“Banking resilience and government response during the COVID-19 pandemic: Evidence from Nigeria” AUTHORS Taofeek Sola Afolabi Thomas Duro Ayodele Oyinlola Morounfoluwa Akinyede Olanrewaju David Adeyanju Harley Tega Williams ARTICLE INFO Taofeek Sola Afolabi, Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede, Olanrewaju David Adeyanju and Harley Tega Williams (2023). Banking resilience and government response during the COVID-19 pandemic: Evidence from Nigeria. Banks and Bank Systems, 18(2), 214-227. doi:10.21511/bbs.18(2).2023.18 DOI http://dx.doi.org/10.21511/bbs.18(2).2023.18 RELEASED ON Wednesday, 14 June 2023 RECEIVED ON Friday, 16 September 2022 ACCEPTED ON Thursday, 20 October 2022 LICENSE This work is licensed under a Creative Commons Attribution 4.0 International License JOURNAL "Banks and Bank Systems" ISSN PRINT 1816-7403 ISSN ONLINE 1991-7074 PUBLISHER LLC “Consulting Publishing Company “Business Perspectives” FOUNDER LLC “Consulting Publishing Company “Business Perspectives” NUMBER OF REFERENCES 28 NUMBER OF FIGURES 2 NUMBER OF TABLES 17 © The author(s) 2023. This publication is an open access article. businessperspectives.org 214 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 Abstract In a global pandemic, there is a need for banks to improve service delivery through financial technologies. Since the fight against COVID-19 is the community responsi- bility, the role of banks in channeling cash to all stakeholders is essential for the con- temporary human race. This study investigated the impact of the government response to COVID-19 on the resilience of banks. A multivariate Structural Equation Model (SEM) was used to specify the links between the exogenous factors (government’s so- cial and financial responses) and the endogenous variables (resilience of bank custom- ers, employees and investors). A research survey approach was used where 543 re- spondents were sampled. A self-constructed online questionnaire was used to harvest responses from customers, employees and investors of the selected banks. The result of the analysis showed a significant relationship between government’s social response and the resilience of bank customers. However, such a relationship does not hold be- tween government’s social responses and other resilience indicators (employees and investors). Furthermore, the result revealed that government’s financial responses do not affect the resilience of banks. The study concluded that the government’s social response during the COVID-19 pandemic influenced bank customers’ resilience in Nigeria. It was recommended that banks, as part of the policy, develop tools to comple- ment government actions during the pandemic, thereby ameliorating its impact on their customers. Taofeek Sola Afolabi (Nigeria), Thomas Duro Ayodele (Nigeria), Oyinlola Morounfoluwa Akinyede (Nigeria), Olanrewaju David Adeyanju (Nigeria), Harley Tega Williams (Nigeria) Banking resilience and government response during the COVID-19 pandemic: Evidence from Nigeria Received on:16th of September, 2022 Accepted on: 20th of October, 2022 Published on: 14th of June, 2023 INTRODUCTION History has noted that the pandemic has had a negative impact on the human race and economic activities. However, medical researchers all over the world have not been able to pinpoint the time and season in which pandemics would occur. There is no mathematical model to predict the next pandemic and its origin; hence some researchers have proven that the mother of all pandemics occurs every 100 years and causes massive mortality (Morens & Taubenberger, 2018). Throughout history, pandemic outbreaks have wrecked mankind and sometimes changed the course of economic and financial transactions while sig- naling the end of the entire business operations system worldwide. Thus, making the impact of the pandemic a system theory approach. From time immemorial, Africans have been seen as a continent in which preparedness is low in combating any outbreak of pandemic, economic and financial crisis in which Nigeria has been noted as a key player. A country’s resilience strategy becomes a key determinant factor in response to such a crisis. Pandemics affect the health and © Taofeek Sola Afolabi, Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede, Olanrewaju David Adeyanju, Harley Tega Williams, 2023 Taofeek Sola Afolabi, Ph.D., Senior Lecturer, Faculty of Management