Greening Bank Financial Innovation for Better Financial Performance. Evidence from Ethiopia.

Abstract

The study objective was to examine the effect of financial innovation on the financial performance of the Ethiopian banking sector, as well as provide insight on environmentally friend financial innovations. The study uses an explanatory research design and quantitative research approach with secondary time series data utilized quarterly over the study period 2013-2020(8 years). More specifically, the study adopts an autoregressive distributed lag (ARDL) model. Furthermore, the long-run relationships of variables are quizzes through bound tests and confirm the existence of a long-run relationship among variables. The short-run relationship was examined through an error correction model. The finding of the study reveals that; automated teller machines, and branch expansion affects the financial performance negatively in the long run. On the other hand, variables like mobile banking, internet banking, debit cards, prepaid cards, and point of sales terminals have a positive effect on the financial performance of commercial banks in the long run. In the short run, automated teller machine, point of sales terminals, mobile banking, and branch expansion are found to positively affect the performance of commercial banks. However, the variables like internet banking, debit cards, and prepaid cards are found to negatively affect the financial performance of the sector. This study suggests that the sector prioritizes environmentally friendly green financial innovations (mobile banking, internet banking, point of sale machines, and debit cards) in adoption over automated teller machines and branch expansion. As a result, both the industry and the environment will benefit in the long run.

Introduction

Green innovation has become a prominent issue from numerous scientific perspectives since the worldwide population is concerned about increasing global warming and environmental deterioration (Aron – Molina, 2020). Green innovation in business generally focuses on enhancements and renovations that reduce emissions, pollution, and costs (Asadi et al., 2020). Companies that are early adopters of green innovation techniques are thought to gain and maintain market competitiveness (Aguilera et al., 2013).

As a result, successful green innovation performance assists businesses in increasing efficiency while also creating and rein­forcing core competencies. From a business perspective, green innovation can be divided into two categories of green technology innovation and green management innovation (Li et al., 2018). In the case of green financial technology, it is believed that the process by which a long tenure is used to label the generation of new and creative approaches to various financial circumstances (Calomiris, 2009).

Furthermore, it can be considered as a shift in the production function’s shape. Hence, innovation, in general, can be explained as a process that allows an existing product or service to be offered at a lower cost, or a „new product or service” to be introduced to the market (Merton, 1992). The application of green innovations increases the competitiveness of a business entity and creates value for its owners (Merton, 1992). Additionally, green financial innovation is used as the strategic planning variable for outstripping the financial institution’s objectives (Calomiris, 2009). Hence having successful green innovation planning generates an exclusive competitive position that grants the firm a competitive advantage and superior performance (Allen, 2012). In the case of the financial sector, green financial technologies aid in the benefits of optimizing taxes (Ahmad – Wu, 2022), increasing the liquidity of market-based products (Li et al., 2018), lowering transaction costs, and lowering agency costs between executive management and shareholders of financial institutions (Aguilera et al., 2013). Apart from that, it creates beneficial opportunities for shareholders and creditors by reducing information asymmetry between corporate insiders and outsiders (Corrocher – Ozman, 2020), increasing risk-sharing opportunities, and making capital intermediation more efficient and cost-effective for clients (Adhikari – Momaya, 2021). In the financial sector, especially banking sectors the widely used products as means of financial innovation are automated teller machines, debit cards, point-of-sale machines, internet banking, and mobile banking (Agarwal – Zhang, 2020). More specifically, internet banking, mobile banking, and debit, and credit cards are classified as the green financial products of the banking sector compared to the others. It is argued that there are many driving forces that initiate the financial sector for the adoption and development of financial innovations.

According to (Benfratello et al., 2008) the driving forces are assumed to be the desire to improve performance, desire to improve customer relationships, rapidly changing customer needs and preferences, desire to improve organizational performance, desire to cover a wide geographical area, desire to build an organizational reputation, and desire to reduce costs.

In general, the majority of empirical studies conducted on green innovation from different perspectives show that the overall importance of innovation is an aid in the advancement of financial performance (Aastvedt et al., 2021; Aguilera et al., 2013; Asadi et al., 2020; Asni – Agustia, n.d. 2020; Corrocher – Ozman, 2020).

