Technological innovation aims to improve product quality, while advertising investment serves as a signal to convey the product quality to consumers. These two elements represent internal and external corporate actions, respectively, and are closely interrelated. Coordinating their intrinsic relationship has become a pressing practical issue in recent years. Focusing on high-tech products characterized by both innovation and marketing intensity, this paper examines the dual dimensions of technological innovation modes (radical/incremental) and advertising investment strategy (no investment/investment). By considering both utility maximization and profit maximization, it constructs four two-stage game models under different scenarios and solved to reveal the interaction mechanism and optimal boundary conditions between internal research and development (R&D) (innovation mode) and external marketing (advertising strategy). Based on this, it further explores macro-level social welfare and micro-level shutdown of enterprises in the context of R&D innovation, thereby deepening the understanding of welfare economics and firm behavior theory. The results show that the two types of innovation modes and advertising investment can exhibit conditional complementarity, which serves as a prerequisite for joint decision-making by firms. In particular, when the unit production cost of innovative products shows a significant advantage, radical innovation and advertising investment exhibit synergistic effects. Macro-level social welfare is influenced not only by the relative degree of innovation, the strategic behavior of consumers, and unit production costs, but also by the chosen innovation-marketing decision combination. Under certain conditions, sales revenue in the first period may not fully cover the total costs incurred during the same period, including production costs, fixed costs, and advertising expenses (if any). In such cases, blindly ceasing operations may be an irrational decision; continuing production may instead represent the optimal course of action. The impact of R&D costs on optimal innovation decisions is primarily reflected in the combined effect of relative R&D efficiency and advertising investment strategy.

Remanufacturing, as an important component of sustainable development of service-oriented manufacturing market, not only promotes energy conservation and the circular economy, but also improves manufacturing efficiency while providing high-quality, affordable products. However, due to the influence of remanufacturing capacity, perceived value, production cost, and competition between new products and remanufactured products, determining optimal price in dynamic two-period scenarios remains a pressing challenge. To address this, a game model between service-oriented manufacturer and customers was developed to explore the effects of perceived value of remanufactured products and remanufacturing capability on pricing decisions. The results show that when the production cost of new products is low or moderate, the prices of new products in two periods remain the same. In other cases, service-oriented manufacturer set a lower price in the first period to stimulate sales, subsequently increasing the remanufacturing volume in the second period. With the increase of production cost of new products, service-oriented manufacturers gradually shift from no manufacturing to partial and finally full remanufacturing, with remanufacturing helping to reduce profit loss. Additionally, service-oriented manufacturers benefit from an increase in remanufacturing capacity and perceived value factor. These findings provide practical insights for service-oriented manufacturers in formulating pricing strategies for remanufactured products.

Against the backdrop of manufacturers selling products through e-commerce platforms and considering consumers’ traceability awareness, this paper develops a game-theoretic model to examine whether the platform should introduce a private label and adopt blockchain technology to enhance consumers’ perceived product quality. It explores the platform’s optimal private label introduction strategy under agency scheme and wholesale scheme when the positioning of the private label can be endogenously determined, and analyzes the impact of private label introduction on manufacturer profits and consumer surplus. The findings indicate that, first, when consumers exhibit low traceability awareness, the platform will always introduce a private label. If the cost of adopting blockchain is also low, the platform will use blockchain alongside the private label, and the application of blockchain will widen the horizontal differences between the private label and the national brand. Second, contrary to intuition, the likelihood of the platform introducing a private label under the wholesale scheme is not necessarily higher than under the agency scheme . This is because, under the wholesale scheme, the platform may invest in private label development but ultimately choose not to introduce it. Additionally, the introduction of a private label consistently harms the manufacturer’s interests; however, compared with the agency scheme, manufacturers are more resistant to the platform adopting blockchain in conjunction with private labels under the wholesale scheme model. Finally, numerical analysis shows that introducing a private label increases consumer surplus, whereas the adoption of blockchain does not necessarily yield the same effect.

