## 5 Empirical Analysis and Results
### The Measurement Model
Furthermore, [63] recommended that the AVE of each latent construct should be 0.50 or more, as demonstrated by this study (see Table 3). Meanwhile, discriminant validity refers to the extent to which a particular latent variable is different from the other latent variables [64]. It was ascertained using AVE as suggested by [65], accomplished by comparing the squared correlation of the paired constructs with the AVE of each construct. Discriminant validity for reflective constructs occurs when the loadings of the items of a construct is an order of magnitude above the loadings for other constructs (loadings higher by 0.1) and the square root of the AVE for each construct is much higher than the correlations among pairs of constructs, above 0.5 [65]. Following [63] criterion, discriminant validity was determined by comparing the indicator loadings with other reflective indicators in the cross-loading table. To achieve adequate discriminant validity, the square root of the AVE should be greater than the correlations among latent constructs. As shown in Table 1, the AVE values are acceptable, ranging between 0.52 and 0.99. The correlations among the first-order variables were compared with the square root of the AVE (values in bold face). Table 4 also shows that the square root of the AVE were all greater than the correlations among latent variables, suggesting adequate discriminant validity [65]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constructs** & **Items** & **Loadings** & \
\begin{tabular}{c} **Composite** \\ **Reliability (CR)** \\ \end{tabular} &
\begin{tabular}{c} **Average Variance** \\ **Extracted (AVE)** \\ \end{tabular} \\ \hline \multirow{10}{*}{Culture} & CUL1 & 0.831 & \multirow{10}{*}{0.932} & \multirow{10}{*}{0.662} \\  & CUL2 & 0.865 & & \\ \cline{1-1}  & CUL3 & 0.834 & & \\ \cline{1-1}  & CUL4 & 0.876 & & \\ \cline{1-1}  & CUL5 & 0.818 & & \\ \cline{1-1}  & CUL8 & 0.727 & & \\ \cline{1-1}  & CUL9 & 0.730 & & \\ \cline{1-1}  & CP1 & 0.819 & & \\ \cline{1-1}  & CP2 & 0.859 & 0.870 & 0.691 \\ \cline{1-1}  & CP3 & 0.815 & & \\ \cline{1-1}  & TMS4 & 0.720 & & \\ \cline{1-1} Top Management System & TMS5 & 0.705 & 0.761 & 0.515 \\ \cline{1-1}  & TMS6 & 0.727 & & \\ \cline{1-1}  & TR2 & 0.848 & & \\ \cline{1-1} Technology Readiness & TR3 & 0.852 & 0.885 & 0.720 \\ \cline{1-1}  & TR5 & 0.846 & & \\ \cline{1-1}  & COM2 & 0.838 & & \\ \cline{1-1} Compatibility & COM3 & 0.879 & & \\ \cline{1-1}  & COM4 & 0.870 & 0.913 & 0.724 \\ \cline{1-1}  & COM5 & 0.815 & & \\ \cline{1-1}  & RA3 & 0.808 & & \\ \cline{1-1} Relative Advantage & RA4 & 0.789 & 0.839 & 0.635 \\ \cline{1-1}  & RA6 & 0.793 & & \\ \cline{1-1}  & REL1 & 0.855 & & \\ \cline{1-1} Reliability & REL2 & 0.841 & 0.904 & 0.703 \\ \cline{1-1}  & REL3 & 0.852 & & \\ \cline{1-1}  & REL4 & 0.803 & & \\ \cline{1-1}  & SEC2 & 0.903 & & \\ \cline{1-1} Security & SEC3 & 0.918 & 0.928 & 0.811 \\ \cline{1-1}  & SEC5 & 0.881 & & \\ \cline{1-1}  & RP1 & 0.903 & & \\ \cline{1-1} Regulatory Policy & RP3 & 0.889 & 0.890 & 0.802 \\ \cline{1-1} Cloud Computing & CCA1 & 0.989 & 0.989 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Item Loadings, Average Variance Extracted and Composite Reliability. The measurement model was also examined for convergent validity [64] which refers to the extent to which items truly represent the intended latent construct and indeed correlate with other measures of the same latent construct [54]. The indicators were evaluated using Average Variance Extracted (AVE) as suggested by [54]. Ref. [63] recommended that the AVE of each latent construct should be 0.50 or more, and in this study they AVE showed high loadings (>0.50) (see Table 4). Discriminant validity can be ascertained by comparing the indicator loadings with cross-loadings [61]. For this study, all the indicator loadings should be higher than the cross-loadings, and Table 5 compares them with other reflective indicators.