Focusing on the carbon information disclosure behavior of low-emission-reduction manufacturers and the emission reduction technology licensing strategies of high-emission-reduction manufacturers in a duopolistic supply chain, this paper constructs three types of game-theoretic models under two scenarios—“no carbon information disclosure” and “carbon information disclosure”: no licensing, per-unit royalty licensing, and fixed-fee licensing. It investigates enterprises’ decisions on carbon information disclosure and emission reduction technology licensing within the supply chain. The results show that when a low-emission-reduction manufacturer chooses not to disclose carbon information, it will hinder the formation of licensing cooperation between the two types of manufacturers. For the low-emission-reduction manufacturer, regardless of whether carbon information is disclosed, accepting any form of emission reduction technology licensing improves its profit, and it shows a preference for the per-unit royalty licensing. In contrast, the high-emission-reduction manufacturer’s preference for licensing mode depends on the level of disclosure cost: when disclosure costs are low, it prefers the fixed-fee licensing mode; otherwise, it favors the per-unit royalty mode. Given that licensing cooperation is achieved, the disclosure of carbon reduction information by the low-emission-reduction manufacturer does not harm its own profit. However, whether this behavior benefits the high-emission-reduction manufacturer depends jointly on the licensing mode and the disclosure cost. When the low-emission-reduction manufacturer does not disclose information, both licensing modes are beneficial to the environment and society. When carbon information is disclosed, the fixed-fee licensing mode can achieve a win-win outcome in terms of environmental performance, consumer benefits, and social welfare.

Commercial vehicle manufacturers are actively deploying freight transportation platforms, which not only provide services to product buyers but also effectively stimulate the commercial vehicle sales market. From the perspective of the manufacturer expanding into freight platform, this paper innovatively explores the interaction between commercial vehicle sales decisions and freight service decisions. It focuses on a freight market composed of commercial vehicle manufacturers, logistics service providers, and shippers. It develops game-theoretic models under three strategies: no platform introduction, platform introduction, and platform openness. It further evaluates the impact of different cost structures and market environments on decision-making behavior. The results show that the introduction and continued operation of freight platforms depend on the scale of fixed costs and the potential demand in existing channels. Under certain conditions, platform introduction can achieve a win-win outcome for all parties. The unit value-added service revenue of the platform and the potential market size influence the platform openness decision. However, firms must undertake additional technological investments, and if investment efficiency is insufficient, excessively high value-added service revenue may instead hinder platform openness. In a monopolistic market, technological investment costs of the platform erode the introducer’s profits, whereas in a competitive market, the introducer can gain a competitive advantage through technological investment and achieve profit growth.

With the gradual deepening of population aging in China, the government has introduced various subsidy policies to promote the development of the elderly care service industry. However, given the large and growing elderly population, the continuous increase in subsidy expenditures has placed significant pressure on public finances. Taking the government’s no-subsidy strategy as a benchmark and incorporating budget constraints, this paper analyzes the impacts of three subsidy strategies on the elderly care service industry: subsidizing nursing homes, subsidizing consumers (the elderly), and mixed subsidies (i.e., subsidizing both nursing homes and the elderly). The findings indicate that subsidy policies do not always improve social welfare; excessive subsidies may instead reduce overall welfare. Compared with subsidizing nursing homes, subsidizing the elderly is not always the optimal strategy; the optimal choice depends on the size of the government’s subsidy budget. The mixed subsidy strategy is most sensitive to the government’s budget level and can achieve the highest level of social welfare.

With the rapid development of the platform economy, the reverse customization model—by directly connecting consumer groups with manufacturing factories—has gradually become a new paradigm for product value co-creation. While reverse-customized products bring great opportunities for platforms and suppliers, they are also accompanied by uncertainty and risks associated with market entry. How to decide whether to explore reverse customization information on platforms and how to share such information are crucial issues for platforms. This paper constructs a closed-loop supply chain system consisting of a single supplier and a single retail platform and designs three strategies considering the risk of reverse-customized products: no information sharing (NI), free information sharing strategy (FI), and toll information sharing (TI). It employs a Stackelberg game to derive equilibrium solutions. The results show that when consumer heterogeneity in product perception is low and the cost of new product improvement is at extreme levels, FI and TI strategies outperform the NI strategy in improving platform profits. However, as consumer heterogeneity and the cost of product improvement increase, both the platform and the supplier tend to choose the NI strategy. In addition, under the TI strategy, the platform’s commission rate has a positive effect on profits, indicating that the TI strategy is generally superior to both FI and NI strategies. As platform profits increase, there is greater incentive to invest in information technology to further mine consumer feedback, enabling more accurate identification of consumer preferences and consequently reducing suppliers’ costs of new product improvement. This paper provides a theoretical foundation and decision support for platform enterprises in terms of information sharing and value co-creation from the perspective of risks associated with new and existing products.

