CBN Cashless Policy and Financial Performance of Deposit Money Banks in Nigeria: Evidence from Point of Sale Transactions

to the Point of Sale channel. The specific objectives are to: 1. Determine the relationship between Point of Sale and Return on Assets of selected deposit money banks in Nigeria. 2. Examine the relationship between Point of Sale and Earnings per Share of selected deposit money banks in Nigeria. Research Questions The following research questions guided the study: 1. What is the relationship between Point of Sale and Return on Assets of selected deposit money banks in Nigeria? 2. What is the relationship between Point of Sale and Earnings per Share of selected deposit money banks in Nigeria? Hypotheses The following null hypotheses guided the study: H01: There is no significant relationship between Point of Sale and Return on Assets of selected deposit money banks in Nigeria. H02: There is no significant relationship between Point of Sale and Earnings per Share of selected deposit money banks in Nigeria. Concept of Cashless Policy Money has historically served as a unit of account, a store of value, a medium of exchange, and a means of deferred payment, evolving over time to reduce the transaction costs associated with exchange (Baddeley, 2004). The transition from barter to coinage, and subsequently to electronic money, reflects a continued search for fungibility, divisibility, and convenience. Adurayemi (2016) describes the CBN's cashless policy as a framework designed to provide mobile payment services, break down traditional barriers to financial inclusion, and extend low-cost, convenient financial services to urban, semi-urban, and rural areas alike. In a similar vein, Okoye and Ezejiofor (2013) define a cashless economy as one in which goods and services are bought and paid for predominantly through electronic media, while Ajayi (2014) stresses that a cashless economy does not imply the total absence of cash, but rather an economic setting in which electronic media is the dominant method of payment. Adewale (2019) frames the cashless society as a gradual, systemic movement of an economy's payment architecture away from physical cash toward non-physical modes of payment across personal, corporate, and governmental transactions. Taiwo, Ayo, Afieroho and Agwu (2016), however, caution that the transition to a cashless economy has, in some assessments, lacked sufficiently strong empirical justification, and that the pace of transition raises legitimate concerns. Overall, the literature converges on the view that a cashless policy minimises, rather than eliminates, the use of JAFM JAFM physical cash by widening the range of electronic payment alternatives available to individuals and businesses (Woodford, 2003). Point of Sale Terminals A Point of Sale terminal is an electronic device, functionally equivalent to an electronic cash register, deployed at merchant locations to enable card-based payment for goods and services in place of cash (Woleola, 2017). Because POS terminals operate online in real time, a customer's account is debited immediately upon completion of a transaction. Oyetade and Ofoelue (2018) note that POS terminals also support related services such as airtime recharge and bill payment. Gerlach (2000) describes the POS system as a retail payment mechanism that substitutes electronic funds transfer for cash or cheques, with transaction data, including the amount, date, and account number, captured electronically and routed to the relevant financial institution for settlement. Concept of Financial Performance Financial performance is a measure of how well a firm employs its assets from its principal line of business to generate revenue, and serves as a general indicator of a firm's overall financial health over a given period. Strong financial performance enhances stakeholder confidence, supports shareholder wealth maximisation, and strengthens a bank's competitive position (Ben- Kinanee, 2021; Akhalumeh & Ohiokha, 2012). This study measures financial performance using two complementary indicators, Return on Assets and Earnings per Share, described below. Return on Assets Return on Assets is an indicator of how profitable a firm is relative to its total assets, calculated by dividing net income (or profit before tax) by total assets and expressed as a percentage (Emekekwue, 2008). Prastowo (2002) describes ROA as a measure of how effectively a company exploits its assets to generate profit, while Brigham (2001) and Riyanto (2001) similarly regard a higher ROA as indicative of superior use of the asset base to generate returns for shareholders. Earnings per Share Earnings per share measures shareholder profitability by indicating how much profit is attributable to each outstanding share