Managerial and Decision Economics最新文献

Unlocking how the emissions trading system (ETS) propels a true green transformation for industrial enterprises and mastering its complex governance require a central research frontier. By integrating partial equilibrium modeling with empirical analysis, this study robustly shows that the ETS significantly promotes the green transformation of industrial enterprises through incentivizes low-carbon innovation and alleviates resource misallocation. The further findings reveal: carbon pricing and its volatility on the green transformation have an inverted U-shaped impact; moderate levels are pivotal for success, while extreme levels prove counterproductive; organizational resilience and managerial green cognition are the key positive moderators that amplify the benefits of the ETS; and the impetus is strongest for technology-intensive, high-slack, and heavy-polluting firms. By innovatively exploring these contingent conditions, this study offers vital insights into optimizing ETS designs and stimulating targeted corporate green initiatives.

Despite growing attention to the economic consequences of CEO dishonesty, its link to corporate green innovation remains underexplored. Using a matched dataset between CEO personal information and the discredited debtor list released by the Supreme People's Court of China, this study investigates the impact of CEO dishonesty on corporate green innovation using a sample of A-share listed firms on the Shanghai and Shenzhen stock exchanges from 2010 to 2021. The results show that CEO dishonesty increases green innovation, but such efforts are largely symbolic rather than substantive. The underlying mechanism lies in heightened public scrutiny, with political connections and environmental awareness moderating the effect. The impact also differs by corporate governance quality, firm-level practices, and regional conditions. The findings suggest that reputational concerns may drive prosocial behavior among dishonest CEOs, enriching the literature on executive integrity and corporate environmental strategy.

This study examines the relationship between the “Revoke County to Urban District” (CTD) policy and the digital transformation (DT) of local enterprises in China, using panel data from A-share listed companies from 2003 to 2020. By leveraging the CTD policy as a quasi-natural experiment, a time-varying difference-in-differences (DID) model is employed, supplemented by robustness checks using propensity score matching (PSM-DID) and a generalized random forest (GRF) model. The findings reveal that the CTD policy significantly enhances enterprise DT, with the effect being more pronounced in non–state-owned enterprises, non–high-tech industries, county-level cities, and regions with high levels of urbanization and digital financial inclusion. Furthermore, a GRF-based conditional average treatment effect (CATE) analysis explores the interplay of firm characteristics—fixed asset proportion, gearing ratio, and return on assets—on the policy impact. The study identifies three core mechanisms driving DT: optimized resource allocation, improved internal controls, and enhanced human capital. These insights not only offer a theoretical framework for understanding the localized implementation of administrative reforms in China but also provide practical recommendations for designing policies to foster digital transformation.

This paper investigates how ESG disclosure affects corporate tail risk by distinguishing between disclosure quantity and quality. Using a dataset of 4044 Chinese A-share listed companies from 2011 to 2022, I employ a machine-learning approach to quantify ESG disclosure from corporate textual data, while tail risk is estimated using extreme value theory. The empirical results show that both ESG disclosure quantity and quality significantly reduce firms' tail risk. The impact is more pronounced for firms in polluting industries, those with high financial constraints, and smaller firms. I identify three potential channels: decreased ESG-related incidents, reduced information asymmetry between managers and outside investors, and enhanced stakeholder relationships. These findings highlight the importance of comprehensive and credible ESG disclosure in promoting market stability, with implications for sustainability reporting regulations and corporate risk management strategies.

To alleviate the financial burden of supporting ESG disclosures, this study introduces third-party institutions and proposes two alternative models: GFSL (Guarantee First, Subsidy Later) and SFGL (Subsidy First, Guarantee Later). Using evolutionary game theory, it analyzes strategic decisions and compares models through numerical simulations. Results show that subsidy caps and premium levels must be balanced carefully. SFGL suits cases with minimal external influence, while FGS with dynamic incentives fits strong responders. For opportunistic enterprises, SFGL is preferred. This study offers theoretical insights and practical guidance for optimizing ESG subsidy strategies and advancing sustainable finance.

After the global financial crisis, systemic risk, especially in the banking sector, has grown increasingly important, stressing the need for better risk management. This study examines how industry-level loan similarity (ILLS) among banks propagates systemic risk through indirect linkages in the Chinese banking system. We have constructed a novel interbank relationship network (ILLS) based on the loan data of 42 listed banks in China from 2013 to 2022. By employing a combination of the GARCH copula quantile regression model (GCQRM) and the dynamic spatial Durbin model (DSDM), the study validated the significant role of the ILLS in explaining interbank systemic risk spillover. Our results indicate that ILLS is a potent spillover channel: banks with similar industry-level loan portfolios exacerbate each other's distress, and the systemic risk caused by indirect spillovers through ILLS is significantly higher than the systemic risk arising from their own financial factors. According to our results, regulators should monitor the overlap in banks' industry-level loan concentration and take macroprudential measures to prevent systemic risks arising from common risk exposures.

This paper constructs a theoretical model to explore the operating strategies of generative AI firms across two markets: one directly using AI products and the other indirectly through AI-enabled industries. The model examines how pricing, mergers, and regulatory strategies are influenced by the AI flywheel effect, a virtuous cycle where AI product adoption and user data feedback enhance performance and promote further adoption. Our two-period model reveals that AI firms can boost profits by lowering prices in the direct market to acquire more usage data. However, excessive data collection per user may decrease profits, depending on the size of the indirect market. The AI flywheel effect's positive externalities and incentive issues impact both data collection strategies and pricing and merger decisions. Our research indicates that the pricing and merger decisions of firms crucially depend on the size of both markets and the rate of improvement in AI technology. If Market B is large, mergers may simultaneously reduce consumer surplus in both markets, a situation that may require regulation; whereas if Market B is small, mergers can actually increase consumer surplus, and policymakers can encourage interfirm connections. These findings provide valuable insights for balancing market efficiency and consumer welfare in the AI era.

Existing research has paid little attention to the impact of tax enforcement independence on corporate financial asset allocation. To fill this gap, we use the merger of the State Tax Bureau and Local Tax Bureau as an exogenous shock, and our DID analysis shows that greater tax enforcement independence reduces corporate financial asset holdings. The mechanism analysis shows that the effect is primarily driven by intensified liquidity constraints and enhanced corporate information disclosure. Moreover, the effect is stronger for firms with greater financing constraints and weaker corporate governance, particularly affecting short-term financial assets.

Participation in the global value chain is crucial for integrating small and medium enterprises (SMEs) into the global economy. However, participation alone does not guarantee success. Other factors may catalyze true internationalization. The digital economy and advancements in information and communication technologies have enabled SMEs to remain competitive. By leveraging digital technologies like cross-border e-commerce (CBEC), SMEs can overcome stagnation at the lower end of the value chain and enhance international market performance. Compared with traditional entry modes, CBEC offers high control and low costs, making it an effective tool for overcoming barriers to internationalization. To investigate how CBEC aids SMEs in ascending global value chains, we developed hypotheses based on organizational learning theory and tested them on a panel dataset of 781 SMEs. Regression analysis results indicate that engaging in CBEC significantly improves an SME's position in the global value chain. We also identified two mediating factors: the firm's entrepreneurial orientation and its ability to reconfigure value chains. These findings underscore the role of CBEC in enhancing the global value of SMEs, offering valuable insights for academics and SME managers alike.