Table of Content

28 September 2026, Volume 35 Issue 5 Previous Issue   

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Promotional Strategies of Competitive Duopoly Retailers Under Financial Constraints Luo Yipin, Wang Haijun, Xu Xiaodong 2026, 35 (5):  1145-1154.  doi: 10.3969/j.issn.2097-4558.2026.05.001 Abstract ( )   PDF (1960KB) ( )   PDF（mobile） (394KB) ( 6 )   Promotion is beneficial for enhancing firms’ market sales and competitive advantage. Analyzing the equilibrium strategy under promotional and financing constraints is of significant theoretical and practical importance. This paper constructs a supply chain consisting of a manufacturer, a capital-constrained retailer, and a well-capitalized retailer. It investigates wholesale pricing, order quantity, and promotional decisions to determine the optimal promotional strategy. It also examines the combined effects of capital constraints and retailer competition on the decision-making processes of each stakeholder. The results indicate that a moderate reduction in wholesale prices can incentivize retailers to increase their promotional investments, thereby expanding market demand and increasing order quantities. However, when the sensitivity coefficient of promotional investment is high, capital-constrained retailers are more. Nevertheless, a reduction in bank loan interest rates does not necessarily alleviate bankruptcy risk, as higher order quantities may increase financing risk. By strategically allocating promotional capital, capital-constrained retailers can enhance market demand while mitigating bankruptcy risk. Meanwhile, well-capitalized retailers must also consider the financial constraints faced by their competitors. Finally, although promotional investment can expand market demand, neither retailer has an incentive to invest in promotion when the capital-constrained retailer considers the potential customer demand of its competitor. Related Articles

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Regional Public Brand Licensing and Quality Inspection Strategies in Contract Farming Supply Chains Sun Yanhong, Chen Yabo, Bian Yiwen 2026, 35 (5):  1155-1168.  doi: 10.3969/j.issn.2097-4558.2026.05.002 Abstract ( )   PDF (1891KB) ( )   PDF（mobile） (386KB) ( 2 )   Based on a contract-farming supply chain comprising a regional public brand (RPB) owner and a farmer cooperative, this paper explores optimal RPB licensing and quality inspection strategies, incorporating the impacts of quality uncertainty and brand spillover effects. It examines two typical quality inspection mechanisms widely adopted in brand licensing: an authentication mechanism based on sampling and a traceability mechanism based on blockchain. Through theoretical analysis, it obtains several interesting findings. Under both quality inspection mechanisms, if the market size of the RPB owner’s on-sale product is relatively small, it is beneficial for the RPB owner to license products with relatively high similarity, as this can enhance the direct spillover effect; otherwise, it is beneficial to raise the licensing standard for the farmer cooperative’s product, as this can help avoid negative reciprocal spillover effects. However, under either inspection mechanism, the RPB owner may reduce the quality of its on-sale product to avoid brand dilution caused by excessive quality differences between the two products. When traceability cost is relatively high, compared with the authentication mechanism, the traceability mechanism may decrease both the farmer cooperative’s product price and the quality of the RPB owner’s on-sale product if the market size of the on-sale product is relatively small. In this case, the RPB owner should choose the authentication mechanism; if the market size of the on-sale product is relatively large, the RPB owner should not consider licensing the brand to the farmer cooperative’s product. Related Articles

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Impact of Electric Vehicle Charging and Discharging Behaviors on the Electricity Market Zhuang Ke, He Zihang, Wei Lai 2026, 35 (5):  1169-1180.  doi: 10.3969/j.issn.2097-4558.2026.05.003 Abstract ( )   PDF (1450KB) ( )   PDF（mobile） (398KB) ( 2 )   This paper utilizes quantitative analysis and constructs an optimization and game-theoretic model to investigate the effects of electric vehicle (EV) charging and discharging behaviors on electricity market stability, with a focus on price volatility. It contrasts individual decision-making with centralized control, revealing that the latter reduces price fluctuations, which are positively correlated with the number of individual EV owners. It further examines potential market manipulation risks associated with centralized control and underscores the importance of market transparency in preventing manipulation and ensuring stability. By comparing both mechanisms through a game-theoretic approach, it innovatively assesses the risks of EV participation in the electricity spot market and centralized control systems, emphasizing the role of information disclosure in maintaining market stability and fairness. The insights contribute significantly to the formulation of electricity market policies, optimization of EV charging and discharging strategies, and promotion of rational decision-making among market participants. Related Articles