of common stock. According to Hock and Nyean (2010), EPS serves two functions: as a direct measure of profitability attributable to ordinary shareholders, and as the denominator in the price-earnings ratio, a key valuation metric used by the investment community. A higher EPS is generally interpreted as a signal of stronger profitability and a potentially more attractive investment. Theoretical Framework This study is anchored on the Diffusion of Innovation Theory (Rogers, 2003) and, complementarily, the Technology Acceptance Model (Davis, 1986). The Diffusion of Innovation Theory explains how, why, and at what rate new technologies, such as POS terminals, spread through a social system. It identifies relative advantage, compatibility, complexity, trialability, and observability as key factors shaping the rate of adoption, and is consistent with the slow uptake of the POS channel observed among Nigerian bank customers. The Technology Acceptance Model, on the other hand, holds that perceived usefulness and perceived ease of use JAFM JAFM jointly determine an individual's intention to adopt a given technology, and offers a useful lens for understanding why, despite its availability, POS has not yet translated into significant measurable gains in bank profitability: adoption, and therefore the scale of usage needed to move profitability indicators, may still be below the threshold required for a statistically detectable effect. Empirical Review A substantial body of empirical work has examined the relationship between the POS channel and bank performance, both in Nigeria and elsewhere, generally alongside other cashless-policy channels and often with mixed results. Several Nigerian studies have examined POS alongside ATM and other channels as drivers of bank performance. Muotolu and Nwadialor (2019) found that ATM had a positive and significant effect on return on assets, while POS, web transactions, and NEFT showed positive but statistically insignificant effects. This pattern recurs in Ighoroje and Okoroyibo (2020) and James and Eloho (2020), both of which employed unit-root tests (Augmented Dickey-Fuller and Phillips-Perron) alongside the Autoregressive Distributed Lag technique: both studies found that POS was positive but insignificant in its relationship with return on equity, even as internet banking and ATM showed significant positive effects. Okafor (2020), using descriptive statistics and regression analysis, reported that POS, alongside internet banking, mobile banking, and ATM, had a significant positive effect on return on assets, while Priye and Percy (2019), using ordinary least squares, found that cashless policy variables (ATM, POS, and mobile banking) collectively had a significant impact on deposit money bank performance. Adekoya (2011) likewise found POS positively related to return on equity, alongside ATM, though web- based transactions were negatively related. Other studies present a less favourable picture of POS specifically. Ugbede, Yahaya and Edicha (2019) found that POS had a positive and significant effect on bank profitability, in contrast to ATM, which was insignificant. Ugwueze and Nwezeaku (2016), applying an Engle-Granger co-integration approach, found that POS was not co-integrated with savings or time deposits, though it was co-integrated with demand deposits, suggesting a deposit-type-specific relationship. Christian and Igwebuike (2016) went further, reporting that POS, alongside mobile banking and NIBSS transactions, had no significant relationship with profit before tax over the 2011-2013 period. More broadly, cashless policy has also been linked to bank performance at a general level rather than through the POS channel alone. Ogbeide and Fapohunda (2017), using Augmented Dickey-Fuller and panel least squares methods, found that cashless policy significantly determined bank financial performance, while Akara and Asekome (2018) reported a high positive correlation between cashless policy adoption and bank profitability using correlation analysis. Jonathan and Uju (2019) found that e-banking had a gradual but positive long-run impact on the performance of Nigerian deposit money banks between 2006 and 2017. Outside Nigeria, Helfasari, Gameyuni and Syaipudin (2020) used multiple regression and a paired-sample t-test to show that most cashless variables improved bank profitability in Indonesia, with the exception of bank checks and bilyet transfers. Taken together, these studies illustrate the inconsistency in the literature regarding the specific contribution of POS to bank performance when it is bundled with other channels, and underscore why the present study isolates POS specifically against both ROA and EPS. Overall, the reviewed studies present mixed and, at times, contradictory