The Effect of E-Payments System on the Efficiency of Banks in Nigeria

The Effect of E-Payments System on the Efficiency of Banks in Nigeria 

Ikoh, Itoro Moses¹, Ocheni, Godwin Arome², Tamuno-Inam Nicholas Wokoma³ 

^1,2Department of Banking and Finance University of Uyo, Akwaibom State Nigeria

³Department of Accounting, University of Calabar, Cross River State Nigeria

DOI : https://dx.doi.org/10.47772/IJRISS.2024.808063

Received: 12 August 2024; Accepted: 18 August 2024; Published: 30 August 2024

ABSTRACT

This study investigated the effect of e-payments system on the efficiency of banks in Nigeria. The objective of the study was to examine the effect of the variables of e-payments of ATM , POS, WEB applications, Mobile applications and NIBSS instant payments (NIP and the profitability of banks in Nigeria from 2009 to 2021. Ex-post facto research design and quantitative methods were adopted in the study. The data on the variables were collected from the Central Bank of Nigeria (CBN) statistical bulletin of various years. The data was analyzed using multiple regression technique. The findings of the study showed that e-payments through Mobile application (MOB) has high and positive effect on the profitability of banks. The result also revealed that there is a shift toward mobile technology in the Nigerian Banks. This might be influenced by the convenience and accessibility of mobile platforms. The study observed that the growth in the traditional Channels of  ATM, POS, and WEB had a slow growth rate, this may be pointing out to cashless policy or that the e-payment variables are being replaced by newer technology (like MOB) and the cashless policy. While the growth in NIP suggests the introduction or increasing popularity of a new service or payment channel in recent years. It also suggests a significant shift towards mobile banking. The findings also revealed that the high rate might correlate with the increasing adoption of smartphones, improvements in mobile payment technologies, and the shift towards digital financial services. While e-payments through POS, WEB and NIP have negative effect on the profitability of banks in Nigeria. It was concluded that e-payments systems has mixed effect on the efficiency of banks in Nigeria. The study recommended that banks should invest more in infrastructures that support e-payments through ATM and mobile applications, and also optimize their roll out and support of the use of POS, WEB applications an NIBSS payments to improve their efficiency.

Keywords: E-payments, mobile payments, ATM, POS, Efficiency

INTRODUCTION

Traditional banking in Nigeria is a system where banking services are provided through brick-and-mortar branches, with little or no emphasis on technology.This system has been in practice for years and has been the standard way of banking in Nigeria. Traditional bank payments which hitherto was mainly characterized by manual and serviced systems has gradually given way since the advent of digitalized financial services pioneered by information and communication technology (ICT), the internet as well as the emergence of Web 2.0, Web 3.0, artificial intelligence and self-service technology (SST).  By this many customers of various banks and other depository institutions may not necessarily visit their banks, draw cheques or other payment or withdrawal slips in the conduct of their everyday financial transactions. In fact, even on the issue of loans, borrowing from banks and other credit institutions has been digitized to the extent that one can access a loan through a mobile device, unlike the other manual process of completing several forms, as well as the extended processing time, and the several delays associated with such process before the loan is granted. This simplicity, flexibility, and real time process is the hallmark of electronic payment system, otherwise called the e-payment system.

The electronic payment system has decimated the traditional payment system and the limitations associated with it, and as such it has revolutionized the banking industry in Nigeria, including building increased capacity of banks to access other channels of earning more income other than interest income. This has led to the delivery of electronic banking services to businesses, governments and individuals by banks in Nigeria over the last decade and half. Apart from these, the increasing depth of innovations including financial technology (fintech) , and the emergence of fast internet (4G and 5G), and cloud computing has  provided more possibilities for electronic payments for those with bank accounts and those without it.  With the increased traffic of financial transactions through the use of mobile telephones and web-based transactions, there is the tendency that more deposits are moved between banks, more loans and advances are processed easily and payments made faster. These transactions no matter how they are conducted start and end with the banks, an implication for payment and receipt of charges, and invariable a good source of revenue from the banks. This is apart from the increased deposits received through the various accounts of customers and clients that are actively involved in e-payment services offered by the banks. This implies that there are charges involved which form a good source of income for the banks.

Even without internet, bank customers are also able to conduct their payment transactions using Unstructured Supplementary Service Data (USSD). This is further deepened through the use of debit cards, cash cards and cardless withdrawals which seem to make life very easy for many bank customers. These have further deepened financial inclusion in Nigeria, and with this come enormous potential for banks and other financial institutions such as payment service banks (PSBs). These enormous potentials created by the e-payment systems have enhanced the capacities of banks to mobilize more deposits, make more loans through the mobilized deposits, and earn more in terms of revenue, hence more profits.

