Search Results (135)

In the context of pursuing the goal of strategic imperatives of sustainable development, the ESG performance of enterprises has become a key yardstick for measuring their comprehensive environmental contribution and economic efficiency. Enhancing ESG performance has far-reaching significance in promoting green and sustainable development of enterprises and society. Drawing on the upper echelons theory, this paper investigates the impact of the chief executive officer’s (CEO’s) green experience on corporate environmental, social, and governance (ESG) performance, utilizing a sample of publicly listed Chinese companies from 2011 to 2023. The study demonstrates that CEOs with green experience significantly enhance corporate ESG performance, a conclusion that remains consistent following a series of rigorous robustness checks. Mechanistic analysis reveals that CEOs’ green experience primarily facilitates corporate ESG performance enhancement through green innovation initiatives. Furthermore, CEO discretion amplifies the positive influence of green experience on ESG performance. Heterogeneity analysis demonstrates that the influence of the CEOs’ green experience on ESG performance is more pronounced in high-tech enterprises, in markets characterized by lower levels of competition, and in firms situated in regions exhibiting higher degrees of social trust. These findings impart both theoretical and practical implications for enhancing corporate ESG performance and offer novel strategic perspective to advance environmental stewardship, social responsibility, and corporate governance frameworks. Full article

Government support has long been viewed as a key driver of sustainable transformation and green technological progress. However, the underlying mechanisms (“how”) through which preferential policies influence green innovation, as well as the contextual conditions (“when”) that shape their effectiveness, remain insufficiently understood. Drawing on resource dependence theory, this study develops a dual-mediation framework to investigate how preferential tax policies promote both the quantity and quality of green innovation—by enhancing R&D investment as an internal mechanism and alleviating financing constraints as an external mechanism. These effects are especially salient among non-state-owned enterprises, firms in resource-constrained industries, and those situated in environmentally challenged regions—contexts that entail higher dependence on external support for sustainable development. Leveraging China’s 2017 R&D tax reduction policy as a quasi-natural experiment, this study uses a sample of high-tech small- and medium-sized enterprises (SMEs) to test the hypotheses. The findings provide robust evidence on how preferential policies contribute to corporate sustainability through green innovation and identify the conditions under which policy tools are most effective. This research offers important implications for designing targeted, sustainability-oriented innovation policies that support SMEs in transitioning toward more sustainable practices. Full article

The rapid development of the digital economy and the pursuit of green transformation are reshaping the innovation landscape of Chinese firms. However, limited attention has been paid to how digital industry agglomeration (DIA) influences corporate green innovation (CGI) at the firm level. Drawing on panel data from China’s A-share listed firms between 2017 and 2021, this study examines the differential effects of specialized agglomeration and diversified agglomeration of digital industry on CGI. The results indicate that DIA can promote CGI, with a 1% increase in DIA associated with a 1.503% increase in green innovation output. Further analysis reveals that specialized agglomeration exerts a significant positive effect, while diversified agglomeration has no evident impact. Our mechanism analysis indicates that knowledge spillovers serve as the key channel through which DIA fosters CGI. Moreover, heterogeneous effects analysis indicates that DIA exerts a stronger influence on non-high-tech enterprises and in regions where environmental regulation is less stringent. Drawing on these insights, fostering specialized digital clusters and strengthening knowledge-sharing mechanisms can help alleviate existing constraints on innovation diffusion, accelerating green innovation and supporting long-term sustainability. Full article

This study investigates the effect of the Green Finance Reform Pilot Zone Policy (GFRPZP) on corporate sustainable development performance (SDP) using a multi-period difference-in-differences (DIDs) regression model. This model incorporates control variables, reflecting firm-level characteristics and regional economic conditions. The results show that GFRPZP significantly enhances corporate SDP, with stronger effects observed among non-state-owned enterprises (Non-SOEs), companies situated in eastern regions, those in non-heavily polluting industries, and high-tech companies. Mediation analysis indicates that the policy enhances sustainable development through four main channels: improving the quality and quantity of green innovation, easing financing constraints, and increasing analyst attention. Moderation analysis further demonstrates that digital transformation and internal control strengthen the policy’s effect. Full article

Despite growing scholarly interest in digital transformation, few studies have systematically explored the mechanisms linking digital transformation capability to firm performance. This study examines both the direct and indirect effects of digital transformation capability on firm performance, offering novel insights by incorporating organizational strategic intuition and digital leadership as mediating variables. These mediators align with the emerging emphasis on strategic risk management in the literature. A survey was conducted among 620 high-tech enterprises in the Yangtze River Delta using a structured questionnaire. The data were analyzed using SPSS 23.0 for descriptive and correlational statistics, SmartPLS 4.0 for structural equation modeling (SEM), and PROCESS 4.2 for mediation analysis. The results reveal a significant direct effect of digital transformation capability on firm performance. Mediation analysis further shows that organizational strategic intuition and digital leadership each significantly mediate this relationship, and a chain mediation pathway involving both variables is also confirmed. These findings deepen our understanding of how digital transformation capability drives performance outcomes and offer practical guidance for high-tech firms seeking sustainable competitive advantages in dynamic digital environments. This study advances the theoretical discourse by clarifying the pathways through which digital transformation capability affects firm performance and provides empirical evidence to inform strategic decision-making in high-tech management. Full article