Online reviews significantly affect consumer decision-making and supply chain operations. This paper uses Bayesian information updating to characterize the influences of online reviews on consumer purchasing decisions and utilizes game theory to explore the influence of existence of online reviews on supply chain operations and product quality. The results show that when the manufacturer is unable to adjust the product quality (i.e., quality is exogenous), online reviews can effectively help consumers reduce the high return rate caused by the uncertainty of product quality information; online reviews increase the profits of the manufacturer and retailer and improve consumer and social welfare if and only if the accuracy of product online reviews is sufficiently high. When the manufacturer can strategically adjust product quality (i.e., quality is endogenous), online reviews not only incentivize the manufacturer to improve product quality but also lead to a Pareto-optimal outcome for both the supply chain members and consumers.

As market competition intensifies, e-commerce platforms have increasingly launched consumer credit services. Suppliers that support such services can stimulate consumer willingness to purchase through consumption in advance but must bear the risk of bad debts caused by repayment defaults. Based on a market composed of an e-commerce platform and two vertically differentiated competing suppliers, this paper examines the launch and adoption strategies of consumer credit services by the platform and suppliers. The findings indicate that the platform will opt to offer the service when bad debt risk is low and the additional utility of consumer credit is high. Although adopting consumer credit allows suppliers to increase their prices and expand demand, it does not necessarily increase their benefits. Specifically, the high-quality supplier never introduces consumer credit independently; when the additional utility of the service is moderate, only the low-quality supplier adopts it; and when the additional utility is substantial, both suppliers are willing to adopt the service. Moreover, greater intensity of quality competition invariably increases high-quality suppliers to adopt consumer credit, while the low-quality suppliers may become less inclined to do so. Further analysis reveals that if both suppliers adopt the same consumer credit strategy, increasing quality competition always harms the platform. However, if the two suppliers adopt different strategies, the platform may benefit from intensified quality competition.

Under the context of asymmetric information regarding manufacturers’ emission reduction efficiency, and considering cap-and-trade system and consumers’ low-carbon preference, this paper develops a two-tier supply chain system consisting of a single dominant retailer and a single manufacturer. It develops three Stackelberg game models—centralized decision-making, decentralized decision-making with symmetric information, and decentralized decision-making with asymmetric information to examine how the retailer can design incentive mechanisms to induce the manufacturer to truthfully disclose its emission reduction efficiency, thereby achieving a coordinated optimization of both economic and environmental performance. The results show that under symmetric information, a two-part tariffs contract can ensure system-wide optimal performance. Under asymmetric information, a menu of contract enables information sharing. For manufacturers with high emission reduction efficiency, such a contract can coordinate the supply chain; however, the retailer must pay an information rent, which may increase or decrease depending on changes in the carbon price and consumers’ low-carbon preferences. For manufacturers with low emission reduction efficiency, the extent of system efficiency loss depends on the retailer’s accuracy in estimating the manufacturer’s emission reduction capability. Numerical analysis further indicates that strengthening carbon price regulations, promoting low-carbon consumption, and providing technical support for emission reduction are effective measures to help achieve emission reduction goals.

Focusing on the practical scheduling scenario of a single workstation task scheduling problem of “carton-transfer-unit” warehouse system under multi-path hybrid environments, this paper investigates a special  multi-trip mixed return vehicle routing problem. First, considering a hybrid path mode that includes both open and closed routes, it proposes an integer linear programming model to minimize the maximum completion time of mixed outbound and inbound tasks for robots. Next, based on the pick-and-place characteristics of robots performing, it integrates the population management mechanism of genetic algorithms to improve the adaptive large neighborhood search process, aiming to avoid premature convergence to local optima while balancing the trade-off between the convergence speed of neighborhood search and population diversity. Finally, the proposed model and method are numerically validated by simulations and comparative analysis using test instances of various scales. Experimental results against several baseline methods demonstrate that the proposed algorithm significantly improves convergence, stability, and convergence speed. The findings provide methodological reference and decision support for robot task scheduling in single-workstation settings in “carton-transfer-unit” warehouse systems.

