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Given the heavy pollution feature of the construction industry, construction corporations need to adopt an effective environmental governance strategy. The quality and quantity of environmental information disclosure (EID) implementation, as an essential part of a corporate environmental governance strategy, is impacted by the characteristics of the top management team (TMT). This paper aims to analyze the relationship between the demographic characteristics of the TMT (i.e. gender, age, tenure, educational level, and duality) and corporate EID.

Using data from listed construction corporations generated between 2014 to 2018 in China, this study employs the Tobit regression model to test the research hypotheses. Also, this study applies a novel analytical approach, necessary condition analysis (NCA), to conduct a series of additional tests.

The results reveal that tenure and educational level are significantly and positively related to EID, while gender, age, and duality in the executive role are not significantly related to EID. When considering the TMT size as a moderator, the TMT age is positively related to the corporate EID, and the size of the TMT acts as a moderator to weaken the positive effect of the TMT age on the EID. The NCA results show that TMT gender, age, tenure, and educational level are necessary when the levels of EID exceed 40%.

Our findings suggest that TMT characteristics have a relatively significant effect on corporate EID levels, which extends EID research to the construction industry. Corporate planners can endeavor to shape TMT characteristics to improve EID levels. The results of NCA provide insights into what TMT characteristics construction corporations need to satisfy in their pursuit of transparent EID, as well as the levels at which these characteristics are desired.

The growing attention to green supply chain transparency prompts firms to disclose their environmental efforts and manage environmental issues along supply chains. Drawn upon diffusion theory, this study aims to investigate how customers’ environmental efforts can be diffused to suppliers for similar actions, as well as how customers’ (diffusors’) characteristics and suppliers’ (followers’) capability in digital technology application moderate the relationship.

This study collects secondary data of 1,514 unique customer-supplier dyad year observations of Chinese listed firms and their disclosed environmental efforts from 2009 to 2022. A fixed-effect regression model is used to test the hypotheses.

This study reveals a positive association between customers’ disclosed environmental efforts and those of their suppliers. Furthermore, the relationship is strengthened when customers are state-owned or when suppliers possess higher levels of digital technology application capability. These findings remain robust when alternative measures of variables are employed.

This study contributes to the supply chain transparency literature by uncovering the diffusion mechanism of environmental efforts from customers to their suppliers. It further identifies moderators for this diffusion, including customers’ (diffusors’) ownership and suppliers’ (followers’) capability. Lastly, our study extends the applicability of diffusion theory within a buyer–supplier context.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Companies are increasingly benefiting from artificial intelligence (AI) applications in various domains, but also facing its negative impacts. The challenge lies in the lack of clear governance mechanisms for AI. While documentation is a key governance tool, standard software engineering practices are inadequate for AI. Practitioners are unsure about how to document AI, raising questions about the effectiveness of current documentation guidelines. This review examines whether AI documentation guidelines meet regulatory and industry needs for AI applications and suggests directions for future research.

A structured literature review was conducted. In total, 38 papers from top journals and conferences in the fields of medicine and information systems as well as journals focused on fair, accountable and transparent AI were reviewed.

This literature review contributes to the literature by investigating the extent to which current documentation guidelines can meet the documentation requirements for AI applications from regulatory bodies and industry practitioners and by presenting avenues for future research. This paper finds contemporary documentation guidelines inadequate in meeting regulators’ and professionals’' expectations. This paper concludes with three recommended avenues for future research.

This paper benefits from the insights from comprehensive and up-to-date sources on the documentation of AI applications.

This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.

The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker.

This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

Digital healthcare manages to grab considerable attention from people and practitioners to avoid severity and provide quick access to healthcare. Entrepreneurs also adopt the digital healthcare segment as an opportunity; nevertheless, their intentions to participate and encourage innovation in this growing sector are unexplored. Drawing upon the social capital theory and health belief model, the study examines the factors that drive entrepreneurship. A novel model is proposed to comprehend entrepreneurial intentions and behavior entrenched in social capital and other encouraging and dissuading perceptive elements with the moderation of trust in digitalization and entrepreneurial efficacy.

The cross-sectional method is used to collect data through a questionnaire from experienced respondents in China. The valid data comprises 280 respondents, analyzed by partial least square structural equation modeling.

Social capital significantly influences monetary attitude, and perceived risk and holds an inconsequential association with perceived usefulness, whereas monetary attitude and perceived usefulness meaningfully explain entrepreneurial activities. Perceived risk has a trivial impact on entrepreneurial intention. Entrepreneurial efficacy and trust in digitalization significantly explain entrepreneurial behavior and moderate the positive relationship between intention and behavior.

The present research proposes a novel research model in the context of entrepreneurship rooted in a digitalized world and offering new correlates. It provides valuable insights by exploring entrepreneurial motivation and deterring factors to get involved in startup activities entrenched in social capital, providing guidelines for policymakers and practitioners to promote entrepreneurship.