Green innovation is widely recognized as an effective pathway for reconciling economic development with carbon emission reduction. However, there is still no consensus on the spatial relationship between green innovation (GI) and carbon emissions (CEs). To address this gap, this study uses panel data from China’s Yangtze River Delta region to examine the spatial spillover effects of GI and its subcomponents on CEs, as well as the role of public R&D investment in this relationship. The findings reveal a non-intuitive and spatially asymmetric effect of GI. Specifically, GI tends to increase CEs in the region where innovation occurs while reducing CEs in neighboring cities. Moreover, the impacts of green technologies (GTs) on CEs vary across regions and channels. Innovation in GTs, indirect CE reduction mechanisms, and direct CE reduction effects all exhibit significant regional spillover impacts on CEs. The results further show that public R&D investment, both locally and through spatial spillovers, weakens the relationship between GI and CEs. This study contributes to the literature by advancing the understanding of how different forms of green technological progress generate heterogeneous spatial spillover effects on CEs. It is among the first to systematically examine how public R&D investment moderates the relationship between GT and CEs. From a policy perspective, the findings provide practical insights into strengthening regional green innovation capacity while simultaneously reducing carbon emissions within an integrated development framework.
Today, turbulence in the economic environment creates numerous challenges across various aspects of firms, requiring organizations to respond quickly to survive and grow. As a result, supply chain flexibility (SCF) has become a strategic priority for companies and a prevalent topic in academia. Grounded in Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study examines how the business environment (ENVI) drives digitalization in operations management activities (DOM) and how DOM enhances SCF both directly and indirectly through supply chain visibility (SCV), supply chain collaboration (SCC), and supply chain risk management (SCRM). In this research, we collected data from 289 Vietnamese firms and analyzed them using PLS-SEM. We checked the reliability and discriminant validity of the measurement scales, then predicted the effect sizes and mediation effects of the relationships. The results show that ENVI significantly drives DOM adoption, which in turn improves SCV, SCC, and SCRM, thereby strengthening SCF. The findings reveal that supply chain visibility and risk management, but not collaboration, mediate the DOM–SCF relationship and function as sequential pathways through which environmental turbulence translates into greater supply chain flexibility. Furthermore, the research offers managerial insights into leveraging digital tools to construct more adaptable and resilient supply chains in volatile economic environments.
Transition economies are attractive destinations for foreign direct investment (FDI), offering lower production costs, emerging consumer markets, and opportunities for strategic positioning, while also facing structural and institutional constraints. This study examines the motives of foreign investors in Serbia, with a particular focus on how investment motivations vary according to firms’ level of internationalization. The analysis is based on a survey of 300 foreign investors that entered the Serbian market between 2001 and 2019 (response rate: 29.3%). The findings indicate that the most important motives include entering a new market, observing consumers, customers, and suppliers, first-mover advantage, the availability of financial resources in the Serbian market, economies of scale, characteristics of the Serbian tax system, and natural resources and their prices in Serbia. However, a more detailed analysis reveals significant differences across investor types. Regional firms are primarily driven by market expansion, economies of scale, and resource-related factors, while global firms place greater emphasis on infrastructure, technological and informational inputs, and integration into broader production networks. Multinational firms display a more balanced pattern of motives, without a clearly dominant driver. These results highlight the importance of investor heterogeneity in shaping FDI patterns in transition economies. They also suggest that policy approaches tailored to specific investor profiles may be more effective than uniform strategies in attracting and sustaining foreign investment.