findings regarding the direction and significance of the relationship between POS and bank performance. Several studies bundle POS with other channels (ATM, internet banking, mobile banking) into a single JAFM JAFM model, making it difficult to isolate its marginal contribution. Others rely on a single performance proxy, most often ROA or ROE, without cross-checking against a shareholder- oriented measure such as EPS. This inconsistency underscores the need for the channel-specific, dual-outcome approach adopted in the present study. Methodology The study adopted an ex-post facto research design, which is appropriate given that the data used were drawn from historical, already-existing annual reports and accounts of the sampled deposit money banks rather than from data generated through direct manipulation of variables (Anao & Uche, 2002; Osuala, 2005). The population of the study comprised the twenty (20) deposit money banks listed on the Nigerian Stock Exchange between 2012 and 2019, drawn from the NSE fact book, and included banks such as Access Bank Plc, Fidelity Bank Plc, First City Monument Bank Plc, First Bank of Nigeria Ltd, Guaranty Trust Bank Plc, Union Bank of Nigeria Plc, United Bank for Africa Plc, Zenith Bank Plc, Ecobank Nigeria Plc, Sterling Bank Plc, Unity Bank Plc, Wema Bank Plc, and others. A simple random sampling technique was used to select ten (10) deposit money banks from the accessible population of twenty (20) listed banks, ensuring adequate representation of the population. The study relied entirely on secondary data manually extracted from the audited annual financial reports of the ten sampled banks for the 2012-2019 period, supplemented by the NSE fact book and relevant academic literature. Because the data were sourced from the audited annual reports of listed banks, filed with and verified by the Nigerian Stock Exchange, they are considered valid, reliable, and free from significant researcher-induced bias (Kalu, 2015). In line with the study's two specific objectives, the model developed by Izedonmi and Okunbor (2014) was adapted and restricted to the channel of interest, POS, as follows: ROA = f EPS = f Expressed econometrically, and consistent with the simple linear regressions reported in the Results section (each tested in line with objectives 1-2): ROA = β0 + β1POS + Ui (used for H01) EPS = β0 + β1POS + Ui (used for H02) Where: ROA = Return on Assets; EPS = Earnings per Share; POS = Point of Sale value; β0 = constant; β1 = coefficient to be estimated; Ui = stochastic error term. Decision Rule: Reject H0 and accept H1 if the two-tailed probability value (p) is less than 0.05; otherwise, accept H0 and reject H1. Descriptive statistics were used to summarise the data on POS, ROA, and EPS. The two stated hypotheses were tested using simple linear regression analysis at the 5 percent significance level, with the aid of the Statistical Package for Social Sciences . Results Table 1: POS and Financial-Performance Data (2012-2019) Year POS (N'Billion) ROA (%) EPS (N.K) 2012 3,348,187 22.71 28.42 2013 4,642,666 26.99 8.51 2014 6,843,059 32.05 29.23 JAFM JAFM Year POS (N'Billion) ROA (%) EPS (N.K) 2015 5,548,772 26.47 30.10 2016 4,828,712 20.84 38.86 2017 3,811,952 32.25 20.88 2018 3,621,044 24.30 25.78 2019 4,149,479 18.01 63.57 Source: Nigerian Stock Exchange , Annual Reports of Sampled Banks. Test of Hypotheses Table 2: Point of Sale and Return on Assets - Model Summary Model R R Square Adjusted R Square Std. Error of Estimate 1 .424a .180 .043 4.94077 a. Predictors: (Constant), POS b. ANOVA Model Sum of Squares Df Mean Square F Sig. Regression 32.181 1 32.181 1.318 .295b Residual 146.467 6 24.411 Total 178.648 7 a. Dependent Variable: ROA b. Predictors: (Constant), POS Coefficients Model B Std. Error Beta t Sig. (Constant) 16.913 7.640 2.214 .069 POS 1.857E-6 .000 .424 1.148 .295 a. Dependent Variable: ROA The regression result shows an R2 of 0.180, indicating that POS accounts for about 18 percent of the variation in ROA. The slope coefficient (1.857E-6) is positive, implying that a unit increase in POS is associated with an increase in ROA. However, this relationship is not statistically significant, as the associated p-value of .295 exceeds the 0.05 threshold. The null hypothesis (H01) is therefore not rejected: there is no significant relationship between POS and ROA among the selected deposit money banks. JAFM JAFM Table 3: Point of Sale and Earnings per Share - Model Summary Model R R Square Adjusted R Square Std. Error of Estimate 1 .018a .000 -.166 17.16187 a. Predictors: (Constant), POS ANOVA Model Sum of Squares Df Mean Square F Sig. Regression .590 1 .590 .002 .966b Residual 1767.179 6 294.530 Total 1767.769 7 a. Dependent Variable: EPS b. Predictors: (Constant), POS Coefficients