57
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Table 4.11 above indicates that for the race variable, the p-value was 0.901, and the Pearson chi-square statistic was χ²(18) = 10.847. For the work experience variable, the p-value was 0.460, and the Pearson chi-square statistic was χ²(18) = 17.931a. Therefore, both variables had a p-value > 0.05, which meant that the researcher could not reject the null hypotheses for these variables.
In other words, there was no statistical association between race or work experience and the participants perception of their firms’ support of MAFR 2023. However, in the case of the work experience variable, the result was contrary to the findings of (Said and Khasharmeh, 2014a), whose study found that it had a significant association with MAFR.
Table 4.11 above shows that for the highest level of academic qualification variable, the p-value was .006, and a Pearson’s chi-square statistic was χ²(12) = 27.813a. For the position variable, the p-value was .000 and the Pearson’s chi-square statistic was χ²(12) = 27.813a. Therefore, both variables had a p-value < 0.05, which meant that the researcher could reject the null hypotheses for these variables and conclude that as a statistical association between position or highest academic qualification and the participants’ perception of their firms’ support of MAFR 2023.
In the case of the position variable, the result was not consistent with that of (Said and Khasharmeh, 2014a), who found that the position of audit professionals did not have any association with MAFR. However, the current study’s finding suggests that positions and qualifications held by audit experts have an influence on their firms’
support of MAFR.
4.4.1.2 Ordinal logistic regression
This study employed the OLR because of the use of a Likert scale in the questionnaire and because it is widely recommended (Armstrong and Sloan, 1989, Ge and Whitmore, 2010, Das and Rahman, 2011). In the case of this study, the ordinal variables were Yes, No and Uncertain.
Therefore, the model equation was log ( 𝑃
1−𝑃) = 𝑎 + 𝑏1𝑥1+ 𝑏2𝑥2+ 𝑏3𝑥3
Table 4.12 below presents the model fitting information.
59 Table 4.12: Model fitting information
Model -2 Log Likelihood Chi-Square Df Sig.
Intercept Only 105.561
Final 96.938 8.623 3 .035
Link function: Logit – Output from SPSS, 2021.
Table 4.12 above shows that the model fitting information (MFI) was statistically significant, because the p-value was <.035. The results indicated a statistically significant development in the fit of the final model over the null model [χ²(3)= 96.938, p<.035]. This meant that the fit of the final model had a p-value < 0.05, and therefore it was suitable for the research.
Table 4.13 below shows the results from the goodness-of-fit test.
Table 4.13: Goodness-of-Fit
Chi-Square Df Sig.
Pearson 279.298 165 .001
Deviance 95.316 165 1.000
Link function: Logit - Output from SPSS, 2021.
Results from the goodness-of-fit (GOF) test (Table 4.13) shown above were non- significant. Meaning the results from the Pearson chi-square test [χ²(165) = 279.298, p=.001] and the deviance test [χ²(165)=95.316, p=1.000] were both an indication that the model was well fitted and non-significant. The outcome from the GOF table suggests a good model fit.
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Table 4.14 below presents the parameter estimates.
Table 4.14: Parameter estimates
Estimate Std.
Error Wald Df Sig.
95% Confidence Interval
Lower Bound
Upper Bound Threshold [MAFR =
1.00] 1.014 4.139 .060 1 .807 -7.099 9.126 [MAFR =
2.00] 3.033 4.168 .530 1 .467 -5.136 11.203 Location B1 1.437 1.034 1.931 1 .165 -.590 3.465
C2 .370 .688 .290 1 .591 -.978 1.719
D3 -2.092 .878 5.668 1 .017 -3.813 -.370
Link function: Logit – Output from SPSS, 2021.
Table 4.14 above shows that B1 (Objective 1 of the research) was a statistically non- significant positive predictor of audit experts’ perceptions on the influence of MAFR on AI. This means that for every unit increase in audit experts’ perceptions of the influence of MAFR on AI, there was a predicted increase of 1.437 in the log-odds of AI being at a higher level. In other words, the result suggests that AI would increase with the implementation of MAFR in 2023, which is consistent with (Polychronidou et al., 2020) findings.
Table 4.14 above indicates that C2 (Objective 2 of the study) was a non-significant predictor of audit experts’ perceptions of the influence of MAFR on AQ. This means that for every unit increase in audit experts’ perceptions of the influence of MAFR on AQ, there was a predicted increase of 0.370 in the log-odds of AQ being at a higher level. In other words, the result suggests that MAFR will improve AQ, which is in line with the findings of (Thornton, 2016).
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Table 4.14 above indicates that D3 (Objective 3 of the research) was a statistically significant predictor of audit experts’ perceptions of the influence of MAFR on AR. This means that for every unit increase (negative coefficient of 2.092) in audit experts’
perceptions of the influence of MAFR on AR, there was a predicted decrease of 0.370 in the log-odds of AR being at a higher level. In other words, the result suggests that MAFR will not have a positive impact on AR, which is contrary to (SAICA, 2016a).
However, it is consistent with the findings of GAO (2003), (Harber and Marx, 2019), (Narayanaswamy and Raghunandan, 2019), as well as the current study, which revealed respondents agreed that MAFR would increase market concentration, thereby impeding AR.
Mathematical representation of the ordinal logistic regression model:
log ( 𝑃
1−𝑃) = 𝑎 + 𝑏1𝑥1+ 𝑏2𝑥2+ 𝑏3𝑥3 , log ( 𝑃
1−𝑃) = 1.014 − 2.092𝑥3 and log ( 𝑃
1−𝑃) = 3.033 − 2.092𝑥3
Table 4.15 below presents the results of the test of parallel lines.
Table 4.15. Test of parallel lines
Model -2 Log Likelihood Chi-Square Df Sig.
Null Hypothesis** 96.938
General 90.196 6.743 3 .081
**The null hypothesis states that the location parameters (slope coefficients) are the same across response categories.
Table 4.15 above presents the results of the test of parallel lines and indicates a statistically non-significant p-value of 0.081. This means that the assumption of proportional odds shown in the table suggested the effects of the explanatory variables were consistent or proportional across the different thresholds.