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This paper explores the association between directors’ and officers’ liability insurance (D&O insurance) and management tone manipulation.

This study uses data from A-share listed non-financial companies from 2009 to 2021 as its sample for empirical tests. In addition, the study relies on text analysis and the construction of models to investigate the relationship between D&O insurance and management tone manipulation.

The authors find that the purchase of D&O insurance will lead to management tone manipulation in the “management discussion and analysis” part of companies’ annual reports, and operating risk and agent cost are the two paths for the effect. Further analysis shows that having a male CEO and employing high-quality auditors can weaken the positive impact of D&O insurance on tone manipulation.

This paper provides a new approach for studying the literature related to D&O insurance and management behavior, and the findings enrich our understanding of the influencing factors and the mechanism of management tone manipulation, thus revealing policy implications for further standardization of the terms and system of D&O insurance in China.

The purpose of this review paper is to provide a review of the most recent advances in the field of manufacturing composite sandwich panels along with their advantages and limitations. The other purpose of this paper is to familiarize the researchers with the available developments in manufacturing sandwich structures.

The most recent research articles in the field of manufacturing various composite sandwich structures were reviewed. The review process started by categorizing the available sandwich manufacturing techniques into nine main categories according to the method of production and the equipment used. The review is followed by outlining some automatic production concepts toward composite sandwich automated manufacturing. A brief summary of the sandwich manufacturing techniques is given at the end of this article, with recommendations for future work.

It has been found that several composite sandwich manufacturing techniques were proposed in the literature. The diversity of the manufacturing techniques arises from the variety of the materials as well as the configurations of the final product. Additive manufacturing techniques represent the most recent trend in composite sandwich manufacturing.

This work is valuable for all researchers in the field of composite sandwich structures to keep up with the most recent advancements in this field. Furthermore, this review paper can be considered as a guideline for researchers who are intended to perform further research on composite sandwich structures.

This study investigates the heterogeneous impact of R&D capital market distortions (CMDs) on the quantity and quality of innovation in Chinese firms by exploring key pathways from a dual-arbitrage perspective.

Using data from Chinese A-share listed companies (2015–2021), we employ a multidimensional fixed effects model to validate the research hypotheses. Under the Systems of National Accounts (SNA, 2008) framework, we use the Bureau of Economic Analysis method to estimate enterprise R&D capital stock and the Cobb-Douglas production function to estimate R&D CMDs.

Results show that R&D CMDs drive firms toward strategic innovation, emphasizing quantity over quality. Policy arbitrage, including policy catering and rent-seeking, emerges as a pivotal mechanism under R&D CMDs, encouraging firms to prioritize quantity over quality in innovation. High-technology firms and those in the decline stage are more inclined to spearhead strategic innovation within the context of R&D CMDs.

These findings help policymakers promote high-quality innovation in Chinese enterprises by enhancing patent review mechanisms and shifting policies from quantity-driven to quality-oriented goals.

This study enriches the research on factor market distortions and innovation in emerging markets from the perspective of R&D CMDs, based on the “emerging + transitional” comprehensive framework. Unlike previous studies, which generally use enterprise R&D expenditure flow data, we apply the theory of R&D capitalization accounting to the micro-enterprise level under the SNA (2008) framework, enhancing the accuracy of R&D CMD estimations.

According to the resource-based theory, a firm’s unique resources and capabilities are the key to its competitive advantage. This paper aims to investigate the effect of top management team (TMT) stability, an important intangible resource of the firm, on the maturity mismatch between investment and financing of companies. Additionally, we explore the moderating effects of state ownership and institutional ownership in this context.

This study conducts an empirical analysis based on the ordinary least squares (OLS) model with a sample of Chinese companies listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2022.

The results show that TMT stability significantly mitigates the degree of maturity mismatch. Both state ownership and institutional ownership weaken the negative effect of TMT stability on maturity mismatch. Besides, alleviating financing constraints is a crucial pathway through which TMT stability influences maturity mismatch.

The findings help firms to effectively retain TMT talents and reduce the occurrence of maturity mismatch.

This paper not only helps to expand the research on the economic effects of TMT stability but also provides new ideas on how to alleviate the maturity mismatch of companies.

