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In today’s competitive and globalized world, innovation is essential for organizational survival, offering a means for companies to address environmental impacts and social challenges. As innovation processes accelerate, managers need to rethink the entire value-creation chain, with digital transformation emerging as a continuous process of organizational adaptation to the evolving societal landscape. The research question focuses on how digital technologies—such as artificial intelligence, Big Data, cloud computing, industrial and service robots, and the Internet of Things—influence innovation-driven revenues among enterprises within the European Union (EU). The paper examines, using neural network analysis, the specific impact of each digital technology on innovation revenues while exploring how these technologies affect various types of social innovation within organizations. Through cluster analysis, the study identifies patterns among EU countries based on their digital technology adoption, innovation expenditures, and revenues and the proportion of enterprises engaged in innovation activities. The findings highlight the central role of digital technologies in enhancing innovation and competitiveness, with significant implications for managers and policymakers. These results underscore the necessity for companies to strategically integrate digital technologies to sustain long-term competitiveness in the rapidly evolving digital landscape of the EU.

Background In the contemporary landscape characterized by digital transformation and sustainable economic development, understanding the dynamics of health status is critically significant. This study investigates the complex relationship among healthcare expenditure, digital transformation, health status, and well-being within the European Union (EU) framework. Methods Through structural equation modeling, the research examines the multidimensional interplay among these variables, while cluster analysis supports identifying distinct patterns within the data. The paper aims to provide a broad understanding of the impact of digital transformation and healthcare expenditure on health status and well-being at the EU level. Results The findings unveil nuanced linkages among healthcare expenditure, digital transformation, health status, and well-being across distinct clusters of EU countries. While certain countries exhibit synergistic advancements resulting in enhanced healthcare outcomes, others confront challenges stemming from disparities in digital infrastructure, healthcare expenditure allocation, and health status. Conclusions This study underscores the imperative of fostering synergies between digital transformation and healthcare expenditure to enhance health status within the EU. Identifying pivotal determinants and barriers enables policymakers to formulate targeted strategies to mitigate disparities and foster inclusive growth to promote equitable healthcare access and advance overall societal well-being.

Digital transformation generates challenges and opportunities at the individual and organizational levels. Implementing digital technologies impacts all countries’ economic growth and orientation toward sustainability. This paper aims to evaluate the effects of digital transformation on the economic performance and sustainability of European Union (EU) countries. The paper employs artificial neural network analysis, structural equation modeling, and cluster analysis to investigate the relationships among digital transformation, economic performance, and sustainability. Economic performance is measured using GDP per capita, while SDG scores represent sustainability. The use of computers and the Internet in enterprises, the volume of e-commerce, and the percentages of implementing new digital technologies, such as cloud computing, Big Data, and the Internet of Things, illustrate digital transformation. The research findings reveal the extent of digital transformation in each country and the significant influence of digital transformation on economic performance and sustainability. The main drivers of digital transformation are the use of computers and the Internet in enterprises and e-commerce. However, new digital technologies exert increasingly noticeable effects, particularly in developed European countries. This study elucidates the profound implications of digital transformation on economic performance and sustainability. It underscores the pivotal role of digital technologies, especially in advanced European countries, in driving economic growth and sustainability. The results can be helpful to regulators in developing digitization strategies that underpin sustainable economic performance.

The digital revolution is reshaping various aspects of society, including having a profound impact on food security and the advancement of Sustainable Development Goals (SDGs). This study investigates the relationship between digital transformation, quantified through the components of the Digital Economy and Society Index (DESI), and SDGs related to food (SDG1, SDG2, SDG3, and SDG10), along with the overall SDG Index score. The data used for investigation are sourced from reports issued by the European Commission concerning DESI, as well as the SDG reports for the period from 2017 to 2022. The paper elucidates how different components of digitalization, such as connectivity, digital skills, internet usage, and digital public services, influence the attainment of food security objectives and broader sustainable development targets using structural equation modeling and cluster analysis. The findings underscore the pivotal role of digital technologies in enhancing poverty alleviation, health and well-being, and, in particular, mitigating inequality. This study contributes to understanding the complex relationship between digital transformation and food security, offering insights for policymakers, practitioners, and stakeholders aiming to leverage technology for advancing SDGs and fostering a more equitable and sustainable future.

Digital innovation in agriculture has become a powerful force in the modern world as it revolutionizes the agricultural sector and improves the sustainability and efficacy of farming practices. In this context, the study examines the effects of digital technology, as reflected by the digital economy and society index (DESI), on key agricultural performance metrics, including agricultural output and real labor productivity per person. The paper develops a strong analytical method for quantifying these associations using predictive models, such as exponential smoothing, ARIMA, and artificial neural networks. The method fully illustrates how economic and technological components interact, including labor productivity, agricultural output, and GDP per capita. The results demonstrate that digital technologies significantly impact agricultural output and labor productivity. These findings illustrate the importance of digital transformation in modernizing and improving agriculture’s overall efficacy. The study’s conclusion highlights the necessity of integrating digital technology into agricultural policy to address productivity problems and nurture sustainable growth in the sector.