 Statement of the problem: The challenges in terms of costs associated with upgrading to digital systems, maintenance of the required e-payment infrastructure, as well as payments to skilled employees’ that man these infrastructures which are expected to run 24 hours every day. These and many more add to the costs of operations for these banks. However, within the context of the revenue-expenses trade-off for these banks, there are several other challenges. These includes telecommunications infrastructural deficits, power supply issues, high cost of mobile banking services, the issue of hidden charges, network failure and downtimes, and the various issues of unsuccessful or failed payments transactions associated with e-payments in Nigeria. These problems are known to be recurring issues even with the wide popularity of electronic payment platforms in the Nigerian banking sector. These problems add up to increase in the cost of operations for banks, and this may decimate their revenue or gains. Perhaps, his may fail to align with improved efficiency of banks in Nigeria, hence this is the focus of this study, which is an investigation of the effect of electronic payments systems on the efficiency of banks in Nigeria.

The specific objectives of this study were:

1. To examine the trend of e-payments system and efficiency of banks in Nigeria from 2009 to 2021.
2. To investigate the effect of the variables of e-payments and the profitability of banks in Nigeria from 2009 to 2021.

Research Hypothesis

H₀₁: There is no significant effect ofthe variables of e-payments on the profitability of banks in Nigeria from 2009 to 2021.

The study of this nature would be important to the policy makers, government and Bank because of the possible implication of the result for policy decision and to provide a guide for the government and Bankers. It would also be useful to the academia, the study would add to the existing body of knowledge. These findings could be valuable for strategic decisions, especially in optimizing transaction channels to enhance profitability

LITERATURE REVIEW

Electronic Payments (E-payments) in Nigeria

Electronic payments refer to digital or non-traditional bank payments.Electronic payment system otherwise

called e-payment is a payment system consisting of electronic mechanisms, which make the exchange of payments possible. Itcan simply be defined as payment or monetary transactions made over the internet or a network of computers Kulkarni (2004). In other words, it involves the provision of payment services and transfers through devices such as telephones, computers, internet, Automated Teller Machine (ATM) and smartcards.This type of payment but is very similar to traditional payments in terms of services rendered has made it easy and convenient for bank customers and non-customers have access to services like online real-time transfer, bank deposits, loan applications and processing, payment of bills and any other numerous banking services like checking of deposit balances, request for bank statement and so on. Accordingly, electronic payments is  includes all digital payment processes that enables customers toaccess and use computer to access account specific information regarding the status of their deposits, and also conduct  payment transactions from any location Sadr, (2013).It has been noted since 2005 that electronic banking transactions was the fastest growing commercial activity on the internet and this has improved payment transactions for global competitiveness in the 21st century Udeze, Okafor, Nwafor and Abarikwu, (2013).

Importantly, this has enhance electronic payments and eased  the transfer of funds between bank accounts, as well as from individuals to personal bank accounts, thus lowering lowered cost of banks operations, improved profits maximization Saleh and Alipour, (2010).Accordingly electronic payments are made through mobile phones, the internet, and electronic cards, hence it has been classified as internet e-payments, digital or electronic card e-payments, and mobile e-payments Bankole, Bankole and Brown, (2011).  Mobile phone e-payment includes the transfer of funds through phone communication units that are linked to an automated system of the bank.  This also involves the use of cell or mobile phones in order to settle some payment transactions though bank applications or USSDs. Internet e-payment system allows customers to make use of the bank’s secured website in order to make transfers, pay bills, and view their bank statement without having to visit the banking hall, while electronic card e-payment involves the use of a physical plastic card that identifies the holder of the card for financial transactions through the point-of-sale (POS) and Automated Teller Machine (ATM) which are used to authorize payments to the sellers. These cards include credit and debit cards issued to customers.

Importance of E-payment System

The following are the importance of electronic payments in a banking system such as Nigeria’s.

