Do you think alternative finance services are improving in South Africa? in %
4.5 Hypothesis testing
4.5.1 User related hypothesis – H1
H1 A: Customer demographic characteristics has an effect on the use of mobile banking and alternative finance
Gender, age, race, educational qualifications and highest level of qualification are demographic characteristics highlighted below.
Gender
Gender is one of the demographic characteristics which was used to test if there is a relationship between mobile banking and alternative finance (see the subsequent Tables). As per Table 4.7 and Table 4.9, the majority of the participants were males (221).
Table 4.7 shows the ordinary score differences across the genders in relation to mobile banking.
For future studies, a larger sample is recommended as it would increase variation and a possible a different outcome.
Table 4.7: Group statistics between gender and mobile banking Group Statistics – Relationship between gender and mobile banking
Gender N Mean Std. Deviation Conclusion
Mobile banking
Male 221 3.1032 0.30606 The average score for Mobile banking differs across the genders.
Female 205 3.1883 0.31647
In Table 4.8, the Levene’s test shows that the equal variance is presumed as the p-value is 0.560 which is above 0.05. Therefore, the information in the first row (equal variance is assumed) is
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accepted. This confirms the low variation of the sample. The second part of Table 4.8 (t-test for similarity of means) indicates that there is a noteworthy difference in mobile banking between females and males (p<0.05, t=-2.822, ∆Mean=-0.08513).
Table 4.8: Independent samples tests between gender and mobile banking Independent Samples Test – relationship between gender and mobile banking
Levene's
Test for Equality of Variances
t-test for Equality of Means
F Sig. T df Sig.
(2- tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower Upper Mobile
banking
Equal variances assumed
.341 .560 - 2.822
424 .005 -.08513 .03017 -.14442 -.02583
Equal variances not assumed
-
2.818
419.045 .005 -.08513 .03021 -.14450 -.02575
Similarly, the relationship between gender and alternative finance was tested and the same outcomes that were derived from gender and mobile banking are noted in Table 4.9.
Table 4.9: Group statistics between gender and alternative finance Group Statistics – relationship between gender and alternative finance
Gender N Mean Std.
Deviation
Conclusion
Alternative finance
Male 221 2.9606 .37385 The average score of Alternative finance differs across the genders.
Female 205 2.9849 .40088
As shown in Table 4.10, the Levene’s test reveals the assumption of equal variance because the p-value is 0.854 which is above 0.05. Therefore, the evidence on the first row (equal variances assumed) is considered. This confirms the acceptance of the hypothesis that gender has an effect on use of alternative finance. This supports previous studies by Zhang et al. (2017) that gender has an effect on the use of alternative finance. It is noted that women are on the land mark for each alternative finance platform, whether as campaigners or funders.
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Section two of Table 4.10 (t-test for equality of means) indicates that there is no significant variation in relations of alternative finance (p>0.05, t=-0.646, ∆Mean=-0.02424) between the two genders. This implies that the average level of alternative finance use is nearly the same for both males and females. While this is different for mobile banking as previous studies confirmed that more males were noted to be mobile banking users because they work, this is in comparison to women who mostly depend on their men (Richard & Mandari, 2017).
Table 4.10: Independent samples test between gender and alternative finance Independent Samples Test – relationship between gender and alternative finance
Levene's
Test for Equality of Variances
T-test for Equality of Means
F Sig. t df Sig.
(2- tailed )
Mean Differenc e
Std. Error Differenc e
95%
Confidence Interval of the Difference Lower Upper
Alternativ e finance
Equal variances assumed
.03 4
.854 - .646
424 .519 -.02424 .03754 - .0980 2
.04953
Equal
variances not assumed
-.644 415.291 .520 -.02424 .03763 - .09822
.04973
Age
Relationship between age and mobile banking
Table 4.11 shows the basics on the relationship between age and mobile banking. The descriptive calculations were completed for each group as shown in the Figure. Similar to alternative finance, no significant relationship was found on age and mobile banking. This further implies that there is a need for future in-depth research focusing on age, mobile banking and alternative finance in order to fully understand the relationship among them.