This research investigates how selected macroeconomic determinants influence stock market returns and volatility across financial systems characterized by varying degrees of market depth and liquidity. The analysis focuses on four stock indices—FTSE 100, FTSE China A50, BUDAPEST SE, and SBITOP—over the period 2012–2022, which is further divided into pre-crisis and crisis/post-crisis subperiods to capture the effects of the COVID-19 shock. To account for time-varying and asymmetric volatility dynamics, the study employs customized multivariate GARCH-type models, including EGARCH, PGARCH, and TGARCH specifications, with model selection guided by information criteria. The empirical results confirm that macroeconomic factors such as inflation, interest rates, exchange rates, and commodity prices exert statistically significant effects on stock index returns and volatility, although the magnitude and direction of these effects differ across markets and across time periods. Developed and less liquid markets display distinct volatility transmission mechanisms, particularly during crisis periods. Overall, the findings highlight the relevance of flexible GARCH-based frameworks for understanding market-specific volatility dynamics and provide useful insights for investors and policymakers operating under conditions of heightened uncertainty.
The modern market economy is characterized by turbulence, uncertainty, and an increasing pace of change. In such conditions, decision-making represents a fundamental and dynamic process in which managerial decisions directly influence organizational effectiveness and overall performance. The primary objective of this study is to identify, using a scientifically grounded methodological framework, the key determinants of managerial decisionmaking styles in public enterprises, with particular emphasis on the organization of work processes, individual demographic characteristics, and economic aspects of management. The research was conducted in public sector enterprises, which have shown insufficient functionality for many years. The study was conducted on a sample of 426 managers across a range of organizations, employing standardized instruments, namely the General Decision-Making Scale (GDMS) and the Work Organization Assessment Questionnaire (WOAQ). The results indicated statistically significant differences in managerial decision-making styles. Furthermore, a significant association was identified between decision-making styles and various dimensions of work organization. In addition, decision-making styles were found to be significantly related to the general demographic characteristics of the participating managers. Based on the research results, improving the decision-making process is necessary in the new digital environment.
The article proposes an approach for assessing the efficiency of sustainabilityrelated investments in the EU-27 member states over the period 2015-2024. This is achieved through a combination of a DEA-based approach, a composite efficiency coefficient, and cluster analysis. The composite coefficient measures the relationship between calibrated outcomes and inputs and, in essence, provides a "value-for-money" assessment of sustainable expenditures. The results show substantial differences between countries. The Baltic states and several Central and Eastern European economies display high efficiency, while some of the traditional "leaders" in sustainability exhibit more modest returns per unit of investment. The cluster analysis groups the countries into four clusters with similar characteristics. These range from groups in which investment volumes play the leading role to groups in which efficiency is the key driver, and clearly show differences in the outcomes of the policies pursued (including target-setting and the design of EU financial instruments). The analysis also takes into accountthe impact of COVID-19 and the war in Ukraine. The main finding is that institutional quality is a stronger factor in maintaining efficiency during crises than the level of economic development. This conclusion underlines that sustainability objectives should also include measurable indicators of administrative capacity in order to improve the system's ability to adapt in times of crisis. The proposed model provides an empirical basis for improving the way European targets and instruments are formulated. It can help direct limited resources more effectively towards countries and regions with the greatest potential for efficiency improvement.
Emissions Trading Scheme (ETS) pilot programs impose binding quota constraints and enable allowance trading, reshaping cost structures and strategic interactions in oligopolistic agri-food supply chains. This paper quantifies the resulting economic impacts, including equilibrium prices, profits, and trade flows, by developing a multi-tier network equilibrium model that links upstream suppliers, downstream manufacturers, domestic and international demand markets, and a carbon trading center under an Emissions Trading Scheme pilot setting. Suppliers invest in low-carbon technologies, while manufacturers undertake labor-efficiency investments that affect unit costs and throughput, with proximity-based spillovers captured via a grid-distance mechanism. The equilibrium conditions are formulated as a variational inequality framework and computed numerically, enabling systematic comparative statics analysis under alternative quota stringency and trading conditions. Using China-EU garlic trade as an illustrative case, the numerical analysis indicates that tighter policy constraints and trading conditions shift production and allowance-trading patterns, with corresponding changes in prices, profits, and emissions across tiers. It also shows that moderate efficiency investment can improve productivity and may reduce aggregate emissions, whereas very high unilateral investment tends to exhibit diminishing returns and can be associated with non-smooth adjustments in network allocations. Finally, coordinated upstream-downstream investment is generally associated with more stable outcomes than isolated initiatives. The framework offers a decision-relevant tool for evaluating Emissions Trading Scheme pilot designs in regulated international agri-food trade networks.
