 mobile banking has been beneficial to both the banks and customers as it reduces operating cost
of the institution and its convenient and cheap as lesser fees are charged on mobile transaction.

Mobile banking is the provision of banking services with the help of mobile devices. The advent
of M-banking was fostered by competition from telecommunication industry mainly Safaricom
with their Mpesa services to their customers and Airtel (formerly Zain) with Airtel Money
services. These services facilitated the customers to deposit money into their account, transfer
money to other user for instance sellers of goods and services, relatives and friend; this brought
convenience. The banking sector has had to adopt technological change to remain competitive.
In search of competitive advantages in the technological financial service industry, banks have
acknowledged value of differentiate themselves from others financial institution through new
service distribution channels. Banks bureaucratic process of account opening cut out many rural
poor, as they could not qualify to own accounts. With competition, banks had to simplify the
process and had to come up with innovative ways of doing so. Mobile banking provides a
number of advantages for both banks and customers. Mobile banking removes geographical
limitation to customers and therefore bringing convenience. There is no time limitation i.e.
banking maybe performed throughout the day and in any place. Mobile banking also provides
efficient cash management and security of cash (Johnson, A.M. 2013).

2.1.8.4 Internet Banking Service

Internet banking (e-banking) is the use of internet and telecommunication networks to deliver a
wide range of value added products and services to bank customers. Internet banking includes
importing data into personal accounting software. Some online banking platforms support
account aggregation to allow the customers to monitor all of their accounts in one place whether
they are with their main bank or with other institutions. Banking through internet is considered as
a complimentary delivery channel for the services rather than a substitute for the brick and
mortar banking branches. Internet has changed the dimensions of competition in the retail
banking sector. Following the introduction of PC banking, ATMs and phone banking, which are
the initial cornerstones of electronic finance, the increased adoption and penetration of Internet
has added a new distribution channel to retail banking: Internet/Online-banking. Internet banking
has gained worldwide acceptance as a new delivery channel for performing various banking
transactions. It provides the opportunity to the customers to conduct banking transactions at their
24
 convenience. There are two ways to offer Internet banking. First, an existing bank with physical
offices can establish a website and offer Internet banking in addition to its traditional delivery
channels. Second, a bank may be established as a "branchless," "Internet-only," or "virtual" bank
(DeYoung, R.2001, Allen et al, 2002, Steven, 2002).

Internet banking is called transactional online banking, because it involves provision of facilities
such as accessing accounts, funds transfer and buying financial products or services online. The
Internet also helps banks penetrate other financial markets without requiring their physical
presence in those markets. The widespread availability of Internet banking is expected to affect
the mixture of financial services produced by banks, the manner in which banks produce these
services and the resulting financial performances of these banks. This therefore is seen by banks
as a better means to serve its wide and ever growing customer base with quality service, fast,
efficient and convenient manner. It is also believed to create good revenue to banks thus leading
to profitability. (Simpson, J. 2002)

2.1.8.5 Debit Card

Debit card is a banking card enhanced with ATM and POS features so that it can be used at
merchant locations. Debit cards allow you to spend only what is in your bank account. It is a
quick transaction between the merchant and your personal bank account. A debit card is linked to
an individual‟s account, allowing funds to be withdrawn at the TM and point of sale without
writing a cheque. When using a debit card to pay for goods and services, the purchase amount is
deducted from the cardholder‟s checking account. The types of debit card include online debit
card and offline debit card. With offline debit card, debit is not made immediately. Benefits of
using a debit card include making the payment process at the checkout counter quicker and more
convenient, eliminating the need to carry a cheque book and a lot of cash, using it at locations
where personal cheques are not accepted, and reducing the possibility of loss or theft of
cash (Okoye, L. 2013).

2.1.8.6 Credit Card

A credit card is a small plastic card issued to users as a system of payment. It allows its holder to
buy goods and services based on the holder's promise to pay for these goods and services. The
issuer of the card creates a revolving account and grants a line of credit to the consumer (or the

25
 user) from which the user can borrow money for payment to a merchant or as a cash advance to
the user (Mavri, M. & Ioannou, G. 2006). A credit card is different from a debit card in that it
does not withdraw money from the users account after every transaction. The issuer lends money
to the consumer to be paid to the merchant. Holders of a valid credit card have the authorization
to purchase goods and services up to a predetermined amount, called a credit limit. The vendor
receives essential credit card information from the cardholder, the bank issuing the card actually
reimburses the vendor, and eventually the cardholder repays the bank through regular monthly
payments. If the entire balance is not paid in full, the credit card issuer can legally charge interest
fees on the unpaid portion.

2.1.8.7 Agent Banking

Bank agents help financial institutions to divert existing customer from crowded branches
providing a “complementary” often more convenient channel of accessing bank services.
Financial institutions in developing markets reach an “additional “client segment or geography.

Reaching poor clients in rural areas is often prohibitively expensive for financial institutions
since transaction numbers and volumes do not cover the cost of a branch. (Kitaka P.2001). In
such environments banking agents that piggy bank on existing retail infrastructure and lower set
up and running cost can play a vital role in offering many low income people their first time
access to a range of financial services. Also low income clients often feel more comfortable
banking at their local store than walking into a mobile branch. Brazil is a pioneer in agent
banking. Since 1999, more than 100,000 retail outlets have been turned into bank agents,
reaching 13 million extra unbanked people. (Adiera, A.1995).

According to NBE Directive, agent banking is the conduct of banking business on behalf of a
financial institution through an agent using various service delivery channes. Mobile banking is
performing banking activities which primarily consists of opening and maintaining
mobile/regular accounts and accepting deposits; furthermore, it includes performing fund
transfer or cash in and cash out services using mobile devices.

In Ethiopia there are different banks that launch mobile and agent banking services. As per
recent data by Belcash technology Solutions (2017), they are vending agent banking service by
brand name called “Hello cash” having feature including account opening, cash depositing, cash

26
 withdrawal, bill payments, top upping mobile balance, transferring money and upcoming
services like receiving international money transfers from worldwide. They currently launch
their service in two banks: Lion International bank, Cooperative Bank of Oromia and one micro-
finance: Somali Micro-finance. With those financial institutions, there are about 5,000 agents
and 825 acceptance networks (Servicing only payments of goods and services) having around
710,000 customers running a total transaction of 3.4 million with the total value balance of 2.1
billion birr. In daily bases, they are running around 20,000 transactions with the value balance of
12 million birr (NBE website).

 Hibir Agent Banking

Following the permission of mobile and agent banking, united bank established a team
responsible for the implementation of the service in line with the Bank‟s strategic focus on
technology led banking which synchronize with its new motto “Beyond Core Banking to
Technology Led Excellence”.

The service enables the Bank to use Banking agents as a kind of branch to process basic banking
services including opening M-wallet account, making deposits and withdrawals, transferring
funds as well as sending and receiving money. United bank received the approval to go on
delivering the service on March 31, 2015. In its Agent Banking Services, United will provide
branchless services banking especially for the unbanked society (NBE website).

2.1.9 Determinants of Banks Profitability

Theoretically factors affecting bank profitability are mainly divided into two categories as
internal and external variables. The internal (bank-specific factors) are factors that are related to
internal efficiencies and managerial decisions. As stated in the above section the efficiency
theory highly assume as bank performance is influenced by those internal factors that are related
to internal efficiencies and managerial decisions. Such factors include determinants such as bank
size, capital adequacy, liquidity risk, operational efficiency (expenses management),
management efficiency, employee efficiency and funding cost. On the other hand, the capital
asset pricing theory assumes as bank profitability is a function of external market factors.

27
 Accordingly, the external factors (variables) that can affect bank profitability are the
macroeconomic factors such as real GDP, foreign exchange rate and inflation rate among others
that are related to both the economic and legal environments in which the banks operate
Athanasoglou et al, (2006).

Bank specific variables are variables that affect the profitability of a specific bank. These factors
are within the scope of the bank and are easy to be manipulated and differ from bank to
bank.Andreas, D. and Gabrielle, W. (2009) stated that the bank profitability is usually measured
by internal determinants which include bank specific variables. Athanasoglou et al, (2006)
argued that profitability is a function of internal factors that are mainly influenced by a bank‟s
management decisions and policy objectives such as the level of liquidity, provisioning policy,
capital adequacy, expense management and bank size, and the external factors such as Gross
Domestic Product, Inflation, Interest rate, macroeconomic policy stability and other
macroeconomic factors.

The impact of size on a bank‟s performance has been greatly argued among researchers. De
Jonghe, O. (2010) concludes that small banks are better able to withstand difficult economic
conditions, while Barros, Ferreira and Williams (2007) argue that small banks are more likely to
get good performance and less chances of getting bad performance. Conversely, large banks are
less likely to obtain good performance and a greater chance of getting bad results. Barros et al.
(2010) responded to the argument of economies of scale and argued that some costs can be
reduced simply by increasing the size.

Needless to say, even though the main focus of this particular study is mainly confined to
quantitative measure of both bank specific and macroeconomic variables; It should be properly
noted that quantitative performance measurements by their nature are not comprehensive enough
since they lack to incorporate qualitative elements such as monetary policy, regulation and
supervision, financial sector openness, institutional environment, financial sector and non-bank,
the management style and risk taking behavior of the bank itself. Any financial sector indicators
lacking these qualitative elements could not be complete enough to capture the true level of the
sector (Creane, et.al 2004).

28
 The exact relationship between these factors and the bank profitability and the significance of the
relationship remain as questions to be addressed more specifically in the context of Ethiopia.

2.1.10 Electronic-Banking and Financial Performance

The banking sector is considered to be an important source of financing for most businesses. The
common assumption, which supports much of the financial performance research and discussion,
is that increasing financial performance will lead to improved functions and activities of the
organizations. The subject of financial performance and e-banking into its measurement is well
advanced within finance and management fields.

From different literatures, electronic banking services like Automated Teller Machines attracted
many people to open up accounts especially those who prefer convenience. These machines are
located in various places of convenience like shopping malls, universities, hotels and airports.
Their installation in such areas have reduced overcrowding in the bank‟s premise and increased
the transactions completed in a day (Baxley, J.B 1987).

The installation of various automated teller machines by commercial banks in their branches is
one of the motives to increase customer base and acquire more deposits available to the bank.
This in the long run increases the bank‟s revenue that determines the profitability level and
finally the general financial performance (Baxley, J.B 1987).Electronic banking plays a big role
in terms of saving to the bank and the client (reduced costs). This is as a result of efficiency and
effectiveness maintained by various systems like electronic fund transfer, Mobile banking and
ATMS.

The elimination of paper work would also minimize costs in stationery and also administrative
costs of human tellers and other personnel that would affect such transactions. As seen from
above, the operating costs determine the firm‟s profitability and therefore the application of
electronic banking system minimizes the level of such costs hence crucial in determining the
financial performance levels of Banks (Ogare, H.O 2013).

According to Nathan, L. (1999), electronic banking services have provided numerous benefits
for both banks and customers. The first benefit for the banks offering electronic banking service
is better branding and better response to the market. Those banks that would offer such service

29
 would be perceived as leaders in technology implementation. As a result, they would enjoy a
better brand image. The other benefits are possible to measure in monetary terms. The main goal
of every company is to maximize profits for its owner and other stakeholders. According to
Allen and Hamilton (2002), an estimated cost of providing the routine business of a full service
branch in USA is $1.07 per transaction, as compared to 54 cents for telephone banking, 27 cents
for ATM banking and 1.5 cent for internet banking. On the other hand, the advantages for the
customers are significant time saving and reduced costs in accessing and using the various
banking products and service, increased comfort and convenience (Pyun, C. Scruggs, L. and
Nam, N. 2002).

Internet and mobile banking are considered beneficial because of low operational costs, time
saving promptness, and interactive ability. For these reasons, banks are able to substantially
reduce overhead expenses by divesting away from physical branch offices, which could be
substituted by internet and mobile banking systems to enhance their profitability (Kim & Park,
2003). Banks could then use the resulting savings to reduce their loan interest rates or increase
their deposit interest rates, thus retaining most profitable customers and attracting new customers
without sacrificing earnings. According to Okiro, O. & Ndungu, J. (2013), the world is becoming
increasingly addicted to conducting business across the internet and World Wide Web (WWW).

