Research background: This paper contributes to the emerging literature on carbon neutrality by addressing the gap in empirical evidence regarding the long-term financial implications of voluntary environmental commitments. While the adoption of carbon offset policies is increasing globally, the relationship between these voluntary climate actions and corporate profitability remains under-explored, particularly in the context of Mediterranean markets like Spain where disclosure has transitioned from voluntary to mandatory. Purpose of the article: The article pursues two primary objectives: first, to map the adoption patterns and strategic trends of corporative voluntary carbon offset policies; and second, to empirically assess whether such environmental engagement translates into measurable improvements in financial performance, specifically analysing if carbon responsibility acts as a driver for corporate value creation. Methods: We conduct a longitudinal study covering a sample of Spanish companies over the 2019–2022 period, a timeframe characterized by voluntary carbon footprint disclosure. The methodology employs panel data and multiple regression analysis to examine the link between voluntary engagement in carbon markets and firm profitability. Financial performance is proxied by Return on Invested Capital (ROIC) to ensure a robust measure of efficiency. Findings & value added: Our findings indicate that companies pursuing voluntary carbon offset strategies exhibit higher ROIC, suggesting that environmental responsibility and financial performance are mutually reinforcing. The study provides critical value by informing policymakers on the benefits of incentivizing voluntary offsetting. Furthermore, it offers investors and managers empirical evidence that sustainability strategies can simultaneously enhance profitability and climate performance, moving beyond the traditional trade-off perspective.
Research background: Tourism accounts for a substantial share of global greenhouse gas emissions, positioning the sector at critical crossroads between remaining a climate liability and contributing meaningfully to global net-zero transitions. Although research on tourism decarbonization has expanded rapidly, existing systematic reviews remain fragmented, typically privileging isolated technological solutions, behavioral interventions, or governance mechanisms. Consequently, there is limited integrative understanding of how technological, ecological, behavioral, and institutional levers interact across contexts to enable scalable, SDG-aligned net-zero tourism pathways. Purpose of the article: The objective of this article is to synthesize and systematize the net-zero tourism literature by identifying dominant decarbonization themes, examining how geographic and institutional contexts shape transition outcomes, and clarifying how tourism's contribution to sustainable development goals (SDGs) can be operationalized. The study advances existing net-zero tourism reviews by offering a multidimensional, SDG-oriented synthesis rather than a single-lens or sector-specific assessment. Methods: This study adopts an integrated mixed-methods review design combining a PRISMA-guided systematic literature review of Scopus-indexed publications (2015–2024), machine learning–based topic modeling using BERTopic to identify latent thematic structures, and a comparative synthesis of empirically grounded case studies across diverse geographic and governance contexts. SDG mapping is embedded throughout the review to enhance analytical coherence and policy relevance. Findings & value added: Five thematic clusters emerge: zero-emission transport and renewable energy, consumer climate engagement, governance integration, ecosystem-based mitigation, and circular economy digitalization. Technological solutions reduce emissions but face infrastructure and institutional constraints, whereas nature-based approaches offer durable sequestration yet remain weakly embedded in policy and accounting. Behavioral initiatives are limited by trust and fairness concerns, and governance fragmentation restricts scaling. The study integrates fragmented research via the ADO lens and demonstrates methodological novelty through PRISMA, BERTopic, and comparative case synthesis, offering actionable guidance for scalable, SDG-aligned net-zero tourism transitions.
Research background: Pursuing entrepreneurship is viewed as a career option that helps migrants survive and thrive in their host countries. To achieve success, these entrepreneurs have overcome not only individual barriers (micro-factors) but also the negative impacts of external barriers, including market context (meso-factors) and institutional and infrastructure context (macro-factors). Purpose of the article: This article aims to explore the main barriers to migrant entrepreneurship (ME) at three levels: micro, meso, and macro; then identify which group of barriers has higher total prominence and which barriers cause the strongest influence on the remaining barriers. Methods: Anchored in mixed embeddedness theory, the research process begins with identifying barriers based on the existing literature and expert opinions through Delphi rounds. Then, the interrelationships of the barriers are analyzed using the DEMATEL approach. Questionnaires were administered to entrepreneurs in the Czech Republic with at least 10 years of business experience. Findings & value added: The results revealed 11 factors hindering ME, with micro-level barriers having greater overall prominence than meso/macro-level barriers. Meanwhile, contextual barriers, especially at the macro-level, including a lack of supportive government policy and favorable infrastructure, are the strongest causal drivers shaping the barrier system. Lack of local language proficiency is the only micro-barrier having a significant impact on other barriers. This research contributes to the ME literature by providing a validated causal map of barriers, using a robust analytical framework that incorporates experienced migrant entrepreneurs’ opinions and a Delphi-based DEMATEL approach. Since the barriers are categorized into drivers (causes) and outcomes (effects), this allows prioritization of barriers and corresponding policy actions to address the most important causal barriers.
