## 4 Results  
Due to the large dataset and extensive structured survey, the results of the descriptive statistics are presented in Table 12. (Appendix B). The results of the linear regression analysis are presented in Table 3.  
In the linear regression, business performance, awareness of ICT importance, intellectual capital, and market assessment were observed as independent variables or predictors, while ICT implementation intentions were the dependent variable. The linear regression results showed that awareness of ICT importance and intellectual capital did not have a significant influence on ICT implementation intentions. Interestingly, the business performance had a low to medium negative influence, indicating that higher business performance would reduce ICT implementation intentions. The market assessment had a low to moderate positive influence on ICT implementation intentions. Further, a binary logistic regression was conducted using stepwise regression to select the significant predictor variables automatically. The results of the binary logistic regression are presented in Table 4.  
The results indicate that classification success is 82%, sensitivity is 85%, and specificity is 88%. In addition, multicollinearity is tested, and the results are presented in Table 5.  
The variance inflation factors (VIFs) are under and slightly above 2.500. The heteroscedasticity is evaluated via the Breusch-Pagan and White test. The results are presented in Table 6. The results indicate that there is no statistically significant correlation. Figure 3: Neural network architecture schematics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Independent Variables (Viewed as Predictor Groups)** & **Dependent Variable** & **Standardized Coefficients** & \(\boldsymbol{p}\) \\ \hline Business performance & & \(-0.158\) & 0.000 \\ \hline Awareness of ICT importance & ICT implementation & 0.112 & 0.722 \\ \hline Intellectual capital & intentions & 0.057 & 0.602 \\ \hline Market assessment & & 0.166 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the linear regression analysis. The \(p\)-values of the Breusch-Pagan test and the White test indicate that the distribution of variances is homoscedastic and that there is no heteroscedasticity present in this model. The QUEST classification decision tree was used to attempt to predict ICT implementation intentions in enterprises. The tree had a yes/no structure. The tree analyzed several influencing features. Cross-validation was also included. In this case, the following features were found to have an effect on ICT implementation intentions:  
* Awareness of ICT importance (Do you think that modern ICTs are important for economic growth-ICTECO); (Do you think that information-communication technologies (ICTs) are important for the enterprise? (ICTIM));
* Market assessment (Main industry in which the enterprise conducts business-INDENT); (In your opinion, what industry is the most competitive? (INDCOMP)); Figure 4 presents the QUEST classification decision tree. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Breusch–Pagan Test** & **White Test** \\ \hline LM (Observed value) & 0.745 & 6.02 \\ \hline LM (Critical Value) & 2.995 & 6.147 \\ \hline DF & 1 & 2 \\ \hline \(p\)-value (Two-tailed) & 0.034 & 0.04 \\ \hline alpha & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the heteroscedasticity test.