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Table 4 There is a significant statistical effect for Artificial Intelligence (AJ) on the Audit Quality (AQ), which confirms the acceptance of (1).

Table 4 There is a significant statistical effect for Artificial Intelligence (AJ) on the Audit Quality (AQ), which confirms the acceptance of (1).

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Fig. 1. Research framework adapted from the unified theory of acceptance and use of technology (UTAUT) model
Fig. 1. Research framework adapted from the unified theory of acceptance and use of technology (UTAUT) model
To make sure the factor structure derived from the EFA is plausible, confirmatory factor analysis (CFA) is performed. Fol- lowing the testing, it was established that 20 indicators had been loaded with a coefficient of more than 0.50 on each of the five latent variables (PE, EE, SI, FC, and AQ); as shown in Fig. 2. This model took into consideration only the relationships between indicators and combination; assuming covariance between latent variables (Anderson & Gerbing, 1988). and table 3 shows model fit measures; extracted from the statistical analysis software AMOS 26.
To make sure the factor structure derived from the EFA is plausible, confirmatory factor analysis (CFA) is performed. Fol- lowing the testing, it was established that 20 indicators had been loaded with a coefficient of more than 0.50 on each of the five latent variables (PE, EE, SI, FC, and AQ); as shown in Fig. 2. This model took into consideration only the relationships between indicators and combination; assuming covariance between latent variables (Anderson & Gerbing, 1988). and table 3 shows model fit measures; extracted from the statistical analysis software AMOS 26.
Table 3
Table 3
Fig. 2. Confirmatory Factor Analysis
Fig. 2. Confirmatory Factor Analysis
Fig. 4. SEM Multi-group analysis (SMEs Model) To examine whether or not the participant’s types of job (auditing office, SMEs) affected the study hypothesis, SEM and a multi-group analytic approach were used as assessment tools. Therefore, data were divided into groups for auditing offices (40) and SMEs (80). To find any changes, the model trajectories of the two separate groups were compared. The variance between the whole structural models of the two groups under study can be learned through a y2 difference test (auditing office vs. SMEs). Concerning the results, it is possible that one or more of the regressions between the two groups under investigation are not equal. The precise differences between the regressions along the path to each group, however, are not supported by the y2 difference test. As a result, two groups were created in the AMOS v.26 graphics for each type of task to evaluate the variations in each route (hypothesis). For each type of employment, two categories were created in the AMOS v.26 graphics: one for auditing offices (which had a total of 40 participants) and another for SMEs (which had a total of 80 participants). Each regression was given a specific name for the test's purposes. Bootstrapping was used during the study to produce the confidence interval between the two tested groups. Table 5 shows that several important distinctions were emphasized for each group.
Fig. 4. SEM Multi-group analysis (SMEs Model) To examine whether or not the participant’s types of job (auditing office, SMEs) affected the study hypothesis, SEM and a multi-group analytic approach were used as assessment tools. Therefore, data were divided into groups for auditing offices (40) and SMEs (80). To find any changes, the model trajectories of the two separate groups were compared. The variance between the whole structural models of the two groups under study can be learned through a y2 difference test (auditing office vs. SMEs). Concerning the results, it is possible that one or more of the regressions between the two groups under investigation are not equal. The precise differences between the regressions along the path to each group, however, are not supported by the y2 difference test. As a result, two groups were created in the AMOS v.26 graphics for each type of task to evaluate the variations in each route (hypothesis). For each type of employment, two categories were created in the AMOS v.26 graphics: one for auditing offices (which had a total of 40 participants) and another for SMEs (which had a total of 80 participants). Each regression was given a specific name for the test's purposes. Bootstrapping was used during the study to produce the confidence interval between the two tested groups. Table 5 shows that several important distinctions were emphasized for each group.
Note **, p-value < 0.05; ***, p-value < 0.001. Significant at the 0.05 level. Critical Ratios for Differences > 1,96 Significant. Table 5
Note **, p-value < 0.05; ***, p-value < 0.001. Significant at the 0.05 level. Critical Ratios for Differences > 1,96 Significant. Table 5
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