Amid the rapid development of the digital economy, data elements—emerging as a new type of production factor—are gradually becoming a key resource for enhancing corporate efficiency and promoting high-quality development. The marketization of data elements is also steadily progressing and playing an increasingly important role. Based on data from Chinese A-share listed companies spanning 2007 to 2023, this study systematically evaluates the impact of data element marketization on corporate investment efficiency using a Double Machine Learning approach. The findings reveal that data element marketization significantly improves investment efficiency. Mechanism analysis further demonstrates that such improvement is primarily driven by reduced information dispersion, enhanced risk-bearing capacity, and improved operational efficiency. Heterogeneity analysis indicates that these effects are more pronounced for firms in high-tech industries, high growth potential firms, enterprises located in regions with strong digital infrastructure, and firms experiencing overinvestment problems. This study provides empirical evidence on how the marketization of data elements in China enhances economic outcomes, improving corporate investment decisions, which could serve as a reference for other countries undergoing digital transformation. Full article

In the context of the explosive growth of the digital economy, how inter-organizational connectivity affects corporate ambidextrous innovation has emerged as a pressing issue in the current digital economy. Based on the perspectives of the innovation ecosystem and digital coupling, this paper explores the inner mechanism of this issue through structural modeling by using the data of China’s high-tech enterprise alliance cooperation from 2015 to 2022. It is found in the empirical study that the local efficiency and reach rate of the digital innovation ecosystem have an inverted U-shaped relationship with exploratory innovation, and the local efficiency and reach rate of the digital innovation ecosystem have a negative effect on firm exploitative innovation. In addition, the level of firms’ digital transformation mediates the relationship between the local efficiency, reach rate, and ambidextrous innovation. The level of market development plays a moderating role in the relationship between the local efficiency, reach rate, and ambidextrous innovation. The findings provide a theoretical basis for the digital innovation ecosystem to realize the role of a “resource pool” through structural connections, which in turn provides important guidance for the digital transformation and innovation development of high-tech enterprises. Full article

The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article

Numerous studies have examined how environmental restrictions affect innovation behavior; however, there has not been enough research focused on how public environmental concerns affect green innovation. This paper utilizes panel data of 4607 Chinese A-share listed companies (29,877 firm-year observations) over the period of 2011–2022 and constructs a dual fixed-effects model to investigate the impact of public environmental concern (PEC) on green innovation quality. Furthermore, we explore the mechanisms underlying this influence through the lenses of external pressure and internal motivation, and the moderating effect of digital transformation. The findings reveal the following: (1) Public concern about environmental issues is positively correlated with the green innovation quality. For every 1% increase in PEC, the companies’ green innovation quality will increase by 0.013%. (2) PEC forces firms to improve the green innovation quality through pressure from institutional investors, while pushing firms to boost the green innovation quality by stimulating ESG performance. (3) Digital transformation reinforces the impact of PEC on the green innovation quality. (4) PEC is more sensitive to the impact of green innovation quality in high-tech and non-heavy-polluting companies, and the enhancement effect is more pronounced in the eastern and western districts. Besides expanding the insights into the factors influencing the green innovation quality, this study also gives pragmatic guidance for governments and companies to enhance the green innovation quality, address environmental challenges, and achieve sustainable development. Full article

This paper examines the firm-level carbon intensity of 83 constituent stocks in the Hang Seng Index, constructs two distinct indexes from the 20 firms with the highest and lowest carbon intensities, and analyzes the connectedness of their annualized daily volatilities with four key external factors over the past 15 years. Our findings reveal that low-carbon stocks—often represented by high-tech and financial firms—tend to exhibit higher volatility, reflecting their more dynamic business environments and greater sensitivity to changes in revenue and profitability. In contrast, high-carbon companies, such as those in the utilities and energy sectors, display more stable demand patterns and are generally less exposed to abrupt market shocks. We also find that oil price shocks result in greater volatility spillovers for low-carbon stocks. Among external influences, the U.S. stock market and Treasury yield exert the most significant spillover effects, while crude oil prices and the U.S. dollar–Chinese yuan exchange rate act as net volatility recipients. Full article

Climate policy uncertainty (CPU) poses formidable challenges to global sustainable development and corporate strategic planning, while intelligent transformation is emerging as a pivotal enabler of organizational sustainability. Using panel data from Chinese A-share listed companies between 2011 and 2022, this study investigates the impact of climate policy uncertainty on intelligent transformation. The results indicate that CPU significantly promotes corporate intelligent transformation, a conclusion that remains robust under various sensitivity tests. Government innovation subsidies, enterprise absorption capacity, and enterprise human capital positively moderate this facilitating effect. A heterogeneity analysis reveals that the effect of CPU on intelligent transformation is more pronounced among firms in sci–tech finance pilot zones, regions with high digital financial inclusion, and those led by CEOs with banking experience. This paper contributes to the literature on climate policy uncertainty by examining its role in corporate intelligent transformation, offering actionable strategies for firms to mitigate climate risks while providing policy insights for developing economies to leverage smart technologies in addressing CPU. Full article