This study examines the persistence of spatial autocorrelation in housing prices within the Brno metropolitan area, focusing on the dynamics between the core city and its peripheral municipalities. The aim of the study is to test whether spatial autocorrelation exists and the extent to which it can be explained by traditional variables. Using a log-linear specification, the analysis employs a hedonic price model estimated separately for the city's central and peripheral areas. The dataset comprises 19,286 residential transactions from the period 2020-2023. Explanatory variables include structural characteristics, land use, crime rates, and accessibility to services. Spatial autocorrelation is measured using Moran's I and Local Indicators of Spatial Association, both on raw data and model residuals. The results reveal that price clustering is substantially stronger in peripheral areas, where lower market liquidity and limited substitutability of dwellings amplify the transmission of price signals across municipal boundaries. The findings suggestthat localized factors such as planning agreements and limited market liquidity affect price transmission across municipal borders. The study's findings highlight that spatial autocorrelation is not merely a statistical pattern but a mechanism shaping housing affordability, market efficiency, and the distribution of price shocks. In peripheral areas, prices are strongly affected by proximity to the city center and access to public infrastructure, indicating the need for coordinated metropolitan policies, particularly in rapidly growing suburban areas where spillover effects are most pronounced. The study contributes to housing economics by quantifying the structural sources of spatial dependence and demonstrating their relevance for metropolitan policy design.
The internationalization of the RMB and China's financial opening represent a continuous process of co-evolution. This process is propelled by a mutually reinforcing cycle where institutional supply, market demand, and network effects interact dynamically. The bidirectional synergistic evolution of the two has unleashed significant institutional dividends, yet its deepening process still faces multiple structural constraints. The synergistic advancement of financial opening-up and RMB internationalization necessitates a holistic framework-one that strategically balances risk containment with growth momentum to ensure these processes are mutually reinforcing. This study examines the synergistic pathways and interactive dynamics between financial opening and RMB internationalization. The objective of this paper is to elucidate the intricate relationship between these processes and their strategic implications, thereby formulating a theoretical and policy-oriented framework to support high-level financial opening and enhance the international standing of the RMB.
Against the backdrop of the digital economy, artificial intelligence (AI) has become a critical strategic resource for firms. Beyond enhancing economic performance, AI has opened new pathways for addressing environmental externalities. Drawing on the resource-based view (RBV), this paper examines the economic mechanisms through which AI resources influence firms' green risk management (GRM) practices. The empirical results show that AI attention has a negative impact on GRM, whereas AI depth and AI-driven innovation exert positive effects. Further analysis indicates that AI technology attention and software depth are the main factors driving the negative impact. A resource crowding-out effect exists between AI attention and green attention, but this effect is weakened when financing constraints are low. Although no crowding-out effect is observed between AI depth and green depth, environmental uncertainty promotes the emergence of such an effect. At the same time, green innovation is identified as an indispensable mediating variable in the process through which AI attention and AI depth influence GRM. Further analysis confirms that environmental subsidies and tax incentives, as key economic policy instruments, can complement AI resources and effectively promote green risk management.
