During business operations, companies often face unexpected challenges such as environmental instability, fluctuating demands, and supply chain disruptions caused by sudden emergencies. This paper explores the relationship between enterprise inventory agility and business performance under the influence of such major emergencies, from the perspective of inventory management. It also examines the moderating effect of enterprise debt financing costs and working capital management capabilities. Using quarterly data from 1533 publicly listed manufacturing enterprises in the A-share market, it employs the method of ordinary least squares to explore the impact of inventory agility on business performance. The findings suggest that under the influence of major emergencies, an enterprise’s inventory agility can enhance its cash flow performance, having a significant positive impact on business performance. The cost of debt financing can constrain an its use of cash flow, negatively moderating the relationship between inventory agility and corporate performance. Specifically, the higher the cost of debt financing, the weaker the positive impact of inventory agility on corporate performance. The stronger an enterprise’s working capital management capabilities, the more it can leverage the advantages brought by inventory agility, showing a positive moderating effect on the relationship between inventory agility and corporate performance. These findings extend traditional management theories that primarily focus on lean inventory practices and provide valuable guidance for enterprises in adjusting their inventory management strategies in response to major emergencies.

Considering the impact of the offline inventory availability on customer behavior, this paper, based on the rational expectation equilibrium theory and newsvendor model, develops store inventory decision models for the physical store in three modes: dual-channel, ship-from-store (SFS), and inventory information disclosure only. It explores the retailer’s optimal mode selection by comparing the equilibrium outcomes. The findings show that under the SFS or inventory information disclosure only mode, retailers should neither always activate the SFS channel nor consistently disclose the actual store inventory. Depending on customers’ waiting costs, travel costs, and the online price of the product, introducing the SFS channel or disclosing inventory information can lead to four effects: demand pooling, demand depooling, market expansion, and market contraction. When the online price is low, merely disclosing inventory information performs no worse than introducing the SFS channel. Conversely, when the online price is high, introducing the SFS channel is no worse than disclosing inventory information. Under certain conditions, retailers should adjust the optimal offline inventory level in response to changes in the online price.

Blind boxes refers to packages which randomly contain selected products of various styles, representing a novel and increasingly popular sales model characterized by uncertainty. In light of issues such as excessive consumption and waste driven by social comparison in current blind box sales, this paper incorporates consumer’s repurchase behavior into the analysis, and investigates the manufacturer’s joint decision-making problem regrading blind box design and inventory, specifically, how to determine the probability of rare (hidden) items and the corresponding inventory levels. Based on a utility function framework, the model characterizes consumer purchasing behavior and derives the relationship between the quantity of blind boxes purchased and the probability of obtaining hidden items. It is found that the expected purchase quantity does not always increase monotonically with the probability of receiving a hidden item. Based on this, the global optimization problem is equivalently transformed into several local optimization problems to obtain the manufacturer’s optimal joint decisions. It is concluded that although the blind box sales model is advantageous for manufacturers, it may harm consumer welfare.

Under the dual background of carbon quota mechanism and blockchain technology, this paper addresses the issue of low-carbon investment strategies in the power supply chain. It develops a two-tier electricity supply chain model consisting of a power generator and a power retailer, capturing four scenarios based on whether blockchain technology is adopted and whether the grandfathering or benchmarking allocation mechanism is used. With a Stackelberg game theory framework and using backward induction, it yields the following findings: Regardless of whether blockchain technology is adopted, the level of low-carbon investment, electricity demand, and retailer profits are always higher under the benchmarking mechanism compared to the grandfathering mechanism. Under any carbon quota mechanism, when the cost of applying blockchain technology is relatively low, adopting blockchain leads to higher levels of low-carbon investment, greater electricity demand, and increased profits for both the generator and the retailer compared to scenarios without blockchain adoption. Moreover, low-carbon investment and the profits of supply chain enterprises increase with consumers’ green trust and environmental preferences, and decrease as the costs of low-carbon technology investment and blockchain application rise.

