## 4 Results
### Model Performance and Variable Importance  
The predictive performance of the Random Forest (RF) model was evaluated using the Root Mean Squared Error (RMSE) on the test dataset. Given the variation in cloud \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Variable** & **Median** & **Trimmed Mean** & **Mad** & **Min** & **Max** & **Range** & **Skewness** & **Kurtosis** \\ \hline
**Cloud Adoption** & \(-0.26\) & \(-0.10\) & \(0.91\) & \(-1.40\) & \(2.71\) & \(4.11\) & \(0.84\) & \(-0.11\) \\
**Higher Education** & \(0.03\) & \(0.01\) & \(1.21\) & \(-1.97\) & \(2.27\) & \(4.24\) & \(-0.04\) & \(-1.03\) \\
**Gdp per Capita** & \(0.22\) & \(0.03\) & \(0.96\) & \(-2.82\) & \(2.64\) & \(5.46\) & \(-0.30\) & \(0.63\) \\
**Unemployment Rate** & \(-0.20\) & \(-0.13\) & \(0.69\) & \(-1.55\) & \(4.59\) & \(6.14\) & \(1.64\) & \(3.80\) \\
**ICT Specialists** & \(-0.23\) & \(-0.09\) & \(0.97\) & \(-1.89\) & \(3.05\) & \(4.94\) & \(0.78\) & \(0.22\) \\
**Broadband Access** & \(0.10\) & \(0.07\) & \(1.07\) & \(-3.04\) & \(1.78\) & \(4.82\) & \(-0.59\) & \(0.00\) \\ \hline \hline \end{tabular} Source: Authors’ calculations using Eurostat data.  
\end{table} 
Table 2: Descriptive statistics (Panel A, EU 27, 2014–2021). adoption rates among EU member states, the default RF model's RMSE of 0.456 suggests an acceptable fit. Using 10-fold cross-validation, we conducted hyperparameter tuning of the random forest model by exploring mtry values ranging from 2 to 10. The model's predictive performance was relatively stable across this range, with the best configuration observed at mtry = 2, yielding an RMSE of 0.5639, R\({}^{2}\) of 0.6887, and MAE of 0.4158 (see Table 3). Performance differences among adjacent mtry values were minor, suggesting that the model was not highly sensitive to this hyperparameter in the absence of country and year fixed effects. Variable importance was evaluated using the percentage increase in mean squared error (%IncMSE), a standard measure in random forest models that reflects how much worse the model performs when a given predictor is permuted. As per Table 4, in the tuned model, ICT specialists and broadband access are the most influential predictors, with importance scores of 29.16% and 21.23%, respectively. This affirms the central role of both digital skills and infrastructure in shaping cloud adoption outcomes across EU member states. Economic and educational indicators played secondary but meaningful roles: GDP per capita (14.26%) and higher education (10.82%) were moderately important. In contrast, the unemployment rate continued to contribute the least (9.67%). These results are consistent with the hypothesis that digital capacity, not just socioeconomic status, is a primary driver of cloud uptake. Figure 2 illustrates the relative importance of each variable visually, reinforcing the dominant influence of ICT-related factors on model predictions. The emphasis on digital enablers highlights key areas for targeted policy intervention. These findings underscore that while economic and educational variables contribute meaningfully, it is digital infrastructure and workforce readiness that most strongly predict cloud adoption. The results further support policy strategies focused on broadband expansion and ICT skill development as levers for accelerating digital transformation across the EU. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Predictor** & **\%IncMSE** & **IncNodePurity** \\ \hline
**ICT Specialists** & 29.16 & 39.84 \\
**Broadband Access** & 21.23 & 34.23 \\
**GDP per Capita** & 14.26 & 11.38 \\
**Higher Education** & 10.82 & 18.56 \\
**Unemployment Rate** & 9.67 & 9.08 \\ \hline \hline \end{tabular} Source: Authors’ calculations.  
\end{table} 
Table 4: Tuned Random Forest Model, Panel A, No Dummies.