Bin Liu

Focusing on the dual-channel supply chain with live broadcasts selling, this paper investigates the service overflow of live broadcasts with Stackelberg game perspective and the impact of retailers’ different market potentials on the pricing decisions of dual-channel members. Meanwhile, it also evaluates the pricing strategy of online retailers after introducing KOL live broadcasts. The results show that when one of the dual-channel retailers adopts live broadcast sales, the live broadcast service overflow will have an adverse impact on it, but the degree of the impact depends on the market potential of supply chain members, and different power structures can be used to offset the adverse impact of live broadcast service overflow. Then, the live broadcast sales service overflow will have a certain beneficial impact on online retailers under certain circumstances. Furthermore, with the increase of live broadcast sales service overflow, the retail prices of both companies will decline, while the live broadcast sales service overflow is more beneficial for consumers.

Based on the conservation of resource theory, this manuscript explores the impact mechanism of the challenge and hindrance stressors on innovation performance, introduces emotional atmosphere as a mediation variable, and on this basis, it examines the moderating role of organizational climate on emotional atmosphere and innovation performance. A two-wave survey of 263 subordinates and 29 supervisors who come from multisource field offered support for our model. Results showed that challenge stressors have a positive effect on innovation performance, positive emotional atmosphere mediates the relationship between challenge stressors and innovation performance; hindrance stressors have a negative effect on innovation performance, and negative emotional atmosphere mediates the relationship between hindrance stressors and innovation performance. Organizational climate strengthens the positive relationship between positive emotional atmosphere and innovation performance and weakens the negative relationship between negative emotional atmosphere and innovation performance. This study enriches the existing literature by identifying the impact of stressors on employee innovation performance and has certain practical significance for optimizing the management of enterprises and improving employee innovation performance.

Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach.

Counterfeiting is common in many industries. For the authenticity of online channel products and to combat counterfeiting, many companies have begun to use blockchain technology to trace product information. This paper investigates a dual-channel supply chain consisting of one manufacturer and one retailer, in which the manufacturer sells its standard products through the retailer and adopts blockchain technology to launch the online channel to sell the traceable products. A Stackelberg game is developed to depict the pricing decision and channel strategy choice for the manufacturer. It shows that both the manufacturer and the retailer can benefit from blockchain adoption. And the longer it takes for product traceability, the lower the price of online traceable products. It is beneficial for the manufacturer when consumers in the market are less concerned about information transparency. For consumer sensitivity, as consumers’ acceptance of direct sales channels increases, the selling prices of online traceable products have risen. Furthermore, as the traceability time-sensitivity coefficient increases, the sales price of the traceable products decreases, and the retail price of offline standard products rises. The conclusion of this paper has a certain guiding role for the dominant manufacturers in choosing whether to apply blockchain technology to improve the demand for traceable products.

Rebate has long been a crucial tool that has attracted researchers from a diverse range of fields including marketing and supply chain management. When a manufacturer uses a retailer for reaching end customers, the rebate strategy undertakes an additional dimension. Here we show whether the two rebate strategies, manufacturer rebate and channel rebate, can be the optimal choice for the manufacturer and the retailer. And we aim at full coordination with rebate. Game theory is exploited to identify the equilibrium rebate decisions, which are fully characterized with two rebate strategies considering rebate sensitivity. Furthermore, we demonstrate how the decisions depend on parameters, such as market size, rebate redemption rate, and competition intensity in monopoly and duopoly supply chain systems. Our work also coordinates the supply chain with two coordination policies and examines if they can achieve full coordination. Counterintuitive findings suggest that the channel rebate with sensitivity and discrimination is not effective and the manufacturer rebate is the unique optimal option. Besides, the coordination can be realized with a centralized rebate in monopoly setting when the manufacturer forgoes her own interest. Then full coordination can be achieved in duopoly setting with a new coordination policy, rebate combination, given the redemption rate for the channel rebate is lower compared with the manufacturer rebate. Managerial insights are suggested that offering rebates with discrimination can have significant inventory and coordination policy implications and can lead to a double win under a well-controlled redemption rate.

Pharmaceutical supply chains are often highly complex with conflicting objectives of social welfare and profit maximization. Furthermore, there are various stakeholders including pharmaceutical manufacturer, distributors, retailers, patients, and the government. In this paper, we consider a two-stage supply chain consisting of one pharmaceutical manufacturer and a pharmacy with online and offline channels. We focus on four price cap models: no price cap regulation, pharmaceutical manufacturer’s price cap regulation, pharmacy price cap regulation, and linkage price cap regulation. We apply game theory, investigate how the price cap regulations affect the firms’ pricing, and evaluate the economic performance and social welfare of the dual-channel pharmaceutical supply chain. Our findings show that first, like the single-channel pharmaceutical supply chain, the profit of the regulated firm always decreases and the profit of the unregulated firm always increases when they are under one-sided price cap regulations. Second, the impacts of the linkage price cap regulation on the supply chain are more complicated depending on the linkage coefficient and market share. Overall, our findings can provide theoretical and practical insights to help the government devise price cap regulations for complex modern pharmaceutical supply chains.