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This paper aims to explore the effect of e‐marketplace on the supply chain's performance under the markdown policy. Profit and risk analyses are both conducted and channel coordination issues are examined.

The paper presents a markdown policy supply chain analytical model with e‐marketplace and examines the optimal markdown policy. The mean‐variance theory is employed to study both the risk and profit residing in the supply chain. Extensive numerical analysis is conducted. The paper investigates both the cases when e‐marketplace selling price is exogenous and endogenous.

The markdown policy can coordinate the supply chain as long as the parameters satisfy certain analytical conditions. The expected profit and risk in the supply chain are both increased when e‐marketplace is introduced. The retailer shares a larger portion of the increased expected profit but at the same time bears a higher risk.

In this study, similar to the mainstream literature in the related area, the supply chain consists of one manufacturer and one retailer, and there is one single selling season with one product. Despite being able to generate interesting analytical results, this model fails to capture the more complicated real world practices.

The existence of e‐marketplace can be beneficial to the whole supply chain in terms of expected profit improvement. When the expected profit increase brought about by e‐marketplace is large enough to compensate for both the operational cost of e‐marketplace and the increase of the risk, the retailer could consider introducing e‐marketplace to dispose of the excess inventory.

This paper is an original work. It is based on the reviewed literature and the model with markdown policy is new. This could be a reference for further research into optimal performance in the supply chain with e‐marketplace.

Drawing on the stress and coping theory, conservation of resources (COR) theory and social role theory, this study aims to investigate the impact of social media overload on knowledge withholding behavior and examine the gender differences in social media overload, engendering knowledge withholding.

By hiring a professional online survey company, this study collected valid responses from 325 general social media users. The structural equation modeling (SEM) technique, bootstrapping method and multi-group analysis were used to test the proposed theoretical model.

The empirical results reveal that three types of social media overload positively affect users' knowledge withholding behavior and that emotional exhaustion significantly mediates the above relationships. The multi-group analysis demonstrates that gender differences do exist in the decision-making process of knowledge withholding; for example, females are more likely than males to become emotionally exhausted from social media overload, while males are more likely than females to engage in knowledge withholding behavior in the case of emotional exhaustion.

This study contributes to the existing body of knowledge by examining the relationship between social media overload and knowledge withholding, verifying the mediating role of emotional exhaustion as the key mechanism linking them, and narrowing the research gap of lacking gender differences research in knowledge withholding literature.

The purpose of this paper is to analyze the influence of different forms of RMB foreign exchange rates on Chinese foreign trade.

This paper constructed spatial panel model and Markov Chain Monte Carlo estimation method and collected the data of 25 countries’ (including China) quarterly macroeconomic data from first quarter of 1993 until third quarter of 2013 to conduct the data analysis.

This paper finds that USD/CNY, which is widely used in trade settlement, is more significant in effecting Chinese export. Totally, 1 percent appreciation of CNY against USD will lead to 1.532 percent decline of Chinese export, while 1 percent appreciation of CNY NEER only 0.42 percent. What is more, 1 percent increases of the volatility of USD/CNY results in 0.579 percent decline of Chinese export. As policy suggestions, we should further reform the foreign exchange derivative market in China, and provide more currency derivatives, so that the ability of Chinese economy to deal with foreign exchange risk could be improved.

Effect of exchange rate on imports and exports relates to the future direction of China’s exchange rate policy. This paper claims that China should accelerate the construction of foreign exchange derivatives market, improving the ability to respond quickly to foreign currency risk.

First, denominated exchange rate has more significant impact on the Chinese export trade to other countries than effective exchange rate. Second, the RMB exchange rate fluctuations also significantly affect the export trade. Third, China’s import and export trade have significant spatial effect.

This paper recommends the construction of the RMB currency futures market as soon as possible, providing a richer foreign exchange derivatives and other risk hedging instruments, thus to enhance the ability to respond to exchange rate risks.

This paper uses spatial panel model with the refined data to study various factors on the import and export trade, and thus more comprehensive analysis on the impact of the exchange rate on the import and export trade with other major countries.

The purpose of this paper is to examine the indigenous Chinese concept of suzhi (素质) with the aim of furthering the development of Chinese human resource management (HRM) research and practice.

An extensive review of the literature on suzhi, published in the West, as well as in China, is the basis for proffering an organizational-level conceptualization of suzhi in the Chinese context.

Instead of understanding it as a free-floating signifier, we argue that suzhi can be considered as a criterion-based framework for HRM research and practice. Suzhi research is classified into two major sources – indigenous Chinese and indigenized Western constructs. We further make a distinction between intrinsic and extrinsic suzhi, and analyze a popular set of suzhi criteria, considering de (morality) and cai (talent), while focusing on de in HRM selection (德才兼备, 以德为先). As multilevel and multidimensional framework, suzhi criteria may form different gestalts in different organizations and industries.

From a social cultural and historical perspective, HRM research that incorporates a combination of indigenous and indigenized suzhi characteristics may receive better acceptance by individuals, organizations and the society in the Chinese context. Accordingly, the reconstruction of suzhi into manageable and measurable dimensions can be undertaken for more effective HRM practice in the Chinese context.

The HRM literature is advanced by linking the indigenous suzhi discourse to Chinese indigenous HRM research and practice.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.