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This paper aims to investigate the impact of China’s Green Credit Guidelines (GCG) policy on the environmental, social and governance (ESG) scores of restricted enterprises and examine firm’s speculative behavior in response to the policy.

This paper views the GCG policy proposed in 2012 as a quasinatural experiment and uses difference-in-differences (DID) model to evaluate its influence on the ESG scores of Chinese nonfinancial A-share listed enterprises from 2007 to 2019. Robustness tests include the propensity score matching (PSM)–DID method and permutation tests.

The GCG policy significantly increases the ESG scores of restricted enterprises, particularly enhancing environmental (E) performance. However, it only improves the social (S) and governance (G) performance of firms heavily reliant on bank credit, indicating speculative behavior by enterprises. Increased Government attention, a higher proportion of female executives and more developed local green finance reduce speculative behavior, while executives with financial backgrounds promote it.

Governments should mandate standardized ESG reporting and monitor restricted enterprises, banks should monitor speculative behavior and firms should integrate ESG into their long-term strategies to support sustainable development.

The results provide evidence of the effectiveness of implementing the GCG policy in China and offer guidance for better promoting green credit policy in developing countries, contributing to the transition toward a more sustainable future.

To the best of the authors’ knowledge, this paper is the first to explore if the GCG policy’s asymmetric effects on ESG components are due to enterprise speculative behavior and examines the factors influencing this behavior, providing insights for regulators to better implement the GCG policy to promote sustainable development.

The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

For survival and prosperity, enterprises need to simultaneously engage in exploitation and exploration. Digital transformation is of great significance to enterprise innovation. However, the impacts of digital transformation on exploitation and exploration remain unclear. Moreover, the impacts of technological diversity on the relationships between digital transformation and exploitation and exploration are also unknown.

Based on an integrated perspective of dynamic capability theory and organizational inertia theory and using data from Chinese listed enterprises from 2007 to 2020, this study clarifies the effects of digital transformation on exploitation and exploration and assesses the moderating effect of technological diversity.

The results show that digital transformation improves exploitation, but negatively impacts exploration. Technological diversity mitigates the negative effect of digital transformation on exploration, but the moderating effect on the relationship between digital transformation and exploitation is not significant.

This study contributes to the existing literature on the digital paradox and provides guidance for enterprises to clarify the direction of digital transformation.

Despite the detrimental effects of supervisor knowledge hiding on employees and organizations, little research has focused on how subordinates cope with it. Drawing on the impression management theory, this study proposes a mediated moderation model to explain who and how cope with supervisor knowledge hiding.

Using a sample of 340 full-time participants in various organizations and industries in China, the proposed model was tested using ordinary least squares regression with the PROCESS 3.5.

The results support the mediated moderation model, indicating that proactive subordinates are motivated to manage their impression towards supervisors and engage in upward ingratiation to cope with supervisor knowledge hiding. In contrast, subordinates with low level of proactive personality trait have less impression management motive and engage in fewer upward ingratiatory behaviors.

Based on the impression management theory, the current paper contributes to the literature on supervisor knowledge hiding by expanding the consequences of supervisor knowledge hiding, identifying a boundary condition of supervisor knowledge hiding on subordinate’s subsequent behaviors and enriching the mechanism underlying the effect of supervisor knowledge hiding with proactive personality.

As a new type of spinning machine, the jet spinning machine absorbs the carding system of the rotating cup spinning series and the nozzle part of the jet spinning. This paper aims to intends to introduce the double carding structure currently studied by the rotating cup spinning into the jet spinning machine, and analyze the influence of the nozzle characteristic number on the flow field in the double carding structure to verify the advantages of the double carding structure.

The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field. The influence of the nozzle characteristic number on the flow pattern in the four models is compared. The advantages and disadvantages of a conventional single comb and a double comb with a bypass channel on the longer side of the transport channel as an additional air supply channel are also evaluated.

At present, the double comb technology of rotary cup spinning is being studied at home and abroad to improve the spinning quality and improve the difficult problem of mixed yarn with large difference in processing fiber properties. At present, the jet spinning machine combines the advantages of rotary cup spinning and jet spinning, absorbing the comb system of rotary cup spinning series and the nozzle part of jet spinning. Therefore, it is found that the introduction of the double-split structure into the wool jet spinning has research value to improve the spinning quality.

The purpose of this paper is to refer to the previous research on the double comb structure in rotary spinning, and to apply the double comb structure in the new jet spinning machine to improve the spinning quality. The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field.

This study aims to respond to the latest research calls to precisely revisit the nexus between corporate green innovation (CGI) and financial decisions through deeply investigating the mediating effect of corporate environmental performance measured by the effectiveness of emission reduction.

This study analyzes nonfinancial-listed firms on the Australian Securities Exchange from 2002 to 2019 using multiple regression analysis on a panel data set. Initially, different static panel data approaches were used. To account for the potential endogeneity issue and generate robust outcomes, the authors apply the one-step system generalized method of moment, two-stage least squares and lagged model approaches.

The results provide a clear indication that the practices of green innovation can favorably contribute to the level of environmental performance, which in turn affect the firm’s ability in opening the new financial doors and shape solid capital structure. In this context, the effective environmental performance fully mediates the nexus between CGI and capital structure of a firm. More importantly, the outcomes are robust and coherent across different estimation techniques.

The originality of this study lies in its utilization of mediation analysis to explore the relationship between CGI and a firm's financial structure. This approach distinguishes it from previous research by offering a thorough and nuanced understanding of how green innovation practices influence the financing decisions of a firm.

Entrepreneurship education (EE) through innovative teaching techniques (ITEE) and entrepreneurial intention (EI) are two essential components of entrepreneurship development. Using the assumptions of the Theory of Planned Behavior (TPB), we took three constructs: attitude (ATT), subjective norms (SUBNs), and perceived behavioral control (PBC), and from the assumption of Human Capital (HC) theory, we constructed another variable, ITEE. Thus, the fundamental objective of this study is to find out the essential predictor of EI between entrepreneurial antecedents (ATT, SUBNs, PBC) and ITEE through an artificial neural network (ANN).

Using the snowball sampling technique, a highly structured questionnaire was sent to respondents. Finally, a sample size of 397 business graduate students was chosen.

The findings revealed that two dimensions of entrepreneurial antecedents (attitude and subjective norm) positively impacted EI. Furthermore, ITEE partially mediated the relationship between two dimensions of entrepreneurial antecedents (attitude and subjective norm) and EI. Moreover, through ANN, we found that attitude (ATT) was a crucial predictor of EI among business graduate students in Bangladesh.

In this study, only business graduate students were included as respondents; thus, further research should include students from other departments or disciplines to generalize the findings.

This study covers numerous actors in terms of practical contributions, including students, academics, the government, and the state. This article should draw the attention of Bangladesh government policymakers regarding the significance of ITEE for developing entrepreneurship. The research framework of this study proposed that ITEE should implement antecedents of entrepreneurship into business education, boosting the ability of students to make judgments, which will also enhance EI in the future.

Integrating the TPB theory with human capital theory represents a significant scholarly advancement in business education for graduate students in Southeast Asia, namely Bangladesh. Furthermore, we developed a novel ITEE scale by synthesizing information from many literary sources, providing valuable insights for future researchers.

Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.

The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.

Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.

The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.