Similarly, mobile banking as an innovation has progressively began to dominate commercial

transactions in major financial and other sectors of the economy and more often than not, the two
are being used simultaneously to achieve efficiency.

Considering that the growth potential of internet and mobile banking consists in its cost
efficiency, it is expected that investment in e-banking and m-banking would ultimately bring
positive outcomes. Simpson, J. (2002) suggests that e-banking is driven largely by the prospects
of operating costs minimization and operating revenues maximization

2.1.11 Performance Measurement in Banks

According to Aburime, T.U (2009), the importance of bank profitability can be appraised at the
micro and macro level of the economy. At the micro level, profit is the essential prerequisite of a
competitive banking institutions and the cheapest source of funds. It is not merely a result, but
also a necessity for successful banking in a period of growing competition on financial markets.
30
 Hence, the basic aim of every bank management is to maximize profit, as an essential
requirement for conducting business.

Various literatures written by academicians also assert that profitability is the bottom line or
ultimate performance result showing the net effects of bank policies and activities in a financial
year. As a matter of fact, numerous factors such as inflation, accounting policy, high level of
competition, etc., may have an influence on a bank‟s profitability. In due course, wide varieties
of ratios are discussed and different measures of profitability of commercial banks have been
suggested.

For instance, Net Interest Margin (NIM), Return on Assets (ROA), and Return on Equity were
identified by Ahmed (2003) are in use in the literature since then. Profitability measures
according to Akinola (2008) include Profit before Tax (PBT), Profit after Tax (PAT), ROE, Rate
of Return on Capital (ROC) and ROA. Some other, studies on profitability have also used returns
on average bank assets (ROAA), net interest margin (NIM) and return on average equity
(ROAE) to measure profitability according to Francis (2013). However, owing to divergent
views among scholars on the superiority of one indicator over the others as measures of
profitability, there is no clear cut stand as to which best fits. Nonetheless, most literatures confine
the profitability measure only to the three widely used measures namely Return on Assets
(ROA), Return on Equity (ROE), and Net Interest Margin (NIM). Accordingly, some scholars
select either of the three and some others preach to select three of them at once.

In line with the above discussion, the researcher has used ROE as measure of profitability for
this particular study owing to the limitations of NIM & ROA. NIM is reported to have two major
limitations. First, it doesn‟t measure the total profitability of the bank as most of them earn fees
and other non-interest income through service like brokerage and deposit account services
without taking account operating expenses, such as personnel and facilities costs, or credit costs.
Besides, net interest margin of two banks can‟t be contrasted as both the banks are poles apart in
their own way in the nature of their activities, composition of customer base, etc. http: //

ons that, it does not account for outstanding liabilities and may indicate a
higher profit level than actually derived, i.e RO is a measure of firm‟s success in using assets to

31
 generate profit without looking at how the assets were financed .Therefore, although, ROA is an
important measure to use and understand, its flaw is that the metric does not account for the size
of the invested capital (Selling, T.I. and Stickney C.P. 1989).

Therefore, for the purposes of this study financial performance was relating more to the
profitability of a company than to the possible wider interpretation of financial performance, i.e.
this study attempts to measure profitability by using ROE similar to most of the under mentioned
researchers: Hall, M. and Weiss, A. (1967); Khrawish, H.A, Al-Sa‟di (2011), Al-Smadi, M. and
Al-Wabel, S. (2011), and Ongare, H.O (2013).

ROE is measured as net profit after tax divided by average shareholders‟ equity, similar to: Al-
Smadi, M and Al-Wabel, S. (2011), Kashif M. k. & Muhammad E.J (2016) and Joseph M.V.
(2017).

ROE=

ROE= β0+β1N TMi,t + β2DCi,t +β3NMOBUi,t + β4V TMTi,t + β5VMOBTi,t

+β6BSi, t +β7INFi, t+ Є i,t

2.2 Review of Empirical

Studies
2.2.1. Related Empirical Studies in the World

A number of empirical studies exist in the literature, which have examined the relative
performance of banks offering internet and mobile banking services. Egland et al. (1998) was the
first important study, which estimated the number of US banks offering electronic banking and
analyzed the structure and performance characteristics of these banks. It found no evidence of
major differences in the performance of the group of banks offering internet banking activities
compared to those that do not offer such services in terms of profitability, efficiency or credit
quality. However, transactional internet banks differed from other banks primarily by size.

32
 In contrast to the results of Egland et al. (1998), Furst et al. (2002) found that banks in all size
categories offering e-banking were generally more profitable and tended to rely less heavily on
traditional banking activities in comparison to traditional banks. Similarly, Hasan et al. (2002)
found that the e-banking institutions were performing significantly better than the traditional
banking groups.

Karimzadeh, D.S (2014) studied electronic banking effect on commercial bank profitability in
Iran. The study sought to establish whether there exists a relationship between the dependent
variable, ROA and the independent variables consisting of No. of ATM, Terminal Branches,
POS, Market Concentration, Bank Size, and Credit Cards for period 2004-12.Result confirmed
that number of terminal branches, ATMs, Credit Cards, POS, Bank Size has a positive and
significant impact on the profitability of banks while Market Concentration has had a negative
and significant impact on bank profitability because it reduces competitiveness and efficiency of
banks so increase in e-banking channels increases the bank services to the customers, which lead
towards increase in deposit and ultimately bank‟s profitability.

A study done by Sujud, H. & Hashem, B. (2017) on the effect of Bank Innovations on
Profitability and Return on Assets (ROA) of Commercial Banks in Lebanon. They sought to
establish whether there was relationship between the dependent variables Profit and ROA and
the independent variables: ATM, POS, Mobile Banking, Debit & Credit Cards, Internet Banking
and EFT. For this study, data was collected from 200 employees and it was found that 59.3 per
cent variation in profitability was explained by these variables and among all Independent
variables it was found that only EFT has significant impact on profitability of commercial banks
in Lebanon. Secondly, 97.7 per cent variation in ROA was explained by these variables and
among all only debit and credit cards had positive and significant contribution in ROA. Overall,
it can be concluded that bank innovations potentially leads to higher profitability and higher
return on assets of commercial banks.

Kashif, M., Kamboh, M. & javaid, M. (2016) examined the impact of cashless banking on
profitability of Pakistani banking industry. To measure cashless banking in the country proxies
of Automated Teller Machines Transactions (ATMT), Point of Sales Transactions (POST), Call
Center Banking Transactions (CCT) and Mobile Banking Transactions (MOBT) were used to
examine their impact on aggregate Return on Equity (ROE) of the banking industry. Ordinary
33
 Least Square (OLS) multiple regressions were used to obtain the results and data from 2nd
quarter of 2007 to 4th quarter of 2014 was used. The results showed that POST and MOBT were
positively significantly related to ROE, CCT and ATMT were negatively significantly associated
with profitability.

Sarokolaei, M. . (2011) “Investigating the effect of E-Banking on Increasing of Bank

Revenues” Factors: Bank Cards, ATM, POS, Internet Bank, Mobile Bank as proxy for e-
banking, to know the effect of E-Banking on service fees. The study was done in Iran on Private
Banks of 2009-2011.By using Regression and Correlation Analysis, It was assessed that ATM,
POS, Bank Card, Internet Bank, Telephone Bank and Service Fee had a positive and significant
relation. So Electronic Banking can increase the revenue of private banks because E-Banking
helps in reducing cost of service.

Alipour, M. and Salehi, M. (2010), in their study entitled “E-Banking in Emerging Economy:
Empirical Evidence of Iran”, focused mainly on advantages of e-banking. The results of this
study showed that e-banking serves several advantages to the Iranian banking sector, however,
the study also showed that the Iranian customers had not enough knowledge regarding e-banking
which was rendered by the banking sector in Iran. The introduction of e-banking in Iran has led
to more use of ATM in Iran. The authors came to conclusion that the active ATM in the banking
sector will cause a decrease in cash circulation and the efficiency of the banking sector will
increase.

Floros, C.H. and Gordian, G. (2015), in their paper showed how useful the number of ATMs is
for modeling and estimating banking efficiency. To estimate banking efficiency they employed
DEA and Free Disposal Hull (FDH) methods. The result of the study showed that large banks
were more efficient than medium and small sized banks in Greece. Furthermore, banks with a
large number of ATMs were more efficient than those with a less number of ATMs. However,
provision of e-banking services by banks did not influence their efficiency scores.

Siam, A Z (2006), examined the impact of e-banking on Jordanian banks and concluded that the
majority of the banks were providing services on the Internet through their websites and the
findings showed that the attention was more on satisfying and fulfilling customers‟ needs
through e-banking. He also concluded that there should be a well-articulated strategy to achieve

34
 success and profits in the long run. Results revealed that electronic banking services had a
negative effect on banks profitability in the short run due to the capital investment by the banks
on infrastructure and training but will be positive in the long run.

Goodarzi, A. and Zebidi, H. (2008), in their study entitled “Impact of e-Banking Development
on Profitability of Commercial Banks”, examined the relationship between e-banking
development and profitability of banks with the help of econometric models. The result of paper
showed that the increase in number of ATM of each bank had a positive effect on profitability of
that bank (ROA) and this effect strengthen by joining of each bank to Interbank Information
Transfer Network (Shetab) of the country. Therefore, the study concluded that e-banking had a
significant effect on banking profitability.

Using a panel data of fifteen Jordanian banks for the period of 2000–2010, Al-Smadi, M. and Al-
Wabel, S. (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. Since adoption of e-banking technology involves cost, this might
take time to recover cost and experience profits.

Using panel data of 13 banks over the period of 2003–2013, Siddik, M., Sun, G., Kabiraj, S.
Shanmugan, J. and Yanjuan, C. (2016), empirically investigated the impact of e-banking on the
performance of Bangladeshi banks measured in terms of Return on Equity, Return on Assets and
Net Interest Margin. Results from pooled ordinary least square analysis showed that e-banking
began to contribute positively to banks‟ Return on Equity with a time lag of two years while a
negative impact was found in first year of adoption.

2.2.2. Related Empirical Studies in Africa

Ongare, H.O (2013), did a study on the effect of electronic banking on the financial performance
of commercial banks in Kenya, the study sought to establish whether there exists a relationship
between the dependent variable, for example, performance measured by profit after tax and the
independent variables consisting of number of ATMS, number of debits and credit cards issued
to customers, number of point of sales terminals and the usage levels of Mobile banking, Internet
banking and Electronic funds transfer, as components of e-banking. The study used secondary
35
 data which was collected from the annual report of commercial banks and Central Bank of
Kenya. The study used both descriptive and inferential statistics in analyzing the data. The
findings of the study were that e-banking has a strong and significant effect on the profitability of
commercial banks in the Kenyan banking industry. Thus, there exists positive relationship
between e-banking and bank performance. The significance test showed that the influence of
bank innovations on bank profitability was statistically significant meaning that the combined
effect of the bank innovations in this research was statistically significant in explaining the
profits of commercial banks in Kenya.

Josiah, A. and Nancy, k. (2012) studied the Relationship between Electronic Banking and
Financial Performance among Commercial Banks in Kenya from 2006 to 2010 using descriptive
and inferential statistics. The study established whether there was relationship between the
dependent variable return on assets and the independent variables: investments in e-banking,
number of ATMS and number of debits cards issued to customers as proxy for e-banking. The
study revealed that e-banking had strong and significance marginal effects on returns on asset in
the Kenyan banking industry by making workers performance more effective and efficient.
ATMs, Debit Card had significant influence on performance of banks by bringing services closer
to its customers and hence improved industry performance. Thus, there exists positive
relationship between e-banking and bank performance.