Research background: Employee performance is a central variable in organizational research that directly influences the competitiveness and sustainability of organisations. The Visegrád Group (V4), comprising Slovakia, the Czech Republic, Poland and Hungary, shares a common post-socialist institutional heritage that has transformed employment relations and organizational management, yet remains largely unexplored from a comparative structural equation modelling perspective. Existing studies on perceived organisational support, job satisfaction and employee performance predominantly concern single-country or single-sector settings, leaving a notable geographical and methodological gap. Purpose: This study aims to identify the determinants of employee performance in V4 countries, with a focus on the role of perceived organisational support and job satisfaction. Specifically, it examines the mediating function of job satisfaction in the relationship between organisational support and employee performance. Methods: A quantitative cross-sectional comparative design was employed. Data were collected via a structured online questionnaire from 1,089 respondents from all four V4 countries between January and March 2026. Partial least squares structural equation modelling was applied as the primary analytical tool, with measurement invariance assessed via Measurement Invariance of Composite Models (MICOM) and multi-group analysis for cross-country comparisons. Findings & value added: Organisational support positively influenced both employee performance and job satisfaction, while job satisfaction partially mediated the relationship between organisational support and employee performance (indirect effect) and independently predicted performance. MGA reveals that the direct effect of organisational support on employee performance was significantly stronger in Slovakia and the Czech Republic than in Poland and Hungary, whereas the mediating mechanism through job satisfaction was homogeneous. This study is the first systematic comparative structural equation modelling analysis of the organisational support → job satisfaction → employee performance model in the entire V4 region. It has empirically grounded implications for management in post-socialist organisational contexts, where the depth of institutional transformation conditions the strength of support–performance relationships and extends the boundary conditions of social exchange theory and organisational support theory beyond their original Western institutional context.
Research background: Traditional socio-economic indicators, such as the Gini coefficient, may suffer from significant data lags and fail to capture the long-term dynamics of capital accumulation and wealth-based inequality. Furthermore, real estate forecasting predominantly focuses on predicting price levels rather than price dispersion, typically relying on ex post accuracy assessments that cannot account for unexpected future market disruptions. Purpose of the article: This study introduces a novel methodological framework to predict wealth-based inequality by leveraging real estate price dispersion as a timely proxy for economic welfare. The primary novelty of the approach lies in: (a) the construction of a Regional Inequality and Volatility (RIV) vector, which simultaneously tracks relative stratification and absolute financial barriers; (b) the development of a parametric bootstrap procedure for ex ante prediction assessment; (c) the implementation of stochastic, unexpected shock simulations (green policy, regulatory, macroeconomic, and climate events) to evaluate model robustness beyond historical data. Methods: The framework is applied to a longitudinal dataset of U.S. prefabricated housing transactions (2015-2023) using log-linear Mixed Models and plug-in predictors. This segment is chosen for its critical role in affordable homeownership. Findings & value added: The analysis reveals a significant structural shift toward 'wealth exclusion', where surging absolute costs create an economic blockade for lower-income households even when relative inequality appears stable. A critical implication is the identification of 'blind spots' in official metrics. For instance, the study uncovers a 'paradox of prosperity' where economic growth may reduce income-based poverty while intensifying wealth-based exclusion due to rising price volatility. These results suggest that a comprehensive policy framework must monitor both income and asset-based indicators to fully address socioeconomic polarization and the broken ladder of wealth accumulation.