Surface anomaly can lead to defects in the appearance, quality, and performance of industrial products, thereby reducing production efficiency and increasing safety risks, ultimately resulting in economic and reputational losses for manufacturing enterprises. Therefore, accurate identification and detection of surface anomalies in industrial products is of paramount importance. With the rapid development of artificial intelligence, computer vision techniques based on deep neural networks (DNNs) have emerged quickly and are extensively used in surface anomaly detection for industrial products. However, due to the scarcity of surface anomalies, uncertainty in defect types, and high cost of manual labeling, the recognition accuracy of DNNs remains suboptimal. To address this issue, this paper proposes a distributionally robust optimization generative (DRO-G) model with label smoothing (LS). This model operates in two phases. In the first phase, the regularization effect of LS is extended and demonstrated to be able to generate new images. In the second phase, the generated images are used to train DNNs for anomaly detection. Moreover, this paper constructs a label smoothing-stochastic gradient (LS-SG) algorithm to approximately solve the model. In the first phase, gradient ascent is used to incorporate the LS regularization effect into existing images and generate new ones. In the second phase, gradient descent is employed to train DNNs to identify anomalous images. Furthermore, this paper conducts simulation experiments using multiple types of surface anomaly data from four products categories: grid, carpet, wood, and screw, in the MVTecAD dataset. The results demonstrate that the proposed algorithm can effectively expand the surface anomaly dataset and improve the recognition accuracy of DNNs for certain product anomalies, while also exhibiting a degree of noise resistance. This approach not only assists enterprises in improving product quality and production efficiency but also offers an innovative solution for anomaly recognition and detection in industrial imaging. This approach not only aids enterprises in enhancing product quality and production efficiency but also provides an innovative solution for anomaly recognition and detection in industrial imaging.

This paper investigates the core challenges constraining inbound logistics operations of automotive parts under the integrated packaging and transportation mode. Two primary issues are identified: first, the lack of a multi-period scheduling mechanism for packaging containers; second, inefficiencies in delivery patterns and vehicle scheduling for full and empty containers within a single period. In response, it proposes a comprehensive solution integrating shared leasing and circular packaging. Based on a two-stage optimization framework, the first stage develops a multi-period supply-demand balanced scheduling model for circular packaging containers. An improved particle swarm optimization algorithm is employed to generate scheduling plans for full and empty containers. The second stage, leveraging the dual-cycle cross-docking operation mode of circular packaging suppliers, establishes a parts delivery route optimization model incorporating cross-docking tasks. An improved brainstorming algorithm is designed to solve the vehicle scheduling problem. Empirical results demonstrate that the proposed solution achieves efficient coordinated scheduling of full and empty containers and reasonable planning of parts delivery routes, providing a viable strategy for cost reduction and efficiency improvement in automotive parts inbound logistics.

Focusing on the resource-constrained project scheduling problem under uncertainty environments, this paper studies a reactive scheduling method that addresses uncertain activity durations. Specifically, when the baseline project schedule is disrupted, the goal is to quickly generate a new optimal schedule. As the new schedule will inevitably deviate from the original baseline, leading to certain impacts and losses, the scheduling objective is set to minimize such deviations and associated losses. First, by introducing resource flows to represent resource constraints, a 0–1 mixed integer linear programming (MILP) model is formulated. Second, given the NP-hard nature of the problem, an iterative algorithm is designed to efficiently and accurately solve the model. This algorithm leverages Lagrangian relaxation, duality and decomposition techniques, and Benders decomposition, combined with the subgradient method, to reduce computational complexity and enhance tractability. Finally, numerical experiments are conducted to test the effectiveness of the proposed algorithm. The results verify that the method can produce more accurate solutions for medium- and even large-scale problem instances, demonstrating both practical feasibility and computational efficiency.

Sudden bifurcations and reversals frequently occur in the evolution of online group opinions, posing significant challenges for governance by both governments and enterprises. As a complex system, online group opinion dynamics are difficult to explain and regulate effectively using traditional opinion dynamics models, system modeling, or control methods. To address this issue, this paper extracts group opinions and their influencing factors from online forums through text analysis, and constructs a cusp catastrophe model to represent opinion dynamics near critical bifurcation points. Additionally, it establishes a resilience index model to quantify the internal accumulation of pressure caused by external shocks, thereby enabling the monitoring of potential opinion mutations. Furthermore, it proposes a Q-learning-enhanced particle swarm optimization (PSO) algorithm to regulate the resilience index and maintain the stability of group opinions. Empirical validation using data from the “Meituan Delivery Forum” demonstrates the effectiveness of the proposed method and uncovers the regulatory patterns of group opinion control. This paper contributes a methodological innovation in controlling nonlinear behaviors in complex systems by integrating catastrophe theory, resilience analysis, and intelligent algorithms. It also provides a framework for identifying key control factors in managing sudden opinion shifts, offering decision-making support for social organization managers.

