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.

The sustainability of the transport sector is targeted by various policies adopted by the European Union, and their impact must be constantly monitored in order to maximize the desired objective. This paper, through a two-stage investigation, aims to present a systemic approach of the sustainability dimensions in transport and to introduce an innovative technique to analyze the interdependencies between them. In the first stage, relevant indicators were selected from the Eurostat database for the content of four dimensions: economic, environmental, social and technological. The robustness of the developed dimensions was assessed and validated through a confirmatory factor analysis. In the second stage, a Gaussian graphical model was estimated as a technique integrating graphical and statistical modeling to identify complex structures of linkages between variables (as components of each dimension of sustainability). The structure of the network clearly highlights the dependence of transport on fossil fuel consumption as the main determinant of pollution in the sector (CO₂ emissions). In addition, the central role of railways in decarbonizing transport is highlighted, in contrast to the limited, and isolated at one end of the network, role of electric vehicles. The findings support that affordability of this new technology plays an important role in its impact on zero-emission transition. Concentrating on the period 2013–2022, at EU27 level, the results are relevant in the context of decarbonization policies, offering useful insights both for future research and policy makers.

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.

The accounting profession plays a crucial role in serving public interest by establishing the foundation for sustainable development and taking on social responsibility. The growing focus on sustainability practices of companies and stakeholders has also had a significant impact on the role of accounting and professional accountants. This has led to increased expectations for greater involvement in integrating sustainability into corporate decision making at every level. We used a bibliometric analysis of academic literature as a research method to identify the relationships between accounting, professional accountants, and sustainability issues (APASI). Bibliometrix R-package and VOSviewer were used to achieve the proposed objectives. This study analyzes the performance of the scientific literature, establishes the conceptual, intellectual, and social structure of research, and identifies new research directions. A period of 37 years (1987–2024) is taken into consideration, with 2556 documents and 859 sources extracted from the Web of Science database analyzed. We offer, in an original manner, descriptive statistics and relevant landmarks of the sources, authors, publications, organizations, and countries that have contributed significantly to the development of research in this field. Interested researchers have the opportunity to identify scholars for potential collaborations and valuable study resources.

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.

The research aims to provide a new approach and insights into the nexus between economic growth and green energy by analyzing their direct, indirect, and total linkages at the European Union level (EU-27). To this end, an advanced empirical analysis performed by network analysis, as Gaussian Graphical Models, is carried out for the period 2012–2022 The originality of network analysis lies in the fact that it allows the simultaneous visualization of all the links between variables in a complex system of dependencies. The novelty of the paper is twofold: first, it addresses the issue of renewable energy in an innovative way that captures three key stages of the EU policy (renewables’ capacity; share of renewable energy; and final consumption); second, it advances the nexus analysis by considering technological investments. Empirical results have validated the vital role of increasing the share of renewable energy in achieving sustainability and reducing greenhouse gas emissions. Also, the Research & Development and Gross Value added in environmental goods and services sector are found to positively influence sustainable development. The results are useful for policymakers and managers of organizations to understand the relationships between these variables better and thus, achieve climate neutrality.