Over the past few decades, the circular economy (CE) has shifted from a normative sustainability vision to a dominant framework in policy agendas, corporate strategies, and academic research. In Europe, for instance, CE has evolved into a pivotal strategy for tackling environmental issues and decreasing the negative impact of high-emitting sectors by translating CE principles into proactive tools encouraging the green transition of European economies. However, while widely promoted as a response to climate change, biodiversity loss, and resource depletion, the CE has also attracted increasing scrutiny due to limited real-world impacts, rebound effects, and the persistence of efficiency-oriented approaches that leave prevailing production and consumption patterns largely unchallenged. To address this tension, this paper presents an analysis of 1,533 CE papers in the context of European countries, focusing on dominant research themes, intellectual structures, and emerging trends in the field. Our findings demonstrate the growing role of the "utopia-paralysis" tension, the relational turn beyond technocentric circularity, circular business model innovation, coopetition initiatives, platformization of the CE, just transition, and circular justice. Furthermore, alongside these dominant themes, we assess the visibility and positioning of sufficiency and regenerative economy perspectives within the broader CE literature. By situating these emerging concepts within the evolving research landscape, the paper contributes to ongoing debates on the maturity, direction, and transformative ambition of the CE as a sustainability paradigm.
Artificial intelligence (AI) applications are currently a new trend, capable of supporting the synthesis, analysis, and discovery of new knowledge. However, concerns have also arisen regarding the unauthorized use of research results and the increase in plagiarism if not controlled. This study develops and tests a new model, with a system of validated indicators, to analyze survey data from 322 public lecturers in the research area. Using a mixed methodology (qualitative and quantitative), a measurement scale system was developed, and a structural equation modeling (SEM) was used to test hypotheses and analyze the suitability of the research model. The research results show that all six factors of AI expectations; and application trends, significantly influence the variables of scientific research and lecturer work performance. Therefore, implications for enhancing the application of AI in scientific research, aimed at improving the work performance of lecturers, are proposed. In addition, the study also found that scientific research is one of the criteria for evaluating the performance of public university lecturers, helping higher education managers to develop appropriate policy implications to enhance the application of AI in scientific research and improve lecturer work performance.
Derivatives markets combine leverage, nonlinearity, and short holding horizons, creating conditions in which behavioral distortions may translate into amplified losses and market stress. This study develops an interpretable, episode-based machine-learning framework to detect bias-consistent trading behavior in exchange-traded derivatives and to translate micro-level signals into a market-level monitoring indicator. Anonymized order-and trade-level records are transformed into position episodes and merged with contract characteristics and microstructure controls. Theory-aligned proxy rules generate weak labels for disposition/loss aversion (time-to-close asymmetry, roll-loser patterns), overconfidence (turnover, leverage-at-entry, short-gamma exposure), lottery demand (low-delta preference), and herding (crowding/flow correlation). Models are trained using time-respecting splits with nested cross-validation and randomized hyperparameter search, and evaluated on a strictly out-of-time test window. Tree-based methods provide moderate but stable discrimination (e.g., Gradient Boosting ROC-AUC approximate to 0.62) with acceptable calibration, while explanation analysis indicates dominance of loss-related and leverage/turnover signals over market-state variables. Aggregating episode probabilities yields a daily Bias Index that is positively associated with next-session realized volatility and supports an alert rule capturing a meaningful share of volatility spikes with manageable false alarms. Limitations include proxy-label noise, market-specific coverage, and associative (non-causal) inference. Practical value arises from transparent scoring and channel attribution that can support targeted nudges, suitability prompts, and surveillance prioritization in periods of elevated behavioral risk.