This study examines the economic consequences of firms' environmental, social, and governance (ESG) performance for audit quality in China's emerging capital market. Using fixed-effects panel regressions, mediation analysis, and a series of robustness, endogeneity, and heterogeneity tests, the study identifies the underlying economic mechanisms. The empirical results show a positive correlation between audit quality and a firm's ESG performance: higher audit quality is associated with better ESG performance. The mechanism analysis indicates that operational risk and information risk act as mediators in this relationship. Strong ESG performance can reduce both operational and information risks for firms, providing auditors with a more stable economic environment and a clearer information basis, thereby improving audit quality. Heterogeneity analysis further reveals that the positive impact of ESG performance on audit quality is more pronounced in eastern regions and in high-tech and non-heavily polluting sectors. Overall, this study highlights the economic aspects of the ESG-audit nexus and suggests that strengthening ESG practices can improve audit quality and promote a more transparent, efficient, and sustainable environment in emerging markets.
In the current conditions of digital transformation of logistics services, omnicultural marketing as a strategy of personalized multichannel customer interaction is becoming especially relevant. Rising consumer expectations regarding the quality of digital communication and service transparency require new approaches to building trust and loyalty in logistics companies. The purpose of the study is to examine the digital behavior of customers of the logistics company Meest and to assess the impact of communication channels and the public presence of management on brand perception. The object of the study is digital communication channels in logistics. The methodological basis is a quantitative online survey of Meest customers (n = 1011) conducted in February 2025, using descriptive statistics to analyze the responses. The results revealed a high level of multimodal interaction: 72.5% of users use at least three channels, and 60% regularly switch between them. The highest level of satisfaction was recorded for the mobile app (96%), although it is inferior to email and messengers in terms of reach. For 92% of respondents, the style of communication affects brand trust, and 68.7% positively assess the public activity of management. A functional typology of channels by strategic purpose (attraction, retention, brand building) has been built, which allows optimization of the digital architecture of logistics companies. The practical significance of the study lies in the possibility of adapting the findings to develop effective omni-cultural strategies, taking into account behavioral and emotional factors of interaction.
Crisis management is an essential component of strategic planning and forecasting in companies, and its importance has grown significantly in the aftermath of the global crisis caused by the COVID-19 pandemic. While insurance companies traditionally focus on preventing and compensating for negative economic events, effective crisis management is equally crucial for accurate risk assessment, as the stability of the insurance sector directly supports the stability of the wider financial system. The aim of this research was to analyze contemporary perspectives on anti-crisis management in insurance and evaluate the effectiveness of measures applied to overcome crisis situations. To achieve this, the study employed bibliographic-analytical methods, induction, deduction, synthesis, generalization, online surveys, and logical, as well as statistical and graphical, comparison. The findings revealed notable differences in risk management approaches across companies of different sizes. Large insurance companies placed considerable emphasis on training staff in crisis management and employing specialists in analytics and risk assessment. Medium-sized firms often relied on external consultations, scenario planning, and employee training, while small firms generally paid insufficient attention to risk management practices. Among anti-crisis measures and management staff training, the work of strategic planning departments, strengthening teamwork, and financial frugality were associated with the effectiveness, speed, and progressiveness of overcoming an economic crisis with a medium-strength connection. That is why large companies that combined these measures were more effective in overcoming crises. In contrast, medium and small enterprises achieved lower effectiveness, reflecting their more limited and inconsistent adoption of crisis management measures.