Sciences, Department of Finance, Redeemer’s University, Nigeria. (Corresponding author) Thomas Duro Ayodele, Ph.D., Associate Professor, Faculty of Management Sciences, Department of Finance, Redeemer’s University, Nigeria. Oyinlola Morounfoluwa Akinyede, Ph.D., Senior Lecturer, Faculty of Management Sciences, Department of Finance, Redeemer’s University, Nigeria. Olanrewaju David Adeyanju, Ph.D., Senior Lecturer, Faculty of Management Sciences, Department of Accounting, Redeemer’s University, Nigeria. Harley Tega Williams, MSc., Lecturer I, Faculty of Management Sciences, Department of Finance, Redeemer’s University, Nigeria. JEL Classification G20, I18, I38 Keywords pandemic, resilience, customers, government, employees, investors This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. www.businessperspectives.org LLC “СPС “Business Perspectives” Hryhorii Skovoroda lane, 10, Sumy, 40022, Ukraine BUSINESS PERSPECTIVES Conflict of interest statement: Author(s) reported no conflict of interest 215 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 well-being of the human race and their financial activities (Ayodele et al., 2021). More often than none, world attention is given to the medical side, while little or no attention is paid to the economic and financial sides, as health is noted to be wealth in general human assumptions. A little comparison be- tween the medical and financial roles in a time of a pandemic shows that both are not mutually exclusive as one seems not to be superior to the other, since the need for health and finance is significant to make life a concern. Therefore, the effective response rate by medical personnel to the pandemic must be the same response rate by monetary authorities to bank stakeholders as one aids the other in a health crisis. The COVID-19 pandemic has affected all financial and economic activities worldwide (World Bank, 2020). Many are affected medically by COVID-19, while some are affected financially. The banking sector is seen from broad and diverse perspectives in the empirical literature as a financial system that provides financial resources to firms and individuals whose impact is essential in the ecosystem. Combating the impact of COVID-19 cannot be done in isolation of the banking system, since it is an important sector of any country. In the first quarter of 2020, the Federal Government of Nigeria declared a total lockdown of her econo- my because the World Health Organization (WHO) characterized COVID-19 as a pandemic and a pub- lic health crisis. Essentially, customers, employees and other stakeholders were already engaged in one form of financial activity. However, the impact of COVID-19 made a clarion call for Nigerian banking to live up to expectations. The call for banks to intensify service delivery through financial technolo- gies became necessary at a time of global uncertainty. The issue of combating COVID-19 isa collective responsibility; the role of banks is necessary for channeling funds to all stakeholders and, as such, is essential for the survival of the modern human race. The Nigerian banking system has always made its roles relevant to financial and economic activities and supported all forms of economic growth policy. Bedford (2020) debated how an efficient banking resilience approach could help banks. This debate has given insights into the action and inaction of the Nigerian government’s response to its citizens in a time of economic instability. Despite the importance of the banking sector in any economy, empirical relationships between banking resilience and the gov- ernment response during the COVID-19 lockdown and the ease of lockdown in Nigeria’s economic and financial activities are yet to be established. Based on this, this study seeks to investigate the impact of government responses to COVID-19 pandemic on the resilience of bank stakeholders. 