## 5 Analysis and Results
### Path Model Assessment
#### 5.2.1 Assessment of Measurement Model
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Construct** & **Items** & **Loadings** &
\begin{tabular}{l} **Average Variance** \\ **Extracted (AVE)** \\ \end{tabular} \\ \hline \multirow{6}{*}{Cybersecurity Prevention} & CP1 & 0.809 & \\ \cline{2-3}  & CP2 & 0.837 & \\ \cline{2-3}  & CP3 & 0.850 & 0.688 \\ \cline{2-3}  & CP5 & 0.818 & \\ \cline{2-3}  & CP6 & 0.834 & \\ \hline \multirow{6}{*}{Government Frequent Alert} & GF1 & 0.751 & \\ \cline{2-3}  & GF2 & 0.768 & \\ \cline{2-3}  & GF3 & 0.785 & \\ \cline{2-3}  & GF4 & 0.783 & \\ \cline{2-3}  & GF5 & 0.666 & \\ \cline{2-3}  & GF6 & 0.621 & \\ \hline \multirow{2}{*}{Organization Training} & OFT1 & 0.849 & \\ \cline{2-3}  & OFT3 & 0.906 & 0.762 \\ \cline{2-3}  & OFT4 & 0.864 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Convergent validity measurement results. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Construct** & **Items** & **Loadings** &
\begin{tabular}{l} **Average Variance** \\ **Extracted (AVE)** \\ \end{tabular} \\ \hline \multirow{6}{*}{Perceived Severity} & PS2 & 0.691 & \\ \cline{2-4}  & PS3 & 0.757 & \\ \cline{2-4}  & PS4 & 0.767 & 0.504 \\ \cline{2-4}  & PS5 & 0.734 & \\ \cline{2-4}  & PS6 & 0.587 & \\ \hline \multirow{6}{*}{Perceived Vulnerability} & PV1 & 0.818 & \\ \cline{2-4}  & PV2 & 0.832 & \\ \cline{2-4}  & PV3 & 0.824 & \\ \cline{2-4}  & PV4 & 0.808 & 0.606 \\ \cline{2-4}  & PV5 & 0.501 & \\ \cline{2-4}  & PV6 & 0.832 & \\ \hline \multirow{6}{*}{Response Cost} & RC1 & 0.856 & \\ \cline{2-4}  & RC2 & 0.840 & \\ \cline{2-4}  & RC3 & 0.821 & 0.704 \\ \cline{2-4}  & RC4 & 0.853 & \\ \cline{2-4}  & RC5 & 0.825 & \\ \hline \multirow{6}{*}{Response Efficacy} & RE1 & 0.745 & \\ \cline{2-4}  & RE2 & 0.733 & \\ \cline{2-4}  & RE4 & 0.822 & 0.514 \\ \cline{2-4}  & RE5 & 0.718 & \\ \cline{2-4}  & RE6 & 0.533 & \\ \hline \multirow{6}{*}{Cybersecurity Leadership} & CL1 & 0.830 & \\ \cline{2-4}  & CL2 & 0.872 & \\ \cline{2-4}  & CL3 & 0.837 & \\ \cline{2-4}  & CL4 & 0.812 & \\ \cline{2-4}  & CL5 & 0.813 & \\ \cline{2-4}  & CL6 & 0.804 & \\ \hline \multirow{6}{*}{Self-Efficacy} & SE1 & 0.818 & \\ \cline{2-4}  & SE2 & 0.843 & \\ \cline{2-4}  & SE4 & 0.861 & \\ \cline{2-4}  & SE5 & 0.885 & \\ \cline{2-4}  & SE6 & 0.676 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Items** & **Indicator Loading** & **Items** & **Indicator Loading** \\ \hline CP4 & 0.436 & PS1 & 0.435 \\ \hline OFT2 & 0.462 & RC6 & 0.352 \\ \hline OFT5 & 0.429 & SE3 & 0.343 \\ \hline OFT6 & 0.495 & CE3 & 0.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: List of eliminated items. In contrast, discriminant validity implies that measurements of constructs that, according to theory, should not be associated are not shown to have a strong correlation with one another [113]. According to [107], the Cornell-Larcker criterion can be used to test discriminant validity. The square root of AVE values is supposed to be bigger than the value of correlations with other constructs, according to the Cornell-Larcker criterion [113; 114]. Each latent construct's square root of the AVE is greater than its connection with the other constructs (see Table 10).