Table 4.11: Descriptive statistics between age and mobile banking Descriptives - age and mobile banking
Age N Mean Std. Deviation Std. Error 95% confidence
Lower Upper 18 years 22 2.9364 .35125 .07489 2.7806 3.0921 21 – 29 126 3.0992 .29973 .02670 3.0464 3.1521
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30 – 39 147 3.1585 .25904 .02137 3.1163 3.2007 40 – 49 100 3.2300 .39402 .03940 3.1518 3.3082 50 – 59 28 3.1179 .18268 .03452 3.0470 3.1887 60 or
older
4 3.1250 .12583 .06292 2.9248
3.3252
Total 427 3.1433 .31373 .01518 3.1135 3.1732
The age aspect of testing the hypothesis for this study is shown in the subsequent tables as having been violated at all testing done as there in no significance in the p-value for
homogeneity of variance testing sig = 0.014 which is less than the 0.05 standard for the p-value (see Table 4.12). Age does not affect the use of mobile banking as no significant relationship was found in this study. The hypothesis of the age relationship is not accepted.
Table 4.12: Homogeneity of variances test between age and mobile banking Test of Homogeneity of Variances – age and mobile banking
Levene Statistic df1 df2 Sig.
2.874 5 421 .014
The ANOVA in Table 4.13 further confirms the violation of the assumption where the sig = 0.001 which is less than the acceptable p-value of 0.05.
Table 4.13: ANOVA between age and mobile banking ANOVA - age and mobile banking
Sum of df Mean F Sig.
Between Within Total
1.992 39.936 41.928
5 421 426
.398 .095
4.200 .001
Even the robust test of equality of means for both the Welch and Brown-Forsythe is below the p-value of 0.05 shown at 0.028 and 0.000 respectively (see Table 4.14).
Table 4.14: Robust tests of equality of means between age and mobile banking Robust Tests of Equality of Means - age and mobile banking
Statistica df1 df2 Sig.
126 Welch
Brown-Forsythe
2.958 5.107
5 5
29.956 158.822
.028 .000
a. Asymptotically F distributed
The multiple comparisons in Table 4.15 also violated the assumption on the relationship between age and mobile banking usage for this study. Therefore, the hypothesis on age for this test is not accepted. This is against the findings from previous studies where the younger generation has highly adopted mobile banking and a low rate is reported among the elderly age groups (Msweli, 2020).
Table 4.15: Multiple comparisons relationship between age and mobile banking Multiple Comparisons – Age and mobile banking
Dependent Variable: Mobile banking Tukey HSD
(I) Age Mean
Difference (I- J)
Std.
Error
Sig. 95% Confidence Interval Lower Bound
Upper Bound
18 years 21 - 29 -.16284 .07117 .201 -.3666 .0409
30 - 39 -.22214* .07041 .021 -.4237 -.0206
40 - 49 -.29364* .07253 .001 -.5013 -.0860
50 - 59 -.18149 .08775 .306 -.4327 .0697
60 or older -.18864 .16741 .870 -.6679 .2906
21 - 29 18 years .16284 .07117 .201 -.0409 .3666
30 - 39 -.05930 .03739 .608 -.1663 .0478
40 - 49 -.13079* .04125 .020 -.2489 -.0127
50 - 59 -.01865 .06435 1.000 -.2029 .1656
60 or older -.02579 .15642 1.000 -.4736 .4220
30 - 39 18 years .22214* .07041 .021 .0206 .4237
21 - 29 .05930 .03739 .608 -.0478 .1663
40 - 49 -.07150 .03992 .473 -.1858 .0428
50 - 59 .04065 .06351 .988 -.1412 .2225
60 or older .03350 .15608 1.000 -.4133 .4803
40 - 49 18 years .29364* .07253 .001 .0860 .5013
21 - 29 .13079* .04125 .020 .0127 .2489
30 - 39 .07150 .03992 .473 -.0428 .1858
50 - 59 .11214 .06585 .531 -.0764 .3007
60 or older .10500 .15705 .985 -.3446 .5546
50 - 59 18 years .18149 .08775 .306 -.0697 .4327
21 - 29 .01865 .06435 1.000 -.1656 .2029
30 - 39 -.04065 .06351 .988 -.2225 .1412
40 - 49 -.11214 .06585 .531 -.3007 .0764
60 or older -.00714 .16463 1.000 -.4785 .4642
60 or older 18 years .18864 .16741 .870 -.2906 .6679
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21 - 29 .02579 .15642 1.000 -.4220 .4736
30 - 39 -.03350 .15608 1.000 -.4803 .4133
40 - 49 -.10500 .15705 .985 -.5546 .3446
50 - 59 .00714 .16463 1.000 -.4642 .4785
*. The mean difference is significant at the 0.05 level.
