This study examines how European automotive companies disclose circular economy (CE) information in light of the Corporate Sustainability Reporting Directive (CSRD) and the European Sustainability Reporting Standards (ESRS) E5. Using a mixed-methods, data-driven approach that combines keyword analysis and latent Dirichlet allocation (LDA) topic modelling on 53 corporate sustainability reports, the research identifies the main CE disclosure themes and evaluates their alignment with ESRS E5 requirements. Results reveal a strong dominance of symbolic communication, focused on compliance rhetoric and waste management, over substantive disclosures that reflect genuine engagement with circular strategies such as reuse, remanufacturing or regeneration. The findings expose the sector's limited preparedness for the new EU sustainability reporting framework and highlight a persistent reliance on linear production models. The study offers empirical evidence on the symbolic-substantive dichotomy in CE reporting and provides actionable insights for policymakers, regulators and firms aiming to strengthen substantive sustainability disclosure.
Coopetition, the interplay of cooperation and competition between firms, becomes increasingly established as a vehicle for enhancing business performance, particularly in innovation-driven industries. While prior research highlights several benefits of coopetition, the literature remains fragmented and focused on specific industries, such as manufacturing or high-tech. Other industries are underrepresented, which limits the generalizability of the results across various contexts. Our study addresses this critical gap by replicating a large-scale examination of coopetition attributes based on manufacturing firms in a contextually distinct sample of 916 creative industries firms. We use quasi-replication for data collection and analytical techniques of the original study to examine the influence of behavioural, strategic, and relational characteristics of coopetition on performance outcomes. Our study offers nuanced insights into the context-dependency of coopetition, providing empirical support for the generalizability of most coopetition-performance mechanisms across industries. Our results confirm the positive impacts of key attributes: paradoxicality of the coopetition strategy, strength of coopetitive relationships, and trust- and investment-oriented behaviours among coopetitors. Additionally, the negative impacts of the dynamics of the coopetition strategy and the asymmetry of coopetitive relationships are also confirmed. Our study also reveals several contextual nuances. Specifically, the effects of two behavioural attributes (competition intensity and formality) are statistically insignificant, whereas the impact of tensions on coopetitive behaviours shows the opposite direction in creative firms, diverging from earlier manufacturing-based findings. Hence, we extend and refine coopetition theory by identifying context-independent coopetition performance drivers, as well as drivers that are context-dependent by degree and context-dependent by substance.
Poland ranks among the world’s leading exporters of goose meat and edible offal, yet domestic consumption remains minimal, revealing a structural imbalance between production and internal demand. This study aims to provide a comprehensive economic assessment of Poland’s foreign trade in goose meat and offal during 2020–2024, examining export specialization, price dynamics, and market resilience. Using official data from the Central Statistical Office (GUS), Eurostat, UN Comtrade, and the National Bank of Poland (NBP), trade flows were disaggregated by CN product codes, destination countries, and unit prices to identify key structural patterns. Results indicate that export volumes remained largely limited by price responsiveness despite sharp price increases and exchange rate fluctuations, confirming stable foreign demand. Exports were heavily concentrated in Germany, which absorbed over 70% of the total trade value, while domestic consumption stayed below 0.5 kg per capita annually. These findings demonstrate both the competitiveness and the fragility of Poland’s export-oriented trade model, characterized by dependence on a single market and limited domestic integration. The study concludes that long-term food system resilience requires diversification of export destinations, stimulation of domestic demand, and stronger alignment with sustainability goals. A forthcoming second part will address environmental impacts and consumer awareness.
Considering the need to explore employee responses to ongoing technological changes and drawing on the assumptions of the Conservation of Resources theory, this study examines the indirect impact of technological social responsibility (TSR, an organizational factor) on employees’ attitudes toward AI integration through their resistance to technological change (a personal factor). It also investigates whether perceived media emphasis on AI-related job loss (an environmental factor) moderates the relationship between TSR and resistance to technological change. It uses data collected from employees working in 504 large companies in Poland and applies PLS-SEM analyses. The empirical findings show that TSR reduces resistance to technological change which is a crucial factor in increasing positive attitudes towards AI integration at work. Resistance to technological change mediates between TSR and employees’ AI-related attitudes, however media activities do not play moderating role in the model. This study contributes to the limited empirical understanding of TSR by demonstrating its indirect effect on employee behavior during digital transformation. The findings offer practical implications for organizations aiming to facilitate AI integration at work by strengthening TSR-based strategies to reduce employee resistance.
The aim of the paper is to develop a method for portfolio construction that maximizes the return-to-risk ratio, based on artificial neural network. It includes separate stages for data preprocessing, ANN parameter optimization, and backtesting. The method was verified using data from stocks listed on the Warsaw Stock Exchange (WSE).In this study, portfolio construction was guided by signals generated through the Triple Barrier Method with ANN models trained on historical financial indicators.
