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The purpose of this paper is to identify the reasons for the differences of executives' compensation across industries from the managerial discretion perspective.

Based on the data from 37 manufacturing industries from 2002 to 2007 in China, managerial discretion for each industry is calculated regarding to the conception raised by Hambrick and Finkelstein which is further clustered into three groups. Then, regression model is used to testify the relation between managerial discretion and executives' compensation.

The executives' compensation is positively related to managerial discretion that is determined by the industrial environment. In the faster growing or higher competing industries, the executives tend to have more managerial discretion, thus they will be better paid due to the extensive latitude of their decision making.

To a certain extent, managerial discretion can be taken to measure the uncertainty or marginal productivity of the executives' work. From the industrial perspective, there are actually some factors far beyond the control of executives but influencing their pay.

When designing the compensation system for the executives, the industrial factors surely should be taken into consideration, to work out a fair and competitive incentive plan.

The paper proves a very important point in the issue of the decisive factors for executives' compensation. Managerial discretion raises the uncertainty and complexity to executives' work, thus it determines the compensation.

Drawing attention to the significant number of unsuccessful (abandoned) cross-border merger and acquisition (M&A) transactions in recent years, the purpose of this paper is to analyze three litigated cross-border inbound acquisitions that associated with an emerging economy – India, such as Vodafone-Hutchison and Bharti Airtel-MTN deals in the telecommunications industry, and Vedanta-Cairn India deal in the oil and gas exploration industry. The study intends to explore how do institutional and political environments in the host country affect the completion likelihood of cross-border acquisition negotiations.

Nested within the interdisciplinary framework, the study adopts a legitimate method in qualitative research, that is, case study method, and performs a unit of analysis and cross-case analysis of sample cases.

The critical analysis suggests that government officials’ erratic nature and ruling political party intervention have detrimental effects on the success of Indian-hosted cross-border deals with higher bid value, listed target firm, cash payment, and stronger government control in the target industry. The findings emerge from the cross-case analysis of sample cases contribute to the Lucas paradox – why does not capital flow from rich to poor countries and interdisciplinary M&A literature on the completion likelihood of international takeovers.

The findings have several implications for multinational managers who typically involve in cross-border negotiations. The causes and consequences of sample cases would help develop economy firms who intend to invest in emerging economies. The study also offers some implications of M&A for telecommunications and extractive industries.

Although a huge amount of extant research investigates why M&A fail to create value to the shareholders during the public announcement and post-merger stages, there is a significant dearth of research on the causes and consequences of delayed or abandoned national and international deals. The paper fills this knowledge gap by discussing an in-depth cross-case analysis of Indian-hosted cross-border acquisitions.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

This research examines whether anthropomorphizing artificial intelligence (AI) chatbots alters consumers' risk preferences toward financial investment options involving differential risks.

An experimental approach has been adopted with three studies, all featuring a between-subjects design.

Through three studies, the findings document that, in a financial decision-making context, anthropomorphizing AI leads to significantly greater risk aversion in investment decision-making (Study 1). This occurs because AI-enabled chatbot anthropomorphization activates greater psychological risk attachment, which enacts consumers to manifest stronger risk aversion tendency (Studies 2 and 3).

Anthropomorphizing AI has undeniable relevance in the contemporary marketing landscape, such as humanoid robotics and emotion AI algorithms. Despite of anthropomorphism's significance and relevance, the downstream impact of anthropomorphism remains unfortunately underexplored.

Wool, mainly composed of keratin, is a relatively high-grade clothing material. Although woollen textile has the advantages of high wearing comfort and excellent warmth retention property as we have known, its inherent disadvantage of easy pilling has easy puzzled people for a long time. Most of the existing technologies for pilling resistance are not eco-friendly or severely damaged the internal structure of wool.

In this work, a controlled and effective surface treatment method was proposed to controllable micro-dissolution the scale layers of wool with minor damage to its internal structure, thereby improving the anti-pilling property of wool. Thiourea dioxide (TD) is used as a dissolving agent to swell and dissolve wool surface flakes. After TD treatment, the morphology changes of wool fibers were observed by scanning electron microscope (SEM) and methylene blue staining. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to characterize the structural changes of TD wool. At the same time, the anti-pilling properties and wettability of wool fabrics were tested.

The results show that the wool scale layer is destroyed after TD treatment, which reduces the friction between fibers and improves the anti-pilling performance of wool fabrics. The methylene blue-stained images further demonstrate that low concentrations of TD can damage the superficial scale layer of wool without significant loss of strength.

This method is simple, eco-friendly and economical, and opens up a new direction for the surface treatment of wool fabrics.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.