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Production and Carbon Emission Reduction Decisions for Construction Machinery Remanufacturing Enterprises Considering Carbon Futures Under Uncertainty of Recycled Product Quality and Demand Chen Weida, Qin Lin, He Wenqi 2026, 35 (5):  1181-1194.  doi: 10.3969/j.issn.2097-4558.2026.05.004 Abstract ( )   PDF (7372KB) ( )   PDF（mobile） (484KB) ( 1 )   This paper investigates the optimization of production and carbon emission reduction investment decisions for carbon emission-dependent construction machinery remanufacturing enterprises under uncertainty in used product quality, considering carbon futures. Faced with uncertain market demand, decision optimization models were constructed for both scenarios both without carbon and with carbon futures. Through numerical simulations, the impacts of quality uncertainty, demand uncertainty, and carbon emission reduction investment on remanufacturing output and profit were analyzed. The results show that after the introduction of carbon futures, the optimal remanufacturing time threshold decreases, leading to an increase in the quantity of remanufactured products, while remanufacturers’ profits increase at lower carbon emission levels. As product quality uncertainty increases, the optimal remanufacturing time threshold increases, the quantity of reclaimed used products decreases, and the optimal remanufacturing quantity increases, resulting in higher profits and total carbon emissions. As demand uncertainty increases, the optimal remanufacturing quantity shows an upward trend. The optimal profit in the carbon futures scenario is consistently higher than that in the non-carbon futures scenario, indicating that carbon futures can effectively mitigate risks caused by market demand fluctuations. As the carbon emission reduction investment coefficient increases, both the optimal carbon emission reduction quantity and the remanufacturing enterprise’s optimal profit decrease. In the carbon futures scenario, the optimal carbon emission reduction quantity is positively correlated with both the futures price and the carbon allowance trading price specified in the carbon futures contract. This paper provides theoretical support and practical guidance for construction machinery remanufacturing companies to optimize production and carbon emission reduction decisions under uncertainty, thereby effectively addressing uncertainties in recycled product quality and market demand. The findings are of practical significance for achieving the China’s “dual carbon” goals. Related Articles

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Optimization of Dynamical Prices for an On-Demand Service Platform with Large-Volume Temporally Varying Requests Ren Xuejie, Ma Yuliang, Zhao Lindu 2026, 35 (5):  1195-1207.  doi: 10.3969/j.issn.2097-4558.2026.05.005 Abstract ( )   PDF (2210KB) ( )   The on-demand service platform provides real-time quotes for customers who are sensitive to waiting time, based on delivery time and production capacity. To optimize dynamic pricing and construct a platform profit-maximization model with waiting time and dynamic capacity as key factors, a partial differential equation is used to simulate the time-varying evolution of order density in a mass production system, motivated by vehicle traffic flow and density theory. The order departure rate, i.e., the number of orders delivered to customers, is derived. Under full production capacity, the optimal price is obtained through mathematical derivation. Under limited production capacity, the continuous profit-maximization model is discretized, and an algorithm is designed and validated to obtain the approximate optimal price. Through extensive numerical experiments, the optimal delivery time and static productivity are shown to exist. Finally, the existence of optimal production capacity is further verified by extending the model to cases of sudden production interruption and continuous linear decay. The application of partial differential equation theory provides a new perspective for characterizing large-scale order production system. Related Articles

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An Adaptive Large Neighborhood Search Algorithm for the Capacitated Modular Hub Location Problem Lu Yongliang, Lin Dianchao 2026, 35 (5):  1208-1220.  doi: 10.3969/j.issn.2097-4558.2026.05.006 Abstract ( )   PDF (2281KB) ( )   The capacitated modular hub location problem (CMHLP), as an important extension of the classical hub location problem, has significant application value in fields such as modern logistics systems, transportation network planning, and telecommunications infrastructure design. Existing research indicates that current solution algorithms for the CMHLP still have notable shortcomings in both solution quality and computational efficiency, making it difficult to meet practical engineering requirements. To address this challenge, this paper proposes an adaptive large neighborhood search (ALNS) algorithm. The algorithm innovatively integrates the following core components: an initialization method based on a randomized greedy strategy, a hybrid destroy-and-repair operator targeting both hub nodes and terminal nodes, a local improvement strategy combined with tabu search, and a dynamic adaptive operator selection mechanism. Computational experiments demonstrate that the proposed algorithm significantly outperforms four mainstream CMHLP algorithms reported in the existing literature in terms of solution quality. Specifically, for 25 standard test instances, the algorithm successfully improves the best-known solution from the existing literature. This paper provides a practical and effective optimization approach for the hub location problem in complex network environments. Related Articles