This study examines the determinants of total factor productivity (TFP) and real GDP growth in 90 middle-income countries over 1990-2020 and contributes by separating the drivers of productivity from the drivers of growth within the same empirical framework to clarify the role of FDI. Using annual panel data, we estimate a TFP equation and a growth equation with country fixed effects and panel EGLS with cross-section weights, reporting White cross-section robust standard errors; this specification is appropriate for large cross-country panels because it controls for time-invariant national characteristics and addresses heteroskedasticity across countries. The results indicate that, conditional on country effects, TFP is linked mainly to domestic structure and macro-fiscal conditions: larger sectoral shares in agriculture and manufacturing and a higher import share are associated with lower TFP, while government consumption is positively related to TFP, and FDI and investment are not robust predictors of productivity. In contrast, GDP growth is positively associated with TFP, FDI, and investment, while inflation and government consumption are negatively related to growth and imports are growth-enhancing. The policy implication is that middle-income countries should prioritise productivity-raising structural upgrading, improve the efficiency and composition of public spending towards skills and infrastructure, and strengthen absorptive capacity so that FDI and trade translate into sustained productivity gains and higher long-run growth.
In many developing countries, rapid economic growth has improved living standards but also intensified environmental degradation. This study investigates whether Information and Communication Technology (ICT) development can mitigate the environmental consequences of economic growth in Vietnam from 1985 to 2023. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach and the ecological footprint as a comprehensive indicator of environmental pressure, the analysis examines the moderating role of ICT in the growth-environment relationship. The results show that while economic growth exacerbates ecological stress, several ICT dimensions-particularly mobile cellular penetration, medium-and high-technology exports, and the composite ICT index-significantly reduce the long-run environmental impact of growth. These findings highlight the potential of ICT development, especially high-tech export, as a strategic policy instrument for achieving sustainable growth in developing economies by reducing environmental degradation.
During the COVID-19 pandemic, the European Central Bank implemented an expansive monetary policy, primarily through quantitative easing, to mitigate the economic downturn and stimulate growth. While QE effectively supported economic activity, it also contributed to inflationary pressures as an unintended side effect. This study investigates the persistence of inflation in the euro area and examines the role of QE across three distinct periods: pre-pandemic, pandemic, and post-pandemic (2009-2022). By employing a panel data approach and estimating a dynamic model using the System Generalized Method of Moments, we aim to capture the lagged effects and potential endogeneity inherent in the inflation-growth relationship. Our findings suggest that inflation in the EA exhibits a high degree of persistence, implying that inflation shocks have lasting effects overtime. Furthermore, QE appears to influence inflation with a one-year lag, indicating a delayed monetary policy transmission to price levels. In contrast, economic growth reacts more rapidly to QE interventions, typically within the same year. These results have important implications for the timing and calibration of monetary policy responses in the context of future economic crises and for non-euro area members to weigh the trade-off between monetary policy flexibility and integration. Finally, it is important to consider the possibility of combining quantitative easing with a moderately restrictive fiscal policy that could mitigate the adverse effects on inflation.
This study examines how the Czech Republic, Hungary, Poland, and Slovakia support innovation in business within EU cohesion policy. Although these countries share a similar pre-1989 history, they differ in their economic development and in how they implement cohesion policy. Existing studies mainly examine how public support relates to economic outcomes. This study addresses this research gap by examining how four Central European countries implement innovation support. This paper aims to analyse and compare the approaches of the Visegrad Group countries to supporting innovation in enterprises from EU cohesion policy funds in the programming period 2014-2020. The comparison focuses on operational programmes, financial allocations, supported projects, institutional arrangements and monitoring indicators. The primary data for this study comes from the European Commission's Cohesion Open Data Platform. The results indicate that the Czech Republic and Hungary apply a similar approach to the structure of their support programmes. Poland, by contrast, adopts a more regionally differentiated model, reflecting the principle that "one size does not fit all." The Czech Republic has also established a strong institutional framework for programme implementation. The greatest number of limitations was identified in Slovakia.