The practice of responding to crisis events, such as the COVID-19 pandemic, has demonstrated that firms can effectively manage production disruption risks through horizontal collaboration and resource sharing, such as capacity sharing. However, there has been limited literature exploring and comparing the coordination mechanisms and risk resilience of different capacity-sharing contracts when horizontally competing firms face production disruption risks. This paper, considering two common types of capacity-sharing contracts: the ex-ante revenue sharing contract and the ex-post transfer payment contract, develops a game-theoretic model between manufacturers with horizontal competition, analyzes and compares the equilibrium between manufacturers across four different models: separate operation, centralized operation, and two capacity-sharing contracts. The findings indicate that both types of capacity-sharing contracts enhance manufacturers’ ability to withstand disruption risks, with the ex-ante revenue sharing contract achieving the same level of resilience as the centralized operation model. Under both capacity-sharing contracts, manufacturers’ expected profits are always higher than those of the separate operation model. The ex-ante revenue sharing contract is appropriate when the disruption probability is high or the shortage penalty cost is low, while the ex-post transfer payment contract is more appropriate when the disruption probability is low and the shortage penalty cost is high. In capacity-sharing transactions, contrary to the ex-post transfer payment contract, the concession ratio of the production interrupter under the ex-ante revenue sharing contract tends to decrease as the shortage penalty cost increases, even approaching zero. Interestingly, in the context of production disruption risks, the implementation of capacity sharing can help competing firms avoid risks and exploit the value of idle capacity, as well as mitigate price competition under certain conditions, which in turn generates additional profits for the manufacturers compared to the risk-free case.

In a supply chain consisting of a manufacturer and a retailer, this paper examines two financing models for a financially constrained manufacturer: financing from a bank or financing from the retailer. Using a Stackelberg game framework and backward induction, it develops and analyzes the models. Under the condition that the retailer undertakes both pre-sales and after-sales services, it compares two financing strategies to determine the manufacturer’s optimal choice. The results show that under certain conditions, financing from the retailer is not always the best option for the manufacturer. For the manufacturer, the retailer, and the total supply chain as a whole, when the retailer offers a relatively low interest rate, financing from the retailer becomes the manufacturer’s optimal strategy. However, when the bank’s interest rate exceeds a certain threshold, the manufacturer may prefer to finance from the bank instead.

Starting from the perspective of community governance structure, this paper analyzes the current issue of difficulty in collecting property management fees in affordable housing communities, proposes new viewpoints, and enriches paradigms in this field. Based on principal-agent theory, it compares the differences in property management structures between affordable housing and commercial housing communities, and proposes optimal incentive mechanisms under the two management models. The research findings show that the management of affordable housing involves a dual-layer principal-agent structure: the government acts both as an agent responsible for ensuring public welfare and as a principal that hires property management companies, requiring a balance of interests among all three parties. This “sandwich-style” principal-agent structure distorts residents’ incentives toward property management companies, leading to a lack of willingness among residents to proactively pay property fees. To balance its own interests, the benefits of residents, and the profits of property management companies, the government adopts fee reductions and subsidies in managing affordable housing property services as a strategy to achieve a three-way equilibrium.

The application of blockchain technology can realize traceability of carbon footprints and improve consumer trust in corporate carbon emission data, through it requires significant investment. Focusing on a two-tier supply chain consisting of a manufacturer and a retailer, this paper explores the operational strategies of supply chain members in different scenarios, regarding whether or not to adopt blockchain technology, and analyzes its impact. The findings show that when demand is heavily sensitive to carbon reduction levels, the manufacturer’s misreporting does not negatively affect the retailer. However, when demand is less sensitive to carbon reduction, the manufacturer’s misreporting of carbon reduction levels harms the retailer’s profits, giving the retailer an incentive to invest in blockchain technology. After adopting blockchain, the manufacturer’s actual carbon reduction levels tend to be higher than when misreporting is possible. When blockchain investment costs are relatively low, both the manufacturer and the retailer can achieve higher profits. Additionally, penalty mechanisms can partially deter the manufacturer’s misreporting. However, excessively high penalties may lead to reduced motivation for carbon reduction, ultimately lowering the overall level of emission reductions.

