## 3 Methodology  
### Research Framework  
The framework and methodology of this study followed the best practices typically applied in this type of research [126; 127; 128; 129]. The research framework included the following steps:  
* Defining the main objective: The goal was to demonstrate how data mining algorithms can be used to predict entrepreneurship intentions based on Internet marketing factors. Various potential predictors were considered. * Literature review: A concise presentation of the relevant theoretical concepts was provided, focusing on modern business environments. Data collection: A structured survey was designed, distributed, and used to gather data. The data were then compiled into a single dataset for analysis using different statistical methods. * Statistical methods: This study applied a range of statistical techniques including linear regression, logistic regression, QUEST, CHAID, SVM, and FNN. * Results and discussion: The findings were analyzed to determine whether it is possible to predict entrepreneurial intentions based on Internet marketing factors. The study was carried out using a structured survey, aligned with the best practices in this field. The dataset comprised responses from 137 enterprises (n = 137). Details on the sample and research methodology summary are provided in Table 1.  
The survey items and additional details can be found in Table A1 (Appendix A). Given that most enterprises in Serbia are small- and medium-sized enterprises (SMEs), it was anticipated that the majority of survey data would come from SMEs, with a few contributions from larger companies. The data collection took place in 2024, and the dataset included information about website optimization, social media marketing, email \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology Aspects** & & **Info** \\ \hline Number of completed surveys & & 137 (n = 137) \\ \hline Participants & \(\bullet\) & Managers at micro-, small-, medium-sized, and large enterprises \\ \(\bullet\) & \(\bullet\) & 95 male participants; \\  & \(\bullet\) & 42 female participants; \\  & \(\bullet\) & The majority had BSc degrees—62 participants \\ \hline Enterprise & & The majority of enterprises were small (35\%) and medium (45,25\%). This was expected as the majority of enterprises in the Republic of Serbia are micro and small enterprises. \\ \hline Study length & & 3 months (finalized in 2024) \\ \hline Sample structure & & Managers/directors/owners of micro-, small-, medium-sized, and large enterprises \\ \hline Conducted data analysis & \(\bullet\) & Regression analysis (linear and binary logistic); \\  & \(\bullet\) & QUEST decision tree algorithm; \\  & \(\bullet\) & Chi-squared automatic interaction \\  & \(\bullet\) & detection—CHAID \\  & \(\bullet\) & Classification supervised learning model—support vector machines/support vector networks. \\  & \(\bullet\) & Feed-forward neural network (Python and Tensorflow) \\ \hline Predictor groups & \(\bullet\) & Website optimization (WOPT) \\  & \(\bullet\) & Social media marketing (SMM) \\  & \(\bullet\) & E-mail marketing (EMAIL) \\  & \(\bullet\) & Content marketing (CMA) \\  & \(\bullet\) & Customer relationship management (CRM) \\  & \(\bullet\) & Online advertising (OAD) \\  & \(\bullet\) & Data management and analytics (DMA) \\ \hline Dependent variable & \(\bullet\) & Entrepreneurship intentions (ENTIN) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the research methodology. marketing, content marketing, and customer relationship management. The dataset was comprehensive and suitable for applying data mining algorithms. The research was conducted in three primary phases. The first phase involved developing a structured survey and thoroughly reviewing existing studies in this area. After the surveys were distributed, the dataset was prepared for data collection. Literature sources were reviewed, leading to the establishment of a theoretical framework. The surveys were anonymous, and respondents had one month to complete them.