## 5 Empirical Analysis and Results
### The Moderation Analysis  
In this study, out of the eight moderating hypotheses, five have been proved to be significant. The indicator method was applied using the PLS SEM to estimate the strength of the moderating effect of culture on each of the selected factors as independent factors and cloud computing adoption as dependent variable; this study applied [PERSON]'s (1988) [69] guidelines for determining the effect size. The indicator approach to test this moderating effect requires the product terms between the indicators of the latent independent variable and those of the latent moderator variable to be created, for use as indicators of the interaction term in the structural model. The procedure was carried out for each of the moderating hypotheses which had been supported (compatibility, relative advantage, reliability, security, and regulatory policy). With respect, the Hypothesis H11, it is found that the relationship is stronger (i.e., more negative) for individuals with high culture than it is for individuals with low culture, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predecessor Latent** & **Dependent** & **(R2)** & **(R2)** & **Excl** & **Effect size (f2)** & **Remarks** \\
**Variables** & **Variable** & **(R2)** & **(R2)** & **Excl** & **Effect size (f2)** & **Remarks** \\ \hline Culture & CCA & 0.84 & 0.79 & 0.23 & Medium \\ Competitive & CCA & 0.84 & 0.79 & 0.12 & Small \\ Pressure & CCA & 0.84 & 0.76 & 0.39 & Large \\ Top Management & CCA & 0.84 & 0.80 & 0.15 & Small \\ Technology & CCA & 0.84 & 0.79 & 0.22 & Medium \\ Relative & CCA & 0.84 & 0.76 & 0.38 & Large \\ Advantage & CCA & 0.84 & 0.82 & 0.06 & Small \\ Reliability & CCA & 0.84 & 0.82 & 0.04 & Small \\ Security & CCA & 0.84 & 0.80 & 0.20 & Medium \\ Regulatory Policy & CCA & 0.84 & 0.80 & 0.20 & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect Size of Predictive Variables.  
Similarly, Hypothesis H10 testing indicated a stronger (i.e., more negative) for individuals with high culture than it is for individuals with low culture. As illustrated by Figure 5, the moderating effect shows a stronger negative relationship between relative advantage and CCA for individuals with high culture than it is for individuals with low culture.  
With respect to Hypothesis H13, which predicted that culture moderates the relationship between reliability and cloud computing adoption, the moderation relationship is significant. Figure 6 depicts that culture moderated the relationship between reliability and cloud computing adoption, such that this relationship is weaker (i.e., more negative) for individuals with high culture than it is for individuals with low culture.  
Figure 4: Interaction Effect of compatibility (COM) and culture (CUL) on cloud computing adoption (CCA).  
Figure 5: Interaction Effect of relative advantage (RA) and culture (CUL) on cloud computing adoption (CCA).  
Furthermore, analysis of Hypothesis H12 proved that culture moderates the relationship between security advantage and CCA. The moderating influence of culture the among between security and cloud computing adoption is depicted in Figure 7, which shows a stronger negative relationship between security and cloud commuting adoption for individuals with high culture than it is for individuals with low culture.  
Regarding Hypothesis H17, the culture is found to moderate the relationship between regulatory policy and cloud computing adoption, as expected. The Figure 8 shows that the relationship between regulatory policy and CCA is stronger (i.e., more negative) for individuals with high culture than it is for individuals with low culture.  
Figure 6: Interaction Effect reliability (REL) and culture (CUL) on cloud computing adoption (CCA).  
Figure 7: Interaction Effect of security (SEC) and culture (CUL) on cloud computing adoption (CCA).