Research background: The gender pay gap (GPG) remains a persistent issue across the EU, with structural factors such as horizontal segregation-particularly in STEM (Science, Technology, Engineering, and Mathematics) industries-contributing to labour market disparities. Despite women increasingly attaining tertiary education, their underrepresentation in STEM careers persists, raising questions about how higher education pathways intersect with the GPG. Specifically, we examine the structural linkages between educational pathways and occupational sorting in STEM-intensive sectors, where it is widely observed that women with STEM degrees do not work in these fields as frequently as men. Purpose of the article: This article aims to identify key determinants of the GPG linked to higher education, with a specific focus on STEM university majors. By analysing the relationship between tertiary education trends and wage gaps, the study seeks to clarify how educational pathways systematically shape occupational segregation and, consequently, the GPG. We examine these dynamics across 27 EU Member States, emphasizing the role of higher educational attainment and gender dynamics in STEM fields as part of broader institutional patterns rather than isolated national cases. Methods: We employ the Bayesian model averaging (BMA) method as an effective approach for modelling uncertainty across a large model space and providing a reliable measure of the importance of the considered determinants. This method is applied to assess the impact of variables such as gender-specific STEM graduation rates on the GPG, ensuring reliable identification of key drivers. Findings & value added: We conclude that STEM tertiary education plays an important role in shaping the GPG, not only through participation rates but through its interaction with labour market structures. Our results suggest that gendered asymmetries in advanced qualifications and early-career entry points may reinforce persistent wage inequalities, even among highly educated cohorts. These findings contribute new insights by demonstrating that educational expansion alone is insufficient to ensure wage convergence if educational trajectories remain horizontally segregated. By highlighting the structural role of higher education in shaping occupational sorting, this study advances current debates on the institutional roots of gender pay inequality and offers implications relevant to a broad set of advanced and emerging economies.
Research background: Green transition is a challenge for EU countries as there are obstacles to its implementation. In Poland, the slow progress of green investments are mainly due to the lack of a coherent climate strategy, social scepticism towards the green transition and a negative perception of public debt. With this in mind, the study analyses the most politically feasible instruments from the perspective of Polish society and the possibility of influencing individual preferences for climate policy implementation. The analysis is noteworthy from an international perspective, particularly for countries whose economies still rely on fossil fuels, meaning they are less advanced in their green transition than the world's richest and most developed countries. Purpose of the article: This article examines social preferences regarding government fiscal measures to counteract climate change and examines the extent to which social preferences can be shaped by the information provided. The following hypotheses have been formulated: H1: Specific social preferences regarding green investment financing modes can be revealed. H2: Individual preferences regarding the trend towards the green transition and the choice of financing instruments can be influenced by the information provided to respondents characterised by profiles of negative perception of public debt and high fiscal conservatism. Methods: A survey was conducted among three representative samples, totalling 1,050 respondents from Poland. The respondents' perceptions of fiscal instruments were examined using the behavioural economics approach. The significance of the obtained results was verified using statistical analysis methods. The relationships between selected variables were also modelled using logit regression. Findings & value added: The study fills the research gap on preferences for financing the green transition depending on the information received by respondents and their individual perceptions of public debt. The conducted analysis reveals that the respondents' aversion to debt as an instrument for financing the green transition decreases thanks to the application of the framing method, despite their initially limited preference for debt. The applied experiment shows that political opposition to both green investments and public debt growth could be reduced through communication with voters.
Research background: Environmental taxation is a foundational pillar of the European Union's strategy for climate neutrality and sustainable development. However, its implementation across Member States remains uneven and structurally fragmented, raising questions about the effectiveness of policy harmonization. Purpose of the article: This study provides a longitudinal, comparative analysis of environmental tax structures from 1995 to 2022 in the EU-27, with a special focus on Slovakia, the V4 countries, and Romania. Methods: Using Eurostat data and a mixed-methods statistical approach, including point estimates, General Linear Models, and repeated-measures ANOVA with post hoc testing, we examine four tax categories: total environmental, energy, transport, and pollution/resource taxes. Findings & value added: The results show that environmental taxation in the EU does not move toward a single model but instead follows different paths shaped by fiscal integration, institutional capacity, and political feasibility. These findings challenge the common belief that policy harmonization causes convergence in environmental taxation across Member States. The prominence of energy taxes, along with the ongoing underuse of transport and pollution/resource taxes, presents a structural barrier to achieving environmental policy goals. Notable regional differences are seen, especially in post-socialist economies, where environmental taxation is more affected by fiscal and institutional limitations than environmental priorities, with agriculture as a key blind spot. By implementing a region-sensitive environmental taxation framework, this study advances environmental tax theory by viewing environmental taxation as a context-dependent policy system rather than a universally applicable tool. The findings contribute to ongoing discussions on green fiscal reform by showing that convergence requires more integration of taxation with environmental goals and supporting policy measures. The results support adaptive reforms targeted at rebalancing tax systems, broadening sectoral coverage, and increasing fiscal resilience in response to changing sustain-ability objectives, with relevance extending beyond the EU to other diverse policy environments.