Model B Std. Error Beta t Sig. (Constant) 31.825 26.537 1.199 .276 POS -2.514E-7 .000 -.018 -.045 .966 a. Dependent Variable: EPS The R2 of 0.000 indicates that POS explains virtually none of the variation in EPS. The slope coefficient (-2.514E-7) is negative, implying that a unit increase in POS is associated with a marginal decrease in EPS; however, this relationship is far from statistically significant (p = .966 > 0.05). The null hypothesis (H02) is therefore not rejected: there is no significant relationship between POS and EPS among the selected deposit money banks. Discussion of Findings H01: There is no significant relationship between POS and ROA. The analysis revealed a positive but insignificant relationship, which is broadly consistent with the prior expectation that POS should improve asset profitability, even though the relationship did not reach statistical significance within the sample. This aligns with Christian and Igwebuike (2016), whose study similarly found no significant relationship between point-of-sale transactions and bank profit before tax in Nigeria. H02: There is no significant relationship between POS and EPS. A negative and statistically insignificant relationship was found, which departs from the prior expectation of a positive association. This is broadly consistent with Abaenewe (2013), who reported an insignificant, negative relationship between cashless-policy variables and bank return measures. Conclusion Based on the findings, the study concludes that, within the 2012-2019 period, the Point of Sale channel had not yet developed a statistically significant relationship with either the asset profitability or the shareholder profitability of the sampled Nigerian deposit money banks. This does not imply that the cashless policy is without value; rather, it suggests that the financial-performance benefits of the POS channel remain latent, and may depend on further JAFM JAFM improvements in transaction volume, digital infrastructure, and customer adoption before they become statistically detectable in banks' profitability metrics. Recommendations The following recommendations were made: i. Bank management should continue to expand and maintain POS infrastructure, particularly in underserved and remote communities, while working to reduce merchant service charges and downtime, so that increased transaction volume can eventually translate into a measurable, positive contribution to return on assets and earnings per share. ii. The Central Bank of Nigeria and other regulators should sustain public enlightenment campaigns on the benefits and safety of POS-based electronic payments, alongside continued investment in the national payment infrastructure, to accelerate the adoption levels required for the intended financial-performance benefits of the cashless policy to materialise. JAFM JAFM References Abaenewe, Z. C. (2013). Electronic banking and bank performance in Nigeria. West African Journal of Industrial and Academic Research, 6(1), 171-187. Adekoya, F. (2011). Cashless economy: The many hurdles before CBN. The Guardian, Lagos. Adewale, A. A. (2019). Evaluating the systemic transition to a cashless economy in Nigeria. Social Science Research Network, 1(1), 56-62. Adurayemi, A. C. (2016). Cashless policy and its effects on the Nigerian economy. European Journal of Business, Economics and Accountancy, 4(2), 81-88. Ajayi, S. I. (2014). Effect of cashless monetary policy on the Nigerian banking industry. Journal of Accounting and Management, 15(2), 20-31. Akara, C. K., & Asekome, M. O. (2018). Cashless policy and commercial banks' profitability in Nigeria. Advances in Social Science Research Journal, 5(3), 395-406. Akhalumeh, P. B., & Ohiokha, F. (2012). Nigeria cashless economy: The imperatives. International Journal of Management and Business Studies, 2(12), 31-36. Anao, A. R., & Uche, C. U. (2002). Research design and implementation in accounting and finance. Institute of Chartered Accountants of Nigeria. Atanda, A. A., & Alimi, O. Y. (2018). Anatomy of cashless banking in Nigeria. Centre for Management, Development & Policy Modelling . Azeez, J. O. (2018). Impact of mobile banking on service delivery in the Nigerian commercial banks. International Review of Management and Business Research, 2(2), 333-344. Baddeley, M. (2004). Using e-cash in the new economy: An economic analysis of micro- payment systems. Journal of Electronic Commerce Research, 5(4), 239-253. Ben-Kinanee, S. M. (2021). CBN cashless policy and financial performance of deposit money banks in Nigeria (Unpublished master's dissertation). Ignatius Ajuru University of Education. Brigham, E. F., & Houston, J. F. (2001). Financial management. Erlangga Publishing. Christian, M. L., & Igwebuike, E. S. (2016). Cashlite policy and profitability of banks in Nigeria. International Journal of Management Studies, Statistics & Applied Economics, 6(1), 17- 36. