This study aims to investigate the influence of emerging digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing, on the digital intensity index (DII). The research method employed involves quantitative analysis of the indicators regarding DII and emerging digital technologies, conducted based on data published by Eurostat for EU members in 2021. During our research, we formulated and tested hypotheses about the relationship between the DII and emerging digital technologies, and the effect on the DII of using AI-based technologies in various economic processes. The formulated hypotheses were validated via four regression models designed during this study, using the most relevant factors. Our research results demonstrate that the DII is positively influenced by emerging IoT and cloud computing digital technologies, as well as the use of AI technologies based on machine learning and AI-based robotic process automation (RPA) software. Furthermore, the same positive influence was identified in human resource management and recruitment processes compared to the intensity with which these technologies are used in other economic processes. Based on these findings, this study offers persuasive arguments for implementing emerging digital technologies at the EU organizational level to achieve significant increases in digitalization levels.

The goal of this paper is to conduct a bibliometric analysis of the scientific literature about the sustainability of digitalization in the energy sector in order to capture the main challenges and trends in the transition towards it. The bibliometric analysis of the scientific literature was carried out by interrogating the Scopus database, using a set of keywords considered relevant for the analyzed field and for the goal of the proposed research. The purpose of the study was, on the one hand, the depth of the research into these topics during the 2013–2021 period, in terms of the number of scientific papers, the topic, abstract and keywords associated, the geographical area of origin and the authors’ affiliation and on the other hand, an analysis of the existence of possible links between these topics formulated through three hypotheses. The results obtained reveal the researchers’ concerns for digitalization in the energy sector, the existing correlations between the keywords analyzed and the tendencies registered in the field of digitalization in the energy industry in order to ensure higher sustainability.

The goal of this paper is to conduct an analysis of the scientific literature on the green economy in Romania and to capture the main challenges in making the transition towards it. Despite the barriers and incomplete legislation, Romania has sufficient resources and the potential to increase the percentage of the green economy, especially as the UE offers incentives for aligning to European standards. An analysis of the scientific literature was conducted via exploratory research in the Scopus database, based on key topics within the thematic range of the green economy. The study focused on the one hand the depth of the research regarding these topics in Romania during the 2007–2021 period and on the other hand, an analysis of the existence of possible links between these topics. The connections between the topics were analyzed through nine hypotheses. The results obtained reveal the researchers’ concerns for the way the principles of the green economy are applied in Romania and the existing correlations between these analyzed topics and how they are reflected in the economic reality. These results can represent a starting point as regards the diversification of policies and measures aimed at improving the implementation of the principles of the green economy in Romania.

During Covid-19 pandemic many employees found themselves in the new position of teleworkers. Proof of the last years, teleworking remains an alternative to the classic system, reason why in this paper we aim to examine, based on an online questionnaire, the perceptions of Romanian employees about the very complex aspects that teleworking involves: the new ICT tools and technologies adoption, impact on work efficiency, work-family balance, and employees physical and mental health. The paper is based on an empirical analysis of data, and the authors have used as research methods: testing the independence of groups (Mann-Whitney U and Kruskal Wallis) and the correlation between variables, categorical principal components analysis (CATPCA) and logistic regression model. Our findings show that several factors, such as flexible working hours, family time, and autonomy have a positive influence on the decision to telework, while IT security risks, interruptions and virtual meetings were not perceived by respondents as negatively influencing their decision to telework. However, the consequences for mental health, such as mental stress, lack of socialisation and the difficulty of separating working time from family time, lead respondents to prefer another working system. The implications of the research are relevant for policy makers, employers, and employees as they reveal which categories of employees are more likely to telework and why.

This paper explores the current attitudes of managers and executives working in the energy sector towards the Data-Driven Organizational Model implied by Big Data. The aim is to explore and understand the current mindset of senior decision makers, since their success depends as much on cognitive and behavioral processes as on their technical competences. We adopt a grounded-theory approach, developing models of understanding and belief abductively, driven by the data obtained from participants through a reflection guide. We find that managers differ significantly in their understanding and engagement with their challenges; they display interest but differ in their commitment and enthusiasm; they identify a lack of strategy and skills as current barriers; and they are currently unwilling to trust data, treating evidence according to their own prior commitments. This is a significant barrier to establishing the Data-Driven Organizational Model. These findings raise concerns, and the paper concludes that by considering initiatives for implementing more agile and forward-looking approaches, establishing a data-driven organizational culture, and managing such changes effectively.

This paper introduces a framework linking AI capability layers (automation, predictive AI, generative AI, AI agents, and agentic AI) to levels of ERP maturity. These levels include: data integration, process standardization, governance clarity, and real-time operational visibility. We argue that AI performance depends critically on the underlying ERP systems that are in place and introduce the idea of “organizational readiness for AI”. This idea is defined through a measure of the alignment between ERP maturity levels and the degree of autonomy that the organization would wish to delegate to AI systems. This, then, views ERP systems as the key enablers for autonomous execution. The framework offers managers a structured diagnostic tool to assess whether their organizations are ready to move from simple automation to greater levels of enterprise autonomy.