1. It deepens the cashless policy of the government. This limits the volume of cash in circulation, helping to curb inflation in the process.
2. It increases liquidity: Banks are required by regulators to hold a certain level of liquidity assets and the reason behind this regulation is to make sure that the commercial banks always possess enough liquidity in order to be able to accumulate enough cash and have in possession other liquid assets as well as having the ability to raise funds quickly from other sources to be able to meet its payment obligation and other financial commitments on time (Rasiah, 2010), E-payments system enhances this.
3. It increases deposits: Deposits consist of money placed in the banking institutions for safekeeping by the public. Banks are said to depend heavily on loans being offered to customers. There is a general notion that deposits are the cheapest sources of funds for banks and so to this extent deposits have positive impact on banks profitability if the demand for bank loans is very high. That is, the more deposits bank is able to mobilize, the greater is its capacity to offer more loans and make more profit.
4. Increase Income: Banks make more non-interest income through e-payment platforms and services provided. This is through charges including fees, commission, and other charges earned through interconnectivity with other banks.
5. Efficiency of Banks: Efficiency of banks refers to the capacity to optimize resources in earning more or generating more income from operations. Hence, an efficient bank can be said to be one that minimizes costs, and maximizes revenue to achieve better profitability. An efficient bank will ensure that it utilizes the available resources towards optimizing profitability. In this case, there is costs are lowered; revenue or income is increased through the various operations. Various measures of efficiency of banks have been identified. This include profitability, net interest margin (NIM), efficiency ratio, and leverage ratios Berger and Humphrey, (2012).

Also, the efficiency of banks can be estimated through asset approach, user cost approach and value-added approach Cava, Junior and Branco, (2016). The value-added approach differs from the asset and user cost approaches in that it considers all liability and asset categories to have some output characteristics rather than distinguishing inputs from outputs in a mutually exclusive way.  The user cost approach determines efficiency from whether an asset or liability category contributes to the financial output of a bank. The operating costs involved in producing nonfinancial services associated with the asset or liability are not explicitly considered. Under the assets approach, loans and other assets are considered to be bank outputs; deposits and other liabilities are inputs to the intermediation process, and these are used in determining the efficiency of the bank. However, in this study, the aggregate profit of the banks was used as the measure of efficiency of banks in Nigeria.

Theoretical Framework: The underlying theoretical literature for this study includes the transaction cost innovative theory, bank-led theory and non-bank led theory.

Transactions Cost Innovative Theory: The transaction cost innovation theory pioneered by Niehans (2006) stated that the dominant factor of financial innovation is the reduction of transaction cost, and in fact, financial innovation is the response of the advance in technology which caused the transaction cost to reduce. The reduction of transaction cost can stimulate financial innovation and improvement of financial payment services through e-payments. This theory posits that financial innovation reduces transaction costs. Transaction costs innovation theory is relevant in the context of this study. The use of e-payments system can substantially reduce a banks transaction costs hence increased level of performances via efficiency in earnings or revenue. Consequently, reduction of operating costs through e-payment platforms may influence banks’ efficiency positively.

Bank-Led Theory: The bank-led theory of branch-less banking was postulated by Lyman, Ivatury and Staschen (2006) and emphasized the role of an agent who acts as a link between the banks and the customers. In this case the retail agents or payments service banks (PSBs) have direct interaction with the banks customers and they perform the role expected of the bank by either paying cash or collecting deposits through e-payments. Finally, this agent  or PSBs transmits all their takings from individuals or banks customers to the bank they represent representing through electronic means (e-payment). This is a popular means adopted by most banks in Nigeria especially in the rural areas.

Non-Bank-Led Theory: This theory was postulated by Hogan (1991). According to the theory, customers do not deal with any bank and they do not maintain any bank account, but are regularly engaged in payments and other financial transactions. All that the customers have to deal with is a non-bank firm such as mobile network operator or prepaid card issuer who they exchange their cash with for e-money account. The e-money account is then stored in the server of this non-bank agent.  This is being deepened by the introduction of the e-Naira by the Central Bank of Nigeria (CBN). However, this tends to represent the most risky platform in the electronic payment methods because of lack of existing regulatory framework upon which these e-agents operate, despite the fact that most of these e-agents operate correspondingly with banks.

Empirical Review

Chiejina (2021) examined the effect of the e-payment system on the efficiency of banks in Nigeria. The specific objective of the study was to examine the impact of e- payments systems on economic growth in Nigeria to determine the implication of mobile payment on the efficiency of Nigerian banks, to identify the significance of Automated Teller Machine on the efficiency of Nigeria banks, and to determine the effect of POS on the efficiency Nigerian banks, collating data of e-payment statistics from Central Bank of Nigeria from the year 2012 to 2016. A linear regression analysis was adopted for this study using SPSS to carry out the analysis. The result of the analysis revealed that connotes that there is no significant effect of the e-payment system on the efficiency of banking in Nigeria. The researcher recommended that the Central Bank of Nigeria should embark on an intensive campaign for complete adoption of e- payments products especially at the grassroots.