In Ethiopia, the financial system is far behind the rest of the world in terms of adopting financial innovations (Temam, 2018). However, the financial sector has shown massive growth and development in terms of branch expansion, adoption of financial innovation, and growth in the capital base in recent years (National bank of Ethiopia, 2020). The widely adopted products as a means of financial innovations in the banking sector are automated teller machines, Debit cards, Point of sales machines, Agency Banking, internet banking, and mobile banking have taken root in various financial institutions.

According to data from the national bank of Ethiopia (2021), the value of transactions made through financial innovations in the past 5 years (2014-2020) was increasing at the highest rate. For example, only in the year 2020, the value of transactions made through financial innovations was increased more than twice compared to the preceding year 2019, and from this total transaction, 69 percent was transacted through an automated teller machine, and the remaining 31 percent was through non-automated teller machine transactions (mobile banking, internet banking, and point of sales machine). In this case, the volume of transactions made through automated teller machines is twice of others innovation transactions.

As observed by many other scholars, adoption of these innovations has positive, negative, or mixed effects on financial performance as well as future investment decisions in financial innovations of the banking sector (Siddik et al., 2016). As a result, identifying the best alternative investment decision of financial innovation, and examining its impact on financial performance is found important (Temam, 2018). As well as making selection on environmentally friend financial innovations.

Taking into account the importance of the study, this study will examine and identify the effect of financial innovation on the financial performance of the Ethiopian banking sector. In doing so, the study used the numbers of automated teller machines, mobile banking, internet banking, debit card, and point of sales machines introduced in the market over 7 years (2014-2020) as a proxy to measure financial innovation. Furthermore, branch expansion is used as a control variable to examine the effect of traditional banking expansion on financial performance. The following section scrutinizes the literature review conducted on the study. Section 3 describes the study’s research methodology, while Section 4 discusses the findings of the study, and the conclusion and policy recommendations are presented in the final section.

Literature review

Green Innovation

According to (Bakari et al., 2018), the academic definition of innovation is a broad phenomenon encompassing any new way of doing things in economic ways. Innovation is any change, modification, improvement, or creation that has been implemented or applied in the market, regardless of its object (product, process, structure, method, etc.) (Corrocher – Ozman, 2020). Thus, innovation entails a multi-stage process in which new ideas must first be created, tested, put into production, and finally placed on the market to affect individuals, businesses, and society as a whole (Agarwal – Zhang, 2020). Currently, because of growing concern about the state of the environment, the business sector has paid close attention to green technology innovation, particularly those in heavily polluting manufacturing industries (Acquah et al., 2021).

Green process innovation and green product innovation are the two primary strategies for green technology innovation (Achi et al., 2022). The one which is aimed to change or modify product designs by using nontoxic compounds or biodegradable materials during the manufacturing process to reduce environmental impact and improve energy efficiency is known as a green product innovation (Cao et al., 2021).

In the financial sector, green financial innovation is defined as the process of developing and commercializing new financial products following various financial technologies, financial institutions, and financial markets (Akdere – Benli, 2018) same with the concept of green products innovation. Furthermore (Allen et al., 2009) defined green financial innovation as a process change that includes a change in various types of financial products.

Green financial innovation, according to (Baah et al., 2021), is related to financial institutions’ costs decreasing or increasing, because every change in their operations through financial technology leads to a change in their financial position. Furthermore, (Akdere – Benli, 2018) backed (Baah et al., 2021) by stating that „financial innovation” is defined as „a technology that decreases risks, lowers transaction costs, or improves products and services.”

According to (Allen, 2012), many financial services are active in various technical breakthroughs and in developing broader financial innovations that aid their business’ overall activities. New goods, services, procedures, and organizational structures have all been part of financial innovation (Bakari et al., 2018). In the contemporary corporate environment, the most well-known financial firms, particularly the banking industry, are most prominent in terms of products and services, organizational structures, and legal and procedural innovations (Amore – Bennedsen, 2016).

Types of financial innovations

According to (Allen, 2012) technological advances in information processes have created many innovations in the financial services business, and financial innovations that have taken place in the last 25 years are of different types and include new production processes, new products, and services, and new organizational forms. The following section of the study focuses on explaining the types of green financial innovation that could be adopted by the banking sector.

