The gradual nature of green transformation in the manufacturing sector means the coexistence and competition of green and non-green products in the market over the long term. Due to the unique environmental attributes of green products, an asymmetric substitution relationship arises between the two, directly affecting the incentives and strategic choices of government subsidy policies. This paper explores the game between green and non-green manufacturers and their dominant retailers under three scenarios: no government subsidy, lump-sum subsidy, and per-unit subsidy, and analyzes the factors affecting subsidy effectiveness and strategy selection. It is found that both subsidy strategies can improve product greenness, promote green consumption, and increase retailer profit, with green manufacturer profits and subsidy coefficients exhibiting a positive U-shaped and inverse U-shaped relationship, respectively. Intensified competition between green and non-green products can raise prices, but through market expansion or enhanced premium capability, all supply chain members can benefit. When competition is sufficiently intense and per-unit subsidies are high, green product prices may fall below levels under no subsidy. Given the same total subsidy expenditure, lump-sum subsidies can better motivate overall green transformation and development of the supply chain, whereas per-unit subsidies can better strengthen government performance across multi-dimensions, including corporate profits, consumer surplus, and environmental improvement. When both subsidy intensity and the marginal return on environmental improvement are high, the government tends to adopt per-unit subsidies to achieve multi-dimensional objectives, although the asymmetric substitution between the two products can weaken this motivation. If cost differences between the two product types are large, the government is more likely to use lump-sum subsidies to ensure policy effectiveness. Reducing green costs or enhancing consumer green preferences helps green products capture market share from non-green products. This clarifies the principles and conditions under which different subsidy policies influence supply chain green transformation, providing micro-decision-making insights for enterprises and theoretical basis for the government policy aimed at promoting the green transformation and development of enterprises.

Under extended producer responsibility (EPR) system, this paper examines a multi-tier closed-loop supply chain in which consumers exhibit environmental awareness. Stackelberg game models are constructed for four alternative recycling modes, with the battery manufacturer, vehicle manufacturer, retailer, and third-party recycler as the main body of recycling, respectively. The optimal decisions and profits of supply chain members under different recycling modes are derived, and the influence of government rewards-punishment policies, consumer environmental awareness, and cascade utilization of power batteries on recycling mode selection are analyzed. The results show that when the government subsidy is higher than a certain threshold, the battery manufacturer serving as the recycling main body is the optimal mode; otherwise, the vehicle manufacturer-led recycling mode is optimal and yields the highest level of social welfare. When the vehicle manufacturer’s investment in battery recycling exceeds a certain threshold, the battery manufacturer-led mode achieves the highest recycling volume; otherwise, recycling outcomes depend jointly on consumer behavior and the degree of battery cascade utilization. Moreover, higher battery recycling volumes and greater cascade utilization ratios significantly lower the environmental impact of the closed-loop supply chain. These findings indicate that supply chain members should focus on improving green processing capabilities and battery recycling efficiency, while the government should encourage producers to actively fulfill extended responsibilities through diversified policies such as fiscal subsidies and tax incentives, thereby promoting green technological innovation.

The abundant data resources embedded in e-commerce platforms provide consumers with valuable references for decision-making and constitute an important factor influencing purchasing behavior. The effective use of product-related data resources can help online retailers optimize pricing strategies. From the perspective of consumer social learning, this paper builds on expected utility theory and considers how consumers enhance product cognition by sequentially browsing online reviews during the purchase decision process. The cognitive updating process is quantified using the anchoring effect and Bayesian theorem, based on which a product revenue-based pricing model is developed. In addition, deep learning methods and Python-based web crawling techniques are employed to conduct four-category sentiment analysis of review texts. This paper examines the impact of the ordering of positive and negative review information and consumers’ “decision threshold” on customer cognition, product pricing, sales volume, and profits. The results demonstrate that, contrary to prevailing conventional wisdom, compared with products with no reviews, a small, moderate, and intermittently appearing amount of negative information can actually be more beneficial for product pricing and marketing. This research provides a theoretical foundation and decision support for data-driven product pricing and operational management by online retailers.