Relationship between age and alternative finance
Table 4.16 describes the relationship of usage of alternative finance and age, with differences noted in the conclusion column.
Table 4.16: Descriptive statistics between age and alternative finance Descriptives - age and alternative finance Alternative finance
N Mean Std.
Deviation
Std.
Error
95%
Confidence Interval for
Mean
Conclusion
Lower Bound
Upper Bound
18 years 22 2.7182 .42273 .09013 2.5308 2.9056 The average score of alternative finance differs across the different age groups.
21 - 29 126 2.9627 .37261 .03319 2.8970 3.0284 30 - 39 147 3.0218 .38079 .03141 2.9597 3.0838 40 - 49 100 2.9730 .35501 .03550 2.9026 3.0434 50 - 59 28 2.9179 .52849 .09987 2.7129 3.1228 60 or older 4 3.0250 .05000 .02500 2.9454 3.1046
Total 427 2.9705 .38817 .01878 2.9336 3.0074
The outcomes as shown in Table 4.17 indicates that the assumption of homogeneity of variances is not violated (P=0.117>0.05).
Table 4.17: Homogeneity of variances test between age and alternative finance Test of Homogeneity of Variances - age and alternative finance Alternative finance
Levene’s Statistic df1 df2 Sig.
1.774 5 421 .117
Table 4.18 points out that there is a significant difference of mean scores across the diverse age groups within alternative finance because the p-value is =0.028< 0.05. This means that the results are not by chance as age plays an important role in the use of alternative finance. This area
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needs more exploration as no studies have focused on age in alternative finance but rather, gender has been the focus.
Table 4.18: ANOVA test between age and alternative finance ANOVA - age and alternative finance Alternative finance
Sum of
Squares
df Mean
Square
F Sig.
Between Groups 1.885 5 .377 2.547 .028
Within Groups 62.303 421 .148
Total 64.188 426
The post hoc test in Table 4.19 demonstrates that the alternative finance average score of the participants that are aged 30-39 is significantly higher (p=0.008<0.05) compared to the ordinary score of the participants that are 18 years old. The corrected alpha was run and showed 0.008 less than the p-value confirming significance.
Table 4.19: Multiple comparisons tests between age and alternative finance Multiple Comparisons - age and alternative finance
Dependent Variable:
Alternative finance
α_corrected Tukey HSD
(I) Age
Mean Difference
(I-J)
Std.
Error Sig.