Online Banking: Online banking, also known as internet banking, e-banking, or virtual banking, is an electronic payment system that enables customers of a bank or other financial institution to conduct a range of financial transactions through the financial institution’s website. According to (Frimpong et al., 2014) online banking is, “to give customers access to their bank accounts via a website and to enable them to enact certain transactions on their account, given compliance with stringent security checks”. Internet banking gives customers access to almost any type of banking transaction at the click of a mouse, except withdrawals.

Mobile Banking: Mobile banking is an application of mobile commerce that enables customers to access bank accounts through mobile devices to conduct and complete bank-related transactions such as balancing cheques, checking account statuses, transferring money, and selling stocks (Buckley et al., 2013). Mobile banking allows customers to perform many activities like mini statements, checking of account history, SMS alerts, access to card statements, balance checks, payment of bills, mobile recharge, etc.

Automated Teller Machine (ATM): Automated Teller Machine (ATM) is the first well-known machine to provide electronic access to customers. With the advent of ATMs, banks can serve customers outside the banking hall. ATM is designed to perform the most important function of banks such as withdrawal of cash, deposits, the printing of mini statements settlements of bills. It does all through access to a personal identification number (PIN), and aplastic that contains a magnetic chip that the customers identified through (Boateng et al., 2014). At first, a bank ATM could only be used by customers who had accounts in that bank, but in the early 1980s with the improvement in telecommunications, banks took advantage and started what is called shared ATMs networks where customers of other banks could access their money through other bank’s ATMs. Banks paid other ATM owners “interchange” fees to cover the marginal cost of the “of us” transactions on the owner’s machines. The ATMs were operated using an ATM card which was a magnetic card (Kinuthia, 2010).

Debit Cards: A debit card (also known as a bank card, plastic card, or check card) is a plastic payment card that can be used instead of cash when making purchases. Unlike a credit card, the money comes directly from the user’s bank account when performing a transaction. Functionally, it can be called an electronic cheque, as the funds are withdrawn directly from either the bank account or from the remaining balance on the card. In some cases, the cards are designed exclusively for use on the internet, and so there is no physical card (Mavri et al., 2020).

Credit Cards: A credit card is a payment card issued to users (cardholders) to enable the cardholder to pay a merchant for goods and services based on the cardholder’s promise to the card issuer to pay them for the amounts so paid plus the other agreed charges. The card issuer (usually a bank) creates a revolving account and grants a line of credit to the cardholder, from which the cardholder can borrow money for payment to a merchant or as a cash advance. In other words, credit cards combine payment services with extensions of credit (Simkovic, 2009).

Point of sale terminals (POS): According to (Huffman – Bolvin, 2014), POS covers a variety of services rendered through machines located at retail establishments. POS terminals are generally clerk-generated devices located at the checkout or convenience counter or retail establishment. Electronic cash register versions of these terminals have been in operation for several years, maintaining store records on sales, inventories, accounts receivable, and the like. Now, POS devices have been linked to financial institution computers, allowing retail customers to receive approval for check cashing and electronically initiate transfers from their accounts to the retailers. In some installations, customers can make deposits to their accounts. POS devices accept either a plastic credit card or a plastic debit card, depending on whether the customer wants to delay the payment by charging the purchase deducted directly from the customer’s account.

Financial performance

investors always look behind the performance of the companies when making an investment decision, which typically includes buying an additional investment, keeping, or selling their current shares (Acar – Yilmaz, 2020). This is why most academic writers have suggested that a company’s performance is comparable to a mirror. The idea is that a firm’s performance is generally defined by how well it achieves its objectives (Achmad Kuncoro – Sari, 2015). Financial performance is largely defined as the measurement of a firm’s asset’s ability to generate revenue, which is determined through return on asset (ROA) and is primarily done by comparing the asset’s overall revenue-generating capabilities (Bartolacci et al., 2020). Hence Firm performance is the sum of the firm’s internal and external actions (Yang – Luo, 2020). Return on assets (ROA), average annual occupancy rate (AAR), net profit after tax (NPAT), and return on investment (ROI) are some of the most often utilized financial or accounting measures by businesses to measure the firm’s financial performance (Temam, 2018). However, the return on assets is the most well-known option for gauging financial performance (Temam, 2018). As a result, return on asset was employed to assess the overall financial performance of Ethiopia’s banking sector in this study. Apart from that, as the firm’s primary objective is profit maximization, the adoption of green financial innovations needs to consider the metrics like profitability, productivity, growth, stakeholder satisfaction, market share, and competitive position (de Oliveira et al., 2018). Hence the following section of the study discusses the relationship between green innovation and financial performance.