Research background: Manufacturing sectors in emerging economies face intensifying regulatory, market, and stakeholder pressures to improve environmental stewardship while sustaining competitiveness. Green supply chain management practices (GSCMP) are increasingly viewed as mechanisms for enabling firms to reduce ecological burdens and create sustainability value. Yet, the mechanisms through which integrated GSCMP translate into sustainable outcomes remain insufficiently specified, particularly in emerging-market manufacturing contexts like Indonesia. Purpose of the article: This study examines how integrated GSCMP convert into environmental, economic, and social performance by activating two forms of eco-innovation-green process innovation (PCI) and green product innovation (PDI). Building on the practice-based view and normalization process theory, the study tests both direct and innovation-mediated pathways linking GSCMP to sustainability outcomes. Methods: A survey of 577 Indonesian manufacturing respondents was analyzed using variance-based structural equation modeling. GSCMP were modeled as a second-order formative construct, with PCI and PDI specified as parallel mediators. Direct, indirect, and total effects were assessed through bootstrapping. Findings & value added: The findings show that integrated GSCMP strongly stimulate both PCI and PDI. PCI is positively associated with sustainable outcomes, whereas PDI is linked to environmental and social outcomes but not to economic gains. GSCMP also exerts effects on all three performance dimensions. The study clarifies that PDI yields more immediate monetizable benefits, while process-oriented greening generates earlier compliance-and legitimacy-based gains. These contributions advance mechanism-level understanding of how GSCMP create sustainability value in emerging-market manufacturing.
Research background: European economies are increasingly differentiated not only by traditional macroeconomic indicators but also by their position in digital trade, artificial intelli gence (AI) ecosystems, and high-technology value chains. While prior research has examined innovation, ICT integration, and cluster dynamics separately, there remains a need for an integrated empirical framework that explains how AI-related human capital, digital trade intensity, and innovation capacity jointly shape structural development gaps across Europe. Purpose of the article: This paper examines how high-technology factors, innovation capacity, information and communication technologies (ICT) trade integration, and artificial intelligence-driven digital transformation impact economic development disparities across European countries and shape distinct development pathways. Methods: A panel dataset of 16 European countries for the years 2019-2022 is analyzed using a three-stage framework. First, indicators from three domains (economic performance, innovation and technological capacity, and AI and digital transformation) are min-max normalised. Second, k-means clustering is applied to group countries, with the optimal number of clusters selected using the Silhouette coefficient. Third, XGBoost is used to identify the variables that most strongly differentiate between clusters, and multinomial logistic regression is employed to interpret the direction and magnitude of the key determinants of cluster membership. Findings & value added: The analysis identifies three distinct development clusters in Europe: advanced AI-intensive economies, ICT trade-driven digitalisers with weaker AI talent bases, and structurally lagging countries with limited digital integration. AI-related human capital, especially AI labour migration and talent concentration, together with ICT trade intensity, emerge as the strongest empirical differentiators of development pathways. Methodologically, the study contributes an integrated framework combining clustering, explainable machine learning, and econometric modelling that can be replicated in other regional contexts. Practically, the results highlight that long-term competitiveness increasingly depends on AI talent ecosystems and digital trade specialization rather than on traditional macroeconomic growth factors alone.
Research background: Amid global pressures to align digital transformation with environmental sustainability, big data policies have emerged as pivotal drivers of green innovation. These policies leverage advanced data analytics to foster eco-friendly technological advancements, yet their long-term impact on corporate innovation across diverse economic contexts remains underexplored. The potential of big data policies to transform environmental constraints into innovation opportunities underscores the need to examine their role in promoting sustainable development, particularly in rapidly digitizing economies. Purpose of the article: The study assesses the role of big data policies in promoting corporate green innovation-measured by green invention patent authorizations as quantity-and their contribution to sustainable technological advancements, utilizing innovation systems theory to rigorously investigate the diverse mechanisms influencing innovation performance in quantity, influence, quality, and diversity dimensions. Methods: Utilizing patent data from 4,728 Chinese listed firms in big data pilot and non-pilot regions to evaluate green innovation outcomes across quantity, influence, quality, and diversity. Using China's National Big Data Comprehensive Pilot Zones (NBDCPZ) policy as a quasi-natural experiment, it applies innovation systems theory to examine mechanisms-including financial channels, policy attention, and R&D incentives-while heterogeneity tests assess variations by firm ownership, innovation capacity, and regional economic conditions, with robustness ensured through placebo tests and instrumental variable approaches. Findings & value added: Findings reveal that the big data policy boosts green innovation quantity by 4.5%, driven by enhanced financing, subsidies, and reduced information asymmetry, with stronger effects in non-state-owned and high-innovation firms. However, its impact on influence, quality, and diversity is limited, suggesting a quantitative focus. The study advocates tax incentives and patent quality platforms to enhance transformative innovation. By illuminating data-driven pathways to sustainability, this research offers strategies for emerging economies and contributes to the global policy-innovation nexus, laying groundwork for future studies on technology-enabled green development.
