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This study uses data from the Chinese banking sector to explore the relationship between green credit and risk-taking in commercial banks. It also examines whether the level of regional green development acts as a moderator regarding this relationship.

Using a dataset composed of annual observations from 57 Chinese commercial banks between 2008 and 2021, this study employs both piecewise and curvilinear models.

Our results indicate that when the scale of green credit is low (<0.164), it increases the risk-taking of commercial banks. Conversely, when the scale of green credit is high (>0.164), it reduces the risk-taking of commercial banks. Moreover, this nonlinear relationship impact exhibits bank heterogeneity. Furthermore, the results show that the level of regional green development and local government policy support negatively moderate the relationship between green credit and commercial bank risk-taking. Furthermore, we find that green credit can directly enhance the net interest margin of commercial banks.

This study is the first to provide evidence of a nonlinear relationship between green credit and risk-taking in commercial banks, and it identifies the significant roles of regional green development level and local government policy support in the Chinese context.

This study aims to investigate the impact of market competitiveness on investment efficiency, and the moderating role of ownership and regulatory structures.

In this study, the Herfindahl–Hirschman Index (HHI), Lerner Index (LI) and industry-adjusted Lerner Index (LIIA) were used to measure market competitiveness. The research population consisted of companies listed on Tehran Stock Exchange (TSE). Using a systematic elimination, 199 companies were selected within eight years during 2014–2021.

The results showed that market competitiveness (based on the LI, LIIA and HHI) positively affected investment efficiency. Moreover, institutional ownership and managerial ownership affected the relationship between market competitiveness (based on all proxies of market competitiveness) and investment efficiency. Blockholders’ ownership also moderated the relationship between market competitiveness (based on LIIA and HHI) and investment efficiency. The hypothesis testing had robustness based on additional analyses.

In recent years, competitive environment and the ownership structure of companies have changed to a certain degree, paving the way for the private sector to enter many areas of activity especially in emerging Asian markets. Moreover, investment drivers and investment efficiency in developed markets may not be generalized to emerging Asian markets. Therefore, the present findings can show the significance of this research to fill the existing gap in the literature and provide insights into ownership and regulatory structures as a governance mechanism in market competitiveness and investment efficiency.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

This study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.

The study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.

The study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.

This study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

Research on decision rights partitioning and its impact on platform performance has predominantly focused on single rights, leading to inconclusive results. This study is driven by a more nuanced objective of exploring diverse governance models that can enhance the performance of sharing platforms across different contexts. Rather than delegating single decision right to users, this approach partitions several essential decision rights concurrently throughout the transaction process. By examining the complex relationships between multiple decision rights partitioning and platform performance, this study identifies and explains suitable governance models that are tailored to specific contextual factors for improving the performance of sharing platforms.

Collecting data from 60 sharing platforms in China, this study employs a combination of cluster and configuration analyses to address research questions.

The study explores three strategic decision rights partitioning modes widely adopted by sharing platforms. It further identifies four governance models for sharing platforms, which are termed as conservative seller model, conservative buyer model, aggressive seller model and aggressive buyer model, related to certain contextual factors.

In addressing platform governance as key to sharing platform success, the study contributes to the literature by investigating how multiple-rights partitioning portfolios and strategic differentiation in decision rights partitioning can enhance platform performance.

This study aims to explore how the extensive implementation of virtual influencers (VIs) in the hospitality and tourism industry shapes tourists’ trust perceptions. Specifically, it compares the differences between human influencers (HIs) and VIs based on mind perception theory and outlines the strategies for hospitality and tourism marketers to efficiently adopt influencers to enhance customers’ trust in diversified consumption contexts.

Three experiments were conducted with online panels (n = 799). Study 1 outlines the anticipated focal effect and the mediating role of perceived experience. Study 2 replicates the effect and investigates its downstream consequences. Study 3 examines the moderating effect of product type.

The results reveal that customers are more likely to distrust VIs than their human counterparts because the former is thought to possess a lower degree of perceived experience. This effect is more prominent in the endorsement of experiential (versus functional) products and services.