Green innovation and Financial Performance

According to (Yusr et al., 2020) there have been several factors that affect the level of green innovation to be used by customers which have an indirect impact on the financial performance of the companies. Hence the relationship between green innovation and financial performance has been studied in different empirical studies. As a result, a higher rate of green innovation has a positive impact on firm performance (Aastvedt et al., 2021). In the study, green innovation’s impact on oil and gas firms’ financial performance is investigated from 2010 to 2018, the study estimates two-panel datasets for oil and gas businesses in the United States and Europe. The findings indicate that a company’s innovation score has a positive impact on its financial performance in both the United States and Europe.

The study conducted by (Barra – Ruggiero, 2021) on firm innovation and local bank efficiency in Italy founds a positive and significant effect of process innovation on banks efficiency. According to the findings of the study conducted by (Zhang et al., 2019), green innovation has a positive and significant effect on firms’ performance. The study’s findings suggest that state-owned companies (SOEs), which are better able to leverage green innovation have a better financial performance.

Furthermore, the study conducted by (Lee – Min, 2015) discovered that green innovation reduces carbon emissions while also improving financial performance in a sample of Japanese manufacturing enterprises. The study conducted by (Lin et al., 2019), showed that green innovation has a favorable impact on the automotive industry’s financial performance. (Rezende et al., 2019) Also founds the positive relationship between green innovation and financial performance. The study’s finding implies that green process and green product innovation have a significant effect on financial performance. On other hand, the study conducted by (Wang et al., 2021) on green process innovation, green product innovation, and its economic performance improvement paths in china implies that; green process innovation and green product innovation can effectively improve the economic performance of enterprises. Furthermore, the study discovered that green process innovation in an enterprise can positively promote green product innovation, and there is also room for technological innovation upgrading. As of the finding (Xie et al., 2019), green process innovation has a beneficial impact on green product innovation, and both green process and green product innovation can improve a company’s financial performance, according to the findings of the study, green product innovation also mediates the association between green process innovation and a firm’s financial performance. On other hand, the study conducted by Walley and Whitehead (1994), showed the negative impact of innovation on financial performance. Furthermore, the study conducted by (Wagner, 2005) lends argument to the study conducted by Walley and Whitehead (1994) suggesting that green innovation has no impact on boosting financial performance. Moreover, Bontis et al. (2018) found a negative influence of green innovation on a company’s financial returns. From various perspectives, the majority of previous studies discovered a positive relationship between financial performance and green innovation. However, some studies revealed a negative relationship between variables. As a result, the current study investigates the impact of green financial innovation on financial performance and forecasts future trends for both variables in the Ethiopian banking sector. Based on previously established theories and empirical studies, we hypothesized the following hypothesis for the study.

Hypothesis 1: Mobile Banking has a positive and significant effect on the profitability of the Ethiopian banking sector.
Hypothesis 2: ATM has a positive and significant effect on the profitability of the Ethiopian banking sector.
Hypothesis 3: Internet banking has a positive and significant effect on the profitability of the Ethiopian banking sector.
Hypothesis 4: Point of sales machine has a positive and significant effect on the profitability of the Ethiopian banking sector.
Hypothesis 5: Debit card has a positive and significant effect on the profitability of the Ethiopian banking sector.
Hypothesis 6: Prepaid card has a positive and significant effect on the profitability of the Ethiopian banking sector.
Hypothesis 7: Branch expansion has a positive and significant effect on the profitability of the Ethiopian banking sector.

Materials used

this study used an explanatory research design and a quantitative research approach to examine the effect of financial innovation on the financial performance of the Ethiopian banking sector. This study used secondary data from different websites, publications, and annual reports.

The data regarding financial innovation determinants and financial performance is collected from the National bank of Ethiopia. The target population of the study was all commercial banks found in Ethiopia, which includes sixteen private banks and one government bank.

Hence all seventeen commercial banks operating in Ethiopia was selected as the study sample by using census method considering the operating period of 2013 up to 2020 G.C. The sampling frame is based on time series quarterly data of the Dependent (Financial performance) and independent (financial innovation, proxies by volume of transactions made through automated teller machine, point of sales machine, mobile banking, internet banking, prepaid card, debit card) variables between 2013 and 2020. Bank branch expansion was used controlling variable. The sample period includes 32 quarterly observations of all variables. The study sampled the period of 2013-2020 based on available data for the financial innovations.

