Do you think alternative finance services are improving in South Africa? in %
4.5 Hypothesis testing
4.5.2 Service providers related hypothesis – H2
H2 A: Service providers of mobile banking and alternative finance understand the demographic characteristics of the customer and how these influence the use of the services offered
Employment status
Service providers normally offer finance to the employed or those who own businesses, hence the employment status plays an important role as a method of vetting, although alternative finance providers are not as strict on their criteria as banks are. Banks are still uptight on lending, regardless of the competition that exists in the market. Table 4.65 demonstrates the relationship between the employment status and mobile banking using the descriptive statistics.
Table 4.65: Descriptive statistics between employment status and mobile banking Descriptives -employment status and mobile banking
Mobile banking
N Mean Std.
Deviation
Std.
Error
95%
Confidence Interval for
Mean
Conclusion
Lower Bound
Upper Bound
Employed 230 3.1774 .31913 .02104 3.1359 3.2189 The average score of Mobile banking differs across the different employment statuses.
Self-employed 63 3.1317 .40354 .05084 3.0301 3.2334 Unemployed 57 3.1649 .21671 .02870 3.1074 3.2224 Student 73 3.0288 .25082 .02936 2.9702 3.0873 Other 2 3.0500 .07071 .05000 2.4147 3.6853 Total 425 3.1428 .31421 .01524 3.1129 3.1728
Table 4.66 reveals that the statement of homogeneity of variances has not been violated as the p-value is greater than 0.05 (p=0.089). This confirms a previous study in Tanzania where the working group was noted to be the dominant user of mobile banking (Richard & Mandari (2017).
Further confirmation by Bhatt (2016) is that mobile usage is higher for working individuals.
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Table 4.66: Homogeneity of variances test between employment status and mobile banking
Test of Homogeneity of Variances - employment status and mobile banking
Mobile banking
Levene’s Statistic df1 df2 Sig.
2.033 4 420 .089
There is significant difference of mean scores across the different employment statuses within mobile banking as the p-value = 0.011< 0.05 (see results in Table 4.67).
Table 4.67: ANOVA relationship between employment status and mobile banking ANOVA - employment status and mobile banking
Mobile banking
Sum of
Squares
df Mean Square F Sig.
Between Groups 1.277 4 .319 3.305 .011
Within Groups 40.583 420 .097
Total 41.861 424
The post hoc test demonstrations in Table 4.68 indicates that the mobile banking average score of the employed respondents is significantly higher than the average score of the respondents who are students (p=0.004< 0.05). This confirms previous studies by Bhatt & Bhatt (2016) and Abayomi et al. (2019) that if a person has an income, they are more likely to use mobile banking.
Furthermore, the income rises with the use of the mobile banking. This makes sense in general as it is not possible to be banking if there is no income. Furthermore, the Alpha test show that the corrected alpha is significant (0.010) which is less than the p-value.
Table 4.68: Multiple comparisons of relationships between employment status and mobile banking
Multiple Comparisons - employment status and mobile banking Dependent
Variable:
Mobile banking Tukey HSD
(I) Employment status Mean
Difference (I-J)
Std.
Error
Sig. 95% Confidence Interval
α_corrected Lower
Bound
Upper Bound
Employed Self-employed .04565 .04420 .840 -.0755 .1667 .010 Unemployed .01248 .04599 .999 -.1135 .1385 .010
Student .14862* .04176 .004 .0342 .2630 .010
Other .12739 .22076 .978 -.4774 .7322 .010
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Self-employed Employed -.04565 .04420 .840 -.1667 .0755 .010 Unemployed -.03317 .05682 .977 -.1888 .1225 .010
Student .10298 .05345 .305 -.0435 .2494 .010
Other .08175 .22327 .996 -.5299 .6934 .010
Unemployed Employed -.01248 .04599 .999 -.1385 .1135 .010
Self-employed .03317 .05682 .977 -.1225 .1888 .010
Student .13615 .05494 .098 -.0144 .2867 .010
Other .11491 .22363 .986 -.4977 .7276 .010
Student Employed -.14862* .04176 .004 -.2630 -.0342 .010
Self-employed -.10298 .05345 .305 -.2494 .0435 .010 Unemployed -.13615 .05494 .098 -.2867 .0144 .010
Other -.02123 .22279 1.00
0
-.6316 .5891 .010
Other Employed -.12739 .22076 .978 -.7322 .4774 .010
Self-employed -.08175 .22327 .996 -.6934 .5299 .010 Unemployed -.11491 .22363 .986 -.7276 .4977 .010
Student .02123 .22279 1.00
0
-.5891 .6316 .010
*. The mean difference is significant at the 0.05 level.
