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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.

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.

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.

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.