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Safety Resilience Improvement Strategy of Maritime Hazardous Chemical Transportation System Considering Resource Consumption and Coupling Effects Yuan Changfeng, Sun Xing, Chen Chen, Wang Taiheng 2026, 35 (5):  1221-1234.  doi: 10.3969/j.issn.2097-4558.2026.05.007 Abstract ( )   PDF (12269KB) ( )   PDF（mobile） (7332KB) ( 3 )   Implementing effective safety resilience improvement strategies is essential for the maritime hazardous chemical transportation system (MHCTS) to rapidly recover stability and achieve overall safety. Based on the entropy flow principle, the resilience improvement process is characterized by the resource consumption of resilience-influencing factors and the entropy changes resulting from the coupling effects among these factors. Therefore, to accurately quantify and evaluate the effectiveness of different resilience improvement schemes, this paper innovatively integrates two dimensions—resource consumption and coupling effects—based on 205 historical accident cases. By employing Pearson correlation analysis and the N–K model coupling degree method, combined with entropy theory, it establishes a mathematical programming model for system safety resilience improvement. Furthermore, it applies the Monte Carlo method to simulate and analyze different coupling improvement schemes, comparing them with single-factor independent improvement schemes. The results demonstrate that the proposed coupling strategies offer superior performance in terms of both resilience improvement effectiveness and resource allocation efficiency. The validity of the strategy is further verified through a real-world case study. This paper provides decision support for MHCTS to achieve proactive safety through the enhancement of its intrinsic resilience capabilities. Related Articles

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Human Resource Hybrid Scheduling Strategies for SDPBFs Considering Outsourcing and Online–Offline Collaborative Modes from the Perspective of Market risk Feng Hui, Chen Xu, Huang Dechun, Xie Chunyan 2026, 35 (5):  1235-1250.  doi: 10.3969/j.issn.2097-4558.2026.05.008 Abstract ( )   PDF (12846KB) ( )   With the intensification of risks in the construction industry’s survey and design market, the limitations of traditional single human resource scheduling strategies in survey and design project-based firms (SDPBFs) under conventional business models have become increasingly evident. Given the high technical requirements and entry barriers, how to innovate business models and adopt hybrid human resource scheduling strategies involving outsourcing and online/offline collaboration remains a critical issue. Considering the difficulty of human resource acquisition in SDPBFs, online/offline collaboration modes, and the choice between in-house staffing and outsourcing, this paper explores optimal strategies for different types of human resources under market fluctuations, with the objectives of maximizing average labor productivity, minimizing human resource costs, and reducing the coefficient of variation in human resources. It constructs a mathematical model, and solves case studies using the NSGA-III algorithm. The findings reveal that the combined effect of outsourcing and online/offline collaboration is more significant, and the proposed hybrid scheduling strategy enables fluctuations in SDPBF human resources to remain below the volatility of total contract value, thereby stabilizing key human resources and achieving cost reduction and efficiency improvement. This paper provides a reference for SDPBFs in selecting hybrid human resource scheduling strategies under different levels of market risks. Related Articles

