Model 2

The second model involved the addition of use of social media and the interaction term (mobile money adoption × use of social media). The interaction term captured how the overall mobile money adoption would be increased by the simultaneous adoption of mobile money technology and use of social media effect, that is, an individual simultaneously used technology and social media. Similar to Model 1, Model 2 is presented as Block 0 and Block 1. Model 2 Block 0 is the null model, the output of which is shown in Table 6.18 below.

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Table 6.18: Zimbabwe Model 2 Block 0

Classification Tablea,b

Observed

Predicted Mobile money adoption

Percentage Correct Non-adoption of Mobile Money technology Mobile Money Adoption Step 0 Mobile money adoption Non-adoption of Mobile

Money technology

1914 0 100.0

Mobile Money Adoption 1836 0 .0

Overall Percentage 51.0

a. Constant is included in the model. b. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant -.342 .115 10.692 1 .001 .710

Variables not in the Equation

Score df Sig.

Step 0 Variables Mobile money adoption × Use of social media

2954.264 1 .000

Use of social media 424.751 1 .000

Overall Statistics 3060.677 2 .000

Source: Author’s compilation

Given the base rates of the two decision options (1836/3750 = 49% chose to adopt mobile money adoption and 51% did not) (see Table 6.18) and with no other information, the best strategy was to predict, for every case, that an individual would choose to adopt mobile money. Using this strategy, the model would be correct 51% of the time. Thus, overall percent of cases that were correctly predicted by the null was 51%, and it could be concluded that the model was a good fit for the data and could be replicated. The intercept-only model is displayed under the “Variables in the Equation” section. The Wald Chi-square tests the null hypothesis that the constant equals zero. This hypothesis was rejected because the p-value (0.001) was less than the critical value of 0.05. Therefore, the study concluded that the constant was

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not zero and the predicted odds of mobile money adoption for Zimbabwe in Model 2 Block 0 were 0.710. Looking at the p-values from the Score test under the Variables not in the Equation section in Table 6.18, mobile money adoption × use of social media (0.000) and use of social media (0.000) were statistically significant at a 95% confidence level. The overall statistics p-value of 0.000 indicated that the result of including the interaction term in the model, it was significant at the 5% level.

Model 2 Block 1 (see Table 6.19 below) displays the outcome of the binary logistic regression model consisting of the use of social media and mobile money adoption × use of social media (interaction term). A discussion of the results follows.

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Table 6.19: Zimbabwe Model 2 Block 1

Omnibus Tests of Model Coefficients

Chi-square Df Sig.

Step 1 Step 1493.283 2 .000

Block 1493.283 2 .000

Model 1493.283 2 .000

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 3703.699a _{.669 .892}

a. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.

Hosmer and Lemeshow Test

Step Chi-square Df Sig.

1 .000 1 .902

Classification Tablea

Observed

Predicted Mobile money adoption

Percentage Correct Non-adoption of Mobile Money technology Mobile Money Adoption Step 1 Mobile money adoption Non-adoption of Mobile

Money technology

1691 223 88.3

Mobile Money Adoption 0 1836 100

Overall Percentage 94.1

a. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a Mobile money adoption × Use of social media 2.279 .614 30.931 1 .002 9.767

Use of social media 1.217 .245 24.757 1 .000 3.377

Constant -.482 .1264 14.626 1 .000 .618

a. Variable(s) entered on step 1: Mobile money adoption × Use of social media, Use of social media. Source: Author’s compilation

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The Omnibus tests of model coefficients gave a Chi-Square of 1493.283 on df 2, and the p-value of the block (mobile money adoption × use of social media; use of social media) was 0.000, and therefore statistically significant at the 95% confidence level. Thus, Model 2 Block 1 model was a significant improvement on Model 2 Block 0 - the addition of the social media and the interaction term to the intercept-only model significantly and positively influenced mobile money technology adoption in Zimbabwe. The block and step p-values were equal to the model’s value since all variables (interaction term, use of social media and the constant) were entered at the same time.

The model summary in Table 6.19 further illustrates that the addition of the interaction term to the null model results in a decrease in the -2 Log likelihood statistic to 3703.699 from 5196.981 (3703.699 + 1493.283 from the Chi-square in the Omnibus tests of model coefficients) in the null model. This reduction of the -2 Log likelihood statistic implies an improvement in model fit after the inclusion of the interaction term. Considering the pseudo 𝑅2, the Nagelkerke 𝑅2 reveals that the interaction term (mobile money adoption × use of social media) and the use of social media accounted for 89.2% of the total amount of variance in the mobile money technology adoption decision. Only 10.8% of the variance in mobile money adoption was explained by other variables which were excluded from Model 2 Block 1, and therefore the Nagelkerke 𝑅2 value indicated a good fit of the model in explaining mobile money technology adoption in Zimbabwe.

At a 95% confidence level, the Hosmer-Lemeshow test was not statistically significant (p-value = 0.902) however. This insignificance indicated that the binary logistic regression was an adequate fit to the data since a good fit model has a p- value that is greater than the 0.05 significance level (Hosmer and Lemeshow, 2000). The classification of the false positive and false negative error rates displayed in Table 6.19 indicates that the decision rule predicted a decision of mobile money technology adoption 2059 times; the prediction was wrong 223 times. Therefore, there was a false positive of 10.83% (223/2059). The decision rule predicted the non-adoption of mobile money 1691 times, and that prediction was correct for a false negative of 0% (0/1691). The overall model correct classification was 94.1%. Thus,

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Model 2 Block 1 showed an improvement in the model fit following the addition of social media and the interaction term to the null model (Model 2 Block 1).

In terms of the Wald test from the Variables in the Equation, Table 6.19 shows that the coefficients of the mobile money adoption × use of social media and use of social media variables were statistically significant at the 95% confidence level. Thus, it was concluded that the interaction term (B = 2.279 and p-value of 0.002) and social media (B = 1.217 and p-value of 0.000) had a positive and statistically significant effect on mobile money adoption decision in Zimbabwe. Therefore, for every one-unit increase in the interaction term, a 9.767 increase in the log-odds of overall mobile adoption was expected, holding the use of social media constant. Also, for every one-unit increase in the use of social media, a 3.377 increase in the log-odds of mobile money adoption was expected, holding the interaction term constant. The fitted Model 2 Block 1 equation is shown below:

Mobile money adoption = -0.482 + 2.279 × Interaction term (mobile money adoption × use of social media) + 1.217 × Use of social media (11)