Figure 1: Conceptual framework of the study

Source: constructed by authors.

Model specification

The general model was developed and tested to achieve the desired purpose of the study. The Explanatory variables in this model were Automated machine (ATM), Mobile Banking (MB), Internet Banking (IB), Debit Card (DC), Prepaid Card (PPC) used as a proxy for determinants of financial innovation. And branch expansion is used as a control variable (BB) The variable representing profitability of commercial banks which was measured by ROA used as the dependent variable in the regression models.

The regression equation was as follows:
ROA = f (ATM, MB, IB, DC, PPC, BB, POS)(1)
ROA = Profitability level of the sector each quarter (measured as net income over total assets).
ATM = number of automated teller machines added to the market each quarter.
MB = Number of mobile banking users added to the market each quarter.
IB = number of internet banking users added to the market each quarter.
DC = Number of debit card users added to the market each quarter.
PPC = Number of prepaid cards added to the market each quarter.
POS = Number of point sales machines added to the market each quarter.
BB = Number of new branches added to the market each quarter.
The above equation number (1) can be rewritten in the following econometric model with its
functional forms.
ROAit = β0 + β1 (ATM)it + β2 (MB)it + β3 (IB)it + β4 (DC)it + βs(PPC)it + + β6 (POS)it + β7 (BB)it + Uit(2)
Where:
Coefficient of variables and Error term
β0 = coefficient of Intercept (Constant) β5 = coefficient of Prepaid card
β1 = coefficient of automated machine β6 = coefficient of branch expansion
β2 = coefficient of mobile banking β7= coefficient of point of sales machine
β3= coefficient of internet banking u = The Error Term
β4 = coefficient of Debit card

Discussion of Model Used

In this study, the study used Autoregressive Distributed Lag (ARDL), Model. The approach was introduced by (Pesaran et al., 2001) to examine the long and short-run relationships among the variables of interest. General to a specific representation of autoregressive distributed lag (ARDL) model is written in the following manner.
Yt = μoi + Σpi = 1αjγt – 1 + Σqi = 1 βjXt – 1 + εit(3)

Whereas Yt is a vector, µoi is the intercept, and variables in Xt are allowed to be purely I (0) or I (1) or fractionally co-integrated; β and a are coefficients; j=1….k is several independent variables. The long-run relationships of variables are tested through the bounds testing approach developed by Peseran et al. (2001).

The following ARDL model is estimated to test for co-integration among the variables.
ΔROAt = β0 + β11 ROAt – 1 + β12 – ROAIt – 2 + β21 ATMt – 1 + β22 ATMt – 2 + β31 MBt – 1 + β32MBt – 2 + β41 IBt – 1 + β42 IBt –2 + β51 DCt – 1 + β52 DCt – 2 + β61 PSOt – 1 + β62 PSOt – 2 + β71 PPCt –1 + β72 PPCt – 2 + β81 BBt – 1 + β82 BBt – 2Σha λ2ΔATMt – α + Σhb λ3ΔMBt – b + Σhc λ4ΔIBt – c + Σha λ5ΔDCt – d + Σhb λ6ΔPOSt – e + Σhc λ7ΔPPCt – e Σhd λ8ΔBBt – f + εt(4)

Whereas
Δ = is the back shift operator
β0 = denotes the intercept term
β = (i = 1…8) represent the long-run coefficients of variables
(ROAt-1, ATMt-1, MBt-1, IBt-1 DCt-1, PPCt-1 BBt-1 POSt-1) represent one period lagged variables
Λ = (i = 1… 8) denote the short-run coefficients of variables at lag orders
h = denotes the lag length that obtained using Akaike information criterion (AIC)
εt = represents the white noise error term.

If the long-run relationship is confirmed after testing the bound testing the following model should use to interpret the short-run relationship between the variables. The error correction model formulated for this study takes the following form.