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Cascading Catastrophe of Operational Risk in Equipment Manufacturing Enterprises Under Unstable and Uncertain Environments Liu Pingfeng, Dou Ding, Wei Chen, Nie Lanshun 2026, 35 (5):  1251-1267.  doi: 10.3969/j.issn.2097-4558.2026.05.009 Abstract ( )   PDF (9791KB) ( )   Equipment manufacturing enterprises face operational challenges arising from multi-source risk superposition and cascading catastrophe effects under unstable and uncertain environments. This paper, adopting text mining techniques, identifies risk factors and their cascading relationships, and integrates fuzzy cognitive maps with catastrophe theory to construct a cascading catastrophe model of operational risk in equipment manufacturing enterprises. Using the model’s inference mechanism and grey relational analysis, simulation studies are conducted to analyze the evolutionary states and control strategies of operational risk in such enterprises. The results show that the structural catastrophe of the operational system state, transitioning from safety to risk, originates from a self-organized phase transition when combinations of internal and external risk variables cross bifurcation thresholds. The system state is more likely to undergo catastrophic shifts under multi-source risk superposition and cascading effects among risk factors, and the correlation strength between each risk factor and the catastrophe varies. The effectiveness and risk mitigation effect of different risk control strategies vary, and although the risk suppression effect increases with the number of control strategies, it exhibits diminishing marginal returns. The optimal combination of risk control strategies is achieved when the marginal increase in risk control cost is balanced with the marginal reduction in expected risk loss. This paper reveals the cascading catastrophe mechanism of operational risk under multi-source risk activation scenarios in equipment manufacturing enterprises, evaluates the effectiveness of risk control strategies and their combinations, and proposes principles for optimizing risk control portfolios, providing theoretical support and strategic guidance for operational risk prevention in equipment manufacturing enterprises. Related Articles

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Blockchain-Empowered Supply Chain Quality Visibility: Information Security Dilemma and Mitigation Strategies Zhang Ziming, Su Qin, Yang Qingyun 2026, 35 (5):  1268-1285.  doi: 10.3969/j.issn.2097-4558.2026.05.010 Abstract ( )   PDF (5735KB) ( )   Blockchain technology has demonstrated substantial potential in enhancing supply chain visibility and safeguarding information security. However, its inherent technical characteristics also render information security risks more concealed and potentially more detrimental. Focusing on the practical scenario in which an information gap exists between physical records and electronic ledgers before supply chain information is uploaded to the blockchain, this paper develops a tripartite evolutionary game model to investigate the formation mechanism of supply chain quality visibility decisions under the influence of information noise, and proposes corresponding mitigation strategies. The results indicate that, under the dual driving forces of proactive benefit seeking and passive risk prevention, information noise significantly undermines the value creation mechanism of supply chain quality visibility. Owing to a threshold effect, the positive role of mandatory information disclosure in regulating firms’ disclosure behavior and improving end-user welfare is replaced by two opposing phenomena: positive value erosion and negative value enhancement. From the perspective of governance, accelerating the withdrawal from public blockchains, promoting the establishment and adoption of private blockchains, and implementing a multi-block-based smooth disclosure mechanism together constitute effective internal blockchain governance strategies. Meanwhile, external regulatory measures—including penalty mechanisms, strongly differentiated subsidy schemes based on the degree of information visibility, and dynamic supervision enabled by smart contract technology—provide practical and feasible solutions for mitigating the adverse effects of information noise. Related Articles

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Risk Assessment and Integrated Control Strategies for Generative AI Healthcare Systems Based on System Dynamics Wang Yali, Deng Zhaohua, Ke Weiling, Zhang Qianfan 2026, 35 (5):  1286-1298.  doi: 10.3969/j.issn.2097-4558.2026.05.011 Abstract ( )   PDF (8501KB) ( )   With the rapid development of intelligent healthcare, technologies such as big data, cloud computing, and generative artificial intelligence (AI) have profoundly reshaped medical services while simultaneously introducing multidimensional systemic risks. To systematically assess the evolutionary mechanisms of these risks, this paper constructs a three-dimensional risk analysis framework of “technology–ethics–environment,” comprehensively identifying and characterizing key risk factors and their interaction pathways in generative AI-based healthcare systems. Based on a system dynamics approach, it develops a dynamic simulation model of risks in generative AI healthcare systems and conducts risk control scenario simulations using Vensim PLE software. The results indicate that, within the overall risk structure, technological risks account for the largest proportion, with algorithmic inference risk being the most prominent, followed by ethical risks, while environmental risks are relatively smaller. Furthermore, the simulation of the risk control system shows that integrated control strategies can effectively reduce the overall level of intelligent healthcare risk. This paper moves beyond the traditional static perspective on intelligent healthcare risks, revealing the mechanisms of risk interaction and evolutionary trends from a systems perspective, thereby providing theoretical support and policy guidance for the governance of intelligent healthcare risks. Related Articles