This paper investigates pre-sale strategies for a retailer ordering multiple products under a quick replenishment mode with inventory capacity constraints, considering both independent products and interrelated products. By constructing mathematical models and conducting optimization analysis, it derives the retailer’s optimal pre-sale pricing and initial order quantity decisions. Further numerical examples demonstrate that, compared with scenarios without pre-sales and quick replenishment, this supply chain can effectively control stockout costs while significantly reducing the risk of excess inventory and unsold products. At the same time, the pre-sale mechanism provides greater flexibility for price reductions, thereby attracting more consumers. The key contribution of this paper lies in incorporating inventory capacity constraints and warehousing costs as critical factors into the framework of pre-sale and replenishment policies, and in examining both independent and complementary multi-product settings. This extends the dimensionality of optimal decision-making from the commonly studied two-dimensional case to a multidimensional framework, greatly enhancing the model’s practical applicability.

Time-of-use (TOU) pricing is an important mechanism that leverages electricity prices as signals to guide users in shifting consumption from peak to off-peak periods, improving the balance between power supply and demand, and promoting renewable energy integration. In recent years, with the continuous increase in the share of renewable energy generation, optimization of TOU pricing policies and enhancing green electricity consumption have become key issues in the industry. Various regions have successively introduced related policy measures to facilitate renewable energy integration. Taking Shandong Province, which took the lead in implementing TOU pricing adjustment, as a case study, this paper treats the policy adjustment at the end of 2022 as a quasi-natural experiment, constructs a difference-in-differences (DID) model, and proposes an indicator termed “electricity consumption share price elasticity” for the first time to capture user consumption behavior. Using industrial and commercial electricity consumption data before and after the policy adjustment in Shandong, it analyzes the causal relationship between TOU pricing changes and variations in electricity usage across different time periods, and further evaluates their impact on renewable energy integration. The results show that both the DID estimates and elasticity measures indicate that service-sector users respond more actively to TOU pricing policies than industrial users. For the sampled firms, under the new pricing policy, during the period of highest renewable energy output (10:00~16:00), an additional approximately 1.77 million kW·h of renewable energy consumption can be accommodated. Based on these findings, it is recommended that TOU pricing policies should fully consider user consumption behavior characteristics, optimize time-period settings for different industries, and be implemented in coordination with other renewable energy support measures to maximize renewable energy integration.

Existing research on buffers mainly focuses on time buffers under single-stakeholder scenarios, while studies on resource buffers in multi-stakeholder contexts remain limited. Notably, current studies on project resource buffer allocation primarily center on commoditized resources and fail to adequately consider the characteristics and requirements of non-commoditized resources (NCRPE) provided by employers in buffer configuration. Therefore, directly applying buffer allocation theories for commoditized resources in single-stakeholder settings to NCRPE buffer configuration in multi-stakeholder project portfolios is neither reasonable nor scientifically sound. To address this issue, this paper considers both the production scale and supply intensity of NCRPE and proposes a method for determining resource buffer sizes that balances NCRPE production costs and idle resource efficiency. First, based on the NCRPE resource usage of each contract project within the project portfolio, the minimum supply intensity is determined, and corresponding supply thresholds are established to define adaptive buffer sizes for each contract project. Then, by incorporating three indicators—resource slack, slack dispersion, and resource tightness—a robustness metric is constructed to reflect the ability of idle resources to withstand disruptions in contract projects, and a robustness optimization model is developed accordingly. Finally, with the dual objective of optimizing NCRPE production scale and claim costs, an optimization model for claim costs in multi-stakeholder project portfolios is established through the reallocation of NCRPE resource buffers. The results show that the proposed method can simultaneously achieve dual optimization of NCRPE production scale and the owner’s claim costs, with a more pronounced improvement observed in the latter. The construction cost of the NCRPE production system and the effects of economies of scale are identified as the two main factors influencing resource buffer settings under NCRPE constraints, which differ from the influencing mechanisms observed in buffer allocation for commoditized resources under single-stakeholder scenarios. This paper provides practical guidance for project owners in setting resource buffers for project portfolios and offers a reference for further research in this area.