H2B Service providers understand the customer needs through background with regards to the use of mobile banking and alternative finance
Understanding the customer as service provider is very important as one is able to reach out accordingly. The subsequent tables indicate the relationship with regards to internet access as one of the measures of customer background. This further shows that the customer lives in an area that has or does not have network to connect to mobile banking and alternative finance services.
Internet access
Relationship between internet access and mobile banking
The descriptive of mean, standard deviation and standard error serve as the basis to understand the sampled population rating on internet access. This assists in understanding the role of the variable that leads to customers utilising the mobile banking services.
Table 4.69: Descriptive statistics between internet access and mobile banking Descriptives - internet access and mobile banking
Mobile banking
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean
Conclusion Lower
Bound
Upper Bound
Yes 382 3.1461 .31875 .01631 3.1140 3.1781
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No 34 3.1824 .17833 .03058 3.1201 3.2446 The average score of mobile banking differs across the respondents’ internet access.
Not sure 10 2.8900 .40125 .12689 2.6030 3.1770
Total 426 3.1430 .31400 .01521 3.1131 3.1729
The outcomes in Table 4.70 reveal that the hypothesis of homogeneity of variances has not been disturbed as the p-value is greater than 0.05 (p=0.084). The hypothesis that internet access plays a critical role for customers to use mobile banking is accepted.
Table 4.70: Homogeneity of variances test between internet access and mobile banking Test of Homogeneity of Variances - internet access and mobile banking
Mobile banking
Levene’s Statistic df1 df2 Sig.
2.486 2 423 .084
In Table 4.71, the results show the significant difference of mean scores across the different internet access statuses within mobile banking since the p-value = 0.029 < 0.05. This means that the groups differs on the level of internet access which affects the utilisation of mobile banking services by customers.
Table 4.71: ANOVA test relationship between internet access and mobile banking ANOVA - internet access and mobile banking
Mobile banking
Sum of
Squares
df Mean Square F Sig.
Between Groups .696 2 .348 3.574 .029
Within Groups 41.208 423 .097
Total 41.904 425
The post hoc test illustrated in Table 4.72 demonstrates that the mobile banking average score of the participants who can access internet on their mobile devices is significantly higher (p=0.029
<0.05) than the average score of the participants who stated that they are not certain if they can access internet on their mobile devices. Additionally, the mobile banking average score of the participants who cannot access internet on their mobile devices is considerably higher (p= 0.026<
0.05) than the average score of the participants who said that they are not certain if they can access internet on their mobile devices.
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Table 4.72: Multiple comparisons test between internet access and mobile banking Multiple Comparisons - internet access and mobile banking
Dependent Variable: Mobile banking Tukey HSD
(I) Can you access internet on your mobile device?
Mean Difference
(I-J)
Std.
Error
Sig. 95% Confidence Interval Lower Bound
Upper Bound
Yes No -.03628 .05586 .793 -.1677 .0951
Not sure .25607* .09998 .029 .0209 .4912
No Yes .03628 .05586 .793 -.0951 .1677
Not sure .29235* .11228 .026 .0283 .5564
Not sure Yes -.25607* .09998 .029 -.4912 -.0209
No -.29235* .11228 .026 -.5564 -.0283
*. The mean difference is significant at the 0.05 level.
Relationship between internet access and alternative finance
Furthermore, the relationship between internet access and alternative finance is explored. As there is a rise in the use of technology in all spheres of the world, alternative finance was not left behind. Online platforms to access alternative finance have been created and made available to consumers. The question now is whether the customers are able to access those platforms, hence having internet access adds great value and needs attention as comparisons are shown in Table 4.73 as per the responses by the respondents.
Table 4.73: Descriptive statistics between internet access and alternative finance Descriptives - internet access and alternative finance
Alternative finance
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean
Conclusion Lower
Bound
Upper Bound
Yes 382 2.9654 .38510 .01970 2.9267 3.0042 The average score of Alternative finance differs across the respondents’
internet access.