1. LITERATURE REVIEW “Coronaviruses” belong to the Corona viridae fam- ily in the Nidovirales order. Corona represents crown-like spikes on the outer surface of the virus; thus, it was named a coronavirus” (Zhong et al., 2003; Wang et al., 2013). In 2019, China witnessed an outbreak of a novel coronavirus in Wuhan, which is one of its main cities and business centers. According to reports, the outbreak killed more than 1,800 people in the city and another 70,000 were infected, all within the first 50days of the pandemic (Madabhavi et al., 2020). Government Response to COVID-19 has different diversions. From the initial case of the announce- ment of COVID-19, the Federal Government of Nigeria was swift in responding to the impact of this pandemic. A Presidential Task Force (PTF) headed by the Secretary to the Federation was able to put facilities in line with the protocol of the World Health Organization (WHO) and UNESCO by ensuring that the disease does not spread to the venerable group in the society. Measures includ- ed setting up isolation centers all over the nation, sensitization and awareness of the public on the danger of contracting the disease, and avoiding infection. The provision of palliatives to the most vulnerable public sector was occasioned by the ef- fect of the lockdown permeating this pandemic. Economically and financially, the government in- tervened by adjusting monetary and fiscal policies to provide relief to the economy in terms of inter- 216 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 fering with interest rates and providing funds for small businesses to survive. Banks were encouraged to lend at low interest and to ensure these funds were made available without stress. In various pro- nouncements from the government and ministries of finance, agriculture, labor and productivity, as well as works and housing, the government had as- sisted in the country’s economic resilience. Resilience is the capacity to adapt or rebound from misfortune, disaster or transition. Resilience also refers to the capability to handle and recover from deliberate attacks, fatalities, or risks or oc- currences that occur naturally. The banks’ support towards economic recovery affected the banking resilience of stakeholders. However, a success- ful bank-supported economic recovery would af- fect the stability and health of banks. The basis for sustained economic growth is a resilient and stable banking sector. Banks are at the center of the credit intermediation process between savers and investors (Bank of International Settlements, 2009). Organizational resilience is defined by the Bank of International Settlements Committee as a bank’s capacity to deliver daily functions through interference. This ability allows a bank to identify threats and protect itself from them. The systems theory defines a system as an organ- ized set of parts, within an organ which are inter- connected and interdependent, such that a unified whole is being produced. The researchers have viewed the COVID-19 pandemic as following a system theory and a stochastic pattern in which the present state determines the future of the hu- man race. The systems theory was first propound- ed by Bertalanffy in 1937 as a lecture note and was published in 1946 as an article; and was later de- veloped as a book titled General System Theory in 1968. Bertalanffy (1950) stated that ‘changing one part of a system may affect other parts or the whole system’. Therefore, changing the pattern of living through social distancing, face masks, and stay-at-home and work-from-home policies affects the world and the Nigerian banking sec- tor. As banking activities and human interactions were perceived to be normal before the suffix of the pandemic, the COVID-19 period and the total lockdown of the economy made customers, em- ployees, and other stakeholders change their ways of life. In the view of the researchers, the implica- tion of this theory to the study is that banking ser- vices are an open system that co-exists with other activities for economic stability. The outbreak of COVID-19 as a public health emergency across the globe made countries to de- velop and apply measures to mitigate its effect. In response to the pandemic, WHO issued several Non-Pharmaceutical Interventions (NPIs) imple- mented by monitoring and documenting govern- ment strategies during the COVID-19 crisis, which are crucial to understanding the epidemic’s pro- gression. Informal workers largely dominate the Nigerian economy; the preventive measures (lock- down, movement restriction, social distancing and interstate travel ban) occasioned by the COVID-19 pandemic affected socio-economic livelihood in Nigeria (World Health Organization, 2020; Cheng et al., 2020; Courtemanche et al., 2020). D’Orazio and Dirks (2021) examined if variations in banking market systems between nations affect local stock market resilience to