With the rapid development of digital technologies, the application of finance technology (fintech) has provided new solutions for supply chain finance. Digital supply chain finance, as a product of the deep integration of fintech and supply chain finance, effectively addresses information asymmetry and plays a crucial role in enhancing efficiency, increasing trust, and reducing risk. This paper, employing knowledge graphs and theoretical analysis methods, based on literature published in CNKI and Web of Science databases from 2016 to 2023, comprehensively reviews and discusses the similarities and differences in publication volume, research methods, focal points, and evolutionary trends of domestic and international literature. Combining the characteristics of fintech, it clarifies the definition, features, and development path of digital supply chain finance, and constructs its theoretical analysis framework. It also systematically analyzes the enabling mechanisms and practical details of relevant literature in the three aspects of efficiency improvement, trust enhancement, and risk management optimization. It summarizes the impact and application of digital supply chain finance in promoting service efficiency, achieving financial innovation, and optimizing risk control. Finally, it outlines the development trends of digital supply chain finance in technology enablement, ecosystem construction, scenario application, and regulatory technology (RegTech) within digital supply chain finance, and proposes feasible future research directions, providing references for theoretical exploration and practical applications in related fields.

The technological innovation chain of major construction projects serves as a core driving force for national infrastructure development, yet its operation faces practical challenges such as insufficient motivation among participants, unstable cooperation, and fragmented innovation resources. To better understand the evolutionary patterns of stakeholder behavior, this paper is grounded in the practice of technological innovation in major construction projects and integrates stochastic evolutionary game theory with prospect theory, while introducing Gaussian white noise to capture real-world uncertainty. First, it constructs a tripartite stochastic evolutionary game model, involving innovation leaders, innovation participants, and government regulators. Based on this model, it analyzes the constraints and optimal strategy combinations governing the evolution of stakeholder behaviors in the innovation chain. It then conducts numerical simulations to reveal the effects of key factors, including initial innovation willingness, benefit-sharing mechanisms, and reward-penalty schemes, on stakeholder behavior, thereby clarifying the evolutionary mechanisms of innovation activities within the technological innovation chain. The results indicate that innovation leaders are the core actors in building the technological innovation chain of major construction projects, and their decisions are sensitive to factors such as initial strategy selection probabilities, risk preferences, and the degree of stochastic disturbances. The benefit-sharing coefficient serves as a crucial lever in motivating participants and requires dynamic adjustment to balance incentives and fairness. Government subsidies play a “catalytic” role, while penalties exert a “targeted deterrent” effect, suggesting the need for differentiated application. Moreover, the risk preferences of innovation agents, such as sensitivity to gains and losses and loss aversion, significantly influence decision directions and should be guided accordingly. These findings contribute to a deeper understanding of decision-making behaviors within innovation chains, providing theoretical support for optimizing the allocation of innovation resources and formulating precise strategies and policies. This paper also offers important practical implications for promoting the stable development of technological innovation chains in major construction projects.

To address group decision-making problems characterized by uncertain information and complex coupling relationships among attributes, this paper proposes a novel bidirectional inference decision model of cloud Bayesian networks based on Dempster-Shafer (D-S) evidence fusion. First, it constructs a fuzzy entropy membership function. By incorporating dynamic entropy weight factor and group consensus to quantify the fuzziness of expert evaluations, and combining Gaussian product fusion strategy, it develops a complete basic probability distribution (BPA) containing composite subsets. Then, it employs a basic trust harmonic function to optimize the evidence fusion process in the high-conflict scenarios. Next, it uses a probability transformation method to accurately allocate the uncertain probabilities within composite subsets to individual subsets, thereby obtaining prior probability distributions of nodes. Afterwards, based on the principle of minimum Gaussian cloud uncertainty, it establishes a bidirectional inference model of a Bayesian network, enabling both forward ranking of alternatives and backward causal analysis. Finally, it provides a numerical example to validate the effectiveness of the proposed model and to identify key sensitive factors. The results indicate that the proposed method can effectively capture the hesitation inherent in expert evaluation information, and enhance the scientific rigor and depth of decision-making by uncovering interactions among decision factors.