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Exploring the Driving Paths of Green Transformation Performance in Heavily Polluting Firms: A TOE Framework Perspective Su Taoyong, Liu Shuling, Liu Xinyang, Su Mingrui 2026, 35 (5):  1299-1310.  doi: 10.3969/j.issn.2097-4558.2026.05.012 Abstract ( )   PDF (1560KB) ( )   Improving green transformation performance is a key challenge for heavy polluting enterprises seeking comprehensive green transformation and sustainable development. Based on the TOE (technology–organization–environment) framework, this paper employs fuzzy set qualitative comparative analysis (fsQCA) to analyze 147 heavy polluting enterprises listed in China’s A-share market and identifies multiple configurational pathways for improving green transformation performance. The results show that no single antecedent condition constitutes a necessary condition for achieving high green transformation performance in heavy polluting enterprises; however, technological conditions, that is, the improvement of green process innovation or green product innovation, plays a more universal role in achieving high green transformation performance. There are four configurational pathways leading to high green transformation performance: green product innovation under government subsidies, green process innovation under market competition, government subsidies under technological innovation, and green process innovation under government intervention. There are two types of inhibitory pathways leading to non-high green transformation performance, characterized by insufficient driving forces. The findings help to deepen the understanding of the complex interactions among multiple factors underlying green transformation in heavy polluting enterprises and provide theoretical and practical support for improving the green transformation performance of enterprises. Related Articles

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Imitative Learning, Technology Concentration and Key Core Technology Innovation of Latecomer Enterprises Yang Shuai, Gong Hong 2026, 35 (5):  1311-1322.  doi: 10.3969/j.issn.2097-4558.2026.05.013 Abstract ( )   PDF (1635KB) ( )   PDF（mobile） (344KB) ( 1 )   Constrained by the dual technological predicament formed by the reconfiguration of global value chain positions and technological decoupling, China’s pursuit of breakthroughs in key core technologies urgently requires the mobilization of latecomer enterprises and the establishment of a “geese formation” technological innovation system. Based on organizational learning theory, this paper, using 1172 specialized, refined, distinctive, and innovative enterprises from 2008 to 2022 as research samples, examines the impact of imitative knowledge learning on key core technological innovation. The findings reveal a U-shaped nonlinear evolution relationship between imitative knowledge learning and key core technological innovation. Specifically, when the intensity of imitative knowledge learning is low, enterprises’ key core technological innovation is suppressed due to resource crowding-out effects and insufficient knowledge absorption. However, once the learning intensity surpasses a certain threshold, imitative knowledge learning significantly promotes key core technological innovation through the integration and internalization of experiential knowledge. Furthermore, it is found that technological concentration significantly mediates the U-shaped relationship between imitative knowledge learning and key core technological innovation. Additionally, heterogeneity analysis indicates that differences in industry opportunity windows and enterprise life cycles affect the validity of this nonlinear relationship. This paper reveals the dynamic path through which latecomer enterprises achieve key core technological innovation via imitative learning, characterized by a “first suppression, then promotion” pattern. It provides a theoretical basis and practical insights for enterprises on how to leverage latecomer advantages to integrate into the innovation ecosystem and achieve breakthroughs in key core technologies. Related Articles

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Key Core Technology of China’s High-End CNC Machine Tool Industry Under a Multi-Network Principal–Agent Coupling Mechanism Guo Benhai, Ouyang Luxuan, Zhang Ruyuan 2026, 35 (5):  1323-1338.  doi: 10.3969/j.issn.2097-4558.2026.05.014 Abstract ( )   PDF (12321KB) ( )   While China’s high-end computer numerical control (CNC) machine tool industry has made significant progress, issues such as disconnects between core systems and supporting components and insufficient system integration capabilities persist. Exploring coupling mechanisms between core systems and supporting components is crucial for achieving high-quality industrial development. Based on global invention patent data, a three-stage “funnel-shaped” model for identifying key core technologies was constructed and applied, revealing that these technologies are concentrated in fields such as H02P, B23Q, and G06F. Building on this, a multi-network framework for core industrial technologies and supporting components was constructed and evaluated to clarify the current development status and coupling effectiveness. Additionally, an evolutionary simulation model of the coupling network was established to reveal the impacts of knowledge matching and government incentives on network evolution, knowledge innovation performance, and coupling intensity. The simulation results indicate that the proportion of knowledge matchmakers exhibits an inverted U-shaped relationship with industrial principal–agent coupling intensity, while government incentive strength is positively correlated with coupling intensity. Higher incentive levels can support sustainable knowledge innovation. Moreover, both knowledge matching and government incentive behaviors promote network evolution, enhance knowledge innovation performance, and strengthen coupling capabilities such as knowledge matching. Finally, based on actual industrial development trends and simulation results, this paper proposes a principal–agent coupling mechanism for key core technologies in China’s high-end CNC machine tool industry. Related Articles