This research advances the understanding of how tourists perceive HIs andVIs differently in social media endorsement, enriching the growing literature on VIs. Hospitality marketers can also gain insights into the advantages and limitations of VIs, providing valuable information to optimize their marketing effectiveness.

The chapter provided a comprehensive overview of lean construction as a transformative paradigm within the building industry. It delved into the core principles, tools, and techniques of lean construction, emphasising its advantages and the challenges associated with its implementation. Furthermore, it highlighted the pivotal role of lean construction principles in streamlining building excellence during the construction stage. The chapter also explored the concept of lean construction for stealth construction, presenting practical applications and a case study to illustrate its efficacy. Overall, it offered a synthesised understanding of lean construction’s significance, potential, and challenges, concluding with a general summary of its implications for the building industry.

The purpose of this paper is to empirically analyze the impact of directors’ and officers’ (D&O) liability insurance on enterprise strategic change. It also explores the mediating role of litigation risk, the moderating roles of enterprise science and technology level and precipitation organizational slack between them. In addition, it examines the joint moderating roles of the top management team (TMT) external social network and enterprise science and technology level, and enterprise scale and precipitation organizational slack.

Using the unbalanced panel data of A-share listed companies in the Shanghai and Shenzhen stock exchanges of China from 2002 to 2020 as the research sample, this paper uses the ordinary least square method and fixed-effect model to study the relationship between D&O liability insurance and enterprise strategic change. The study also focuses on the mediating mechanism and moderating mechanisms between them.

The authors find that D&O liability insurance has an “incentive effect,” which can significantly promote enterprise strategic change. Litigation risk plays a partial mediating role between D&O liability insurance and enterprise strategic change. Enterprise science and technology level and precipitation organizational slack negatively moderate the relationship between D&O liability insurance and enterprise strategic change. TMT external social network and enterprise science and technology level, and enterprise-scale and precipitation organizational slack have joint moderating effects on the relationship between D&O liability insurance and enterprise strategic change.

This paper confirms the “incentive effect hypothesis” of the impact of D&O liability insurance on enterprise strategic change, which not only broadens the research perspective of enterprise strategic management but also further expands the research scope of D&O liability insurance. Besides, this paper thoroughly explores the influencing mechanisms between D&O liability insurance and enterprise strategic change, providing incremental contributions to the research literature in the field of enterprise risk management and corporate governance. The findings have practical guiding significance for expanding the coverage of D&O liability insurance, promoting the implementation of strategic changes and improving the level of corporate governance of Chinese enterprises.

National spirit, as a powerful legitimacy trait, shapes the consistency of a firm’s financial decisions, employee engagement and sustainability strategies. Combining this with resource-based view (RBV) theory, the study empirically examines the dual impact of national spirit on corporate environmental, social and governance (ESG) performance.

This paper utilizes data from Chinese A-share listed companies from 2009 to 2022 and employs machine learning methods to construct enterprise-level indicators of national spirit. In addition, the paper scrapes nearly 3 million ESG-related online news articles from the Baidu news website and uses machine learning methods to measure media ESG attention and sentiment.

The findings reveal that national spirit significantly enhances corporate ESG performance, operating through both internal and external channels: promoting social financing and boosting employee morale. Further analysis indicates that the positive influence of national spirit on corporate ESG performance is more pronounced in private enterprises, companies facing higher levels of credit constraints and firms in polluting industries. Additionally, managerial shortsightedness weakens the sustainable value of national spirit, while external media ESG attention and regional ESG governance efforts further strengthen this effect. Furthermore, different dimensions of national spirit exhibit varying impacts on corporate ESG performance.

This study provides new insights for promoting sustainable development systems in emerging economies and understanding the role of national spirit in corporate social responsibility investments.

This paper shifts the study of national spirit from macro-level cultural analyses to a micro-level perspective. It bridges gaps in the literature by providing empirical evidence on the role of national spirit as a soft resource that influences corporate financial behavior and employee morale. This study provides new insights into promoting sustainable development systems in emerging economies and understanding the role of national spirit in corporate social responsibility investments.