CHAPTER 6 THE IMPACT OF EFD USE ON AUDIT EFFECTIVENESS,
6.2 Audit effectiveness
6.2.1 Demographic variables and audit effectiveness
This subsection seeks to know the categorical relationship between demographic variables and the audit effectiveness in an EFD-enabled environment. Demographic information presented in Chapter 4 set an important background; therefore, the same level of analysis is not repeated in the current chapter. This chapter is interested in the significance of categorical relationships between demographic variables (age, gender, education, and business experience) and audit effectiveness. According to Figure 6-2, in a Likert scale of five variables, those who reported that they disagree or strongly disagree showed very low counts. Therefore, the study combined the two elements of the scale into one. To have a uniform scale, the study combined the input for those who strongly agree and those who agree to form one scale. The new scale has the following elements: Agree, moderately agree, and disagree. This scale is the one used in the next analysis.
In the analysis, One Way ANOVA was used to examine the categorical relationship between all demographic variables and the audit effectiveness, and the summarised results are given in Table 6-1. In addition, the ordinal regression was used to evaluate the impact of demographic variables on the audit effectiveness, and the results are presented in Table 6-2.
The first demographic variable used in the analysis was the age of respondents. The objective was to determine the significance of the relationship between the age group and the perceived effectiveness of audit in a business environment supported by the use of EFDs. According to results in Table 6-1, the observed One Way ANOVA p-value was 0.400. In this case, there no categorical relationship is confirmed. From the mean values provided, it is evident that all mean values are inclined toward 2. The lowest mean value is 1.88 while the highest is 2.18. Regardless of the observed insignificance of the relationship, it is important to highlight that the youngest (age 18–30) population had the lowest mean, suggesting that the group had a larger percent of respondents who agreed that
the audit exercise is effective, compared to the rest. Further to this, the oldest group had the largest mean, suggesting them to be more inclined to disagree. According to Berkowsky, Sharit and Czaja (2017), it is possible that the group of senior respondents finds audit to be less effective because they are not adequately attached to technology use; therefore, their pace of adoption is low.
Apart from a poor categorical relationship shown in Table 6-1, the ordinal regression results in Table 6-2 indicate the lack of a significant impact caused by the age group to the perceived audit effectiveness. The observed p-value for the model-fitting information was 0.396, and the Nagelkerke r-square was 0.0013. Therefore, the age group is not suitable for predicting the perceived effectiveness of the audit process supported by EFD operations.
Table 6-1: Demographic variables and audit effectiveness – ANOVA extracts
Input variable Descriptive information One Way ANOVA P-Value
Scale Mean Freq.
Age 18-30 1.88 98 0.400
31-40 2.15 96
41-50 2.03 68
51 and above 2.18 17
Overall 2.03 279
Gender Female 2.03 173 0.859
Male 2.01 106
Overall 2.03 279
Education Primary Education 1.96 79 0.023
Secondary
Education 1.78 90
Certificate or
Diploma 2.27 88
Degree and above 2.27 22
Overall 2.03 279
Business experience 2 years and below 2.10 148 0.444 Between 3 and 5 1.90 78
years
6 years and above 2.00 53
Overall 2.03 279
Source: (Author’s Design, 2020)
Accordingly, the study determined whether the gender of respondents offered a significant categorical relationship with the perceived audit effectiveness. Findings in Table 6-1 indicated the One Way ANOVA p-value as 0.854. This value is greater than the threshold, therefore, the gender of respondents does not have a significant categorical relationship with the perceived audit effectiveness. Therefore, the perceived audit effectiveness was almost uniform across all genders. According to Gurama, Mansor and Pantamee (2015), this might be an indicator of a fair business ground to all participants, regardless of their gender. In addition, the study used the ordinal regression analysis to determine the impact of gender on the perceived audit effectiveness. According to results presented in Table 6-2, the study did not detect a significant impact of gender on the perceived audit effectiveness.
The observed p-value for the model-fitting information was 0.854, while the Nagelkerke r- square was 0.000.
Furthermore, the study tested the relationship between the level of education and the perceived audit effectiveness. It was the assumption of the study that the level of education could define the perception of respondents on audit effectiveness in an EFD-enabled environment. In this case, the level of education showed a significant categorical relationship with the perceived audit effectiveness in an EFD-enabled environment. The P- value for the One Way ANOVA was 0.023, which is less than the threshold (0.05). A closer look at the observed mean value suggests that those with primary and secondary education were more associated with suggesting that the audit process is effective, compared with those with a college-level education.
Accordingly, the study tested the impact of education on the perceived audit effectiveness, using Ordinal Regression. Based on results presented in Table 6-2, the level of education is the only variable which reflects a significant impact on audit effectiveness. The model- fitting information suggests the p-value =0.025, and the Nagelkerke r-square=0.091. In addition, parameter estimates suggested that all parameters of the level of education differed significantly with the parameter set as a reference value. The reference value was
the parameter representing taxpayers with at least a Bachelor‟s degree. It is possible that those with low education perceived more benefits because the system supported them with basic computations and record-keeping, which would be more difficult for them in a traditional environment (Chen, Harold, Little, Mark, & Zhao, 2012). In addition, it is possible that those with superior education have different expectations of the benefit compared to the rest of the parameters. The difference in expectations resulted in differences in the perceived value of EFDs in the audit process (Naibei & Siringi, 2011).
Table 6-2: Demographic variables and audit effectiveness – ordinal regression extracts Input variable Model Information Value
Gender Model-fitting information p-value = 0.854 Nagelkerke Pseudo r-square R-square = 0.000 Parameter
estimates
Female p-value = 0.680
Male Ref. Value
Education Model-fitting information P-value = 0.025 Nagelkerke Pseudo r-square R-square = 0.091 Parameter
estimate
Primary 0.005
Secondary 0.002
Certificate/Diploma 0.050 Degree or above Ref. Value Business
experience
Model-fitting information P-value = 0.038
Nagelkerke Pseudo r-square R-square = 0.007 (0.7%) Parameter
estimate
2 years or below 0.572
3-5 years 0.882
6 years or above Ref. value
Age Model-fitting information P = 0.396
Nagelkerke Pseudo r-square R-square = 0.013 (1.3%) Parameter
estimate
18-30 years 0.288
31-40 years 0.713
41-50 years 0.732
51 and above years Ref. value Source: (Author’s Design, 2020)
The last demographic variable that was studied was the experience of the user in the business. The first part of the analysis tested whether user experience in business offered a significant categorical relationship with the perceived audit effectiveness. Results of the One Way ANOVA presented in Table 6-1 suggested no significant categorical relationship observed between the business experience and the perceived audit effectiveness. The observed One Way ANOVA p-value was 0.444, which is greater than the threshold. In addition, the study conducted another analysis to determine the significance of the impact of user experience on the perceived label of audit effectiveness. The results of the ordinal regression suggested that the model is not suitable for this analysis, because the model- fitting information p-value was 0.396. Moreover, the study reported a very small Nagelkerke value, where the r-square was 0.007. All parameters estimated had the p-value above 0.05; therefore, no significant impact is detected.