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Imbalanced Credit Evaluation Model of Deep Learning Based on Class Difficulty Perception Loss Yang Lian, Liu Wenxiu, Chai Nana, Shi Baofeng 2026, 35 (5):  1339-1352.  doi: 10.3969/j.issn.2097-4558.2026.05.015 Abstract ( )   PDF (3219KB) ( )   PDF（mobile） (3576KB) ( 1 )   Effective management of credit risk is crucial for the stable operation of the financial system. In response to the problem of low accuracy in identifying high-risk default customers in traditional credit evaluation models, a dynamic weight term that reflects the real-time identification difficulty of each sample type is constructed to improve the cross-entropy loss function. On this basis, a back propagation neural network–class difficulty perception loss (BPNN-CDPL) credit evaluation model is established. The default prediction performance of the model is validated using 11 comparative models and five model evaluation metrics. The results show that the proposed model has good adaptability to credit data with different imbalance ratios and credit characteristics, and can significantly improve the identification accuracy of high-risk default customers, which is of primary concern to financial institutions. Compared with existing credit evaluation models, the proposed model exhibits superior default prediction performance. The innovations and contributions of this paper are as follows: introducing dynamic weight terms into the cross-entropy function breaks through the limitations of traditional deep learning models with weak applicability to imbalanced samples, improving the poor recognition performance of the back propagation neural network–focal loss (BPNN-Focal Loss) model on default samples due to not considering differences in sample size  between classes, and avoiding the excessive suppression of non-default samples in back propagation neural network–class balanced cross entropy (BPNN-CBCE) model caused by “uniform” sample weighting. This paper extends the theoretical development of deep learning methods and provides new ideas for credit risk management in financial institutions. Related Articles

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Supply Chain Whistleblowing and Dealer Slackness: A Sensemaking Perspective Shen Lu, Zhang Hailong, Zhang Chuang, Jiang Sihan 2026, 35 (5):  1353-1365.  doi: 10.3969/j.issn.2097-4558.2026.05.016 Abstract ( )   PDF (2371KB) ( )   In a multi-agent situation, does whistleblowing among agents mitigate agency problems by reducing information asymmetry between the principal and agents, or does it exacerbate such problems by undermining agents’ trust in the principal’s competence? To answer this important question, this paper examines a downstream supply chain network consisting of a manufacturer, a focal dealer, and other dealers. Based on sensemaking theory, it investigates how supply chain whistleblowing intensity affects the focal dealer’s competence-based trust in the manufacturer and, subsequently, its impact on the focal dealer’s slackness, using paired survey data collected from the automobile industry. The results show that supply chain whistleblowing intensity undermines the focal dealer’s competence trust in the manufacturer, and this erosion of trust further increases the focal dealer’s slackness. Moreover, the negative effect of whistleblowing intensity on the focal dealer’s competence trust is amplified when horizontal competition among dealers decreases. This paper reveals the inherent tension between whistleblowing’s role in reducing information asymmetry and its potential to weaken organizational trust, offering insights for managers to carefully weigh the trade-offs of relying on whistleblowing as a peer monitoring mechanism. Related Articles

