# WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition
## 4 Results
### Results of SEM-PLS  
This study assessed the reliability and validity of the proposed variables within the measurement model [39]. Based on the composite reliability and Cronbach's alpha values, each measure surpassed the general threshold of 0.6, confirming the internal consistency reliability. Additionally, the average variance extracted (AVE) surpassed the 0.50 cutoff [40], reinforcing the convergent validity (see Table 3).  
The Cornell-Larcker criterion was used to evaluate the discriminant validity (see Table 4), where the square roots of the AVE for each construct were greater than their correlations with other constructs, satisfying the required standard.  
Regarding hypothesis testing, all of the structural associations highlight their importance and significance through the magnitude of standardized values. The results from the SEM analysis are presented in Figure 13. This study used SEM to test the hypotheses, and the findings supported the extension of TAM theory, validating the model's applicability to the field of text mining [41].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Demographic Information** & **Frequency** & **Percent (\%)** \\ \hline \multirow{2}{*}{Gender} & Male & 82 & 55.7 \\  & Female & 65 & 44.2 \\  & 18–20 & 36 & 24.4 \\ Age in year & 21–25 & 57 & 38.7 \\  & 26–30 & 54 & 36.7 \\  & Undergraduate & 51 & 34.6 \\ Education & Master & 59 & 40.1 \\  & Doctor & 37 & 25.1 \\ Past experience & Yes & 89 & 60.5 \\ (Text mining-related) & No & 58 & 39.4 \\ Frequency & Always & 65 & 44.2 \\ (Text mining tools) & Sometime & 49 & 33.3 \\  & Never & 33 & 22.4 \\  & Yes & 78 & 53 \\ Used other tools before & No & 69 & 46.9 \\ Attitude toward text & Yes, Need knows more & 55 & 37.4 \\ mining knowledge & No & 58 & 39.4 \\  & Not sure & 34 & 23.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ATT** & **BI** & **PEOU** & **PU** \\ \hline ATT & **0.721** & & & \\ BI & 0.674 **\({}^{**}\)** & **0.766** & & \\ PEOU & 0.545 **\({}^{**}\)** & 0.546 **\({}^{**}\)** & **0.719** & \\ PU & 0.514 **\({}^{**}\)** & 0.385 **\({}^{**}\)** & 0.431 **\({}^{**}\)** & **0.722** \\ \hline \hline \end{tabular} Note: The values on the diagonal represent square roots of AVE; **\(p<0.01\)**.**  
\end{table}
Table 4: Analysis results of discriminant validity.  
Figure 13: SEM results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Construct** & **Item** & \begin{tabular}{c} **Factor** \\ **Loading** \\ \end{tabular} & \
\begin{tabular}{c} **Cronbach’s** \\ **Alpha** \\ \end{tabular} & **AVE** &
\begin{tabular}{c} **Composite** \\ **Reliability** \\ \end{tabular} \\ \hline \multirow{4}{*}{PU} & PU1 & 0.751 & & & \\  & PU2 & 0.750 & & & \\  & PU3 & 0.618 & 0.696 & 0.521 & 0.812 \\  & PU4 & 0.758 & & & \\  & PEOU1 & 0.709 & & & \\ PEOU2 & 0.758 & & & & \\  & PEOU3 & 0.646 & 0.689 & 0.517 & 0.810 \\  & PEOU4 & 0.758 & & & \\  & ATT1 & 0.744 & & & \\ ATT2 & 0.786 & & & & \\  & ATT3 & 0.676 & 0.691 & 0.520 & 0.812 \\  & ATT4 & 0.672 & & & \\  & BI1 & 0.719 & & & \\ BI & BI2 & 0.745 & 0.649 & 0.586 & 0.809 \\  & BI3 & 0.828 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Critical indicators of CFA results.  
**H1 (PU \(\rightarrow\) ATT).**_Supported. PU had a significant positive effect on ATT (\(\beta\) = 0.343, \(t\) = 4.599, \(p\) < 0.001)._  
**H2 (PEOU \(\rightarrow\) ATT).**_Supported. PEOU also had a significant positive influence on ATT (\(\beta\) = 0.397, \(t\) = 5.246, \(p\) < 0.001)._  
**H3 (PEOU \(\rightarrow\) PU).**_Supported. PEOU had a positive impact on PU (\(\beta\) = 0.431, \(t\) = 5.688, \(p\) < 0.001)._  
**H4 (ATT \(\rightarrow\) BI).**_Supported. ATT had a strong positive impact on BI (\(\beta\) = 0.674, \(t\) = 13.767, \(p\) < 0.001)._  
Overall, these results provide strong empirical support for the proposed TAM in this study. They confirm the significance of each hypothesized relationship and the practical relevance of the TAM in explaining the users' behavioral intentions.