In recent years, reducing carbon emissions has become a shared global challenge, requiring international cooperation and coordination. Multinational enterprises can lower carbon emissions during production by adopting low-carbon technologies; however, determining the optimal timing for adopting such technologies has become a key concern for firms. This paper builds a transnational manufacturing network influenced by carbon tax policies, consisting of an original equipment manufacturer (OEM) and two contract manufacturers (CMs). By employing optimal stopping theory and game theory, it analyzes the critical and optimal production quantities for adopting low-carbon technologies under collaborative and Stackelberg decision-making scenarios, and explores the optimal timing for adoption. To mitigate the cost pressures from carbon taxes and rapidly increasing exchange rates, it proposes a cost-sharing contract. This contract effectively reduces international trade risk and helps the supply chain achieve optimal performance. The findings reveal that under collaborative decision-making, the critical production threshold for optimal stopping is affected by both carbon tax rates and market volatility. Raising the carbon tax rate lowers the threshold for investing in low-carbon technologies and accelerates their adoption; similarly, market stability also encourages adoption. Under Stackelberg decision-making, the optimal production quantity of firms is jointly influenced by exchange rates, carbon taxes, carbon emission volumes, and the carbon reduction efficiency of the low-carbon technology. As carbon taxes, exchange rates, and emission reduction efficiency increase, firms become more inclined to adopt low-carbon technologies. Overall, the government plays a vital role in promoting corporate adoption of low-carbon technologies and in advancing carbon emission reduction efforts.

From the intersecting perspective of carbon price volatility and market panic sentiment, and based on complex network theory, this paper develops a risk contagion model for corporate carbon trading networks under a coordinated carbon tax–carbon trading strategy. It theoretically analyzes how carbon prices and market panic influence counterparty risk contagion in carbon trading and simulates the disturbance effects of carbon price fluctuations, market panic intensity, and herd behavior. The findings indicate that under conditions of fluctuating compliance periods and insufficient carbon allowance fulfillment, the number of corporate bankruptcies exhibits an upward trend with volatility, leading to the largest scale of carbon trading counterparty risk contagion. In the same scenario, heterogeneity in asset size significantly affects the number of bankruptcies. Enhancing carbon risk awareness and consumer green preferences, along with reducing asset sell-offs and noise, and coordinating with moderate levels of negative media disclosure, information dissemination, and policy risk control, can effectively suppress counterparty risk contagion in carbon trading networks. When the carbon tax rate and the optimal carbon price under the coordinated carbon tax–trading strategy reach the same level, the emission reduction effect of the coordinated strategy is maximized, and the interactive effects among carbon price determinants have the least impact on counterparty risk contagion. Market panic sentiment amplifies the disruptive effect of carbon price on counterparty risk contagion, whereas carbon pricing can mitigate the disruptive effect of market panic on contagion among trading counterparties.

Preventing and defusing risks in the real estate sector is essential for maintaining national economic stability. The key lies in clarifying the intrinsic interrelationships among real estate risk factors and uncovering the underlying risk formation mechanisms. This paper proposes a node importance evaluation method that integrates node degree and constraint coefficients to address the limited discriminative power of traditional network analysis methods in identifying critical factors. By applying a TOP1 network to filter key interaction paths among risk factors, it reveals how risks emerge and compares the risk structures across different cities. The results indicate that consumer purchasing power and the availability of development funds are the core determinants of real estate market risk nationwide. However, city-level heterogeneity exists. In first-tier cities, risk is predominantly driven by land prices, whereas in second-tier cities, urbanization levels play a more significant role. Specifically, the key risk pathways in first-tier cities exhibit a “chain-like” structure (land price→consumer purchasing power→economic development level→land supply), while in other cities, the structure is more “star-shaped”, with consumer purchasing power as the central hub directly linked to multiple influencing factors.

