This paper maps how management scholarship has taken up the United Nations' Sustainable Development Goals (SDGs) across the past two decades,with a particular focus on how Management and Organization Review(MOR) compares to 18 flagship journals in accounting,finance,management,marketing,and operations. Building on a 55-year,18-journal dataset,the authors zero in on 2005—2024—the decade before and after the SDGs' 2015 launch—and add MOR as a 19th journal to assess whether Chinese management research has been especially receptive to SDG-oriented work

While conventional sustainability models prioritize environmental and economic factors,they  largely neglect the critical dimension of human capital.The provided commentary explores this overlooked aspect by synthesizing a broad range of research to develop and systematically analyze the concept of human capital sustainability (HCS).The work argues that while corporate rhetoric often labels employees as their “most valuable resource”,organizational systems rarely treat the workforce with the long-term stewardship given to natural or financial capital.The analysis unpacks this concept from two distinct but interconnected viewpoints:the institutional perspective,which focuses on societal policies and systemic risks,and the managerial perspective,which examines workplace conditions and psychological mechanisms.While the institutional perspective diagnoses the large-scale societal problem,the managerial perspective offers crucial insights into how these problems manifest within organizations through daily practices.


From an institutional perspective,the societal costs of unsustainable work practices are staggering.Global data indicate an estimated 3 million fatalities from occupational causes annually,with the economic burden of such incidents costing nearly 4% of the world's Gross Domestic Product.In the United States,the incidence rate remains significant despite a downward trend,translating to a 75% chance of an individual experiencing a workplace injury over a 30-year career.These systemic harms are linked to specific institutional risk factors,which are often embedded in public policy,legal frameworks,and widespread economic practices.For example,a study found that legislation limiting shareholder litigation rights was followed by a significant 28% increase in workplace injury rates,suggesting that diminished oversight can lead to a reduced focus on employee safety.


Beyond regulatory oversight,other institutional risks directly impact employee well-being.A primary concern is inadequate health insurance,as underinsured workers often face higher fatality rates.In contrast,studies have shown that gaining universal health coverage can reduce mortality rates by a remarkable 20%.In addition to health coverage,job security itself is a critical factor,as layoffs also have severe health consequences,with studies reporting a 44% increase in mortality rates in the years following job loss and a dramatic 600% increase in violent behavior.Work-family conflict,driven by excessive hours like the “996” culture,is a significant contributor to poor health,increasing hypertension risk by 29%.Furthermore,organizational inequality creates profound health disparities,with the landmark “Whitehall Studies”demonstrating that employees in the lowest hierarchical grades have mortality rates nearly three times higher than those in top positions,a gap attributed to psychosocial factors like job control.These factors collectively are estimated to cause approximately 120000 excess deaths and $180 billion in additional healthcare costs each year in the U.S.alone.


Shifting to the managerial perspective,the commentary examines how specific management practices and workplace conditions directly impact employees.A key indicator of unsustainable management is employee turnover,which carries substantial hidden costs,with one longitudinal study finding that productivity declined for seven months following a manager-employee departure.Common policies like pay-for-performance,while intended to motivate,have been linked to a more than 5% increase in the use of anxiety and depression medication.Workplace aggression and incivility are also identified as a potent threat.Experimental research has proven that even mild rudeness significantly impairs cognitive functions like memory and creativity,creating a hidden tax on employee productivity and innovation,and in high-stakes medical simulations,it has led to critical performance errors.


The commentary delves into the cognitive mechanisms behind these impacts.From a cognitive psychology standpoint,rudeness depletes finite working memory resources as employees are forced to interpret the ambiguous threat.Beyond individual cognition,incivility also harms team performance by reducing “social value orientation”,causing individuals to become self-focused and less effective at sharing information.The negative effects extend beyond direct victims; studies show that even witnesses to workplace injuries can exhibit declines in cognitive functioning,such as slower processing speed and reduced impulse control,which in turn predict future injury risk.These findings underscore that managerial practices have measurable and often unconscious physiological and cognitive consequences for the workforce.


Building on this analysis,the commentary proposes a forward-looking research agenda.The central challenge is to reconcile the tension between short-term organizational pressures and the long-term viability of the workforce.The agenda calls for addressing emerging challenges that threaten HCS,including the impact of artificial intelligence (AI) on employment security and workload,the precarity of the gig economy,and the needs of an aging workforce.Future work should also apply theoretical lenses like the Conservation of Resources (COR) theory to understand how employees dynamically allocate personal resources over time under different work conditions,providing a richer model of employee strain and recovery.Furthermore,practical interventions like on-site health services and no-layoff policies warrant further investigation.


