From Infrastructure to Productivity: A Partial Least Squares Structural Equation Modelling Analysis of Cloud and Artificial Intelligence Adoption in Germany

Purpose This study aims to investigate whether digital infrastructure and the adoption of frontier digital technologies catalyze innovation and productivity on a national scale. Research method This exploratory study develops and tests a theory-driven model linking digital infrastructure, cloud adoption, and emerging technology intensity to innovation output and, ultimately, productivity. Using secondary indicators from the United Nations (UN), UN-affiliated sources, and the Organisation for Economic Co-operation and Development (OECD), a country-level dataset is assembled for Germany covering the years 2013-2024. A partial least squares structural equation model is then estimated using the R programming language. Findings The paper presents an integrated measurement and structural specification for macro-level digital transformation, providing transparent construct-to-indicator mappings that are suitable for replication using public data, as well as policy-relevant assessments of institutional contexts. Limitations The study relied on secondary data sources from a single country, Germany, which limited the number of observations used. Implications The results framework is intended to offer guidance to both academic analyses and evidence-based digital policy design. Originality/value The study makes three main contributions. First, it develops a unified empirical framework linking digital infrastructure, technology adoption, innovation, and productivity. Second, it leverages internationally comparable data from UN and OECD sources to move beyond sectoral studies. Third, it provides policy-relevant insights by testing whether cloud adoption fundamentally improves productivity; emerging technologies strongly drive innovation, with institutional readiness shaping the extent of these effects.