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Accounting scandals and earnings management problems at large firms such as Global Crossing and Enron have resulted in lots of wealth loss not only to corporate investors but also led tremendous damage to societies. Hence, policymakers and academic researchers have started to explore mechanisms to prevent improprieties in financial reporting and further enhance firm value. Using data from United States (US)-listed companies between 2000 and 2018, this article explores the effect of ex-military executives on earnings quality, the role of financial analysts in their interplay and the firm value implication of earnings quality driven by ex-military executives.

This study employs a firm fixed-effects model to validate the main conjecture and adopts the weighted least squares, Granger causality analysis, instrumental variable approach, propensity score matching, entropy balancing approach and dynamic system Generalized Method of Moments (GMM) estimator to address robustness and endogeneity issues.

Authors reveal that companies run by ex-military senior executives exhibit lower levels of accruals-based and real earnings management than those without. The effect of management military leadership on constraining earnings management is more prominent for companies with low analyst coverage, suggesting that the military experience of executives could be a substitute for external monitoring. Authors also find that these ethical managers alleviate the negative impact of earnings management on firm value and that companies managed by these managers exhibit higher firm performance.

This study highlights the importance of the intrinsic motivation behind the effect of military experience on senior managers' personalities and offers essential stakeholder-related implications regarding the effect of military experience. The military experience of senior managers helps facilitate the attainment of broader corporate governance and economic objectives.

This article adds new insights to the literature on the role of managerial military experience in decision-making processes, financial reporting outcomes and firm performance by employing the upper echelons and imprinting theoretical perspectives.

The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots.

This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans.

Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation.

This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.

China is a significant energy consumer with increasingly severe resource constraints and environmental problems, requiring low-carbon energy transformation and encouraging high-quality energy development (HED). Green finance significantly affects the effect on HED as a cutting-edge financial strategy to support environmental improvement and encourage green development.

Using panel data from 30 provinces from 2007 to 2019 and the system-generalized method of moments method, this paper investigates the impact of green finance on HED, and further explores their threshold effect, heterogeneous and asymmetry analysis.

The main results indicate that: (1) green finance positively affects HED in China; in other words, a 1% increase in the green finance index will boost HED by an average of 0.767%; (2) as the economy improves, the positive impact of green finance on HED will be even more significant and (3) the contribution of green finance to HED is more significant in the northern provinces and areas with lower HED levels.

This paper puts forward relevant policy suggestions to further improve the construction of the green financial system.

According to construal level theory, close (versus far) psychological distance is associated with low (versus high) construal level. Despite the evidence for discount frame effect, it is unclear whether psychological distance and product nature play moderating roles. In addition, little has been known whether the effect of discount frame can extend to other dependent variables such as willingness to pay (WTP). Driven by construal level theory, five experiments were conducted to explore whether the effect of discount frame is dependent on psychological distance and product nature (i.e. utilitarian versus hedonic product).

The experimental method was used, with discount frame, psychological distance and product type as the independent variables and purchase intention, attitude towards the advertisement, perceived value and WTP as the dependent variables. Participants were presented with promotion scenarios in which psychological distance and discount format were manipulated. In order to test the generalizability of results, promotional scenarios for both utilitarian (i.e. backpack bag and shampoo) and hedonic products (i.e. scenery ticket and perfume) were presented. Data were collected via the online experiment platform (i.e. www.Credamo.com).

The authors found an interaction between discount frame and spatial distance in that consumers had more positive attitude toward percent off than amount off under near-spatial distance. However, no interaction was observed between discount frame and temporal, social or hypothetical distance.

Taken together, the current study for the first time reveals that the effect of discount frame is contingent on a specific dimension of psychological distance (i.e. spatial distance), regardless of whether the product is utilitarian or hedonic. Findings from this study for the first time pose a challenge to the notion that construal-level match necessarily leads to more favorable consumer responses, suggesting that there may be a unique mechanism underlying the joint effects of spatial distance and discount frame. The current findings can provide important implications for marketers and retailers in an effort to design effective promotional messages.

