Community-based natural resource governance is often promoted in the name of sustainability, yet long-term outcomes remain elusive. This study examines the impact of various types of trust as the underpinning factor for the long-term resilience of collaborative resource governance. Using a four-type trust framework, dispositional, affinitive, rational, and procedural, the study compares trust trajectories across the prior to-, during-, and post-project phases of matsutake mushroom management in two communities within the S National Protected Area, Yunnan Province, China. Drawing on qualitative interviews and comparative case analysis, the study finds that while neither community sustained co-management mechanisms in full, X Village retained partial continuity through embedded procedural and rational trust. In contrast, Y village experienced accelerated institutional decline after project withdrawal, as market changes and weak enforcement undermined collective norms and trust in formal structures. These findings underscore that institutional resilience is shaped not only by institutional design but also by the evolution and interaction of trust types over time. By tracing trust trajectories across project stages, this study contributes a micro-level explanation of how trust mediates post-project governance outcomes. It emphasizes the importance of layering trust and embedding it within local social and institutional practices. The findings highlight the need to consider how co-management strategies can support trust continuity after project withdrawal, particularly by acknowledging the underlying structural conditions that shape trust resilience.

Traditional production function theory treats executive characteristics as exogenous fac-tors, failing to capture how managerial heterogeneity dynamically influences productiv-ity. This study integrates Upper Echelon Theory and Dynamic Capabilities Theory to develop an endogenous production function framework where executive attributes serve as dynamic parameters reshaping capital and labor elasticities. Using fixed effect-s panel-data models, we analyze Chinese listed companies, examining how executives’ education, experience, and compensation interact with production parameters. Re-sults show executive characteristics significantly moderate factor productivity: higher education and board independence enhance capital productivity, while compensation intensity transforms labor elasticity from negative to strongly positive values. Hetero-geneity analysis reveals effects vary across ownership types and technology intensity, with technology-intensive firms showing strongest responsiveness. This research recon-ceptualizes executives as active architects of production processes, provides a rigorous framework for endogenizing management factors, and offers evidence-based guidance for corporate governance and talent strategy optimization.

Research Question/Issue: This study investigates whether and how political connections affect customer complaints in the Chinese market, using a comprehensive dataset from the country’s largest online complaint platform. Research Findings/Insights: Analyzing 22,644 firm-year observations from 2018 to 2023, we find that politically connected firms experience significantly fewer customer complaints. A one-unit increase in political connection strength is associated with a 9% reduction in complaints relative to the sample mean. This effect operates through two primary mechanisms: a reputation-motivation channel and a financial resource channel. The mitigating effect is more pronounced for firms in highly marketized regions, those with higher advertising expenditures, companies facing greater earnings pressure, and those with lower tangible asset ratios. Theoretical/Academic Implications: Our study contributes to the literature on political connections and corporate governance by demonstrating how political capital translates into tangible consumer experience advantages. It also advances research on the determinants of customer complaints by highlighting the role of internal governance mechanisms, particularly managerial political ties. Our findings support the Corporate Reputation and Financial Resource Hypotheses while challenging alternative explanations based on regulatory shielding or managerial complacency. Practitioner/Policy Implications: For corporate leaders, our results underscore the importance of reputation management and quality investment, particularly when political connections are absent. Policymakers should consider strengthening public monitoring institutions to reinforce reputational incentives across markets. Investors may use customer complaints as an indicator of product quality and operational stability in their investment decisions.