In advancing sustainable economic growth, the affiliation between foreign direct investment and economic growth (EGR) has become increasingly prominent given the dual emphasis on attainment of sustainability development goals. The research evaluated the affiliation between China's foreign direct investment inflows (CFDI) and EGR in Africa. The research consists of 36 African regions for the period 2003-2023. The econometric model of Common Correlated Effect Mean Group, Driscoll and Kraay standard errors, and fully modified ordinary least square was employed to assess the affiliation between the variables and the direction of affiliation through the Durmitrus Hurlin causality test. The findings indicated that China's foreign investment inflow, human capital (HMC), and technological innovation (TINN) had a significant positive effect on EGR. However, trade openness (TRAD) and total natural resources rent (TNRR) had an inverse impact on EGR. Furthermore, total natural resource rents had a positive moderating influence on the affiliation between CFDI and EGR. The current research provides governments and institutions with the ability to develop policies on China's FDI inflows and natural resources to promote economic growth in Africa. By having bilateral cooperation, factors such as human capital, technological innovation, and trade openness will upsurge in the future.
The study analyses sustainable development in the context of economic and environmental dimension in EU countries according to economic level. The aim is to evaluate the impact of selected indicators of economic and environmental sustainability on the economic and socio-economic development of two groups of EU countries in the period 2010-2022. The selected set is represented by EU countries with a higher and a lower economic level than the EU average. Using log-log panel regression analysis, the study provides a closer evaluation of the impact of selected economic and environmental sustainability indicators not only in EU countries (27), but also to evaluate differences in the impact of sustainable development indicators between groups of EU countries (with higher and lower economic levels). The results of EU countries with higher and lower economic levels confirmed the impact of different economic and environmental sustainability indicators on the economic and socioeconomic development. The research on the examined areas of sustainable development contributes to a more comprehensive assessment of economic and environmental sustainability, reflecting not only long-term goals, but also some newer trends, with an emphasis on the socio-economic level of development of EU countries. The findings may be useful not only for economic policymakers and environmental analysis in the European dimension, but also for the development of national sustainable development concepts and strategies.
This study examines the effect of corporate sustainability on tax aggressiveness, with political connections as a moderating variable. The population of this study consists of companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2022. The research method used is panel data analysis with the Moderated Regression Analysis (MRA) approach. The results indicate that corporate sustainability positively influences tax aggressiveness (consistent with agency theory). While political connections significantly increase tax aggressiveness, their moderating effect not only attenuates but completely inverts the positive sustainability-tax aggressiveness relationship, resulting in a negative association (aligning with legitimacy theory). These findings suggest that sustainability reporting may serve as a facade for legitimacy, whereas political connections present both advantages and risks. This study makes theoretical contributions by agency theory and legitimacy perspectives to examine corporate behaviour in emerging markets, particularly Indonesia, while offering practical implications for enhancing regulatory oversight of sustainability disclosures and politically affiliated firms.
This study examines the impact of herd behavior on investment performance by investigating the serial mediating effects of perceived risk and investment decisions, drawing on the principles of behavioral finance theory. Using a crosssectional design, data were collected through a structured questionnaire from 640 investors in the Nepal Stock Exchange (NEPSE) and analyzed with SPSS AMOS, applying SEM and Hayes' Process Macros to test the hypotheses. The results reveal that herding behavior has a non-significant direct effect on investment performance (IP) ((beta = 0.064, p = 0.138) but significantly affects IP indirectly through risk perception (RP) ((beta= 0.086, p < 0.001) and investment decisions (ID) ((beta = 0.035, p < 0.001). The serial mediation pathway via RP -> ID significantly transmits the influence of herding on investors' performance ((beta = 0.028, p < 0.001). The study contributes to behavioral finance theory by extending the basic mediation model to include risk perception and investment choices as serial mediators in the Nepalese context, which has been rarely examined before. Practically, the findings offer valuable insights for constructing robust investment models, raising awareness of behavioral biases, and informing investors, researchers, and policymakers in the development of more effective strategies and policies.