95% Confidence Interval Lower Bound
Upper Bound
18 years 21 - 29 -.24452 .08889 .068 -.4990 .0100 0,008
30 - 39 -.30359* .08794 .008 -.5554 -.0518 0,008
40 - 49 -.25482 .09059 .057 -.5142 .0045 0,008
50 - 59 -.19968 .10960 .453 -.5134 .1141 0,008
60 or older -.30682 .20910 .685 -.9055 .2918 0,008
21 - 29 18 years .24452 .08889 .068 -.0100 .4990 0,008
30 - 39 -.05907 .04670 .804 -.1928 .0746 0,008
40 - 49 -.01030 .05152 1.000 -.1578 .1372 0,008
50 - 59 .04484 .08037 .994 -.1853 .2749 0,008
60 or older -.06230 .19538 1.000 -.6216 .4970 0,008
30 - 39 18 years .30359* .08794 .008 .0518 .5554 0,008
21 - 29 .05907 .04670 .804 -.0746 .1928 0,008
40 - 49 .04877 .04987 .925 -.0940 .1915 0,008
50 - 59 .10391 .07932 .779 -.1232 .3310 0,008
60 or older -.00323 .19495 1.000 -.5613 .5549 0,008
40 - 49 18 years .25482 .09059 .057 -.0045 .5142 0,008
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21 - 29 .01030 .05152 1.000 -.1372 .1578 0,008
30 - 39 -.04877 .04987 .925 -.1915 .0940 0,008
50 - 59 .05514 .08225 .985 -.1803 .2906 0,008
60 or older -.05200 .19616 1.000 -.6136 .5096 0,008
50 - 59 18 years .19968 .10960 .453 -.1141 .5134 0,008
21 - 29 -.04484 .08037 .994 -.2749 .1853 0,008
30 - 39 -.10391 .07932 .779 -.3310 .1232 0,008
40 - 49 -.05514 .08225 .985 -.2906 .1803 0,008
60 or older -.10714 .20563 .995 -.6958 .4815 0,008
60 or older 18 years .30682 .20910 .685 -.2918 .9055 0,008
21 - 29 .06230 .19538 1.000 -.4970 .6216 0,008
30 - 39 .00323 .19495 1.000 -.5549 .5613 0,008
40 - 49 .05200 .19616 1.000 -.5096 .6136 0,008
50 - 59 .10714 .20563 .995 -.4815 .6958 0,008
*. The mean difference is significant at the 0.05 level.
Relationship between race and mobile banking
Race was also considered as one of the variables that required to be tested in order to understand if it has an impact on mobile banking usage. Table 4.20 shows the responses and outcomes from the data collected.
Table 4.20: Descriptive statistics between race and mobile banking Descriptives - race and mobile banking
Race N Mean Std. Deviation Std. Error 95% confidence
Lowe r
Upper
Black 328 3.1768 .32783 .01810 3.1412 3.2124 Coloured 49 2.9857 .26061 .03723 2.9109 3.0606 Indian/Asian 22 3.0545 .20172 .04301 2.9651 3.1440
White 28 3.0964 .17739 .03352 3.0276 3.1652
Total 427 3.1433 .31373 .01518 3.1135 3.1732
Like age, similar outcomes based on the hypothesis of the race assumption which is a demographic characteristic that affects the use of mobile banking were established. This hypothesis was also not accepted as shown in Table 4.21. The testing for homogeneity of variances was found to be less (sig = 0.011).
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Table 4.21: Homogeneity of variances between race and mobile banking Test of Homogeneity of Variances – race and mobile banking
Levene Statistic df1 df2 Sig.
3.753 3 423 .011
Similar to age, the ANOVA for race was also not significant based on the assumption of race hypothesis, hence it was rejected as the p-value (0.05) is greater than sig. 0.000 as illustrated in Table 4.22.
Table 4.22: ANOVA relationship between race and mobile banking ANOVA - race and mobile banking
Mobile Banking Sum of df Mean F Sig.
Between Within Total
1.820 40.108 41.928
3 423 426
.607 .095
6.400 .000
Furthermore, the robust test of equality of means was also violated at sig. equals to 0.000 for both the Welch and Brown-Forsythe (see Table 4.23). This means that there is no relationship between mobile banking and race in this study. This is against the findings in study by Nam, Lee
& Kim (2022) who found out that blacks use more mobile payment services and whites use less of the services. The researchers further mentioned that prior experiences explain these disparities in the use of technological applications by blacks.
Table 4.23: Robust tests of equality of means between race and mobile banking Robust Tests of Equality of Means - race and mobile banking
Mobile Banking Statistica df1 df2 Sig.
Welch
Brown-Forsythe
8.355 11.95
3 3
62.757 130.001
.000 .000
a. Asymptotically F distributed
Table 4.24 shows that the mean difference is not significant as it is greater than 0.05 significant level. This then leads to the relationship between race and mobile banking to be seen as violated.
