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This study adopts self-determination theory and stimulus-organism-response framework to develop a model that explores the motivations of such donors by considering their self-determination needs and extrinsic and intrinsic motivations.

Based on online survey data collected from 436 crowdfunding donors in China, this study follows a structural equation modeling analysis to test hypotheses.

The results indicate that perceived ease of use, perceived self-efficacy and social connection have positive effects on the donation intentions of backers through a combination of extrinsic and intrinsic motivations.

The findings shed light on various extrinsic and intrinsic motivations advancing knowledge of individual fund motivation in donation-based crowdfunding and provide guidelines for the development of donation-based crowdfunding theory and practice.

The metaverse enables users to create their own avatars in a shared virtual space, giving rise to a new avatar personality that differs from their real-self personality. The aim of this research is to explore how users' real-self and avatar personalities may affect their behavioral engagement and satisfaction in the metaverse context.

This research applies self-discrepancy theory to investigate how the big five traits of both real-self and avatar personalities influence users' engagement and satisfaction in the metaverse. The present research employed a mixed-methods approach, beginning with a qualitative study to identify prevalent personality cues among users on metaverse social media platforms. Subsequently, a quantitative study was conducted to further validate the findings of the qualitative study.

The results indicated that avatar personality scored higher than the real-self personality in the dimensions of openness, conscientiousness and extraversion, while scored lower in the dimensions of agreeableness and neuroticism. Both real-self and avatar personality traits positively influenced metaverse satisfaction via behavioral engagement in the metaverse. Notably, avatar personality traits had a stronger impact on behavioral engagement compared to real-self personality traits, which further influence metaverse satisfaction.

The present study offers practical insights for metaverse developers and managers to enhance user satisfaction by focusing on users’ big five traits of both real-self and avatar personality. It suggests implementing personalized tools, organizing personality-based social activities and other initiatives to encourage user’s behavioral engagement and ultimately enhance metaverse satisfaction.

Unlike existing research that concentrates on a single facet of personality traits, this research employs a mixed-methods approach to conceptualize users' real-self personality and avatar personality, further exploring their impacts on metaverse satisfaction.

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.

This paper argues that currently management accounting is simply too narrow and proposes how to broaden its scope to make it more relevant and useful.

The approach is to provide a critique of the extent to which management accounting sufficiently deals with three primary areas that classic management accounting has been myopic about at least to some extent: Organizational control, Organizational measurement, and Intellectual assets.

The paper argues that management accounting has not taken a “deep dive” into these areas and has placed itself at risk of being marginalized. It presents potential frameworks and tools of organizational control, organizational measurement, and intellectual assets as “add-ons” to management accounting to increase its relevance and utility.

The paper shows how management accounting must be broadened to include all organizational measurement and accountability for planning and control.

The paper describes several global applications of the proposed revised frameworks, methodologies, and tools presented as potential add-ons to management accounting. These applications demonstrate the feasibility, utility, and generalizability of the broader management accounting “tool box” presented.

The paper proposes a revised paradigm for management accounting. This paradigm is original and its value is in serving as a catalyst for academics as well as practitioners to rethink and broaden the current paradigm of management accounting in order to be more relevant and useful. It provides a potential new set of tools for management accounting.

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.

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.

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 objective of this study was to analyse the influencing factors of citizens' dissatisfaction with government services during the COVID-19 pandemic to help government departments identify problems in the service process and possible countermeasures.

The authors first used cosine interesting pattern mining (CIPM) to analyse citizens' complaints in different periods of the pandemic. Second, the potential evaluation indices of customer satisfaction were extracted from the hotline business system through a hypothesis analysis and modelled using multiple regression analysis. During the index transformation and standardization process, a machine-learning algorithm of clustering and emotion analysis was adopted. Finally, the authors used the random forest algorithm to evaluate the importance of the indicators and obtain the indicators more important to citizen satisfaction.

The authors found that the complaint topic, appeal time, urgency of citizens' complaints, citizens' emotions, level of detail in the case record, and processing timeliness and efficiency significantly influenced citizens' satisfaction. When the government addresses complaints in a more standardized and efficient manner, citizens are more satisfied.

During the pandemic, government departments should be more patient with citizens, increase the speed of the case circulation and shorten the processing period of appeals. Staff should record appeals in a more standardized manner, highlighting themes and prioritizing urgent cases to appease citizens and relieve their anxiety.

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.