With the growing appeal of user-generated content (UGC) for attracting viewers, two-sided video platforms (e.g., iQIYI) have begun introducing UGC business line to attract viewers and increase advertising revenue. However, this also poses a challenge: some advertisers who originally placed advertisements directly on the platform may perceive UGC as more effective for promoting their products and shift their advertising to UGC content. For two-sided long-form video platforms, the introduction of UGC represents both an opportunity and a risk. Based on this, this paper applies two-sided market theory to comprehensively analyze the dual impact of UGC adoption on long-form video platforms. A three-party game model involving viewers, the platform, and advertisers is constructed, and platform profits are compared across three scenarios: no UGC adoption, UGC adoption by a single platform, and UGC adoption by all platforms. It examines whether and when platforms should introduce UGC and summarizes the operational impact of UGC adoption. It further explores how platforms should adjust pricing strategies to maintain optimal profits in response to changes brought by UGC adoption. The results indicate that, in competitive markets, when UGC adoption significantly affects platform advertising revenue, the key factor influencing adoption decisions is whether the platform can obtain a value advantage from UGC content. When the impact is moderate, the primary factor is the switching cost for viewers between platforms. The introduction of UGC by a platform simultaneously affects both viewer and advertiser behavior, and this effect propagates through the platform’s inherent two-sided market dynamics. Platforms that secure a substantial UGC content value advantage can attract competitors’ advertisers by lowering ad prices to offset potential losses, while competing platforms should raise ad prices to preserve profits.

Surgery appointment platforms can help alleviate the problem of “difficulty in accessing medical care”. However, the development of this model has long been constrained by relatively low medical quality and insufficient traffic conversion. How platforms and primary-level hospitals can enhance medical quality through online-offline collaboration is therefore key to overcoming these challenges. From the perspective of medical quality, this paper constructs a complete-information dynamic game model between platforms and primary-level hospitals to examine their collaborative strategies. The findings show that the efforts made by both parties to improve medical quality are related to the initial medical quality of primary-level hospitals. When the platforms collaborate with primary-level hospitals whose overall initial medical quality is relatively low, a higher initial medical quality leads to a corresponding increase—either monotonic or wave-like—in the equilibrium improvement of medical quality, until a maximum is reached. Due to the free-rider effect, when platforms collaborate with primary-level hospitals whose overall initial medical quality is relatively high, a higher the initial medical quality instead results in a smaller equilibrium improvement in medical quality. When platforms collaborate with primary-level hospitals with a moderate level of initial medical quality, a moderate level of difficulty in improving medical quality is most conducive to overall quality enhancement.

Project material demand forecasting is a critical component of project management, playing an important role in ensuring the smooth progress of projects and reducing procurement costs. Traditional methods heavily rely on complete project information; however, in the early stages of engineering projects, information incompleteness constrains the forecasting lead time, reduces procurement flexibility, and poses significant challenges to demand planning. Moreover, project material procurement data are characterized by high dimensionality, sparsity, and heavy-tailed distributions, for which existing forecasting models exhibit limited predictive performance on extremely imbalanced datasets. To address these issues, this paper proposes a long-term project material demand forecasting method under conditions of incomplete information. By constructing an association graph structure, the method leverages complete information from similar projects to enhance prediction capabilities for early-stage projects, thereby significantly extending the forecasting lead time. In addition, to cope with data imbalance, a dedicated matching module and customized loss function are designed to improve prediction accuracy. Empirical analysis based on real project data from the State Grid Corporation of China spanning 2015–2023 demonstrates that transferring source-domain information from projects with complete information to early-stage projects with incomplete information can effectively enhance forecasting performance. Furthermore, when detailed project information is lacking in the early stages, standardized and fine-grained material coding schemes play a particularly important role in demand forecasting, with the precision contribution of a single indicator exceeding 20%. The proposed method not only achieves strong predictive accuracy in the early stages of projects but also outperforms traditional demand forecasting models overall. This paper provides an effective tool for project material demand forecasting and contributes to the enhancement of digital and intelligent operational management of engineering projects.

Deep-sea mineral development is a key potential pathway for achieving a green energy transition, but its commercialization is constrained by high technological risks and the complexities of research and development collaboration. This paper, employing a novel dual-type game-theoretic approach, constructs a mathematical model involving two deep-sea mining companies and one deep-sea mineral research institution, with polymetallic nodules used as a case study for simulation analysis. The results indicate that the effective application of high-quality research outcomes significantly influences pricing decisions and capacity investment in deep-sea minerals, while high market entry barriers encourage firms to engage more in non-price competition. The feedback mechanisms within the research and development cooperation loop enhance the optimal effort level of research institutions, incentivizing the production of more applied, high-level outputs. Additionally, government research funding provides direct support and incentives to research institutions, indirectly affecting the revenues of deep-sea mining companies and their investment decisions in research and development. This paper aims to provide a theoretical basis for the commercialization of deep-sea minerals, helping stakeholders balance competition and R&D cooperation in deep-sea mining, thereby promoting the sustainable development of the industry.