the COVID-19 pan- demic. The findings show that nations with more integrated banking systems, a greater number of foreign banks, and a larger percentage of Islamic banks are more resilient to the epidemic, based on a sample of 66 countries covering the period be- tween January 2020 and July 2020. Considering the disparities in banking regulations among nations, it was found that equity markets in countries with stronger capital and liquidity regulations are more resilient to COVID-19. Finally, the study revealed that ‘while stock market movements in countries with more stable banking systems are more resil- ient to the pandemic, countries with higher cred- it-to-deposit ratios, overhead costs, high provisions, and nonperforming loans are more vulnerable’. Policymakers, regulatory agencies, and investors should take note of the findings. The financial system’s resilience, particularly banks, was evaluated during the Global Financial Crisis and COVID-19 (Giese & Haldane, 2020). Findings show that banks are now part of the solu- tion rather than the issue because of regulatory and institutional improvements over the last dec- ade. Paying attention to the lessons learned from the Global Financial Crisis has paid off, and some of the early lessons learned from the COVID-19 problem for the financial system will assist in the 217 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 future. COVID-19 has resulted in a global de- cline in economic activities, thereby resulting in job losses at an unprecedented rate. Both custom- ers and employees of business organizations have thus been affected and made to adjust to this new normal. Recently, AlZgoola et al. (2020) examined the relationship between leaders’ emotional intelli- gence and work engagement, using the media- tion of self-efficacy and resilience. The study used Structural Equation Modeling (SEM) for data collected from employees of five major banks in Bahrain. The findings demonstrated the impor- tance of emotional intelligence among leaders in raising employee work engagement. The connec- tion between leaders’ emotional intelligence and work engagement was also significantly mediat- ed by self-efficacy and resilience, supporting the mediation hypothesis. According to the research presented here, leaders’ emotional intelligence may effectively manage challenging situations like the COVID-19 pandemic and increase employ- ee engagement, enthusiasm, and vigor at work. According to the study, leaders’ emotional intelli- gence also plays a key role in increasing their team members’ psychological resourcefulness, which increases their efficacy and resilience and leads to higher engagement levels. Korzeb and Niedziólka (2020) study allows us to examine the impact of the loan portfolio’s indus- try structure on commercial banks’ resistance to the COVID-19 pandemic-related crisis. It employs two approaches to assess the impact of the pan- demic on industry risk and a system that allows industries to be prioritized in terms of the cri- sis’s possible negative consequences. The ability of commercial banks functioning in the Polish financial sector to withstand the possible conse- quences of the COVID-19 outbreak was one of the diagnostic criteria used to choose 13 commercial banks for implementation. The TOPSIS strategy and the Hellwig method were used as linear order- ing methods. The parameters for the parametric assessment of financial institution resilience were capital adequacy, liquidity level, the profitability of economic activity, the share of portfolio levels of exposure with recognized impairment, and resil- ience of the bank’s credit portfolio to risk resulting from exposure in economic sectors. The analysis found that the biggest banks operating in Poland were the least vulnerable to the pandemic’s effects. 2. AIMS AND HYPOTHESES The study aims to investigate the impact of govern- ment responses during the COVID-19 pandemic on bank resilience. To achieve this, the following hypotheses are formulated: H01: Government social response to the COVID-19 pandemic has no significant impact on bank resilience. H01a: Government social response to the COVID-19 pandemic has no significant impact on cus- tomer resilience. H01b: Government social response to the COVID-19 pandemic has no significant impact on em- ployee resilience. H01c: Government social response the COVID-19 pandemic has no significant impact on in- vestor resilience. H02: Government financial response to the COVID-19 pandemic has no significant im- pact on bank resilience. H02a: Government financial response to the COVID-19 pandemic has no significant im- pact on customer resilience. H02b: Government financial response to the COVID-19 pandemic has no significant im- pact on employee resilience. H02c: Government financial response to the COVID-19 pandemic has no significant im- pact on investor resilience. 