Joseph M.V. (2017) studied the Impact of Electronic Banking on the Profitability of Commercial
Banks in Kenya. Ordinary Least Square (OLS) multiple regressions were used to obtain the
results and Data collected from 43 commercial banks from January 2007 to June 2015 (34
Quarters). The study concluded that ATM Transactions and POS transactions had positive and
significant effect on ROE whereas mobile banking transactions had negative and insignificant
effect on bank profitability.

Makur, P.M (2013) examined the effects of Financial Innovations on the Financial Performance
of Commercial Banks in South Sudan. Using Regression and Correlation Analysis data was
collected from 16 Commercial banks from 2009-13.The study revealed that No. of Daily
Transactions through ATM and financial performance of Commercial Banks had Positive
Relation. No. of Daily Transactions using Phones had positive but weak relations with financial
performance. Money Borrowed using Internet Transactions had also positive relation with
36
profitability of Commercial Banks. So Financial Innovation had a positive and significant impact
on Financial Performance of Commercial Banks but there was need to fast and secure payment
system for development of business and economic growth of all sectors and facilitating financial
deepening.

Gambo.J, Arikpo, I. (2013) studied-banking and Bank Performance: Evidence from Nigeria, The
study sought to establish whether there exists a relationship between the dependent
variables=ROA,ROE and Net Interest Margin, Independent Variables=Loan/ Assets,
Loan/Deposit, Equity/Total Assets, Operating Expenses/Total Assets, Logarithm of Total Assets,
Log of Operating Expenses, E-banking. Macroeconomic Variables= Inflation, Cyclical
Output=GDP, Bank consolidation. Data was collected from 8 commercial Banks of 1999-2010.
Using Ordinary Least Square Regression Model, It was reported that in the first year of adoption,
negative impact was observed but e-banking contributes positively to bank performance after
two years of adoption in terms of ROA and NIM due to financial cost of adopting e-banking. So
investment in E-Banking should be rational so as to justify cost and revenue implications on
bank performance.

2.2.3. Related Empirical Studies in Ethiopia

Solomon, W. (2016) conducted a research on the Roles of E-banking on Financial Performance

of Commercial Banks in Ethiopia. The study used secondary data and employed purposive
sampling technique to select ten commercial banks operating in Ethiopia covering the periods
from 2013 to 2015.Using ROA as one of the most fundamental indexes of profitability, key
explanatory variables were identified to disclose their relationship and influence on financial
performance of commercial banks. These independent and other control variables are value or
price of transaction of ATM, value or price of transaction of POS, debit card, number of
automated teller machine terminals, number of point of sale terminal and market share of banks.

Result exhibited that numbers of ATM terminals, number of POS terminals and bank market
share had positive and significant role on financial performance of commercial banks measured
by return on asset. The study showed that increased number of ATM, POS and market share had
a positive role on the financial performance of commercial banks with many banking

37
 institutions indicating that increased market share allowed a company to achieve greater scale
in its operations which generally improved its profitability.

Girma, A. 2016 conducted a research about the impact of ICT on the performance of
Ethiopian banking industry using secondary data over the period 2010 – 2014. Data
analysis was carried out in panel environment. The study employed purposive sampling
technique to select the required sample of banks from commercial banks in Ethiopia. Using ROA
as a measure of performance in the study and the explanatory variables were ICT investment,
ATM, POS, INF, BRAN and GDP. The finding shows that the ICT, ATM and POS have no
statistically significant effect on return on asset on commercial banks in Ethiopia. Moreover
result showed that the POS, ICT and number of branches have negative effect on return on asset
on commercial banks in Ethiopia.

A research undertaken by Uvaneswaran (Dr) S.M, Eldana, M., Kassa, C. & Hamid, M. (2017),
on Challenges in e- banking Services and its impact on profitability of public sector bank in
Ethiopia particularly Commercial Bank of Ethiopia (CBE) before and after introduction of e-
banking services. To meet this objective, a stratified-random sampling design was used. Data
were collected both from primary and secondary sources. The primary data were collected from
e-banking customers of the seven Dessie town branches and the secondary data were collected
from the banks financial statement and analyzed to see the relationship between e-banking
service and profitability of CBE. Finally, presentation and appraisal was illustrated in simple
descriptive statistics, relative ranking score and t test. This paper highlights that the e-banking
services has any impact on the profitability of CBE in terms of three financial performance
indicators of ROA, ROE and, NIM.

A research undertaken by Ayana, G. (2014) on factors affecting adoption of e-banking

system in Ethiopian banking industry, focused on factors that affect adoption of e banking in
Ethiopian banking industry. The study statistically analyzed data obtained from survey of staffs
of 4 purposely selected banks using qualitative and quantitative research approach on a research
framework developed based on Technology Organization Environment mode (TOE). And
concluded e-banking system such as ATM, mobile banking, internet banking and others were not
well adopted by Ethiopian banking industry, due to low level of ICT infrastructure and lack of
legal frameworks at NBE. In addition the result of the study also showed that security risk and
38
 lack of trust on the use of technological adoption were other major barriers for the system.
Limited technical and managerial skills availability in Ethiopian banks were also mentioned as
an influential factor for the choice of technology.

A study conducted by Gardachew, W. (2010), on practices, opportunities and challenges of E

banking in Ethiopia, analyzed the main challenges and opportunities of E-banking. After
conducting a survey he came up with the challenges being low level of internet
penetration and poorly developed telecommunication infrastructure, lack of suitable legal and
regulatory framework for e-commerce and e-payment, high rates of illiteracy, high cost of
internet, absence of financial networks that link different banks, lack of reliable power supply
and cyber security issues. And mentioned opportunities offered by ICT through e-learning
programs, with the help of nongovernmental agencies like ECA and World Bank to developing
countries to design national strategies like e-commerce and commitment of the government
on ICT as prospects for E banking development.

Abraham (2012) described that among the common problems known in Ethiopian which
were related to electronic banking few of them were lack of banking services through the web or
other electronic means such as using mobile phone, weak telecommunications, lack of
Internet awareness, broken and slow Internet connections, data and network security and
privacy, lack and limitation of government policies, regulations and e-commerce laws, as well
as legislation to protect workers and to make the Internet secure.

2.2.4 Gaps in Literature

Information technology is considered as the key driver for the changes taking place around the
world. Due to a pervasive and steadily growth of information and communication technology,
the world banking industry is entering into new phenomena of unprecedented form of
competition supported by modern information and communication infrastructure. The Ethiopian
banking system is very much behind compared to the rest of the world. Cash is still the most
dominant medium of exchange.

Previously the banking industry was without simple electronics like ATM and SMS alert. This
made all customers of banks to personally walk to the banking hall to be able to transact simple
transactions like checking account balances, verifying deposits and making withdrawals. This led
39
 to long queues, energy exacting and time consuming, and on the whole it was costly. Physical
cash, long distant travelling and paperwork characterized most of the payment systems in
Ethiopia. However the situation has changed in recent times due to the introduction of electronic
banking services into the Ethiopian banking industry by various financial institutions.

Technological follow ups like the ATMs, Electronic Funds Transfer at Point of Sale, internet
banking, SMS alert, and debit cards have graced the Ethiopian banking environment. These
highly economic innovations go a long way to decrease drastically the pressure on manual
services to banks‟ customers which enhance services delivery and also improve banks
profitability (Appiah, A. & Agyemang, F. 2005).

While the rapid development of information technology has made some banking tasks more
efficient and cheaper, technological investments are taking a larger share of bank‟s resources.
Currently, apart from personnel costs, technology is usually the biggest item in the budget of a
bank, and the fastest growing one. It is therefore important that e-banking innovations are made
through sound analysis of risks and costs associated so as to avoid harms on the bank
performance. On one hand the bank performance is directly related to efficiency and
effectiveness of electronic banking, but on the other tight controls and standards are needed to
prevent losses associated with electronic banking (Josiah, A. and Nancy, k. 2012).

From the review of the relevant literature relating to the roles of electronic banking on financial
performance of commercial banks, it‟s possible to see the existence of knowledge gap. Even
though studies were undertaken by (Solomon, 2016) and Girma, A. (2016), they failed to include
important variables such as number of mobile banking users and value of transaction using
mobile banking. These variables were very important variables which can significantly affect
ROE of commercial banks in Ethiopia.

Besides, other research works conducted in Ethiopia in relation to electronic banking focused on
e-banking adoption, barriers and benefits, challenges and prospect, customer satisfaction and
behavior towards e-banking but, this research focused on the roles of e-banking on the financial
performance of commercial banks. This makes the study more relevant and therefore intends to
fill these relevant gaps in literature by examine the roles of e-banking on the financial
performance of commercial banks in Ethiopia by adding variables, number of mobile banking

40
 CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

Research methodology is the procedures by which researchers go about their work of describing,
explaining and predicting phenomena (Rajasekar et al. 2013). This section explains the research
methodology that was applied to obtain representative data from commercial banks in Ethiopia.
This study also aims to examine the role of electronic banking on financial performance of
commercial banks in Ethiopia. Accordingly, this chapter discusses the research procedure that is
used to carry out this study. In case, it starts by discussing research design followed by the nature
and instruments of data collection and sampling design. The subsequent section presents and
discusses method of data process and analysis. The final two sub parts present variables
definition & hypothesis development and operationalization of study variables.

3.1 Research Design

Research design is a plan outlining how information is to be gathered for an assessment or

evaluation that includes identifying the data gathering method(s), the instruments to be used,
how the instruments would be administered, and how the information would be organized and
analyzed (Assumptah, M. J., & Muhari, W. J. 2017). Quantitative research design was used to
meet the overall objective of the study and to test hypotheses under it.
A panel data study design which combines the attributes of cross-sectional (inter-firm) and time
series data was used. A Panel data provides results that are simply not detectable in pure cross
sections or pure time series studies Brooks, (2008). In addition Hsiao, (2003) described panel or
a longitudinal data set is one that follows a given sample of individuals over time, and thus
provides multiple observations on each individual in the sample.

The advantage of panel data analysis is that more reliable estimates of the parameters in the
model can be obtained and have space and time dimensions, can take heterogeneity explicitly
into account, give more variability, less co-linearity among variables, more degree of freedom
and more efficiency(Gujarati, 2004).

42
 3.1.1 Population of the Study and Sampling Techniques

The target population of the study was all commercial banks adopting e-banking service in
Ethiopia. However to conduct the research, commercial banks operating in Ethiopia, have no
complete data related with e-banking service before 2015 especially data related with mobile
banking service.

Therefore, those banks having organized e-banking service report to NBE since 2015 were
considered as a sample. Due to this reason, by using purposive sampling technique from eighteen
commercial banks operating in Ethiopia the study took ten banks based on information available
on their annual reports with regard to their extensive investment and application of e-banking
service, and based on being pioneer in implementing e-banking services. The selected banks
were: Commercial bank of Ethiopia, Awash International bank, Dashen Bank, Bank of
Abyssinia, Wegagen Bank, United Bank, Nib International bank, Oromia International Bank,
Berhan International bank and Zemen Bank.

3.1.2 Source and Types of Data

The study employed a quantitative research approach by using secondary data gathered from
National Bank of Ethiopia and published annual audited financial statements, which were
calculated in Ethiopian Birr as of June 30 of each year from 2015to 2018 of ten purposively
selected banks out of 18 existing commercial banks which were readily available on their
website and archives as well as the bank specific variable data: No of ATM installed, No of debit
cards issued, Number of mobile banking users, Value of ATM transactions and Value of
transactions executed by mobile banking were gathered from each selected banks‟ head office (e-
banking departments) by the researcher. Financial statements and other published and
unpublished documents were also used to construct the literature part of this thesis and cited
accordingly.