In conclusion,the work advocates for a paradigm shift toward a more proactive and cognitively informed approach to designing work systems.It argues that HCS is not a peripheral concern but a core component of the overall sustainability strategy.The findings highlight the urgent need to integrate human well-being into organizational and societal priorities to foster work environments that support both high performance and the long-term health and sustainability of the workforce

This lecture discusses the emergence and implications of the Open Science Framework (OSF) Movement within the fields of management, industrial-organizational psychology, and related disciplines. The concerns over questionable research practices (e.g., replication failure, lack of transparency) have driven the adoption of Open Science practices. These practices, such as preregistration, data sharing, and transparent reporting, aim to enhance research credibility, replicability, and collaboration across the scientific community

Compared to existing academic guidelines such as Transparency and Openness Promotion (TOP) Guidelines, OSF empowers researchers and practitioners with more flexibility to adopt Open Science practices based on their own conditions. Examples from the Journal of Applied Psychology illustrate practical implementation, including voluntary data disclosure and ethical adjustments during peer review. 


Challenges remain and ought to be addressed, such as resistance from scholars wary of increased workload or methodological scrutiny. The lecture underscores the need for discipline-specific adaptations, acknowledging unique barriers in management research (e.g.,proprietary data constraints). Additionally, it calls for further exploration of how different predictors shape attitudes toward Open Science. Ultimately, this lecture advocates for a balanced approach:promoting openness while respecting contextual limitations. By fostering transparency and collaboration, Open Science is positioned not only as a corrective to questionable practices but also as a catalyst for robust, cumulative knowledge-building in management studies

The study offers a systematic and comparative framework of three ideal-typical models of global artificial intelligence (AI) governance: Paternal Prevention (exemplified by the European Union),Guardian Supervision (characteristic of the United States),and Companion Regulation (manifested in differing forms in China and  the United Kingdom). Through in-depth analysis across four key governance dimensions—legal tools and policy orientation,administrative enforcement and  structure,judicial review and mechanisms of checks and balances,and local experimentation and power allocation—the study reveals the normative foundations,institutional logics,and strategic approaches each model employs to balance AI innovation with risk mitigation. Particular emphasis is placed on the rising prominence of Companion Regulation as a potentially adaptive and globally influential governance path.

The Paternal Prevention model pursued by the European Union embodies a risk-averse,precautionary logic,centered on preemptively constraining AI applications that may infringe upon fundamental rights. Anchored in the binding AI Act,the EU constructs a unified,risk-tiered regulatory framework that applies directly across member states. This framework mandates ex-ante compliance measures for high-risk systems,such as human oversight and conformity assessments,while prohibiting certain high-risk uses altogether. The EU's  approach is supported by a multilevel enforcement architecture,including the European AI Board and designated national supervisory authorities,with substantial sanctioning powers. The model is undergirded by a strong judiciary capable of reviewing both administrative actions and legislative compliance,further reinforcing fundamental rights. However,the supranational and centralized nature of this model limits member states' autonomy and scope for localized experimentation,potentially constraining innovation flexibility.

By contrast,the Guardian Supervision model exemplified by the United States emphasizes post hoc oversight within a market-oriented,innovation-friendly environment. Lacking a comprehensive federal AI law,the U.S. relies on a decentralized patchwork of sector-specific regulations,supplemented by soft law instruments such as the NIST AI Risk Management Framework and executive guidance like the “Blueprint for an AI Bill of Rights”. Enforcement is fragmented across existing agencies (e.g.,FTC,FDA,EEOC),with no centralized authority for AI regulation. The judiciary intervenes only after harm has occurred,adjudicating AI-related disputes through the application of general legal principles rather than AI-specific norms. Local jurisdictions,particularly states and municipalities,serve as regulatory innovators,adopting diverse measures that reflect localized priorities but also contribute to regulatory fragmentation. This model privileges technological dynamism but raises concerns about delayed responses to systemic harms and governance incoherence.

The Companion Regulation model,observed in both China and the United Kingdom,seeks to align public governance with industry innovation through flexible,collaborative,and context-sensitive regulatory mechanisms. In the UK,this model is instantiated through a “pro-innovation” approach that emphasizes principles-based,sector-led guidance over comprehensive legislative codification. Regulators such as the Information Commissioner's Office and Financial Conduct Authority lead AI oversight within their sectors,supported by coordination platforms like the Digital Regulation Cooperation Forum. Judicial interventions,as seen in key cases on facial recognition and algorithmic bias,reinforce rights-based accountability. While the UK  system is more centralized than that of the U.S.,it still permits targeted experimentation through regulatory sandboxes and devolved competencies.

