## 3 Methodology  
### Research Framework and Dataset  
The framework and the methodology of this study are in accordance with the best practices in this type of research [67]. The research methodology includes the following:  
* Identifying the primary goal of the research (which is to present the application of data mining algorithms and attempt to predict enterprises' intentions of implementing and applying ICTs with multiple potential predictors);
* Literature review (obtaining and concisely presenting a theoretical background in the domain of modern business environments);
* Data acquisition (developing a structured survey, survey distribution, data collection, and creating an integrated dataset for data mining via various statistical methods);
* Statistical methods (including descriptive statistics, linear regression, binary logistic regression, multicollinearity test, heteroscedasticity test, QUEST classification tree algorithm, support-vector machine-SVM);
* Information extraction and discussion (presenting and determining whether it is possible to predict intentions of ICT implementation and application with different statistical methods). The study was conducted through a structured survey in accordance with best practices in this domain. The dataset included survey data from 214 enterprises (\(n=214\)), with details presented in Table 1.  
The survey items and additional details are presented in Table A1 (Appendix A). Since the majority of enterprises in Serbia are SMEs, it was expected that the survey data would primarily consist of data from SMEs, with a few large enterprises included. The data collection was conducted in 2021, and the dataset included information about the respondent (gender, age, education), enterprise size, business performance, awareness of ICT importance, intellectual capital management, and market assessment. Future research may consider other potential predictors. The dataset is robust and suitable for applying data mining algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Information** & & **Detail** \\ \hline Number of participants & & 214 \\  & \(\bullet\) & Managers; \\  & \(\bullet\) & Directors; \\  & \(\bullet\) & Owners of micro-, small-, medium-, and large-sized enterprises; \\  & \(\bullet\) & 135 male participants; \\  & \(\bullet\) & 79 female participants; \\  & \(\bullet\) & Average age of 43; \\  & \(\bullet\) & Youngest participant 29; oldest participant 61; \\  & \(\bullet\) & The majority has BSc degree—159 participants; \\  & & The majority of enterprises are micro-sized (36.45\%) and small-sized (49.07\%). This is expected as the majority of enterprises in the Republic of Serbia are micro- and small-sized enterprises. Industries in which the enterprises conduct their business: manufacturing (58); textile (28); \\ Enterprise & & agriculture (4); mining and quarrying (2); information and communication (41); \\  & & wholesale and retail (9); construction (9); \\  & & finance and insurance (26); healthcare and social work (9); water, electricity, gas supply (12); \\  & & education (10); other (6). \\ Note: & & Additional details are presented in the Appendix A. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. The research was conducted in three main phases. The first phase involved developing a structured survey, analyzing existing studies in this domain, and setting up the data collection process. The literature sources were analyzed, and the theoretical framework was developed. The anonymous surveys were distributed, and participants were given one month to complete them. An integrated dataset was created for statistical analysis. The second phase focused on data analysis/data mining, utilizing statistical methods ranging from simple to complex, such as descriptive statistics, linear regression, binary logistic regression, multicollinearity test, heteroscedasticity test, QUEST classification tree algorithm, support-vector machine-SVM, and feed-forward neural network.