## 4 Results
### First-Order Measurement Model
To measure the discriminative validity, the square root of the AVE of each construct may be compared to correlations from one construct with any other construct [57; 63]. The square root of the AVE should be greater than the relationship among latent variables, according to the concepts of [PERSON] et al. [11]. Table 7 demonstrates that the outcome validates the discriminant validity of the measurement model. Nonetheless, several academics have rejected [PERSON] and [PERSON]'s  
Figure 4: Structure model along with the coefficient of the path (outer loading results are shown on arrows). (1981) definition of discriminative validity. Consequently, [PERSON], [PERSON], and [PERSON] suggested an alternative way of evaluating discriminative validity (i.e., the HTMT ratio criterion) [10]. The HTMT method, which analyzes the relation among two constructs provided they are reliably calculated, is a unique approach for analyzing the discriminant-variance-based SEM validity. The HTMT was well employed in this work to analyze the validity of discriminants. HTMT values should be less than 0.85 and 0.90, indicating that the two conceptions are distinct [6]. The HTMT value should be less than 0.90 if the model's constructs are conceptually remarkably similar and less than 0.85 if the model's constructs are conceptually dissimilar. Table 8 displays the HTMT values for all investigated constructions. Therefore, the notions have shown sufficient discriminating validity. This research also used the cross-loading criteria to establish discriminatory validity. This approach aims to identify that the indicator loading of certain constructs latent in nature must be greater than the indicator's loading for alternative constructs latent in nature for each row [65; 66]. Their constructions' loading indicators must be more valuable than the alternative construct. Table 9 reveals that the allocated latent construct indicator load is more significant from their cross-loadings on varying constructs. Each construct exhibits a significant degree of one-dimensionality, as seen by the outcome [67]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Construct** & **Cost** & **Creativity** & **Evaluation** & **Function** & **Knowledge** & **Normalization** & **Quality** & **Regulation** \\ \hline Cost & & & & & & & \\ \hline \hline \multicolumn{1}{c}{Creativity} & 0.459 & & & & & & \\ \hline \hline \multicolumn{1}{c}{Evaluation} & 0.375 & 0.298 & & & & & \\ \hline \hline \multicolumn{1}{c}{Function} & 0.486 & 0.281 & 0.243 & & & & \\ \hline \hline \multicolumn{1}{c}{Knowledge} & 0.160 & 0.447 & 0.373 & 0.5 & & & \\ \hline \hline \multicolumn{1}{c}{Normalization} & 0.258 & 0.176 & 0.221 & 0.268 & 0.258 & & \\ \hline \hline \multicolumn{1}{c}{Quality} & 0.375 & 0.298 & 0.146 & 0.243 & 0.373 & 0.221 & & \\ \hline \hline \multicolumn{1}{c}{Regulation} & 0.233 & 0.248 & 0.090 & 0.271 & 0.210 & 0.163 & 0.090 & \\ \hline \hline \multicolumn{1}{c}{Time} & 0.459 & 0.241 & 0.298 & 0.281 & 0.447 & 0.176 & 0.298 & 0.248 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: (Fornell–Larcker) discriminant validity and the relationship between latent variables.