## 5 Empirical Analysis and Results
### The Structural Model
For Hypothesis H16, which stated that culture moderates the relationship between competitive pressure and CCA, the results indicated that the interaction terms representing competitive pressure and culture (\(\beta=0.026\), t = 0.909, \(p<0.364\)) were statistically not significant. Therefore, Hypothesis H16 is not supported. Finally, Hypothesis 17 stated that culture moderates the relationship between regulatory policy and CCA. The result showed the interaction terms representing regulatory policy and CCA was statistically significant (\(\beta=-0.099\), t = 2.367, \(p>0.018\)), and this hypothesis is supported. Table 6 summarizes the hypotheses testing of this study. To assess the structural model in PLS-SEM, there is an \(R^{2}\) value, known as the coefficient of determination [65; 66; 67]. The \(R^{2}\) values represent the proportion of variation in the dependent variable that can be explained by the independent variables and are interpreted in the same way as those obtained from multiple regression analysis. While the \(R^{2}\) value indicates the amount of variance in the construct that is explained by the variables, an acceptable level depends on the research context [66]. Ref. [68] suggested an \(R^{2}\) value of 0.10 as a minimum acceptable level. In addition, [63] considered values of 0.67, 0.33, and 0.19 in PLS-SEM as substantial, moderate and weak, respectively. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**H**} & \multirow{2}{*}{**Relation**} & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**T Value**} & \multirow{2}{*}{**\(p\) Value**} & \multirow{2}{*}{**Finding**} \\  & & **Sample** & & & & \\ \hline  
P \({}^{*}\) CUL \(\cdot>\) CCA & \(-0.099\) & 0.042 & 2.367 & 0.018 & Supported \\ \hline \hline \end{tabular} Note. H = Hypothesis, COM = Compatibility, CCA = CCA, CUL = Culture, CP = Competitive Pressure, RA = Relative Advantage, REL = Reliability, RP = Regulatory Policy, SEC = Security, TM S= Top Management System, TR = Technology Readiness, CCA = Cloud Computing Adoption. “\({}^{**}\) means moderating between IV and DV.  
\end{table} 
Table 6: Structural Model Assessment with All Values. values of the dependent variable in this study, showing that all the independent variables collectively explain 84% of the variance in CCA. The effect size (f2) indicates that the relative effect of a particular independent variable on dependent variable(s) by means of changes in the \(R^{2}\) value [61]. It is calculated as the increased in \(R^{2}\) of the latent variable, to which the path is connected, relative to the latent variable's proportion of unexplained variance; an effect size of 0.02 is small, 0.15 is medium, and greater than 0.35 is large [69]. The results provided by PLS-SEM analysis for the effect size of the independent latent variable on the dependent latent variable in this study's structural model are illustrated in Table 7.  
As illustrated in Table 7, the effect sizes for the nine independent variables; (a) relative advantage, (b) compatibility, (c) security, (d) reliability, (e) technology readiness, (f) top management support, (g) regulatory policy, (h) competitive pressure, and (h) culture were mentioned respectively. Therefore, following the [69] guideline, the effect sizes of these independent variables on cloud computing adoption can be considered variously as small, medium, and large. Moreover, predictive relevance Q2 is further explored through the employment of a blindfolding procedure using the cross-validated redundancy approach. Q2 values of above zero for a specific endogenous latent variable is an indication of the predictive relevance of that latent variable in explaining the endogenous latent variable [57]. As for this research model, the obtained Q2 value for CCA is 0.75, indicating the large predictive relevance [70].