3.1.3 Data Analysis

The data collected using data collection sheet were edited, coded and cleaned. To achieve the
broad research objective, the paper was primarily based on a panel secondary data, which was
collected through structured document review. Thus, the collected data was analyzed using

43
 descriptive statistics, correlation analysis and multiple linear regression analysis. Descriptive
analysis deals with a simple description of variables. It includes mean, maximum, minimum and
standard deviation of each variable to analyze the general trends of the data from 2015 to 2018
based on the sector sample of ten banks. Correlation analysis was also used to examine the
relationship between the dependent variable and explanatory variables. On the other hand, the
regression analysis known as OLS was used to estimate the relationship between profitability
and its determinants. The multiple linear regressions model was run, and thus OLS was
conducted using Stata 13 econometric software package, to test the casual relationship between
banks‟ financial performance (Profitability) and their potential determinants and to determine the
most significant and influential explanatory and other control variables affecting the financial
performance (ROE) of commercial banks in Ethiopia. According to Gujarati (2004) regression
analysis is concerned with the study of the dependence of one variable, the dependent variable,
on one or more other variables, the explanatory variables, with a view to estimating and/or
predicting the (population) mean or average value of the former in terms of the known or fixed
(in repeated sampling) values of the latter.

3.2 Model Specification

The researcher formulates some econometric model which is a representation of the basic
features of an economic phenomenon so as to achieve the broad research objective. It is an
abstraction of the real world. The specification of a model is based on the available information
relevant to the study in question. This is to say, the formulation of an economic model is
dependent on available information on the study as embedded in standard theories and other
major empirical works, or else, the model would be theoretical.

The literatures reviewed in the previous chapter identified the roles of e-banking on financial
performance of commercial banks. This chapter presents a framework of analysis on the basis of
these studies, and involves adopting a model that would help demonstrate the significance
(responsiveness) of certain key variables in influencing the financial performance of commercial
banks in Ethiopia. The performance indicator utilized for this particular study was Return on
equity (ROE) and the major determinants (independent variables) considered were: No of ATM
terminals, Debit cards issued, number of mobile banking users, value of ATM transactions,
Value of transactions executed by Mobile banking, bank size and annual inflation rate.
44
Accordingly, the study adopted a model that existed in most literatures, like: Hall, M. and Weiss,
A. (1967); Khrawish, H.A, Al-Sa‟di (2011), Al-Smadi, M. and Al-Wabel, S. (2011), and Ongare,
H.O (2013), Kashif, M. ,Kamboh, M. & javaid, M. (2016) and Joseph M.V. (2017).

According to Brooks (2008), the general multivariate regression model with K independent
variables can be written as follows:

Yi = β0 + β1X1i +β2X2i + …+ βkXki + εi (i 1, 2, 3…, n)

Where Yi is the ith observation of the dependent variable, X1i,…, Xki are the ith observation of
the independent variables, β0,…,βk are the regression coefficients, εi is the ith observation of the
stochastic error term, and n is the number of observations. Hence, the roles of e-banking on
profitability of commercial banks can be modeled as described below:-

ROE= β0+β1N TMi,t + β2DCi,t +β3NMOBUi,t + β4V TMTi,t + β5VMOBTi,t

+β6BSi, t +β7INFi, t+ Є i,t

Where;

ROE = Profit after tax / Average stockholder equity

NATM= Number of ATM installed by banks

DC = Number of debit cards issued by banks

NMOBU= Number of mobile banking users

VATMT = Value of transactions executed by ATM = (Natural logarithm of the value of ATM
transactions)

VMOBT=Value of transactions executed by Mobile banking = (Natural logarithm of the value of

Mobile banking transactions)

BS = Bank size (other control variable) which is measured by the natural log of total assets

INF= annual inflation rate

βo = Constant term

45
β1, 2, 3…8 are parameters to be estimated

Є = is the error component for Bank i at time t assumed to have mean zero E [Є it] =0

i = commercial banks i = 1. . . 10; and

t = the index of time periods and t = 1- 4

3.3 Variables Definition and Hypothesis Development

It is the process of strictly defining variables into measurable factors. The process defines vague
concepts and allows them to be measured, empirically and quantitatively (Creswell, J.W. 2009).

A variable is a measure characteristic that assumes different values among subject, Mugenda, O.
and Mugenda, A. (2003). Independent variables are variables that a researcher manipulates in
order to determine its effect of influence on another variable. Kombo, K.D. and Tromp, D.L.
(2006), states that independent variable (explanatory variable) is the presumed change in the
cause of changes in the dependent variable; the dependent variable attempts to indicate the total
influence arising from the influence of the independent variable Mugenda, O. and Mugenda, A.
(2003).

3.3.1 Dependent Variable

More than any other accounting measure, profits demonstrate how well management is doing in
investment and financing decisions. Profitability ratios measure how effectively a firm‟s
management is generating profits on sales, total assets, and stockholders‟ investment. Therefore,
anyone whose economic interests are tied to the long-run survival of a firm will be interested in
profitability ratios (Moyer, C. James, M. & William, K. 2006).

As concluded by extensive Prior academic research there are different accounting based
measures for banks‟ profitability of which ROA and ROE are the major ones Alexandru et al
(2008).While these measures of profitability are widely accepted as reliable and strong measures
of profitability they have certain shortfalls. Most commonly, that they are based on accounting
information and thus accounts for neither the time value of money nor the investment risks faced
by the shareholders.

46
 Although ROE disregards the risks associated with high financial leverage, is an internal
performance measure of shareholder value, and by far the most popular measure of performance,
since: (i) it proposes a direct assessment of the financial return of a shareholder‟s investment; (ii)
it is easily available for analysts, only relying upon public information; and (iii) it consider the
effect of borrowed capital in financing the assets to generate profit. Therefore, this study
attempts to measure profitability by using ROE similar to most of the aforementioned
researchers.

ROE is measured as net profit after tax divided by average shareholders‟ equity similar to
Kashif, M. ,Kamboh, M. & javaid, M. (2016) and Joseph M.V. (2017).

Return on Equity

ROE is an internal and by far the most popular measure of performance that reflects how much
profit a bank earned compared to the total amount of shareholder equity invested or found on the
balance sheet and it measures how effectively a bank management is using shareholders‟ funds.
ROE is the product of ROA and assets-to-equity ratio (equity multiplier that measures financial
leverage).It brings together theand the where net income or
profit is compared to the shareholders‟ equity. The number represents the total return on equity
capital and shows the firm‟s ability to turninto profits. To put it another way, it measures
the profits made for each dollar from shareholders‟ equity. Essentially the ROE–ROA
relationship clearly illustrates the fundamental tradeoff that banks face between risk and return,
whereas the equity multiplier reflects the leverage or financing policies, i.e. the debt-equity
proportion that the bank management used to fund the bank.

A sustainable and increasing ROE over time can mean a company is good at generating value
because it knows how to reinvest its earnings wisely, so as to increase productivity and profits. In
contrast, a declining ROE can mean that management is making poor decisions on reinvesting
capital in unproductive assets. However, it doesn‟t fully show the risk associated with that return.
A company may rely heavily onto generate a higher net profit, thereby boosting the ROE
higher. According to Khrawish, 2011, it is measured by the ratio of net profit to average
shareholders‟ equity as follows:

47
ROE=

3.3.2 Independent and Other Control Variable

Independent variables are explanatory variables that explain the dependent variables. In case the
independent variables included in this study were: number of ATMs installed, debit cards issued,
number of mobile banking users, value of transactions executed by ATM, value of transactions
executed by mobile banking , bank size (BS) and inflation.

Bank Specific Variables

3.3.2.1 Number of Automated Teller Machine Terminals (NATM)

The combined services of both the Automated and human tellers imply more productivity for the
bank during banking hours. Also, as it saves customers time in service delivery as alternative to
queuing in bank halls, customers can invest such time saved into other productive activities.
ATMs are a cost-efficient way of yielding higher productivity as they achieve higher
productivity per period of time than human tellers (an average of about 6,400 transactions per
month for ATMs compared to 4,300 for human tellers Rose (1999). Furthermore, as the ATMs
continue when human tellers stop, there is continual productivity for the banks even after
banking hours.

Holden, K. and El-Bannany, M. (2004) checked profitability of UK banking industry due to

introduction of modern e-banking equipment along with traditional measures like bank size,
market share etc. Results confirm that ATMs significantly increase profitability of UK banking
industry.

Gambo.J, Arikpo, I. (2013) studied e-banking and Bank Performance in Nigeria using dependent
variables=ROA, ROE and Net Interest Margin. Result confirmed that in the first year of
adoption, negative impact was observed but e-banking contributes positively to bank
performance after two years of adoption in terms of ROA, ROE and NIM due to financial cost of
adopting e-banking.

48
 Karimzadeh, D.S (2014) studied “The Effects of Electronic Banking Expansion on Profitability
of Commercial Bank of Iran”. The study used RO as dependent variable and No. of ATM,
Terminal branches, POS, Market Concentration, Bank Size, and Credit Cards as independent
variables for the period from 2004 to 2012. The result depicted that Terminal branches, ATMs,
Credit Cards, POS, Bank Size has a positive and significant impact on the profitability of banks
while Market Concentration has had a negative and significant impact on bank profitability
because it reduces competitiveness and efficiency of banks so overall E-Banking has a positive
impact on bank profitability.

Goodarzi, A. and Zebidi, H. (2008), in their study entitled “Impact of e-Banking Development
on Profitability of Commercial Banks”, examined the relationship between e-banking
development and profitability of banks with the help of econometric models. The result of paper
showed that the increase in number of ATM of each bank has a positive effect on profitability of
that bank (ROA).

Evidence from other empirical studies conducted on the contribution of automated teller
machines ( TM) to bank‟s profitability reveal that investment in ATMs increases both the
volume and value of deposit accounts, reduces banking transaction costs, reduces number of staff
and the number of branches, a decrease in cash circulation and consequently improves bank‟s
profitability (Abdullah, 1985).

ATM is considered in terms of total number of ATM terminals and value of transaction executed
by ATM (VATMT)

3.3.2.2Debit Card (DC)

Debit cards are prepaid cards which incorporate a computer chip/integrated circuit on which
value is loaded, either from the card holder‟s bank account or in return for cash. Value is then
removed from the card as purchases are made using special POS terminals.

Debit cards have surpassed credit cards to become the most common form of Visa point-of-sale
(“POS”) transaction in most parts of the world. Overall, debit cards were used for over 15.5
billion POS transactions totaling $700 billion in the year 2002 in the United States. This
represented about 35% of electronic payment transaction volume and 12% of POS noncash

49
 payments (Gerdes & Walton, 2002).Industry observers predict continued strong growth for debit
cards. Debit card improves efficiency and flexibility to customers. Customers can still access
their bank accounts and other details without necessarily visiting the banking halls. This has
attracted more customers since they enjoy banking services that are convenient and flexible.

Fu-Qiang, S and Sajid, K. (2014) investigated effect of debit card usage on profitability of
banking industry in form of ROA over the period of 2004 to 2013 quarterly in the banking sector
in Pakistan. The results showed that increased in debit card usage enhance the profitability of
banking industry in form of ROA over the period of 2004 to 2013 quarterly.

Polatoglu and Ekin (2001) identified that users of debt cards were more satisfied with the cost
saving factor of electronic banking including train reservations, energy bills, taxes and
investment in stocks (Wise, 1995). The increased usage of debit cards has significantly reduced
transaction costs and enhanced convenience among credit and debit card users. This has attracted
prospective customers leading to increased sales and profitability.

A study conducted by Josiah, A. and Nancy, K. (2012) on the Relationship between Electronic
Banking and Financial Performance among Commercial Banks in Kenya from 2006 to 2010
using ROA as dependent variable and investments in e-banking, number of ATMS and number
of debits cards issued to customers as proxy for e-banking. The study result revealed that number
of ATMs, Debit Card have significant influence on performance of banks by bringing services
closer to its customers and hence improved industry performance. Thus, there exists positive
relationship between e-banking and bank performance.

Similar studies were conducted by Njoroge, M. N. & Mugambi, F. (2018) on the effect of
electronic banking on financial performance in Kenyan commercial banks. The study revealed
that taking all other independent variables at zero, a unit increase in debit cards leads to an
increase in the Bank performance in Kenya. Further the study established that Increase in debit
card usage enhances the profitability of banking industry in form of ROA and increased usage of
debit cards has significantly reduced transaction costs and enhanced convenience among credit
and debit card users.

