## 3 Materials and Methods
### Data Preparation
#### 3.1.2 Variable Construction and Data Processing  
Each variable was extracted, filtered, and transformed to ensure conceptual alignment, statistical consistency, and comparability across EU countries and years. All data originate from Eurostat and follow standard EU statistical codes and classifications. Table 1 summarizes their definitions, measurement units, and sources. All variables are available annually from 2014 to 2024, except for Broadband Access, which is limited to the period until 2021. Due to this data limitation, two separate analytical panels must be created: Panel A, which covers the period from 2014 to 2021 and includes internet infrastructure as a significant predictor, and Panel B, which covers the period from 2014 to 2024 but excludes broadband access. In addition to providing a thorough analysis of the temporal dynamics underlying cloud adoption, this dual-panel approach also acts as a robustness check, enabling an evaluation of the effects of including or excluding digital infrastructure on model estimates and the reliability of the conclusions reached. It is important to note that no automated feature selection techniques were applied, as all variables were deliberately chosen based on theoretical relevance and empirical support from the existing literature. Data from each Eurostat data file are cleaned, filtered, and merged at the country-year level. Additionally, the two datasets are harmonized by converting all time indicators to integers and filtering for the specified EU countries, whereas missing data are dealt with through listwise deletion (complete-case analysis), ensuring that only observations with complete records across all selected predictors were retained for modeling. Finally, each variable is standardized using Z-score normalization as per Equation (1):  
\[z_{i}=\frac{x_{i}-\mu_{i}}{\sigma_{i}} \tag{1}\]  
where \(\mu_{i}\) and \(\sigma_{i}\) denote the mean and standard deviation of \(x_{i}\), respectively. This guarantees the required numerical stability for training and interpretation by forcing all predictors to be centered around zero and carry a unit standard deviation. \begin{table} \begin{tabular}{l l l l l l} \hline \hline
**Variable** & **Definition** & **Source (Eurostat Code/URL)** & **Unit** & **Years Available** & **Notes** \\ \hline \multirow{3}{*}{Cloud Adoption} & \% of enterprises (10+ & & & & Dependent variable; filtered \\  & employees) using cloud & isoc\_cice\_use/ & PC\_ENT & 2014–2024 & by size GE10, unit PC\_ENT, indicator E\_CC \\ \multirow{3}{*}{Higher Education} & \% of population aged & & & & \\  & 25–64 with ISCED 5–8 & & & & \\  & GDP per person & & & & \\  & (constant prices) & & & & \\ \multirow{3}{*}{Unemployment Rate} & \% of active labor force & & & & \\  & unemployed & & & & \\ \multirow{3}{*}{ICT Specialists} & \% of employed & & & & \\  & workforce in ICT roles & & & & \\  & \% of households with & & & & \\  & fixed broadband & & & & \\ \hline \multirow{7}{*}{Broadband Access} & \% of households & & & & \\  & fixed broadband & & & & \\ \hline \multirow{7}{*}{Broadband Access} & \% of individual & & & & \\  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}