3. METHODS The research survey method was adopted for this study, where 543 respondents were sampled through a self-constructed questionnaire. These responses were harvested using an online form of the questionnaire. 218 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 The validity of the measuring instrument was car- ried out through factor analysis. The factor load- ings for the measuring items were computed using the Amos 26 software, and these were used to es- tablish the composite reliability for the construct variables. The Average Variance Extracted (AVE) was computed for each construct to ascertain the convergent and discriminant validities. Chin (1998) recommends a minimum acceptable AVE value of 0.5 for convergent validity. The Fornell-Larcker cri- terion (Fornell & Larcker, 1981) requires that the inter-construct correlations must be less than the square roots of the AVEs. Garson (2016) also sug- gests composite reliability greater than 0.7. The relationships between the exogenous varia- bles (Government responses – social & financial) and the endogenous variables (Banking resilience – customers, employees & investors) are specified through a multivariate Structural Equation Model (SEM) in Figure 1. Path analysis was conducted on the SEM using the Amos 26 software. The test of model fit was specified through the Comparative Fit Index (CFI) and the Root Mean Square Error Approximation (RMSEA). 4. RESULTS Table 1 shows that a total number of 543 respond- ents completed the research questionnaire, of which 295 (54.3%) were female and 248 (45.7%) were male. Further results show that most respondents fall within the age brackets of 19-29 years (31.5%) and Source: Amos 26 (2022). Note: CR = Customer’s Resilience; ER = Employee’s Resilience; IR = Investor’s Resilience; GSR = Government Social Response to Covid-19 Pandemic; GFR = Government Financial Response to Covid-19 Pandemic; CR1, CR2, … CR7 = Measurement items for Customer’s Resilience; ER1, ER2, …ER7 = Measurement items for Employee’s Resilience; IR1, IR2, … IR6 = Measurement items for Investor’s Resilience; SR1, SR2, … SR8 = Measurement items for Government Social Response; FR1, FR2, … FR7 = Measurement items for Government Financial Response; e1, e2, e3, e4, …, e35 = error terms of the measurement variables; e36, e37, e38 = residual terms of the regression estimates. Figure 1. Path diagram showing the relationship between government responses and banking resilience 219 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 30-39 years (30.9%). These age groups belong to the working class groups of those who are either bank customers or their employees. Lastly, the distribu- tion of the respondents’ level of education reveals that a large percentage has an HND/BSc degree (41.1%). This is followed by those with postgrad- uate degrees (27.8%). These results agree with the distribution of the age bracket. Table 1. Demographic characteristics of respondents Source: IBM SPSS Amos 26 (2022). Characteristics Frequency Percent Cumulative percent Sex Female 295 54.3 54.3 Male 248 45.7 100 Age group less than 18yrs 23 4.2 4.2 19-29yrs 171 31.5 35.7 30-39yrs 168 30.9 66.7 40-49yrs 124 22.8 89.5 50-59yrs 950 9.2 98.7 60yrs and above 7 1.4 100 Education O-levels 49 9 9 OND/A-levels 62 11.4 20.4 HND/BSc 223 41.1 61.5 Professional Certificate 58 10.7 72.2 Postgraduate Degrees 151 27.8 100 Table 2 presents the t-statistics of the skewness and kurtosis of the construct variables. The skew- ness and kurtosis values help assess the variables’ normality of the measurement items. A value which falls within the range of ± 2.58 is consid- ered normal. Table 2. Descriptive statistics Source: IBM SPSS Amos 26 (2022). Variables Skewness Kurtosis Statistic Std. error Statistic Std. error Government Social Response –0.046 0.319 –0.127 0.628 Government Financial Response 0.341 0.319 –1.123 0.628 Customer’s Resilience 0.356 0.319 