Abdulmumin (2020)investigated the role of e-payment systems on economic growth in Nigeria throughout 2010- 2018. Specifically, the study analysed the role of e-payment systems on economic growth using the value of e-payment transactions and the volume of e-payment transactions. The study used quarterly time series data for the value of POS, ATM, mobile, Internet transactions, and real GDP for model 1 and volume of POS, ATM, mobile, internet transactions, and real GDP for model 2. The multiple regression analysis, Johansen Cointegration test, Granger causality test, and Vector error correction model (VECM) were employed for analysis in the study. The results of the multiple regression analysis for models 1 and 2 show that ATM and internet transactions are positive and insignificantly related to economic growth while there is a negative and insignificant relationship between POS transactions and real GDP in Nigeria. The result also shows that the volume of mobile transactions is positive and significantly related to economic growth while the value of mobile transactions is positive but insignificantly related to economic growth in Nigeria.

Okifo andIgbunu (2015) examine e-payment system in Nigeria: economic benefits and challenges. They authors cited that the arrival of the internet has taken electronic payments and transactions to an exponential growth level. By this, consumers could purchase goods and services from the internet and send unencrypted credit card numbers across the network, which did not provide much security and privacy. Accordingly, the researchers stated that the benefits of e-payment are unquantifiable in that it would galvanize Nigeria into a cashless society and eliminate fear of the unknown.

Ogunlowore and Oladele (2014) investigated the impact of electronic banking on the satisfaction of customers in GTB bank, Lagos. A total respondent of100 respondents were sampled using a carefully structured questionnaire. Data obtained were analyzed using descriptive measures such as simple tables and percentages. The formulated hypotheses were validated using the chi-square statistical measure. The empirical result fromthe chi-square analysis revealed that electronic banking has significant relationship with customer satisfaction in GTB bank in particular and the general banking customers in general.The result also revealed that the introduction of electronic banking has enhanced bank profitability level. Finally, the results showed the application of electronic banking has increasedthe market share of banks in Nigeria.

Ogini, Mohammed, El-Maude and Gambo (2013), did a study on e-banking and bank performance:evidence from Nigeria.The researchers examined the impact of electronic banking on banks performance in Nigeria.Panel data comprised audited financial statements  of eight banks that have been adopted and retained their brand name banking between 2000 and 2010 as well macroeconomic control variables were employed to investigate the impact of e-banking on return on assets (ROA),return on equity (ROE) and net interest margin (NIM). Result from pooled OLS estimations indicate that e-banking begins to contribute positively to bank performance in terms of ROA and NIM with a time lag of two years while a negative impact was observed in the first year of adoption. It was recommended that investment decision on electronic banking should be rational so as to justify cost and revenue implementation on bank performance.

Mahotra and Singh (2007) examined the impact of internet banking on banks performance and risk in India. The study examined a comprehensive set of 10 measures of financial performance that made it possible for the authors to critically look into banks performance. The results of the study revealed that on average, internet banks are more profitable than non-internet banks and are operating with lower cost as compared to non-internet banks, thus, representing the efficiency of the internet banks. The reasons of lower profitability of these banks were pointed out to be higher cost of operations, including fixed cost and labour cost.

METHODOLOGY

The research design used in this study was the ex-post facto research design. This design supports the objectives of the study which is establishing the effect of e-payment on the efficiency of banks using quantitative or secondary data on the variables of e-payments and efficiency of banks. The variables of e-payments include the value of payments through ATM, value of payments through POS, value of payments through Web, value of payments through Mobile, and value of payments through NIBSS instant payments (NIP) while the variable for efficiency is profit of the banks. The data for this study were mainly secondary data. The sources of the data include Central Bank of Nigeria (CBN) Statistical Bulletin and Central Bank of Nigeria (CBN) Annual Reports and accounts for the various years. Thedatawere collected from the Central Bank of Nigeria (CBN) Statistical Bulletin and Annual Reports and accounts of various years through intensive library search and archival retrieval methods. Some of the materials used were photocopied and also downloaded from internet-based sources.

Model Specification: Multiple regression analysis was used, hence the need for the specification of the hypothesis stated in the study. These models specified the nature of the relationship between the variables of e-payments and variable of efficiency.

The model for the hypothesis is presented as follows:

PROFIT = f (ATM, POS, WEB, MOB, NIP )                                                                       1

PROFIT = α₀ +β₁ATM+ β₂POS + β₃WEB+ β₄MOB+β₄NIP +ϵ₁                                                         2

Where: PROFIT is the profit of banks in Nigeria

ATM is the value of e-payments through ATM

POS is the value of e-payments through POS

WEB is the value of e-payments through Web platforms

MOB is the value of e-payments through mobile applications or USSD

NIP is the value of e-payments through NIBSS instant payments

α₀is Regression constant

β_1, β₂, β_3, β_4, β₅ are the regression coefficients

ϵ₁ is the error term

Data Analysis Techniques: Multiple regression technique was adopted in the analysis of the data. This was through, the Ordinary Least Square (OLS) method. Specifically, the t-stat and F-stat were used for the determination of the statistical significance of the independent variables and testing of the overall hypotheses of the study at 5% level of significance and at degrees of freedom given as n-k-1, where n is the number of years covered in the study and k is the number of independent variables.