Against the backdrop of China’s vigorous promotion of shared manufacturing, rapid transformation and upgrading of manufacturing enterprises and the scientific design of sharing schemes have become important research topics. This paper focuses on the shared scheduling of machine resources in manufacturing enterprises. By considering characteristics such as fixed rental costs, unit variable rental costs, and shared-machine discount policies, it constructs a mathematical programming model with the objective of minimizing the sum of the maximum makespan and the total shared-machine rental cost. It examines two scenarios: identical and non-identical processing times. For the case of identical processing times, it derives key properties of the optimal schedule and designs an exact algorithm. For the case of non-identical processing times, it establishes a lower bound and proposes a 4/3-approximation algorithm. Finally, it conducts numerical experiments to verify the effectiveness of both algorithms. The results provide scientific decision-making support for machine resource sharing practices and production scheduling management in manufacturing enterprises.

The connection between supply chains, economic activities, and social life is becoming increasingly close. Terrorists may exploit freight systems to detonate destructive weapons, targeting densely populated areas and critical infrastructure, thereby posing serious security threats. Enhancing the capacity to respond to such risks requires interventions from the logistics security system. However, few studies systematically analyze supply chain security from the perspective of cooperation between government and enterprises. To address this gap, this paper applies game-theoretic models to supply chain logistics scenarios, investigating the mechanisms by which public-private partnership (PPP) models enhance emergency management effectiveness. The findings reveal that under a non-cooperative model, corporate supply chain security management tends to suffer from “principal responsibility failure”. In contrast, a government-dominated model may lead to emergency management efforts that are only formally rational, lacking substantive effectiveness. By comparison, public-private cooperation emerges as a key approach to improving supply chain security. Within such a cooperative framework, enterprises are better positioned to focus on specific emergency scenarios, while the role of government shifts from “regulation” to “governance”.

The empowerment of grassroots governance through digital technology has brought unprecedented opportunities and challenges to grassroots work in the contemporary era. How to effectively avoid the formalism trap in digital streamlining and solve the dilemma of “digital suspension” has become the core issue of empowering grassroots governance with digital technology. This paper constructs an SEIQR evolutionary game model, integrating the strategic interactions between local governments and the public into an improved infectious disease model. It deeply explores the dissemination and evolutionary mechanisms of the “digital suspension” phenomenon in grassroots governance, focusing on effective solutions from both behavioral evolution and value perception perspectives. The research findings reveal that higher cost of public information absorption hinders the control of negative administrative behavior, while higher public satisfaction helps to suppress its spread. Solely relying on government credibility-building cannot significantly improve the effectiveness of controlling negative behavior. The government needs to pay attention to factors related to the implementation of digital systems, such as costs associated with digital system construction, in order to better mobilize the enthusiasm of local governments and the public. Under conditions of bounded rationality, the strategic evolution of game participants is influenced by perceived gains and losses, with changes in the reference point for gains exerting a particularly significant controlling effect on negative administrative behavior.

To address the limitation of traditional binary financial distress prediction models in providing fine-grained, tiered early warnings, this paper constructs an interpretable multi-class financial crisis early warning model based on the fusion of financial and non-financial information. First, management discussion and analysis (MD&A) tone information is incorporated to enrich data sources for small and medium-sized enterprises (SMEs). Subsequently, random forest (RF), light gradient boosting machine (LightGBM), and support vector machine (SVM) models are utilized to predict the financial performance of SMEs, which are then further integrated using an improved Dempster-Shafer (D-S) evidence theory. Finally, the SHapley Additive exPlanations (SHAP) is introduced to facilitate interpretable analysis. The results show that the information-fusion model exhibits a 1.3% improvement in the F1 score compared with the best-performing base classifier, effectively avoiding prediction “disaster points.” The model also identifies key early warning indicators such as the debt-to-asset ratio, undistributed earnings per share, and return on equity. Overall, the proposed model achieves more accurate financial crisis classification and more stable predictive performance, thereby providing a novel perspective for financial crisis early warning research on SMEs.

Using management discussion and analysis (MD&A) texts of A-share listed companies in Shanghai and Shenzhen stock markets from 2005 to 2020, this paper constructs a green transformation index system for firms using Word2Vec, and enables an empirical examination of the spillover effects of peers' green transformation on focal corporate green innovation. The results show that peers' green transformation significantly promotes corporate  green innovation. This spillover effect is strengthened by higher media attention, greater reliability of information disclosure, and more intense industry competition. Further analysis explores the roles of corporate governance and financing constraints as mechanisms and transmission channels, and demonstrates that the spillover effect varies significantly depending on industry pollution levels, regional economic development, policy uncertainty, and ownership structure. This paper uncovers the real drivers of corporate green innovation from a novel perspective, extends the existing literature, and provides empirical and theoretical support for promoting industry-wide green coordination and informing government policy-making.

