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Flooding is China’s most frequent and catastrophic natural hazard, causing extensive damage. The aim of this study is to develop a comprehensive assessment of urban flood risk in the Hubei Province of China, focusing on the following three issues: (1) What are the factors that cause floods? (2) To what extent do these factors affect flood risk management? (3) How to build an effective comprehensive assessment system that can be used to reduce flood risk?

This study combines expert opinion and evidence from the extent literature to identify flood risk indicators across four dimensions: disaster risk, susceptibility, exposure and prevention and mitigation. The Criteria Importance Through Intercriteria Correlation (CRITIC) and the Grey Relational Analysis (RA)-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making approach were applied to calculate the weighting of factors and develop a model of urban flood risk. Then, ArcGIS software visualizes risk levels and spatial distribution in the cities of Hubei Province; uncertainty analysis verified method accuracy.

The results show that there are significant differences in the level of urban flood risk in Hubei Province, with cities such as Tianmen, Qianjiang, Xiantao and Ezhou being at high risk, while cities such as Shiyan, Xiangyang, Shennongjia, Yichang, Wuhan and Huanggang are at lower flood risk.

The innovative method of combining CRITIC-GRA-TOPSIS reduces the presence of subjective bias found in many other flood risk assessment frameworks. Regional data extraction and uncertainty analysis enhance result reliability, supporting long-term decision-making and urban planning. Overall, the methodological approach developed provides an advanced, highly effective and efficient analysis and visualization of flood risk. This study deepens the understanding of flood risk assessment mechanisms and more broadly supports the development of resilient cities.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study aimed to analyze the impact of enterprise digital transformation on bank loan contracting from 2007 to 2021.

This study used an empirical approach to examine the impact of digital transformation on bank loan interest rates and the possible mechanisms. The digital transformation data of firms were obtained by Python, and the bank loan contracting information of Chinese listed firms was hand-collected from the notes of the annual report.

The results show that digital transformation can significantly reduce the bank loan interest rate and show heterogeneity in the nature of property rights, industry type and firm size. The above results remain significant after conducting a series of robustness tests. Channel tests suggest that digital transformation can promote total factor productivity, improve firms’ information environment and reduce the risk of financial distress, thus helping them reduce their loan interest rate. In addition, banks’ digital transformation can also affect the link between enterprise digital transformation and bank loan interest rates.

First, this paper deeply investigates the relationship between enterprise digital transformation and bank loan contracting, and the mechanisms behind it which expands the research on economic consequences of digitalization. Developing digitalization society has been a top priority in China as it is an urgent task to survive in the competitive global economic environment as a developing country. Second, in developed countries, the evidence relating to bank loan contracting is plentiful. However, the Chinese studies are still very limited as it has no database like Dealscan in US.

This research aims to address the knowledge gaps in determining factors affecting relationship satisfaction in buyer–seller relationships, with a specific focus on sustainability orientation and asymmetric power dynamics.

Drawing upon the Social Exchange Theory (SET) and using a sample of 436 US-based companies, this research uses structural equation modelling to investigate the impact of sustainability orientation and asymmetric power on relationship satisfaction within these relationships. Furthermore, this research explores the moderating role of asymmetric power in the connection between differences in sustainability orientation and relationship satisfaction.

The results uncover a significant negative association between the extent of divergence in sustainability orientation between buyers and sellers and the level of relationship satisfaction. Notably, asymmetric power in buyer–seller relationships positively moderates the influence of sustainability orientation differences on relationship satisfaction.

This research sheds light on the increasingly vital issue of sustainability orientation in buyer–seller relationships by enhancing our understanding of asymmetric power’s role in shaping relationship satisfaction within business-to-business relationships.

Drawing upon social information processing theory, this study investigates the correlation between self-serving leadership and employee expediency. It also explores the mediating effect of self-interest motivation and the moderating effect of trait mindfulness.

A total of 147 part-time MBA students were enlisted to participate in a scenario experiment (Study 1), and 291 valid employee questionnaires were collected through a multiple-time point survey (Study 2). SPSS 23.0, MPLUS 8.0 and PROCESS programs were used to analyze the data and test the hypotheses.

Study 1 illustrated a positive correlation between self-serving leadership and employee expediency. It also identified self-interest motivation as a mediating factor in the correlation between self-serving leadership and expediency. Study 2 replicated the results obtained in Study 1 and expanded upon them by demonstrating that trait mindfulness moderates the association between self-serving leadership and self-interest motivation. Additionally, trait mindfulness moderates the indirect effect of self-serving leadership on expediency.

This research argues that organizations should take steps to prevent self-serving leadership in order to reduce employee expediency. Furthermore, it is advisable to provide ethics training to employees who exhibit high trait mindfulness, as they show increased sensitivity to self-serving leadership and are more likely to engage in unethical behavior.

This study expands the existing research on the ethical outcomes of self-serving leadership and contributes to a deeper understanding of the negative aspects of trait mindfulness.