The accelerating pace of digital transformation has positioned the digital economy as a key driver in advancing the Sustainable Development Goals (SDGs). However, the mechanisms through which digitalization influences sustainability remain underexplored. This study examines the extent to which digital progress, captured through the Digital Economy and Society Index (DESI), impacts sustainable development outcomes across EU member states, measured by the Sustainable Development Goals Index (SDGi). Utilizing data spanning the period 2017–2022, the analysis applies a multi-method approach—combining exploratory factor analysis, multiple regression, artificial neural networks, and predictive modeling—to identify structural relationships and forecast future trends. The findings reveal strong linkages between human capital development, digital technology integration, and SDG performance, while also highlighting significant heterogeneity among EU countries. Forecasts indicate that digitalization is likely to accelerate in the coming years. Still, its contribution to sustainability will depend on the degree to which policy frameworks succeed in fostering inclusive and context-sensitive digital transitions. By integrating empirical precision with predictive insight, this study offers a robust framework for aligning digital transformation with long-term sustainability objectives in a diverse European context.

With the increasing concern about climate change and its impacts on agriculture, understanding the dynamics of greenhouse gas (GHG) emissions in the European Union (EU) agricultural sector is essential for devising effective mitigation strategies. This study aims to assess the impact of agriculture on GHG within the EU and to examine how climate-smart agricultural practices can affect these emissions. The research investigates the complex relationship between agricultural activities and GHG emissions within the European Union during the period of 2017–2022 using structural equation modeling based on data from Eurostat and the European Commission. Furthermore, the study examines the influence of the digital economy on labor productivity in agriculture, recognizing the pivotal role of digital technologies in fostering climate-smart agricultural practices. The findings unveil significant positive influences encompassing the digital economy, agricultural productivity, agricultural output, and GHG emissions, underscoring the imperative of integrating climate-smart methodologies into agricultural frameworks. However, the influence of digital technologies is not significant as a result of opposing forces. Digital technologies exert positive indirect influences by increasing agricultural productivity and agricultural output, while they have negative influences by improving production processes through automation and precision agriculture. Digitalization and climate-smart agricultural practices have a significant potential to improve the efficiency and sustainability of the agricultural sector, contributing to food security and environmental protection by reducing GHG emissions. This study highlights the EU’s potential to achieve its environmental objectives through the reduction of GHG emissions and the enhancement of resilience within the agricultural sector, emphasizing the necessity of adopting climate-smart strategies.

This study explicitly assesses how crop and livestock production, along with real labor productivity, affect greenhouse gas emissions in agriculture across the European Union (EU), considering both per capita and total emissions. Using annual Eurostat data for EU Member States from 2008 to 2024, the research applies multiple regression models and a multivariate General Linear Model (GLM) to evaluate structural relationships, complemented by Holt exponential smoothing and ARIMA models to analyze temporal dynamics and generate forecasts. The empirical results indicate that crop and livestock production have a statistically significant positive effect on emissions, while real labor productivity has a significant negative impact. The models explain over 92% of the variation in total emissions and over 95% of the variation in per capita emissions, confirming strong explanatory power. Forecasts show continued growth in agricultural output but a declining trend in per capita emissions, primarily driven by productivity improvements. These findings demonstrate that improvements in labor efficiency and technological progress can partially offset the environmental pressures associated with increased agricultural production. The study concludes that achieving climate-neutral agriculture in the EU is feasible through sustained productivity gains and innovation-driven transformation.

The European agricultural sector has increasingly faced volatility in input and output prices, raising concerns about income stability and long-term performance. This study examines the relationship between agricultural price dynamics and sectoral performance across European countries from 2006 to 2024, with a particular focus on countries’ capacity to translate price movements into economic outcomes. Using Eurostat data, the analysis combines factor analysis to construct latent price and performance indicators, structural equation modeling to assess the structural association between price dynamics and real factor income and gross value added, and cluster analysis to identify cross-country heterogeneity. The results reveal a positive and statistically significant association between favorable price dynamics and agricultural performance at the aggregate level. Beyond this general relationship, the findings point to pronounced asymmetries across European agricultural systems. While some countries consistently convert favorable price dynamics into higher income and value creation, others remain structurally constrained and benefit less from similar market conditions. These differences give rise to identifiable groups of relative “winners” and “losers” within the EU agricultural market. The results indicate that price dynamics alone are insufficient to explain convergence in agricultural performance and that structural capacity plays a critical role in shaping outcomes. From a policy perspective, the study highlights the need for differentiated agricultural and regional policy approaches to strengthen resilience and reduce persistent structural disparities across European agriculture.

Agriculture in the European Union operates in a context where productivity, output growth, and sustainability increasingly shape policy priorities and economic choices. This research explores how these elements have interacted and influenced one another from 2000 to 2024, focusing on the dynamic relationships among economic performance, sustainability, labor productivity, and agricultural output across EU member states. The methodology is straightforward: it starts with factor analysis to uncover the fundamental structures linking key variables and to clarify connections that are often hidden in aggregated data. Building on these insights, a General Linear Model provides a clearer picture of how economic performance and sustainability affect changes in labor productivity and agricultural output, revealing the mechanisms through which these factors promote or hinder agricultural progress. To enhance understanding, cluster analysis groups EU countries according to shared patterns, enabling interpretation of national differences within broader structural trends rather than as isolated cases. The findings show that countries with stronger economies and more consistent sustainability initiatives tend to achieve higher productivity and output, while the clusters identified demonstrate significant differences that explain the diverse development paths within the Union.