0.142 0.628 Employee’s Resilience –0.285 0.319 –0.589 0.628 Investor’s Resilience 0.297 0.319 –1.984 0.628 Results from Table 2 show that all the variables have skewness and kurtosis values within the ac- ceptable normal range of ± 2.58. This confirms the normality of the data for the variables. The measurement items for the construct variables must satisfy the factor loading requirement before being used as indicators of their respective con- structs. The minimum acceptable threshold for standardized factor loading is 0.5. Factor loadings were computed for all the items in the question- naire, and those with loadings less than0.5 were removed from the model. Table 3 gives a summary of the retained items for each construct and their respective loadings. Table 3. Standardized factor loadings Source: Output from IBM SPSS Amos 26 (2022). Items Estimates SR7 ← GSR 0.501 SR4 ← GSR 0.770 SR3 ← GSR 0.843 SR2 ← GSR 0.728 FR1 ← GFS 0.558 FR7 ← GFS 0.937 CR7 ← CR 0.612 CR5 ← CR 0.682 CR6 ← CR 0.824 ER5 ← ER 0.937 ER6 ← ER 0.655 ER7 ← ER 0.533 IR1 ← IR 0.808 IR2 ← IR 0.720 IR3 ← IR 0.972 IR6 ← IR 0.904 The result from Table 3 indicates that all the re- tained items have factor loadings greater than the acceptable minimum threshold of 0.5, suggesting that they share significant variance with their con- struct variables. Table 4 reveals the result of the Composite Reliability (CR) and the Average Variance Extracted (AVE). The rule of thumb requires that all the constructs must have a CR value greater than 0.7 and an AVE value of at least 0.5. The re- sults show that all the construct variables satisfy both the composite reliability and the convergent validity requirements. The inter-construct correlation between the exog- enous variables is 0.004. To satisfy the condition for discriminant validity, the square roots of the AVEs of these variables must be greater than their inter-construct correlation. A comparison of the re- sults reveals that the square roots of the AVEs (0.721 220 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 & 0.774) are greater than the inter-construct corre- lation (0.004). This implies that the exogenous vari- ables satisfy the discriminant validity test. The Comparative Fit Index (CFI) and the Root Mean Square Error Approximation (RMSEA) in- dices were computed in order to test the model fit of the SEM. The threshold for a good model fit re- quires a CFI between 0.9 and 1.0and an RMSEA value less than 0.08. Table 6 gives a summary of the model fit result model. Table 5. Model fit indices Source: Outputs from Amos 26 (2022). Index Values CFI 0.901 RMSEA 0.036 Both the CFI value of 0.901 falls within the accept- able range while the RMSEA value is less than 0.08. These imply a good model fit for the path analysis. Table 6. R-Squared estimates of the model Source: Outputs from Amos 26 (2022). Endogenous variables R-squared estimates IR 0.019 ER 0.093 CR 0.664 The R-squared values in Table 6 reveal the total variations in the endogenous variables explained by the exogenous variables. The results show that the government responses indicators explain 66.4% of the total variations in the Customer’s Resilience variable (CR). Further results reveal that only 2% and 9% of the variations in Investor’s Resilience (IR) and Employee’s Resilience (ER) are respectively explained by government response. Results of the regression estimates of the multivar- iate SEM are presented in Table 7. All the estimates are positive, indicating a direct relationship be- tween the government response to the COVID-19 pandemic and the resilience of customers, em- ployees and investors. Further details reveal a sig- nificant relationship between government social response and customer resilience at the 5% level (β = 0.819, p = 0.016). This implies that a unit change- in the government’s social response will result in an 81.9percent change in the resilience of bank customers, while other variables remain constant. However, such a significant relationship does not hold between government social responses and other resilience indicators, as revealed by their p-values greater than the 5% level. Similarly, the government’s financial response has no significant influence on all the resilience indicators. 