Global Journal of Management and Business Research, A: Administration and Management, Volume 22 Issue 7

Francesca, and Peter (2008), conducted a comparative analysis of the effect of electronic banking on performance in four European countries namely UK, Spain, Finland and Italy. The study adopted panel data method from 1995 to 2004 using 46 banks. The dependent variables were return on assets (ROA) and return on equity (ROE), Findings revealed that banks involved in only on line banking services and those involved in mixed intemet,banking services do not have any clear differences. However, the study showed that internet banking has a significant impact on European countries Enoruwa, Ezuem , and Nwani (2019) examined the relationship between electronic banking and bank performance in Nigeria adopting data sourced from the Central Bank of Nigeria (CBN) bulletin for the period 2009 to 2017. Regression Analysis was used to test the strength and nature of relationship between the dependent and independent variable. The performance of the Nigerian banking sector was proxied by Total Bank Deposit while transaction values of Automated Teller Machine (ATM Debit Cards), Mobile Banking, Point of Sales (POS) and Web Pay was used as proxy for electronic banking. The correlation results show that electronic channel products (ATM, POS, Web pay, Mobile Pay) are positively and significantly related to bank performance. The regression result also showed that all the predictors are highly correlated to each other. Abaenewe, Ogbulu, and Ndugbu (2013) this study investigated the profitability performance of Nigerian banks following the full adoption of electronic banking system. The study became necessary as a result of increased penetration of electronic banking which has redefined the banking operations in Nigeria and around the world. Judgmental sampling method was adopted by utilizingdata collected from four Nigerian banks. These four banks are the only banks in Nigeria that have consistently retained their brand names and remain quoted in the Nigerian Stock Exchange since 1997. The profitability performance of these banks as measured in terms of returns on equity (ROE) and returns on assets (ROA). With the data collected, we tested the pre- and post-adoption of ebanking performance difference between means using a standard statistical technique for independent sample at 5 percent level of significance for performance factors such as ROE and ROA. The study revealed that the adoption of electronic banking has positively and significantly improved the returns on equity (ROE) of Nigerian banks. On the other hand and on the contrary, it also revealed that e-banking has not significantly improved the returns on assets (ROA) of Nigerian banks.. The findings of this study have motivated new recommendations for bank customers, bank management and shareholders with regard to electronic banking adoption for banking operations. III. M ethodology The study adopted an ex-post facto design since it dealt with data that had already been compiled. Also, since the study is focused on the cause-effect relationship among variables and investigates variables that cannot be observed experimentally, such as those studies in this work. Descriptive Research design complimented the ex-post facto design given that the study is quantitative in nature. The data used for the study was secondary data. The datasets were sourced from the central bank of Nigeria statistical bulletin for various years, the World Bank database and from other relevant websites. In addition, the datasets were annualized time series. The model for this study was structured to empirically reveal the impact of electronic banking on economic growth in Nigeria. The variables used include Mobile Banking, Internet Banking, Automated Banking and economic growth proxied by Gross Domestic Product (GDP). The model follows the classical linear regression equation adapted from Books (2014) thus: Y = β 0 + β 1 x 1 +- β 2 x 2 + β 3 x 3 + βn + e eq.1 To capture the impact of electronic banking on economic growth in Nigeria, the essential variables are fitted into the classical linear regression model (CLRM) as shown thu GDP = f (Electronic Banking System) To capture the various sectors, GDP is unbundled into Mobile Banking, Internet Banking, Automated Banking and Economic Growth to reflect the GDP hypotheses. The model were presented as follows thus: Model: GDP= β o + β 1 MB + β 21 B + β 3 AB +e Where: GDP = Gross Domestic Product proxy for Economic Growth MB = Mobile Banking lB = Internet Banking AB = Automated Banking e = Error Term Constant, β 1 , β 2 , β 3 Coefficient of the independent variables. Apriori expectation = β 1+ β 3>0 and β 4< An Empirical Investigation into the Impact of Electronic Banking on the Economic Growth of Nigeria 35 Global Journal of Management and Business Research Volume XXII Issue VII Version I Year 2022 ( ) A © 2022 Global Journals