## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Item** & **Feature Group** & \(\boldsymbol{\beta}\) & \(\boldsymbol{p}\) & **95\% CI** \\ \hline Financial goals are easily achieved. & Business performance & 1.20 & 0.001 & 1.17 & 1.54 \\ \hline Strategic long-term profitability is sustainable. & Business performance & 0.74 & 0.000 & 0.68 & 0.78 \\ \hline Quality of products is satisfactory. & Business performance & 0.94 & 0.001 & 1.01 & 0.99 \\ \hline Do you think that information-communication technologies (ICTs) are important for the enterprise? & Awareness ICT importance & 0.96 & 0.000 & 0.68 & 0.96 \\ \hline Do you think that enterprises that implement modern ICTs are more competitive compared to enterprises that don’t? & Awareness of ICT importance & 0.38 & 0.01 & 0.44 & 1.01 \\ \hline Intellectual capital is effectively applied in the enterprise. & Intellectual capital & 1.14 & 0.000 & 1.02 & 1.33 \\ \hline Intellectual capital is reported periodically within the enterprise. & Intellectual capital & 0.80 & 0.188 & 0.83 & 1.02 \\ \hline Do you think the COVID-19 pandemic has negatively affected the enterprise’s market performance? & Market assessment & 0.81 & 0.055 & 0.81 & 0.94 \\ \hline Do you think the market is competitive within your enterprise’s industry? & Market assessment & 0.79 & 0.006 & 0.72 & 1.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the binary logistic regression. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Business Performance** & **Awareness of ICT Importance** & **Intellectual Capital** & **Market Assessment** \\ \hline Tolerance & 0.454 & 0.422 & 0.351 & 0.490 \\ \hline Variance Inflation Factor (VIF) & 1.955 & 1.947 & 1.844 & 2.505 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multicollinearity test. The rules explaining the QUEST algorithm are as follows:  
* N1: if (ICTECO = y), then class y = 80.70% and class \(n=19.30\%\);
* N2: if (ICTECO = \(n\)), then class y = 71.00% and class \(n=29.00\%\);  
Figure 4: QUEST algorithm decision tree for predicting intentions on ICT solution implementation. * N3: if (ICTIM = \(n\)), then class y = 79.63% and class \(n\) = 20.37%;
* N4: if (ICTIM = y), then class y = 86.67% and class \(n\) = 13.33%;
* N5: if (INDENT = [finance and insurance] OR [education]), then class y = 56.25% and class \(n\) = 43.75%;
* N6: if (INDENT = [manufacturing] OR [textile] OR [water electricity] OR [information and communication] OR [healthcare] OR [other]), then class y = 73.81% and class \(n\) = 26.19%;
* N7: if (INDENT = [healthcare] OR [other]), then class y = 68.42% and class \(n\) = 31.58%;
* N8: if (INDENT = [manufacturing] OR [textile] OR [water electricity] OR [information and communication]), then class y = 76.92% and class \(n\) = 23.08%;
* N9: if (INDCOMP = [manufacturing] OR [information and communication])), then class y = 72.22% and class \(n\) = 27.78%;
* N10: if (INDCOMP = [textile] OR [agriculture] OR [wholesale and retail] OR [construction] OR [education]), then class y = 75.86% and class \(n\) = 24.14%. In the first step, the model was able to predict 80.7% of the time if the participant thinks that modern ICTs are important for economic growth. In the next step, the model was able to predict ICT application 86.67% of the time. On the other side of the first split, managers/directors/owners of enterprises who do not think that ICTs are not important for economic growth but who would implement an ICT solution are affected by the industry in which their enterprise conducts business and by the opinions on the most competitive industries. The predictive precision value of this model (risk value) is 0.224 (0.005), which means that a misclassification occurs in 22.4% of the cases. Next, the summary of the optimized SVM classifier is presented in Table 7.  
The confusion matrix of the SVM analysis is presented in Table 8.  
Following Table 8, the receiver operating characteristic curve-ROC curve is presented in Figure 5.