In an era marked by rapid technological advancements, the relationships among organizational digitalization, innovation, and sustainability are receiving growing academic and managerial attention. This paper employs bibliometric analysis to examine the scientific literature on these interconnected terms, based on 775 relevant publications retrieved from the Web of Science database and analyzed using MS Excel, Bibliometrix, and VOSviewer software packages. The findings reveal a rapid increase in research on digital transformation and sustainability since 2017, with key themes including Industry 4.0, artificial intelligence, blockchain, and circular economy. However, the analysis also highlights notable conceptual fragmentation, emphasizing the need for a more integrated theoretical framework, especially in fields such as performance measurement and corporate social responsibility. From a practical standpoint, the study identifies research gaps—including CSR alignment, SME digitalization, and evaluation metrics—where organizations and policymakers continue to face challenges. These findings can support targeted capacity building, policy development, and strategic research funding aligned with sustainability-oriented innovation. By synthesizing key patterns from the literature, this study contributes to a deeper understanding of how digital transformation drives sustainable innovation, while suggesting several directions for further investigation in both academia and practice.

Artificial intelligence (AI) is a key driver of the energy transition and sustainable economic development. However, the specific mechanisms through which AI adoption impacts renewable energy production versus consumption remain poorly understood. This study addresses this research gap by empirically analyzing how three AI dimensions (investments, readiness, and projects) differently influenced renewable energy production and consumption across 30 countries (EU-27, USA, China, and UK) during 2020–2023. Additionally, the AI–energy transition nexus is analyzed in relation to economic development (GDP per capita) and carbon emissions (CO₂). Employing robust regression, Gaussian graphical modeling, and cluster analysis, the study provides robust multidimensional validation. Empirical findings reveal that AI investments predominantly stimulate renewable energy production, while AI readiness and institutional ecosystems primarily drive renewable energy consumption. The following two country clusters emerge: advanced economies (USA, China, Germany, UK, and France) characterized by higher AI readiness and superior green-energy integration, and developing economies with significant catch-up potential. The study demonstrates AI’s dual role as both direct determinant and systemic mediator in the energy transition. Moreover, CO₂ emissions show an asymmetric role, being positively correlated with renewable energy production but negatively linked with renewable energy consumption. These insights highlight the need for targeted policies that bridge economic and technological divides, thereby accelerating the renewable energy transition and enriching academic debates on technology-driven sustainability.

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO₂ emissions as well as between production-based CO₂ emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO₂ efficiency.

Given the importance of foreign direct investment (FDI) in the economy, the purpose of this study is to identify and investigate the economic indicators that can explain the development of FDI in the economies of Central and Eastern European countries such as the Czech Republic, Poland, Hungary, and Slovenia throughout the period 1995–2020. When developing multiple linear regression models, the following explanatory variables were considered: exports, imports, import concentration and diversification indices, the balance of trade, the balance of payments, and different components of the economic freedom index. Therefore, it was shown that a rise in exports and imports has a beneficial impact on enhancing the flow of foreign direct investment (FDI) in each of the nations examined for this study. Furthermore, an increase in the value of the import diversification index is shown to have a beneficial effect on the levels of foreign direct investment (FDI) in the Czech Republic, Hungary, and Slovenia, as determined by this study. On the other hand, the import concentration index has been shown to benefit foreign direct investment in Poland. Furthermore, it was discovered that the balance of payments was a positive factor in the Hungarian economy. In contrast, the trade balance was shown to be a positive element in Poland and Slovenia. Both indicators have positively impacted foreign direct investment (FDI) flow.

Research background: Artificial intelligence (AI) has become a central element of firms’ digital transformation agendas, yet empirical evidence on how AI affects firm performance remains fragmented across theories, sectors, and performance dimensions, obscuring the mechanisms through which AI shapes organizational outcomes. Purpose of the article: The article aims to systematically map the scientific literature that explicitly examines the relationship between AI use and firm performance in the digital transformation era, and to clarify the main thematic areas, theoretical perspectives, and research gaps that structure this field. It addresses seven research questions covering the evolution of scientific production; key contributing countries, institutions, authors, and journals; dominant themes and conceptual clusters; the intellectual structure of the field; and the conceptualization of AI as a driver of business model and organizational transformation. Methods: The study uses a quantitative bibliometric design, drawing on 422 journal articles indexed in the Web of Science (WoS) Core Collection from 2000 to 2025 that explicitly address AI and firm performance. The Bibliometrix R package integrates performance indicators with science-mapping techniques, including co-word analysis, thematic mapping, and source co-citation networks. Findings & value added: The results show rapid post-2018 growth in research on AI and firm performance, strong internationalization, and a dual intellectual core that combines applied, technology-oriented journals with strategic management and organizational outlets. Conceptual analyses present AI as a strategic organizational meta-capability that affects performance through mechanisms of efficiency, agility, innovation, governance, and sustainability, while also identifying the field as fragmented and heavily reliant on cross-sectional designs and coarse measures of AI adoption. The article adds value by integrating dispersed contributions into a coherent bibliometric map, highlighting underexplored areas related to business model transformation, governance, sustainability issues, small businesses, and human-AI complementarity, and by outlining a future research agenda and implications for AI investment and governance.

In the current context, the labor market volatility in the information and communications technology sector (ICT), the challenges faced by companies in identifying and hiring these specialists, as well as the challenges faced by specialists in this field in terms of employment and job retention, necessitate a detailed and comprehensive analysis of the ICT labor market developments in close correlation with the current trends of digitalization of the economy. On this premise, the research presented in this article seeks to examine data about European firms that have engaged or tried to hire ICT specialists in the period 2014-2020, as well as the trends for the period 2021-2030. The purpose of the research will be to provide, on the one hand, the development of the selected variables for research during the analysis period and, on the other hand, the trends related to the ICT labor market for the categories of enterprises under study. The study aim will serve as the basis for the development of research questions and hypotheses, which will be tested using two types of methodological techniques, namely linear analysis and linear regression-based predictions.