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Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

The purpose of this study is to use the grounded theory to summarize the types of celebrity persona and to construct a theoretical model for celebrity persona on consumer purchase intention. Based on the study results, it provides better suggestions for merchants and live streamers and is an expansion of previous research on live-streaming e-commerce.

The grounded theory is recognized as the most scientific qualitative research method and is the ideal explorative method for generating theory. First, the participants were interviewed, and interview data were collected. Then the interview data were organized and analyzed. Finally, this paper summarizes the types of celebrity persona and constructes a theoretical model framework of celebrity persona on consumers' purchase intention.

The results show that the celebrity live streamer persona can be divided into two types: personalized persona and professional persona. Through emotional attachment, the celebrity's persona affects the consumer's purchase intentions. As well as, product type plays a moderating role between celebrity persona and consumer purchase intentions.

The contribution of this research is to start from the celebrity persona, link the celebrity persona with the consumer purchase intentions and expand the research scope of the celebrity persona. It opens the “black box” of the heterogeneity of celebrity live streamers' characteristics on consumer purchase intentions.

Based on the cognitive appraisal theory of emotion and social comparison theory, this study explores the mediating mechanism and boundary conditions of supervisor bottom-line mentality on employee presenteeism.

Using hierarchical regression and bootstrapping, we test the hypothesized relationships with three-stage data from 265 full-time employees in China.

Supervisor bottom-line mentality has a significant positive influence on employee presenteeism. Workplace fear of missing out plays a mediating role between supervisor bottom-line mentality and employee presenteeism. Employees’ status-striving motivation positively moderates the influence of supervisor bottom-line mentality on employees’ workplace fear of missing out and enhances the mediating effect of workplace fear of missing out.

Presenteeism can be detrimental to employees’ health, and ultimately leads to a decrease in organizational productivity. Research conclusions warn companies to be vigilant about supervisors’ bottom-line mentalities and to strengthen employee health management.

This study explains when and how supervisor bottom-line mentality affects employee health, contributing to the literature on the antecedents of presenteeism and enriching the research on supervisor bottom-line mentalities and employee and organizational outcomes. This study clarifies the emotional mechanisms and boundary conditions of supervisor bottom-line mentality affecting presenteeism.

The silicone modifications of two-component epoxy resin coatings are commonly built on epoxy resins rather than on epoxy curing agents. The silicone-modified epoxy curing agent system is rarely reported yet. This study aims to prepare the polysiloxane (PS)-modified waterborne epoxy coatings based on aqueous curing agents technology.

Waterborne epoxy curing agents with different contents of terminal epoxy PS were synthesized by reacting with triethylenetetramine, followed by incorporating of epoxy resin (NPEL-128) and polyethylene glycol diglycidyl ether. The waterborne epoxy coatings were prepared with the above curing agents, and their performance was investigated through thermogravimetric analysis, scanning electron microscopy, mechanical characterization, gloss measurement, chemical resistance test and ultraviolet (UV) aging experiment.

The results showed that the epoxy coating prepared by silicon-modified curing agent has higher gloss, better chemical resistance and UV resistance than the coating from unmodified curing agent with terminal epoxy PS and commercially available waterborne epoxy curing agent (Aradur 3986), as well as the competitive mechanical properties and heat resistance. Reduced water absorption on fibrous paper was also obtained with the help of silicon-modified curing agent.

These findings will be valuable for resin researchers in addressing the modification issues about waterborne epoxy resin and curing agent.

The elderly service industry is emerging in China. The Chinese government introduced a series of policies to guide elderly service enterprises to improve their service quality. This study explores novel differentiated subsidy strategies that not only promote the improvement of service quality in elderly service enterprises but also alleviate the financial burden on the government.

Evolutionary game and Hotelling models are employed to investigate this issue. First, a Hotelling model that considers consumer word-of-mouth preferences is established. Subsequently, an evolutionary game model between local governments and enterprises is constructed, and the evolutionary stable strategies of both parties are analyzed. Finally, simulation experiments are conducted.

The findings indicate that local government decisions have a significant influence on the behavior of elderly service enterprises. Increasing the proportion of local governments opting for subsidy strategies helps incentivize elderly service enterprises to improve their service quality. Furthermore, providing differentiated subsidies based on the preferences of the customer base of elderly service enterprises can encourage service quality improvement while reducing government expenditure. The findings offer valuable insights into the design of government subsidy policies.

Compared with previous research, this study examines the role of consumer preferences in a differentiated subsidy policy. This enriches the authors’ understanding of the field by incorporating neglected aspects of consumer preferences in the context of the emerging elderly service industry.

The study specifically focused on ethical leadership and help seeking during innovation and tested the moderating effect of happiness on the path from help seeking to innovation.

In this longitudinal study, 472 employees from an HR outsourcing company in China participated in the first survey. However, only 184 employees completed two surveys (1 month apart). We used path analysis to test all hypotheses through Mplus.

We found that ethical leadership, through help-seeking, is correlated to innovation. We also found that one’s happiness level is related to innovation, and when employees are chronically happy, the positive effect of help-seeking on innovation is stronger.

Working in an ethical environment, employees are comfortable acknowledging mistakes, hearing feedback and making corrections, which fosters continuous innovation. Conversely, without the influence from ethical leaders, employees may choose to remain silent and abandon continuous innovation due to the concern of losing “face” the context of China.

Prior research on user-generated content (UGC) contributions has primarily focused on self-centered or other-centered motives, paying limited attention to the concept of enlightened self-interest, in which both motives coexist in a single organism. Additionally, the factors influencing enlightened self-interest and their effects in different circumstances are yet to be explored. Drawing on theoretical lenses rooted in the switching barriers perspective and stimulus–organism–response framework, this study posits that dedication-based switching barriers (community–member relationship quality, member–member relationship quality, and content attractiveness) positively relate to enlightened self-interest, whereas constraint-based switching barriers (switching costs) moderate the relationship between dedication-based switching barriers and enlightened self-interest in social media communities (SMCs). Members' enlightened self-interest in turn influences both the creation and co-creation of UGC.

This study comprised two quantitative studies: an online survey-based study (Study 1) and an online scenario-based experiment (Study 2). Study 1 surveyed 613 respondents, while Study 2 included 749 participants. Both studies employed structural equation modeling and bootstrapping techniques for analysis.

The findings indicate that dedication-based switching barriers positively affect users' enlightened self-interest, which in turn is positively associated with UGC creation and co-creation. Switching costs moderate the relationship between relationship quality (community–member and member–member) and enlightened self-interest.

This study complements the current understanding of how the association between dedication- and constraint-based switching barriers and users' enlightened self-interests influence user-generated contributions.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.