Technology-based enterprises have become a key carrier of China’s innovation-driven development strategy, and improving their green innovation capabilities is an important pathway toward achieving the “dual carbon” goals and advancing innovation-driven development. Spatially, technology-based enterprises have increasingly clustered into diversified and coordinated high-tech industrial agglomerations, providing new directions and impetus for achieving green innovation-oriented transformation. Using data from high-tech industrial zones and enterprise-level data of technology-based enterprises from 2011 to 2018, this paper empirically examines the impact and mechanisms of high-tech industrial agglomeration on corporate green technology innovation from the perspectives of substantive innovation and strategic innovation. The results show that high-tech industrial agglomeration significantly improves both substantive and strategic innovation, and the findings remain robust across a series of robustness checks. Comparatively, the effect on substantive innovation is more pronounced. High-tech industrial agglomeration promotes green innovation outcomes by increasing the research and development personnel and research and development expenditure in enterprises, thereby facilitating both substantive and strategic innovation. Government intervention not only has a negative impact on corporate green technology innovation, but also weakens the innovation effect of high-tech industry agglomeration, which has a restraining effect on corporate green technology innovation. Heterogeneity analysis shows that the positive effects of high-tech industrial agglomeration on green innovation are stronger in eastern regions, capital-intensive industries, and non-state-owned enterprises. This paper deepens the understanding of micro-level mechanisms through which high-tech industrial agglomeration promotes corporate green innovation and provides theoretical reference for the precise formulation of regional high-tech industrial development strategies and green innovation policies.

The rise of digital and platform economy has fostered new forms of employment represented by ride-hailing services, where workers provide services through internet platforms, with their labor processes deeply regulated by algorithms. Platforms and workers are connected through service agreements, which are less binding than traditional employment contracts, thereby highlighting the central role of psychological contracts. Accordingly, this paper examines the formation mechanism of psychological contracts among workers in new employment forms. Drawing on a survey of ride-hailing drivers’ occupational conditions and experiments simulating ride-hailing work scenarios, it examines how the characteristics of platform algorithmic allocation rules influence drivers’ psychological contracts. The findings show that the clarity of the platform’s compensation rules and the effectiveness of reward-and-penalty rules promote the formation of psychological contracts among drivers, with procedural justice playing a mediating role. This paper reveals the distinctive features of psychological contract formation between workers and firms mediated by algorithmic platforms, and, by comparing decision-making procedures in new and traditional contexts, elucidates the critical role of procedural justice in this process. The findings provide theoretical foundations and practical implications for optimizing platform economy governance and building harmonious “platform-worker” relationships.

Based on macro panel data from 2000 to 2020 across 109 countries, this paper, employing nonlinear regression and panel threshold regression methods, investigates the determinants of real effective exchange rates. Additionally, it explores the buffering effect of foreign exchange reserves and identifies their threshold values. The results indicate that real effective exchange rates are influenced by global economic shocks at both the trade and financial levels, with foreign exchange reserves providing a buffering effect against both types of global economic shocks. The heterogeneity analysis indicates that for countries with high levels of financial development, the impact of financial shocks is not significant, and more attention should be paid to trade shocks. For countries with lower levels of financial development, both types of shocks have significant impacts, and they should hold sufficient foreign exchange reserves to guard against these shocks. For countries with large industrial capacity and strong technological competitiveness, global economic shocks have no significant impact. However, for countries with large industrial capacity but weak technological competitiveness, there is an impact. The most significant impact of global economic shocks is on countries with small industrial capacity and weak technology. The threshold effect shows that when a country’s foreign exchange reserves as a proportion of GDP exceed a certain threshold, it can effectively resist global economic shocks. This paper provides policy implications for preventing global economic shocks, optimizing the scale of foreign exchange reserves, and maintaining the basic stability of our country’s exchange rate.

