Search Results (944)

The rapid development of digital technology is reshaping the global economic landscape. However, its impact on firms’ capacity utilization rate (CUR), particularly through technological innovation, remains unclear. This study investigates this issue by developing an endogenous growth model that connects digital technology to CUR. The empirical analysis is based on data from Chinese A-share manufacturing firms. The methods employed include quantile regression, instrumental variable techniques, and various tests to explore underlying mechanisms. CUR is calculated using a special model that looks at random variations, and digital transformation is assessed using text analysis powered by machine learning. The findings indicate that digital transformation significantly enhances CUR, especially for firms with average capacity utilization levels, but has a limited effect on low- and high-end firms. Moreover, technological innovation mediates this relationship; however, factors like “double arbitrage” (involving policy and capital markets) and “herd effects” tend to prioritize quantity over quality, which constrains innovation potential. Improvements in CUR lead to enhanced firm performance and productivity, generating industry spillovers and demonstrating the broader economic externalities of digitalization. This study uniquely applies endogenous growth theory to examine the role of digital transformation in optimizing CUR. It introduces the “quantity-quality” technology innovation paradox as a crucial mechanism and highlights industry spillovers to address overcapacity while offering insights for fostering sustainable economic and social development in emerging markets. Full article

This study evaluates the effectiveness of the B Impact Assessment (BIA) as a catalyst for integrating sustainability into industrial firms through a qualitative case study of LC Paper, the first B Corp-certified tissue manufacturer globally and a pioneer in applying BIA in the pulp and paper sector. Based on semi-structured interviews, organizational documents, and direct observation, this study examines how BIA influences corporate governance, environmental practices, and stakeholder engagement. The findings show that BIA fosters structured goal setting and the implementation of measurable actions aligned with environmental stewardship, social responsibility, and economic resilience. Tangible outcomes include improved stakeholder trust, internal transparency, and employee development, while implementation challenges such as resource allocation and procedural complexity are also reported. Although the single-case design limits generalizability, this study identifies mechanisms transferable to other firms, particularly those in environmentally intensive sectors. The case studied also illustrates how leadership commitment, participatory governance, and data-driven tools facilitate the operationalization of sustainability. By integrating stakeholder and institutional theory, this study contributes conceptually to understanding certification frameworks as tools for embedding sustainability. This research offers both theoretical and practical insights into how firms can align strategy and impact, expanding the application of BIA beyond early adopters and into traditional industrial contexts. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

The purpose of this paper is to analyze how China’s outward foreign direct investment (OFDI), driven by different motivations, affects the bilateral global value chain (GVC) relationship between the home country (China) and host countries, evaluating both bilateral GVC trade value and relative GVC positions. Employing the OECD Trade in Value Added (TiVA) database combined with Chinese listed firm data, we found the following results: (1) Strategic asset-seeking OFDI strengthens the GVC relationship between China and host countries while enhancing China’s GVC position relative to host countries. (2) Efficiency-seeking OFDI increases the domestic value-added exported from host countries to China but does not improve China’s relative GVC position. (3) Natural resource-seeking OFDI enhances bilateral GVC trade volumes but has no significant impact on the relative GVC positions of China and host countries. (4) China’s OFDI, not driven by these motivations, generates a trade substitution effect between home and host countries. We also examined the heterogeneity of these effects. Our findings suggest that China’s OFDI fosters equitable and sustainable international cooperation, supports mutually beneficial GVC trade and host-country economic growth, and therefore, progresses toward Sustainable Development Goal (SDG) 8. Full article

This study uses financial data from 872 Chinese listed companies (2018–2022). It tests the triple dividend effect and threshold effect of China’s environmental protection tax (EPT) using high-dimensional fixed effects models and panel threshold models. We document that (1) EPT creates an environmental dividend for Chinese listed companies. It significantly reduces pollution emissions. A 1-unit tax increase reduces LnTPPE by 2.5%. (2) EPT creates a significant innovation dividend. It forces enterprises to improve the quality of authorized patents. A 1-unit tax increase raises patent technological complexity by 0.79%. (3) EPT creates an economic dividend. It significantly improves firm performance. A 1-unit tax increase raises relative corporate revenue by 38.1%. (4) EPT exerts significant threshold effects on micro-level triple dividend outcomes among Chinese listed companies. A heterogeneity analysis shows significant differences in threshold effects between non-heavily polluting and heavily polluting industries. This study confirms that China’s EPT generates a micro-level triple dividend effect alongside coexisting threshold effects for listed companies. This provides literature references for China to design and implement differentiated policies and offers a quantitative empirical case for implementing globally sustainable EPT strategies. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