ΔROAt = β0 + β1 Σha i (ROAt – 1) + β2 Σhb i (ATMt – 1) + β3 Σhc i (MBt – 1) + β4 Σhd i (IBt – 1) + β5 Σhe i (DCt – 1) + β6 Σhf i (POSt – 1) + β7 Σhe i (PPCt – 1) + β8 Σhf i (BBt – 1) + μECM(–1) εt(5)

Whereas
ECM (– 1) = error correction term lagged by one period.
εt = vector of white noise error terms.
h = the optimal lag length of each variable in the autoregressive process.
,μ = error correction parameter that measures the speed of adjustment towards the long-run equilibrium.

Discussion

The researchers conducted several experiments to see if the model was visible and useful for policy recommendations. The classical linear regression model assumptions are used to test the model. The tests conducted was multicollinearity test, normality, heteroscedasticity, and Ramsey reset tests were then performed and confirm that the model is viable.

In the following section of (table 1), the study discusses descriptive statistics of the variables for both dependent and independent variables. The detailed information of each variable, which includes the mean, median, minimum, maximum, and standard deviation is given under the following section.

Table 1: The result of Descriptive statics. Source: Compiled by taking row data from NBE.

This study employs 32 quarterly data observations spanning the years 2013 to 2020. In this study, the dependent variable was financial performance, which was calculated by dividing net income by total bank assets. According to the above descriptive analysis, the average result of the return on asset (profitability) during the study period was 2.7414 percent, with a maximum of 3 percent and a minimum of 2.6 percent. This means that during the study period (2013-2020), the profitability of the Ethiopian banking sector ranged from 2.6 percent to 3 percent. Furthermore, each observation in the study deviated from the mean value by 0.001207 percent. The descriptive analysis of independent variables as measured by the transaction volume made through automated teller machines, mobile banking, internet banking, debit cardholders, postpaid cardholders, and branch expansion each quarter is given in Table 4 of the study. Hence the following section of the study discusses the long-run and short-run relationship of the variables.

Error Correction Model and Short-run coefficients of the variables

Error Correction Model

The error correction model is a useful model for identifying the short-run coefficients of co-integrated variables in the long run. The results of a bound test for this study confirmed the existence of a long-run relationship among variables, allowing the researcher to run an error correction model.

The error correction term (ECM), which indicates the rate of adjustment, has a value of -0.935574. It is considered correctly signed and statistically significant. This means that the short-run disequilibrium, as well as inconsistencies, are being adjusted and corrected in the long run at a rate of 97.74 percent. The negative sign confirms the existence of long-term equilibrium. Furthermore, the values of R-square and adjusted R-square are 0.966398 and 0.927196, respectively. This indicates, 92 percent of the change in independent variables explains the change in the dependent variable. However, the remaining 8 percent of the variation change in the dependent variable will be explained by other independent variables which are not included in this study. The adjusted R- squared which is an indication of goodness of fit, In comparison to the R Square (Brooks, 2002), is used in this study to discuss the explaining power of independent variables. Because, the adjusted R square is better and more precise goodness of fit measurement, since, it allows a degree of freedom to the sum of squares. Therefore, even after the addition of a new independent variable (s,), the residual variance does not change. In addition, the F-statistics (an overall test of significance) take a value of 24.65162 with a P-value of zero. The null hypothesis of F-statistics (an overall test of significance) that is equal to zero R-square is rejected at a 1 percent significance level since P-value is 0.00001. Thus, all the variables had a jointly statistically significant effect on financial performance. The discussion for the short-run regression analysis is discussed below.

Discussion of long-run estimation of variables

Financial Performance and Automated teller Machine (ATM): The short-run regression analysis implies that; automated teller machine has a positive and significant effect on the financial performance of the Ethiopian banking sector. The regression result implies that a 1 percent increase in the number of automated teller machines each quarter causes the financial performance to increase by 0.307000 percent and statically significant at a 5 percent significant level. The implication behind the positive relationship of the variables is that; in the short-run increase in the number of ATMs has a positive result on the financial performance of the Ethiopian banking sector. The finding of this variable is found the same as the working hypothesis, and previously established study by (Siddik et al., 2016). However, the long-run relationship between automated teller machines (ATMs) and banking sector financial performance has emerged as a negative relationship. The results of the regression analysis indicate that, in the long run, ATMs have a negative but insignificant impact on the financial performance of commercial banks.

