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The purpose of this paper is to explain how individuals form microblogging habits and why they continue to use microblogs from the perspective of direction social networks.

Drawing on the social network theory and the social presence theory, the authors develop a theoretical framework to explain how individuals form microblogging habits and why they continue to use microblogs. To test the proposed model and examine its external validity, the authors collected data from two microblogs: Twitter and Sina Weibo.

Satisfaction and habit have a significant influence on microblogging continuance intention. Whereas, users’ microblogging habits are developed by two key factors – satisfaction and frequency of past behavior – that are further determined by social presence and social network centrality.

Larger sample size with diverse populations is highly recommended for future studies. In addition, exploring the distinct technical functionalities of microblogs when conceptualizing habit formation would be of benefit in future studies.

In this study, it was found that social presence increases both the satisfaction of users and the frequency of past use behavior. Hence, microblog designers should provide users with greater freedom to modify the form and content of their interface, and enable these modifications to be visible in real time to increase the interactivity of microblogs.

In contrast to past studies that have largely neglected the impacts of the directed social network structure, this study aims to focus on microblogging continuance intention from the directed social network perspective. The results from two independent data sets converge on the conclusion that users’ continuance intention to use is affected by both their conscious evaluations (i.e. satisfaction) and unconscious reactions (i.e. habit).

This study aims to complement existing studies by investigating the impact of different corporate social responsibility (CSR) information disclosed by peer listed stars (i.e. governance information [GI] and output information [OI]) on focal firms’ responsive CSR (RCSR) and strategic CSR (SCSR) practices. The authors also investigate the influence of different boundary conditions (i.e. founders’ social status [SS] and industry pollution intensity).

Based on the listed stars of 16 industries and their 4,096 private peers in China, the authors use the least squares method and logistic regression models to analyze the data set.

The results indicate that the GI of peer listed stars can only positively affect firms’ RCSR behavior. The OI of peer listed stars has a positive effect on firms’ SCSR behavior while negatively affecting firms’ RCSR behavior. The SS of focal firms’ founders and their interaction with the industry’s pollution level strengthen the abovementioned positive relationships while weakening the negative ones.

This study provides insights into the role of listed stars in influencing peer firms’ CSR activities, offering important practical implications for both policymakers and managers.

This study extends the recent discussion on peer effects of CSR by elucidating the peer star effect on CSR and confirms that firms may adopt heterogeneous CSR practices to achieve sustainable growth by investigating peer firms’ different responses to their listed stars’ different CSR information. Moreover, by introducing the SS of founders and the pollution intensity of the industry as boundary conditions, this study enriches the research context on CSR activities.

The purpose of this paper is to discuss the influence of different aspects of top management team (TMT) functional background on short-term performance, long-term performance, innovation performance and oversea performance separately. This research aims to verify whether the social categorization theory and information and decision-making theory are applicable in listed companies of China’s information technology (IT) industry so as to provide key theoretical references for TMT enhancement ad corporate performance improvement.

This paper takes A-share listed companies in Shanghai Stock Exchange and Shenzhen Stock Exchange as its study subjects, and it chooses the data from 2004 to 2010 in all of the 105 companies in IT industry in terms of the classification of Wind Database. The stepwise multiple regressions were run utilizing the regression program in Statistical Product and Service Solutions (SPSS).

The research results show that the social categorization theory can better explain TMT’s influence on corporate performance. TMT functional heterogeneity does not contribute to improving corporate performance and shows significant negative influence on short-term performance and innovation performance in particular. Among the three basic functional backgrounds, TMTs dominated by “throughput” backgrounds show significant positive influence on short-term performance, long-term performance, innovation performance and overseas performance, and the influence turns out to be the largest among these three backgrounds. In terms of the three special professional experiences, top executives with overseas backgrounds have significant positive influence on all of short-term, long-term, innovation and overseas performances. Externally hired executives, however, would impede corporate innovation development, while those with government background would increase corporate overseas performance.