Against the backdrop of the “carbon peaking and carbon neutrality” goals and the global green transition, the construction of green collaboration networks to enhance green innovation capability and thereby promote green growth performance has become an important pathway to tackle global environmental challenges. Based on social network theory, this paper categorizes green collaboration networks into three dimensions: enterprise participation, government participation, and higher education and research institution participation. It constructs a theoretical framework linking “green collaboration networks-green innovation capability-green growth performance”, and further explores the underlying contextual mechanisms from the perspectives of environmental regulation, digitalization, and knowledge spillovers. Utilizing collaboration data from green projects under the European Union Framework Program involving 23 EU countries, the empirical analysis yields the following findings: First, the participation intensity of enterprises, governments, higher education and research institutions in green collaboration networks positively influence green growth performance, with enterprise participation exerting the most significant impact. Second, green innovation capability functions as a mediator between the green collaboration networks and green growth performance. Third, market-based environmental regulation exerts a positive moderate effect on the relationship between green collaboration networks and green growth performance, while command-and-control environmental regulation shows a negative moderating effect. In addition, the level of digitalization positively moderates the relationship between the green collaboration networks and green innovation capability, and knowledge spillovers positively moderate the relationship between green innovation capability and green growth performance. These findings elucidate the mechanisms of different participants in the green collaboration networks and provide theoretical support and practical insights for maximizing green growth performance through such networks.

For many high-tech and internet-related products, consumer utility partly depends on the scale of user groups, a phenomenon known as network externality. With the rapid development of information technology, network externality has become an important factor influencing product sales. This paper, employing a differential game approach, investigates the optimal dynamic product innovation strategies of firms competing in markets with network externalities. It considers both horizontal and vertical product differentiation, as well as the impact of reference quality on consumer purchasing decisions, and establishes sufficient conditions for the existence of a unique saddle-point steady-state equilibrium. The results show that network externalities stimulate competing firms to increase investment in product innovation and network expansion, while the reference quality effect plays an inhibitory role. In the long run, firm profits, social welfare, and consumer surplus increase with stronger network externalities and decrease with higher product substitutability, while being largely unaffected by reference quality. In addition, full cooperation among firms suppresses investment in product innovation and network expansion. Compared with the equilibrium under government regulation, firms’ independent decision-making leads to underinvestment in product innovation.

In the digital economy era, the entrepreneurial environment has undergone profound changes, and startups urgently need to accurately assess their ecological niche to overcome resource constraints. Existing studies mainly focus on ex-post performance evaluation and lack a dynamic niche measurement system. To address this gap, this paper is grounded in niche “state-situation” theory and integrates the SSIP process with factor analysis to develop a niche measurement scale for startups from the perspective of the entrepreneurial incubation chain. A system archetype of the startup ecological niche is constructed, and simulation analysis is conducted using Vensim PLE software to examine the positive feedback relationships among the system’s components and subsystems. Furthermore, an “one-body–two-wings” eigenvalue calculation model is designed, with niche breadth as the core dimension and niche overlap and niche suitability as auxiliary dimensions, to quantitatively evaluate the growth potential of startups at different incubation stages. The findings not only fill a gap in the application of niche theory to startup growth research but also provide a quantitative tool, through the “one-body–two-wings” eigenvalue model, to address key entrepreneurial challenges such as the “valley of death” survival dilemma and the identification of potential “unicorn” firms.

To promote the development of data trading and markets and address the challenges of high cost and computational complexity in traditional data valuation methods, this paper proposes a novel causal inference-based approach for assessing the incremental value of data: the data synthetic control method (DSCM). From the perspective of data buyers, DSCM treats newly acquired data as an intervention and innovatively constructs a counterfactual inference framework to accurately quantify the contribution of additional data to the performance of supervised learning models. In simulation experiments, DSCM’s estimates of data value for different models closely match the true data value, with an average error of only 0.0032. In a real-world application involving advertisement click-through rate prediction, DSCM achieves an average error rate of only 9% in estimating the value of user-centric data, significantly outperforming traditional evaluation methods. These results demonstrate that DSCM provides accurate and stable data valuation, effectively supporting data-driven decision-making for enterprises.

Amid the rapid development of the digital economy, core digital economy enterprises face significant challenges related to the mismatch between short-term borrowing and long-term investment. Leveraging the establishment of bankruptcy courts as a quasi-natural experiment, this paper examines how legal system development promotes high-quality development of core digital economy enterprises from the perspective of short-term loans used for long-term investment. Baseline regression results show that the establishment of bankruptcy courts significantly curbs such mismatched financing behavior, and this conclusion remains robust after multiple robustness checks. Further analysis reveals that bankruptcy courts mitigate short-term borrowing for long-term investment through two channels: the long-term capital supply effect and the risk suppression effect. This mitigating effect is more pronounced among non-state-owned enterprises, enterprises in digital technology application sectors, regions with more efficient law enforcement, and areas with higher levels of financial development. Economic consequence analysis indicates that the suppression of mismatched financing significantly improves total factor productivity and operational performance of core digital economy enterprises. These findings enrich the literature on the micro-level effects of legal system development and provide practical insights for promoting high-quality development of the digital economy and advancing bankruptcy system reforms.

