Jurgita Bruneckienė

The textile and fashion (T&F) industry faces increasing pressure to transition towards a circular economy (CE), yet progress remains limited. Based on a qualitative multiple-case study of secondary data, this study examines how three Baltic T&F companies listed on Nasdaq Baltic advance circularity through circular-related responses (CRR). The findings reveal a predominance of process- and product-related responses, while consumer engagement remains comparatively underdeveloped, pointing to a misalignment between CE theory and corporate disclosure practices and to underutilised capacity to shape sustainable textile consumption. In response, the study develops a framework that conceptualises T&F companies’ CRR across these dimensions and links them to sustainable textile consumption. The study contributes to a deeper understanding of corporate circularity disclosure practices and offers practical insights for managerial decision-making related to circularity.

The transition to a low-carbon economy generates structural, technological, energy-related, and social transformations within economies, calling for a renewed understanding of regional convergence dynamics. This study presents a systematic literature review of 109 articles examining regional convergence in the context of the low-carbon transition. The findings show that the low-carbon transition broadens the traditional understanding of convergence by introducing new determinants, analytical approaches, and methodological techniques. The review highlights that regional convergence is a context-sensitive process shaped by spatial, temporal, change-related, and interregional linkages. Building on these insights, this study proposes a conceptual framework that integrates convergence determinants with contextual factors influencing regional development pathways. The results also reveal important gaps in the existing literature and outline directions for future research, aiming at improving the analytical and policy relevance of convergence studies in the context of a low-carbon transition.

This article examines intergenerational social mobility as a driver of sustainable development on the case of Lithuania, a country in Eastern Europe. Using data from the European Social Survey (2020), the analysis explores how parental education and occupation shape individual educational and occupational outcomes. Descriptive, correlational, and regression analyses reveal that while structural mobility has been facilitated by Lithuania’s transition from Soviet rule to a market economy and subsequent European integration, family background remains a decisive factor. The study also highlights the key factors that promote or constrain social mobility in contemporary Lithuania. The interaction between mothers’ and fathers’ education fosters the attainment of higher levels of education, whereas mothers’ occupational status and respondents’ gender (male) appear to limit it. Similarly, the achievement of a higher occupational level is positively influenced by the interaction between parental education and the respondent’s own highest educational attainment, while negatively influenced by gender. These findings demonstrate that education not only determines social mobility but also supports the broader objectives of sustainable development by reducing inequality, fostering human capital, and advancing gender equality. The results underscore the importance of inclusive social policies that ensure equitable access to quality education and align educational opportunities with labor market demands, thereby reinforcing social mobility as a foundation for long-term social resilience and sustainability.

As global challenges escalate—economic shocks, environmental issues, and evolving work expectations—the four-day workweek (4DWW) is gaining traction as a sustainable and viable alternative. This study investigates the transition from a traditional 5-day workweek to a 4DWW in the context of private companies in the Baltic States, supplemented by the insights from informal interviews with 17 representatives across Latvia, Lithuania, and Estonia and an in-depth case study of a Latvian wholesale company. Employing a mixed-methods approach, the research integrates document analysis, structured interviews, and quantitative assessment of key performance indicators over an 8-year period. The findings elucidate the phased transition process, detailing the multifaceted challenges encountered at individual, operational, and strategic levels. The study evaluates the impact of the 4DWW on organizational productivity, employee well-being, and environmental outcomes. Results indicate that, while the 4DWW can sustain or enhance operational efficiency and substantially improve work-life balance and stress reduction, these benefits are not uniformly observed across all economic sectors. This paper not only advances the literature on workweek restructuring by clarifying the algorithm for transitioning to a 4DWW but also underscores its potential as a sustainable business practice. The results offer valuable decision-making insights for private companies considering similar transitions, while also highlighting the need for context-specific strategies and policy support—including relevant legislative frameworks in the Baltic region—to ensure successful implementation.

Even though the importance of economic resilience has increased as economic shocks have become more frequent in the world, there is still a knowledge gap on how to measure it. In search for effective ways to measure national resilience, this article identifies and mathematically proves the existence of direct correlation between resilience and competitiveness through time-specific global correlation rate. This research proposes an economic performance evaluation method that measures the competitiveness of countries facing economic shocks and recovering from them. A quantified method for identification of global economic shocks through industrial portfolio is proposed as well. The holistic approach internalises most externalities and a nation’s resilience is pared down to its ability to compete in the international trade, linking the main determinants of resilience to the basics of human behaviour. The proposed methodology can be used effectively for national and global economic performance estimations. It also opens a range of new possibilities for economic resilience studies.

The EU climate policy affects the competitiveness of both the European Union (EU) as a whole and individual member states, leading countries to search for new knowledge to increase their national competitiveness. However, there has been little empirical research about the implementation of green competitive strategies in the common European space from different countries’ perspectives. Using the Porter Hypothesis and system theory, this paper explores national competitive strategies that align with climate neutrality in the EU. We used index construction, clusterization, principal components analysis and trajectories change analysis to analyze data from the 24 EU countries from a 10-year period (2012–2021). The main findings reveal three green competitiveness profiles and five green competitiveness progress strategies in the EU. We found that EU countries have different strategies and conditions in terms of their transition towards climate neutrality and competitiveness, which ultimately lead to different rates of progress. Our results provide an initial basis for the development of nation-specific policies to achieve green competitiveness.

The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice.