## 4 Data Analysis
### Development of Structural Models  
#### 4.3.1 Measurement Model  
Estimating reflective measurement models (or barriers) inside PLS-SEM calls for research on internal consistency, discriminant validity, and convergent validity. The whole model's construction was deemed valid since it met the criteria and pc > 0.600. In addition, the whole construction has been AVE-approved. AVE values over 0.500 are required to meet the criteria. The PLS algorithm version 4.0 used in this study yielded overall construct and AVE value approximations greater than 50% [74,78]. Indications so far point to the fact that the measurement model is both internally consistent and convergent. This indicated that all constructs in the study model were accurately measured by the research variables. When there are many important aspects associated with a notion, its external load will be high. Figure 3 shows that external loadings over 0.600 are considered to be within  
Figure 2: Conceptual framework. acceptable ranges [76; 82]. The observed correlation between all constructs can be easily compared with the square root of AVE, providing greater strength to the analysis and also utilizing the Fornell and Larcker hypothesis. A smaller value of correlations is required than the square root of AVE. Table 5 presents the detailed outcomes of the measurement model analysis involving AVE and correlations. Figures 4-7 present the normal probability distribution curves of item loading, Cronbach alpha, composite reliability, and AVE. All the observed parameters are in the normal range, which qualifies the model in terms of reliability and validity. The level of shared variance between the model's latent variables is often evaluated using the Fornell-Larcker (1981) criterion [74; 78]. With these standards in mind, we can use the Average Variance Extracted (AVE) and Composite Reliability to assess the convergent validity of the measurement model (CR) [89]. The output generated from smart pls for discriminant validity should be in such a way that in each column of every construct, the first highest value should be compared with the rest values; in this way, the Fornell linker criteria will be satisfied. Table 6 displays the discriminant validity Fornell linker values. The Fornell-Larcker method of evaluating hypotheses suggests that contrasting the variance captured by the concept (AVEj) with the variance shared with other constructs is one way to establish discriminant validity (j). Figure 3: Developed structural model. Figure 4: Normal probability curve of item loadings. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Safety Phase**} & \multirow{2}{*}{**Assigned Code**} & \multicolumn{2}{c}{**Loadings**} & \multirow{2}{*}{**Cronbach Alpha**} & \multirow{2}{*}{**Composite Reliability**} & \multirow{2}{*}{**AVE**} \\ \cline{3-4}  & & **Initial** & & **Modified** & \\ \hline \multirow{4}{*}{Technology Barriers} & F1 & 0.880 & 0.880 & 0.863 & 0.907 & 0.709 \\  & F8 & 0.816 & 0.816 & - & - & - \\  & F9 & 0.810 & 0.810 & - & - & - \\  & F14 & 0.861 & 0.861 & - & - & - \\ \hline \multirow{4}{*}{Database Barriers} & F11 & 0.711 & 0.771 & 0.774 & 0.855 & 0.596 \\  & F13 & 0.758 & 0.758 & - & - & - \\  & F7 & 0.808 & 0.808 & - & - & - \\  & F4 & 0.806 & 0.806 & - & - & - \\  & F2 & 0.563 & Deleted & - & - & - \\ \hline \multirow{4}{*}{Privacy Barriers} & F10 & 0.813 & 0.813 & 0.779 & 0.859 & 0.606 \\  & F16 & 0.803 & 0.803 & - & - & - \\  & F21 & 0.825 & 0.825 & - & - & - \\  & F20 & 0.660 & 0.660 & - & - & - \\ \hline \multirow{4}{*}{Operational} & F6 & 0.930 & 0.930 & 0.703 & 0.865 & 0.763 \\  & F15 & 0.813 & 0.813 & - & - & - \\  & F19 & 0.594 & Deleted & - & - & - \\ \hline \multirow{4}{*}{Management Barriers} & F23 & 0.916 & 0.916 & 0.838 & 0.925 & 0.86 \\  & F22 & 0.938 & 0.938 & - & - & - \\ \cline{1-1}  & F3 & 0.502 & Deleted & - & - & - \\ \cline{1-1}  & F5 & 0.564 & Deleted & & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Loadings of Variables along with CA, CR and AVE output.