This paper analyzes the relationship between TMT functional background and corporate performance in a comprehensive way for the first time and then takes the lead in considering the dynamics and complexity of corporate performance as well as discussing the influence of TMT functional background on four corporate performances. This study not only supports the effect that the social categorization theory has on TMTs but also offers some inspirations on the development of China’s IT companies.

Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.

This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.

Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.

The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.

First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.

The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.

This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.

This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.

Traditional simulation research of geological and similar engineering models, such as landslides or other natural disaster scenarios, usually focuses on the change of stress and the state of the model before and after destruction. However, the transition of the inner change is usually invisible. To optimize and make models more intelligent, this paper aims to propose a perceptible design to detect the internal temperature change transformed by other energy versions like stress or torsion.

In this paper, micron diamond particles were embedded in 3D printed geopolymers as a potential thermal sensor material to detect the inner heat change. The authors use synthetic micron diamond powder to reinforced the anti-corrosion properties and thermal conductivity of geopolymer and apply this novel geopolymer slurry in the direct ink writing (DIW) technique.

As a result, the addition of micron diamond powder can greatly influence the rheology of geopolymer slurry and make the geopolymer slurry extrudable and suitable for DIW by reducing the slope of the viscosity of this inorganic colloid. The heat transfer coefficient of the micron diamond (15 Wt.%)/geopolymer was 50% higher than the pure geopolymer, which could be detected by the infrared thermal imager. Besides, the addition of diamond particles also increased the porous rates of geopolymer.

In conclusion, DIW slurry deposition of micron diamond-embedded geopolymer (MDG) composites could be used to manufacture the multi-functional geological model for thermal imaging and defect detection, which need the characteristic of lightweight, isolation, heat transfer and wave absorption.

This paper aims to develop a resonant accelerometer for structure optimization. The dynamic analysis of the resonator for resonant accelerometer are investigated.

First, the working principle and mechanical model of the resonator are introduced. Moreover, dynamic analysis of the resonator is used for the purpose of investigating the dynamic characteristics of the resonant accelerometer. Finally, to verify the feasibility of the proposed dynamic analysis method, resonant accelerometer 1g static tumbling experiments of resonant accelerometer are built.

It can be seen from the natural frequency and the resonator mode that only when the resonator root stiffness is much greater than the resonant beam stiffness, there will be appear corresponding interference mode, therefore,the resonator root stiffness is avoid too large in design. The stability analysis result of resonant beam under axial force show that the resonant beam parameters should be maintained a constant. At the same time, it is concluded from the vibration mode analysis for resonant beam that the influence of the beam thickness and beam errors on the first and second order modes is great. On the other hand, it is concluded from the test result that the designed resonant accelerometer sensitivity is 98 Hz/g, which shows that the dynamic analysis method is feasible.

The research may be significant in the field of resonant sensors, supporting a variety of practical applications such as phone and game.

This paper seeks to establish a foundation for designing and optimizing resonant accelerometer structure. To this end, the dynamic analytical method of the resonator for resonant accelerometer was discussed. The results of this research have proved that the dynamic analysis based on a resonator is an effective approach and instructional in practical resonant sensor design.

The purpose of this paper is to examine the effects of China’s Food Safety Law on its food industry.

First, an event study is employed to investigate the impact of regulatory changes on the food industry. Then the authors examine the association between the magnitude of cumulative abnormal returns (CARs) and firm characteristics in a cross-sectional regression framework.

The results suggest that the announcements of some important events during the legislative process do affect the investors’ expectations. Further analysis shows that some sub-industries, especially the dairy industry, underperform the others around the enforcement date of the Food Safety Law, indicating that investors expect more costs of compliance for the sub-industries with lower levels of food safety. Moreover, CARs are found to be positively correlated with firm size, implying that larger firms may benefit more from this food legislative reform than small ones.

Measuring the impact of regulatory changes on food producers and investors by stock market response could help regulators assess the effectiveness of regulation and amend the law accordingly.

Previous studies seldom empirically examine the effect of Food Safety Laws on China’s food industry and this study attempts to fill this gap, which contributes to extending the understanding of the impact of legislative reform or regulatory changes on related industries.