Debit Card is considered in terms of total number of Debit Card users

50
 3.3.2.3 Number of Mobile Banking Users (NMOBU)

According to Rose, P.S. (1999) Mobile banking allows customers with busy lives to
conveniently do their banking using their phones anytime. It is about getting banking services to
the unbanked, those who do not have bank access or bank accounts, and those who are at the
bottom of the economic pyramid, often living in remote areas. They receive the benefits of
banking services such as being able to save and borrow in a cost-efficient and secure way.

According to the German mobile operator Mobilcom, mobile devices, especially smart phones,
are the most promising way to reach the masses and to create “stickiness” among current
customers, due to their ability to provide services anytime, anywhere, high rate of penetration
and potential to grow. A study conducted by Hernando and Nieto (2007) on the effect of mobile
banking and financial performance of Spanish commercial banks. It was concluded that banks
that implemented mobile banking were able to attract more customers and this led to increased
access to customer deposits leading to financial performance.

Kathuo S., (2015) studied the effect of mobile banking on the financial performance of banking
institutions in Kenya. The study used (ROA) and (ROE) as a measure of financial performance
while the overall operating cost as well as other Bank specific factors in form of ratios as
independent variables and applied descriptive research design. The target population included the
42 commercial banks operating in Kenya as at December 2014. The study established that the
number of mobile banking transactions has tremendously increased in the last five years since
the introduction of M-banking, thus concludes that, banks that have adopted M-banking services
have to a large extent increased their customer outreach, and hence have improved their financial
performance.

Donner, J. and Tellez, C.A. (2008) did a study on mobile banking and economic development
where they sought to link adoption, impact, and use. The study established that through offering
a way to lower the costs of moving money from place to place and offering a way to bring more
users into contact with formal financial systems, m-banking/m-payments systems could prove to
be an important innovation for the developing world.

Tiwari, R., Buse, S. and Herstatt, C. (2006) studied mobile banking as business strategy: impact
of mobile technologies on customer behavior and its implications for banks. The study sought to
51
 examine the opportunities for banks to generate revenues by offering value added; innovative
mobile financial services while retaining and even extending their base of technology-savvy
customers

Mobile banking is considered in terms of total number of Mobile banking users and value of
transaction executed by mobile banking (VMOBT)

3.3.2.4 Value of Transaction of Automated Teller Machine (VATMT)

Joseph M.V. (2017) studied the Impact of Electronic Banking on the Profitability of Commercial
Banks in Kenya and Data collected from 43 commercial banks from January 2007 to June 2015
(34 Quarters). The study concluded that ATM Transactions and POS transactions have positive
and significant effect on ROE whereas mobile banking transactions have negative and
insignificant effect on bank profitability.

Price of transaction of ATM was considered as independent variable by Hamed et al.2016, the
results of study showed that the effect of price of ATM and POS transaction on ROA of selected
banks in Iran was positive and significant. The effect of POS on bank ROA was higher than that
of ATM transaction.

Automated Teller Machines Transactions (ATMT), Point of Sales Transactions (POST), Call
Center Banking Transactions (CCT) and Mobile Banking Transactions (MOBT) were used to
examine their impact on aggregate Return on Equity (ROE) of Pakistani banking industry by
Kashif, M. ,Kamboh, M. & javaid, M. (2016). Results showed that POST and MOBT were
positively significantly related to ROE, CCT and ATMT were negatively significantly associated
with profitability (ROE)

3.3.2.5 Value of Transaction of Mobile Banking (VMOBT)

Number of registered mobile banking customers by the banks, investment in mobile banking
measured in Kenya shillings and the number of mobile banking transactions by the banks were
considered as the factors of m-banking by Mwange, A. J (2011) to examine the impact of
mobile banking on financial performance of commercial banks in Kenya using ROA as factor of
profitability. The study results showed that the investment in mobile banking measured in Kenya
shillings and the number of mobile banking transactions by the banks have a positive relation to

52
 the ROA in that a unit increase in each / or all would result in an increase in the performance
indicator ROA, while on the other hand the number of mobile banking registered customers by
banks has an inverse relation to the ROA in the model meaning a unit increase in it would result
in a decline in the performance indicator ROA.

Wadhwa. S (2016) studied the Impact of m-Banking on Profitability of Scheduled Commercial

Banks in India. The study has considered Volume of transactions and Value of transactions
performed through m-banking as the factors of m-banking and Profitability has been considered
in terms of Return on Assets (ROA) and Return on Equity (ROE), for the period from 2012 to
2016. It was found in the study that although the usage of m-banking has tremendously increased
during the period of study, it has not played any significant role towards improvisation in the
profitability of these banks. It has been opined in the paper that if all the banks could proceed to
the path of m-banking in a proper manner, the overall profitability due to m-banking will
improve in coming years.

The study conducted by Rachael W.M. (2011), examined the effects of mobile banking on the
financial performance of commercial banks in Kenya considered ROA as measure of bank
performance and total amounts transferred via the mobile (value of transaction executed by
mobile banking) number of mobile banking users as the factors of m-banking for five years
period from 2007 to 2011 using Monthly data analysis. During the study period, the amount of
money transacted through the mobile money transfers increased steadily from 0.06 billion in
2007 on its launch to 118.08 billion by the last month of the analysis. The growth was motivated
by the convenience offered by the service. The study however found that there exist a weak
positive relationship between mobile banking and the financial performance of commercial
banks in Kenya.

Controlled Variable

In order to isolate the effects of e-banking on bank performance, it is needed to control for other
factors that are expected to have some influence on profitability. The control variable which is
expected to influence bank‟s profitability included in this study is bank size, Although there are
other variables that affect bank performance the study focus on the below variable only:

53
 3.3.2.6 Bank Size (BS)

Bank size which is measured by the natural log of total assets (Smirlock, M. 1985) is one of the
control variables that determine the financial performance of the commercial banks. Studies
conducted on determinants of bank profitability took bank size, as an important determinant
variable of bank performance (Kosmidou K., 2008). In the literature, mixed relationships are
found between size and profitability. Increasing bank size can increase bank profitability by
allowing banks to realize economies of scale. For example, increasing size allows banks to
spread fixed costs over a greater asset base, thereby reducing their average costs. Increasing
banks‟ asset size can also reduce risk by diversifying operations across product lines, sectors, and
regions (Mester 2010). Lower risk can promote profitability directly by reducing losses or
indirectly by making liability holders willing to accept lower returns, thereby reducing banks‟
funding costs. Furthermore, as the scale of operations increases, banks may be able to better use
specialized inputs such as loan officers with expertise in commercial and industrial business
lines, resulting in greater efficiency. Realizing economies of scale may lead to a healthier
banking system by eliminating inefficiencies and reducing risks (Rao & Tekeste, 2012 and Alper
and Anbar, 2011). On the other hand, in the diversification of bank branches, for instant, the
operational expense may get higher and due to possible bureaucratic bottlenecks and managerial
inefficiencies the variable may exhibit negative effects Ameur, I. and Mhiri, G. (2013) and
Sufian, F. and Chong, R. 2008.

There are two opposing arguments both theoretically as well as empirically regarding to the
relationship between bank performance and size. The effect of size could therefore be nonlinear;
meaning that banks‟ performance is likely to increase up to a certain level by achieving
economies of scale and decline from a certain level. Hence, the expected sign of the coefficient
of bank size is unpredictable based on academic literature.

The researcher used the natural logarithm of total Assets as a proxy for bank size.

Both Njogu, J.N (2012) and Karimzadeh, D.S. (2014) studied the effect of electronic banking on
profitability of commercial banks in Kenya and Iran respectively, considering size of the bank as
one of the control variable. The results of these studies found that Bank Size has a positive and
significant impact on the profitability of banks.

54
Macroeconomic variables

3.3.2.7 Inflation (INF)

Inflation is used to represent the changes in the general price level or inflationary conditions in
the economy and it is measured by annual country inflation rate. It is an important
macroeconomic condition which may affect both the costs and revenues of banks.

In this regard, some authors introduce the issue of the relationship between bank profitability and
inflation, stating that the effect of inflation on bank profitability depends on how inflation affects
both salaries and the other operating costs of the bank. In this context, Staikouras, C. & Wood,
G. (2003) point out that as inflation may have direct effects, that is, increase in the price of labor,
and indirect effects, that is, changes in interest rates and asset prices, on the profitability of
banks. Perry (1992) also suggests that as the effects of inflation on bank performance depend on
whether the inflation is anticipated or unanticipated. In the anticipated case, the interest rates are
adjusted accordingly, resulting in revenues to increase faster than costs and subsequently, having
positive impact on bank profitability. On the other hand, in the unanticipated case, banks may be
slow in adjusting their interest rates resulting in a faster increase of bank costs than bank
revenues and consequently, having negative effects on bank profitability. Thus, the expected sign
of the inflation is unpredictable based on prior research.

Girma . (2016) on his study on: “The Impact of Information and Communication Technology
on Performance of Commercial Banks in Ethiopia.” Considered inflation as one of the
macroeconomic variable

55
 3.4 Operationalization of Study Variables

Table 3.1 Definitions, Notation and Expected Sign of the Study Variables

Variables Notation Measurement Used by Expected

(source) result
Return on ROE Net income Al-Smadi, M. and
Dependent
Variable

equity after tax Al-

/Average (2011)
shareholder‟s
equity

Number of NATM Natural log of Gambo.J, +

ATM number Arikpo,
terminals of ATM I. (2013)
Debit DC deployedlog of Josiah, A. and
Natural +
Cards number Nancy, K.
issued of debit card (2012)
Independent variables

Number of NMOBU users

Natural log of Hernando, I. +
Mobile number and
banking of MOB users Nieto, M.J.
users (2007)
Value of VATMT & Kathuo S.,
Natural log of Joseph +
ATM Value (2017)
transaction of ATM
Value of VMOBT transactions
Natural log of Wadhwa,S. +
MOB MOB (2016)
transaction transactions
Bank size BS Natural log of Ameur I., +/-
total Mhiri, G. 2013 and
assets Sufian, F.
Chong, R. 2008
Inflation INF Annual inflation Demirguc-Kunt, A. +/-
rate & Huizin
(1999).
Source: Compiled by researcher

56
 CHAPTER FOUR

DATA ANALYSIS AND INTERPRETATION

This chapter deals with the analysis and presentation of the results of the study. The data were
analyzed by using Stata 13 econometric software package. The descriptive statistics and the
correlation analysis were discussed. Followed by the diagnostic test, which is necessary to
fulfill the assumption of the classical linear regression model. Then, econometric analysis
and discussion of the main finding of the study were presented. Finally, the results of the
regression analysis were discussed by supporting empirical evidence.

4.1. Descriptive Statistics

Table 4.1 provides a summary of the descriptive statistics of the dependent and independent
variables for ten commercial banks from the year 2015 to 2018 with a total of 40 observations.
The table demonstrates the mean, minimum, maximum, standard deviation and number of
observations for the dependent variable Return on equity (ROE) and independent variables:
Number of ATM terminals (NATM), Number of debit cards (DC), Number of mobile banking
users (NMOBU), Value of ATM transactions (VATMT), Value of mobile banking transactions
(VMOBT), Bank size (BS) and Inflation (INF).

Table 4.1 Descriptive Statistics

Source: STATA13 result descriptive statistics

57
 Table 4.1 above presents the descriptive statistics of the 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 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. It is a measure of the average distance
between the values of the data in the set and the mean. A low standard deviation indicates that
the data points tend to be very close to the mean; a high standard deviation indicates that the data
points are spread out over a large range of values.

As shown in the table 4.1 above, the mean value of ROE of commercial banks was around 25.21
percent for the sampled commercial banks in Ethiopia. This implied that, the sampled banks on
average earned 25.21 percent of each birr invested in equity. It could be noticed that the banks
ROE growth fluctuates on average between 14.3 and 67.7percent. That means the most profitable
bank among the sampled banks earned 67.7% of profit after tax for a single birr invested in the
equity of the firm. On the other hand, the least profitable bank of the sampled banks earned 14.3
% of profit after tax for each birr invested in the Equity of the firm. The standard deviation
statistics for ROE was (0.126) which indicated that there were higher variations of performance
growth among commercial banks during the study period. The result implied that these banks
need to optimize the use of their equity to increase the return on their equity.

