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The directional solidification Fe-B alloy was prepared. The microstructures and three-body abrasive wear behaviors of directional solidification alloy were investigated.

Fe-B alloy was melted in medium frequency induction furnace. The hardness was measured on HRS-150 Rockwell-hardness tester and HXD-1000TMC tester. The wear characteristic of the alloy was examined with a block-on-ring geometry. The worn surface of the alloy was investigated by scanning electron microscopy and laser scanning microscopy.

The wear weight loss and worn surface roughness increase with the increasing contact load in wear tests. When the worn surface is perpendicular to the boride growth direction, the highest hardness plane of the boride can resist abrasive effectively under the surrounding and supporting of the martensite matrix.

The relation between boride growth direction and wear direction will cause different boride breaking tendency and wear weight loss.

The purpose of this paper is to discuss characteristics of organic food value chain governance and policy tools that can increase the supply of good quality of agri-products.

This paper discusses a national organic food supply system in China, identifying the link between an organization form with a social confidence crisis and information asymmetry as the main challenges. It develops an analytical model of the market structure of organic certification based on the contract theory, which considers the certification incentive driven by both farmers and processors. Two cases of raw milk producers and processors provide empirical data.

The argument which is brought forward is that product information asymmetry together with strict requirement for ensuring organic food integrity brings the organic milk value chain into a highly integrated organization pattern. A tight value chain is effective in the governance of organic food supply chain under third party certification (TPC), while a loose value chain discourages producing organic products because of transaction costs. TPC is found to be a positively correlation with a tight value chain, but it brings high organizational cost and it raises cost for consumers.

This is the first paper discussing the governance of organic food value chain in Chinese milk industry.

Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing.

It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience.

The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics.

The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences.

This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.

An increasing number of users are inspired by enterprises to repost social media messages, which greatly contributes to the dissemination of such messages in an online social network. The purpose of this paper is to discover the repost patterns of users regarding enterprise social media messages to help enterprises improve information management abilities for social media.

This paper proposes a novel method to discover the repost patterns of users in enterprise social networking (ESN) at the macro-level through topic analysis. Specifically, it proposes the message-diversity metric to measure the latent topic diversity degree of the social media messages. Through this technique, the paper analyzes the message-diversity characteristics of the enterprise social media messages and then explores the repost patterns of users.

The experimental results show that a high repost rate is more prominent for the messages with diverse latent topics, where message-diversity is as high as 0.5.

The findings have great potential in several management areas, such as employing social media marketing, predicting popular messages, helping enterprises strengthen their online presence, and gathering more potential customers.

This study explores how the repost patterns of users in ESN can be determined through general macro-level behavior of users instead of their micro-level processes. The patterns can also lead to a deeper understanding of which contents can drive people to diffuse information. This study gives an important insight into the information behavior of social media users for enterprise management researchers.

The purpose of this paper is to investigate the impact of political instability at the local level on foreign firms in China. Building on the literature on political embeddedness and business power, the authors propose a theoretical framework to explain how political turnover can affect foreign firms’ performance and how they respond to such challenges by leveraging their power bases.

To test the hypotheses, the authors apply fixed effects regression to an unbalanced panel data set comprising 13,360 foreign firms from 1998 to 2013 and the political replacement that involved changes in provincial governors.

The findings confirm that political turnover incidents have a negative impact on the performance of foreign firms in China. However, the authors also found that this negative relationship is weaker for firms that can choose various types of power sources. Specifically, the study reveals that foreign firms with large firm size, government ownership and a strong foreign direct investment community are better qualified to mitigate the negative effects of political instability.

This study contributes to the literature by developing the understanding of how political uncertainties and risks affect the performance of foreign firms in China and the importance of firms’ power in counterbalancing these effects. The research provides valuable insights into how multinational corporations can exploit their power to manage the effects of local political turnover, which has practical implications for the strategy and management of foreign firms operating in China.

The purpose of this paper is to analyze the impact of diplomatic activities on outward foreign direct investment (OFDI).

The paper first develops a set of hypotheses drawing insights from politics, international business and institutional theory. It then tests these hypotheses by estimation of Panel Corrected Standard Error models, using the data of Chinese OFDI flow to 131 countries over the period of 2003-2010.

The main findings are: friendly bilateral diplomatic activities help OFDI in general; bilateral diplomatic activities provide effective support to some sensitive and important investments; and bilateral diplomatic activities play an important role in host countries where institutions are absent or poor in quality.

Friendly bilateral diplomatic activities provide strong support to multinationals investing abroad.

The paper incorporates a neglected but important factor, diplomacy, into a model to analyze its influences on OFDI. It investigates not only the direct impact of diplomatic activities on OFDI but also their moderating effect on other OFDI determinants, such as economic and institutional factors.

Multinational enterprises (MNEs) play a dominant role in the international business (IB) literature. Traditionally, by far the majority of IB studies deal with issues at the micro level of the individual MNE, or at the meso level of a sample of individual MNEs in industries. This paper focuses on the impact of MNE behavior through foreign direct investment (FDI) on a country’s international trade, and vice versa. In so doing, this study responds to a recent plea for more macro‐level studies in IB into the effect of MNE behavior on the macroeconomic performance of countries as a whole, particularly developing and emerging economies. In the current study, we focus on the largest developing or emerging economy of all: China. Applying sophisticated econometric techniques, we unravel the causality and direction of FDI‐trade linkages for the Chinese economy in the 1980‐2003 period.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.

To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.

The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.

The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.

This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

This paper comparatively assesses the connections between individual demographic traits, occupational characteristics, and organizational factors and officers' attitudes toward important groups in China and Taiwan.

Survey data used in this study were collected from 722 police officers from mainland China and 531 officers from Taiwan. Multivariate regression analyses were conducted to assess the correlates of police attitudes toward peers, supervisors, and citizens.

The Chinese and Taiwanese officers do not differ in their trust in peers, but the Chinese officers hold significantly more positive views on the trustworthiness of supervisors and citizens compared to the Taiwanese officers. Supervisor justice and organizational identification are significant predictors of officers' attitudes toward all three groups in both countries.

A major limitation revolves around the inability to test and explain exactly why findings from the two groups vary in their ways. Future research should include specific social, political, and cultural predictors.

This study represents one of the few studies that compare police attitudes toward important groups of peers, supervisors, and citizens across nations/cultures.