## 1 Introduction  
Cloud computing is emerging as a key enabler in the digital transformation of organizations and governments, significantly contributing to increased productivity, cost reduction, and improved efficiency [1; 2; 3]. Through cloud services, organizations such as local governments can focus on their strategies, as the scalability and reliability of their systems are enhanced [4]. Strategies that incorporate multi-scale feature extraction and dual attention mechanisms effectively capture informative patterns at different levels of detail, enhancing decision-making and system responsiveness [5]. The cloud is now a foundation of digital transformation and is closely linked to service-oriented architecture. It integrates storage, applications, and business processes into a dynamic, reusable environment. In Digital Open Government (DOG), the use of multi-layered cloud structures reduces infrastructure needs and software costs [6]. Despite the benefits, cloud adoption is not uniform internationally. Factors such as the quality of the legal framework and broadband penetration influence its adoption. In particular, countries dependent on export-oriented businesses find it difficult to move to the cloud due to past investments in traditional technologies [7]. Cloud readiness, therefore, is not only about technological infrastructure but also about developing unique skills and knowledge. Multinational companies that have strategically invested in the cloud have recorded benefits in terms of operations and competitiveness, strengthening their sustainable development and external networks [8]. Additionally, the adoption of the cloud is also associated with quantifiable economic benefits, according to new empirical data, particularly when paired with enabling infrastructure like high-speed broadband. Nonetheless, the effects continue to vary among industries and nations, indicating the necessity of designing policies according to the circumstances [9]. Prior research has mostly concentrated on firm-level cloud computing adoption, with little investigation into the macro-level factors that influence cloud computing adoption [7; 10; 11; 12]. [PERSON] et al. [13] state (in their review of the cloud computing literature) that research on cloud computing at the macro level (national level) will create more awareness and support towards favorable policies for cloud computing. The European Commission aims to increase the access of European businesses and public authorities to cloud infrastructures and services in order to achieve the objective of the EUs Digital Decade, where 75% of European businesses should use cloud-edge technologies for their activities by 2030. Driven by this critical gap in the literature and its growing policy relevance, this study investigates the macro-structural determinants of cloud computing adoption across 27 EU countries. Existing research predominantly emphasizes firm- or sector-level factors, often using traditional econometric models constrained by linearity and limited interaction handling. In contrast, national-level adoption dynamics likely reflect more intricate, nonlinear relationships involving digital infrastructure, human capital, and socioeconomic context. To capture this complexity, we propose an integrated methodology that combines interpretable machine learning (XAI) with panel econometric validation. Using harmonized Eurostat indicators, we build a dual-panel dataset covering 2014-2021 and 2014-2024. Random Forests [14], XGBoost [15], and Support Vector Machines are deployed to model adoption outcomes, while SHAP and ICE visualizations enable transparent interpretation of predictor influence [16]. This flexible architecture overcomes the functional limitations of fixed-effects and linear models [17; 18], capturing both cross-country heterogeneity and temporal evolution. Furthermore, we estimate a dynamic panel system GMM model to benchmark causal inferences, reinforcing the centrality of digital skills while highlighting methodological complementarity.