This analyzes reviews the significance of brand power in the digital age and explores its central role in brand management. Brand power is not only a critical factor for enterprises to maintain competitive advantage but also serves as a pivotal driver for economic growth. Brand power can be understood as the market manifestation and reflection of brand equity. Therefore, research perspectives on the conceptual definition, structural dimensions, measurement methods, and evaluation models of brand equity are crucial for a deep understanding of brand power. Through a systematic review of literature on brand equity and brand power, this paper identifies the core influencing factors and evaluation methods of brand power. Based on this understanding, this paper comparatively explores various brand power evaluation models proposed by scholars and market institutions, and subsequently proposes a new framework for measuring and managing brand power in the digital age. This framework aims to provide both theoretical support and practical guidance for enterprises seeking effective brand power management in the evolving digital landscape.

With the extensive and in-depth application of new media technologies, the interaction between brands has become increasingly diversified. In recent years, brands have begun to interact with each other in the form of one-to-one, one-to-many, many-to-one or alternate teasing on social media, using anthropomorphic expressions, such as mutual blackmail, mutual teasing, and mutual praising, which is collectively defined as brand teasing. Brand teasing not only captures the attention of consumers, but also promotes brand communication effectiveness. Currently, research on brand teasing between two or more brands on social media platforms is still in its infancy, and the research on the influence mechanism of brand communication effect is scarce. Therefore, this field needs to be further explored to enrich its theoretical foundation. This paper focuses on typical cases of brand teasing and uses a multi-case study approach to explore the effect of brand teasing on brand communication effectiveness and its underlying mechanisms. The findings indicate that brand teasing has a significant positive impact on brand communication effectiveness. Both brand identification and brand experience play an intermediary role in the relationship between brand teasing and brand communication effectiveness. This paper not only interprets the influence mechanism of brand teasing on brand communication effectiveness, but also expands the typology of brand teasing, providing valuable references for future academic research, and offers practical managerial implications for brand practitioners engaging in brand teasing.

Based on a quasi-natural experiment of margin financing and securities lending, this paper uses a sample of A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2022, and empirically examines the impact of short selling via securities lending on firms’ ambidextrous innovation and its underlying mechanisms by applying a multi-period difference-in-differences approach. The findings reveal that short selling significantly promotes ambidextrous innovation. From a firm lifecycle perspective, short selling positively influences ambidextrous innovation in firms at the growth and maturity stages, but shows no significant effect on firms in the decline stage. Moreover, stock liquidity and stock volatility negatively moderate the innovation incentives induced by short selling. Mechanism tests indicate that short selling enhances market pricing efficiency through the information siphoning effect in capital markets. Further analysis finds that market pricing efficiency and analyst attention constitute the primary pathways through which short selling fosters exploratory innovation, while analyst attention serves as the key channel for promoting exploitative innovation.

With the rapid promotion of environmental, social, governance (ESG) concepts and the continuous advancement of environmental protection policies in China, corporate environmental performance disclosure and its impact have become a hot topic in academic research. This paper empirically studies the effect of environmental performance disclosure on institutional shareholding, using ESG overall and dimension scores of 1 172 A-share listed companies from 2009 to 2022 sourced from the Bloomberg database. The findings reveal that corporate environmental performance disclosure has a significant positive promoting effect on institutional investors’ shareholding behavior. This result remains robust under various tests, including difference-in-difference regression, alternative independent variables, instrumental variables, Heckman two-step method, varying lag periods, and quantile regression. The effect is more pronounced in state-owned enterprises, enterprises with environmental management systems, and heavily polluting enterprises. In addition, the impact of corporate environmental performance disclosure on institutional shareholding mainly promotes short-term institutional investors, with no significant positive effect on long-term institutional investors. This is attributed to the fact that environmental performance disclosure mainly promotes short-term stock price appreciation, but has no significant improvement on firms’ long-term value indicators. This paper suggests that although environmental performance disclosure positively influences overall institutional shareholding, this effect is primarily driven by speculative trading by short-term investors and fails to effectively enhance firms’ long-term value or attract long-term institutional investors. Therefore, stronger guidance from government and regulatory authorities is urgently needed to promote a virtuous interaction between ESG disclosure and sustainable corporate development.