Alexandra-Andreea Mironescu

This study has the scope to identify circular economy performance patterns across EU states using a multivariate clustering approach. The analysis is based on secondary data from Eurostat for the year 2022 and includes four key indicators, circular material use rate, recycling rate of municipal waste, recycling rate of waste electrical and electronic equipment (WEEE) and recycling rate of packaging waste. After standardization of the variables through Z-scores, the cluster analysis was performed through Ward and Euclidean method. The optimal number of clusters was determined through agglomeration coefficients and dendrogram inspection. Cluster robustness was assessed through distance beteen clusters analysis and ANOVA. The results reveal three distinct circular economy performance, a high, low and transitional performing clusters Municipal and packaging waste recycling are the strongest differentiators between clusters, highlighting the important role of waste management infrastructures in this field. To improve circular economy, the findings of this research recommned adapted policy approaches to the caracteristcs of each group of countries.

This study empirically examines the role of Artificial Intelligence (AI) in conflict management, focusing on its impact on workplace safety and sustainable development. The literature review focuses on three thematic areas: (1) conflict management in organizations, (2) workplace safety, including improved efficiency from automation and risks related to job displacement, and (3) sustainable development in the AI era. This study analyzes Eurostat’s 2021 dataset on AI adoption, workplace safety, and sustainability. It is examined the relationship between AI adoption in enterprises and workplace accident rates across EU Member States. To ensure data reliability, only countries with complete and verified records in both categories were included in the analysis. Descriptive statistics were employed to summarize the key trends and Spearman’s rank correlation coefficient was applied to assess the strength and direction of the association between AI adoption and workplace accident rates. The chosen methodological approach enables an impartial assessment of potential correlations while considering variations in data distribution across different EU Member States. Preliminary findings reveal a significant positive correlation, indicating that rapid AI adoption may coincide with heightened organizational tensions and increased risk of conflict triggers. These results underscore the need for comprehensive conflict resolution strategies that explicitly integrate sustainability objectives, ensuring that AI technologies are implemented responsibly and equitably. By shedding light on the interplay between AI, conflict management, and sustainable development, this study contributes new insights into how organizations can harness innovative tools without compromising workforce well-being or long-term societal progress.

This study embarks on an empirical examination of the relationship between digitalization intensity and economic performance among Small and Medium-sized Enterprises (SMEs) within the European Union (EU) in 2022. Utilizing data from European Commission, the research specifically compares the “SMEs with at least a basic level of digital intensity” and the “Proportion of total SME value added for all industries”. The aim is to unravel the potential correlation between digital integration in SMEs and their contribution to the economy, measured in terms of value added. The methodology encompasses a quantitative approach, involving the collection of relevant data across EU member states. The study first applies descriptive statistics to summarize the data and then conducts a correlation analysis. In preparing the data for analysis, we adjusted the value-added figures by the number of SMEs to derive a value-added per SME metric, facilitating a more accurate and comparable evaluation across countries with varying numbers of SMEs.Our preliminary analysis reveals significant variations in digital intensity among SMEs across different EU countries. The core of the analysis investigates whether higher levels of digital adoption correlate with an increased economic output per SME. This study is particularly relevant in the context of the EU’s digital single market strategy and the increasing emphasis on digital transformation in the business sector. The findings of this study are expected to contribute to the understanding of the digital economy’s impact on SMEs, providing valuable insights for policymakers and business leaders.

The COVID-19 pandemic has accelerated the digitization of the logistics industry, exceeding the expectations of some companies. Ensuring customer satisfaction throughout the online ordering and delivery process has major implications for their profitability and market position. This study aims to classify firms in the logistics sector based on their financial accounting performance and determine if software designed for the logistics sector can improve their performance. By using advanced data analysis methods such as cluster analysis and neural network training, the study identifies three distinct classes. One company from a lower class was given a scenario for implementing digitization, improving its performance indicators, and enabling its movement to a higher class. The practical implications of the study are related to the domestic and international business environment, providing an overview of the logistics sector in Romania and a direction for growth for companies operating in the sector.

For this study, we conducted a systematic review of the literature on digitalization in energy production, distribution, and consumption over a sufficiently long period in order to reveal the trends and particularities of this phenomenon at the sectoral level. For the systematic review of the literature, representative articles on the subject indexed in the Web of Science and Scopus databases were selected using the PRISMA 2020 flow diagram. As a result of the systematic review of the literature, a significant number of articles on the subject of digitalization in the energy sector were found—both over the entire period considered and especially in the last five years—indicating the magnitude of the digitalization process in this field. The impacts of digitalization in the energy production, distribution, and consumption sectors materialized in the aspects of health, safety, and environmental improvement; process improvements; and cost reductions. The most important technologies used in the digitalization process include data mining and machine learning, smart grid/smart metering/smart home, Internet of Things, cybersecurity, and automation solutions (e.g., robotics, drones, and distribution automation).