No 34 3.1088 .41514 .07120 2.9640 3.2537 Not sure 10 2.6900 .24698 .07810 2.5133 2.8667 Total 426 2.9704 .38862 .01883 2.9334 3.0074
Table 4.74 reveals that the supposition of homogeneity of variances has not been disrupted (p=0.535 > 0.05).
Table 4.74: Homogeneity of variances test between internet access and alternative finance
Test of Homogeneity of Variances - internet access and alternative finance Alternative finance
Levene’s Statistic df1 df2 Sig.
.626 2 423 .535
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The outcomes in Table 4.75 shows that there is significant variation of mean scores across the different internet access statuses within alternative finance because the p-value = 0.008 < 0.05.
Table 4.75: ANOVA test between internet access and alternative finance ANOVA - internet access and alternative finance Alternative finance
Sum of
Squares
df Mean
Square
F Sig.
Between Groups 1.447 2 .724 4.878 .008
Within Groups 62.740 423 .148
Total 64.187 425
The post hoc test demonstration in Table 4.76 reveals that the alternative finance average score of partakers who cannot access the internet on their mobile devices is significantly higher (p=0.007 <0.05) than the average score of partakers who pointed out that they are not certain if they can access internet on their mobile devices.
Table 4.76: Multiple comparisons between internet access and alternative finance Multiple Comparisons - internet access and alternative finance
Dependent Variable:
Alternative finance Tukey HSD
(I) Can you access internet on your mobile device?
Mean Differenc
e (I-J)
Std.
Error
Sig. 95% Confidence Interval Lower
Bound
Upper Bound
Yes No -.14338 .06893 .095 -.3055 .0187
Not sure .27545 .12337 .067 -.0147 .5656
No Yes .14338 .06893 .095 -.0187 .3055
Not sure .41882* .13854 .007 .0930 .7447
Not sure Yes -.27545 .12337 .067 -.5656 .0147
No -.41882* .13854 .007 -.7447 -.0930
*. The mean difference is significant at the 0.05 level.
Mobile banking service improvement
Relationship between mobile banking services improvement in South Africa and mobile banking
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Improved mobile banking services is important as customers are always excited to use the service at the comfort of their own space. According to Baptista & Oliveira (2015), rapid modernisation of communications, mobile technologies and the spread of smart devices have improved the impact of mobile banking services for banks, financial services providers and users.
This is noted in Table 4.77, as the ordinary score of mobile banking varies across mobile banking services improvement.
Table 4.77: Descriptive statistics between mobile banking services improvement in South Africa and mobile banking
Descriptives - mobile banking services improvement in South Africa and mobile banking Mobile banking
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean
Conclusion Lower
Bound
Upper Bound
Yes 383 3.1381 .30853 .01577 3.1071 3.1691 The average score of Mobile banking differs across Mobile banking services improvement.
No 38 3.2026 .38025 .06168 3.0776 3.3276
Not sure 2 3.1000 .14142 .10000 1.8294 4.3706 Total 423 3.1437 .31503 .01532 3.1136 3.1738
Table 4.78 indicates that the postulation of homogeneity of variances has not been violated as the p-value is greater than 0.05 (p=0.382).
Table 4.78: Homogeneity of variances test between mobile banking services improvement in South Africa and mobile banking
Test of Homogeneity of Variances - mobile banking services improvement in South Africa and mobile banking
Mobile banking
Levene’s Statistic df1 df2 Sig.
.964 2 420 .382
The outcomes in Table 4.79 indicates a non-significant variation of mean scores across mobile banking services improvement within mobile banking because the p-value is greater than 0.05 (p=0.476). Consequently, the post hoc effects are disregarded.
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Table 4.79: ANOVA relationship between mobile banking services improvement in South Africa and mobile banking
ANOVA - mobile banking services improvement in South Africa and mobile banking Mobile banking
Sum of
Squares
df Mean Square F Sig.
Between Groups .148 2 .074 .743 .476
Within Groups 41.733 420 .099
Total 41.881 422
ANOVA relationship between mobile banking services improvement in South Africa and alternative finance
As noted with mobile banking, similar users’ needs have to be a priority to service providers. This is confirmed by Pankomera & Van Greunen (2018) who note that stakeholders need to create an awareness to users’ protection against potential cybersecurity exposure and threats. The security of users is also a factor in alternative finance online platforms, hence improvements are critical.