Regarding the explanatory variables of the model, the mean value of the number of ATM
installed was 2 percent; the standard deviation was 0.56 percent, while 2.9% and 0.9% were the
maximum and minimum numbers of ATM installed, respectively, which exhibited a lower
dispersion to the mean value. That means there was lower difference among banks with regard to
number of ATM terminals. This implied that there was competition between commercial banks
to attract customers with a motive of ATM under the study period.

The mean value of the banks‟ debit cards (DC) over the study period was 4.59 percent; the
standard deviation was 0.5 percent with the maximum and minimum number of debit cards 5.8
% and 3.6 % respectively. There was a lower dispersion in DC towards its mean value. This
implied that there was lower difference among banks regarding debit cards issued.

The mean value of the banks‟ number of mobile banking users (NMOBU) over the period under
study was 4.3 percent; the standard deviation was 0.65 percent with maximum (5.7 %), and
58
 minimum (2.2 %) number of mobile banking users. This implied that there was a lower
dispersion in NMOBU towards its mean value.

The average value of ATM transactions was 7.3 percent while the average value of mobile
banking transactions was 6.9 percent.

4.2 Correlation Analysis

Table 4.2 Correlation Matrix of Dependent and Independent Variables

Source: STATA13 result for correlation matrix

Correlation is a way to index the degree to which two or more variables are associated with or
related to each other. The most widely used bi-variant correlation statistics is the Pearson
product-movement coefficient, commonly called the Pearson correlation which was used in this
study. Before the regression result, it is important to check the correlation between variables that
are used in the regression. Correlation analysis is the statistical tool used to study the closeness
of the relationship between variables Gujarati (2004). This section of the study deals with the
correlation analysis of the studied variables. The purpose of undertaking correlation analysis is to
check whether there is multicollinearity problem in the model and to indicate whether the
variables move together or not in the same direction and the correlation coefficient indicates the
strength of a linear relationship between two variables. The correlation coefficient ranges
between +1 and -1. +1 indicates the strongest positive correlation possible, and -1 indicates the
strongest negative correlation possible. Therefore the closer the coefficient to either of these
numbers the stronger the correlation of the data it represents. On this scale 0 indicates no

59
 correlation, hence values closer to zero highlight weaker/poorer correlation than those closer to
+1/-1.

The above correlation matrix table 4.2 showed the relationship between the dependent variable
and independent variables, and also between the independent variables each other used in this
study. Based on the correlation matrix ROE had a positive correlation with number of ATM
installed, number of debit cards, number of mobile banking users, value of ATM transactions,
value of mobile banking transactions and bank size which indicated when those variables
increased ROE would also be increased with different correlation coefficient. Moreover inflation
was negatively correlated with ROE. The negative correlation figure implied if this independent
variable increased ROE would be decreased.

4.3 Classical Linear Regression Model (CLRM) Assumptions and Diagnostic

Test

As noted in Brooks (2008), CLRM is based on sets of assumptions: Such as the errors have zero
mean, the variance of the errors is constant and finite over all variables of Xt, the errors are
linearly independent of one another, there is no relationship between the error and corresponding
X-variate, and the error terms are normally distributed. Hence, if these CLMR assumptions hold,
the estimators determined by OLS will have a number of desirable properties that is consistent,
unbiased, and efficient. Thus In order to determine the validity of the model, it should pass
diagnostic tests such as; heteroscedasticity, autocorrelation, multicollinearity and normality tests.

4.3.1 Test for Average Value of the Error Term is Zero (E (ut) = 0)

The first assumption required is that the average value of the errors is zero. In fact, if a constant
term is included in the regression equation, this assumption will never be violated. Therefore,
since the constant term (i.e. βo) was included in the regression equation, the average value of the
error term in this study is expected to be zero.

4.3.2 Test for Heteroskedasticity Assumption (var(ut ) = σ2 )

It has been assumed that the variance of the errors is constant. This is known as the assumption
of homoscedasticity (Brook, 2008). If the errors do not have a constant variance, they are said to

60
be heteroskedastic. This study used white test to examine the presence of hetroskedasticity across
the range of explanatory variable. In table 4.3 below the p-value in white‟s test of
heteroskedasticity was 0.2826, since the p-value was considerably in excess of 0.05 we couldn‟t
reject the null hypothesis of homoskedasticity. Implying that, there was no significant evidence
for the presence of heteroskedasticity.

Table 4.3 Heteroskedasticity Test

Source: STATA13 result for white‟s test

4.3.3 Test for Multicollinearity

Multicollinearity indicates a linear relationship between explanatory variables which may cause
the regression model biased (Gujarati, 2004). If an independent variable is an exact linear
combination of the other independent variables, then we say the model suffers from perfect
collinearity, and it cannot be estimated by OLS Brooks (2008). When independent variables are
multicollinear, there is overlap or sharing of predictive power. This might lead to the paradoxical
effect, whereby the regression model fitted the data well, but none of the explanatory variables
(individually) had a significant impact in predicting the dependent variable Gujarati,
(2004).Perfect multicollinearity will usually be observed only when the same explanatory
variable is inadvertently used twice in a regression. This assumption does allow the independent
variables to be correlated but they cannot be perfectly correlated. How much correlation causes

61
 multicollinearity however, is not clearly defined. While Hair et al (2006) argue that correlation
coefficient below 0.9 may not cause serious multicollinearity problem. Malhotra (2007) stated
that multicollinearity problem exists when the correlation coefficient among variables is greater
than 0.75.Although there is no one unique method of detecting multicollinearity, or measuring its
strength, among several indicators variance inflation factor (VIF) and the explanatory variables
correlation coefficients (CC) were used for this particular study (Gujarati, 2004). Therefore, in
examining the correlation matrix of the independent variables shown below in table 4.4 the
highest correlation was 0.8015 which was between value of ATM transactions and value of
mobile banking transactions.

Table 4.4 Correlation Matrix between Independent Variables

Source: Stata 13output

The other test used for the presence of multicollinearity was the variance inflation factor (VIF) or
tolerance value (1/VIF).Variance inflation factor (VIF) or tolerance value is used
interchangeably. According to Gujarati (2004), if the variance inflation factor (VIF) is more than
10 and tolerance level is less than 0.10 it indicates a serious multicollinearity problem. The
tolerance value is between zero and one if it approaches zero it indicates collinearity problem
and when it approaches 1 no multicollinearity problem. According to Table 4.5 the variance
inflation factor (VIF) was less than 10 for both the model (mean VIF) and for each independent
variable. This test confirmed the presence of lower degree of collinearity among explanatory
variables.

62
Table 4.5 Multicollinearity Test:

Source: STATA13 result for Variable inflation factor test

4.3.4 Test For ra t u t (ut (0, σ2)

It is assumed that the distribution of residuals is normal. 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. This means that the p-value given at the bottom of the normality test screen
should be bigger than 0.05 to not reject the null of normality at the 5% level (Brook, 2008). The
test result for the model provides a p-value of greater than 5% evidencing that residuals were
normally distributed. As per table 4.6 below the Jarque-Bera statistic had a P-value of 0.3806 and
both the probability of skewness and kurtosis was above 5% which implied that there was no
evidence for the presence of abnormality in the data. Thus, the null hypothesis that the data was
normally distributed was failed to reject since the p-value exceeded 0.05.
Table 4.6 Test for Normality Assumption: Jarque–Bera

Source: STATA13 result for Skewness/Kurtosis test

63
Figure 4.1: Histogram for Residual

4.3.5 Test for Choosing Random Effect (RE) Versus Fixed Effect (FE)

Models

The econometrics model used to identify the effect of bank specific factors on the financial
performance of commercial banks in Ethiopia was panel data regression model which should be
either fixed effects or random effect model. In order to analyze this panel data there are broadly
two classes of panel data estimator approaches that can be employed in empirical research: fixed
effects models and random effects models. The first issue is, therefore, that choosing between
fixed effects (FE) and a random effects (RE) model based on the Hausman test where the null
hypothesis says that random effects model is appropriate than the fixed effects model. If p value
is higher than 0.05 (insignificant) random effects is preferable and if p value is lower than 0.05
(significant) fixed effects model is appropriate than the random effects model

Hausman Test of Hypothesis

H0: Random effect model is appropriate

H1: Fixed effect model is appropriate

Decision Rule: Reject H0 if P value is less than significant level 0.05. Otherwise, do not reject
H0.
64
 Table 4.7 Choosing Random Effect (RE) Versus Fixed Effect (FE) Models Using Hausman
Test

Source: STATA13 result for Hausman test

According to Hausmantest shown in Table 4.7, the model was better off if fixed effect model
was used since the p value for the model was 0.0050 which was lower than 0.05.

4.4 Result of the Regression Analysis

This section presents the overall results of the regression analysis on the role of electronic
banking on financial performance of commercial banks in Ethiopia. In the study ROE was used
as a financial performance indicator. The relationship between the dependent and independent
variables will be discussed on the basis of the findings on this empirical study of fixed effect
model. Under the following regression results the beta coefficient may be negative or positive;
beta indicates each variable‟s average level of influence on the dependent variable. The positive
beta coefficient indicates that the variable has on average a positive impact on the dependent
variable; and negative beta indicates a negative impact on the dependent variable. Specifically it
shows that when independent variables increase/decrease by one percent the dependent variable
will increase/decrease by beta amount on average but the independent variables should
statistically have significant impact on the dependent variable. P-value indicates at what
percentage or precession level each variable is significant.

65
 4.4.1 Operational Model

ROE= β0+β1 (N TMi,t) + β2(DCi,t) +β3(NMOBUi,t) + β4(V TMTi,t) + β5(VMOBTi,t)

+β6(BSi,t) +β7(INFi,t)+ Є i,t

The output of the econometrics model by fixed effect regression showed the explanatory power
of the model based on the result of R2. The R2 measured the success of the regression in
predicting the values of the dependent variable in the sample. In standard settings, it could be
interpreted as the fraction or percentage of the variance of the dependent variable explained by
the independent variables collectively. The statistic would equal one if the regression fitted
perfectly, and zero if it fitted no better than the simple mean of the dependent variable. As it said
before, R2 value indicated the explanatory power of the model and adjusted R2 value which took
into account the loss of degrees of freedom associated with adding extra variables were inferred
to see the explanatory powers of the models.

Table 4.8: Fixed Effect Model Regression Results

Source: STATA13 result for fixed effect regression

ROE= β0+β1 (N TMi,t) + β2(DCi,t) +β3(NMOBUi,t) + β4(V TMTi,t) + β5(VMOBTi,t)

+β6(BSi,t) +β7(INFi,t)+ Є i,t

66
 ROE= 545.67–7.67NATM + 3DC+6.57NMOBU +3.25VATMT – 0.97VMOBT– 58.7BS +
0.43INF + Є ,t

Table 4.8 above showed fixed effect estimation on 40 observations taken from 10 commercial
banks over four year‟s period from 2015 to 2018 with a balanced panel data. The R2, goodness of
fit of the model for the model was 61.7% which was fairly good. This means variation in return
on equity (ROE) of commercial banks in Ethiopia was 61.7% explained by number of TM‟s
installed (NATM), number of debit cards issued (DC), number of mobile banking users
(NMOBU), value of ATM transactions (VATMT), value of mobile banking transactions
(VMOBT), Bank size (BS) and annual inflation rates (INF).The rest 38.3% variation in return on
equity (ROE) of commercial banks in Ethiopia was not explained by either bank specific or
macroeconomic variables used in this model. This means that the remaining 38.3% of the
changes was explained by other variables which were not included in the model. In addition over
all test of significance (Prob> chi2 was 0.0000) which showed joint statistical significance of the
coefficients and linearity in parameters. So this implied that the overall model was statistically
significant since p-value was 0.000 which was below 5%.