This paper takes the gig workers in Chinese delivery platform as the research object and adopts a questionnaire survey to explore the complex influence of job gamification perception on the job involvement of gig workers, via the mediating role of cognitive assessment and moderating role of overwork, in order to provide research and data support for the development of platform gamification.

The study conducted a three-wave online questionnaire survey to obtain 300 final samples from Chinese delivery platforms. Hypotheses were tested hierarchical regression and bootstrap methods.

Drawing on cognitive appraisal theory, we observed an inverted U-shaped relationship of gig workers between job gamification perception and job involvement. The mediating role of cognitive assessment and moderating role of overwork were also considered. Both challenge and threat assessment mediate the relationship between job gamification perception and job involvement. Direct effect of job gamification perception on job involvement and indirect effect of job gamification perception on cognitive assessment are moderated by overwork.

In the past, the research on job gamification mostly focused on the traditional forms of employment, but this study focuses on the new forms of employment and from the perspective of individual self-perception, explores the influence of job gamification perception on the job involvement of gig workers in Chinese delivery platform and investigated the dialectical role of job gamification perception. The findings enrich the literature and theoretical research on job gamification perception and job involvement and provide new references and perspectives for management practice.

This study aims to investigate the mechanism of knowledge management within multinationals during the post COVID-19 era, with particular consideration given to the relationship between the cultural intelligence of top managers and knowledge-oriented leadership using fear of COVID-19 as a moderating factor.

Derived from upper echelons’ theory and research on knowledge management success (KMS), a theoretical model and associated hypotheses have been developed and tested. Structural equation modeling was used with statistics collected from 288 top managers and executives of multinational corporations dominated by knowledge-intensive industries through a network investigation.

Results indicate that the levels of executives’ cultural intelligence and knowledge-oriented leadership contribute to KMS, while knowledge-oriented leadership acts as a mediator between them. In addition, the fear of COVID-19 of senior executives negatively affects both the direct and mediated influence of cultural intelligence on KMS.

The current research uses an empirical approach to examine cross-border KMS. Further research is needed to develop more comprehensive measurement tools for KMS and more detailed research by further developing the subdimensions of cultural intelligence. In addition, this paper used cross-sectional research that limits the capability to establish causal relationships over time.

The research explores the “human side” of the key antecedents of KMS, fills the gap in research about the impact of cultural intelligence and knowledge-oriented leadership on the achievement of KMS, paves the way for emerging knowledge-oriented leadership from the initial phase to the mature phase and contributes to the literature on environmental uncertainty and crisis, using the COVID-19 as a representative context.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Passive chipless RFID (radio frequency identification) sensors, devoid of batteries or wires for data transmission to a signal reader, demonstrate stability in severe conditions. Consequently, employing these sensors for metal crack detection ensures ease of deployment, longevity and reusability. This study aims to introduce a chipless RFID sensor design tailored for detecting metal cracks, emphasizing tag reusability and prolonged service life.

The passive RFID sensor is affixed to the surface of the aluminum plate under examination, positioned over the metal cracks. These cracks alter the electrical length of the sensor, thereby influencing its amplitude-frequency characteristics. Hence, the amplitude-frequency profile generated by various metal cracks can effectively ascertain the occurrence and orientation of the cracks.

Simulation and experimental results show that the proposed crack sensing tag produces different frequency amplitude changes for four directions of cracks and can recognize the crack direction. The sensor has a small size and simple structure, which makes it easy to deploy.

This research aims to deploy crack detection on metallic surfaces using passive chipless RFID sensors, analyze the amplitude-frequency characteristics of crack formation and distinguish cracks of varying widths and orientations. The designed sensor boasts a straightforward structural design, facilitating ease of deployment, and offers a degree of reusability.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

This paper develops a debt-run model to study the effects of liquidity injections on debt markets in the presence of a renegotiation option. In the model, creditors decide when to withdraw their funding and equityholders can renegotiate the contract terms of debt. We show that when equityholders have a large bargaining power, liquidity injections into distressed firms can rather cause more aggressive runs from their creditors, hurting the debt value. This outcome occurs because equityholders can strategically utilize the renegotiation option as a bankruptcy threat, pushing down the debt value below the potential liquidation value of the firm. In such a scenario, a deterred default resulting from emergency capital injections could be detrimental to creditors.