## 5 Study 2: Quantitative Validation of the Technology Acceptance Model in AI Recruitment
### Method
#### 5.1.3 Data Analysis  
We conducted data analysis in two stages using SPSS 29 and JASP [52]. First, regarding quantitative data, confirmatory factor analyses (CFAs) were performed using maximum likelihood estimation to evaluate the proposed measurement model, assess discriminant validity, and address concerns regarding common method bias (CMB). The model's fit was assessed using multiple indices, including \(\chi^{2}/\mathrm{df}\) (degree of freedom), RMSEA (root-mean-square error of approximation), CFI (comparative fit index), and SRMR (standardized root mean square residual), following guidelines by [PERSON] [53]. A model was considered acceptable if \(\chi^{2}/\mathrm{df}<5\), RMSEA and SRMR \(<0.08\), and CFI \(>0.90\)[53].  
As shown in Table 3, the hypothesized four-factor model, comprising perceived usefulness, perceived ease of use, attitude, and intention to use, was compared against three alternative models: (a) a three-factor model combining perceived usefulness and perceived ease of use, (b) a two-factor model combining perceived usefulness and perceived ease of use, and another factor combining attitude and intention to use into separate factors, and (c) a single-factor model collapsing all items into one factor. The three-factor model was included to evaluate potential overlap between perceived usefulness and perceived ease of use, given possible similarities in their scale items. The two-factor model was tested to detect potential conflations between attitudes and intention to use. Finally, the single-factor model was used to examine the risk of CMB [54].  
The confirmatory factor analysis (CFA) results indicated that the proposed four-factor model provided a superior fit compared to alternative models (see Table 3). Specifically, the four-factor model demonstrated better performance than the three-factor model, underscoring clear empirical distinctions between the variables. These findings were further corroborated by the square roots of the Average Variance Extracted (AVE) values (see Table 4), which were consistently greater than the correlations between each variable, as recommended by prior guidelines [44].  
To evaluate discriminant validity--demonstrating the uniqueness of the indicators for each latent variable--the Maximum Shared Variance (MSV) was also analyzed. Results showed that for all constructs, the MSV was lower than the AVE, providing additional support for discriminant validity.  
Convergent validity, which assesses the extent to which indicators reliably correlate with their respective latent constructs, was also evaluated. The AVE values for all latent constructs in the study exceeded the recommended threshold of 0.50, demonstrating sufficient convergent validity. Additionally, the AVE for each construct was higher than its correlations with other constructs, further affirming the distinctiveness and coherence of each  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & \(\chi^{2}/\mathrm{df}\) & **CFI** & **TLI** & **RMSEA** & **SRMR** \\ \hline Model 1 & 3.64 & 0.94 & 0.93 & 0.08 & 0.05 \\ Model 2 & 6.38 & 0.89 & 0.87 & 0.12 & 0.07 \\ Model 3 & 7.48 & 0.87 & 0.85 & 0.14 & 0.07 \\ Model 4 & 8.41 & 0.85 & 0.82 & 0.14 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confirmatory factor analyses.  
latent variable. In summary, the study successfully confirmed the reliability, convergent validity, and discriminant validity of the measurement model. These findings strengthen the robustness and validity of the constructs examined in this research.  
In the third stage of analysis, we tested the study hypotheses using Structural Equation Modeling (SEM) with JASP software, version 0.19.3 [52]. This approach allowed for a robust examination of the relationships between the constructs in the proposed model, providing insights into both direct and indirect effects.