China's version of Companion Regulation is more interventionist and regulatory. It combines robust top-down mandates with strategic state—industry coordination. Regulatory instruments include binding measures for specific technologies (e.g.,generative AI,recommendation algorithms),supported by broader legal frameworks such as the Cybersecurity Law and the Personal Information Protection Law. Enforcement is led by the Cyberspace Administration of China and implemented through a vertically integrated regulatory matrix spanning multiple ministries. While judicial review plays a supplementary role,local pilot zones (e.g.,in Shanghai and Beijing) enable experimentation with regulatory approaches under central guidance. Successful local practices are often scaled nationally,reflecting a model of iterative governance rooted in strong administrative capacity.

This tripartite framework also applies to understanding other countries in global AI governance. For example,South Korea,Bahrain,Brazil,Canada,and Turkey,as well as many international forums,tend toward EU-style Paternal Prevention,while India,Saudi Arabia,the UAE,and Israel are closer to U.S.-style Guardian Supervision,and Singapore,Japan,Australia,and New Zealand exhibit Companion Regulation characteristics similar to those of the UK and China.

The study concludes that these three models represent divergent responses to the governance challenges posed by AI's rapid development and social entrenchment. The EU model prioritizes legal certainty and rights protection through preemptive regulation; the U.S. approach champions innovation and institutional pluralism but often lags in anticipatory oversight; the China's and UK pathway,through different institutional arrangements,attempt to harmonize regulatory responsiveness with developmental goals. Among these,Companion Regulation emerges as a particularly salient alternative,offering a dynamic balance between flexibility and control. Its success,however,depends on the state's capacity to deploy technical expertise,coordinate across sectors,and adapt regulatory strategies in real time. As AI technologies continue to evolve,this model—grounded in adaptive governance and collaborative oversight—may offer a more effective pathway toward a responsible and sustainable AI future

Against the backdrop of the deep integration of the digital economy into the public governance system,public data,as a strategic infrastructure for the modernization of national governance,requires effective governance,which  is not only a core means to break down “data silos” and “flow barriers” but also a key support for promoting institutional innovation and the market-oriented allocation of data elements. The 2024 “Guiding Opinions of the General Office of the Communist Party of China Central Committee and the General Office of the State Council on Accelerating the Development and Utilization of Public Data Resources” clearly put forward the goal of public data circulation under the premise of security and controllability. However,in practice,the problem of governance failure caused by interest conflicts among multiple subjects is prominent:regulators face incentive imbalances,with a non-linear relationship between regulatory intensity and the willingness of suppliers to share; suppliers face a mismatch between costs and benefits,as costs such as data desensitization and storage squeeze the motivation to share; demanders face insufficient compliance constraints,leading to a negative cycle of illegal use and the shifting of governance costs. These contradictions highlight the urgency and necessity of optimizing public data governance strategies.


Based on evolutionary game theory,this paper constructs a tripartite dynamic game model involving public data regulators (strategies:active regulation / passive regulation),suppliers (strategies:high sharing / low sharing),and demanders (strategies:high compliance / low compliance). Through numerical simulation using Matlab2024a,it systematically analyzes the impact mechanism of core parameters on system equilibrium. The model design strictly follows the logic of “bounded rationality-dynamic feedback-policy intervention”,incorporating key variables such as regulatory revenue (α,including incremental social benefits),regulators' own revenue (ω,such as fiscal rewards),collaborative performance bonuses (φ,additional benefits from tripartite collaboration),supply revenue (δ,direct benefits for suppliers from sharing data),supply costs (η,costs such as data cleaning and desensitization),and demand penalties (μ,intensity of punishment for illegal use) into the revenue function. It deduces the system's evolutionary trajectory through replicator dynamics equations and determines the stability of equilibrium points using the Jacobian matrix.


