This article investigates the macroeconomic and labor market conditions that shape the adoption of artificial intelligence (AI) technologies among large firms in Europe. Based on panel data econometrics and supervised machine learning techniques, we estimate how public health spending, access to credit, export activity, gross capital formation, inflation, openness to trade, and labor market structure influence the share of firms that adopt at least one AI technology. The research covers all 28 EU members between 2018 and 2023. We employ a set of robustness checks using a combination of fixed-effects, random-effects, and dynamic panel data specifications supported by Clustering and supervised learning techniques. We find that AI adoption is linked to higher GDP per capita, healthcare spending, inflation, and openness to trade but lower levels of credit, exports, and capital formation. Labor markets with higher proportions of salaried work, service occupations, and self-employment are linked to AI diffusion, while unemployment and vulnerable work are detractors. Cluster analysis identifies groups of EU members with similar adoption patterns that are usually underpinned by stronger economic and institutional fundamentals. The results collectively suggest that AI diffusion is shaped not only by technological preparedness and capabilities to invest but by inclusive macroeconomic conditions and equitable labor institutions. Targeted policy measures can accelerate the equitable adoption of AI technologies within the European industrial economy.

In this article, we analyze the combined impact of sustainability activities and family governance on firm-level risk, measured by earning volatility, with particular attention to the timing of ESG involvement. Using panel regression models, we distinguish between short- and long-term ESG performance and between family ownership and family management. The empirical analysis reveals a negative correlation between long-term ESG performance and corporate risk, but short-term ESG impact is insignificant. Family ownership and having a family CEO both decrease firm risk; however, family ownership moderates the link between ESG risks and firm risk.

This article investigates macroeconomic factors that support the adoption of Artificial Intelligence (AI) technologies by large European Union (EU) enterprises. In this analysis, panel data regression is combined with machine learning to investigate how macroeconomic variables like health spending, domestic credit, exports, gross capital formation, and inflation, along with health spending and trade openness, influence the share of enterprises that adopt at least one type of AI technology (ALOAI). The results of the estimations-based on fixed and random effects models with 151 observations-show that health spending, inflation, and trade and GDP per capita have positively significant associations with adoption, with significant negative correlations visible with and among domestic credit, exports, and gross capital formation. In adjunct to this, the regression of machine learning models (KNN, Boosting, Random Forest) is benchmarked with MSE, RMSE, MAE, MAPE, and R² measures with KNN performing perfectly on all measures, although with some concerns regarding data overfitting. Furthermore, cluster analysis (Hierarchical, Density-Based, Neighborhood-Based) identifies hidden EU country groups with comparable macroeconomic variables and comparable ALOAI. Notably, those with characteristics of high integration in international trade, access to credit, and strong GDP per capita indicate large ALOAI levels, whereas those with macroeconomic volatility and under-investment in innovation trail behind. These findings suggest that securing the adoption of AI is not merely about finance and infrastructure but also about policy alignment and institutional preparedness. This work provides evidence-driven policy advice by presenting an integrated data-driven analytical framework to comprehend and manage AI diffusion within EU industry sectors.

This article investigates macroeconomic factors that support the adoption of Artificial Intelligence (AI) technologies by large European Union (EU) enterprises. In this analysis, panel data regression is combined with machine learning to investigate how macroeconomic variables like health spending, domestic credit, exports, gross capital formation, and inflation, along with health spending and trade openness, influence the share of enterprises that adopt at least one type of AI technology (ALOAI). The results of the estimations—based on fixed and random effects models with 151 observations—show that health spending, inflation, and trade and GDP per capita have positively significant associations with adoption, with significant negative correlations visible with and among domestic credit, exports, and gross capital formation. In adjunct to this, the regression of machine learning models (KNN, Boosting, Random Forest) is benchmarked with MSE, RMSE, MAE, MAPE, and R² measures with KNN performing perfectly on all measures, although with some concerns regarding data overfitting. Furthermore, cluster analysis (Hierarchical, Density-Based, Neighborhood-Based) identifies hidden EU country groups with comparable macroeconomic variables and comparable ALOAI. Notably, those with characteristics of high integration in international trade, access to credit, and strong GDP per capita indicate large ALOAI levels, whereas those with macroeconomic volatility and under-investment in innovation trail behind. These findings suggest that securing the adoption of AI is not merely about finance and infrastructure but also about policy alignment and institutional preparedness. This work provides evidence-driven policy advice by presenting an integrated data-driven analytical framework to comprehend and manage AI diffusion within EU industry sectors.

The present study aims to contribute to the exploration of the combined effect of sustainability strategies and family governance on corporate risk-taking, with a focus on the temporal dimension of ESG engagement and the multidimensional nature of family involvement. Although a number of research papers have attempted to investigate the interrelations between ESG performance, corporate risk, and ownership structure, the existing research is fragmented, static, and limited in terms of its research methodology. Therefore, this article aims to provide a framework that (a) distinguishes between long-term and short-term ESG performance and (b) distinguishes between ownership incentives and management control in family firms. The present study uses a sample of listed corporations and combines three different research methods: fixed effects panel regressions, cluster analysis, and machine learning regression methods. The results of this study suggest that long-term ESG performance is negatively related to corporate risk, as proxied by the volatility of operating performance. The effect of short-term ESG performance on corporate risk is less robust and less consistent. Furthermore, this study finds that family cash-flow rights and family CEOs are negatively related to corporate risk, in accordance with the SEW hypothesis. Finally, this study reveals that the positive interaction effect of long-term ESG performance and family cash-flow rights mitigates the negative effect of sustainability on corporate risk in family-controlled corporations. The results of this study show that cluster analysis reveals a high degree of heterogeneity in corporate strategic profiles, with different clusters characterized by specific configurations of ESG orientation, family involvement, corporate governance structure, firm size, and corporate risk. The results of this study also show that the proposed model has a high predictive power in relation to corporate risk levels, as indicated by the results of the random forest regression analysis. Finally, this study reveals that financial fundamentals are more important in determining corporate risk than ESG engagement and family control; however, ESG engagement and family control are secondary but significant and nonlinear determinants of corporate risk.