## 3 Methodology
### SVM Algorithm for Data Mining  
Machine learning presents a significant advancement in the field of algorithms. It is a megatrend in various fields, including clustering (unsupervised learning), classification (supervised learning), and dynamic programming (reinforcement learning) [73]. Classification is an important topic in machine learning and is commonly used for predictive modeling [73]. Decision trees (such as the aforementioned QUEST algorithm) are one of the several types of machine learning classification methods. Another well-known classification algorithm is support vector machines (SVMs), which are widely applicable in various fields [74]. SVMs are trained to separate training data into two classes by determining the hyperplane that acts as the separator. The position of the hyperplane is defined by a subset of vectors called support vectors. For non-linear problems, the data are mapped into higher-dimensional spaces using kernel functions. Figure 1 illustrates the main principles of SVM for a linear problem. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology Aspects** & & **Info** \\ \hline Number of completed surveys & & 214 (\(n=214\)) \\ Study length & & Less than a year (finalized in 2021) \\ Sample structure & & Managers/directors/owners of micro-, small-, medium-, and large-sized enterprises \\ Study research instrument & & Survey (attached as an Appendix A) \\  & & Basic (descriptive) statistics; \\  & & Regression analysis (linear and binary logistic); \\ Conducted data analysis & & QUEST decision tree algorithm; \\  & & Classification supervised learning model-support vector machines/support vector networks. \\  & & Feed-forward neural network (Python and Tensorflow) \\  & & Demographic and basic information (gender, age, education, enterprise size) \\  & & Business performance \\ Predictor groups & & Awareness of ICT importance \\  & & Intellectual capital \\  & & Market assessment \\ Dependent variable & & Intention to implement and apply ICT within the enterprise \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Research methodology summary. Next, in Figure 2. the main principle for a non-linear problem is presented. This includes higher dimensional spaces via kernel functions. The main mathematical principle of SVM includes [76] the input parameters, which are a series of data points (x\({}_{1}\), y\({}_{1}\)), (x\({}_{2}\), y\({}_{2}\)),... (x\({}_{i}\), y\({}_{i}\)), where x\({}_{i}\) is the normalized input parameter, while y\({}_{i}\) is the normalized intention to implement an ICT under the input sample \(i\). From here, SVM approximates the function in the following form:  
\[\text{f(x)}=\omega\cdot\phi(\text{x})+\text{b} \tag{3}\]  
where \(\phi(\text{x})\) presents the higher-dimensional space, which is non-linearly mapped. In order to find \(\omega\) and b, the regularized risk function is used:  
\[\frac{1}{2}\left\|\omega\right\|^{2}+C\frac{1}{l}\sum_{i=1}^{l}L_{\varepsilon }\left(y_{i},\ f(x_{i})\right) \tag{4}\]  
where \(\left\|\omega\right\|^{2}\) is the regularized term, and by minimizing it, the function can be as flat as possible. \(\frac{1}{l}\sum_{i=1}^{l}L_{\varepsilon}\left(y_{i},\ f(x_{i})\right)\) is the empirical error. The error is measured by insensitive loss function (\(\varepsilon\)). This function has the following form:  
\[L_{\varepsilon}(y_{i},\ f(x_{i}))=\left\{ \begin{matrix}0,y_{i}-f\left(x_{i} \right)\leq\ \varepsilon\\ \left|y_{i}-f(x_{i})\right|-\varepsilon,\ otherwise\end{matrix} \right. \tag{5}\]  
This way, the range of \(\varepsilon\) values is determined in a way that, if the prediction is accurate, the loss will be zero. If the predicted value is outside the range, the loss is the difference between the predicted value and the distance of \(\varepsilon\) value range.