Simulation results show that, firstly,core incentive and constraint parameters play a decisive role in the direction of equilibrium. Increasing total regulatory revenue (α+ω+φ) can cover regulators' resource input costs (γ),stimulating their motivation for active regulation; increasing supply revenue (δ) can offset suppliers' sharing costs (η),significantly enhancing the willingness for high sharing; strengthening demand penalties (μ) can make illegal costs higher than compliance costs (λ),prompting demanders to shift to high compliance behaviors. The combined effect of these three factors can drive the system to converge to a benign evolutionary stable strategy (ESS) of “high regulation-high sharing-high compliance”. Secondly,the rise in supply costs (η) has a significant inhibitory effect:when η exceeds supply revenue (δ),suppliers' enthusiasm for sharing drops sharply,and the system tends to fall into an inefficient locked state of “low sharing-low compliance”,making it difficult to realize data value. Thirdly,initial conditions have a path-dependent impact on governance effectiveness:if there is a lack of sufficient incentives (such as low ω and insufficient δ) or constraints (such as weak μ) in the early stage of reform,the system may quickly converge to a low-level equilibrium (E1(0,0,0)),and subsequent reversal would require exponential institutional costs,highlighting the importance of the policy window.


The research innovation lies in breaking through the limitations of traditional static equilibrium analysis,deeply integrating abstract simulation parameters with concrete institutional tools,and constructing a closed-loop optimization framework of “parameters-tools-goals”. For regulators,it proposes to increase α+ω+φ through fiscal performance linkage (such as incorporating regulatory effectiveness into KPIs) and reputation constraints (such as inter-departmental excellence evaluations) to strengthen regulatory motivation. For suppliers,it suggests enhancing δ through benefit distribution mechanisms (such as data usage sharing) and data productization design (such as API-based services),while reducing η through common technology platforms (such as privacy computing platforms) and process reengineering (such as “one assessment,multiple reuses”) to resolve the cost-benefit mismatch. For demanders,it advocates establishing a hierarchical punishment system (such as fines,credit penalties,and data usage bans) and a compliance incentive mechanism (such as compliance whitelists and process facilitation) to stabilize μ and reduce the space for illegal activities.


This study not only theoretically reveals the dynamic evolutionary laws of multi-agent strategic interactions in public data governance,addressing the core issues of interest conflicts and institutional coordination but also provides an operable path for formulating precise governance policies in practice. By dynamically adjusting parameters and matching institutional tools,it can achieve Pareto improvements in data value release and risk control,offering important theoretical support and practical references for the construction of digital governments and the market-oriented reform of data elements

This study is grounded in the rapidly evolving context of the digital economy and aims to revisit and extend the traditional New Keynesian macroeconomic framework. Specifically,it explores how the rise of online sales channels affects price stickiness,the transmission mechanism of monetary policy,and overall social welfare. With the widespread application of digital technologies—such as the internet,big data,cloud computing,and artificial intelligence—the digital economy has become a key engine driving China's economic and social development,profoundly transforming traditional modes of production and consumption. In particular,the rapid expansion of major e-commerce platforms such as Taobao,Tmall,and JD.com has broken the temporal and spatial constraints of traditional retail,reduced information asymmetries between firms and consumers,and significantly improved the flexibility of price adjustment and resource allocation efficiency. By 2024,China's online retail sales reached RMB 15.5 trillion,accounting for 26.8% of total retail sales of consumer goods,maintaining its position as the world's largest online retail market for 12 consecutive years and becoming an increasingly important driver of economic growth.


However,this profound transformation also presents challenges to conventional macroeconomic theories and policy frameworks. One of the core assumptions of New Keynesian macroeconomics is price stickiness—i.e.,the sluggish adjustment of prices in response to changes in market conditions. This assumption underpins the short-run non-neutrality of monetary policy and its effectiveness in stabilizing economic fluctuations. Yet,the development of the digital economy significantly reduces price stickiness in online markets,potentially weakening the effectiveness of monetary policy and increasing inflation volatility. At the same time,improvements in production efficiency and reductions in search costs enabled by digital technologies may offset inflationary pressures and enhance the transmission of monetary policy.


Using quarterly data from A-share listed companies in China from 2015 to 2019,this paper empirically investigates the heterogeneous effects of monetary policy shocks on online and offline sales via the local projection method. The results show that,under both quantitative and price-based expansionary monetary policy shocks,the year-over-year growth rate of online sales is consistently higher than that of offline sales. To provide a deeper explanation for these empirical findings,we construct a dynamic New Keynesian DSGE model that incorporates both online and offline sectors. In the model,the online sector,due to the adoption of digital technologies,enjoys lower search costs,higher production efficiency,and lower price stickiness compared to the offline sector. Through numerical simulation,we find that the relative strength of the price stickiness and search cost mechanisms determines the differential impact of monetary policy on online versus offline sales. On the one hand,lower price stickiness attenuates the stimulus effect of monetary policy on online sales; on the other hand,improvements in production efficiency and reductions in search costs can counteract,and under certain conditions even outweigh,the negative effects of reduced price stickiness—thus making online sales more responsive to monetary policy shocks. Specifically,when the labor output elasticity in the online sector exceeds a certain threshold,the search cost mechanism becomes dominant,and expansionary monetary policy significantly boosts online sales growth.


