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This study examines the impact of firm-level climate risk exposure (FCRE) on firm stock liquidity by using a sample of Indian-listed firms from the financial years 2003–2004 to 2022–2023. Further, it endeavors to investigate the moderating role of environmental, social and governance (ESG) disclosure in this relationship.

A novel text-based FCRE metric is introduced using a sophisticated Word2Vec model through a Python-generated algorithm for each firm and year based on the management discussions and analysis (MD&A) reports. The panel fixed effect model is used to study how FCRE affects stock liquidity.

The result shows that FCRE negatively affects firms’ stock liquidity, and the effect remains robust after addressing endogeneity concerns. In addition, we find that a high ESG disclosure rating significantly moderated the adverse effect of FCRE. Furthermore, our analysis reveals that investor sentiment, information quality, corporate life cycle and institutional holdings moderate the impact of FCRE on liquidity.

The study offers valuable insights for investors, managers and policymakers on integrating climate risk into investment strategies, improving corporate climate governance and shaping policies that incentivize sustainable corporate behavior.

To the best of our knowledge, this study is an early study to explore the relationship between firm-specific climate risk exposure and stock liquidity using advanced machine learning techniques. It contributes to the existing literature by illustrating how climate risk can lead to adverse market reactions while highlighting the critical roles of corporate ESG practices, investor sentiment and disclosure quality in influencing this relationship.

The paper explores the notion of worker voice in terms of its implications for supply chain justice. The paper proposes the value of the recognition perspective on social justice for framing workers’ experiences in global supply chains and identifies opportunities for the advancement of the worker voice agenda with recognition justice in mind.

The paper adopts a conceptual approach to explore the notion of worker voice in supply chains in terms of the recognition perspective on social justice.

Sustainable supply chain management (SSCM) scholarship has considered worker voice in terms of two key paradigms, which we term communication and representation. To address recognition justice for workers in global supply chains, the worker voice agenda must consider designing worker voice mechanisms to close recognition gaps for workers with marginalised identities; the shared responsibilities of supply chain actors to listen alongside the expectation of workers to use their voice; and the expansion of the concept of worker voice to cut across home-work boundaries.

The paper offers conceptual clarity on the emerging notion of worker voice in SSCM and is the first to interrogate the implications of recognition justice for the emergent worker voice agenda. It articulates key opportunities for future research to further operationalise worker voice upon a recognition foundation.

This study addresses the remarkable research gap in the existing literature on Chat Generative Pre-training Transformer (ChatGPT), which has primarily explored its functional benefits rather than the psychological states of its users. By integrating the self-concept theory and functional theory of attitudes, this study develops a moderated-mediating model to examine the impact of the bandwagon effect on users’ habit formation and subsequent feelings of pride associated with the ChatGPT application.

This study analyzed self-reported survey data from 568 respondents from mainland China using partial least squares structural equation modeling.

The findings reveal that the bandwagon effect indirectly influences users’ pride through the formation of habits related to ChatGPT applications. This study also identifies the boundary condition of social-adjustive attitude, which strengthens both the direct relationship between the bandwagon effect and habit formation and its indirect relationship with pride.

This study contributes to the field by offering a novel perspective on ChatGPT adoption, highlighting the role of self-concept and attitudinal functions in driving users’ intentions to utilize the technology, with a focus on the desire for pride as a motivating factor.

Focusing on the growth of artificial intelligence (AI) in education, this research reveals that AI can create and improve English language assessments for learners in order to optimize and enhance test questions as a bilateral tool in the traditional way for English Language Teaching (ELT) is possible.

The research adopted a qualitative methodology by conducting semi-structured interviews with a varied range of language institutes’ lecturers, revealing new beneficial effects of AI on test time, content and human variables.

Several interviewees agreed that AI should be used in ELT exam creations because of its overt advantages in making test items automatically, adaptive testing, enhanced feedback mechanisms and quality assurance and innovative formats. Simultaneously, some disadvantages are recorded, including complexity and nuance of language, technical limitations, ethical and bias concerns and human oversight and validation.

The study was also limited by the time frame of the research, which may not have fully captured the complex dynamics between the different actors, such as using AI in preparing questions for reading tasks such as automatic creation of pre-reading questions as well as possible answers.

For future studies, as AI-generated material is becoming more ubiquitous, from music to artwork, it presents crucial legal problems regarding who owns the rights to the work or construct ELT exams. It has also become the next problem that the writers should concentrate on.

The aim of the study is to understand the transformative impact of ChatGPT on artificial intelligence (AI) research, its applications, implications, challenges and potential to shape future AI trends. The study also seeks to assess the relevance and quality of research output through citation and bibliographic coupling analysis.

This study employed a comprehensive bibliometric analysis using Biblioshiny and VOSviewer to investigate the research trends, influential entities and leading contributors in the domain of AI, focusing on the ChatGPT model.

The analysis revealed a high prevalence of AI-related terms, indicating a significant interest in and engagement with ChatGPT in AI studies and applications. “Nature” and “Thorp H.H.” emerged as the most cited source and author, respectively, while the USA surfaced as the leading contributor in the field.

While the findings provide a comprehensive overview of the ChatGPT research landscape, it is important to note that the conclusions drawn are only as current as the data used.

The study highlights potential collaboration opportunities and signals areas of research that might benefit from increased focus or diversification. It serves as a valuable resource for researchers, practitioners and policymakers for strategic planning and decision-making in AI research, specifically in relation to ChatGPT.

