Search results

With the continuous deepening of China's mixed-ownership reform, the participants in the reform have gradually expanded from state-owned enterprises to private enterprises. Whether state-owned equity participation in private enterprises can facilitate the development of environmental, social and governance (ESG) performance in private enterprises is a question that needs urgent examination. This study aims to investigate the impact of state-owned equity participation on the ESG performance of private enterprises.

Using Chinese listed companies as the research sample, this study uses econometric methods such as multiple regression to analyze the relationship between state-owned equity and the ESG performance of private enterprises. Additionally, it explores the underlying mechanisms and influencing factors of this relationship.

There is a significant inverted U-shaped relationship between state-owned equity and the ESG performance of private enterprises. Mechanism analysis reveals that resource effects and governance effects play a mediating role in this nonlinear relationship. Furthermore, the authors find that environmental regulation and managers' attention to the environment positively moderate the relationship between state-owned equity participation and ESG performance.

A reasonable equity structure is crucial for enhancing corporate ESG performance. Moderate state-owned equity participation helps to leverage resource integration and governance advantages, which will assist private enterprises in maximizing ESG performance and achieving sustainable development.

In advancing the process of mixed-ownership reform, the government should maintain an appropriate proportion of state-owned equity to avoid excessive intervention in enterprise decision-making. At the same time, it should ensure that enterprises can genuinely undertake their social and environmental responsibilities while pursuing economic benefits. This is of great significance for promoting sustainable economic and social development.

This study integrates state-owned equity, ESG and nonlinear relationships into a single research framework. It explores the internal mechanisms and influencing factors of their relationship, overcoming the limitations of previous studies and provides a new perspective for understanding the impact of state-owned equity on corporate ESG performance.

Two friction models of Fe-Fe and Diamond-like carbon (DLC)-Fe were established by molecular dynamics (MD) method to simulate the friction behavior of traditional fracturing pump plunger and new DLC plunger from atomic scale. This paper aims to investigate the effects of temperature and load on the friction behavior between sealed nitrile butadiene rubber (NBR) and DLC films.

In this study, MD method is used to investigate the friction behavior and mechanism of DLC film on plungers and sealing NBR based on Fe-Fe system and DLC-Fe system.

The results show that the friction coefficient of DLC-Fe system exhibits a downward trend with increasing load and temperature. And even achieve a superlubricity state of 0.005 when the load is 1 GPa. Further research revealed that the low interaction energy between DLC and NBR promoted the proportion of atoms with larger shear strain in NBR matrix and the lower Fe layer in DLC-Fe system to be much lower than that in Fe-Fe system. In addition, the application of DLC film can effectively inhibit the temperature rise of friction interface, but will occur relatively large peak velocity.

In this paper, two MD models were established to simulate the friction behavior between fracturing pump plunger and sealing rubber. Through the analysis of mean square displacement, atomic temperature, velocity and Interaction energy, it can be seen that the application of DLC film has a positive effect on reducing the friction of NBR.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

In a world with over 8 billion people, ensuring sustainable food sources is paramount. This chapter explores the pivotal role of aquaculture in addressing the challenges of marine conservation and sustainable resource use. Aligned with the United Nations’ Sustainable Development Goal 14, aquaculture emerges as a solution to relieve pressure on wild fish stocks and enhance food security. The chapter emphasises the rapid growth of this sector and underscores the importance of international cooperation and policies like the Global Ocean Treaty in ensuring marine biodiversity. While acknowledging the potential of aquaculture, the chapter delves into environmental concerns surrounding fishmeal and fish oil in feed. It advocates for innovative technologies and ingredients to establish a circular bioeconomy. The significance of higher education in advancing sustainable aquafeed technology, breeding, and genetics is highlighted, with a discussion on milestones achieved by experts like Dr John E. Halver and Professor Simon J. Davies. Examining technological advances, the chapter explores molecular genetics, transgenics, and gene editing, particularly CRISPR biosciences, as transformative tools for enhancing aquaculture productivity and sustainability. Environmental impacts are addressed, proposing solutions such as Recirculating Aquaculture Systems (RAS) and Multitrophic Aquaculture Systems (MTA) to minimise ecological footprints. Throughout, there is a strong emphasis on the integral role of research and education in fostering sustainable aquaculture practices. The chapter advocates for specialised courses and programs in higher education to prepare the next generation for the challenges and opportunities in aquaculture, ensuring its contribution to global food security and environmental stewardship.

This study examines whether common directors are guilty of contagion of corporate frauds from the customer side and, if so, how contagion occurs. Moreover, it explores a way to mitigate it, which is the increased digital orientation of firms.

Secondary data analysis is applied in this paper. We extract supply chain relations from the China Stock Market and Account Research (CSMAR) database as well as corporate fraud data from the same database and the official website of the China Securities Regulatory Commission (CSRC). Digital orientations are estimated through text analysis. Poisson regression is conducted to examine the moderating effect of common directors and the moderated moderating effect of the firms’ digital orientations.

By analysing the 2,096 downstream relations from 2000 to 2021 in China, the study reveals that corporate frauds are contagious through supply chains, while only customers’ misconduct can contagion to upstream firms. The presence of common directors strengthens such supply chain contagion. Additionally, the digital orientation can mitigate the positive moderating effect of common directors on supply chain contagion.

This study highlights the importance of understanding supply chain contagion through corporate fraud by (1) emphasising the existence of the contagion effects of corporate frauds; (2) understanding the potential channel in the process of contagion; (3) considering how digital orientation can mitigate this contagion and (4) recognising that the effect of contagion comes only from the downstream, not from the upstream.

While government is known to provide political guidance and exercising its executive function, it is also has regulatory powers through laws it enacts. In fostering sustainability, it is important to inquire how government's role can be innovated to facilitate sustainability, particularly in the travel and tourism industry. By reviewing tourism governance literature and mapping governance roles in the travel and tourism industry, this chapter creates a policy framework that underscores a new approach to tourism governance. We underscore that government's role must pivot toward being more developmental than regulatory so that it can effectively stimulate the market to sustainability by fostering value creation, supporting manpower capacitation, ensuring health and safety, and protecting the environment. This can be done if government will not fix the market and promote free market policymaking.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

This paper aims to examine the consequences of board diversity (BD) in Kuwait. In particular, it examines the impact of BD (gender, age and nationality) on earnings management (EM).

The research uses data from 103 non-financial Kuwaiti-listed companies from 2010 to 2017. The data is collected from the companies’ data from secondary sources such as their annual reports. The data analysis methods are correlation, multi-regression and robust regression. EM is measured using the modified Jones model (1995) and Kothari et al. (2005).

The findings show a negative association between gender diversity (GD) and EM. It also found a positive relationship between age diversity (AD) and EM and no relationship between national diversity (ND) and EM.

This study’s results have significant implications for investors. The practical empirical findings indicate that GD on the board did not impact on EM. Also, it is more important to have senior directors on the board than AD to reduce EM. There is no need to employ any foreigners because they do not affect EM.

It contributes to the growing body of literature on BD by investigating its effect on EM. Furthermore, building on the broader literature on gender, age and ND by highlighting the critical role that women, young people and foreign directors play in improving boards' monitoring role on EM. More specifically, it contributes to existing knowledge, provides a theoretical contribution and makes a methodological contribution.