Multi-level leadership play a vital role in handling crises and is also crucial for the smooth recovery from crisis. This is equally significant for establishing collective well-being. So far, literature has not demonstrated how a university could approach and establish multi-level leadership in a disruptive environment; thus, authors attempted to understand the multi-level leadership attributes during a crisis. In the premise of social exchange and social learning theories, this study aims to explore multi-level leadership attributes during crisis in the context of higher education and its influence on collective well-being. This has been approached qualitatively by interviewing higher education faculty, executives, and support staff in Egypt, Estonia, India, and United Kingdom. The interview questions revolve around crisis leadership and well-being aspects that cover the interviewee themselves, their team and departments. The study concludes that higher education has shown good adaptability with considerable reactionary leadership approaches at governance-strategic, tactical and operational levels during the crisis. To build collective well-being in higher education, top-down and bottomup approaches to leadership should work in shared or distributed leadership approaches to establish system continuity with a healthy index of collective well-being. The present study contributes firstly, by exploring multi-level leadership attributes and its influence on the collective well-being of faculty, executives, and support staff. Secondly, the study has derived a framework from the findings that shows the multi-level leadership attributes that act as enablers and a list of a matrix which could ensure collective well-being during the crisis in the context of higher education.
This study examines the drivers of firm-level eco-innovation in Slovenia, a country that ranks among the best performers in environmental sustainability but faces the challenge of translating its strong environmental framework into widespread adoption of eco-innovation practices at the firm level. The study focused on three areas of eco-innovation, which are the implementation of energy management systems, the adoption of waste minimization and recycling strategies and introduction of measures to control air pollution. These areas were used to investigate the impact of determinants - ten predictor (independent) variables such as own R&D, energy consumption and CO2 emissions, on the adoption of ecoinnovation in 409 Slovenian firms. The analysis shows that CO2 emissions goals and own R&D are crucial for promoting eco-innovation in the country analysed. The findings highlight the importance of investment in R&D and stringent CO 2 emissions goals to promote eco-innovation, while implications for practice include the need to integrate environmental objectives into strategic management, systematically monitor energy consumption, establish an environmental manager and obtain environmental certifications that promote organizational learning and regulatory readiness. This study offers insights for policymakers and business leaders, indicating that tailored strategies and supportive policies can effectively enhance sustainable practices. By improving the environment for eco-innovation, Slovenia can serve as a model for other CEE countries that are striving for sustainability and environmental responsibility.
Public hospitals depend on nurses' discretionary efforts to sustain care quality, yet these settings are marked by heavy workloads and spillovers between work and family life, making supportive HR systems especially consequential. This study examines how High-Performance Work Systems (HPWS) relate to nurses' Organizational Citizenship Behavior (OCB) in Jordan's public hospitals, testing core competencies as a mediator. The study also assesses whether work stress moderates the HPWS and core-competencies link and whether family stress moderates the core-competencies and OCB link. Using a quantitative, cross-sectional survey of nurses (n = 372), hypotheses were evaluated with Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicate that HPWS positively predict OCB and significantly enhance core competencies; core competencies, in turn, are positively associated with OCB and mediate the HPWS and OCB relationship. Work stress moderates the association between HPWS and core competencies, and family stress moderates the association between core competencies and OCB. Taken together, the findings clarify how HPWS translate into extra-role behaviors through competency development and under what stress conditions these effects are stronger or weaker. Theoretical and practical implications are outlined, and avenues for future research are discussed.
In the context of transition to green, digital and knowledge economy, measuring innovation performance is of great importance to understand and improve national and regional competitiveness. Institutional structures in innovation systems that ensure the coordination and interaction of innovation activities within the country, undertake the tasks of regulating innovation activity and executing and carrying out competitive development programs based on reported assessments and performance metrics. This paper brings a comparison of selected innovation performance indices. The primary aim is to evaluate how these indices measure, assess and categorize innovation performance in the European contexts. Four important frameworks are considered in the study: the Global Innovation Index (WIPO), the European Innovation Scoreboard (European Commission), Global Competitiveness Index (World Economic Forum) and the Bloomberg Innovation Index framework. The study assesses innovation success by analysing different assessment methods, indicators and weighting systems. The analysis shows that innovation assessment is multidimensional, while European indices reflect regional characteristics and assessments are concentrated across different actors, scales and sectors. By providing a comparative analysis of innovation assessment frameworks, this study improves the understanding of methodological differences and indicator weights and provides important conclusions on how these changes affect the assessment of innovation performance in European countries. This contributes to the ongoing debate on improving innovation measurement approaches to better respond to the dynamic challenges of contemporary innovation ecosystems.
