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Following the hierarchy-of-effects model, this study aims to propose a two-step process framework to investigate social media post efficacy via attraction and likes.

The study analyzes 113,785 social media posts from 126 WeChat official accounts to explore how external (headline features and account type) and internal (content features and media type) features impact social media post attractions and likes, respectively.

The antecedents of post attraction differ from those of post likes. First, headline features (punctuation, length, sentiment and lexical density) and account type significantly influence social media post attraction. Second, content features (depth, tone, domain specificity, lexical density and readability) and media type affect social media post likes.

First, this study considers online user engagement as a two-step process regarding social media posts and explores different influencing factors. Second, the study constructs new variables (account type and domain specificity) in each stage of the two-step process model.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

The problem of parameter identification for biaxial piezoelectric stages is still a challenging task because of the existing hysteresis, dynamics and cross-axis coupling. This study aims to find an accurate and systematic approach to tackle this problem.

First, a dual-input and dual-output (DIDO) model with Duhem-type hysteresis is proposed to depict the dynamic behavior of the biaxial piezoelectric stage. Then, a systematic identification approach based on a modified differential evolution (DE) algorithm is proposed to identify the unknown parameters of the Duhem-type DIDO model for a biaxial piezostage. The randomness and parallelism of the modified DE algorithm guarantee its high efficiency.

The experimental results show that the characteristics of the biaxial piezoelectric stage can be identified with adequate accuracy based on the input–output data, and the peak-valley errors account for 2.8% of the full range in the X direction and 1.5% in the Y direction. The attained results validated the correctness and effectiveness of the presented identification method.

The classical DE algorithm has many adjustment parameters, which increases the inconvenience and difficulty of using in practice. The parameter identification of Duhem-type DIDO piezoelectric model is rarely studied in detail and its successful application based on DE algorithm on a biaxial piezostage is hitherto unexplored. To close this gap, this work proposed a modified DE-based systematic identification approach. It not only can identify this complicated model with more parameters, but also has little tuning parameters and thus is easy to use.

The determination of parameters of Duhem model that can describe piezoelectric hysteresis is usually a great challenge. The purpose of this paper is to find a way to identify the parameters of Duhem model by using a modified bee colony algorithm.

The promising bee colony algorithm has great potential to identify hysteresis nonlinearity, but has not yet been used to identify the Duhem-type hysteresis in the literatures. To explore this possibility, the classical bee colony algorithm is modified to enhance its performance regarding both searching capability and convergence speed. In the modification, the current optimal solution is used to guide the search direction, which can balance the local and global searching ability. Moreover, a new searching formula for scout bees is proposed to enhance the convergence ability of the algorithm.

Through a series of experiments, the modified algorithm can attain the optimal parameters with a 0.61 µm peak valley error and a 0.12 µm root-mean-square error. Compared to the particle swarm optimization and classical bee colony algorithms, the modified bee colony algorithm can reach higher parameter identification accuracy. Based on 50 trials, the robustness of the posed algorithm was also proved.

The well-performed modified bee colony algorithm is a good candidate in parameter identification of Duhem-type hysteresis nonlinear systems. As there is no work studying the parameter identification of Duhem model using a bee colony algorithm in the literatures, this work closed this gap and explored the ability of bee colony algorithm to identify piezoelectric hysteresis with superb accuracy and robustness.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

In this letter, based on the infinitesimal transformations with respect to the generalized coordinates and generalized momentums, we obtain the definition, determining equations and structure equation of the momentum‐dependent symmetry for the systems. This study directly leads to the non‐ Noether type conserved quantity for the systems. Further we also give the inverse issue of the momentum‐dependent symmetries of the systems. However, a theory of momentum‐dependent symmetries of the nonconservative Hamiltonian systems is established. Finally, an example is discussed to illustrate the results.

The purpose of this paper is to summarize the progress in grey system research during 2000-2015, so as to present some important new concepts, models, methods and a new framework of grey system theory.

The new thinking, new models and new methods of grey system theory and their applications are presented in this paper. It includes algorithm rules of grey numbers based on the “kernel” and the degree of greyness of grey numbers, the concept of general grey numbers, the synthesis axiom of degree of greyness of grey numbers and their operations; the general form of buffer operators of grey sequence operators; the four basic models of grey model GM(1,1), such as even GM, original difference GM, even difference GM, discrete GM and the suitable sequence type of each basic model, and suitable range of most used grey forecasting models; the similarity degree of grey incidences, the closeness degree of grey incidences and the three-dimensional absolute degree of grey incidence of grey incidence analysis models; the grey cluster model based on center-point and end-point mixed triangular whitenization functions; the multi-attribute intelligent grey target decision model, the two stages decision model with grey synthetic measure of grey decision models; grey game models, grey input-output models of grey combined models; and the problems of robust stability for grey stochastic time-delay systems of neutral type, distributed-delay type and neutral distributed-delay type of grey control, etc. And the new framework of grey system theory is given as well.

The problems which remain for further studying are discussed at the end of each section. The reader could know the general picture of research and developing trend of grey system theory from this paper.

A lot of successful practical applications of the new models to solve various problems have been found in many different areas of natural science, social science and engineering, including spaceflight, civil aviation, information, metallurgy, machinery, petroleum, chemical industry, electrical power, electronics, light industries, energy resources, transportation, medicine, health, agriculture, forestry, geography, hydrology, seismology, meteorology, environment protection, architecture, behavioral science, management science, law, education, military science, etc. These practical applications have brought forward definite and noticeable social and economic benefits. It demonstrates a wide range of applicability of grey system theory, especially in the situation where the available information is incomplete and the collected data are inaccurate.

