## 5 Empirical Analysis and Results
### The Structural Model  
This study applied the standard bootstrapping procedure with 5000 bootstrap samples and 328 cases to assess the significance of the path coefficients [65; 66; 67]. Bootstrapping is a non-parametric procedure that can be applied to test whether coefficients such as outer loadings, outer weights and path coefficients are significant, by estimating the standard errors. Figure 3 shows the estimates for the full structural model. First, after examining the influence of relative advantage (Hypothesis 1) on cloud computing adoption (CCA), the result revealed a significant positive relationship (\(\beta\) = 0.131, t = 4.392, \(p<0.000\)). Hence, hypothesis H1 is supported. Next, hypothesis H2, which predicted that compatibility has a significant effect on CCA, revealed a significant positive bond between compatibility and CCA (\(\beta\) = 0.179, t = 3.562, \(p<0.000\)), supporting this hypothesis. Similarly, Hypothesis H3, that proposed a relationship between security and CCA is fully supported as it is statistically significant (\(\beta\) = 0.176, t = 4.591, \(p>0.000\)). For Hypothesis H4, the influence of reliability on CCA, there is a positively significant relationship (\(\beta\) = 0.106, t = 2.601, \(p>0.009\)), supporting the hypothesis. In examining the influence of top management support on CCA, Hypothesis H5 is found to have a significant positive relationship (\(\beta\) = 0.136, t = 4.971, \(p>0.000\)) and is thus supported. With respect to Hypothesis H6, which predicted that technology readiness is positively related to CCA, is found to have a significant relationship with CCA (\(\beta\) = 0.158, t = 4.319, \(p<0.000\)), supporting Hypothesis H6. Next, regulatory policy was predicted in Hypothesis H7 to be positively related to CCA, and the result shows a significant negative relationship (\(\beta\) = 0.149, t = 2.550, \(p<0.011\)), so Hypothesis H7 is supported. With respect to the results for Hypothesis H8, on the influence of competitive pressure on CCA indicate a positively significant relationship (\(\beta\) = 0.107, t = 3.470, \(p>0.001\)), supporting this hypothesis. Hypothesis H9 predicted that culture has a statistically significant negative relationship with CCA. The result (\(\beta\) = -0.299, t = 4.286, \(p<0.000\)) clearly supports this hypothesis, showing that when culture increases CCA decreases. On the hand, Hypothesis H10 stated that culture moderates  
Figure 3: The Structural Model. “*” means moderating between IV and DV. the relationship between relative advantage and CCA. The results show that this is statistically significant (\(\beta=-0.049\), t = 2.081, \(p>0.038\)). With respect to the Hypothesis H11, which stated that culture moderates the relationship between compatibility and CCA. As expected, the results indicate that the interaction terms representing compatibility x culture (\(\beta=-0.133\), t = 2.902, \(p<0.004\)) are statistically significant. Hence, Hypothesis H11 is fully supported. Furthermore, Hypothesis H12 stated that culture moderates the relationship between security and CCA; this was found to be statistically significant (\(\beta=-0.075\), t = 2.084, \(p>0.037\)), clearly supporting this hypothesis. Hypothesis H13 predicted that culture moderates the relationship between reliability and CCA, and this is found to be statistically significant (\(\beta=-0.124\), t = 2.927, \(p>0.009\)), supporting the hypothesis. With respect to Hypothesis H14, which predicted that culture moderates the relationship between top management support and CCA, the results show that this was not statistically significant (\(\beta=-0.005\), t = 0.230, \(p>0.818\)), so this hypothesis is not supported. Meanwhile, Hypothesis H15 stated that culture moderates the relationship between technology readiness and CCA, but this was not found to be statistically significant (\(\beta=-0.011\), t = 0.288, \(p<0.773\)) and this hypothesis is also not supported.