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The objective of this study is to examine the effects of pictures (consumer pictures vs. product pictures vs. no pictures) in online consumer reviews on product evaluation and to determine the mechanism and boundary conditions behind such effects.

The research consisted of three laboratory experiments.

The results showed that consumer pictures led to the most favorable product evaluation. Study 1 showed that persuasive effect was the mechanism behind the main effect. Study 2 showed that for problem-solving products, consumer pictures increased product evaluation significantly; for enhancing products, there was no significant difference of product evaluation among consumer pictures, product pictures and no picture. The results of Study 3 showed that for the unfamiliar brand, consumer pictures significantly enhanced product evaluation; for the highly familiar brand, there was no significant difference among consumer pictures, product pictures and no picture. The present research used persuasive effects to examine the mechanism behind the interaction effects.

The study provides managerial implications for online store owners about how to manage pictures in online reviews.

This study supplements the literature on online consumer reviews and enriches the study of effects of pictures.

The aim of this study is to investigate how social media users' experience of seeking emergency information affects their engagement intention toward emergency information with a reciprocity framework integrated with information adoption model.

Drawing on reciprocity theory, indebtedness theory, and information adoption model, an integrative research model is developed. This study employs a questionnaire survey to collect data of 325 social media users in China. Structural equation modeling analyses are conducted to test the proposed theoretical model.

Social media users' experience of seeking emergency information has a strong effect on their perceived information usefulness and indebtedness, while perceived information usefulness further influences community norm, indebtedness, and engagement intention. The authors also found that perceived information usefulness mediates the relationships between experience of seeking emergency information and community norm/indebtedness.

This study offers a new perspective to explain social media users' engagement intention in the diffusion of emergency information. This study contributes to the literature by extending the theoretical framework of reciprocity and applying it to the context of emergency information diffusion. The findings of this study could benefit the practitioners who wish to leverage social media tools for emergency response purposes.

In recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal.

Combined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C-marking method and Z-marking method.

Throughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately.

The G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.

Greenhouse gas (GHG) emission has a detrimental impact on climate change. There is an increasing trend for firms to use disclosure to signal stakeholders about its environmental responsibilities and performance in dealing with climate change. China is one of the countries producing the most carbon emissions. Over the last decade, Chinese state-owned enterprises (SOEs) are becoming important players in international trade. However, the existing literature provides limited evidence on how Chinese SOEs influence GHG disclosure. Through the lens of stakeholder–agency theory, this chapter studies the top 300 listed firms to examine the relationship between Chinese SOEs and the likelihood of GHG disclosure. The result suggests a negative relationship between Chinese SOEs and the likelihood of GHG disclosure. This could be explained as a consequence of the managers' political self-interests, economic and policy-oriented decision-making process and the power differentials between the government and SOE managers. This research extends the GHG literature to Chinese SOEs context, providing direct evidence on how state ownership impacts on GHG disclosure.

The purpose of this paper is to apply an empirically based approach to develop a decision-making model that comprehensively incorporates the potential affecting factors and the related significant drivers that support network designers in selecting the appropriate strategic supply chain configuration or checking the coherence of an existing supply chain structure in four industry sectors.

The decision-making model is developed based on an empirical study that integrates multiple case studies and statistical analyses. In total, 113 best-in-class manufacturing firms in four sectors are studied to investigate their strategic supply chain configurations and the information of identified affecting drivers. The factor analysis and regression analysis are conducted to classify the drivers into five factor groups, and to identify the significant drivers used to develop the decision-making model.

The findings of this research are three-pronged. First, 12 significant drivers related to 5 factor groups affecting strategic supply chain network design (SCND) are identified. Second, a decision-making model is developed to support users in strategic SCND. Last, the main characteristics of various strategic supply chain configurations are summarized in four industry sectors.

The authors identified valuable insights for both academics and practitioners based on the identified significant affecting drivers and the developed decision-making model. In addition, this study also proposes two potential research lines on the study of additional contextual affecting factors and decision issues in strategic SCND.