RESULTS AND DISCUSSION

Table 1: Descriptive Statistics

Source: Researcher’s Computation (2024)

The results in Table 1 shows that descriptive statistics and the estimation of the six variables under study. These were: PROFIT, ATM, POS, WEB, MOB, and NIP. All the variables were measured in billions of Naira (N’Billions). Using the central tendency, the Mean result indicated that PROFIT was ₦4395.249 billion, ATM was ₦1873.346 billion, POS was ₦236.510 billion, WEB was ₦1942.479 billion, MOB was ₦64283.72 billion while NIP was ₦1898.765 billion. In this context, the findings suggest that the averages of the profitability of the banks appear to be swaging with the increase in the averages of the various e- payments channels of the banks. The average values of the variables show that MOB transactions have the highest mean. This shows that MOB is the most efficient E-Payment variable, followed by NIP, and ATM among the six variable under study indicating they are the most substantial contributor among the variables to bank efficiency in Nigeria. But POS was observed to have the lowest average value. the result is in line with the earlier study conducted by Okifo and Igbunu (2015) who examine e-payment system in Nigeria, and concluded that the economic benefits and arrival of the internet has taken electronic payments and transactions to an exponential growth level positively.

The Median as a measure of central tendencyindicated that, E-payment variables that is less affected, the result showed PROFIT of ₦3970.250 billion, ATM of ₦448.510 billion, POS of  ₦91.580 billion, WEB of  ₦442.350 billion, MOB of ₦47137.37 billion and NIP of ₦2216.790 billion. The result indicated that MOB and NIP have notably high median values among the six variables under study. POS also showed the lowest median, consistent with its low mean value.  The table also indicated that the Maximum and Minimum range of values spread and just as in the mean and median. The results showed that MOB has the highest maximum value and the highest range, suggesting significant variability and potential. Also POS has the smallest range, indicating less variability. When Standard Deviation was used in the measurement of the spread of the data, the result showed PROFIT of ₦2981.551 billion, ATM of ₦2857.924 billion, POS of ₦249.5629 billion, WEB of ₦2898.330 billion, MOB of ₦55853.02 billion and NIP of ₦1092.262 billion.  Again MOB has the highest standard deviation, reflecting high variability in mobile transactions. POS has the lowest standard deviation, indicating relatively consistent values. The result also showed a positive skewness values for most variables indicate a longer tail on the right side of the distribution, suggesting that there are a few high values in the data set. NIP is slightly left-skewed, indicating a few lower values. Therefore the use of the descriptive statistics analysis suggested that PROFIT and MOB have the highest means and standard deviations, indicating high variability and significant influence on the dataset. ATM and WEB also show high variability, with ATM being notably right-skewed and leptokurtic. POS and NIP exhibit lower variability and less deviation from normality, although most variables are right-skewed, with ATM showing a significant deviation from normality.

Figure 1 below is a graph showing the trend of the various E-payment system variables transaction under study from 2009 to 2021. The y-axis represents the transaction volume, which is likely in Millions, while the x-axis shows the years. Each line on the graph corresponds to a different transaction type, with a color legend provided at the bottom. This includes: PROFIT (Blue), ATM (Red), POS (Green), WEB (Black), MOB (Cyan) and NIP (Pink). The Graph showed that between (2009-2021), MOB (Cyan) showed a significant and rapid increase, especially from around 2015 onwards. By 2021, it has grown exponentially, far outpacing the other variables. This suggests that mobile (MOB) payments or services have seen a massive surge in usage or transactions, likely due to increased smartphone penetration and mobile banking services. It also climaxes the shift toward mobile technology in the financial sector, probable influenced by the convenience and accessibility of mobile platform. NIP (Pink), starts to grow more noticeably around 2018 but remains relatively low compared to MOB. It shows some upward trend, indicating that it’s gaining traction but not at the same level as MOB. But other PROFIT (Blue), ATM (Red), POS (Green), WEB (Black) remain relatively flat with minimal growth over the period. This suggest the gradual prevalence of digital transformation in finance, a state of the nation where mobile platforms have become far-reaching development. (Technological Adoption).