Table 4.80: Descriptive statistics between mobile banking services improvement in South Africa and alternative finance
Descriptives - mobile banking services improvement in South Africa and alternative finance Alternative finance
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean
Conclusion Lower
Bound
Upper Bound
Yes 383 2.9661 .38404 .01962 2.9275 3.0046 The average score of Alternative finance differs across Mobile banking services improvement.
No 38 3.0026 .43464 .07051 2.8598 3.1455
Not sure 2 3.1500 .21213 .15000 1.2441 5.0559 Total 423 2.9702 .38786 .01886 2.9331 3.0073
Table 4.81 indicates that the postulation of homogeneity of variances has not been disturbed as the p-value is greater than 0.05 (p=0.858).
Table 4.81: Homogeneity of variances test between mobile banking services improvement in South Africa and alternative finance
Test of Homogeneity of Variances - mobile banking services improvement in South Africa and alternative finance
Alternative finance
Levene’s Statistic df1 df2 Sig.
.154 2 420 .858
160
There is a non-significant variation of mean scores across mobile banking services improvement within alternative finance as the p-value =0.692 > 0.05 (see Table 4.82). Therefore, the post hoc results are ignored.
Table 4.82: ANOVA relationship between mobile banking services improvement in South Africa and alternative finance
ANOVA - mobile banking services improvement in South Africa and alternative finance Alternative finance
Sum of
Squares
df Mean
Square
F Sig.
Between Groups .111 2 .056 .368 .692
Within Groups 63.373 420 .151
Total 63.485 422
Relationship between alternative finance service improvement and alternative finance The constant improvement of alternative finance service by providers is important and, in that process, users need to be kept updated. In return, with changes that add value to users, more buy-in is critical to the benefit of the service providers.
Table 4.83: Descriptive statistics between alternative finance services improvement in South Africa and alternative finance
Descriptives – alternative finance services improvement in South Africa and alternative finance
Alternative finance
N Mean Std.
Deviation
Std.
Error
95%
Confidence Interval for
Mean
Conclusion
Lower Bound
Upper Bound
Yes 140 2.9757 .29133 .02462 2.9270 3.0244 The average score of Alternative finance differs across alternative finance service improvement.
No 24 3.0292 .44083 .08998 2.8430 3.2153 Not
sure
240 2.9642 .41796 .02698 2.9110 3.0173 Total 404 2.9720 .37974 .01889 2.9349 3.0092
Results in Table 4.84 show that the postulation of homogeneity of variances has been disturbed (p=0.002< 0.05) as the p-value is significantly less than 0.05.
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Table 4.84: Homogeneity of variances test between alternative finance services improvement in South Africa and alternative finance
Test of Homogeneity of Variances - alternative finance services improvement in South Africa and alternative finance
Alternative finance
Levene’s Statistic df1 df2 Sig.
6.313 2 401 .002
The outcomes in Table 4.85 of the robust tests of equality of means are reflected. The given results in Table 4.85 show no significant variations where Welch p-value=0.778; Brown-Forsythe p-value=0.736 in alternative finance service improvement with alternative finance. For this reason, the statement of homogeneity of variances has not been violated.
Table 4.85: Robust tests of equality of means between alternative finance services improvement in South Africa and alternative finance
Robust Tests of Equality of Means - alternative finance services improvement in South Africa and alternative finance
Alternative finance
Statistica df1 df2 Sig.
Welch .252 2 61.887 .778
Brown-Forsythe .308 2 63.900 .736
a. Asymptotically F distributed.
The results in Table 4.86 indicate that there is a non-significant difference of mean scores of alternative finance service improvement within alternative finance because the p-value of 0.720 is greater than 0.05, thus the post hoc results are ignored.
Table 4.86: ANOVA test between alternative finance services improvement in South Africa and alternative finance
ANOVA - alternative finance services improvement in South Africa and alternative finance Alternative finance
Sum of
Squares
df Mean
Square
F Sig.
Between Groups .095 2 .048 .329 .720
Within Groups 58.019 401 .145
Total 58.114 403
4.5.3 Relationship between mobile banking and alternative finance - H3