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The purpose of this paper is to prepare a multifunctional nanocomposite that is slow-release and resistant to seawater corrosion and biofouling corrosion and to explore the synergistic effect between the two corrosion inhibitors.

The morphology, structure and release properties of CAP@HNTs, BTA@HNTs and CAP/BTA@HNTs were investigated by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, specific surface area analysis and UV spectrophotometry. The corrosion resistance and antimicrobial properties were investigated by electrochemical measurements and bioinhibition rate tests, and the synergistic effect between the two corrosion inhibitors was explored by X-ray photoelectron spectroscopy.

The CAP/BTA@HNTs are responsive to acidic environments and have significantly improved antibacterial and corrosion resistance compared with CAP@HNTs and BTA@HNTs. CAP and BTA have a positive synergistic effect on anticorrosion and antifouling.

Two types of inhibitors, anticorrosion and antifouling, were loaded into the same nanocontainer to prepare a slow-releasable and multifunctional nanocomposite with higher resistance to seawater corrosion and biocorrosion and to explore the synergistic effect of CAP and BTA on corrosion resistance.

The purpose of this research note is to investigate the drivers and market reaction to firms’ decision to release general COVID-19-related announcements and to withdraw earnings forecasts and dividends during the COVID-19 pandemic in the continuous disclosure environment of Australia.

The authors first tracked the market reaction of all firms in the Australian Securities Exchange All Ordinaries, Top 300, Top 200 and Top 100 indices during the early period of the COVID-19 pandemic between 1 January and 21 September 2020. The authors then focus the investigation on the incidence of firms deciding to withdraw earnings forecasts and dividends and how the market responded to these incidences during that period.

The market reacted negatively during the March/April 2020 period but then bounced back to the pre-March 2020 level. The market reaction is mainly driven by three industries, including consumer discretionary, health care and utilities. Firms in industry sectors such as consumer discretionary, materials, health care and information technology contribute to the highest percentage of COVID-19 announcements. It is interesting to document that firms issuing COVID-19 announcements and withdrawing earnings forecasts and dividends tend to be larger firms with stronger financial performance and higher financial leverage. Regarding the stock market reaction, while the market generally reacted positively to COVID-19-related announcements, the decision to withdraw earnings forecasts and dividends is significantly regarded as bad news.

The COVID-19 pandemic has provided a unique natural event to examine firms’ disclosure behaviour in the continuous disclosure environment of Australia during this period of extreme uncertainty. The incidences of earnings forecasts and dividend withdrawals are mainly driven by larger, better performing and higher leverage firms in the consumer discretionary, health care, materials and information technology industry sectors. The market generally reacted favourably to COVID-19-related announcements, despite a significant stock price drop during the March/April 2020 period. The findings provide important regulatory and practical implications.

The objective is to develop an empirical model estimating the relationship and interaction amongst the factors affecting and enhancing green finance (GF) in developing economies like India.

Around nine growth-accelerating enablers of green financing were found through literature and unstructured interviews and analysed using the total interpretive structural modelling (TISM) method. The hierarchical link between each factor is established using TISM, and further to evaluate the driver-dependent relationship the Matriced’ Impacts Croises Appliquee Aaun Classement (MICMAC) approach is utilised.

The findings demonstrate an interrelationship between growth-accelerating factors, where the political environment and information and communication technology (ICT), have minimal dependency but a strong driving force. Political environment and ICT are found as strategic-level factors lying at the bottom of the model driving towards the dependent variables. The government should focus on enacting effective policies such as the green credit guarantee scheme and carbon credit and establishing a regulatory framework to enhance green financing.

This study examines the literature to generalise the findings and focus on the primary motivators for developing green financing. To increase green financial activity, practitioners must concentrate on aspects with significant driving forces. Furthermore, it makes organisations more profitable, efficient and competitive and promotes long-term growth.

The study is the first in the literature which identifies the growth-accelerating factors of green financing using the TISM and MICMAC-based hierarchical models.

The purpose of this study is to examine the influence of green technology innovation on sustainability performance with the moderating effect of innovation capabilities in the Malaysian manufacturing listed companies.

This was a quantitative study and carried out by applying a research survey. The questionnaire was used to collect the data from 204 Malaysian manufacturing companies of the “consumer products and services” sector listed at Bursa Malaysia, incorporating a five-point Likert scale. All the hypothesized relationships were tested by using the partial least square structural equation modeling (PLS-SEM).

The empirical results showed that the comprehensive adoption of green technology innovation significantly promotes sustainability performance including economic, environmental and social performance. In addition, innovation capabilities significantly and positively moderate the relationship between green technology innovation and sustainability performance.

The scope of this study is specifically confined to the Malaysian manufacturing listed companies, operating within the consumer products and services sector listed at Bursa Malaysia. Consequently, the findings of this study may not be generalized to manufacturing companies of the different geographical contexts.

The findings of this study may help the top management and policymakers of the Malaysian manufacturing listed companies to scrutinize green technology innovation and innovation capabilities to achieve higher sustainability performance.

This study magnifies and provides new insights into the extant literature by developing a comprehensive research model that concurrently tests the direct and moderation effects between green technology innovation, innovation capabilities and sustainability performance. Additionally, this is the first study to examine the influence of green technology innovation on sustainability performance with the moderating effect of innovation capabilities in the Malaysian manufacturing listed companies. This distinct approach significantly bolsters the originality of this study.

The purpose of this paper is to describe the mediating effects of transformational and transactional leadership styles on the relationship between organizational ethics and workplace incivility.

The study employed cross-sectional survey design. Data were obtained from 209 employees from the telecommunication sector. The structural equation model was employed as the analytical tool to test the hypotheses of the study.

Organizational ethics was negatively related to workplace incivility. Both transformational and transactional leadership styles mediated the relationship between organizational ethics and workplace incivility.

Human resource developers and managers can employ, appraise, train and develop managers who can ethically demonstrate transactional, and transformational leadership behaviors to deal with workplace incivilities.

The paper makes an important contribution to the existing organizational literature by establishing the relevance of transformational and transactional leadership styles as mediators of the nexus between organizational ethics and workplace incivility in the telecommunication sector.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

The purpose of this global study is to investigate the critical failure factors (CFFs) in the deployment of operational excellence (OPEX) programs as well as the key performance indicators (KPIs) that can be used to measure OPEX failures. The study also empirically analyzes various OPEX methodologies adopted by various organizations at a global level.

This global study utilized an online survey to collect data. The questionnaire was sent to 800 senior managers, resulting in 249 useful responses.

The study results suggest that Six Sigma is the most widely utilized across the OPEX methodologies, followed by Lean Six Sigma and Lean. Agile manufacturing is the least utilized OPEX methodology. The top four CFFs were poor project selection and prioritization, poor leadership, a lack of proper communication and resistance to change issues.

This study extends the current body of knowledge on OPEX by first delineating the CFFs for OPEX and identifying the differing effects of these CFFs across various organizational settings. Senior managers and OPEX professionals can use the findings to take remedial actions and improve the sustainability of OPEX initiatives in their respective organizations.

This study uniquely identifies critical factors leading to OPEX initiative failures, providing practical insights for industry professionals and academia and fostering a deeper understanding of potential pitfalls. The research highlights a distinctive focus on social and environmental performance metrics, urging a paradigm shift for sustained OPEX success and differentiating itself in addressing broader sustainability concerns. By recognizing the interconnectedness of 12 CFFs, the study offers a pioneering foundation for future research and the development of a comprehensive management theory on OPEX failures.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.