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It is also important to note that most previous studies that have focused on race are mostly American, and they cannot explain the South African context as these countries are different.
Table 4.24: Multiple comparisons relationship between race and mobile banking Multiple Comparisons – race and mobile banking
Dependent Variable: Mobile banking Tukey HSD
(I) Race Mean
Difference (I- J)
Std.
Error
Sig. 95% Confidence Interval Lower Bound
Upper Bound
Black Coloured .19111* .04716 .000 .0695 .3128
Indian /Asian .12228 .06782 .273 -.0526 .2972
White .08040 .06063 .547 -.0760 .2368
Coloured Black -.19111* .04716 .000 -.3128 -.0695
Indian /Asian -.06883 .07903 .820 -.2727 .1350 White
-.11071 .07295 .428 -.2989 .0774
Indian / Asian Black -.12228 .06782 .273 -.2972 .0526
Coloured .06883 .07903 .820 -.1350 .2727
White -.04188 .08773 .964 -.2682 .1844
White Black -.08040 .06063 .547 -.2368 .0760
Coloured .11071 .07295 .428 -.0774 .2989
Indian /Asian .04188 .08773 .964 -.1844 .2682
*. The mean difference is significant at the 0.05 level.
Relationship between race and alternative finance
Furthermore, the relationship between race and alternative finance was explored to test the hypothesis. Table 4.25 shows the outcome based on each race group of participants.
Table 4.25: Descriptive statistics between race and alternative finance Descriptives - race and alternative finance
Alternative finance
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean
Conclusion Lower
Bound
Upper Bound
Black 328 2.9713 .40799 .02253 2.9270 3.0157 The average score of alternative finance differs across the different races.
Coloured 49 2.9959 .42030 .06004 2.8752 3.1166
Indian/Asian 22 2.9545 .15653 .03337 2.8851 3.0239
White 28 2.9286 .15836 .02993 2.8672 2.9900
Total 427 2.9705 .38817 .01878 2.9336 3.0074
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Table 4.26 shows that the assumption of homogeneity of variances has been dishonoured (p=0.002<0.05), hence the outcomes in Table 4.27 where Robust tests of equality of means tests were considered.
Table 4.26: Homogeneity of variances test between race and alternative finance Test of Homogeneity of Variances – race and alternative finance Alternative finance
Levene’s Statistic df1 df2 Sig.
5.108 3 423 .002
The results in Table 4.27 reveal no significant differences (Welch p-value=0.640; Brown-Forsythe p-value=0.783) in the means of race within alternative finance. For that reason, the hypothesis of homogeneity of variances has not been violated.
Table 4.27: Robust tests of equality of means between race and alternative finance Robust Tests of Equality of Means – race and alternative finance
Alternative finance
Statistica df1 df2 Sig.
Welch .565 3 72.528 .640
Brown-Forsythe .359 3 104.258 .783
a. Asymptotically F distributed.
The results in Table 4.28 tells us that there is a non-significant difference of mean scores across the different races within alternative finance because the p-value=0.903>0.05. Consequently, the post hoc results are not deliberated.
Table 4.28: ANOVA relationship between race and alternative finance ANOVA – race and alternative finance
Alternative finance
Sum of
Squares
df Mean
Square
F Sig.
Between Groups .087 3 .029 .191 .903
Within Groups 64.101 423 .152
Total 64.188 426
Basically, previous studies on race in South Africa continue to emphasise that race is still the driving force of inequality, where 80% of financial assets are still owned by the 10% rich population (Hill, 2022). This includes other African countries such as Botswana, Eswatini, Lesotho and Namibia which are also affected by racial inequality. This has an impact on and requires
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more exploration on how it affects alternative finance in the South African context as studies have been based on black Hispanics abroad.
Highest educational qualification is related to mobile banking
According to Mothobi & Gryzybowski (2017), the education level plays an important role in mobile banking. The means that without education, it would be difficult to understand and utilise mobile banking services. Table 4.29 shows the responses from the respondents based on the level of education.