Purpose – Estimates disclosure of human capital management for Chinese listed companies. Investigate the patterns ofthe disclosure of human capital management acrossindustries and regions. Examine the determinants of human capital management disclosure in China. Examine the association between human capital management disclosure and firm performance. Design/methodology/approach – We employ natural language processing techniques on annual reports’ management discussion and analysissections.We construct exposuremeasuresforten human capitalmanagement dimensions and synthesize them into one comprehensive measure of human capital management disclosure. We conduct empirical analysis on the measure using a sample of Chinese listed companies during 2009–2022. Findings – We construct a measure of human capital management disclosure for 5,153 Chinese companies during 2009–2022. We find that firms with high HCM disclosure are more labor intensive and have more cash holdings and R&D expenditure but have lower sales growth, market-to-book ratio and leverage. HCM disclosure is associated with better future accounting performance but poor future market valuation. There are substantial variations in HCM disclosure across industries, geographic regions and ownership types. HCM disclosure has increased significantly during the COVID-19 pandemic. Social implications – The increased HCM disclosure and its association with firm operating performance and market valuation indicate the relevance of HCM in corporate management and underscore the need for more robust and standardized disclosure of HCM in China. Our findingssupport recent regulatory efforts by CSRC to enhance the transparency and accountability in HCM disclosures and advocate for more explicit and specific HCM disclosure requirements in the future. Originality/value – We propose a quantitative measure of human capital management disclosure, which can be modified to apply to other markets. We construct a comprehensive, ready-to-use dataset for HCM disclosure for Chinese listed companies and conduct descriptive analysis on the dataset. We identify the patterns of human capital management disclosure and its determinants in China.

The essay delves into the impact of environmental uncertainty on asymmetric mispricing utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact, and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improve he efficiency of emerging capital markets.

This study examines the role of financial derivatives in managing operational and financial risks within China's chemical manufacturing sector. While prior research has primarily focused on financial determinants of hedging decisions, we highlight the significant influence of operational metrics—particularly inventory levels and turnover rates—in shaping firms’ engagement in derivatives markets. Drawing from a sample of 289 publicly listed chemical firms from 2016 to 2022, we employ probit regression and K-means clustering to explore how operational and financial factors jointly determine derivatives adoption. Our empirical results reveal that operational metrics have a non-negligible impact on hedging decisions. Specifically, inventory and turnover rates emerge as primary determinants of firms' initiatives, while pre-tax operating profit remains significant from a financial perspective. The moderation analysis of cash flow reveals that financially constrained firms prioritize derivatives for operational risk mitigation, while resource-abundant firms employ them selectively for strategic optimization. Furthermore, our robustness tests, which control for geographical distinctions and the COVID-19 effect, confirm that firm-specific operational characteristics consistently dominate firms' hedging decisions despite regional heterogeneity. Finally, clustering analysis underscores the interplay between operational efficiency and capital robustness, showing that firms exhibiting superior operational efficiency and capital robustness demonstrate higher engagement in derivatives hedging. These findings contribute to the corporate risk management literature by expounding on the primacy of operational considerations in derivatives usage, particularly in asset-intensive industries. The study also provides practical implications for manufacturing firms navigating volatile market conditions, emphasizing that integrating operational and financial strategies is crucial for effective risk management.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

This study investigates how venture capital (VC) reputation affects initial public offering (IPO) exits in the Chinese VC market using a two-sided matching mechanism. Research that distinguishes the sorting and influence effects of VCs in the Chinese market is lacking. To address this gap, Chinese VC transaction data, comprising 3,606 VC firms and 8,173 investment transactions, was used to construct a structural econometric model. The Markov Chain Monte Carlo Bayesian estimation techniques were employed to identify the sorting and influence effects of VC reputation. We demonstrate that the likelihood of IPO exits is considerably increased by VC reputation, whereas historical investment experience has a dampening effect on exit outcomes. The IPO success rates are significantly higher for firms in the biotechnology, electronics, medical, and late-stage industries. The difficulty of IPO exits increases with investment age. Compared to influence effects, sorting effects were the dominant mechanism. VCs with a high reputation systematically selected firms with potential advantages, such as high-quality management teams, to promote IPO success. This study’s novelty lies in its application of an endogenous two-sided matching solution to the Chinese VC market. Using a structural model, we discovered the importance of the reputation sorting effect in the Chinese VC market and refined the VC’s investment preferences in high-tech industries. This study’s practical significance lies in the findings that enterprises must pay attention to the sorting capabilities of VC institutions, the government can guide capital flows to efficient exit industries, and VC institutions should optimize the resource allocation structure.