As of December 2022, there were 119,000 gas stations, 10,800 gas stations and 4,488,000 charging piles nationwide, while the number of vehicles reached 312 million, including 11.49 million new energy vehicles. The imbalance between transportation energy supply and energy replenishment demand leads to crowded queues of vehicles at some stations and idle resources in others. How to reduce the phenomenon of large queues and improve the utilization rate of idle resources is the key to alleviating the imbalance between supply and demand.

Therefore, from the perspective of spatio-temporal equilibrium of urban transportation energy supply stations, multi-energy supply station cooperation is established in view of the phenomenon of large spatio-temporal differences among different energy supply stations, and corresponding inducing strategies are adopted for energy supplement vehicles in the road network, so that part of queued users go to energy supply stations with fewer vehicles, so as to balance the supply and demand of transportation energy in the region. On this basis, the income distribution of urban transportation energy supply station is discussed.

The total revenue after the cooperation was 13,095, an increase of 22.9%. Secondly, in terms of distribution rationality, three impact factors are selected and Shapley correction value is used to distribute the total income. Compared with independent operation, both sites have a certain degree of increase.

Traffic congestion at energy supply stations is closely related to the number, location and number of vehicles at energy supply stations. Therefore, using a cooperative approach of energy trading cannot solve the queuing problem. In addition, there are a few research results on the equalization of energy supply station services considering time-of-use pricing. However, these studies do not consider the vehicular grooming at congested stations. As far as the authors know, there are no relevant research results in the research on the service equilibrium of energy supply stations based on cooperative games.

Positive reviews can enrich the favorable impression of peer-to-peer accommodation products, and seizing this impression is vital for hosts. This study aims to focus on hosts’ response strategies to positive reviews and their effects.

This study categorizes hosts’ response strategies to positive reviews into cordial and tailoring responses. This study empirically analyzes the influence of these response strategies on subsequent review volumes using 1,283 valid listings and zero-inflation negative binomial regression models.

While hosts use cordial responses more, tailoring responses are more likely to drive subsequent reviews. In addition, when the host chooses entirely shared accommodation or sets a high price, the facilitating effect of the two response strategies on subsequent reviews weakens.

This study enriches the knowledge system on managerial responses by proposing two specific response strategies to positive reviews that can be adopted by peer-to-peer accommodation hosts and by finding the promoting impact of these strategies on subsequent review volumes.

This study recommends that peer-to-peer accommodation hosts adopt cordial and tailoring responses to encourage subsequent consumer reviewing behavior.

As an early attempt to explore hosts’ responses to positive reviews and their impacts on subsequent review volumes, this study provides valuable insights into further research on positive review response strategies in the digital space.

The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

Scientific impact is traditionally assessed with citation-based metrics. Recently, altmetric indices have been introduced to measure scientific impact both within academia and among the general public. However, little research has investigated the association between the linguistic features of research article titles and received online attention. To address this issue, the authors examined in the present study the relationship between a series of title features and altmetric attention scores.

The data included 8,658 titles of Science articles. The authors extracted six features from the title corpus (i.e. mean word length, lexical sophistication, lexical density, title length, syntactic dependency length and sentiment score). The authors performed Spearman’s rank analyses to analyze the correlations between these features and online impact. The authors then conducted a stepwise backward multiple regression to identify predictors for the articles' online impact.

The correlation analyses revealed weak but significant correlations between all six title features and the altmetric attention scores. The regression analysis showed that four linguistic features of titles (mean word length, lexical sophistication, title length and sentiment score) have modest predictive effects on the online impact of research articles.

In the internet era with the widespread use of social media and online platforms, it is becoming increasingly important for researchers to adapt to the changing context of research evaluation. This study identifies several linguistic features that deserve scholars’ attention in the writing of article titles. It also has practical implications for academic administrators and pedagogical implications for instructors of academic writing courses.