Furthermore,this paper examines the welfare implications of technology shocks and the optimal design of monetary policy rules. By incorporating a welfare loss function,we find that the development of the digital economy—via lower search costs and higher production efficiency—substantially improves social welfare. Compared to a counterfactual scenario without digital economy development,welfare increases by approximately 6.54%. In addition,our findings suggest that as the digital economy expands,monetary policy should place greater emphasis on stabilizing output fluctuations rather than focusing solely on inflation volatility. This implies that in the digital era,central banks should adjust their policy rules to better accommodate the cost advantages and efficiency gains brought by digital transformation.


The findings of this paper make the following contributions. First,on the theoretical front,we go beyond existing literature that primarily focuses on the negative implications of reduced price stickiness for monetary policy effectiveness. By introducing new perspectives related to declining search costs and rising production efficiency,this study re-evaluates the monetary transmission mechanism in the digital economy. We show that the cost advantages brought by digital transformation may not only offset the negative effects of reduced price stickiness,but also enhance monetary policy effectiveness under certain conditions—thereby enriching the applicability and explanatory power of New Keynesian macroeconomic theory in the digital age. Second,in terms of model innovation,this study builds on Glocker and Piribauer (2021) by developing a more refined and realistic two-sector DSGE model that explicitly captures the cost advantages and productivity enhancements of the online sector driven by digital technology adoption. Through parameter calibration and numerical simulation,we successfully account for the empirical observation that online sales are more responsive to monetary policy shocks,offering a solid theoretical framework and methodological reference for future research. Third,from a policy perspective,this study provides welfare-based analysis and discusses optimal monetary policy rules,emphasizing that in the digital economy era,monetary policy should place greater emphasis on output stabilization rather than targeting inflation alone. This conclusion offers important theoretical and practical guidance for the formulation of current and future monetary policy in China,especially in light of the continuing expansion of the digital economy and the rising share of online sales. Central banks must promptly adapt their policy frameworks and instruments to ensure macroeconomic stability and maximize social welfare

As China continues its rapid economic transformation and moves toward innovation-driven development, the mental health of entrepreneurs has become an increasingly important yet insufficiently examined factor influencing business success.In a context where entrepreneurial competition is intensifying and strategic decision-making is more complex, gaining a deeper understanding of how mental health affects entrepreneurial effectiveness is essential.Based on strategic leadership theory, this study constructs an integrated conceptual framework to investigate the role of mental health in shaping venture performance, with innovation behavior serving as a mediating mechanism and resource slack functioning as a moderating contextual factor.


To empirically examine these theoretical relationships, the study employs a dual-dataset strategy.The first dataset is derived from a comprehensive provincial-level survey conducted in Shandong Province, offering detailed insights into entrepreneurial dynamics within a major economic region of China.The second dataset is sourced from the nationally representative China Family Panel Studies (CFPS), which ensures broader generalizability and external validity across different regions and demographic groups in the country.


The empirical analysis leads to three major findings.First, entrepreneurial mental health has a significantly positive effect on venture performance.Entrepreneurs who report higher levels of mental health tend to demonstrate clearer strategic thinking, greater market sensitivity, and more consistent decision-making.These mental health advantages translate into stronger leadership performance, more effective stakeholder engagement, and greater organizational responsiveness to external changes.The cumulative impact of these factors contributes to improved business outcomes across multiple dimensions.


Second, the study identifies a counterintuitive moderating role of resource slack.Although prior literature often suggests that resource abundance strengthens organizational performance, the findings of this research indicate the opposite in the context of mental health.Specifically, when firms possess surplus resources beyond their immediate operational needs, the positive relationship between entrepreneurial mental health and venture performance is significantly weakened.This may be due to reduced pressure to act strategically, diminished urgency for innovation, or lower sensitivity to environmental threats, which together can offset the benefits that mental health would otherwise generate.
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Third, innovation behavior functions as a partial mediator in the relationship between mental health and performance, but this mediation effect is context-dependent.While the direct association between mental health and innovative behavior is not statistically significant, further analysis using bootstrap methods confirms a significant indirect effect.This suggests that mental health can influence innovation outcomes, but only when certain organizational or environmental conditions are present to support innovative action.Moreover, resource slack also moderates this indirect pathway, with the mediating role of innovative behavior becoming less effective as resource slack increases.This multilevel interaction reveals the complexity of how mental health, organizational behavior, and contextual factors jointly shape performance outcomes.