This study examines the complex relationship between environmental, social, and governance (ESG) and financial performance in the automotive industry, with a particular focus on how financial constraints shape this relationship. Using a global data set for the period 2008 to 2023 and employing a range of panel data techniques, including those addressing endogeneity concerns, we find that higher ESG scores positively affect financial performance. Specifically, a one-point rise in ESG score corresponds to an estimated 1–1.7% increase in the market-to-book ratio, with the effect reaching approximately 1.6% for firms facing financial constraints. These findings highlight the economic significance of ESG engagement, particularly for resource-constrained companies. The novelty of this study is that it focuses on the automotive sector, an industry with limited ESG-specific research, and that it makes a theoretical contribution by linking ESG performance outcomes to financial constraints, an angle largely overlooked in prior research. The findings offer critical policy insights, emphasizing the strategic importance of ESG initiatives for value creation under varying financial conditions. Full article

Public–private partnership (PPP) logistics infrastructure projects have become increasingly prevalent globally. Consequently, the effective management of these projects as their concession periods expire presents a crucial challenge for governments, vital for the sustainable management of PPP logistics infrastructure. This study addresses this challenge by focusing on the pivotal post-concession decision: whether the government should self-operate the mature logistics infrastructure or re-delegate its management to a private entity. Our theoretical model, built on a principal–agent framework, first establishes a social welfare baseline under government self-operation and then analyzes delegated operation under symmetric information, identifying efficiency frontiers. Under symmetric information, we find that government self-operation is more advantageous when its own operational efficiency is sufficiently high, irrespective of the private enterprise’s efficiency; conversely, delegating to an efficient private enterprise is optimal only when government operational efficiency is low. We also demonstrate that if the government can directly specify the demand quantity and service level and delegates operation via a fixed fee, the enterprise can be incentivized to align with the social optimum. However, under asymmetric information, potential welfare gains from delegation are inevitably offset by informational rent and output distortion. We further uncover non-monotonic impacts of parameters like the proportion of low-cost firms on social welfare loss and demonstrate how information asymmetry can indirectly compromise the long-term resilience of the infrastructure. Ultimately, our work asserts that delegation is only superior if its potential efficiency gains sufficiently offset the inherent losses stemming from information asymmetry. Full article

Globalization and market saturation have led Portuguese textile companies to seek international markets not only for growth but also to contribute to their country’s international image. This study aims to explore how the internationalization of the Portuguese textile sector into the Chinese market contributes to Portugal’s destination image and identify the critical success factors in this process. The research follows an inductive, qualitative methodology based on semi-structured interviews with two groups of companies: those already operating in China (n = 5) and those preparing to enter the market (n = 5). The interviews were thematically analyzed to extract key patterns and insights. The findings reveal that successful companies operate in the luxury segment, rely on prior international experience, and often use local intermediaries. Firms planning to internationalize highlight quality differentiation, brand authenticity, and innovation as strategic advantages. These insights support the role of niche positioning and cultural adaptation in building both commercial success and a refined international image of Portugal. This study contributes to the literature by linking internationalization and destination branding through industry-specific case evidence and offers practical implications for managers targeting emerging markets like China. Full article

In the era of climate change, the strategic importance of forestry products for sustainable development is increasingly recognized. Amid a global resurgence of industrial policy aimed at addressing environmental challenges, this study investigates the impact of China’s central and provincial green industrial policies on the export competitiveness of wood-processing enterprises. Utilizing firm-level data from the China Industrial Enterprise Database and China Customs Export Database (2000–2013), we apply a double machine learning (DML) approach and construct a heterogeneous competitiveness model to evaluate policy effects along two dimensions: export quantity (volume and intensity) and export quality (product complexity and consumer-perceived quality). Our findings reveal a clear dichotomy in policy outcomes. While industrial policies have significantly improved export product complexity—reflecting China’s comparative advantage in labor-intensive production—they have had limited or even negative effects on export volume, intensity, and product quality. This suggests that current policy frameworks disproportionately reward horizontal innovation (product diversification) while neglecting vertical upgrading (quality enhancement), thereby hindering comprehensive export performance gains. Those results highlight the need for more balanced and targeted policy design. By aligning industrial policy instruments with both complexity and quality objectives, policymakers can better support the sustainable transformation of China’s forestry sector and enhance its competitiveness in global value chains. Full article

Global supply chains face unprecedented challenges from geopolitical conflicts, climate change, economic volatility, and technological disruptions, highlighting the critical role of supply chain resilience as a core strategy for firms to maintain stability and competitive advantage. Grounded in the resource-based view and dynamic capability theory, this study examines how supply chain capability—that is, entrepreneurial leadership, collaborative capability, and digital transformation—enhances resilience, which mediates its impact on performance. Using structural equation modeling on survey data from Chinese firms, we find that resilience, comprising absorptive, reactive, and recovery capability, significantly mediates the relationship between supply chain capability and performance. Environmental uncertainty moderates this relationship, particularly in highly uncertain contexts, where resilience becomes a key driver of competitive advantage. Theoretically, this study extends dynamic capability theory by disaggregating resilience and exploring its mediating role. Practically, it emphasizes strengthening entrepreneurial leadership, collaborative capability, and digital transformation to improve resilience and performance in uncertain environments. Full article