According to fixed effect estimation of the model out of seven explanatory variables five of them
had statistically significant impact on profitability. Among the significant variables, number of
mobile banking user and bank size were significant at 1% since the p-value for these variables
were 0.002 and 0.001 respectively. Inflation and number of ATM installed were significant at
5% with p-value 0.013 and 0.041 respectively. Whereas value of ATM transactions was
significant at 10% with p-value=0.10.

While assessing coefficients of correlation, number of debit cards issued, number of mobile
banking users, value of ATM transactions and inflation had a positive or direct relationship with
return on equity (ROE) of commercial banks, which suggested that, an increase in these
independent variables would result in an increase in ROE and a decrease in these explanatory
variables would result in a decrease in ROE of commercial banks in Ethiopia. Whereas the rest
variables such as: number of ATM installed, value of mobile transactions and bank size had a
negative coefficient, that means these explanatory variables had an inverse relation with return
on equity (ROE) of commercial banks in Ethiopia.

67
 The panel fixed effect estimation regression result in the above table 4.8 also showed that, the
coefficient intercept (β0) was 545.6695. Implying that, when all explanatory variables took a
value of zero, the average value ROE would take 545.6695 unit and statistically significant at 1%
level of significance.

4.4.2 Interpretations on Regression Results and Research Hypothesis

This section discusses in detail the analysis of the results for each explanatory variable and their
importance in determining financial performance. Furthermore, the discussion analyzes the
statistical findings of the study in relation to the previous empirical evidences.

4.4.2.1 Number of ATM Terminal and Return on Equity

The proxy used to measure number of ATM terminals is natural logarithm of number of ATM
deployed. The result of fixed effect regression model in table 4.8 above indicated that number of
ATM terminals was statistically significant with P-value 0.041 and had coefficient of -7.67.
Holding other variables constant, when number of ATM terminals was increased by one percent,
return on equity (ROE) of sampled commercial banks would be decreased by 7.67 percent on
average and statistically significant at 5% level of significance. In other words, there was
significant negative relationship between ATM terminals (NATM) and return on equity (ROE)
of sampled Ethiopian commercial banks. Therefore, the researcher rejected the null hypothesis
that there was a positive relationship between NATM and ROE, as there was no sufficient
evidence to support the positive relationship between NATM and ROE.

In contrary to the hypothesis of this research, NATM showed a negative relationship with return
on equity (ROE) of sampled Ethiopian commercial banks. The result was consistent with other
researchers‟ findings: Kashif, M., Kamboh, M. & javaid, M. (2016) and Uchechukwu, N.;
Chubuzor, E.E (2017), who reported a negative relationship between the cost of building ATM
locations on bank profit. The negative effect found by these studies were attributed to the
increased in investment spending as a result of the high cost of building up an ATM stand i.e. the
physical mounting of the ATM machines and cost of building of ATM locations were significant
and reduced the performance of banks. According to Simon, O., Mohammed A., & Elmaude,
J.G. (2013) it was reported that in the first year of adoption, a negative impact was observed but

68
 e-banking contributed positively to bank performance after two years of adoption in terms of
ROA and ROE due to financial cost of adopting E-Banking.

4.4.2.2 Debit Card Issued and Return on Equity

The proxy used to measure debit cards issued is natural logarithm of number of debit card users.
The result of fixed effect regression model in table 4.8 above indicated that debit card had
coefficient of 3.00 and its P-value was 0.284. Holding other variables constant, when number of
debit card users increased by one percent, return on equity (ROE) of sampled commercial banks
on average would be increased by 3.00 percent but statistically insignificant i.e. even if the
coefficient of debit card issued was positive as expected, it was not statistically significant even
at 10% significance level (p-value= 0.284), suggesting that its influence was negligible. The
hypothesis that stated there was a positive significant relationship between debit cards and
profitability (ROE) would be rejected as data did not support the hypothesis. Therefore, the
researcher failed to accept the null hypothesis that debit card had a positive significant role on
return on equity of commercial banks in Ethiopia. Referring to previous studies, result was
consistent with the findings of Sujud, H. & Hashem, B. (2017), Ongare, H.O (2013), Josiah, A.
and Nancy, k. (2012), Fu-Qiang and Sajid (2014) and Polatoglu and Ekin (2001) that increased
in debit card usage enhanced the profitability of banking industry in form of return on banks
equity. Therefore, conclusion about the impact of debit cards on financial performance (ROE)
remained ambiguous and further research was required.

The possible reason for the insignificant association between DC and ROE could be attributed to
the fact that, the increased number of inactive cardholders‟ and lack of efficient delivery
mechanism of cards to customers because after production cards would not be delivered to
customers immediately.

4.4.2.3 Number of Mobile Banking Users and Return on Equity

The proxy used to measure number of mobile banking users (NMOBU) is natural logarithm of
number of mobile banking users. The result of fixed effect regression model in table 4.8 above
indicated that the coefficient of number of mobile banking users was 6.57 and its P value was
0.002. Holding other variables constant, when number of mobile banking users (NMOBU)
increased by one percent, return on equity (ROE) of sampled Ethiopian commercial banks on

69
 average would be increased by 6.57 percent and statistically significant at 1% level of
significant. This means that, commercial banks with large number of mobile banking users were
more profitable than commercial banks with low number of mobile banking users. Therefore, the
researcher failed to reject the null hypothesis that NMOBU had a positive effect on ROE, as
there was no sufficient evidence to support the negative relationship between ROE and NMOBU.

The relationship was positive as expected and this positive relationship between NMOBU and
ROE could be attributed to the fact that mobile banking can made basic financial services more
accessible by minimizing time and distance to the nearest retail bank branches (CGAP, 2006) as
well as reduced the bank„s own overheads and transaction- related costs and had the potential to
extend the limited nature and reach of the formal financial services to various customers thereby
increasing their profitability (Lee, Lee and Kim, 2007). In addition with mobile banking
customers could control their bank account at any location around the country with the use of
mobile internet. This finding was consistent with previous studies of Kathuo S., (2015), Donner
and Tellez (2008), Tiwari, Buse and Herstatt (2006), Mwange, A. (2013), Rachael W.M. (2013)
& Mustapha, S.A. (2018). According to those researchers banks that had adopted M-banking
services had to a large extent increased their customer outreach, and hence had improved their
financial performance. Their findings revealed that many mobile banking products were being
offered by banks such as Fund Transfer between Accounts/ E-funds transfer, Bill Payment, order
for cheque books and bank statements and therefore concluded that the financial performance of
the banks that provide these mobile banking products had improved because they ensured
efficiency of the banking services.

This empirical finding was also consistent with the findings of Ngumi, M.P (2013), which
pointed out that bank innovation had significant influence on income, ROE, Profitability and
Deposits of commercial bank in Kenya. It was found that bank innovations had the highest
positive influence on mobilization of consumer deposits.

4.4.2.4 Value of ATM Transactions and Return on Equity

The proxy used to measure value of ATM transactions (VATMT) is natural logarithm of the
value of ATM transaction. The result of fixed effect regression model in table 4.8 above
indicated that the coefficient of value of ATM transactions was 3.25 and its P value was 0.10.

70
 Holding other variables constant, when value or price of transactions of ATM (VATMT)
increased by one percent, return on equity (ROE) of sampled Ethiopian commercial banks would
be increased by 3.25 percent on average and statistically significant at 10% level of significant.
Therefore, the researcher failed to reject the null hypothesis that value or price of transactions of
ATM had a positive role on return on equity, as there was no sufficient evidence to support the
negative relationship between VATMT and ROE.

The relationship was positive as expected and this positive relationship between VATMT and
ROE could be attributed to the fact that more transactions of ATM led to have more return on
equity. This finding was consistent with previous studies of Joseph M.V. (2017), Itah and
Emmanuel (2014), Cook, Seiford and Zhu (2004). According to those researchers value of ATM
transactions had a positive and significant role on return on equity.

The possible reason for the significant positive relationship could be that, the more transactions
executed by ATM, the more commission would be generated by commercial banks. Moreover as
more transactions were processed by ATM, banks would benefit from transaction related costs.
According to Allen and Hamilton (2002), an estimated cost of providing the routine business of a
full service branch in USA was $1.07 per transaction, as compared to 54 cents for telephone
banking, 27 cents for ATM banking and 1.5 cent for internet banking. On the other hand, the
advantages for the customers were significant time saving and reduced costs in accessing and
using the various banking products and service, increased comfort and convenience.

4.4.2.5 Value of Mobile Banking Transactions and Return on Equity

The proxy used to measure value of mobile banking transactions (VMOBT) is natural logarithm
of the value of mobile transactions. The result of fixed effect regression model in table 4.8 above
indicated that the coefficient of the value or price of transactions executed by mobile banking
was -0.97 with p-value (0.586). In other words, there was insignificant negative (indirect)
relationship between value of mobile banking transactions (VMOBT) and return on equity
(ROE) of sampled Ethiopian commercial banks. Therefore, the researcher rejected the null
hypothesis that there was positive relationship between VMOBT and ROE, as there was no
sufficient evidence to support the positive relationship between VMOBT and ROE.
71
 In contrary to the hypothesis of this research, VMOBT showed a negative relationship with
return on Equity (ROE) of sampled Ethiopian commercial banks. The result was consistent with
the findings of Wadhwa. S (2016) and Ene, (2017) that value of mobile banking transaction had
no significant relationship with return on equity. In contrast, many previous studies for instance
Kashif, M., Kamboh, M. & javaid, M. (2016), Uchida, Ahmed, and Ahmed (2011) and Rauf and
Qiang 2014, stated that VMOBT had significant positive effect on ROE.

The possible reason for the negative association between VMOBT and ROE could be attributed
to the fact that, commercial banks recruited mobile banking customers for the sole purpose of
providing bank account information via test message and might not encourage their customers to
transact through mobile banking, which enabled them perform banking transaction, hence
resulted in a decrease in the value or price of transactions performed by mobile banking, which
in turn resulted to be negatively correlated with profitability. In addition many individuals could
have been skeptical with regard to the functionality of mobile banking, as a result of the
increased number of unsuccessful mobile banking transactions due to network interruption which
discouraged customers from using the medium, thus resulted to be negatively associated with
profitability (ROE).

4.4.2.6 Bank Size and Return on Equity

The proxy used for bank size is natural logarithm of total asset. The result of fixed effect
regression model in table 4.8 above indicated that the coefficient of bank size was -58.7 and its
P-value was 0.001. Holding other variables constant, when bank size (BS) increased by one
percent, return on equity (ROE) of sampled commercial banks would be decreased by
58.7percent and statistically significant at 1% level of significance.

The result indicated that a negative (indirect) size-profitability relation had existed which implied
that smaller commercial banks tend to earn higher profits than larger commercial banks. It
supported the studies of (Kosak & Cok, 2008) and Jonghe (2010), Barros, Ferreira and Williams
(2007) who argued that small banks were more likely to get good performance and less chances
of getting bad performance. Conversely, large banks were less likely to obtain good performance
and a greater chance of getting bad results.

72
 The result was consistent with the previous studies Ameur, I. and Mhiri, G. (2013) ,Sufian, F.
and Chong, R. (2008) and Athanasoglour et at 2008 who suggested that in the diversification of
bank branches operational expenses (rent, payroll, marketing etc) might get higher and the
variables might exhibit a negative effects. Moreover size and profitability of banks might show a
negative relationship due to bureaucratic bottlenecks and managerial inefficiencies. In contrast,
Rao & Tekeste, (2012) and Alper and Anbar, (2011) found positive relationship between bank
size and performance, suggesting that Large banks were likely to had an advantage of engaging
in higher investment diversification than small banks. Since this diversification reduced risks and
economies of scale led to increase operational efficiency through minimizing costs.