This study offers several important theoretical contributions.First, it extends strategic leadership theory by incorporating mental health into its framework within the entrepreneurial context.Traditional research in this domain has focused largely on leadership behaviors and organizational strategy, often overlooking the psychological characteristics of the entrepreneur.By demonstrating that mental health is a fundamental component of entrepreneurial human capital—shaping innovation behavior and performance—the study introduces a more comprehensive understanding of individual-level leadership traits.


Second, the findings challenge the linear assumption that positive psychological traits automatically translate into strategic action.The results show that the effect of mental health on innovation behavior depends heavily on contextual conditions, particularly the level of resource slack.Mental health significantly drives innovation when resources are constrained or moderately available, but this effect diminishes under high resource slack.This context-dependent mechanism not only enriches the theoretical understanding of how individual traits interact with organizational conditions but also offers a more dynamic interpretation of strategic leadership in entrepreneurial settings.


Third, the study contributes to the resource-based view (RBV) of the firm by highlighting a potential downside of excessive resource availability.While entrepreneurship policy in China has traditionally focused on increasing resource support—through subsidies, tax incentives, and incubator platforms—this research reveals that such overprotection may lead to policy dependency and strategic inertia.This so-called “greenhouse effect” reduces sensitivity to market opportunities and weakens the motivational role of mental health in driving innovation.In contrast, moderate resource constraints appear to sharpen entrepreneurs' strategic focus and enhance the performance-enhancing effects of good mental health.This insight provides important refinements to RBV by suggesting that intangible psychological resources such as mental health require carefully balanced organizational environments to generate their full strategic value.


Taken together, these findings carry valuable practical implications.They underscore the need to incorporate mental health as a core component in entrepreneurship support systems, particularly in high-risk, high-pressure environments.Policymakers should promote mental health services tailored to entrepreneurial challenges, while also designing resource allocation strategies that avoid excessive buffering.By fostering both internal resilience and external discipline, such interventions can help maximize the strategic and economic value of entrepreneurial mental health in China's evolving economic landscape

In the context that air,as a public resource,is difficult to exclude and lacks clearly defined property rights,free-riding behavior becomes prevalent during the governance process. This makes air pollution a typical externality issue,leading to market failure. The government,as a key supplier of environmental public goods,has a well-established theoretical foundation for its intervention in pollution control. However,in real-world governance,the motivation and capability of  government to manage pollution can be influenced by multiple factors,such as the spillover effects of environmental governance outcomes and the inherent tension between environmental constraints and administrative performance evaluations. Furthermore,the local government-led governance model often faces problems of weak enforcement and a lack of public trust. With the shift in policy approach,environmental governance is gradually evolving from a single government-led model to a multi-actor co-governance structure involving government,enterprises,and the public. Government information disclosure is increasingly regarded as a crucial institutional instrument for breaking down information barriers,activating social supervision,and enhancing governance performance. Building a co-governance framework based on information transparency has become a key direction in current policy development. This transition reflects the growing demand for institutional mechanisms that promote accountability,responsiveness,and participatory governance in tackling environmental issues.


Based on the above background,this paper investigates whether and how government information disclosure can promote improvements in pollution governance performance. The study systematically analyzes the Government Work Reports of 282 prefecture-level cities in China from 2012 to 2022. Using the Word2Vec model,semantically similar terms are identified,and word frequency is calculated to develop a city-level government information disclosure index that is both dynamic and comprehensive in scope. This index captures the evolving emphasis of local governments on information disclosure over the years. In terms of empirical methodology,we adopt a panel fixed-effects model to identify the impact of government information disclosure on the effectiveness of air pollution governance,with the annual average PM2.5 concentration serving as a proxy variable for pollution intensity. City and year fixed effects are included to control for unobservable heterogeneity across time and space. In addition,mediation analysis is employed to explore the pathways through which information disclosure affects air quality,and heterogeneity analysis is conducted under different city characteristics to identify the boundary conditions of institutional effectiveness. This multi-dimensional empirical design enables a more nuanced understanding of both the direct and indirect effects of institutional transparency on environmental outcomes.


