This paper constructs a dual-channel Stackelberg game model including the benchmark wholesale model B, the manufacturer encroachment model E, and the front-end and back-end AI-enabled model A. Through comparative static analysis, it is found that the manufacturer's encroachment profit increases with the increase of consumers' green preferences and channel substitutability. When the effective market capacity is positive, the manufacturer's profit under the encroachment strategy is better than that of the benchmark model, and the retailer's profit under the encroachment model is not lower than that under the benchmark model. Additionally, the optimal AI investment increases monotonically with the effective market capacity and efficiency parameters and decreases monotonically with the cost coefficient. When reaching a certain threshold, it shows a significant upward trend. Moreover, the stronger the channel substitutability, the lower the threshold. Under given constraints, the profits of both the manufacturer and the retailer in model A are significantly higher than those in other models. Therefore, moderate front-end and back-end AI collaboration is the key to achieving the triple goals of "encroachment + green + digitalization" and achieving a win-win profit situation between the manufacturer and the retailer. This provides a theoretical basis for enterprises to design direct sales strategies, AI investment intensity, and for governments and platforms to formulate subsidy and cost-sharing policies.

Amid increasingly stringent environmental regulations and Chinese “dual-carbon” goals, the manufacturing sector faces simultaneous pressures of economic performance and environmental responsibility. Remanufacturing, with its advantages in energy savings, emissions reduction, and resource reuse, has become a key pathway to green manufacturing; however, market growth is hampered by consumers’ distrust of product quality and information opacity. This study incorporates potential manufacturer encroachment and introduces blockchain technology into the remanufacturing market. We build a two-echelon supply chain model consisting of a manufacturer and a retailer, and develop three variants: a baseline model, a blockchain cost-sharing model, and a manufacturer-encroachment model. Using a Stackelberg game framework, we analyze the optimal decisions and profit outcomes of the manufacturer and the retailer under different blockchain cost levels, degrees of consumer awareness, and encroachment intensities. The results show that blockchain enhances consumer trust by improving information transparency, but its economic benefits depend critically on implementation costs and consumers’ understanding of product information. When encroachment intensity is moderate, both the manufacturer and the retailer can achieve a win–win outcome, with higher overall supply-chain efficiency and increased sales of remanufactured products. Further analysis reveals a misalignment between the manufacturer’s profit-maximizing point and the environmentally optimal outcome; this can be reconciled through piecewise government subsidies. This paper enriches research at the intersection of blockchain and green supply-chain governance and provides theoretical guidance and practical implications for firms formulating remanufacturing strategies and for policymakers designing green incentive mechanisms.

Amid increasingly stringent environmental regulations and Chinese “dual-carbon” goals, the manufacturing sector faces simultaneous pressures of economic performance and environmental responsibility. Remanufacturing, with its advantages in energy savings, emissions reduction, and resource reuse, has become a key pathway to green manufacturing; however, market growth is hampered by consumers’ distrust of product quality and information opacity. This study incorporates potential manufacturer encroachment and introduces blockchain technology into the remanufacturing market. We build a two-echelon supply chain model consisting of a manufacturer and a retailer, and develop three variants: a baseline model, a blockchain cost-sharing model, and a manufacturer-encroachment model. Using a Stackelberg game framework, we analyze the optimal decisions and profit outcomes of the manufacturer and the retailer under different blockchain cost levels, degrees of consumer awareness, and encroachment intensities. The results show that blockchain enhances consumer trust by improving information transparency, but its economic benefits depend critically on implementation costs and consumers’ understanding of product information. When encroachment intensity is moderate, both the manufacturer and the retailer can achieve a win–win outcome, with higher overall supply-chain efficiency and increased sales of remanufactured products. Further analysis reveals a misalignment between the manufacturer’s profit-maximizing point and the environmentally optimal outcome; this can be reconciled through piecewise government subsidies. This paper enriches research at the intersection of blockchain and green supply-chain governance and provides theoretical guidance and practical implications for firms formulating remanufacturing strategies and for policymakers designing green incentive mechanisms.

Under the dual carbon target regulation, this article constructs a collaborative research and development carbon reduction model for the photovoltaic industry supply chain from the perspective of carbon tax and consumer green preferences, using differential game theory. Considering three different scenarios of no research and development carbon reduction, independent research and development carbon reduction, and collaborative research and development carbon reduction, the optimal factors and profit values are obtained, and case analysis and sensitivity analysis are conducted. Research has found that: 1) The optimal carbon reduction achieved by photovoltaic industry supply chain entities through cooperative research and development is higher than that achieved through independent research and development. 2) The increase in carbon tax rates has to some extent increased carbon emissions, but at the same time reduced the overall profit of the photovoltaic industry chain. 3) The higher the proportion of research and development costs borne by photovoltaic system manufacturers, the higher the carbon emission reduction of photovoltaic silicon wafer suppliers' research and development. 4) Consumer green preferences are beneficial for increasing carbon emissions reduction in the photovoltaic industry chain.