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Risk-Taking or Innovativeness: Social Class Origins and the Spirit of Entrepreneurship Ge Jianhua, Li Ge 2026, 35 (5):  1366-1383.  doi: 10.3969/j.issn.2097-4558.2026.05.017 Abstract ( )   PDF (1566KB) ( )   How do entrepreneurs’ social class origins influence their spirit of entrepreneurship? Private entrepreneurs with diverse identities and backgrounds have emerged amid China’s economic reforms. While portraying their spiritual characteristics, prior studies have paid insufficient attention to the heterogeneity of entrepreneurs’ social origins and have overlooked the distinction between risk-taking and innovative dimensions of entrepreneurial spirit. By integrating research on entrepreneurship and social class, this paper argues that entrepreneurs’ social class origins shape their cognitive and behavioral tendencies, leading to differentiated configurations of risk-taking and innovative spirit, which in turn, influences firms’ innovation and risk-taking activities. Using national survey data of Chinese entrepreneurial firms, it is found that entrepreneurs from lower social classes, shaped by early-life experiences of resource scarcity and weak institutional protection, tend to develop stronger psychological resilience and upward mobility motivation. As a result, they are more inclined toward risk-taking and exhibit higher levels of risk tolerance. However, due to limited access to human capital, constrained resources, and a short-term time orientation, they show weaker recognition of innovativeness and invest less in innovation and long-term development. This paper reveals the differentiated impact of social class origins on entrepreneurs’ perceptions of risk-taking and innovativeness, enriches the understanding of entrepreneurial behaviors within China’s institutional context, and offers policy implications for stimulating the spirit of entrepreneurship across different social groups. Related Articles

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Environmental Judicial Reform and Corporate Leverage Manipulation: Evidence from the Establishment of Environmental Courts Yu Lianchao, Yang Zhenzhen, Qiao Ruochen, Li Zhaoyi, Ma Ning 2026, 35 (5):  1384-1403.  doi: 10.3969/j.issn.2097-4558.2026.05.018 Abstract ( )   PDF (2057KB) ( )   Although the original intention of environmental judicial reform is to strengthen environmental governance by applying the concept of environmental rule of law, it may also send potential risk signals to stakeholders, thereby potentially leading to distorted corporate behavior. Taking the establishment of environmental courts as an exogenous shock, this paper systematically examines the impact of environmental judicial reform on corporate leverage manipulation using data from heavily polluting companies listed on the Shanghai and Shenzhen A-share markets from 2004 to 2023, employing a multi-period difference-in-differences approach. The results show that the establishment of environmental courts significantly exacerbates corporate leverage manipulation, with the core mechanism being the intensification of environmental homogeneity pressure and the emergence of debt financing disadvantages. Heterogeneity analysis indicates that this exacerbating effect is more pronounced when companies face higher environmental risks or have fewer collateralizable assets. Governance mechanism analysis reveals that this effect is mitigated when green subsidies are relatively sufficient, green finance is relatively well-developed, and corporate governance quality is relatively sound. Economic consequence analysis further shows that this effect significantly increases corporate debt default risk and stock price crash risk. Spillover effect analysis confirms that this effect exhibits spatial spillovers, meaning that the establishment of environmental courts in neighboring regions also exacerbates local corporate leverage manipulation behavior. These findings reveal the unintended consequences of environmental judicial reform from the perspective of corporate leverage manipulation, providing theoretical support and practical insights for judicial authorities to optimize environmental judicial reforms, prevent systemic financial risks, and for enterprises to improve their debt decisions to control implicit debt risks. Related Articles

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Industrial Chain Institutional Innovation and Enhancement of Corporate Resilience: A Quasi-Natural Experiment of the “Chain Leader Policy” Cheng Feiyang, Xia Binyuan, Shi Jing 2026, 35 (5):  1404-1414.  doi: 10.3969/j.issn.2097-4558.2026.05.019 Abstract ( )   PDF (1837KB) ( )   PDF（mobile） (998KB) ( 0 )   As a crucial component of the comprehensive evaluation capability system, corporate resilience has emerged as a core topic in strategic management research. This paper, using A-share listed companies from 2011 to 2023 as the research sample, constructs a stacked difference-in-differences model to systematically examine the microeconomic effects and potential mechanisms of the “Chain Leader” policy, an industrial chain institutional innovation, on corporate resilience. The evidence shows that implementation of the policy significantly enhances corporate resilience, and this finding remains robust after addressing endogeneity concerns and conducting various robustness tests. Mechanism analysis indicates that the policy effectively strengthens corporate resilience by reducing principal–agent costs, improving financial allocation efficiency, promoting industry–academia–research integration, and stimulating labor and capital market vitality. Furthermore, the policy effect is more pronounced for firms located in eastern regions and areas with better business environments. It is particularly effective in non-labor-intensive industries, high-tech sectors, and highly competitive industries. Additionally, the policy effect is stronger for firms in chain leader positions and those facing significant financing constraints. Overall, this paper further offers theoretical support and policy implications for enhancing the resilience of China’s industrial and supply chains and for adapting to uncertainties in global economic development. Related Articles