Firms are increasingly adopting circular economy (CE) practices to meet the growing demand for sustainability, but most small and medium-sized enterprises (SMEs) have been unable to adopt them due to limited resources, capabilities, and a lack of environmental culture. However, SMEs with an environmental orientation and diverse cooperation networks tend to find this adoption easier. This article examines the role of environmental orientation and the moderating effects of cooperation with diverse networks on the adoption of the following CE practices: CE-based service provision, CE-based communication, CE-based design for production, and CE-based manufacturing. The study is based on a survey conducted with managers from 300 SMEs in the Spanish manufacturing sector. The results, analysed through structural equation modelling, confirm that both environmental orientation and the diversity of cooperation networks significantly influence the adoption of CE practices among SMEs. These findings have significant implications for business managers, urging them to adopt an environmental orientation and diversify their cooperation networks to access the resources necessary to implement CE practices. Furthermore, policymakers are encouraged to promote the diversification of SME cooperation networks, for example, by fostering partnerships through incentives that support digital platforms for these interactions, ultimately contributing to a more sustainable economy.
This paper aims to reveal the factors influencing investors' intention to accept AI advice in financial decision-making. By integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), it proposes a comprehensive model that elucidates the intricate relationships between social norms, attitude, perceived behavioral control, and the intention to accept AI advice, with a particular focus on examining risk tolerance as a moderating factor. A questionnaire survey was conducted with 569 Vietnamese investors to collect data in three different times. Partial least squares structural equation modeling (PLS-SEM) was utilized to analyze the measurement model and test the hypotheses. Results indicate that perceived usefulness, perceived ease of use, attitude, subjective norms, and perceived behavioral control positively influence the intention to accept AI advice. Furthermore, risk tolerance significantly moderates the link between attitude, subjective norms, perceived behavioral control, and intention to accept AI advice. This pioneering study introduces a comprehensive model unveiling the dynamics of AI advice acceptance in finance. It explores the novel concept of risk tolerance as a moderator, marking an important step in understanding human-AI interaction for financial decisions. Findings provide valuable insights into evolving AI adoption, especially in high-risk contexts.
The presented paper offers an in-depth look at mesh routing protocols, focusing on how well they perform under different conditions of node density and mobility. To make accurate comparisons, the study uses robust non-parametric statistical methods, including the Kruskal-Wallis, Mann-Whitney, correlation and covariance tests, to pinpoint which protocols stand out across crucial Quality of Service (QoS) metrics. These metrics cover energy consumption, end-to-end delay, packet delivery ratio, throughput, and network overhead. The research simulates several mesh network scenarios to collect important data for a comprehensive assessment of the reliability and effectiveness of each protocol. The Kruskal-Wallis test identifies large differences in performance across different circumstances. Following that, the Mann-Whitney test offers a closer look, comparing protocols in detail to identify those that work best under specific conditions. The study finds that protocol performance can vary dramatically depending on network density and how mobile the nodes are. It outlines which protocols excel in more stable setups and which remain dependable in fast-changing, dynamic networks. For network designers and operators, these insights are incredibly valuable, offering practical guidance on optimizing performance in real-world deployments. Furthermore, the research underscores why non-parametric statistical methods are so crucial. Network data often show high variability and don't always follow normal distribution patterns, making traditional parametric techniques less reliable. By choosing a more flexible and accurate approach, this study makes a meaningful contribution to wireless communication, providing a clearer picture of how different routing protocols hold up under real-world conditions.