Based on the perspective of supply-demand imbalance, this article constructs a collaborative innovation decision-making model for the photovoltaic industry supply chain using differential game theory. It analyzes two different decision-making scenarios: decentralized decision-making and centralized decision-making, and focuses on studying the impact of cost sharing contracts under decentralized decision-making and revenue sharing contracts under centralized decision-making on the decision-making behavior of photovoltaic industry supply chain entities. Research has found that: 1) The supply-demand imbalance coefficient affects the pricing of photovoltaic silicon wafer suppliers. 2) In the case of decentralized decision-making, considering the cost sharing contract situation in the photovoltaic industry supply chain system can better optimize the profit value and innovation level of each subject in the photovoltaic industry supply chain. 3) The benefit sharing contract under centralized decision-making is more effective than the cost sharing contract under decentralized decision-making.

The study firstly tries to segment the value of telecommunication customers through data mining methods, and introduces variable convolution on the basis of depth-weighted K-mean algorithm for improvement. Meanwhile, grid search is introduced on the basis of extreme gradient boosting tree for optimization, and finally a telecom customer recommendation marketing model is proposed by combining the two optimization algorithms. The experiments use a publicly available dataset from Kaggle that contains telecom customer behavior data, call records, billing records, and service usage from China in 2019, totaling about 2 million pieces of information. The experimental results show that the highest value of classification accuracy of the improved depth-weighted K-mean algorithm is 95.5%, and the highest separation degree is 96.3%. In summary, the proposed model can effectively categorize telecom customers and rationally implement telecom product recommendation. The study aims to provide telecom companies with more accurate marketing decision support to improve customer satisfaction and market competitiveness.

This study investigates the generalized stiffness of laterally functionally graded materials (LFGMs) and applies these findings to dynamic beam elements. The generalized stiffnesses of LFGM, coupled with material and cross-sectional properties such as flexural and axial rigidity, mass per unit length, and mass-moment of inertia, are explicitly formulated. In the context of LFGM, material properties depend on an asymmetrical power law function with respect to cross-sectional depth. An example of the generalized numerical stiffness of a circular cross-section is provided for various material properties. To illustrate the application of generalized stiffness to dynamic beam elements, free vibration of LFGM beams with rotary inertia is considered. The dimensionless differential equation governing the free vibration of such beams is derived and numerically solved to obtain natural frequencies and corresponding mode shapes. Numerical results demonstrate a good consistency with the finite element method.

Colleges/universities outside the Seoul metropolitan area (hereafter “non-Seoul universities”) are in a crisis of existence due to a decline in the school-aged population in South Korea. Most non-Seoul universities are making efforts to implement self-rescue measures for survival. This study examines the relationships among different types of organizational culture (innovative, collective, rational, and hierarchical), organizational silence (acquiescent, prosocial, and defensive), and faculty–student interaction as perceived by non-Seoul university professors to provide useful implications for effective human resource management plans for the professors, who are responsible for entrance examinations and education at non-Seoul universities in crisis. An online survey was employed to collect data from professors at private community colleges/universities outside the Seoul metropolitan area. Responses from 421 professors were used for data analysis. Results are as follows. First, when professors perceived innovative and collective cultures, their defensive silence decreased while prosocial silence increased. Second, when rational culture was perceived, defensive silence increased while prosocial silence decreased. Third, acquiescent silence increased while prosocial silence decreased when hierarchical culture was perceived. Fourth, faculty–student interaction was enhanced when innovative and collective cultures were prevalent. Finally, prosocial silence had a positive effect on faculty–student interaction. The findings imply that if university managers change policy to encourage professors to perceive the university culture as innovative and collective, prosocial silence, which is positive from the organization’s perspective, will increase, facilitating interactions between professors and students.

The main objective of this paper is to investigate the effect of affective and cognitive trust in bank advertising toward its potential customers. An advertising experiment was conducted by creating two bank advertisements focusing on affective and cognitive trust in bank advertising. SEM analysis is conducted with a sample of 254 customers who banked with the Central Bank of Ghana using STATA 14.1. Results showed that customers of a bank are more drawn to affective advertising than cognitive advertising. Results also showed that cognitive trust is a stronger predictor of anticipation towards future interactions with a bank than affective trust. Uncertainty and commitment had insignificant relationships with affective trust. Cognitive trust had a positive relationship with commitment, while affective trust had no significant relationship with commitment. Managerial implications and future research of the findings are discussed.

This study considers a sequential batching problem with a minimum quantity commitment (MQC) constraint in N-level non-exclusive agglomerative hierarchical clustering structures (AHCSs). In this problem, batches are created for item types included in clusters according to the sequence of the levels in a given AHCS such that the MQC constraint as well as the maximum and minimum batch size requirements are satisfied, simultaneously. The MQC constraint ensures that more items than a committed minimum quantity must be batched at a level before items not batched at the level are sent to the next level. We apply the MQC constraint to control effectively the degree of heterogeneity (DoH) in the batching results. We developed a sequential batching algorithm for minimizing the total processing cost of items using properties identified to find better solutions of large-sized practical problems. Results of computational experiments showed that the developed algorithm found very good solutions quickly and the heuristic algorithm could be used in various practical sequential batching problems with the MQC constraint such as input lot formations in semiconductor wafer fabrication facilities, determination of truckloads in delivery service industry, etc. Also, we found some meaningful insights that dense cluster, small batch size, and tight MQC constraint are effective in reducing the total processing cost. Additionally, small batch size with loose MQC constraint seem to be helpful to reduce the DoH in the batching results. Finally, we suggested that the density of cluster, batch size, and MQC tightness should be determined simultaneously because of interactions among these factors.