Service innovation in manufacturing enterprises is highly context-dependent. The rise of digital intellectualization not only provides fertile ground for such innovation but also complicates decision-making regarding optimal innovation levels. Considering that service innovation in this context is driven by both product support and relationship maintenance, and influenced by product intelligence and long-term supplier-customer interaction, while also exhibiting both firm-initiated and customer-incentivized characteristics, this paper uses a reputation model from principal-agent theory. By combining reputation-based incentives with customer reward incentives and aiming to maximize value co-creation between suppliers and customers, it constructs a dynamic contract model for service innovation in the context of digital intellectualization. The key findings indicate that under digital intellectualization, influenced by product intelligence and supplier-customer interaction, there exists an optimal level of service innovation for manufacturing enterprises. Future reputation effects and current customer rewards have comparable impacts on motivating optimal service innovation levels. To further stimulate customer-driven reward sharing, firms should focus on sharing customer data, maintaining brand reputation, and enhancing the perceived value of services. As supplier-customer cooperation deepens over time, the optimal level of service innovation evolves in a gradual and convergent manner. This paper analyzes the dynamic decision-making of service innovation levels in the context of digital intellectualization and offers theoretical insights for promoting the sustainable development of service innovation in manufacturing enterprises.

Point-to-point direct transportation is one of the key approaches for efficiently dispersing passengers at transportation hubs. However, the stochastic passenger demand between these hubs poses significant challenges to vehicle dispatching decisions for transportation service providers. If providers make decisions solely based on the observed demand in the current stage to maximize immediate revenue, such an approach can be short-sighted. This is because mismatches between vehicle supply and uncertain future demand in the subsequent stage may lead to a decline in expected overall revenue. Therefore, the core operational challenge lies in how to schedule vehicles to maximize the combined profit of the current stage and the expected profit in the next stage. This paper investigates the vehicle dispatching problem in a two-stage point-to-point transportation network under stochastic demand. First, it develops a two-stage stochastic integer programming model to formulate the problem. Then, leveraging the structural characteristics of the model, it proposes a polynomial-time TRAC (treerec with arc combination) algorithm to efficiently solve the expected value function in the second stage. Numerical experiments demonstrate that the TRAC algorithm can obtain exact optimal solutions with high computational efficiency, making it particularly suitable for large-scale real-world applications.

This paper proposes a consensus algorithm for power trading based on quantum blockchain and microgrids, aiming to address the issues of high trading risk, low efficiency, and long consensus cycles in electricity trading in microgrids. Compared with existing research on blockchain applications in microgrid electricity trading, it further incorporates quantum technology, enabling more secure and stable transmission of transaction data, thereby improving the efficiency and security of electricity trading. Leveraging the powerful data-processing capabilities of quantum computing, it constructs a quantum blockchain framework tailored to microgrid electricity trading and introduces a quantitative node trust evaluation model to assess the trust level of each node, achieving deep integration between trust values and prosumer (producer-consumer) users. By combining node trust values with a quantum random number generator, it probabilistically selects consensus participants, significantly enhancing the block header identification performance of the quantum blockchain’s anonymous voting protocol. Based on this, it adopts a trust-value-based accounting rights competition mechanism to form the final block, achieving efficient consensus among nodes within each trading cycle. This mechanism allows trust values to influence the probability of nodes gaining accounting rights, thus incorporating economic incentives to encourage honest and trustworthy behavior during transactions. Simulation results show that the proposed scheme can effectively improve consensus efficiency among prosumer nodes in electricity trading, while also ensuring the security and operational efficiency of internal transactions within microgrids.

In the context of increasingly intense global competition in industrial and supply chains, rebuilding high-value and resilient supply chain networks has become a critical national strategy. Modeling methods for complex supply chain networks are key technologies for improving the visibility of deep structural layers within supply chains and enhancing the perception of potential risks. Drawing on big data and complex network theory, this paper systematically reviews the current state of research on complex supply chain network modeling, with the goal of exploring the theoretical frontier in response to major national needs. First, based on the evolution of supply chains, it clarifies the development process and conceptual connotation of complex supply chain networks. Next, by integrating domestic and international theoretical developments and modeling approaches, it categorizes and summarizes the main modeling methods. Finally, it identifies existing limitations and emerging trends in the field of complex supply chain network modeling. This paper aims to provide strategic guidance for technological innovation and breakthroughs in China’s supply chain management, and to offer theoretical support for fostering new quality productive forces and optimizing industrial chain layouts amid a volatile global landscape.

