# Perceived Usefulness of a Mandatory Information System
## 6 Results
\begin{table}
\begin{tabular}{p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt}} \hline \multirow{2}{*}{**Item**} & **Original** & **Arosinhto1** & **Box-Cox** & **Center,Scale/0** & **Epo/0** & **Lamber’s W** & **Log,Dec+a** & **orderNorm** & **sqtif(\_\_\) + a** & **Vev-Johnson** & **Best Normalizations** \\  & **Distribution** & & & & & & & & & & & & \\ \hline Perceived case of use 1: My interaction with the system is clear and understandable & Binomial & 3.78 & 3.75 & 3.71 & 3.88 & 3.8 & 3.9 & 3.73 & 3.68 & 3.78 & \begin{tabular}{} \end{tabular} \\ \hline Perceived case of use 2: It is easy for no temperature how to perform 190 segments using the system & Binomial & 2.33 & 2.33 & 2.34 & 4.17 & 2.29 & 2.41 & 2.24 & 2.29 & 2.34 & \begin{tabular}{} \end{tabular} \\ \hline Perceived case of use 3: Overall,1 find the system easy to use & Binomial & 2.71 & 2.6 & 2.6 & 3.99 & 2.6 & 2.76 & 2.88 & 2.67 & 2.6 & \begin{tabular}{} \end{tabular} \\ \hline Perceived usefulness 1: Using the more efficient & Binomial & 2.79 & 2.92 & 2.91 & 3.73 & 2.96 & 2.93 & 2.91 & 2.81 & 2.92 & \begin{tabular}{} \end{tabular} \\ \hline Perceived usefulness 2: Using the system will increase my job performance & Binomial & 3.24 & 3.25 & 3.2 & 4.06 & 3.25 & 3.37 & 3.34 & 3.23 & \begin{tabular}{} \end{tabular} \\ \hline Perceived usefulness 3: Using the system will increase my job performance & Binomial & 3.01 & 2.76 & 2.74 & 3.81 & 2.78 & 2.81 & 3.03 & 2.82 & 2.79 & \begin{tabular}{} \end{tabular} \\ \hline Perceived influence 1: As far as 1 know, may change things I should use the system & Binomial & 3.65 & 3.32 & 3.35 & 3.94 & 3.4 & 3.77 & 3.38 & 3.5 & \begin{tabular}{} \end{tabular} \\ \hline Supervised influence 3: I will have the system in performing some of my tasks because any systems expects no do & & & & & & & & & \\ \hline Symbolic adoption 1: I am enthusiastic about using the system & Binomial & 2.58 & 2.45 & 2.44 & 4.45 & 2.47 & 2.6 & 2.47 & 2.49 & 2.45 & \
\begin{tabular}{} \end{tabular} \\ \hline Symbolic adoption 3: It is my desire to see the full utilization and deployment of the system & Binomial & 2.23 & 2.18 & 2.22 & 4.29 & 2.16 & 2.25 & 2.13 & 2.18 & 2.15 &
\begin{tabular}{} \end{tabular} \\ \hline \end{tabular} Note: * Estimation method: Out-of-sample via CV with 10 folds and 5 repeats:\({}^{\circ}\) Estimated Normality Statistics ENS (Penson P/4/l, lower \(z\) more normal).  
\end{table} 
Table 1: Normalization transformations for 10 questionnaire items with 72 observations. Figure 3: Comprehensive overview of the final structural equation model (N = 72; * \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001). Figure 2: Latent structures derived from standardized questionnaire statements. Model Validation:  
In order to validate the current model, several additional models were prepared that included the same structure as the original model. Comprehensive validation was conducted using various estimation methods, including:  
Maximum likelihood (ML) estimation: This method is widely used in SEM due to its efficiency and consistency. Results from ML estimation were compared with those from other estimation methods. Generalized Least Squares (GLS) Estimation: This method is used for its robustness in certain scenarios, providing a comparative benchmark. Unweighted Least Squares (ULS) Estimation: This method, although less efficient in some cases, was included for a thorough comparative analysis. Asymptotically Distribution-Free (ADF) Estimation: This method was used to ensure that model estimations were not biased by non-normality in the data. Bootstrapping to Compare Estimation Methods:  
Bootstrapping was employed to compare the performance of these estimation methods. This technique is particularly useful for assessing the stability and reliability of parameter estimates in SEM. Here is a summary of the approach: