## 4 Factors Influencing Cloud Adoption for HPC
### Model Evaluation  
The omnibus test of model coefficients explores whether the independent variables in the model can explain variations in the dependent variable. A significant result suggests that the independent variables can improve the prediction of the dependent variables. In this case, a chi-squared of 44.54, with a degree of freedom of 10 and a significance value below 0.01 (\(p\) = 0.00) reveals that the ten factors of interest can significantly improve the prediction of the cloud computing adoption decision.  
**Goodness-of-fit statistics.** The Hosmer and Lemeshow test gives a c2 (8) of 3.12, with a significance value of 0.93. This non-significant result suggests an acceptable match between the predicted adoption decision and actual adoption decision. \(-2\) log likelihood (\(-2\) LL) and Nagelkerke pseudo R-squared were also calculated to show the power of the research model in explaining the data variation. The lower the \(-2\) LL value is, the better the model fit is. In this case, a \(-2\) log likelihood value of 117.23 was acceptable. Nagelkerke Pseudo R-squared represents the amount of variation explained by the model. The model explains 42.3% of the variance in cloud computing adoption.  
**Statistical test for individual predictors.** Wald chi-square statistics were used to check the predictive ability of individual predictors. The results show that indirect benefits (\(p\) < 0.01) and HPC  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Adopters** & **Non-Adopters** & \(\mathbf{T}\)**-Value** \\ \hline Personal innovativeness & 3.89 (0.73) & 3.75 (0.63) & 1.08 \\ HPC competence & 3.48 (1.01) & 2.98 (0.98) & 2.71 ** \\ IT/IS competence & 2.94 (0.97) & 2.67 (1.02) & 1.43 \\ Complexity & 3.40 (0.87) & 3.49 (0.73) & \(-0.60\) \\ Compatibility & 3.75 (0.71) & 3.23 (0.78) & 3.86 ** \\ Reliability and security & 3.32 (0.94) & 3.19 (0.69) & 0.84 \\ Top management support & 3.14 (0.85) & 2.67 (0.83) & 3.02 ** \\ Adequate resources & 3.14 (0.99) & 2.67 (0.77) & 2.85 ** \\ Indirect benefits & 3.71 (0.65) & 3.10 (0.66) & 6.13 ** \\ Cost reduction & 3.40 (0.64) & 2.94 (0.75) & 3.66 ** \\ \hline \hline \end{tabular} Note: Numbers in brackets are standard deviations; **_p_ < 0.01.  
\end{table}
Table 4: \(T\)-test results of perceptions between adopters and non-adopters.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **B** & **S.E.** & **Wald** & **Sig.** & **Tolerance** & **VIF** \\ \hline Personal innovativeness & 0.04 & 0.36 & 0.01 & 0.91 & 0.93 & 1.07 \\ HPC competence & 0.65 * & 0.32 & 4.03 & 0.04 & 0.53 & 1.89 \\ IT/IS competence & \(-0.56\) & 0.34 & 2.75 & 0.09 & 0.56 & 1.78 \\ Complexity & \(-0.24\) & 0.32 & 0.56 & 0.45 & 0.86 & 1.16 \\ Compatibility & \(-0.30\) & 0.42 & 0.50 & 0.48 & 0.47 & 2.12 \\ Reliability and security & \(-0.54\) & 0.35 & 2.36 & 0.12 & 0.72 & 1.39 \\ Top management support & 0.49 & 0.37 & 1.75 & 0.18 & 0.47 & 2.14 \\ Adequate resources & 0.40 & 0.37 & 1.16 & 0.28 & 0.48 & 2.09 \\ Indirect benefits & 2.02 * & 0.55 & 13.51 & 0.00 & 0.47 & 2.14 \\ Cost reduction & 0.04 & 0.43 & 0.01 & 0.92 & 0.49 & 2.04 \\ Constant & \(-6.76\) & 2.42 & 7.83 & 0.01 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Logistic regression results.  
competence (\(p<0.05\)) are statistically significant predictors of cloud computing adoption. Thus, H3 and H9 were supported. Indirect benefits and HPC competence are positively related to an organization's likelihood of adopting cloud computing for HPC. Perceptions of IT/IS competence is approaching significance (\(p=0.09\)).  
**Discriminating power.** The logistic regression analysis also reveals the predictive accuracy of the research model (Table 6). The model yields a correct prediction rate of 79% for non-adopters and of 75% for adopters. The overall correct prediction rate is 77%. These results illustrate that these predictors have sufficiently higher discriminating power than the random choice model.