Table 4.29: Descriptive statistics between highest educational qualification and mobile banking
Descriptives – Highest educational qualification and mobile banking Mobile banking
N Mean Std.
Deviation
Std.
Error
95%
Confidence Interval for
Mean
Conclusion
Lower Bound
Upper Bound
Grade 11 or lower 34 3.0618 .26055 .04468 2.9709 3.1527 The average score of Mobile banking differs across the highest educational qualifications.
Grade 12 (Matric) 138 3.1942 .39629 .03373 3.1275 3.2609 Post Matric diploma or
certificate
120 3.1367 .28430 .02595 3.0853 3.1881 Degree 104 3.1087 .24539 .02406 3.0609 3.1564 Post Graduate degree 31 3.1484 .23363 .04196 3.0627 3.2341
Total 427 3.1433 .31373 .01518 3.1135 3.1732
The outcome in Table 4.30 reveals that the postulation of homogeneity of variances has been dishonoured (p=0.001<0.05).
Table 4.30: Homogeneity of variances test between highest educational qualification and mobile banking
Test of Homogeneity of Variances - Highest educational qualification and mobile banking Mobile banking
Levene’s Statistic df1 df2 Sig.
4.827 4 422 .001
As a result, the outcomes in Table 4.31 of the robust tests of equality of means is considered.
These results confirm studies by Mothobi & Grzybowski (2017) that mobile banking usage increase side by side with level of education.
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Table 4.31: Robust test of equality of means between highest educational qualification and mobile banking
Robust Tests of Equality of Means - Highest educational qualification and mobile banking Mobile banking
Statistica df1 df2 Sig.
Welch 1.720 4 123.671 .150
Brown-Forsythe 2.212 4 311.816 .068
a. Asymptotically F distributed.
Supportive to Table 4.31 is that there is no significant variation (Welch p-value=0.150; Brown- Forsythe p-value=0.068) in the means of highest educational qualifications within mobile banking, thus the supposition of homogeneity of variances has not been disturbed.
The disclosures in Table 4.32 indicate that there is a non-significant difference of mean scores across the highest educational qualifications within mobile banking as the p-value=0.122>0.05 which is high. For that reason, the post hoc outcomes are not deliberated.
Table 4.32: ANOVA relationship between highest educational qualification and mobile banking
ANOVA - Highest educational qualification and mobile banking Mobile banking
Sum of
Squares
df Mean Square F Sig.
Between Groups .715 4 .179 1.829 .122
Within Groups 41.214 422 .098
Total 41.928 426
Relationship between highest educational qualification and Alternative Finance
As noted by Pankomera & Van Greunen (2018) that the level of education contributes to increase usage of mobile banking, this assumption was also applied to alternative finance as potential users would need to have an understanding of the use of alternative finance in order to be able to utilise the services offered by alternative finance providers. Table 4.33 shows the outcome of the analysed relationship from the respondents.
Table 4.33: Descriptive statistics between highest educational qualification and alternative finance
Descriptives - Highest educational qualification and alternative finance Alternative finance
N Mean Std.
Deviation
Std.
Error
95%
Confidence Interval for
Mean
Conclusion
Lower Bound
Upper Bound Grade 11 or lower 34 2.8559 .48317 .08286 2.6873 3.0245
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Grade 12 (Matric) 138 2.9884 .38171 .03249 2.9242 3.0527 The average score of alternative finance differs across the highest educational qualifications.
Post Matric diploma or certificate
120 2.9783 .40318 .03680 2.9055 3.0512 Degree 104 2.9683 .33071 .03243 2.9040 3.0326 Post Graduate degree 31 2.9935 .42421 .07619 2.8379 3.1492 Total 427 2.9705 .38817 .01878 2.9336 3.0074
The supposition of homogeneity of variances has not been disrupted (p=0.390>0.05) as revealed by the outcome statistics in Table 4.34.
Table 4.34: Homogeneity of variances test between highest educational qualification and alternative finance
Test of Homogeneity of Variances – Highest educational qualification and alternative finance Alternative finance
Levene’s Statistic df1 df2 Sig.