5. DISCUSSION The results from the data analysis have established a significant relationship between government ac- tions during the COVID-19 pandemic and banks’ Table 4. Composite reliability and average variance extracted Source: Authors’ computation using outputs from Amos 26 (2022). Constructs CR AVE The square root of AVE Inter construct correlation Government Social Response 0.81 0.520 0.721 0.004 Government Financial Response 0.74 0.599 0.774 Customer’s Resilience 0.75 0.502 0.709 – Employee’s Resilience 0.76 0.530 0.728 – Investor’s Resilience 0.92 0.732 0.856 – Table 7. Estimates of the regression coefficients Source: Outputs from Amos 26 (2022). Hypothesized path Estimate S.E. C.R. P Decision CR ← GSR 0.819 0.341 2.402 0.016 Accepted ER ← GSR 0.040 0.220 0.181 0.856 Rejected IR ← GSR 0.101 0.163 0.617 0.537 Rejected CR ← GFS 0.402 0.216 1.861 0.063 Rejected ER ← GFS 0.134 0.095 1.409 0.159 Rejected IR ← GFS 0.033 0.047 0.698 0.485 Rejected 221 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 resilience. Specifically, government’s social re- sponse has a significant impact on bank custom- ers in Nigeria. This result agrees with the work of Siahaan (2020) and Awofeso and Irabor (2020). According to the survey “Indonesian Consumer Response to COVID-19” by Siahaan (2020), up to 50% of Indonesians have reduced their activities outside the home, and 30% have stated that they want to shop more regularly online. According to Awofeso and Irabor (2020), informal workers large- ly dominate the Nigerian economy; Government preventive measures (lockdown, movement re- striction, social distancing and interstate travel ban) occasioned by the COVID-19 pandemic af- fected socio-economic livelihood in Nigeria. According to Al-Nawayseh (2020), perceived benefits and social norms have a substantial im- pact on the intention to use FinTech applications, which has a good effect on consumer adoption. According to Ikeda et al. (2021), with the dramatic drop in economic activity at the start of the ep- idemic, banks avoided deleveraging in this crisis, and lending grew. CONCLUSION This study investigated the impact of government responses during COVID-19 on the resilience of banks in Nigeria. A survey research method was used to elicit data from customers, employees and investors in the selected banks. The result of the data analysis has shown that government’s social re- sponse during the COVID-19 pandemic significantly influenced bank customers’ resilience. However, such significant influence was not seen on bank employees’ and investors’ resilience during the same pe- riod. Furthermore, the results show that government financial responses do not influence the resilience of banks. Based on these findings, the study concludes that the government’s social intervention during the COVID-19 pandemic in Nigeria affected bank customers as stakeholders in the banking sub-sector. For this reason, the study recommends that banks improve their e-banking platforms so that customers can access banking channels while away from the banking hall. It is also recommended that banks, as part of the policy, devise means of complementing government actions during the pandemic, thereby ameliorating its impact on their customers and the nation as a whole. IMPLICATION OF FINDINGS The study found that the government’s actions during the COVID-19 pandemic had more of an impact on customers than any other Nigerian bank stakeholders (employees and investors). This implies that bank customers felt the hard effect of the lockdown and social distancing enforced by the government in relation to banking activities in the country during the pandemic. The finding therefore suggests that the actions of the government in any nation, during pandemic, are felt most by its citizens. This calls for a more careful and pragmatic approach to policy-implementation by the government, especially in a period of health pandemic. AUTHORS CONTRIBUTIONS Conceptualization: Taofeek Afolabi, Oyinlola Akinyede, Thomas Ayodele, Harley Tega Williams, Olanrewaju Adeyanju. Data curation: Harley Tega Williams. Formal analysis: Taofeek Afolabi Funding acquisition: Taofeek Afolabi, Oyinlola Akinyede, Thomas Ayodele, Harley Tega Williams, Olanrewaju Adeyanju. Investigation: Oyinlola Akinyede. Methodology: Taofeek Afolabi. 222 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 Project administration: Oyinlola Akinyede, Thomas Ayodele. Software: Taofeek Afolabi. Supervision: Oyinlola Akinyede, Thomas Ayodele. Validation: Harley Tega Williams, Olanrewaju Adeyanju. Visualization: Olanrewaju Adeyanju. Writing – original draft: Taofeek Afolabi, Oyinlola Akinyede, Harley Tega Williams. 