This study constructs a climate risk attention indicator for Chinese funds by applying Word2Vec-based text analysis to annual fund reports. This study examines how climate risk attention affects excess returns in actively managed funds, using the Brinson model for performance decomposition. The results show that climate risk attention increases fund excess returns with notable heterogeneity: a 1% increase in acute physical risk attention raises excess returns by 24.675%, whereas the same increase in transition risk attention leads to a much smaller effect—only 2.746%, approximately 11% of the acute risk impact. Chronic physical risks, such as rising temperatures, do not significantly affect returns. These findings underscore the importance for fund managers to prioritize acute and transition risks, particularly acute physical risks, in climate-related investment strategies. This study extends the empirical experience in Chinese climate finance, validates a machine-learning-constructed climate risk dictionary, and provides practical insights for fund managers and regulators. A limitation is the reliance on annual reports, which may not capture real frequency shifts in risk attention.
The quality of life in cities is reflected in satisfaction with city life. Therefore, it is necessary to assess satisfaction with living in a city in relation to the quality of city life. The objective of this study is to specify the relationship between respondents’ satisfaction with life in the city and selected indicators characterizing the quality of life within the studied set of cities. The data utilized in this study were obtained from Perception survey results (Eurostat, 2024a) and derive from 77 cities across Europe in 2023. The OLS method was employed in this study. The linear model and the logarithmically transformed models were verified. In selecting a model that would satisfy the given criteria, an econometric approach was employed. This approach entailed the implementation of several tests, including the normality test, the homoscedasticity test, and the Ramsey test. The resulting logarithmic model demonstrates a substantial relationship between the indicator satisfaction with city life and the indicators of perception of safety, satisfaction with greenery in public spaces, satisfaction with educational institutions and education, and evaluation of local government expenditures. As the values of the independent variables increase, the satisfaction indicator in the city also increases, but at a decreasing rate.
This study explores the global business and policy determinants of sustainable energy efficiency across the 27 European Union member states from 2013 to 2023, using the ODEX index to measure aggregate progress in reducing energy intensity in industry, transport, and households. Based on balanced panel data from Eurostat, the World Bank, WIPO, and the Worldwide Governance Indicators, we apply fixed-effects and System GMM estimations to assess the contributions of foreign direct investment (FDI), per capita GDP growth, government effectiveness (GE), and the Global Innovation Index (GII), while controlling for the 2022 energy crisis. The results reveal strong persistence in energy efficiency patterns, a robust positive influence of the Global Innovation Index (GII), and a negative effect of GDP per capita growth through rebound mechanisms, which becomes statistically significant in the dynamic specification. Government effectiveness exerts a strong structural effect in the static model. These findings underscore the strategic role of innovation and governance in improving energy performance and inform EU policymakers on aligning investment, innovation, and institutional frameworks with the Union’s 2030 energy efficiency and 2050 carbon-neutrality objectives.
The primary aim of this study is to examine consumers’ neuro-emotional and cognitive responses to digital game advertisements using EEG. Within this framework, 18 visual stimuli, totaling 150 seconds, were selected from five publicly accessible online advertisements for PUBG, one of the leading games in the digital gaming market. These visuals were chosen to trigger and measure specific emotional responses. The study focuses on assessing five fundamental emotions identified in Plutchik’s Wheel of Emotions: joy, sadness, anticipation, fear, and anger. In the study, the results indicate that participants exhibited varying levels of neural responses to all visual stimuli. Overall, this finding can be attributed to the high average values observed in the frontal region, indicating the active involvement of the prefrontal cortex in attention, planning, and decision-making processes. Notably, this study provides practical implications for advertisers and producers in selecting similar scenes and identifying the most impactful visuals in advertisement production. In this regard, the study can also be considered a useful resource for evaluating advertisement feedback.
