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This study empirically examines the impact of announcements on supply chain strategic collaboration (SCSC) on companies' shareholder value.

This study analyzes changes in shareholder value of companies listed in China based on data of 208 SCSC announcements. The signaling theory is applied to determine correlation among SCSC announcements and the market. An event study is used to estimate the stock market reaction to SCSC announcements. The common market model estimates stock abnormal returns after the event. The least squares method and regression model calculate the model parameter value.

There is a positive and statistically significant relationship between SCSC announcement and shareholder value. Market reaction to product development collaboration is significantly higher than to technology-sharing collaboration, market collaboration, and other SCSC types. The market reacts more positively to suppliers and companies with greater supply chain control power than to buyers and companies with lower control power. Announcements from the service supply chain can lead to stronger market reactions than those from manufacturing supply chains.

The findings provide a systematic assessment of how SCSC announcements contribute to firms' shareholder value. The result provides a benchmark of value promotion that can be expected from SCSC announcements.

This study fills the research gap that using secondary data to assess changes in companies’ shareholder value caused by SCSC announcements and firstly examines these changes by constructing the signaler–signal–receiver progress based on signaling theory. The research results provide a new reference and inspiration for deeper understanding of the impact mechanism of SCSC. Furthermore, this study contributes to the development of the signaling theory using an empirical study in an emerging market, China.

The purpose of this study is to examine the impact of the implementation of supply chain strategic collaboration (SCSC) on companies’ operating performance.

Based on 181 SCSC announcements of listed companies in China, this study analyzes changes in the operating performance of the sample companies in the 20 quarters after the announcement. The changes in different operating performance metrics for the sample firms are compared against their metrics before the announcement. This study uses a self-control model based on historical performance and uses a combination of adjustment percentage changes and adjustment level changes to measure performance changes.

SCSC helps to improve firm operating performance, although this effect is only evident after two years. Companies that collaborate on product development have better performance improvements than do companies that implement market collaboration. The operating performance of buyer companies is better than that of supplier companies. Finally, strategic collaboration in the service supply chain improves performance more than that in the manufacturing supply chain.

The finding that company performance varies in different situations can help managers better understand and manage SCSC.

This study newly uses secondary data to assess changes in companies’ operating performance brought about by the implementation of SCSC.

We investigate the impact of the creation of a new incentive structure for CEOs resulting from firms introducing equity-based compensation (EBC) as a means of paying top executives on policy decisions. Contrasting a firm’s stock and operating performance in the period the CEO is compensated with EBC (EBC period) and the period when EBC is not a component of the same executive’s pay (No EBC period) leads us to conclude that awarding stock options and restricted shares to executives is not associated with improved firm performance. However, firms initiate EBC after superior performance suggesting that CEOs are awarded compensation in this form as a reward for past performance. Firms have higher unsystematic and total risk levels in the EBC period suggesting EBC influences CEOs’ risk-taking behavior and reduces agency costs arising from managerial risk aversion. While there is no change in R&D expenses and cash ratios there is a decrease in capital expenditures in the EBC period, which is consistent with reduced overinvestment agency costs. Finally, leverage and payout ratios are similar in both periods implying that firms’ financing policy is not influenced by changes in CEOs’ compensation structure.

This study investigates the way in which acquisition-related human factors affect knowledge transfer in the context of Chinese cross-border M&A for strategic assets. The authors find that the process of knowledge transfer is reciprocal for revenue and cost synergies, including explicit and tacit knowledge. The establishment of joint ventures (JV) in China after the takeover boosts product-oriented knowledge transfer from overseas-acquired firms in mature markets to Chinese acquirers. The promotion of overseas synergies stimulates complementary knowledge transfer flow, which is reversely transferred from Chinese acquirers to overseas-acquired subsidiaries such as low-saving sourcing and new market applications. This study identifies three acquisition-related human factors that impact overseas knowledge senders for knowledge transfer. These human factors are implemented by Chinese strategic investors as new shareholders during the loosen integration phase. The first facilitator is all-round communication programs with top management involvement, aiming to build up constructive communication channels to boost knowledge transfer. The second facilitator is competence-based trust, which stimulates cooperation and application based on similar professional competence between Chinese acquirers and their overseas-acquired subsidiaries. The impeder is a high turnover of key skilled workers at Chinese acquirers to undermine the effectiveness of knowledge transfer.