Using stock codes search data from A-share mainboard listed companies in Shanghai and Shenzhen markets, this paper constructs retail investor attention indicators at market, industry, and individual stock levels to investigate their impacts on stock returns. The findings reveal positive correlations between retail investor attention and same-day stock returns across all three dimensions. However, distinct patterns emerge in longer-term performance: industry-level and individual stock-level attention indicators exhibit long-term reversal effects on returns, whereas market-level attention demonstrates persistent influence without significant subsequent reversal. Further analysis confirms that market-level attention exhibits more remarkable persistence than industry and individual stock dimensions. After controlling for contemporaneous market-wide attention, industry- and stock-specific attention measures maintain their long-term reversal effects, consistent with their impacts on individual stock returns. These results remain robust after excluding extreme risk events, controlling for the December effect, and employing alternative measurement methodologies. Additionally, the impact of attention on stock returns displays heterogeneity across market risk states, manifesting more pronounced effects during high-risk market conditions.

Financial security is an important guarantee for the stable operation of a country’s macroeconomy. Faced with an increasingly complex global financial system, it is particularly important to accurately perceive and measure systemic risks and financial security status across countries. This paper builds a multi-quantile intelligent risk perception model and uses financial market index, commodity prices, and national macroeconomic state variables of G20 countries from October 2004 to September 2023 to measure systemic financial risks, risk sources, and security levels of different countries under different risk states. The results show that the spillover of financial risks faced by countries mainly originate from global stock markets, especially neighboring countries, while commodities and macroeconomic factors generally play a risk-mitigating role. China’s systemic financial risk and security level are relatively less affected by shocks from the global financial system but are more sensitive to its own economic conditions. In addition, systemic risks in developing countries are generally higher than those in developed countries, and their financial security levels—calculated under the unified consideration of external shocks and macroeconomic characteristics—are also generally lower. The intelligent risk perception model expands the research perspective on systemic risk and provides regulatory authorities with a practical tool for monitoring systemic risks and financial security levels under various risk conditions.

Based on the resource-based view (RBV), this paper elucidates the internal mechanism through which digital transformation generates an “incremental and quality-enhancing” effect on firms’ collaborative innovation, and analyzes the moderating role of organizational inertia and absorptive capacity. Using panel data from Chinese listed manufacturing firms from 2007 to 2021, the hypotheses are empirically tested with a two-way fixed-effect OLS (ordinary least squares) model. The results show that digital transformation significantly promotes both the quantity and quality of firms’ collaborative innovation. Organizational inertia positively moderates the effect of digital transformation on both the quantity and quality of collaborative innovation. Absorptive capacity positively moderates the effect of digital transformation on the quality of collaborative innovation, but does not significantly affect the positive correlation between digital transformation and the quantity of collaborative innovation. Digital transformation contributes more to the quantity and quality of firm collaborative innovation in high-tech industries and state-owned enterprises than in non-high-tech industries and non-state-owned enterprises. The findings extend the literature by shifting the focus of digital transformation’s impact on innovation from intra-organizational processes to inter-organizational relationships, enriching the application of the resource-based view in the context of the new wave of technological revolution, and providing further support for organizational evolution theory’s positive perspective on organizational inertia.

In response to the cross-border integration, digitalization, and intelligentization characterizing the modern service sector, this paper introduces value network theory and develops a competitiveness evaluation framework for modern service enterprises across three dimensions: “value creation, value delivery, and value acquisition.” Large-scale data imputation is performed using machine learning combined with the SCIS method, enabling the identification and positioning of modern service enterprises, and resulting in an analysis dataset covering 580 000 enterprises, including 791 listed companies. For indicator weighting, a combined approach of the CRITIC method and entropy method is employed. The evaluation results indicate that although competitiveness in industries and their sub-sectors has improved in vertical comparisons, overall levels remain relatively low, and multidimensional imbalances exist across industries, within industries, and among indicators. The framework developed in this paper is applicable to the analysis of modern service enterprise competitiveness in China, offering valuable theoretical and practical guidance for relevant enterprises and policymakers.

This paper examines a supply chain system composed of a manufacturer, an offline retailer, and an e-commerce platform. It develops a game-theoretic model under two online sales modes, resale and agency, and two scenarios of whether the e-commerce platform shares information, to explore how information-sharing strategies affect the manufacturer’s selection of online sales mode. The results indicate that the existence of an offline retailer can increase the manufacturer’s profit and influence its sales mode. When demand volatility is low (high), the manufacturer tends to adopt the agency (resale) mode. For moderate demand volatility, the platform’s information-sharing behavior can alter the manufacturer’s mode selection. When product substitutability is high, information sharing enables the manufacturer to select agency mode, achieving a triple-win situation for the manufacturer, offline retailer, and e-commerce platform. A shift from resale to agency mode by the manufacturer may also lead to Pareto improvement for the platform. Moreover, cost considerations affect the e-commerce platform’s information sharing decision, which in turn influences the manufacturer’s mode selection.