Research background: After the lessons of supply chain disruptions caused by environmental changes such as the pandemic, political and commercial restrictions, and natural disasters, companies make strategic decisions to design more sustainable, resilient, and flexible supply chains able to reduce the effect of uncertainty. Nevertheless, most companies lack the skills and understanding of how to systematically develop a network of sustainable supply chain partners that would take advantage of new technological capabilities to meet changing consumer expectations and exploit market opportunities. Therefore, the problem this research addresses is how to develop sustainable supply chain networks in conditions of global uncertainty.
Research background: The banking sector plays a vital role in maintaining global economic stability, making corporate governance (CG) a crucial mechanism for mitigating systemic risk. Although the relationship between board structures and firm performance has been extensively examined, the specific impact of CG on banking institutions remains insufficiently understood, particularly given the sector's unique regulatory environment. Moreover, previous studies rarely distinguish between short-term and long-term governance effects, resulting in a limited understanding of how qualitative board characteristics shape bank outcomes over time. Purpose of the article: This study investigates the short-term and long-term effects of board characteristics on bank performance and financial stability. The analysis covers 409 banks from 27 developed countries over the period 2001-2023, capturing substantial variation in governance systems and institutional contexts. Methods: Bank performance is assessed using Return on Assets (ROA) and Return on Equity (ROE), while financial stability is measured with the Z-score, which reflects the distance to default. Corporate governance attributes-including board size, experience, skills, gender diversity, and independence-are operationalized through detailed board composition metrics. The empirical analysis employs Feasible Generalized Least Squares (FGLS) to correct for heteroscedasticity, supplemented by the two-step System Generalized Method of Moments (GMM) to address potential endogeneity and the dynamic persistence of bank performance. Findings & value added: Managerial experience and gender diversity are found to enhance bank profitability and stability in both the short and long term. While board independence exerts a significant positive effect on performance in the long run, larger boards negatively influence profitability over the same horizon. In contrast, board skills show a positive short-term association exclusively with bank stability. The paper's main theoretical contribution lies in demonstrating that the effects of corporate governance in the banking sector are not static but strongly horizon-dependent, and that governance mechanisms influence profitability and resilience through distinct pathways. The practical and policy implications suggest that effective board structure should be regarded as a long-term component of prudential governance. Regulators and bank stakeholders can enhance institutional resilience and reduce systemic vulnerability by promoting managerial experience, gender diversity, and well-designed board independence-rather than focusing solely on formal compliance with governance codes.
Research background: Responsible consumption and production, articulated in Sustainable Development Goal 12 (SDG 12), has heightened global expectations for credible sustainability disclosure. Despite this, firms across sectors continue to use selective, vague, or unverifiable environmental claims that contribute to greenwashing. Although research on greenwashing has expanded, consolidated knowledge on how misrepresentation patterns vary across industries and how these practices undermine SDG 12 objectives remains limited. A clearer understanding of sector-specific disclosure behaviors is essential for strengthening accountability and supporting responsible production–consumption transitions. Purpose of the article: This study aims to provide a cross-sectoral synthesis of greenwashing mechanisms and sustainability misrepresentation, examining how disclosure tactics differ across the manufacturing, energy, fast-moving consumer goods (FMCG), automotive, technology, and service sectors. The objective is to map these practices against SDG 12 expectations and highlight how they hinder progress toward responsible production and consumption. Methods: Using a PRISMA-based systematic review of Scopus-indexed studies, the analysis applies thematic coding and comparative sectoral assessment to identify patterns of misrepresentation. The review integrates evidence across multiple industries to highlight differences in performance-based, claim-based, symbolic, and impression-management tactics. Findings & value added: The results show that manufacturing and energy firms predominantly engage in performance-related sustainability misrepresentation, whereas FMCG and service firms more frequently employ claim-based, symbolic, and impression-management approaches. Across all sectors, recurring practices include overstated certifications, selective reporting, and ambiguous SDG commitments, which collectively impede transparency and weaken the achievement of SDG 12. By offering one of the first comprehensive cross-industry evaluations of greenwashing within an SDG framework, the study advances the theoretical understanding of sustainability misrepresentation and identifies sector-specific risks relevant for regulators and policymakers. It also provides actionable insights into enhancing reporting integrity and accountability aligned with SDG 12.