The importance of drug supply and the efficiency and effectiveness of the drug supply chain as a strategic commodity are not embedded in many economic analyses. Supplying raw materials for the production, storage, and distribution of medicines within a supply chain network is crucial. Today, the development of the Internet of Things and the analysis of vast amounts of data have enabled the rapid preparation of large data production resources, allowing decisions in the drug supply chain network to be made quickly with the aid of appropriate tools. This article aims to provide a framework for agile drug supply chain management, focusing on the Internet of Things and big data analysis to manage the country's drug supply chain effectively. Rebif-22-44 drugs, which are used in the treatment of MS disease, are investigated in this paper, and an attempt is made to minimize the costs of the drug supply chain network design and minimize the maximum unmet demand of drug distribution based on big data sources by using a framework. MOGWO analysis tools and epsilon constraints are discussed. Considering the existence of uncertainty in drug demand and transportation costs, the robust possibilistic planning method has been used to control uncertain parameters. The analysis of big data shows that, in order to reduce the shortage of drugs in the country, it is necessary to use more supplies and increase the number of drug production centers and warehouses. This leads to an increase in the design costs of the supply chain network. In the analysis of the model, 11 efficient solutions were obtained by the MOGWO algorithm, and seven efficient solutions were obtained by the epsilon constraint method. It was also observed that, in Iran, with the increase in the uncertainty rate, the demand for Rebif-22-44 drugs has increased, and this has led to a rise in network design costs and drug shortages in the country. Concretely, lowering the maximum unmet demand from 78 to 47 units required raising the total network cost from 1,303,212.72 to 1,498,842.94 (USD), quantifying the cost-equity trade-off, and MOGWO produced a broader, faster Pareto set than the epsilon-constraint method (11 solutions in 68.49 s, MSI = 195,630 vs. seven solutions in 1,342.15 s, MSI = 184.4.
This study provides a causal estimate of the impact of logistics-focused coordination policies on regional economic growth in China. While the role of logistics in development is widely acknowledged, the existing literature offers limited causal evidence on the economic mechanisms through which policydriven coordination unlocks growth. Addressing this gap, we employ a quasiexperimental design, treating the staggered implementation of coordination policies in three major urban agglomerations-Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) - as natural experiments. We estimate a heterogeneous Difference-in-Differences (DID) model complemented by an instrumental variable (IV) approach to address endogeneity. Our analysis, structured around supply-side production scale, spatial coordination efficiency, and demand-side market intensity, reveals significant positive effects. However, we find heterogeneous treatment effects: the growth mechanism is infrastructure-driven in BTH, marketintegration-led in YRD, and reliant on cross-border cooperation in PRD. By quantifying these distinct causal pathways, this research contributes to the discourses in economic geography and regional policy, underscoring the importance of regionally-tailored economic strategies for achieving efficient resource allocation and sustainable development.
Against the backdrop of rapid advancements in intelligent technologies and the convergence of China's "dual carbon" strategic goals, how enterprises leverage artificial intelligence to enhance economic and environmental benefits while achieving sustainable development has emerged as a critical research question. This study adopts a dual-perspective approach, examining both financial and environmental performance within the framework of sustainable development. Based on a sample of Chinese A-share listed companies from 2013 to 2023, this study employs a multi-period Difference-in-Differences model to empirically examine the impact of applications of artificial intelligence on corporate sustainable development performance, as well as the underlying mechanisms. The findings reveal: (1) Applications of artificial intelligence enhance both the financial and environmental performance of corporations, thereby improving their overall sustainable development performance. The conclusion has passed a series of robustness tests, including heterogeneity tests and double machine learning. (2) Mechanism analysis reveals that artificial intelligence influences corporate sustainable development performance through green innovation effects, efficiency enhancement effects, and information acquisition effects. The Porter Hypothesis, the Resource-Based View, and the Information Asymmetry Theory, among other theories, have been verified. (3) The promotional effect of artificial intelligence applications on sustainable development performance exhibits heterogeneity. Specifically, the promotional effect is more pronounced in non-heavily polluting enterprises, high-tech enterprises, and enterprises with senior executives possessing environmental protection backgrounds. (4) When enterprises are engaged in intense market competition, the positive relationship between artificial intelligence adoption and sustainability performance strengthens. Our findings offer valuable insights for managers and policymakers aiming to leverage AI for achieving sustainable growth.
This study explores the intersection between financial development and environmental, social, and governance (ESG) performance using a comprehensive bibliometric analysis. Drawing on 443 academic publications extracted from the Scopus database between 2000 and 2023, the research identifies major contributors, trending topics, and thematic evolutions in this interdisciplinary field. Using co-authorship, co-citation, and keyword co-occurrence analyses, the study reveals increasing scholarly attention on sustainable finance, ESG disclosure, and the role of institutions in fostering ESG practices. The results highlight a growing convergence between financial systems and sustainability goals, with significant research clusters emerging in Europe, North America, and Asia. The study contributes to the literature by mapping the intellectual structure of ESG-finance research and identifying gaps and future research directions. These insights are valuable for academics, practitioners, and policymakers aiming to align financial development with sustainability imperatives.