This study could be the first attempt to use an empirically based method to develop a decision-making model aimed at supporting the preliminary design of a supply chain network. Therefore, the drawbacks of a pure qualitative conceptual model and optimization model are eliminated.

The purpose of this paper is to propose a novel jump control method based on Two Mass Spring Damp Inverted Pendulum (TMS-DIP) model, which makes the third generation of hydraulic driven wheel-legged robot prototype (WLR-3P) achieve stable jumping.

First, according to the configuration of the WLR, a TMS-DIP model is proposed to simplify the dynamic model of the robot. Then the jumping process is divided into four stages: thrust, ascent, descent and compression, and each stage is modeled and solved independently based on TMS-DIP model. Through WLR-3P kinematics, the trajectory of the upper and lower centroids of the TMS-DIP model can be mapped to the joint space of the robot. The corresponding control strategies are proposed for jumping height, landing buffer, jumping attitude and robotic balance, so as to realize the stable jump control of the WLR.

The TMS-DIP model proposed in this paper can simplify the WLR dynamic model and provide a simple and effective tool for the jumping trajectory planning of the robot. The proposed approach is suitable for hydraulic WLR jumping control. The performance of the proposed wheel-legged jump method was verified by experiments on WLR-3P.

This work provides an effective model (TMS-DIP) for the jump control of WLR-3P. The results showed that the number of landing shock (twice) and the pitch angle fluctuation range (0.44 rad) of center of mass of the jump control method based on TMS-DIP model are smaller than those based on spring-loaded inverted pendulum model. Therefore, the TMS-DIP model makes the jumping process of WLR more stable and gentler.

In light of the stimulus-organism-response (SOR) theoretical paradigm, this paper explores how information relevance and media richness affect social network exhaustion and, moreover, how social network exhaustion ultimately leads to health anxiety and COVID-19-related stress.

The conceptual model is explicitly analyzed and estimated by using data from 309 individuals of different ages in mainland China. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were utilized to validate the proposed hypotheses through the use of online data.

The findings suggest that information relevance is negatively associated with social network exhaustion. In addition, social network exhaustion is a significant predictor of health anxiety and stress. Furthermore, information relevance and media richness can indirectly influence health anxiety and stress through the mediating effect of social network exhaustion.

Theoretically, this paper verifies the causes and consequences of social network exhaustion during COVID-19, thus making a significant contribution to the theoretical construction and refinement of this emerging research area. Practically, the conceptual research model in this paper may provide inspiration for more investigators and scholars who are inclined to further explore the different dimensions of social network exhaustion by utilizing other variables.

Although social network exhaustion and its adverse consequences have become prevalent, relatively few empirical studies have addressed the deleterious effects of social network exhaustion on mobile social media users’ psychosocial well-being and mental health during the prolonged COVID-19. These findings have important theoretical and practical implications for the rational development and construction of mobile social technologies to cultivate proper health awareness and mindset during the ongoing worldwide COVID-19 epidemic.

To achieve stable gait planning and enhance the motion performance of quadruped robot, this paper aims to propose a motion control strategy based on central pattern generator (CPG) and back-propagation neural network (BPNN).

First, the Kuramoto phase oscillator is used to construct the CPG network model, and a piecewise continuous phase difference matrix is designed to optimize the duty cycle of walk gait, so as to realize the gait planning and smooth switching. Second, the mapper between CPG output and joint drive is established based on BP neural network, so that the quadruped robot based on CPG control has better foot trajectory to enhance the motion performance. Finally, to obtain better mapping effect, an evaluation function is resigned to evaluate the proximity between the actual foot trajectory and the ideal foot trajectory. Genetic algorithm and particle swarm optimization are used to optimize the initial weights and thresholds of BPNN to obtain more accurate foot trajectory.

The method provides a solution for the smooth gait switching and foot trajectory of the robot. The quintic polynomial trajectory is selected to testify the validity and practicability of the method through simulation and prototype experiment.

The paper solved the incorrect duty cycle under the walk gait of CPG network constructed by Kuramoto phase oscillator, and made the robot have a better foot trajectory by mapper to enhance its motion performance.