Figure 1: Trend Analysis of Variables

The rapid growth in Mobile (MOB)informs the swing in the direction of mobile technology in the financial sector. This is likely influenced by the convenience and accessibility of mobile platforms. This may cause a Stagnation or Slow Growth in Traditional Channels for ATM, POS, and WEB may point out that these are either being replaced by newer technology (like MOB) or that their growth potential is limited. While the growth in NIP suggests the introduction or increasing popularity of a new service or payment channel in recent years. It also suggests a significant shift towards mobile banking or mobile payment solutions during this period. The exponential growth might correlate with the increasing adoption of smartphones, improvements in mobile payment technologies, and the shift towards digital financial services.The NIP transactions showed a modest increase over time but remain far below the MOB transactions. This could indicate a steady yet less dramatic adoption of this payment method compared to mobile transactions.ATM (Automated Teller Machine) transactions, seem to have minor fluctuations but no significant growth trend, possibly indicating a plateau in the use of physical cash withdrawals. POS (Point of Sale) and WEB (Web-based transactions) are also relatively stable, suggesting that while these methods are used, they are not experiencing the same growth as mobile transactionsMOBe-payments also appeared to have the most gains in terms of growth for all the e-payments that were considered. In fact the trend of MOBshows a surge that outpaced the changes in the profit level of banks in Nigeria.  This is an indication that all the e-payments carried out in the country from 2009 and 2021, the POS. ATM, NIP, WEB and MOB were shown to flow with the profit level of the banks. This may imply that as the value of the transactions through ATM, POS, WEB, MOB, and NIP increases, the profitability of the banks also increases over the period covered in this study. The result mayindicate that the growth in the values recorded through the e-payment channels, the more the profits of the banks grow as shown in Figure 1.

Data Adequacy and DataAnalysis

In knowing the stationary of the data series under study, the Augmented Dickey Fuller (ADF) Test was conducted to test if the data contains a unit root and to ensure that all the data for each of the variables did not exhibit non-stationary properties which render estimation results from such data spurious and unreliable. The ADF unit root analysis result is presented in Appendix for all the variables and summary is presented in Table 2

Table 2: Summary of ADF Unit Root Analysis Results

Source: Researcher’s Computation (2024)

The result from table 2 above shows that the data series under study is non-stationary because the data series does not has a constant mean, variance, and autocorrelation over time, making it appropriate for modeling when compared to the critical value at a 5% level of significance to determine whether to reject the null hypothesis of a unit root, the series is non-stationary. At the 5% Critical Value.

The Table 2 also showed that PROFIT had ADF t-statistic of -4.307768, Critical Value of 3.259808, Probability of 0.0116  and Order of Integration: I(1). Showing that the ADF t-statistic for PROFIT is less than the critical value at the 5% level, and the probability is 0.0116, which is less than 0.05. This suggests that the PROFIT series is non-stationary in levels but becomes stationary after first differencing, making it I(1). The Variable ATM series is non-stationary in both levels and first differences, as indicated by its ADF t-statistic and probability. However, after second differencing, it becomes stationary, making it I(2). The POS series is non-stationary in levels and after first differencing but becomes stationary after second differencing, making it I(2).The WEB series is non-stationary in levels and first differences, becoming stationary only after second differencing, making it I(2).The MOB series is close to the critical value, with a p-value just above 0.05. This suggests borderline stationarity after second differencing, making it I(2). While the NIP series is non-stationary in levels but becomes stationary after first differencing, making it I(1).

The results suggest that most of the data series (ATM, POS, WEB, MOB) are integrated of order two, I(2), that requires second differencing to achieve stationarity. It therefore be said that this variables exhibit strong trends or volatility over time, and simply differencing them once is insufficient to stabilize them. PROFIT and NIP are integrated of order one, I(1), indicating they only require first differencing to become stationary.The varying orders of integration among the variables indicate different underlying dynamics. Variables like ATM, POS, and WEB may have more persistent effects or shocks that take longer to dissipate, requiring more differencing to achieve stationarity. In contrast, PROFIT and NIP might have less persistent shocks, requiring only first differencing. Therefore the ADF unit root test results showed that, the need for careful consideration of the stationarity properties of each variable in time series analysis, particularly in terms of how many differencing steps are needed to achieve stationarity. At second difference the variables ATM, POS, WEB, and MOB were to have no unit root. This implies that at the second difference, all the variables were found to have no unit root,were adjudgedstationary, hence they were free to be used in statistical or econometric estimation using linear regression technique.