The primary objective of this study is to examine the influence of various educational factors on the entrepreneurial potential of university students as a prerequisite for the stability of business and economic systems, especially in EU candidate countries. Education is crucial for developing entrepreneurial skills and potential entrepreneurs who contribute to the development of national economies. The research sample comprised students from two non-EU countries and one EU member state. Data were collected from a total of 1,008 university students across these three countries. The instruments utilized in the study were the Questionnaire on Entrepreneurial Traits (QET) and the Scale of Entrepreneurial Potential (SEP). Different educational factors were analysed in this research: entrepreneurial education, different educational profiles, and the influence of faculties with a supportive environment toward entrepreneurship. Canonical discriminant analysis confirmed the significance and structure of differences among students who belong to faculties with different: a) orientations toward entrepreneurship, b) types of faculties, and c) experiences of previous entrepreneurial education compared with those who did not receive such education, considering their performance across the dimensions of the Entrepreneurial Traits Model (QET) and the Entrepreneurial Potential Model (SEP). Results from this research could shed light on the development of entrepreneurship and the importance of this phenomenon, which proves to be crucial for the regional economy.
China’s exports face long-standing challenges of low quality, low price, and low profit margins. Amid intensifying global economic competition and deeper value chains, breaking the ‘low-value-added lock-in’ to boost export enterprises’ profit margins is urgent for addressing foreign trade bottlenecks. This study systematically examines artificial intelligence (AI)’s effect and mechanism on export enterprises’ price markup. It integrates 2007–2016 data from China’s Customs Database and Shanghai and Shenzhen A-share manufacturing listed companies, combined with AI indicators built by extracting annual report text via large language models. Results show AI significantly raises export enterprises’ price markup, with the ‘intelligent empowerment’ model having a stronger promotional effect than ‘machine replacing human’. Mechanism analysis reveals AI’s dual impacts: positive effects from efficiency improvement and technological innovation, and negative effects from intensified market competition and higher information transparency. Heterogeneity analysis finds AI benefits state-owned enterprises, high-productivity enterprises, quality-competitive enterprises, and those exporting to developed countries more. Additionally, it reduces markup dispersion and improves resource allocation efficiency. Overall, AI drives export enterprises to break low-value-added dilemmas, optimise resource allocation, and further boost China’s export economy.
This study examines how digital technology adoption moderates the relationship between strategic orientation and economic performance in construction enterprises. Drawing on the resource-based view and dynamic capabilities theories, we surveyed 193 construction firms in Guangdong Province, China, to investigate the economic effects of market orientation, entrepreneurial orientation, and technology orientation on performance, with digital technology adoption as a boundary condition. Using partial least squares structural equation modeling (PLS-SEM), our findings reveal differential impacts of strategic orientations on performance. Technology orientation emerges as the strongest predictor of economic performance (β = 0.246, p < 0.01), followed by market orientation (β = 0.187, p < 0.05), while entrepreneurial orientation shows no significant direct effect. Critically, digital technology adoption significantly moderates both the market orientation–performance (β = 0.134, p < 0.05) and technology orientation–performance (β = 0.198, p < 0.01) relationships, amplifying their economic value creation under conditions of high digital adoption. The model explains 38.4% of performance variance, with simple slope analysis revealing that strategic orientations generate substantial performance benefits only when supported by adequate digital infrastructure. These findings challenge the universal applicability of entrepreneurial frameworks in traditional industries and suggest that digital transformation fundamentally reconceptualizes which strategic orientations remain economically viable in increasingly digitized business environments.