Empirical results show that improvements in government information disclosure are significantly associated with reductions in city-level PM2.5 concentrations,indicating that government information transparency plays a positive role in promoting more effective pollution governance. Furthermore,the effects exhibit significant spatial spillovers,suggesting that information disclosure not only improves local environmental performance but also strengthens governance incentives in neighboring cities through mechanisms such as competition or imitation. Regarding the mechanisms,information disclosure enhances the observability and enforceability of environmental regulations,thereby increasing policy deterrence. At the same time,transparency reduces uncertainty and raises the expected cost of pollution,encouraging firms to increase investment in green innovation to reduce emissions. These mechanisms demonstrate that transparency is not merely symbolic but materially alters the cost-benefit calculus for local governments and enterprises alike. In addition,the study finds a substitution effect between government information disclosure and public participation. In other words,as the supply of information becomes more sufficient,the effect for the public to spontaneously engage in environmental governance diminishes. Heterogeneity analysis reveals that the policy demonstrates stronger effects in non-resource-based cities,cities with lower political-business integrity,cities with stronger digital infrastructure,and cities with less frequent government turnover. This suggests that information disclosure systems can serve a compensatory governance function,especially in areas with better technological foundations or weaker institutional environments. The effectiveness of such disclosure,therefore,hinges not only on the volume of information but also on the local socio-political and digital context.


Based on the above findings,this study proposes five policy recommendations. First,improve the institutional framework for government information disclosure by enhancing the quality and coverage of released information to support cross-regional coordination. Second,strengthen the institutional incentive effects of information transparency,encouraging local governments to intensify environmental regulation and increase enforcement transparency. Third,enhance policy clarity and stability to reduce firms' uncertainty and promote green innovation,forming a positive feedback loop driven by transparency. Fourth,foster complementary interaction between government disclosure and public participation by tailoring mechanisms to local strengths and weaknesses,thereby amplifying governance synergy. Fifth,implement context-specific information disclosure strategies that address regional disparities,such as infrastructure constraints or weak institutional foundations,to reinforce governance capacity in less-developed areas. Together,these strategies aim not only to reduce pollution but also to reinforce institutional trust,policy responsiveness,and public accountability in environmental governance. Collectively,these recommendations aim to systematically activate the governance potential of information disclosure,facilitate a multi-actor co-governance structure led by governments,supported by enterprises,and coordinated with public engagement,thereby contributing to improved air quality and the broader goal of green transformation

In the current era of globalization,Chinese enterprises are increasingly expanding their operations overseas to better utilize global resources,enhance their core competitiveness,and achieve sustainable development. This trend is supported by China's national strategy to promote high-standard opening-up and  to deepen international economic cooperation. However,the complex and volatile international economic environment,characterized by trade protectionism,geopolitical tensions,and macroeconomic uncertainties,poses significant challenges to the overseas operations of Chinese enterprises. Against this backdrop,understanding how overseas operations influence corporate expense decisions becomes crucial for ensuring the healthy and orderly development of enterprises and for implementing national strategies such as the Belt and Road Initiative.


Expense decisions are a critical component of corporate management,directly affecting resource allocation and long-term development. When operating overseas,enterprises face a more complex and uncertain environment,making the scientific nature of expense decisions essential for the success of overseas operations. Previous research has shown that firms may adjust their expense structures in response to various external factors,such as market conditions,regulatory environments,and competitive pressures. However,the specific impact of overseas operations on expense decisions remains underexplored,especially in the context of Chinese firms. This gap in the literature motivates the current study,which aims to investigate how overseas operations influence corporate expense stickiness—a measure of how expenses adjust asymmetrically in response to changes in business conditions. Understanding this relationship is important for both theoretical advancement and practical application,as it provides insights into how firms optimizes their expense management strategies in a global context.


The study employs a comprehensive dataset of Chinese A-share listed firms from 2008 to 2023 to empirically examine the relationship between overseas operations and corporate expense stickiness. The findings reveal several key insights:Firstly，The research shows that overseas operations significantly enhance a firm's ability to mitigate risks. By diversifying their operations across different markets,firms can better manage and hedge against various risks,leading to an increase in expense stickiness. This suggests that firms are more likely to maintain higher levels of expenses even when facing declines in revenue,as they anticipate potential future gains from their overseas activities. Secondly,the study further explores the impact of overseas operations in different regions. It finds that the effect of overseas operations on expense stickiness is morepronounced for firms operating in OECD countries and those operating in both OECD and non-OECD countries simultaneously. This indicates that the quality of the institutional environment and market conditions in OECD countries may play a significant role in influencing firms' expense decisions. Thirdly,The research also distinguishes between different types of expenses and finds that the impact of overseas operations is more significant on cost stickiness rather than on selling,general,and administrative (SG&A) expense stickiness. This suggests that the primary mechanism through which overseas operations influence expense stickiness is by affecting production and operational costs,rather than through changes in SG&A expenses. Finally,from the perspective of the moderating role of environmental factor,the study examines how the impact of overseas operations on expense stickiness is moderated by various environmental factors. It finds that the positive effect of overseas operations on expense stickiness is more evident during periods of high economic uncertainty,in industries with high competition,and in regions with high market segmentation. This implies that firms are more likely to adjust their expense structures in response to overseas operations when facing greater external challenges and uncertainties.