Drawing on a mission approach, this study investigates the application of integrated reporting (IR). It also explores its role in shaping organisations' internal processes in Chinese state-owned enterprises (SOEs) and the China General Nuclear Power Corporation (CGN).

We employed a case study method and collected data by conducting semi-structured interviews with CGN managers and analysing their reports and appropriate documents.

Our findings reveal that the CGN is motivated by its mission and vision to adopt IR rather than by other common motivations, such as legitimacy, strategy and stakeholder pressure. IR practice contributes to implementing its mission and vision through direct and indirect methods, covering the changes in design archetypes and subsystems that meet the nature of reorientation changes that belong to first-order transition.

This study extends empirical evidence of IR at the firm level in China and provides in-depth insights into how IR is implemented in Chinese SOEs. Our findings may be helpful for policymakers to review and develop policies. For instance, the government might consider integrating IR frameworks into current reporting and mandate listed companies to adopt IR. However, our data are from one company, the only Mainland Chinese Company recognised by the IIRC database.

This study provides an innovative approach to analysing IR and offers managers insight into how IR practice benefits the mission’s implications.

During the COVID-19 pandemic, an infodemic erupted on social media, leading to a surge in negative disclosure behaviors such as expressing dissatisfaction and releasing negative emotions. By extending the elaboration likelihood model and the Big Five personality theory to the domain of online self-disclosure, we aimed to identify the factors that influence negative disclosure behavior.

We investigated how the features of negative information content, information sources and recipients’ social perceptions influence how social media users disclose negative information. We also examined the moderating roles of personality traits in this process. To validate the model and test our hypotheses, we collected cross-sectional data from 456 social media users.

Empirical results reveal that (1) information overload, topic relevance, attractiveness of information sources, peer approval of negative disclosure and social influence on negative information strengthen the intention to disclose negative information. (2) The perception of social risk weakens the intention to disclose negative information. (3) Openness to experience, extraversion and neuroticism strengthen the relationship between the intention to disclose negative information and actual disclosure behavior.

Our results not only provide new perspectives on the decision-making mechanisms behind negative disclosure behavior but also extend personality research within the context of the dissemination of negative information. Furthermore, it offers insights into negative information dissemination on social media platforms, with significant implications for various stakeholders.

In Ghana, the adoption and application of green building concepts and technologies have not been fully explored. The study aimed to look into the key barriers and how they affect this.

Purposive and snowball sampling techniques were used to select a total of 292 construction industry stakeholders in Ghana who provided information via a questionnaire survey used for the data collection. Exploratory factor analysis and Partial least squares structural equation modelling (PLS-SEM) were used for computing the data analyses.

According to the study findings, the top five most critical barriers to the uptake of green building concepts and technologies (GBCs and Ts) in Ghana are: lack of government incentives/supports for implementing green building technologies (GBTs), lack of knowledge and awareness of GBTs and their benefits, lack of GBTs databases and information, Lack of green building (GB) expertise/skilled labour and Higher costs of GBTs. Principal Component Factor Analysis was used to further analyse the data, which allowed for the reduction of the 27 (27) factors to just four (4) underlying critical barriers: (1) government and knowledge-associated barriers, (2) technical barriers, (3) cost and finance barriers and (4) stakeholders’ attitude barriers. PLS-SEM techniques were used to analyse this collection of barriers, and the results showed that stakeholders’ attitude-associated barriers and cost and finance-related barriers have a significant negative influence on the uptake of GBCs and Ts in Ghana. This study’s findings have provided empirical evidence of the critical barriers to the uptake of GBCs and Ts from all stakeholders. Stakeholders desirous of implementing GBCs and Ts would work against the negative influences on the uptake of GBCs and TS.

Although there has been an abundance of research to examine the critical barriers to GB, however, the uniqueness of this study is nested in modelling the influence of the barriers on the adoption of GBCs and Ts using the PLS-SEM path modelling.

This paper aims to investigate the impact of environmental risk on corporate governance through market reaction to bank loan announcements.

Using the establishment of environment court in China as a quasi-natural experiment, this paper adopt the difference-in-differences approach based on listed firms during 2003–2013 to explore the impact of environment court on corporate governance.

This paper find that the environment court would weaken the cumulative abnormal return of loan announcements. Then, this paper confirm that the potential reason is that environment court worsens the interest conflict between majority and minority shareholders. Further, cross-sectional analysis suggests that bank’s supervision, market competition and analyst coverage can alleviate the impact of environment court on corporate governance.

Environment courts intensify firms’ internal interest disputes, thus causing the decrease of corporate governance, which can be observed through the effect of bank loan announcements.

This paper provide reference for environmental policy formulation and implementation, firms’ decision-makings and improving the banking regulatory system.

This paper makes a contribution to the studies about the impact of environment court on firms’ decision-making and investors’ reaction, the impact of external factors on corporate governance and bank loan announcements effect.

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.