As the main carrier of green and low-carbon transformation, improving environmental performance and promoting sustainable development by enterprises is an important manifestation of their active fulfillment of environmental and social responsibilities. Focusing on corporate environmental performance, this paper proposes a novel model that integrates self-evaluation and mutual evaluation. Based on the conventional concept of cross-efficiency, it constructs a mathematical programming model and an interactive iterative algorithm to jointly and continuously optimize environmental performance. By introducing a set of soft constraint variables to continuously promote the approximate Pareto improvement with minimal cost, it obtains the optimal weight combination through the interactive joint decision-making, thereby effectively enhancing the recognition of the evaluation results among all participants. The proposed interactive evaluation model is applied to an empirical study on the environmental performance of listed companies in China’s manufacturing industry from 2019 to 2022. The results show that the overall environmental performance of the sample enterprises is relatively satisfactory, with an average upward trend year by year, indicating that China’s manufacturing sector is beginning to make progress in its green and low-carbon transformation. However, there remain significant differences in environmental performance among enterprises across different years, with a noticeable trend of polarization, which warrants attention. To promote high-quality development across the entire industry, it is essential to strengthen both policy support and regulatory oversight, while establishing and promoting benchmark enterprises that exemplify green production practices.

Accelerating the development of new quality productivity and realizing high-quality growth are of great significance to the manufacturing industry. Based on a clear understanding of the formation logic, manifestations, and functional roles of new quality productivity forces in manufacturing industry, this paper constructs an evolutionary game model involving core (chain-leading) enterprises, participating (in-chain) enterprises, and the governments, to analyze the collaborative cultivation dynamics of new quality productivity and the law of evolution, and to investigate the effectiveness of different incentive mechanisms. The research findings indicate that collaborative cultivation of new quality productivity in manufacturing depends heavily on internal collaboration within the industrial and supply chains, where costs and benefits play a critical role. Due to the significant dynamics and inconsistency of decision-making among multiple actors, internal collaborative for fostering new quality productivity is often misaligned. However, external incentives from the government can effectively improve the alignment and efficiency of internal collaboration. Clarifying the influence of subjective factors such as risk preferences and reference effect—and establishing appropriate control mechanisms—is key to overcoming inherent path dependence and realize the transformation from traditional productivity to new quality productivity. Based on these findings, this paper proposes several policy recommendations from the aspects of constructing a dual-center synergy mechanism led by both the “chain leader + chain master” enterprises, constructing a dual incentive mechanism system of “reward and punishment mechanism + economic means”, and strengthening risk assessment and early warning systems related to the qualitative leap in productivity, etc., to support the coordinated cultivation of new quality productivity across the manufacturing industrial supply chains.

The intense global patent litigation, especially opportunistic lawsuits, have a detrimental impact on the continuity and stability of corporate innovation, hindering the achievement of strategic goals for achieving high-level technological self-reliance and self-improvement. A thorough investigation into the timing strategies of patent litigation is conducive to elucidating the essence of strategic litigation in industrial practices, guiding patent litigation back to its fundamental purpose of protecting and incentivizing innovation. Therefore, building upon evolutionary game theory, this paper establishes a three-stage game model between patent-holding enterprises and those accused of patent infringement, which enables an analysis of the game dynamics and stable strategies of both parties. Moreover, it contextualizes the time strategies of patent litigation within the scenario of Star Market IPO. Additionally, utilizing Matlab software, it conducts a simulation to elucidate how pivotal factors influence the strategic behaviors of both parties in patent litigation. The research findings indicate the existence of evolutionarily stable equilibrium strategies in the behavioral games of both parties in patent litigation. The timing of litigation significantly impacts the game strategies of both parties, while the duration of litigation demonstrates a more moderate effect. Specifically, among factors related to litigation timing and duration, the influences of settlement fees, long-term litigation benefits, and significant indirect costs are particularly pronounced. Furthermore, as patent validity transitions from low to high, the effects of litigation timing and duration on the game strategies of both parties in patent litigation become more (less) pronounced. The discussion on the time strategy of patent litigation from the perspective of evolutionary game enriches and expands the literature on time and patent litigation, and has strong practical significance for guiding enterprises how to initiate or respond to strategic patent litigation.