This study analyzes the impact of 5G technology on the electronic turnover (e-turnover) of SMEs in 26 EU Member States between 2020 and 2023, using the ENET methodology. It examines key digitalization drivers: 5G coverage, 5G spectrum, ICT specialists, and very high-capacity networks (VHCN). The results show that 5G coverage has a strong positive effect on e-turnover, particularly in countries with lower regulatory constraints, where implementation flexibility enhances connectivity and performance. Similarly, 5G spectrum allocation improves operational efficiency, but this effect weakens under stricter regulation, suggesting that rigid frameworks may hinder potential benefits. An unexpected finding is the negative relationship between ICT specialists and e-turnover, likely due to high integration costs in small firms. Although the impact lessens in regulated environments, it remains significant, indicating the need for SMEs to adopt cost-effective strategies such as upskilling or outsourcing. Granger causality analysis confirms a one-way influence from ICT specialists to e-turnover. The most substantial positive effect is linked to VHCN, especially in regulated settings, where fast and reliable data transmission drives innovation and revenue through advanced digital services. These results highlight the importance of coordinated 5G and VHCN strategies and strategic digital investment for SME competitiveness in the digital economy.
Amid severe global climate change and environmental degradation, energy systems worldwide are undergoing a significant transformation from traditional, high-carbon, and inefficient energy structures to more efficient, secure, and sustainable ones. This study develops an indicator system for green finance and an Energy Net Zero Transition Potential (ENTP) index using data from 280 cities between 2004 and 2020 to examine how green finance development affects the net-zero transition of energy systems. The findings indicate that growth in green finance notably boosts the ENTP. Mechanism analysis shows that green finance promotes the ENTP by enhancing resource allocation efficiency and optimizing industrial structure. However, it has not effectively spurred green technological innovation to advance energy toward a net-zero goal. Heterogeneity analysis reveals that the positive impact of green finance development on the energy net-zero transition is stronger in resource-based cities, large cities, and provincial border regions. Additionally, this study verifies the robustness of its results through Difference-in-Differences (DID) analysis of green finance innovation pilot zones, lagged effects, two-stage least squares estimation, and spatial econometric analysis. These conclusions can guide policy recommendations for China’s green finance reforms and address the global energy security challenge.
The swift adoption of artificial intelligence (AI) across EU economies has sparked heightened debate among scholars and policymakers about its association with labor market dynamics, economic outcomes, and sustainability objectives. This research investigates the cross-sectional links between enterprise-level AI adoption and key socio-economic indicators across EU countries, including total employment, the proportion of highly educated science and technology workers, GDP per capita, and the Sustainable Development Goals Index (SDGI). Using a comparative and multi-method approach, the study combines exploratory factor analysis, general linear model estimations, and cluster analysis to identify structural patterns and group countries with similar digital and developmental traits. Results show consistent links between AI adoption and higher economic performance, as well as a larger share of science and technology professionals. The relationships with overall employment and sustainability indicators are weaker but still present. The cluster analysis reveals diverse yet cohesive national profiles, reflecting differences in digital readiness, human capital, and institutional factors across the EU. The study’s primary contribution is to combine employment structures, economic performance, and sustainability into a comprehensive cross-sectional framework, providing a detailed comparison of AI-related patterns across the EU. Its findings provide policymakers with a solid empirical foundation for assessing how the diffusion of AI supports inclusive growth and sustainability goals.
Following successive global health, energy, and geopolitical shocks, this research investigates the persistent transformation of retail consumption patterns. While extant literature examines crisis-driven behavior, a methodological gap remains in synthesizing rich qualitative data with advanced computational techniques. This study addresses this gap by employing a mixed-methods design, integrating Principal Component Analysis (PCA) and Sentiment Analysis, to uncover the latent behavioral and affective dimensions of post-crisis decision-making. Conducted in 2024 within the Romanian emerging market, the study utilizes focus group data to identify core drivers of behavioral change. Results reveal an accelerated adoption of retail technologies, a fundamental recalibration of purchasing priorities, and heightened expectations regarding the shopping experience. Sentiment analysis highlights significant variance in consumer adaptation, offering a granular perspective on emotional responses to the “new normal.” Theoretically, this work contributes to interdisciplinary scholarship on economic uncertainty and digital transformation. Practically, the findings provide actionable intelligence for firms to develop engagement and innovation strategies that align with emerging consumer needs in volatile environments. By bridging structural patterns with emotional dynamics, the paper offers a robust framework for understanding consumer resilience in the wake of systemic disruptions.
