## 5 Analysis and Results
### Path Model Assessment
#### 5.2.1 Assessment of Measurement Model  
Measurement model assessment includes measuring the internal consistency reliability, convergent validity and discriminatory validity. According to [PERSON] and [PERSON] ([PERSON] and [PERSON], 2017), the measurement model stipulates evaluating the reliability (internal consistency reliability) and validity (convergent and discriminatory validity) of each construct in the model. Measurements of internal consistency, reliability, convergent validity, and discriminatory validity are part of the assessment of measurement models. * Construct Reliability  
The measurement scales for internal correlation among items for the specified latent construct is referred to as reliability ([PERSON] and [PERSON], 2017). A contract's reliability is determined by assessing the scale measurement's consistency and reliability. Cronbach's alpha and composite reliability are two measures that can be used to assess construct reliability. The Cronbach's alpha coefficient measures how effectively a scale's items are positively correlated with one another. It is calculated as the mean of the correlations between the items used to measure the concept ([PERSON] et al., 2015). A Cronbach's Alpha value of 0.7 or higher indicates construct reliability ([PERSON] et al., 2015). An alternative internal consistency reliability measure, called composite reliability (CR), can be used more effectively because Cronbach alpha has limitations in the population ([PERSON] et al., 2015). It is acceptable to have a construct reliability value of 0.70 or greater ([PERSON] et al., 2015). The construct reliability values for each construct are displayed in Table 7. With composite reliability ratings ranging from 0.835 to 0.929 and Cronbach's Alpha values ranging from 0.753 to 0.908, the reliability of all constructs is acceptable. * Construct Validity  
The degree to which a construct captures the intended or desired effect is known as its construct validity [112]. The convergent validity and discriminant validity components of construct validity are assessed [111; 113]. According to [PERSON], [PERSON]. [113], convergent validity refers to the degree of strong correlation between measures of the same constructs, whereas discriminant validity refers to how dissimilar one construct is from another. Convergent validity addresses loading and average variance extracted (AVE) of construction and assesses the degree of correlation of same-definition measurements [113]. The larger outer loading of the indicator indicates that there is a high degree of agreement between the connected measurements or that items used to test the same definition are coherent with the construct [113]. Factor loadings above 0.7 are thought to be extremely important [63]. According to Table 8, all of the loadings were higher than the suggested threshold of 0.7. However, there were a few items eliminated because of low outer loadings (between 0.50 and 0.60) as shown in Table 9. Additionally, the average variance extracted (AVE) value should be 0.5 or greater, indicating that constructs often account for more than half of the variance in their indicator. On average, there are more errors in measurement items with AVE below 0.5. Table 4 displays the AVE results as well as the initial outer loadings of the measurements. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Construct** & \multicolumn{2}{c}{**Cronbach’s Alpha**} & **Composite Reliability** \\ \hline Cybersecurity Prevention & (CP) & 0.887 & 0.917 \\ Government Frequent Alerting & (GF) & 0.824 & 0.873 \\ Organization Training & (OFT) & 0.845 & 0.906 \\ Perceived Severity & (PS) & 0.753 & 0.835 \\ Perceived Vulnerability & (PV) & 0.864 & 0.900 \\ Response Cost & (RC) & 0.895 & 0.922 \\ Response Efficacy & (RE) & 0.757 & 0.838 \\ Cybersecurity Leadership & (CL) & 0.908 & 0.929 \\ Self-Efficacy & (SE) & 0.875 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Construct reliability and internal consistency measures.