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology Press. Devinaga, R. (2020). User acceptance of ATM biometric authentication. Global Journal of Computer Sciences Theory and Research, 10(1), 11-17. Emekekwue, P. E. (2008). Corporate financial management (5th rev. ed.). African Bureau of Educational Sciences. Gerlach, D. (2000). Money in the real and the virtual world: E-money, C-money, and the demand for CB-money. Netnomics, 1(1), 11-35. Helfasari, N. A., Gameyuni, R. R., & Syaipudin, U. (2020). Cashless banking and financial performance of banks in Rakyat Indonesia. Proceedings of the First International Conference of Economics. Hock, N., & Nyean, T. (2010). Understanding earnings per share. Journal of Finance and Accounting Research. Ighoroje, E. J., & Okoroyibo, E. E. (2020). Cashless policy and the performance of deposit money banks in Nigeria. European Journal of Accounting, Auditing and Finance Research, 8(5), 85-104. JAFM JAFM Izedonmi, F., & Okunbor, J. A. (2014). The impact of cashless economy on Nigeria financial system. Jaiyeola, L. (2011). Benefits of cashless economy by experts. Business Day. James, I. E., & Eloho, O. E. (2020). Cashless policy and the performance of deposit money banks in Nigeria. European Journal of Accounting, Auditing and Finance Research, 8(5), 85-104. Jonathan, O. O., & Uju, V. O. (2019). Impact of electronic banking on the performance of money deposit banks in Nigeria. Noble International Journal of Economics and Financial Research, 4(9), 83-90. Kalu, I. (2015). Environmental cost and financial performance of selected oil companies in the Niger Delta region of Nigeria. Unpublished M.Sc. dissertation, University of Port Harcourt. Lamikanra, B. (2019). Managing the transition to a cashless economy in Nigeria: The challenges and strategies. Journal of Accounting and Management, 15(2), 20-31. Moses-Ashike, H. (2020). Cashless economy can reduce risk of carrying huge cash. Business Day. Muotolu, P. C., & Nwadialor, E. O. (2019). Cashless policy and financial performance of deposit money banks in Nigeria. Ogbeide, S. O., & Fapohunda, F. M. (2017). Cashless policy and banks' financial performance in Nigeria. International Journal of Current Research, 9(7), 55247-55252. Ogu, I. A. (2011). Faulting CBN's rationale for daily cash withdrawal limits. Africana-First Publisher. Okafor, C. A. (2020). Cashless policy for business purpose and the performance of deposit money banks in Nigeria. International Journal of Innovative Finance and Economics Research, 8(3), 1-13. Okoye, P. V. C., & Ezejiofor, R. (2013). An appraisal of cashless economy policy in development of Nigerian economy. Research Journal of Finance and Accounting, 4(7), 237-252. Ongore, V. O. (2018). Determinants of financial performance of commercial banks in Kenya. International Journal of Economics and Financial Issues, 3(1), 237-252. Osuala, E. C. (2005). Introduction to research methodology (3rd ed.). Africana-First Publishers. Otuori, O. H. (2018). Influences of exchange rate determinants on the performance of commercial banks in Kenya. European Journal of Management Science and Economics, 1(1), 76-89. Ovat, O. O. (2017). The central bank of Nigeria's cashless policy in Nigeria: Benefits and challenges. Journal of Economics and Sustainable Development, 3(14), 128-133. Oyetade, A., & Ofoelue, F. (2018). Costs of cashless society. Research Journal of Finance and Accounting, 4(7), 87-93. Prastowo, D. (2002). Analisis laporan keuangan: Konsep dan aplikasi. Yogyakarta. Priye, W. A., & Percy, A. B. (2019). Impact of cashless policy on deposit money bank performance in Nigeria. Global Journal of Education, Humanities and Management Sciences, 1(2), 89-93. Riyanto, B. (2001). Dasar-dasar pembelanjaan perusahaan. BPFE. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. Taiwo, J. N., Ayo, K. O., Afieroho, E., & Agwu, M. (2016). An appraisal of cashless policy on the Nigerian financial system. Journal of Accounting and Management, 15(2), 35-45. JAFM JAFM Ugbede, J. T., Yahaya, A., & Edicha, M. J. (2019). Effect of electronic payment on financial performance of deposit money banks in Nigeria. Lafia Journal of Economics and Management Sciences, 4(1), 2550-2732. Ugwueze, A. C., & Nwezeaku, N. C. (2016). E-banking and commercial bank performance in Nigeria: A cointegration and causality approach. International Journal of e-Education, e- Business, e-Management and e-Learning, 6(3), 50-70. Woleola, O. (2017). Nigeria in 2012: The vision of cashless economy. The Nigeria Economic Summit Group, Abuja. Woodford, M. (2003). Interest and prices: Foundations of a theory of monetary policy. Princeton University Press.