The Journal of Business Economics and Management (JBEM) was established in 2003 as a continuation of the Almanach des praktischen Managements in Mittel- und Ost-Europa, that was founded in 1999. Today, it has evolved into a recognised platform at the intersection of economics, management, and decision science. This study conducts a comprehensive bibliometric analysis of JBEM’s publication record, employing a dual-database approach that examines 1,182 articles published between 2003 and 2024 in Scopus and 1,020 documents in Web of Science from 2007 to 2024. Using VOSviewer for network mapping, the analysis reveals a stable intellectual core focused on multi-criteria decision-making methods, alongside a growing emphasis on sustainability, ESG, and corporate responsibility. Ginevičius, Tvaronavičiene, and Zavadskas emerge as the most prolific authors, while Vilnius Gediminas Technical University, Bucharest University of Economic Studies, and Vilnius University lead the institutional contributions. At the country level, Lithuania, China, and Spain are the main contributors. The findings offer relevant insights for understanding how thematic priorities evolve, and inform future expected research and journal development. From a practical perspective, the results may guide editors and researchers in identifying key contributors and collaboration networks. This study’s originality lies in the integration of dual-database coverage with the combined use of quantitative indicators and visual analysis, offering a richer longitudinal perspective on the journal’s evolution.
This study challenges the “win-win” narrative in corporate sustainability by identifying a key trade-off between environmental performance and shortterm profitability. Using a global panel of the 1,000 largest companies across 23 developed markets, we employ a two-way fixed-effects estimation to test 24 potential mediation pathways linking board governance to financial outcomes via ESG performance. Our results reveal what we term the “Green Board Paradox.” We find that board independence and gender diversity are robustly associated with improved environmental scores, confirming their role in advancing corporate sustainability. However, this environmental performance acts as a significant mediator that is, in turn, associated with lower Earnings Per Share (EPS). This finding highlights a central tension: the very governance mechanisms that promote environmental responsibility simultaneously create a drag on short-term profits. Furthermore, we find that board independence and diversity act as substitutes, suggesting firms can achieve similar environmental outcomes through alternative governance configurations. Ultimately, our study provides critical evidence on the governance-sustainability-performance nexus, offering a nuanced framework for navigating the trade-offs between corporate ESG ambitions and financial realities.
Identifying the causes of stock mispricing is crucial for stabilizing capital markets. Utilizing panel data from Chinese A-share listed firms, this paper investigates the causal association between ESG rating disagreement and stock mispricing. We reveal that ESG rating disagreement significantly exacerbates stock mispricing. Further analysis shows that cross-shareholding investors, media attention and marketization weaken the relationship between ESG rating disagreement and stock mispricing. We also demonstrate that the influence of ESG rating disagreement on stock mispricing is more pronounced in state-owned enterprises, heavy pollution enterprises and enterprises with short-term institutional investor holdings. These findings help to provide some insights into ESG rating disagreement as a determinant influencing stock mispricing among emerging markets.
This study investigates when and how sustainability disclosure in higher education translates into superior financial performance, and whether green marketing initiatives strengthen that relationship. Drawing on a multi-country panel of Middle Eastern universities covering the period (2014–2024), the analysis develops a disclosure index aligned with the Global Reporting Initiative (GRI). Green marketing intensity is coded from verifiable institutional communications, while financial performance is assessed through operating outcomes and revenue-related indicators. Using panel regression models and complementary dynamic specifications, supported by thorough diagnostic and robustness checks, the results indicate a clear positive association between disclosure quality and financial performance, a separate positive association for green marketing, and, most importantly, a reinforcing interaction whereby credible, consistent green communication amplifies the financial payoff of disclosure. The study recommends that universities move beyond compliance reporting toward communicative integration for value: align disclosure with recognized standards, anchor messages in verifiable evidence, coordinate disclosure and marketing functions, and monitor communication outcomes related to enrolment, partnerships, and funding. The findings offer actionable guidance for leaders and policymakers seeking to couple sustainability commitments with sound financial stewardship in an increasingly competitive academic landscape.