Relationship Analysis and Hypothesis Testing

The Multiple Regression analysiswas used test the relationship between dependent variable (PROFIT)  and the independent variables ATM, POS, WEB, MOB, and NIP) the results are provided in Appendix while the summary of the result is presented in the Table 3below

Table 3: Summary of Regression Result for Hypothesis

Source: Researcher’s Computation (2024)

From Table 3 above:

1. PROFIT = 2803.0+ 0.311 ATM -7.1091 POS- 1.718 WEB-0.142 MOB-0.409 NIP with a high R² value of 0.9528
2. The F-statistic of 109.653, with a corresponding p-value of 0.00023, indicates that the overall regression model is statistically significant. The result shows that collectively, the independent variables explains the variation in the PROFIT
3. The Durbin-Watson statistic of 2.55 is close to 2, which suggests that there is no significant autocorrelation in the residuals. This adds to the reliability of the regression results

The regression model, stated above seeks to explain how PROFIT (the dependent variable) is influenced by ATM transactions, POS transactions, WEB transactions, MOB transactions, and NIP transactions. The high R² value of 0.9528, indicating that 95.28% of the variation in PROFIT is explained by the independent variables. This suggests that the model fits the data well and that the chosen variables are strong predictors of PROFIT.The model is overall statistically significant, explaining a large portion of the variability in profits, but further research might be needed to explore the specific factors that make certain transaction types less profitable.

The ATM (Automated Teller Machine Transactions)  has a positive coefficient of(0.311), indicating that an increase in ATM transactions is associated with an increase in PROFIT. However, the p-value (0.1111) suggests that this relationship is not statistically significant at the 5% level. While ATM transactions may contribute positively to profit, the evidence is not strong enough to confirm this conclusively.

POS (Point of Sale Transactions) The coefficient for POS is negative (-7.1091), indicating a negative relationship between POS transactions and PROFIT. This means that increase in POS will result decrease in profit. Although the t-statistic is relatively strong (-2.4924), the p-value (0.0673) is slightly above the 5% threshold, meaning the relationship is not statistically significant. This suggests that while there is some evidence that POS transactions might negatively impact on PROFIT, this finding is not conclusive.

The coefficient for WEB transaction is negative (-1.718), with a statistically significant p-value of 0.0067, indicating existence of strong and significant negative relationship between WEB transactions and PROFIT. The negative impact of WEB transactions on PROFIT could be due to factors like higher costs associated with online transactions or lower margins compared to other channels. In the same line, the coefficient for

The coefficient for MOB is slightly negative (-0.142), but more importantly, the p-value is very significant at 0.0048. This suggests a significant negative relationship between MOB transactions and PROFIT. Despite the relatively small coefficient, the significant negative effect indicates that increases in mobile transactions might possibly reduce profits, due to comparable reasons as WEB transactions.

But the coefficient for NIP is negative (-0.409), although the relationship is not statistically significant (p-value of 0.3486). it also showed that NIP transactions do not have a strong impact on PROFIT.

Therefore the study suggest that, Bank profitability (PROFIT) will remain positive at an average of 2803.00 units if value of e-payments through ATM, POS, WEB, MOB and NIP are held constant. A unit change in the value of e-payments through ATM will lead to a 0.0311 units change in PROFIT of banks. This positive effect of ATM on Profit is statistically insignificant with the computed t-statistic probability value of 0.1111. Also, a unit change in e-payments through POS Will decrease the PROFIT of banks by 7.109 units, and a unit change in the value of e-payments through WEB will decrease the PROFIT of banks by 1.718 units. This inverse effect of WEB e-payments is statistically significant with a probability value of 0.0067. Furthermore, a unit change in the e-payments value through Mobile applications (MOB) will lead to a positive increase of 0.142 units in the PROFIT of banks. This positive effect of MOB on the PROFIT of banks is statistically significant with a probability value of 0.0048. Finally, a unit change in the e-payments through NIBSS instant payments (NIP) will lead to a decrease of 0.409 units in PROFIT of banks in Nigeria. This inverse effect of NIP on PROFIT is statistically insignificant with probability value of 0.3486.

The coefficient of determination (R²) value of 0.9528 indicates that 95.28% of the variations in PROFIT of banks have been explained by the independent variables (ATM, POS, WEB, MOB and NIP). This implies a high predictive power of the independence variables on the changes in profitability or efficiency of banks in Nigeria. The remaining 0.742%  would be due to other variables not included in the study. This is given by the error term. The Durbin-Watson statistic value of 2.55 indicates the absence of autocorrelation, as indicated using the rule of thumb which provides that a Durbin-Watson statistic value that is close to 2, indicates the absence of serial correlation.