1.033 4 422 .390
Table 4.35 indicates that there is no significant variation of mean scores across the highest educational qualification within alternative finance since the p-value=0.492 > 0.05. Consequently, the post hoc outcomes are disregarded. This confirms the findings by Mothibi and Rahulani (2021) that knowledge improvement though education on different financing instruments for users is critical so that they are able to access alternative finance.
Table 4.35: ANOVA relationship between highest educational qualification and alternative finance
ANOVA - Highest educational qualification and alternative finance Alternative finance
Sum of
Squares
df Mean
Square
F Sig.
Between Groups .515 4 .129 .854 .492
Within Groups 63.673 422 .151
Total 64.188 426
H1B: Customer background has an effect on the use of mobile banking and alternative finance. Background is represented by account holding and access to internet.
Account holding
Holding an account contributes to the users’ use of mobile banking and alternative finance. It is worth noting that holding an account can be easily linked to the mobile application of an institution if it allows for banking innovatively and the ability to obtain finance through the mobile application.
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Table 4.36 shows the relationship between account holders and mobile banking. The ordinary score of mobile banking is different on both categories of account holders.
Table 4.36: Group Statistics between account holding and mobile banking Group Statistics - relationship between the account holding and mobile banking Are you an account holder of a South
African bank
N Mean Std. Deviation Conclusion
Mobile banking
Yes, I currently am 406 3.1470 .31619 The average score of Mobile banking differs across both types of account holders.
No, I haven't been in the past 5 years
19 3.0737 .26634
The Levene’s test shows that the equal variance which is assumed as the p-value is more than 0.05 shown as 0.522 (see Table 4.37). Consequently, the information on the first row (equal variances is assumed) is considered, meaning that the assumption of the relationship between mobile banking and account holding is accepted.
The second section of Table 4.37 (t-test for equality of means) exposes that there is no significant difference of mean scores in terms of mobile banking (p>0.05, t=0.995, ∆Mean=0.07336) between the two types of account holders.
Table 4.37: Independent samples test between account holding and mobile banking Independent Samples Test – relationship between the account holding and mobile banking
Levene's Test
for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig.
(2- tailed)
Mean Differen ce
Std. Error Differenc e
95% Confidence Interval of the Difference Lower Upper
Mobile bankin
g
Equal variances assumed
.411 .522 .995 423 .320 .07336 .07376 - .07162
.21834
Equal variances not assumed
1.163 20.4
49
.258 .07336 .06308 - .05805
.20477
Table 4.38 displays the relationship between account holders and alternative finance which shows the same outcome as that shown earlier in Table 4.36 with mobile banking.
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Table 4.38: Group statistics between account holding and alternative finance Group Statistics – relationship between account holding and alternative finance Are you an account holder of a South
African bank
N Mean Std.
Deviation
Conclusion
Alternative finance
Yes, I currently am 406 2.9739 .39285 The average score of Alternative Finance differs across both account holders.
No, I haven't been in the
past 5 years
19 2.8947 .26347
The Levene’s test as per Table 4.39 shows that the equal variance is assumed because the p- value is above 0.05 where sig. value is 0.193. Therefore, the information on the first row (equal variances assumed) is accepted. The hypothesis of relationship is accepted.
Part two of Table 4.39 (t-test for equality of means) discloses that there is no significant difference of mean scores in relation to alternative finance (p>0.05, t=0.869, ∆Mean=0.07915) between the two categories of account holders.
Table 4.39: Independent samples test between account holding and alternative finance Independent Samples Test – relationship between account holding and alternative finance
Levene's Test
for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig.
(2- tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference Lower Upper Alternative
finance
Equal variances assumed
1.700 .193 .869 423 .386 .07915 .09112 - .09996
.25827
Equal variances not assumed
1.246 21.930 .226 .07915 .06351 - .05258
.21089
Conclusively, this implies that the typical level of both mobile banking and alternative finance differs for the current account holders compared to those who have not been account holders in the past five years.