Our research investigates the dynamic relationship between economic growth and environmental sustainability in Serbia, a country situated at the intersection of Western environmental standards and Eastern development models. Specifically, it examines whether economic growth, as measured by GDP per capita, conflicts with environmental sustainability. It also considers the roles of renewable energy consumption (RENC), urbanization (URB) and trade openness (TO) as mediating variables. The analysis is based on annual time series data for Serbia from 1995 onward. A Vector Error Correction Model (VECM) framework is employed to assess both short-run and long-run relationships among the variables. Our research addresses the underexplored question of how countries like Serbia can reconcile growth with sustainability in a policy space shaped by contrasting regional norms. The results indicate a short-run trade-off between GDP and environmental sustainability, as lagged CO₂ emissions and RENC negatively affect GDP growth. However, in the long run, growth is positively associated with TO and URB, while RENC is strongly driven by URB. CO₂ emissions appear to evolve relatively independently of TO and URB. The adjustment coefficients confirm that GDP, CO₂ and RENC significantly respond to deviations from long-run equilibrium, with URB playing a central role in stabilizing the system.
This study investigates the impact of SUS on CIN, with a specific focus on the mediating role of ENX. Utilizing a panel dataset of firms spanning the period from 2010 to 2022, the study employs the fixed effects model (FEM), 2SLS, and the system generalized method of moments (GMM) to ensure robust estimation and address potential endogeneity concerns. The findings reveal that SUS harms CIN, suggesting that firms facing heightened ESG uncertainty are more cautious in their capital allocation. Moreover, the results confirm that ENX plays a mediating role, as firms tend to increase environmental expenditures in response to higher ESG uncertainty, which in turn reduces their capital investment. The study provides valuable social and practical implications. From a social perspective, it underscores the importance of stable and transparent ESG policies in mitigating uncertainty and promoting sustainable investment practices. Practically, firms should balance their environmental expenditures and investment strategies to ensure long-term financial stability. The study’s novelty lies in integrating ESG-related uncertainty with CIN decisions through the mediating role of environmental expenditures, offering a fresh perspective on how firms respond to ESG-related risks in capital allocation.
In the digital economy, the importance of robust cybersecurity governance for corporate financing stability has become increasingly salient. Using a sample of Chinese A-share listed companies from 2011 to 2023, we employ the Word2vec natural language processing technique to develop a measure of corporate cybersecurity governance commitment. Our study empirically examines its impact on trade credit financing. The results indicate that a firm’s expressed commitment to cybersecurity governance is positively associated with the trade credit it receives from suppliers. We find that information asymmetry and corporate reputation are key mechanisms through which this effect operates. Heterogeneity analysis reveals that this effect is more pronounced for firms that have received regulatory inquiry letters, those without political ties, those operating during periods of high economic policy uncertainty, and those located in regions with low social trust. Furthermore, we show that when firms’ stated commitments align with their actions, suppliers adjust their business relationships by allocating a greater share of procurement volume to them, thereby strengthening long-term supply chain trust. Our findings offer valuable insights for firms in emerging economies seeking to enhance their cybersecurity governance and optimize their financing environment.