The possible reason for the negative association could be due to the fact that, Small banks might
be able to form stronger relationships with local businesses and customers than large banks,
which allowed them access to proprietary information useful in setting contract terms and
making better credit underwriting decisions (Berger and others). Indeed, these informational and
pricing advantages might fully offset any loss of scale economies.

4.4.2.7 Inflation and Return on Equity

Annual inflation rate is used as a proxy to measure inflation. The result of fixed effect regression
model in table 4.8 above indicated that the coefficient of inflation was 0.43 and its P-value was
0.013. Holding other variables constant, when inflation (INF) was increased by one percent,
return on equity (ROE) of sampled commercial banks on average would be increased by 0.43
percent and statistically significant at 5% level of significance. The result indicated that inflation
(NF) was positive and statistically significant to bank profitability (ROE). This implied that
during the period of the study, inflations was anticipated which gave banks the opportunity to
adjust the interest rates accordingly, resulting in revenues that increased faster than costs, with a
positive impact on profitability.

Referring to previous studies, results concerning inflation were mixed. Demirguc-Kunt, A. &
Huizinga, H. (1999) found a positive relationship between inflation rate and bank profitability.
However, Pasiouras & Kosmidou (2007) found a negative relationship between inflation rate and
bank profits.

73
 Thus, this study accepted the hypothesis which stated that there was a positive relationship
between inflation and bank performance in Ethiopia.

Table 4.9: Summary of Variables, Hypothesis Test and Decisions

Explanatory Variables Expected Signs Actual Sign Decision

Number of ATM Positive and Negativ and Rejected

terminals Significant e

Debit cards issued Positive and Positive and Rejected

Significant
Number of mobile Positive and Positive and Accepted at 1%
banking users
Significant
Value of transactions Positive and Positive and Accepted at 10%
of ATM
Significant
Value of transactions Positive and Negativ and Rejected
of mobile e
Significant
Banking
Bank size Positive/negative Negativ and Accepted at 1%
and Significant e

Inflation Positive/negative Positive and Accepted at 5%

and Significant

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 CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

The previous chapter presented the analysis of the findings and discussions of the study. The
purpose of this chapter is to discuss the conclusions and recommendations. Accordingly, the
chapter is organized in three sections; the first section presents the conclusions of the study, the
second section presents the recommendations provided based on the findings of the study and
finally further research area is presented.

5.1 Conclusion

The main objective of this research was to examine the roles of e-banking service on financial
performance of commercials banks in Ethiopia for the period 2015 to 2018. A balanced panel
data of ten purposively selected commercial banks with 40 observations have been used for
analysis. The sample data of ten commercial banks have been analyzed using descriptive
statistics and fixed effect panel regression model. The dependent variable used as a financial
performance indicator was return on equity. ROE represented net income after tax divided by
average stockholders‟ equity. The dependent variable, i.e ROE is regressed with independent
variables such as: number of TM‟s terminals, number of debit cards, number of mobile banking
users, value of ATM transactions and value of mobile banking transactions from bank specific
variables which were used as a proxy of electronic banking service. Whereas bank size (BS) was
the other control bank specific variable and inflation rate (INF) from macroeconomic variables
included in this study.

The finding of the study confirmed that from bank specific variables number of mobile banking
users and value of ATM transaction had significant and positive roles on financial performance
of commercial banks in Ethiopia measured by return on equity. This indicated that increasing the
number of mobile banking users and increasing the value or price of transactions executed by
ATM had positive roles on the financial performance of commercial banks in Ethiopia, as these
made basic financial services more accessible by minimizing time and distance to the nearest
retail bank branches as well as reduced the bank„s own overheads and transaction- related costs
and had the potential to extend the limited nature and reach of the formal financial services to

75
 various customers thereby increasing their profitability. In the contrary number of ATM
terminals and bank size had significant negative impact on financial performance at 5% and 1%
level of significance respectively. This implied that as those explanatory variables increased,
financial performance of commercial banks measured by ROE would be decreased. The negative
association between bank size and performance suggested that the more banks getting bigger in
its asset size, the lesser profitable it became.

With respect to macroeconomic variables, inflation (INF) was positive and statistically
significant to bank profitability (ROE). This implied that during the period of the study,
inflations was anticipated which gave banks the opportunity to adjust the interest rates
accordingly, resulted in revenues that increased faster than costs, with a positive impact on
profitability.

The rest variables number of debit cards and value of mobile banking transactions were not
powerful variables to influence financial performance of commercial banks in Ethiopia.

To sum up although the result indicated some negative influences by the selected variables due
to financial cost of adopting e-banking, in general, it could be concluded that the effect of
electronic banking on the financial performance of commercial banks in Ethiopia was positive.

The adoption of E-banking by commercial banks had a high potential of improving financial
performance in the long run 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.

5.2 Recommendations

The findings of the study showed that number of mobile banking users, value of ATM
transactions, bank size and inflation were the significant drivers of financial performance of
commercial banks in Ethiopia during 2015 to 2018. Hence, focusing and taking the necessary
action on these indicators could further enhance financial performance of commercial banks in
Ethiopia. Based on the findings of the study the following possible recommendations were
forwarded:

 Mobile-Banking is one of the biggest social changes being witnessed by current

generation. It has eventually affected the modes of trade and payment systems keeping in

76
 mind the convenience, demands and lifestyle of the current generation. However, the
concept of mobile banking is still in the infant stage and yet unable to explore its
potential in order to increase the profitability of commercial banks in Ethiopia. Thus,
commercial banks should focus on communicating information that emphasizes the
relative advantage and usefulness of mobile banking compared to traditional branch-
based banking and should encourage their customers to transact via mobile banking in
order to maximize the full effect of these innovations.
 It is evident that the increase of regularity of mobile users is cumulating at very high
speed but the regularity of banks account holder is very less. Therefore, mobile banking
is a new technological platform to the banks to increase their customers. Thus the study
further recommend that commercial banks should keep adopting and using mobile
banking in their operations aggressively as a way of moving to a cashless society which
is a key driver towards achieving economic growth of a country because the number of
people with access to a mobile hand set is increasing every day.
 With regard to ATM transactions, commercial banks should improve their ATM
transaction reconciliation process, either by assigning more personnel or by automated
means, as it creates customer dissatisfaction and discouragement. Because, ATM
transactions processed but not paid to customers will take more time to be credited to
customers‟ bank account.
 Commercial banks should also ensure proper maintenance of ATM outlets to ensure
quality service delivery to their clients. ATM outlets should also be strategically selected
to be accessible to as many clients possible.
 Commercial banks in Ethiopia should avoid overexpansion in its asset size, as it may
affects profitability negatively due to higher operational expenses, bureaucratic
bottlenecks and managerial inefficiencies.
 Commercial banks in Ethiopia should properly anticipate the future inflation rates to
avoid its negative impact on banking profitability.

5.3 Further Research Consideration

This study demonstrated that electronic payment systems had positive effects on financial
performance (profitability) of commercial banks in Ethiopia. However, not all financial

77
performance factors related with e-banking were studied. It is therefore recommended that future
studies be carried out on:

 The effect of e-banking on the financial performance of Micro- Finance institutions that
have adopted these innovations
 The roles of digital finance of commercial banks in Ethiopia on financial inclusion
 The effect of agency banking on either financial performance of commercial banks or
on financial deepening in Ethiopia.
 Investigation of the effect of e-banking on Ethiopian commercial banks Deposit

78
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 Appendix I
Data for E- Banking and Financial Performance

ID BAN YEA LOGN LOGN LOGNM LOGVA LOGMO BS INF ROE

KS R ATM DC BU TMT BT
1 CBE 2015 2.42 5.80 5.67 8.01 8.00 11.48 92.0000 67.6514
2
1 CBE 2016 2.59 5.71 5.42 8.47 8.24 11.59 98.9000 62.8519
5
1 CBE 2017 2.89 5.53 5.11 8.59 8.40 11.69 107.2000 55.9518
1
1 CBE 2018 2.93 5.44 5.09 8.10 8.67 11.75 125.2000 50.6230
6
2 AIB 2015 2.22 4.68 4.14 7.06 7.20 10.40 92.0000 27.2453
2 AIB 2016 2.36 4.67 3.95 7.34 7.01 10.49 98.9000 22.9828
8
2 AIB 2017 2.48 4.28 3.87 7.39 7.32 10.62 107.2000 21.5399
7
2 AIB 2018 2.61 4.66 4.02 7.66 7.17 10.74 125.2000 24.2324
6
3 DB 2015 2.34 4.81 4.44 7.92 7.35 10.39 92.0000 30.6885
5
3 DB 2016 2.44 4.52 4.17 7.93 7.57 10.46 98.9000 26.4106
5
3 DB 2017 2.51 4.09 4.08 7.80 7.38 10.56 107.2000 23.1481
9
3 DB 2018 2.68 4.89 4.65 8.01 7.66 10.66 125.2000 30.8642
6
4 BOA 2015 2.30 4.18 4.95 7.01 6.87 10.14 92.0000 33.9392
9
4 BOA 2016 2.39 4.04 4.52 6.36 7.18 10.23 98.9000 17.4720
7
4 BOA 2017 2.46 4.17 4.82 7.40 7.11 10.40 107.2000 18.3263
9
4 BOA 2018 2.47 5.04 4.84 7.61 7.57 10.50 125.2000 19.1990
8
5 WB 2015 2.09 4.46 4.01 6.67 6.66 10.14 92.0000 15.3422
3
5 WB 2016 2.13 4.56 4.33 7.72 6.80 10.21 98.9000 15.4630
4
5 WB 2017 2.28 4.32 4.51 7.70 7.16 10.32 107.2000 14.3910
6
5 WB 2018 2.30 4.81 4.51 7.73 7.27 10.44 125.2000 16.0841
2
6 UB 2015 1.95 4.79 4.51 6.72 6.69 10.16 92.0000 14.2824
4
6 UB 2016 1.94 4.98 4.40 7.91 7.40 10.24 98.9000 17.2507
9
6 UB 2017 2.14 5.14 4.58 7.91 7.60 10.34 107.2000 18.0388
8
6 UB 2018 2.14 5.24 4.56 8.01 7.49 10.45 125.2000 22.2833
2
7 NIB 2015 1.48 4.26 3.39 6.96 5.64 10.12 92.0000 16.3826
9
7 NIB 2016 1.80 4.39 3.70 7.06 6.45 10.20 98.9000 16.2772
5
7 NIB 2017 1.97 4.42 4.00 7.31 6.55 10.32 107.2000 16.6033
5
7 NIB 2018 2.05 4.74 3.98 7.40 6.87 10.43 125.2000 17.2674
9
8 BB 2015 1.60 4.11 2.23 5.10 4.35 9.62 92.0000 17.9464
3
8 BB 2016 1.08 4.34 3.28 5.15 5.42 9.86 98.9000 22.5211
7
8 BB 2017 1.08 4.48 4.52 5.40 5.20 10.02 107.2000 22.8856
1
8 BB 2018 1.75 4.43 4.70 6.55 6.48 10.15 125.2000 21.4552
6
9 OIB 2015 0.90 4.25 3.13 5.71 6.22 9.98 92.0000 23.7433
3
9 OIB 2016 1.43 4.77 3.79 6.37 6.88 10.05 98.9000 24.8678
6
9 OIB 2017 1.30 4.33 4.47 7.01 6.96 10.21 107.2000 19.2519
9 OIB 2018 1.30 4.68 4.52 7.33 6.47 10.38 125.2000 23.3049
3
10 ZB 2015 1.00 3.64 3.41 7.70 6.57 9.69 92.0000 31.9776
5
10 ZB 2016 1.08 3.79 3.65 7.78 6.97 9.87 98.9000 21.5725
8
10 ZB 2017 1.11 4.00 3.88 7.84 6.69 9.99 107.2000 22.9573
3
10 ZB 2018 1.90 4.01 4.33 8.00 6.15 10.09 125.2000 22.9573
3
 Appendix II

1. Heteroskedasticity Te t: wh te’ te
t
 Appendix III
Correlated Random Effects- Hausman Test
 Appendix IV
Fixed Effects Test Result