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H., Tan, S. Y., Chang, Q., Xie, J. P., Liu, X. Q., Xu, J., Li, D. X., Yuen, K. Y., Peiris, J., & Guan, Y. (2003). Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People’s Republic of China in February, 2003. The Lancet, 362(9393), 1353-1358. https://doi.org/10.1016/S0140- 6736(03)14630-2 224 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 Table A1. Regression weights: (Group number 1 – Default model) Items Estimate S.E. C.R. P CR ← GSR 0.819 0.341 2.402 0.016 ER ← GSR 0.040 0.220 0.181 0.856 IR ← GSR 0.101 0.163 0.617 0.537 CR ← GFS 0.402 0.216 1.861 0.063 ER ← GFS 0.134 0.095 1.409 0.159 IR ← GFS 0.033 0.047 0.698 0.485 SR7 ← GSR 1.000 – – – SR4 ← GSR 2.846 0.809 3.517 *** SR3 ← GSR 3.211 0.889 3.610 *** SR2 ← GSR 2.914 0.848 3.437 *** FR1 ← GFS .491 .175 2.802 .005 CR6 ← CR 1.394 .342 4.075 *** CR7 ← CR 1.000 – – – ER5 ← ER 1.637 .506 3.236 .001 ER6 ← ER 1.238 .346 3.573 *** ER7 ← ER 1.000 – – – IR1 ← IR 1.000 – – – IR2 ← IR .814 .135 6.048 *** IR3 ← IR 1.700 .187 9.075 *** IR6 ← IR 1.118 .134 8.364 *** FR7 ← GFS 1.000 – – – CR5 ← CR 1.066 .307 3.473 *** Note: *** – 0.000. Figure A1. Path Diagram Source: Outputs from Amos 26. APPENDIX A. Supplementary materials 225 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 Table A2. Standardized regression weights: (Group number 1 – Default model) Items Estimate CR ← GSR .422 ER ← GSR .027 IR ← GSR .092 CR ← GFS .695 ER ← GFS .303 IR ← GFS .101 SR7 ← GSR .501 SR4 ← GSR .770 SR3 ← GSR .843 SR2 ← GSR .728 FR1 ← GFS .558 CR6 ← CR .824 CR7 ← CR .612 ER5 ← ER .937 ER6 ← ER .655 ER7 ← ER .533 IR1 ← IR .808 IR2 ← IR .720 IR3 ← IR .972 IR6 ← IR .904 FR7 ← GFS .937 CR5 ← CR .682 Table A3. Covariances: (Group number 1 – Default model) Items Estimate S.E. C.R. P GFS ← GSR .002 .065 .031 .976 e15 ← e9 –.386 .371 –1.040 .298 e21 ← e20 –.169 .154 –1.096 .273 Table A4. Correlations: (Group number 1 – Default model) Hypothesized path Estimate GFS ← GSR20 .004 e15 ← e9 –.945 e21 ← e20 –.284 Table A5. Variances: (Group number 1 – Default model) Variable Estimate S.E. C.R. P GSR .133 .072 1.850 .064 GFS 1.497 .788 1.901 .057 e36 .169 .143 1.176 .240 e37 .266 .138 1.926 .054 e38 .157 .043 3.617 *** e2 .398 .080 4.967 *** e5 .742 .195 3.799 *** e6 .557 .195 2.851 .004 e7 1.002 .241 4.160 *** e15 .798 .272 2.938 .003 e21 .459 .201 2.285 .022 e22 .838 .179 4.672 *** e27 .108 .180 .601 .548 e28 .597 .153 3.899 *** 226 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 Variable Estimate S.E. C.R. P e29 .738 .156 4.733 *** e30 .085 .018 4.762 *** e31 .098 .020 5.012 *** e32 .028 .020 1.353 .176 e35 .045 .012 3.693 *** e9 .209 .720 .291 .771 e20 .771 .204 3.789 *** Note: *** – 0.000 Table A6. Squared Multiple Correlations: (Group number 1 – Default model) Variable Estimate IR .019 ER .093 CR .664 CR5 .425 FR7 .877 IR6 .816 IR3 .944 IR2 .519 IR1 .653 ER7 .284 ER6 .429 ER5 .879 CR7 .374 CR6 .680 FR1 .311 SR2 .530 SR3 .711 SR4 .592 SR7 .251 Table A7. CMIN Model NPAR CMIN DF P CMIN/DF Default model 57 131.504 95 .008 1.384 Saturated model 152 .000 0 Independence model 16 503.548 136 .000 3.703 Table A8. Baseline comparisons Model NFI Delta1 RFI rho1 IFI Delta2 TLI rho2 CFI Default model .739 .626 .911 .858 .901 Saturated model 1.000 – 1.000 – 1.000 Independence model .000 .000 .000 .000 .000 Table A9. RMSEA Model RMSEA LO 90 HI 90 PCLOSE Default model .036 .019 .050 .953 Independence model .095 .086 .104 .000 Table A5 (cont.). Variances: (Group number 1 – Default model) 227 Banks and Bank Systems, Volume 18, Issue 2, 2023 http://dx.doi.org/10.21511/bbs.18(2).2023.18 Table A10. Demographic Distribution Sex Value Frequency Percent Valid percent Cumulative percent Valid Female 295 54.3 54.3 54.3 Male 248 45.7 45.7 100.0 Total 543 100.0 100.0 – Age group Valid Less than 18 23 4.2 4.2 4.2 19-29 171 31.5 31.5 35.7 30-39 168 30.9 30.9 66.7 40-49 124 22.8 22.8 89.5 50-59 50 9.2 9.2 98.7 60yrs and above 7 1.4 1.3 100.0 Total 543 100.0 100.0 – Highest level of education Valid O-levels 49 9.0 9.0 9.0 OND/A-levels 62 11.4 11.4 20.4 HND/BSc 223 41.1 41.1 61.5 Professional Certificate 58 10.7 10.7 72.2 Postgraduate Degrees 151 27.8 27.8 100.0 Total 543 100.0 100.0 – “Banking resilience and government response during the COVID-19 pandemic: Evidence from Nigeria”