## 4 Results
### Explainable AI (XAI) Analysis  
SHapley Additive exPlanations (SHAP) were used to better interpret the Random Forest (RF) model, providing detailed information on the ways in which each predictor influences cloud adoption. SHAP is a crucial tool for model transparency and policy interpretation since it helps quantify the average influence of each feature as well as the variability of its contribution across various projections.  
The SHAP-based feature importance plot for Panel A (2014-2021), derived from the tuned Random Forest model (mtry = 10, ntree = 500), trained on the standardized dataset without country fixed effects, is shown in Figure 4. Of note, SHAP values were calculated using the iml package in R [57; 58], which implements model-agnostic feature attribution based on the concept of marginal contributions from cooperative game theory [59]. Specifically, we used the FeatureImp class, which approximates permutation-based Shapley values by quantifying each feature's marginal impact on model error (measured as increased MSE) when its values are permuted. This approach captures global importance while preserving consistency and local accuracy. The visualized SHAP plot is derived from the tuned Random Forest model trained on standardized data without fixed effects and reflects feature contributions aggregated over all test observations. The predictor object was instantiated using the actual trained randomForest model and the test dataset held out during training.  
With the biggest average contributions to lowering model error (as determined by mean squared error, or MSE), the ICT specialists (ICT) and broadband access (Broadband) variables emerged as the dominating predictors. These findings demonstrate the critical role that labor competencies and digital infrastructure play in propelling cloud adoption throughout the EU.  
Economic and educational metrics, such as GDP per capita (GDPpc) and higher education (HighEd), showed moderate importance, while the unemployment rate (Unemp) had the least average influence.  
Notably, the plot also illustrates variability (horizontal bars), indicating how consistently each feature influences predictions. ICT and broadband access show both high average importance and wide variability, suggesting their impact fluctuates across different country-year contexts, likely reflecting disparities in both workforce skills and digital infrastructure maturity. In contrast, higher education and GDP per capita demonstrate smaller average importance and lower variability, underscoring their stable but less dominant roles.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Training Time (s)** & **Notes** \\ \hline
**Random Forest** & 3.26 & 500 trees; mtry optimized \\
**XGBoost** & 8.02 & Tuned over nrounds, eta, max\_depth \\
**Elastic Net** & 0.20 & glmnet-based regularization (fastest) \\
**SVM (Linear Kernel)** & 0.14 & Tuned C with linear kernel \\
**Ensemble (Stacked)** & 12.36 & Aggregation of base learners via greedy RMSE \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training time for each model.  
These SHAP results reinforce and deepen the earlier random forest importance findings, confirming that broadband connectivity and ICT workforce capacity are critical drivers of cloud adoption. The combination of high mean importance and substantial variability for ICT and broadband access suggests that policy interventions to boost human resource skills and digital infrastructure can have transformative effects, particularly in lagging regions.