As a result, a 1 percent increase in each quarter’s ATM number compared to the previous quarter causes a 0.0373 percent decrease in financial performance. The rationale for the negative association between ATMs and financial performance could be attributed to the fact that the cost incurred by banks for the installation of ATMs is incapable of generating enough profit regardless of their cost of installation. Additionally, as previously conducted studies argued that automated teller machines are not supposed to be beneficial to the financial sector because of the high cost of installation, restrictions on withdrawals, risk of robbery, run out of coins, energy consumption level, and unavailability of the product in the rural areas (Temam, 2018). The finding of this study is consistent with the study conducted by (Khodaei Valahzaghard – Bagherzadeh Bilandi, 2014).

Financial Performance and Mobile Banking: The variable mobile banking has a positive and significant effect on financial performance in the short run. It indicates that where other explanatory variables remain constant, increasing the number of mobile banking users has a positive influence on financial performance and implies that when the number of mobile banking users increases by 1 percent then the financial performance will increase by 0.042170 percent and statistically significant at 5 percent. Furthermore, the long-run relationship between the two variables is still positive.

According to the regression output, a 1percent increase in the number of mobile banking users each quarter causes the financial performance of each quarter to increase by 0.043431 percent and is statistically significant at a 1percent significance level. The rationale for the variables’ positive relationship is that mobile banking provides an alternative service delivery channel for transferring balances from one account to another and facilitating some online mini payments. Hence the service charge optimized from the transaction helps boost financial performance. And also mobile banking has the capability of generating enough profit regardless of their cost of installation. Finally, it argued that commercial banks with a high number of mobile banking users are more profitable than commercial banks with a low number of mobile banking users. The result of this finding is consistent with the study conducted by (Sujud – Hashem, 2017).

Financial Performance and Internet Banking: Internet banking has a negative and statically significant effect on financial performance in the short run. The result of regression analysis implies that a 1 percent increase in internet banking users causes the financial performance to decrease by 0.016016 percent and statically significant at a 1 percent significance level. This could be attributed to the lack of infrastructure and financial inclusion in Ethiopia. However, the long-run relationship between the two variables is found positive. The regression output indicates that internet banking has a positive and significant impact on the financial performance of Ethiopia’s banking sector. Furthermore, the result implies that a 1 percent increase in transaction volume of internet banking each quarter causes a 0.020666 percent increase in financial performance and is statistically significant at a 1percent significance level. The finding of the study is consistent with the working hypothesis as well as a previous study conducted by (Lasmini et al., 2020).

Financial performance and Debit Cards: In the short run, the variable Debit card has a negative and significant effect on the financial performance of the commercial banks in Ethiopia. Furthermore, the result of regression analysis implies that a 1 percent increase in the number of debit card users causes the financial performance to decrease by 0.227179 percent and is statically significant at a 5 percent significance level. The possible reasons for the negative relationship between Debit cards and financial performance in the short run could be attributed to lack of customer awareness and illiteracy level. However, the long-run relationship between the two variables is found positive.

The result envisaged that when other explanatory variables remain constant as the number of debit cardholders increases by 1pecent, financial performance would increase by 0.020383 percent but statistically insignificant in long run. The finding of this study is consistent with the study conducted by (Akhisar et al., 2015). The justification behind the possible positive relationship between Debit cards and the financial performance of commercial banks in Ethiopia is basically because of many numbers of active debit card users.

Financial performance and Point of sales (POS): The variable point of sales machine has a positive relationship with the banking sector’s financial performance in the short run as well as in the long run. The positive association between POS machines and the financial performance of commercial banks could be attributed to the fact that POS terminals have provided an opportunity for commercial banks to establish agent banking in non-traditional bank locations. In addition, it helps to improve the profitability of banks through income generated in the form of charge fees and by reduction of banks’ operational costs. And also it can increase customer satisfaction because it allows customers to purchase, pay bills, and access statements without going to the bank. However, POS terminals are a recent phenomenon in the Ethiopian banking industry, many commercial banks have introduced this innovative product very soon and the distribution of POS terminals is also restricted to the major cities of the countries and can’t cover the rural areas. This could be the major reason that the relation between POS terminals and return on Asset (ROA) is not statically significant. The finding of this study is consistent with the finding established by (Khodaei Valahzaghard – Bagherzadeh Bilandi, 2014).