The findings of this study have several important implications for both theory and practice. Theoretically,the study enriches the literature on the economic consequences of overseas operations by providing new insights into how these operations influence corporate expense decisions. It extends the understanding of expense stickiness by highlighting the role of overseas operations as a strategic tool for risk management and resource allocation. The study also contributes to the broader literature on international business by demonstrating the complex interplay between global expansion and internal financial management.


Practically,the results offer valuable guidance for firms considering overseas expansion. They suggest that while overseas operations can enhance risk-mitigation capabilities and support long-term growth,they also require careful management of expenses to ensure financial sustainability. Firms should be aware of the potential increase in expense stickiness and adjust their strategies accordingly,especially in challenging economic environments. Additionally,the findings highlight the importance of considering regional differences and industry characteristics when making overseas investment decisions.


In conclusion,this study provides a comprehensive analysis of how overseas operations influence corporate expense decisions,shedding light on the mechanisms and conditions under which these effects occur. It offers valuable insights for both researchers and practitioners interested in understanding the financial implications of global business expansion

The frequent occurrence of firm-specific stock price crashes in China adversely affects investor wealth and economic development. Preventing financial risks and suppressing stock price crashes are of paramount importance for economic development. Existing scholarly research on stock price crash risk predominantly adopts information asymmetry and agency theory perspectives, exploring influencing factors through the perspective of “information disclosure” and “investor reaction.” However, few studies have explored the sources of bad news that trigger stock price crash risk from the perspective of enterprise operations. In reality, numerous companies ultimately face stock price crashes because of poor management and risk accumulation. The common thread among these cases is the neglect of genuine market demand and the competitive environment, indicating a lack of Market Orientation (MO).


Market orientation emphasizes focusing on customer needs and competitor actions, enhancing sustainable development and profitability through the effective integration of internal and external resources. Prior research has revealed MO's positive effects on various financial and non-financial performance outcomes, including profits, market share, and innovation. Some scholars have also pointed out the significant influence of marketing on the stock market. Nevertheless, the existing literature has not definitively answered whether and how MO affects stock price crash risk. In particular, in the current context of intense competition and rapid development of digital technologies, how does the role of MO differ?


Focusing on the above questions, this study employs a sample of A-share listed companies from 2010 to 2020. Utilizing machine learning methods to measure firm-specific MO based on MD&A texts and grounded in the Resource-Based View(RBV) and Dynamic Capabilities theory, this study investigates the impact of MO on stock price crash risk. The findings indicate that: ① MO significantly inhibits stock price crashrisk, a result that remains robust after a series of tests for robustness and endogeneity. ② Operating risk partially mediates the relationship between MO and stock price crash risk. Market-oriented enterprises can keenly perceive market dynamics, seize opportunities, and effectively integrate resources through their dynamic capabilities. This reduces the operational risks of the enterprise, thereby minimizing the generation of negative news and ultimately lowering the risk of stock price crashes. ③ This inhibitory effect is more pronounced in firms operating in highly competitive markets and those with a greater degree of digitalization. ④ Heterogeneity analysis shows that the inhibitory effect of MO on crash risk is stronger in firms with smaller size, smaller board size, and lower external audit quality. This suggests that the pathway proposed in this study serves as a substitute mechanism for the conventional route, whereby the governance level affects stock price crash risk through information disclosure.


The contributions of this study are threefold: First, this study expands the research on the economic consequences of market orientation, extending it from the performance level to the field of capital market risk. It reveals the important mechanism through which market orientation promotes financial stability by enhancing the quality of physical operations, thereby enriching the literature on“marketing-finance integration”. Second, it supplements the pathways of stock price crash risk. Unlike the focus on information concealment, this study, based on RBV and Dynamic Capabilities theory, demonstrates a new path through which MO strategy reduces crash risk by curtailing the generation of bad news itself. Finally, by incorporating the contemporary digital context, it examines the significant moderating role of digital technology in the process through which MO influences crash risk, offering practical insights for enterprises to use MO strategies and digital technologies to mitigate risks