The Free Trade Area (FTA) strategy and the participation of enterprises in global value chains (GVCs) are important aspects of China’s high-quality economic development stage. This study matches trade data from the China Customs Import and Export database with information from listed firms in the CSMAR database, calculating the firms’ GVC embeddedness and the depth of trade agreements at the firm level. On this basis, this research employs a gravity model with fixed effects to empirically analyze the impact and mechanism of the FTA strategy on the embedding of Chinese listed firms in GVCs, utilizing data from 2000 to 2006. The results demonstrate that the FTA strategy substantially enhances the embeddedness of Chinese listed enterprises in GVCs. The heterogeneity analysis indicates that state-owned enterprises, those located in the central and western regions, manufacturing firms, and high-tech industry enterprises derive greater advantages from the FTA strategy in terms of their embeddedness in GVCs. Moreover, the mechanism analysis indicates that the FTA strategy enhances the embeddedness of enterprises in GVCs by increasing their technological innovation levels. Additionally, the internal control costs of enterprises negatively moderate the impact of the FTA strategy on their embedding in GVCs, and a “substitution effect” exists between asset operating efficiency and the FTA strategy in promoting the GVC embedding of listed firms. These findings provide empirical evidence and policy recommendations for the Chinese government to enhance the FTA strategy and sustainably improve the embeddedness of Chinese listed enterprises in GVCs. Full article

Geographic accessibility is an important determinant of the quality of interactions between firms and stakeholders and has a significant impact on the technological innovation of enterprises. By using a quasi-natural experiment implemented with China’s high-speed rail service and designing a high-speed rail network centrality indicator using social network analysis, we examine the impact of geographic accessibility on corporate technological innovation. The results show that geographic accessibility significantly promotes the technological innovation of enterprises, especially for enterprise exploratory innovation. Mechanism analysis indicates that geographic accessibility promotes enterprise technological innovation by reducing financing constraints and increasing technicians’ mobility. Cross-sectional analysis reveals that the prompting effect is more obvious in high-tech firms and firms in less-developed regions. This study enriches the research on geographic accessibility and corporate technological innovation, and has significant implications for enhancing the core competitiveness and sustainable development of enterprises. Full article

Temporary book-tax differences (BTDs) serve as critical proxies for understanding corporate earnings management and tax planning. However, the drivers of large negative BTDs (LNBTDs)—where book income falls below taxable income—remain underexplored. This study investigates the determinants and components of LNBTDs, focusing on their relationship with deferred tax assets (DTAs) and liabilities (DTLs). Utilizing hand-collected data from the tax disclosures of S&P 500 firms’ 10-K filings (2007–2023), I analyze 4685 firm-year observations to identify specific accounting items driving LNBTDs. Findings reveal that deferred revenue, goodwill impairments, R&D, CapEx, environmental obligations, pensions, contingency liabilities, leases, and receivables are significant contributors, often generating substantial DTAs due to timing mismatches between book and tax recognition. Notably, high-tech industries, like the pharmaceutical, medical, and computers and software industries, exhibit pronounced LNBTDs, driven by upfront revenue recognition for tax purposes and deferred recognition for financial reporting, capitalization, amortization and depreciation effects, and other deferred tax components. Regression analyses confirm strong associations between these components and LNBTDs, with asymmetry in reversal patterns suggesting that initial differences do not always offset symmetrically over time. While prior research emphasizes large positive BTDs and tax avoidance, this study highlights economic and industry-specific characteristics as key LNBTD drivers, with limited evidence of earnings manipulation via deferred taxes. These insights enhance the value relevance of deferred tax disclosures and offer implications for reporting standards, tax policy, and research into BTD dynamics. Full article

The influence of digital transformation on ESG performance has garnered considerable interest; however, previous research in this area has not adequately considered the influence of environmental uncertainty factors. This study utilized a dataset comprising Chinese A-share listed companies from 2009 to 2023 to explore how environmental uncertainty affects the correlation between digital transformation and ESG performance. Furthermore, we also examined potential pathways and heterogeneity. Our findings demonstrate that digital transformation significantly enhances ESG performance, with the positive effects persisting for up to three years post-implementation, although gradually diminishing in intensity. However, environmental uncertainty substantially reduces this positive impact across all pivotal technologies. Improvements in ESG performance are more pronounced in firms that are high-tech, technology-intensive, and capital-intensive and that do not produce heavy pollution. Quantile regression reveals that firms in the upper–middle ESG performance range benefit most. Our mediation analysis confirms that digital transformation enhances ESG performance by increasing firm value, media attention, and analyst coverage. Overall, this study contributes to the existing literature by providing empirical evidence of the impacts of environmental uncertainty. These findings provide strategic guidance for companies navigating digital transformation initiatives in turbulent business environments, while also offering concrete recommendations for regulatory authorities developing ESG disclosure frameworks and digital infrastructure investment priorities tailored to different uncertainty conditions. Full article