Furthermore, , the critical value of F-statistic was obtained as 5.192 (F_4,5) from the F-table. Thus as the computed F-statistic value of 109.653 is greater than the 5.192 and the probability of F-statistic value of 0.00023 is less than 0.05, the null hypothesis will fail to hold and is hereby rejected and the alternative accepted. The alternative which states that there is a significant effect   of the variables of e-payments on the profitability of banks in Nigeria is accepted. This indicates that e-payments have a significant effect on the efficiency of banks in Nigeria.

Findings of the Study

The research work was undertaken to assess the effect of e-payment efficiency of banks in Nigeria. The findings indicated that value of e-payments through ATM and Mobile applications showed positive effect on the profitability of banks in Nigeria. This implies that ATM and mobile payments affects the efficiency of banks positively, hence as the e-payments through the ATM and mobile application surges, there is the likelihood that banks would be more efficient in terms of profitability.  This is in line with the propositions of the Transactions Cost Innovative theory and the findings in Abdulminin (2020).  However, e-payments through POS, WEB and NIP showed an inverse effect on the profitability of banks in Nigeria. There is the likelihood that POS attract more patronage than the bank’s ATM and this has service charges accruable to the banks and their customers. This finding agrees with Chiejina (2021), who reported an insignificantly negative effect of e-payments on the efficiency of banks. In another findings, Puschel et al (2010) had argued that e-payment conducted through FinTech would affect the fortune of banks as increased cost associated with these e-payment channels may not easily be optimized in the banking industry.

Summary of findings

1. The ATM transactions have a positive coefficient, but not statistical significance. This therefore calls for further investigation to confirm their impact on PROFIT.
2. The significant negative coefficients for WEB and MOB transactions might suggest that these e-payment channels may be popular but could be less profitable, possibly due to high cost of operation associated with it, even the additional cost of network or may be a lower revenue realize per transaction.
3. The mixed results for POS and NIP suggest that their impact on profitability is complex and might depend on other factors not captured in this model

CONCLUSION AND RECOMMENDATIONS

The demand for new and innovative payment platforms for accessing financial services and for payments has been boosted with increase in the number of ATMS, POSs, and mobile applications. These are known to allow payments and transfers to and from different accounts, to other recipient accounts in various banks. The empirical results of the study indicate the existence of positive relationship between e-payments through ATMs, and mobile applications, and e-payments through POS, WEB and NIBSS instant payments. However, which ATM positively predicted banks profitability, POS, WEB, MOB and NIP did not, rather a  strong negative  significant  impact on PROFIT. Based on the mixed results, the following recommendations are made:

1. There is need for banks to sustain their investments in infrastructures that sustain Automated Teller Machines (ATM) and mobile applications as e-payments platform, in order to enhance their profitability.
2. Banks should maximize the e-payments platform of POS, Web applications and Nigeria Interbank Settlement System (NIBSS) instant payments. This will help improve their efficiency over time.

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APPENDIX

Multiple Regresion Results

Dependent Variable: LOG (PROFIT)

Method: Least Squares

Date: 02/06/24  Time: 05:27

Sample (adjusted): 2012 2021

Included observations: 10 after adjustments

Dependent Variable: PROFITS

Method: Least Squares

Date: 02/06/24   Time: 05:24

Sample (adjusted): 2012 2021

Included observations: 10 after adjustments

Unit Root Analysis

Null Hypothesis: D(PROFIT) has a unit root

Exogenous: Constant

Lag Length: 2 (Automatic – based on SIC, maxlag=2)

Warning: Probabilities and critical values are calculated for 20 observations and may not be accurate for a sample size of 11.

Null Hypothesis: D(WEB,2) has a unit root

Exogenous: Constant

Lag Length: 2 (Automatic – based on SIC, maxlag=2)

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(WEB,3)

Method: Least Squares

Date: 02/06/24  Time: 05:33

Sample (adjusted): 2014 2021

Included observations: 8 after adjustments

Null Hypothesis: D(MOB,2) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic – based on SIC, maxlag=1)

*MacKinnon (1996) one-sided p-values.

Warning: Probabilities and critical values calculated for 20 observations and may not be accurate for a sample size of 7

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(MOB,3)

Method: Least Squares

Date: 02/06/24   Time: 05:35

Sample (adjusted): 2015 2021

Included observations: 7 after adjustments

Null Hypothesis: D(NIP) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic – based on SIC, maxlag=2)

*MacKinnon (1996) one-sided p-values.

Warning: Probabilities and critical values calculated for 20 observations and may not be accurate for a sample size of 11

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(NIP,2)

Method: Least Squares

Date: 02/06/24   Time: 05:37

Sample (adjusted): 2011 2021

Included observations: 11 after adjustments