Financial performance and Prepaid cards (PPC): The variable prepaid cards have a negative and statistically significant effect on the financial performance of the banking sector in the short run. The result of regression analysis implies that a 1 percent increase in the numbers of prepaid cards causes the financial performance to decrease by 0.003461 percent and is statically significant at a 5 percent significance level. The implication of the result implies that; the commercial banks with the high number of prepaid cards have the lowest financial performance in a given quarter. However, the long-run result implies that the number of prepaid cards has a long-term positive and significant impact on the financial performance of Ethiopia’s banking sector. The result implies that a 1 percent increase in the number of prepaid cards in each quarter causes a 0.011912 percent increase in financial performance, which is statistically significant at a 5 percent significance level. The positive relationship between the variables implies that, in the case of prepaid cards, customers should deposit money ahead of time before receiving any services or goods. As a result, banks can use pre-deposited customer funds to earn an additional return by investing elsewhere, increasing the bank’s profit.

Financial performance and branch expansion (BB): The number of bank branches has a positive and significant effect on the financial performance of commercial banks in the short run. The result of regression analysis implies that a 1 percent increase in a bank branch in each quarter causes the financial performance to increase by 0.147164 percent and statically significant at a 1 percent significance level. The result implies that the commercial banks with a larger number of branches have the highest financial performance compared to others. However, branch expansion and financial performance have a negative relationship in the long run. The regression output further implies that; 1 percent increase in branch expansion each quarter causes a decrease in financial performance by 0.085627 percent and is statically insignificant. The negative relationship between the two variables is an implication that the return earned by opening new branches is less likely to cover the costs incurred compared to other financial services.

ECM Regression
Case 3: Unrestricted Constant and No Trend

Source: E-views 10 software.

Long run estimation of the model
(Financial Performance and Financial Innovation)

Source: generated from E-views 10 software

Conclusion

A green Bank innovation considers all social and environmental/ecological concerns as part of its normal banking activities, with an extra goal of environmental resource protection, and also includes the ideas of sustainability, ethical investing, conservation, and energy efficiency to protect the environment and natural resources. Most developing and undeveloped countries’ banking systems, on the other hand, are more concerned with reaping the benefits of new technology to increase financial performance without taking into account environmental conservation. However, taking environmental concerns into account when implementing financial innovation is critical. This study was conducted to examine the effect of financial innovation on the financial performance of the Ethiopian banking sector and to make recommendations on financial innovation selection for greening the banking industry innovation.

According to the study’s findings, automated teller machines and branch expansion have a positive and significant effect on financial performance in the short run. However, the long-term impact is negative. Variable mobile banking and point-of-sale machines have a positive and significant impact on financial performance in both the short and long run. Variable internet banking, debit cards, and prepaid cards (credit cards), on the other hand, have a negative relationship with financial performance in the short run. However, the long-term relationship is positive.

As a result, the study concludes that; due to the widespread usage of debit and credit cards, mobile banking, and internet banking, ATMs may become outdated over time. The use of actual cash is gradually dwindling as transactions become more digital. In addition, the most common reason people use ATMs is to withdraw cash, which may now be done at any point of sale. Like an ATM, any point of sale (POS) vendor with sufficient funds can issue cash swiftly and conveniently. As a result, ATM usage may decrease in the near future.

Based on the study’s findings, we recommend that the banking sector focuses on financial innovations to improve financial performance by taking into account both the short-run and long-run benefits of the products. At the same time, we recommend that the sector prioritize environmentally friendly green financial innovations (mobile banking, internet banking, point of sale machines, and debit cards) over automated teller machines. As a result, both the industry and the environment will benefit in the long run.

The study was limited to examining the effect of green financial innovation on financial performance based on aggregate data. However, we were interested to examine the response of each bank’s financial performance to financial innovation based on disaggregated data but could not find the data because of poor data management and the unwillingness of some banks. As a result, the researchers advise other scholars to find a way to collect disaggregated data and examine how financial innovation responded to each bank’s financial performance.

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Goshu Desalegn, PhD student
Hungarian University of Agriculture and Life Sciences, Doctoral School of Economics and Regional Sciences, Hungary;

Dr. Anita Tangl, Associate professor
Hungarian University of Agriculture and Life Sciences, Institute of Rural Development and Sustainable Economy,

Dr. Maria Fekete-Farkas, Professor
Hungarian University of Agriculture and Life Sciences, Institute of Agricultural and Food Economics,