Globalization has greatly changed the way logistics firms function, improving speed, accuracy, and efficiency in everything from logistic management to warehousing. Robotics and automation technologies driven by artificial intelligence improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging kinds, and order profiles. Nevertheless, more research is still needed to fully comprehend how automation has affected logistics and how it has evolved. In addition, to date, no scholarly work has provided a thorough analysis of particular automated logistic process automation strategies used by Lithuanian businesses. Although many of the assessments that are currently available in this field offer valuable insights, they are frequently overly broad. In order to tackle this problem, we conducted a methodical study that attempts to offer a strong and pertinent basis, focusing on the automation of logistics processes that are used in supply chain management together with artificial intelligence. This study’s objective was to examine conditions for increasing logistics automation processes in Lithuanian logistic companies. The novelty of this article is the consideration of the main factors influencing the automation of logistics processes, which include the key drivers of AI-powered warehouse automation processes to evaluate the real level of automation. Full article

Against the backdrop of the accelerated transition to sustainable development in global emerging markets, the synergistic mechanism between environmental governance innovation and corporate green transformation has become a key issue in realizing high-quality development. As the world’s largest emerging economy, China’s new Environmental Protection Law (EPL), implemented in 2015, has promoted green technology innovation and performance improvement of heavily polluting enterprises by strengthening environmental regulation. This paper takes Chinese A-share listed companies as samples from 2012–2023, treats the EPL as a quasi-natural experiment, and applies the DID method to explore the path of its impact on the performance of heavily polluting firms, with a focus on analyzing the mediating effect of green technological innovation and the moderating role of firm size and regional differences. The study revealed the following findings: the implementation of the EPL significantly improves the performance of heavily polluting enterprises, which verifies the applicability of “Porter’s hypothesis” in emerging markets; green technological innovation plays a partly intermediary role in the process of policy affecting enterprise performance, indicating that environmental regulation achieves win–win economic and environmental benefits by driving the innovation compensation mechanism; and there is significant heterogeneity in policy effects, with large-scale firms and firms in the eastern region experiencing more pronounced performance improvements, reflecting differences in resource endowments and institutional implementation strength within emerging markets. This study provides empirical evidence for emerging market countries to optimize their environmental governance policies and construct a “regulation–innovation–performance” synergistic mechanism, which will help green economic transformation and ecological civilization construction. Full article

This study examines the effects of the International Maritime Organization’s (IMO) market-based measures, with a particular focus on the greenhouse gas (GHG) levy and on the financial and operational performance of Korean shipping companies. The analysis estimates that these companies, which play a vital role in global trade, consume approximately 9211 kilotons of fuel annually and emit 28.5 million tons of carbon dioxide. Under the lowest proposed carbon tax scenario, the financial burden on these companies is estimated at approximately KRW 1.07 trillion, resulting in an 8.8% reduction in net profit, a 2.4% decrease in return on equity (ROE), and a 1.1% decline in return on assets (ROA). Conversely, under the highest carbon tax scenario, costs rise to KRW 4.89 trillion, leading to a significant 40.2% decrease in net profit, thereby posing a serious threat to the financial stability and competitiveness of these firms. These findings underscore the urgent need for strategic policy interventions to mitigate the financial impact of carbon taxation while promoting both environmental sustainability and economic resilience in the maritime sector. Full article

In Europe, corporate sustainability reporting through the double materiality assessment was formally introduced with the Corporate Sustainability Reporting Directive in response to the European Sustainability Reporting Standards. The double materiality assessment is essential not only to determine the scope of corporate sustainability reporting but also to guide companies toward an efficient allocation of resources and shape corporate sustainability strategies. However, although EFRAG represents the technical adviser of the European Commission, there are numerous “interoperable” standards related to the assessment of double materiality, including the Global Reporting Initiative (GRI), or UNI 11919-1:2023. This research intends to systematically analyze similarities and divergences between the most widespread double materiality assessment standards at the global scale, highlighting their strengths and weaknesses and trying to identify a comparable path toward the creation of a set of common guidelines. This analysis is carried out through the systematic study of seven standards and by answering nine questions ranging from generic ones, such as “what is the concept of double materiality?”, to more technical questions like “does the standard identify thresholds?”, but adding original prospects such as “does the standard refer to different types of capital?”. Findings highlight that EFRAG, UNI 11919-1:2023, and GRI represent the most complete and least-discretionary standards, but some methodological aspects need to be enhanced. In the double materiality assessment, companies must identify key stakeholders, material topics and material risks, and must develop the double materiality matrix, promoting transparent disclosure, continuous monitoring, and stakeholders’ engagement. While comparability is principally required among companies operating within the same sector and of similar size, this does not preclude the possibility of comparing firms across different sectors with respect to specific indicators, when appropriate or necessary. Full article