## 4 The Results of Hypothesis Testing
### The Estimation Results of Model  
The Naive Bayes model validation for decision quality assessment, such as mean squared error (MSE) and R-squared (\(R^{2}\)), and to assess the model's predictive accuracy are presented in Table 3, and a graphical representation is shown in Figure 7.  
In the context of Quality of Decision assessment (QOD), the Naive Bayes model exhibits good predictive efficacy, as evidenced by an \(R^{2}\) value of approximately 0.989 and a small mean squared error (MSE) of 0.0151. Figure 5: Mean and standard deviation of constructs. Source: original. Figure 6: Illustration of construct measures of reliability and validity. Source: original. This high \(R^{2}\) value is indicative of the model's ability to account for the variance in the dependent variable, thereby offering a highly accurate representation of the underlying data. The model's generalizability is further supported through a rigorous nested cross-validation approach, which yielded a robust mean \(R^{2}\) value of 0.688. These empirical results validate the model's applicability and reliability, rendering it reliable and effective for tool decision-making scenarios, thereby contributing a methodologically rigorous, statistically robust, and practically relevant model to the extensive literature on decision analytics. Table 4 shows the correlation between latent variables, and a heterotrait-monotrait (HTMT) ratio graph is provided in Figure 8 and a heatmap visualization graph is presented in Figure 9. It shows a considerable negative correlation between extraversion (EXC) and various other characteristics. This suggests that individuals with higher levels of extraversion may exhibit lower performance on knowledge tests. The trait of conscientiousness (COA) is found to have a significant positive association with neuroticism (NEF) while displaying a negative association with Self-Assessed Knowledge (SAK). This suggests that persons who score high in conscientiousness may also have neurotic tendencies but tend to underestimate their level of knowledge. The presence of a strong association between Neuroticism (NEF) and Openness (OPA) implies that persons with higher levels of neuroticism may potentially exhibit more acceptance of new experiences. The heterotrait-monotrait (HTMT) ratio graph is provided in Figure 8 and provides valuable insights into the discriminant validity of the constructs. Evaluating discriminant validity is essential to confirm that the constructs measured are distinct and do not overlap significantly. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Nested Cross-Validation Score (Mean \(R^{2}\)) & 0.688 \\ Mean Squared Error (MSE) & 0.015 \\ R-squared (\(R^{2}\)) & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Naive Bayes model validation. Figure 7: Naive Bayes model prediction for quality of decision. Source: original. Most construct pairs depicted in the graph have HTMT values significantly below the 0.90 threshold, which is commonly accepted for establishing discriminant validity. A few construct pairs, such as EC5 and SIC, OC3 and CC1, demonstrate higher HTMT values nearing or exceeding 0.50. These instances suggest a potential overlap and require further scrutiny to ensure that the constructs are adequately distinct. Comparative Analysis with Cornell-Larcker Criterion: The Cornell-Larcker criterion assesses discriminant validity by comparing the square root of the average variance extracted (AVE) with the correlations between the constructs. While effective, it can be less sensitive in cases where constructs are highly correlated but still distinct. The application of HTMT in our study reinforces the discriminant validity of most constructs, with a few exceptions that warrant further investigation. By using HTMT alongside traditional methods like Fornell-Larcker, we can achieve a more comprehensive validation of our measurement model, enhancing the reliability and interpretability of our research findings.