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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 aims to explore the association between environmental sustainability thoughts and environmental performance. Mediation through social innovation and moderating role of green innovation is also tested.

A quantitative research design was used for analyzing the personal/lived experience of 387 small–medium enterprises (SMEs) managers. Data were collected from persons who were performing their job, that is, it is based on current data. Cross-section data were collected through questionnaires. Correlation and regression analysis was used to test the study hypotheses.

Green innovation encourages firms to grow to be competent and increase their environmental performance. Results proved that businesses can work in a sustainable way through environmental sustainability thoughts and green innovation. Moreover, social innovation leveraged the interplay of environmental sustainability thoughts and the environmental performance.

The uniqueness of this research is that it combined environmental sustainability thoughts, social innovation and green innovation for developing environmental performance model for SMEs. This research includes new approach on sustainability domain and offered newest relevant factors to achieve a wider perceptive and multifaceted analysis of all dimensions that influence environmental performance.

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.

This paper aims to examine the role of local/provincial government in influencing corporate social and environmental reporting (CSER) in China, and more specifically, how the underlying economic and political factors associated with local government have influenced the quality of CSER.

The authors used 234 environmentally sensitive companies listed on the Shanghai and Shenzhen Stock Exchanges during 2013 and 2015 as the research sample to test the relationship between CSER and local government’s political connection and economic prioritisation and the potential mediating effect of local economic prioritisation.

The analysis provides evidence that local/provincial government’s political geographical connectedness with the central government has directly and positively influenced the level of CSER, while local prioritisation of economic development has a direct but negative effect on CSER in China. In addition, local/provincial prioritisation of economic development has mediated the relationship between local–central political geographical connectedness and CSER.

While local/provincial governments are heavily influenced by the coercive pressure from the central government, they also act in their own political and economic interests in overseeing CSER at the local level. This study raises the question about the effectiveness of the top-down approach to improving CSER in China and suggests that the central government may need to focus more on coordinating and harmonising different local/provincial governments’ interests to enable achieving a common sustainability goal.

The authors provide evidence revealing how the economic and political contexts of local government have played a significant role in shaping CSER in China. More specifically, this paper addresses a gap in the literature by highlighting the importance of local government oversight power for CSER development and how such oversight is determined by local prioritisation of economic development and political geographical connectedness of local and central governments.

With the exponential growth of mobile applications, recommending suitable mobile applications to users becomes a critical challenge. Although existing methods have made achievements in mobile application recommendation by leveraging graph convolutional networks (GCNs), they suffer from two limitations: the reliance on a singular acquisition path leads to signal sparsity, and the neighborhood aggregation method exacerbates the adverse impact of noisy interactions. This paper aims to propose SMAR, a self-supervised mobile application recommendation approach based on GCN, which is designed to overcome existing challenges by using self-supervised learning to create an auxiliary task.

In detail, this method uses three distinct data augmentation techniques node dropout, edge dropout and random walk, which create varied perspectives of each node. Then compares these perspectives, aiming to ensure uniformity across different views of the same node while maintaining the differences between separate nodes. Ultimately, auxiliary task is combined with the primary supervised task using a multi-task learning framework, thereby refining the overall mobile application recommendation process.

Extensive experiments on two real datasets demonstrate that SMAR achieves better Recall and NDCG performances than other strong baselines, validating the effectiveness of the proposed method.

In this paper, the authors introduce self-supervised learning into mobile application recommendation approach based on GCNs. This method enhances traditional supervised tasks by using auxiliary task to provide additional information, thereby improving signal accuracy and reducing the influence of noisy interactions in mobile application recommendations.

Drawing on bargaining power and the inequality aversion perspective, this study aims to probe employees’ influence on addressing income inequality between top executives and nonexecutive employees. Meanwhile, it examines the moderating role of employee-related factors and plan attributes.

This study uses a staggered difference-in-differences design with a propensity scoring match approach and verification of the parallel trend assumption to test the hypotheses.

The results support the hypothesis that employee stock ownership plans (ESOPs) significantly reduce within-firm income inequality. The negative effect is amplified by both the presence of trade unions and the unemployment rate at the regional level, as well as the duration of the lock-in period and the scale of participants within the stock ownership plan.

This study has implications for income inequality research and ESOP design and provides theoretical support for policymakers and corporate governance.

This study contributes to the literature on income inequality by examining the implementation of ESOPs from the employee perspective. Furthermore, it extends the current literature by investigating the strengthening effects of regional factors and ESOP attributes on the relationship between ESOPs and income inequality. The conclusions provide new empirical evidence to promote the effective implementation of ESOPs by combining internal and external factors.

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.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.