This paper aims to present an unconditionally energy-stable scheme for approximating the convective heat transfer equation.

The scheme stems from the generalized positive auxiliary variable (gPAV) idea and exploits a special treatment for the convection term. The original convection term is replaced by its linear approximation plus a correction term, which is under the control of an auxiliary variable. The scheme entails the computation of two temperature fields within each time step, and the linear algebraic system resulting from the discretization involves a coefficient matrix that is updated periodically. This auxiliary variable is given by a well-defined explicit formula that guarantees the positivity of its computed value.

Compared with the semi-implicit scheme and the gPAV-based scheme without the treatment on the convection term, the current scheme can provide an expanded accuracy range and achieve more accurate simulations at large (or fairly large) time step sizes. Extensive numerical experiments have been presented to demonstrate the accuracy and stability performance of the scheme developed herein.

This study shows the unconditional discrete energy stability property of the current scheme, irrespective of the time step sizes.

The purpose of this paper is to establish a stiffness model of fixed joint considering self-affinity and elastoplasticity of asperities.

The proposed model considers that asperities of different scales are interrelated rather than independent. For elastoplastic contact, a spring-damper model and an elastic deformation ratio function were proposed to calculate the contact stiffness of asperities.

A revised fractal asperity model was proposed to calculate the contact stiffness of fixed joint, the impacts of the fractal dimension, the fractal roughness parameter and the Meyer index on the contact stiffness were discussed, and the present experimental results and the Jiang’s experimental results showed that the stiffness can be well predicted by proposed model.

The contradiction between the Majumdar and Bhushan model and the Morag and Etsion model can be well explained by considering the interaction among asperities of different scales. For elastoplastic contact, elastic deformation ratio should be considered, and the stiffness of asperities increases first and then decreases with the increasing of interference.

Achieve a lightweight design for a bogie frame while ensuring it meets strength requirements by conducting static and fatigue strength assessments and optimizing plate thickness.

Establish a finite element model and determine loads according to the UIC615-4 standard. Fatigue strength assessments are conducted using the structural stress method. Size optimization for plate thickness is performed with constraints on maximum static strength and total fatigue damage of the weld. Multi-objective optimization design is carried out using Isight software, with sensitivity analysis to identify key plates. The neural network model is chosen as the approximation model, and the NSGA-II multi-objective genetic algorithm is selected as the optimization algorithm.

The strength assessment reveals a significant margin. Through size optimization of plate thickness with constraints on static strength and fatigue damage, the frame’s mass is reduced by 9.59%, achieving a lightweight design while meeting strength requirements.

In many lightweight studies, the inclusion of fatigue assessment through the structural stress method in the optimization process is often overlooked. However, this paper addresses this gap by incorporating it and providing a detailed operational procedure. Such consideration holds reference value for the design of lightweight optimization, especially when fatigue strength is a critical consideration.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

The spatial concentration of the manufacturing employment and production is the general feature in all the economy. Indian economy is not an exception from this scenario. The spatial pattern of Indian manufacturing sector reports a significant regional disparity. Since manufacturing is becoming more vital in the Indian economy, it is essential to understand the spatial distribution of the manufacturing sector. This chapter provides a detailed picture of India’s spatial distribution pattern in formal and informal manufacturing sectors. Using the well-known Ellison–Glaeser index, we measure the industrial agglomeration for all National Industry Classification (NIC) three-digit industries and further incorporate statewise manufacturing activity distribution. From the analysis, it is evident that there is a substantial regional concentration of manufacturing activity; however, the recent regional distribution pattern also depicts some signs of industrial dispersion.