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Table 2: Correlation Matrix

Table 2 Correlation Matrix

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Source: Authors’ Design: 2021 sector as a control variable impacts both return of asset and return on equity.
Source: Authors’ Design: 2021 sector as a control variable impacts both return of asset and return on equity.
As expected, point of sales (POS) transactions also moved in the direction of the ATM LID NANNING OI Mt CREAR QR TD SERA ERIE ANE AID, RAINE, AINE, PINON AD RN SADA LDN ARS NT Bs SRR Be Nite in Nigeria, witnessing huge acceptance and patronage. In 2015, CBN e-payment report shows a total of N448,512,548,727.00 as value of POS transactions. The years 2016, 2017 and 2018 ended with N 758,996,505,702.00, N 1,409,813,091,608.35, N 2,383,108,901,148.12 respectively. Total value of POS transactions as at August 2021 stood at N2, 806,304,086,834. in Nigeria, witnessing huge acceptance and patronage. In 2015, CBN e-payment report shows
As expected, point of sales (POS) transactions also moved in the direction of the ATM LID NANNING OI Mt CREAR QR TD SERA ERIE ANE AID, RAINE, AINE, PINON AD RN SADA LDN ARS NT Bs SRR Be Nite in Nigeria, witnessing huge acceptance and patronage. In 2015, CBN e-payment report shows a total of N448,512,548,727.00 as value of POS transactions. The years 2016, 2017 and 2018 ended with N 758,996,505,702.00, N 1,409,813,091,608.35, N 2,383,108,901,148.12 respectively. Total value of POS transactions as at August 2021 stood at N2, 806,304,086,834. in Nigeria, witnessing huge acceptance and patronage. In 2015, CBN e-payment report shows
From the diagrams above, it is apparent that in the period, the total value of businesses This is shown in the chart below as figure 3. respectively. Total value of POS transactions as at August 2021 stood at N2, 806,304,086,834.
From the diagrams above, it is apparent that in the period, the total value of businesses This is shown in the chart below as figure 3. respectively. Total value of POS transactions as at August 2021 stood at N2, 806,304,086,834.
Aside mobile money and payments, the ATMs are also getting more and more popular xrowth in mobile money transactions is shown below: on mobile money alone hit 377million GHC while total value stood at 81.83billion GHC. The transactions continued to increase, with debit card being the most popular means for cai payment on account of affordable technologies which enabled merchants to accept card
Aside mobile money and payments, the ATMs are also getting more and more popular xrowth in mobile money transactions is shown below: on mobile money alone hit 377million GHC while total value stood at 81.83billion GHC. The transactions continued to increase, with debit card being the most popular means for cai payment on account of affordable technologies which enabled merchants to accept card
in figure 5 below:
in figure 5 below:
Figure 6: Mobile Financial Services Volume in CoteD’Ivoire A KPMG (2018) report on payment developments in Africa shows that the banking system in West African Monetary Union is enjoying robust growth with about 115 active banks
Figure 6: Mobile Financial Services Volume in CoteD’Ivoire A KPMG (2018) report on payment developments in Africa shows that the banking system in West African Monetary Union is enjoying robust growth with about 115 active banks
all variables are defined in the previous equations above, w is the coefficient of the error correction model, while ECT,_, is the error correction term. In theory, it is assumed that the coefficient of the error correction model has a negative sign and is statistically significant to measure the speed of adjustment from the short-run to the long- run after a short-run disequilibrium.
all variables are defined in the previous equations above, w is the coefficient of the error correction model, while ECT,_, is the error correction term. In theory, it is assumed that the coefficient of the error correction model has a negative sign and is statistically significant to measure the speed of adjustment from the short-run to the long- run after a short-run disequilibrium.
Table 1: Descriptive Statistics in table 1, is the results of the descriptive statistics.
Table 1: Descriptive Statistics in table 1, is the results of the descriptive statistics.
not deviate much from each other and such was observed for median, standard deviation,
not deviate much from each other and such was observed for median, standard deviation,
Table 3: Unit Root Tests
Table 3: Unit Root Tests
(2020), Lee et al. (2019), and Enoruwa et al. (2019) among others. the ADF statistic for the specified variables is less than 0.05. These findings are in line with
(2020), Lee et al. (2019), and Enoruwa et al. (2019) among others. the ADF statistic for the specified variables is less than 0.05. These findings are in line with
Note: ***, **, and * represents 1%, 5% and 10% level of significance and (.) represents the probability values Source: Computed by the researcher. We furthered our investigations by employing the ARDL bounds test to examine if there
Note: ***, **, and * represents 1%, 5% and 10% level of significance and (.) represents the probability values Source: Computed by the researcher. We furthered our investigations by employing the ARDL bounds test to examine if there
therefore present the estimated ARDL long-run bounds test result in table 5 below.
therefore present the estimated ARDL long-run bounds test result in table 5 below.
Table 6: Results from Diagnostic Tests technological innovations and bank performance in each of the specified countries.
Table 6: Results from Diagnostic Tests technological innovations and bank performance in each of the specified countries.
Source: Compiled by the Researcher. Note: Decisions was taken at 5% level of significance.
Source: Compiled by the Researcher. Note: Decisions was taken at 5% level of significance.
models since in all the results; it has the least values across model] (see table 6).
models since in all the results; it has the least values across model] (see table 6).
Note: AIC=Akaike Information Criterion, SC= Schwartz Information Criterion (SC) and HQ=Hannan-Quinn Information Criterion Source: Computed by the Researcher. Estimated ARDL Results for Long Run Relationship
Note: AIC=Akaike Information Criterion, SC= Schwartz Information Criterion (SC) and HQ=Hannan-Quinn Information Criterion Source: Computed by the Researcher. Estimated ARDL Results for Long Run Relationship
Table 8: Results for Estimated ARLD Models Madugba et al. (2021) among others.
Table 8: Results for Estimated ARLD Models Madugba et al. (2021) among others.
Note: ***, ** and * represents the 1%. 5% and 10% level of significance Source: Computed by the Researcher.
Note: ***, ** and * represents the 1%. 5% and 10% level of significance Source: Computed by the Researcher.
Table 9: ARDL Results for Short-Run Dynamics al. (2021), and Eze & Egoro (2016) among others.
Table 9: ARDL Results for Short-Run Dynamics al. (2021), and Eze & Egoro (2016) among others.
Robustness Checks
Robustness Checks
Table 10: Estimated FMOLS and DOLS Results equations for the specified models
Table 10: Estimated FMOLS and DOLS Results equations for the specified models
Note: ***, ** and * represents the 1%. 5% and 10% level of significance Source: Computed by the Researcher.
Note: ***, ** and * represents the 1%. 5% and 10% level of significance Source: Computed by the Researcher.
Table 11: Estimated Results of FMOLS and DOLS for the Selected Countries specified models for each country and the results are presented in table 11 below.
Table 11: Estimated Results of FMOLS and DOLS for the Selected Countries specified models for each country and the results are presented in table 11 below.
Note: ***, ** and * represents the 1%. 5% and 10% level of significance Source: Computed by the Researcher. banking (INB), mobile banking (MBN), and inflation rate (INFR) portrayed negative and
Note: ***, ** and * represents the 1%. 5% and 10% level of significance Source: Computed by the Researcher. banking (INB), mobile banking (MBN), and inflation rate (INFR) portrayed negative and
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