The reader is given a general picture of grey systems theory as a new model system and a new framework for studying problems where partial information is known; especially for uncertain systems with few data points and poor information. The problems remaining for further studying are identified at the end of each section.

This study aims to deem the new policy – talk for environmental protection – promoted in the second half of 2014 to be the exogenous event and adopts PSM and DID to verify whether and how the central government’s mechanism of supervision of environmental enforcement improves firm environmental performance and reveals the micro effect and working mechanism of the supervision of environmental enforcement.

The researchers first select reasonable control groups for target districts by means of PSM, then apply DID to compare corporations in the treatment group with those in the control group for the change of environmental performance before and after the talk for environmental protection, so as to evaluate the micro-level effect of such talks on corporate environmental performance; after that, the research examines the working mechanism of such talks on corporate environmental performance; then, it goes a step further to find out the environmental impact of such talks on corporations of different natures of property right.

It is found from the research that the talk for environmental protection will effectively improve the environmental performance of corporations in the target districts, and the improvement of environmental performance in state-owned corporations in the target districts will be more evident. However, such improvements, to a certain extent, are achieved by reducing the output value, and corporations do not increase environmental investments from a long-term perspective.

First, the targets of the talk for environmental protection are mainly principals of municipal governments, but the research expands the scope to the whole province due to the small sample at the municipal level. Despite evidences showing that such a pressure of supervision impacts the whole province, the results obtained based on the data at the municipal level will be accurate. Second, the research selects a relatively short research period. Third, due to the limited data on corporate environmental performance in China, the research selects only listed companies from key monitored and controlled firms by state.

First, for the central government, environmental policy making is not the end of its job; it shall also supervise local governments’ work at environmental governance and properly handle its relationship with local governments. Second, for the local governments, in the course of implementing environmental policies, they should not only strengthen law enforcement but keep the continuity of law enforcement to avoid moving law enforcement. Third, in the long run, corporations must start from the source of production to enhance environmental governance and make cleaner production, so as to keep boosting corporate competitiveness and their ability of fighting risks.

First, the research innovatively provides empirical evidence about the effect of China’s supervision of environmental enforcement. Previous studies on this topic are mostly theoretical discussions only, while this research makes the talk for environmental protection the exogenous event about the supervision of law enforcement and achieves breakthroughs in empirical studies of administrative enforcement supervision. Second, the research pushes the studies on the implementation effect of environmental policies from a medium level to a micro level. Third, the research achieves some breakthroughs in the data for measuring corporate environmental performance.

This paper aims to investigate whether and how air pollution affects auditor behavior and audit quality. Specifically, the authors draw from studies of behavioral economics and psychology to develop a new prediction that air pollution-induced negative mood causes pessimistic bias in auditors’ risk assessments of client firms, which motivates them to put more effort into achieving higher audit quality.

This study uses a sample of Chinese public firms for the period 2013 to 2018 and an ordinary least squares model to examine the effects of air pollution on audit quality.

The results suggest that auditors exposed to higher levels of air pollution are more likely to put more effort into their audits, resulting in higher audit quality. Furthermore, the impacts of air pollution on audit quality are more pronounced when an auditor has a higher level of education, a major in accounting or a related subject and a position as a partner. A series of identification tests and sensitivity tests further support the main findings.

This study provides deeper insight into how air pollution affects auditors’ decision-making through its effect on mood.

The findings have broad potential implications for auditing and other high-skill professions. Because air pollution-induced negative mood is a common occurrence and numerous psychological experiments have demonstrated the potentially adaptive and beneficial role of negative mood in decision-making for professions like auditing that need a more conservative, alert and detail-oriented cognitive style, negative mood may to some extent facilitate decision-making. Professionals may benefit from paying closer attention to the adaptive benefits of different moods.

Few studies empirically discuss the effects of auditors’ psychology on audit outcomes. This study responds to this research gap with analyzes of how air pollution-induced negative mood can affect auditors’ professional judgment and audit outcomes. Further, this study adds to the growing literature that examines how air pollution affects various aspects of the economy and enriches the literature on behavioral economics, providing empirical evidence from a large sample of the effects of an environmental stressor on individual auditors’ professional judgment.

The main purpose of this study is to investigate the mechanism through voice behavior mediates the relationship between ethical leadership and employees' creativity. This study also examines the moderating role of psychological empowerment and innovative climate between ethical leadership and employee creativity.

We used a survey questionnaire to collect multi-wave data from 295 employees working in the IT sector to test the proposed hypotheses of this study.

The findings revealed that ethical leadership boosts employee creativity, and voice behavior mediates the positive relationship between ethical leadership and employee creativity. Moreover, the results confirm the significant moderating role of psychological empowerment on the relationship between ethical leadership and voice behavior. A positive moderation of innovative climate was also confirmed in the association between voice behavior and creativity. Employees with supportive innovative climate adopt creative behavior when they can voice their concerns freely.

Ethical leadership is a vital tool for fostering employee's creativity by providing autonomy to raise their voice at the workplace in the emerging markets.

This is one of the leading researches to emphasize the role of ethical leadership for employee creativity, and the key contribution is to discover voice as a potential mediator for ethical leadership and an innovative climate as a potential moderator in the relationship between voice behavior and employee creativity.