This study examines the profitability of a simplified short-term pairs trading strategy for retail investors in real-time equity markets, comparing the performance of individual stock pairs versus clusters of stocks. The research employs a simplified strategy implemented using accessible tools like “eToro” for trading and “MS Excel” for calculations. Stock pairs were selected based on historical correlations. The strategy was tested over a six-week trading period, comparing the performance of individual pairs versus a cluster of pairs. Key risk factors such as market trends, divergence risk, idiosyncratic news, and transaction costs were analysed. The research revealed that while the strategy can mitigate large losses when well-diversified, profitability is limited in stable or rising markets, idiosyncratic events also significantly impacted profitability. Trading clusters of stocks offers greater stability but reduced profit potential, whereas individual pairs provide higher but riskier returns. This research provides practical insights for retail investors seeking the simplified methods for pairs trading. It emphasises the importance of effectively managing key risks, diversifying portfolios, and adjusting trading strategies to improve investment outcomes. By concentrating on real-time market dynamics and a simplified framework, this study distinguishes itself from prior studies by offering practical guidance for retail investors, emphasizing the accessibility and flexibility of its methodology, in contrast to the complex algorithmic methods discussed in other studies.
Considering the nature, complexity and importance of the current managerial challenges, there is a need for a systematic study that offers guidance to their holistic analysis, and no overarching structure to guide this type of research could have been identified to date. This article lays the groundwork for a comprehensive examination of managerial challenges. Based on the review of 78 empirical works, the key practices in studying managerial challenges are synthesized and presented as a reference framework. The framework is designed using the 5W and 1H method. It offers an up-to-date understanding of the substance of managerial challenges, which contributes to both the theoretical understanding and practical execution of managerial work.
Corruption remains a significant constraint for firms in Europe, despite ongoing institutional reforms. The main goal of this paper is to obtain a list of firm-level variables that can serve as predictors of corruption perception using a machine learning approach. Drawing on agency and institutional theory, we analyse firm-level data from European firms from the World Bank Enterprise Survey (WBES). We employ a Random Forest classifier, which is well-suited for high-dimensional, categorical survey data, capturing non-linear relationships and interactions often missed by traditional models. The model achieves strong predictive performance (ROC AUC = 0.755; Accuracy = 79%). Results show that the most important prediction factors of corruption perception include firm age, size, ownership concentration, legal form, external financial audits, bribery experiences, sector, country group (EU vs. WB), innovation activity, and informal sector competition. The findings support the design of risk-based audits and encourage reforms to reduce informality through streamlined registration processes. The study contributes methodologically by applying machine learning to the field of political economy and expands theoretical insights into firm-level institutional barriers. It is one of the first research to apply Random Forest to firm-level corruption perception in both EU and Western Balkans.
Enterprise resource planning (ERP) systems are evolving to support organisations in addressing their climate impact. Yet, there is a paucity of empirical and cross-sectoral data on how these solutions can mitigate organisations’ negative climate impact through changes in business models. By focusing on a dataset of ERP-related patents published between 2020 and 2024, within a climate change classification, this study aims to shed light on how ERP-based solutions can enable business model transformation to improve environmental performance and to investigate Industry 4.0 technologies that facilitate such mitigations. Through a pragmatic inductive approach employing mixed methods, the study uncovers three main areas of business model transformation for climate change mitigation: production optimisation, sustainability management and monitoring, and supply chain performance improvement. While most of the examined patents prioritise production optimisation, the findings reveal the emergence of novel applications designed to enhance organisational sustainability management and monitoring. Furthermore, the research emphasises unexploited opportunities to enhance ERPs through the integration of Industry 4.0 technologies. This study provides a substantial contribution to the existing literature by focusing on a significant yet underexplored area: ERP-based solutions designed to enable business model transformation to mitigate climate change, with implications for researchers, organisational adopters, and system developers.
Given that structural efficiency serves as a significant instrument, this paper applies a novel approach to measure structural efficiency levels within Chinese banks from the perspective of potential improvement. To further investigate the patterns of structural efficiency, the overall structural efficiency is disaggregated into a series of variable-specific structural efficiencies. It reveals that fixed assets and non-interest incomes constitute the primary sources of structural inefficiency during the study period. Furthermore, the structural efficiencies of small-medium commercial banks surpass those of large state-owned commercial banks, although the efficiency gap between the two types of banks has narrowed. Based on the variable-specific structural efficiencies, this paper further explores the structural efficiency patterns within the Chinese banking sector.