To address situations in which firms fall into liquidity-driven financial distress, this paper designs a ratchet-style contingent write-down debt (RWDCs) instrument and develops a corresponding mitigation approach. The ratchet-style design of the coupon write-down clause overcomes the limitations of traditional “one-size-fits-all” full write-down provisions. Furthermore, it employs a finite-maturity structural model of RWDCs to construct a corporate security pricing model, within which the firm’s optimal capital structure is endogenously determined. The results show that issuing RWDCs can significantly reduce default risk and enhance firm value, demonstrating strong effectiveness and applicability in alleviating financial distress. Specifically, the optimal initial coupon write-down ratio of RWDCs is positively correlated with the firm’s operational risk, while the optimal proportion of RWDCs in total debt exhibits a decreasing-then-increasing trend. In addition, the ratchet-style design of the coupon write-down clause effectively reduces credit spreads, thereby enhancing the firm’s ability to mitigate liquidity financial distress.

Enhancing communication between external investors and corporate management through information technology and internet platforms is an important means to promote market efficiency and a key topic in the study of information asymmetry in the stock market. The online interactive platforms launched by China’s stock exchanges not only provide a venue for online interactions between small and medium-sized investors (SMIs) and management but also offer an ideal setting for empirical study of the above issues. This paper investigates the relationship between SMIs-management interaction and stock mispricing based on data from the online interactive platforms of the Shanghai and Shenzhen Stock Exchanges. The empirical results show that both the frequency of corporate responses and the timeliness of replies can significantly reduce the degree of stock mispricing. Further analysis of the underlying mechanisms from the perspectives of information demand and sentiment transmission reveals that the corrective effect of online interactions on mispricing is influenced by the level of information asymmetry and investor sentiment. Moderation analysis reveals that higher levels of information asymmetry and stronger investor sentiment weaken the effectiveness of online interactions in correcting stock mispricing. This paper provides additional evidence on the mechanisms through which communication between listed companies and retail investors affects stock mispricing, offers new empirical support for research on investor-management interaction, and enriches the literature on multi-agent problems involving major shareholders, minority shareholders, and corporate management in China’s stock market.

The use of visual contrast techniques in green brand advertising, specifically through juxtaposing different ecological scenes from left to right, can effectively capture consumers’ attention and stimulate reflection on environmental issues, thereby playing a significant role in the development of green industries. Drawing on four experiments, this paper systematically examines the matching effect between image presentation order in green brand advertisements and consumers’ thinking modes. The findings reveal that consumers with an experiential thinking mode exhibit more favorable brand evaluations when exposed to a “inferior-left, superior-right” image sequence, whereas those with a rational thinking mode are largely insensitive to such image order. Moreover, thinking mode significantly moderates this effect. This paper not only extends the application of visual contrast techniques in the context of green brand advertising but also, for the first time, deepens the understanding of how left-right image presentation order based on temporal metaphor influence consumer information processing and judgment mechanisms from the perspective of the interaction between processing fluency and thinking modes, providing novel strategic implications for enhancing green brand advertising communication.

In modern supply chain management, building efficient collaboration capabilities has become a key approach for firms to integrate supply chain resources, respond to market changes, and gain competitive advantages. However, traditional information technology (IT) applications are increasingly becoming a bottleneck to further improvement. Driven by contextual generative artificial intelligence (GenAI), cloud services have profoundly transformed firms’ IT usage patterns, reshaped IT governance mechanisms in supply chains, and significantly enhanced collaboration capabilities. Based on IT ambidexterity theory, this paper proposes the concept of “cloud service ambidexterity capability,” which is divided into two core dimensions: cloud service architecture modularity and cloud-business integration. Using survey data from 194 middle and senior managers of firms adopting cloud services, it employs partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the mechanisms and configurational pathways through which cloud services affect supply chain collaboration via IT governance. The results indicate that cloud service ambidexterity capability plays a significant role in promoting supply chain collaboration. Moreover, the higher the level of contextual GenAI orientation, the stronger the positive impact of cloud-business integration on IT governance. This paper reveals multiple configurational paths for achieving high levels of supply chain collaboration and provides new theoretical insights and practical implications for managers seeking to leverage cloud services to enhance collaboration under a GenAI-oriented context.