Whether and how relationship networks can improve credit availability is of great importance for micro entities. Based on the syndicated loan data in China from 2016 to 2021, this paper investigates the relationship between network centrality in the syndicated loan network and firms’ credit availability by constructing a bimodal financing network consisting of both lending institutions and borrowing firms. It analyzes the mechanism of firms’ syndicated loan network centrality and further explores the moderating role of the different syndicate natures. The findings reveal that firms occupying advantageous positions within the network tend to receive more favorable lending responses from banks. The higher the firms’ network centrality, the larger their credit availability. Mechanism analysis indicates that network centrality enhances credit access by mitigating information asymmetry and improving monitoring efficiency of lending banks. Leveraging the bipartite structure of the syndicated loan network, the centrality of lead banks positively moderates the relationship between borrowing firms’ network position and their credit availability. Conversely, factors such as the legal system of the bank’s home country, the degree of bank internationalization, and the geographical dispersion of syndicate members exert negative moderating effects. This paper enriches the literature on how bank-firm relational networks influence credit access and offers practical implications for both firms seeking financing and banks making lending decisions.

For project-based enterprises, both project-oriented and functional (departmental) organizational structures have their respective advantages and disadvantages. Some enterprises adopt a matrix structure that combines features of both, but this often leads to conflicting directives and increased internal friction. In response to these challenges, the hybrid organizational structure has emerged as a potential solution. Given the long-standing issues of lack of operational awareness and core competitiveness in Chinese project-based enterprises, this paper analyzes the fundamental features of the project-based and functional structures. Using operations strategy (OS) priorities as the optimization objective, it develops a trade-off model for organizational structure design in project-based enterprises and constructed a two-level nested algorithm to solve the model using case studies. The results show that the optimal form of a hybrid structure depends on the specific context of each enterprise—there is no universally optimal solution. Additional findings show that mall enterprises tend to rely more on individual heroism. It is challenging for enterprises to optimize human resource costs. There is a correlation between team stability and development and the organizational flexibility of the enterprise.

This paper develops a dynamic stochastic general equilibrium (DSGE) model incorporating green and polluting firms with financing constraints. The model parameters are calibrated and estimated by Bayesian techniques based on China’s data. The simulation results verify the incentive effect of the green credit policy on green firms and the punishment effect on polluting firms. In other words, the green credit policy increases the cost of labor and capital by stimulating the green firms’ demand for employment and investment, thereby inhibiting the polluting firms’ employment, investment, and financing behaviors, resulting in the reduction of polluting firms’ output and pollution emissions. Therefore, the green credit policy helps to promote the industrial structure and realize the sustainable development of the green economy. Additionally, a comparative analysis reveals that the integration of green technology innovation with permanent green credit policies outperforms the combination of pollution fees and permanent green credit policies in curbing inflation, reducing emissions, and enhancing output.

Based on data from A-share listed private enterprises in China from 2008 to 2022, this paper studies how “reverse mixed-ownership reform” affects the digital transformation and reveals the potential transformation pathways, and analyzes the economic implications of this reform for private enterprises. The empirical results show a U-shaped relationship between “reverse mixed-ownership reform” and the digital transformation in private enterprises. As the reform progresses, the level of digital transformation initially declines and then rises. Moreover, green innovation capability plays an intermediary role in this relationship. Specifically, green innovation capability exhibits an inverted U-shaped pattern, which leads to the observed U-shaped transformation in digitalization. Heterogeneity analysis reveals that the U-shaped relationship is more pronounced in contexts with weaker digital economy policies and higher market competition intensity. From the perspective of economic consequences, “reverse mixed-ownership reform” contributes to the long-term growth and sustainable development of private enterprises.