This study is one of the first to provide a comprehensive bibliometric analysis of the ChatGPT research domain, its multidimensional impact and potential. It offers valuable insights for a range of stakeholders in understanding the current landscape and future directions of ChatGPT in AI.

The purpose of this study is to conduct a comprehensive review of ChatGPT in the education sector. By delving into the published literature, the research aims to uncover the benefits, drawbacks, present applications and prospective uses of ChatGPT for various stakeholders.

The research employs quantitative methodologies. Utilizing the Scopus database, the authors applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework to gather data. Additionally, the study includes a bibliometric analysis conducted through the VOSviewer visualization tool and R Studio to achieve the research objectives.

ChatGPT is making a transformative impact on the education sector. A thorough literature review revealed that ChatGPT has several benefits and drawbacks for students and educators. Additionally, the study sheds light on present applications of ChatGPT and explores its prospective uses for its key stakeholders.

PRISMA methodology in systematic reviews faces challenges in handling publication bias and evaluating study quality. Systematic reviews are limited by their inability to comprehensively cover all relevant research and depend on the quality of included studies. Bibliometric analyses may oversimplify research landscapes, neglecting qualitative insights. The research relies on existing literature, introducing potential biases due to varied accessibility. The study’s focus on the Scopus database and time constraints may exclude recent significant studies.

The study has several recommendations for educational institutions, students, educators, administrative staff and ChatGPT service providers. These recommendations collectively aim to provide comprehensive guidance to stakeholders, fostering an environment where ChatGPT can effectively transform the education sector.

This research conducts a comprehensive examination of ChatGPT in the education sector, with a primary emphasis on exploring its prospective uses for students, educators and administrative staff. By highlighting the potential benefits, the study aims to provide key stakeholders with opportunities to leverage ChatGPT for the transformation of the education sector.

The purpose of this study is to investigate the accuracy and creativeness of ChatGPT in the domain of quantitative aptitude.

ChatGPT 3.5 is used to generate multiple-choice quantitative aptitude questions. A total of 1,100 questions were created across 11 different areas of quantitative aptitude. A dataset is obtained through ChatGPT prompts. Human specialists assessed the accuracy and creativity of these questions. Every question is evaluated and classified into six distinct grades to indicate its level of accuracy. Likewise, the procedure of assessing each question includes providing a grade that showcases originality. Subsequently, we generate hypotheses to evaluate the accuracy and creativity of ChatGPT’s response. The hypotheses are evaluated through the application of statistical methods, such as the one-tailed test.

Our study indicates that ChatGPT exhibits a moderate degree of accuracy when solving mathematical aptitude questions. Our work shows that, for instance, when prompted to generate 10 questions regarding a specific quantitative aptitude topic, ChatGPT is unlikely to produce more than five questions that are accurate in terms of solution and explanation, and it seldom generates more than three new questions. This study also compares the accuracy of ChatGPT in answering questions related to quantitative aptitude with that of questions related to medical science. This study illustrates that ChatGPT is less precise in its responses to quantitative aptitude questions than it is in medical science questions. However, including it as a tool for producing a wide range of quantitative aptitude questions poses a significant problem in terms of creativeness.

The study is focused on a topic set that encompasses approximately 50% of the topics studied within the realm of quantitative aptitudes. In addition, the inclusion of human experience in verifying the correctness of ChatGPT may potentially undermine the study’s accuracy.

Our study shows that ChatGPT demonstrates poor originality and quantitative correctness, thereby limiting its teaching value. This is particularly worrying for students, as ChatGPT does not assist in assessing an answer, making human verification necessary.

Our research will be valuable for individuals residing in countries such as India who are actively preparing for competitive examinations to secure employment in diverse government and private enterprises and are utilising the ChatGPT platform for this purpose.

The purpose of this study was to determine whether cognitive factors mediate the relationship between parental knowledge/support and delinquency escalation.

Using data from early adolescent youth enrolled in the Gang Resistance Education and Training (GREAT) study, two analyses were performed. The first analysis cross-lagged parental knowledge and cognitive impulsivity as predictors of delinquency escalation and the second analysis cross-lagged parental support and moral neutralization as predictors of delinquency escalation.

In both analyses, the indirect effect of a change in parenting on delinquency escalation via a change in cognition attained significance, whereas the indirect effect of a change in cognition on delinquency escalation via a change in parenting did not. In neither case did the direct effect of parenting on delinquency achieve significance.

This study was limited, however, by exclusive reliance on self-report measures to assess all variables in this study and the use of explicit rather than implicit measures of cognitive impulsivity and moral neutralization.

The practical implications of these results are that they point to ways in which improved parenting can lead to crime deceleration; reduced cognitive impulsivity and moral neutralization can lead to crime deceleration.

These results imply that social variables like parental knowledge and support stimulate a change in cognition as part of the process by which delinquency escalates during early adolescence.

The unique contribution this study makes to the field is that it highlights the role antisocial cognition plays in mediating between social factors and delinquency as part of the crime acceleration process that often occurs in early adolescence.

In the field of engineering, the fractional moments of random variables play a crucial role and are widely utilized. They are applied in various areas such as structural reliability assessment and analysis, studying the response characteristics of random vibration systems and optimizing signal processing and control systems. This study focuses on calculating the fractional moments of positive random variables encountered in engineering. This study focuses on calculating the fractional moments of positive random variables encountered in engineering.

By integrating Laplace transforms with fractional derivatives, both analytical and practical numerical solutions are derived. Furthermore, specific practical application methods are provided.

This approach allows for the stable and highly accurate calculation of fractional moments based on the integer moments of random variables. Data experiments included in this study demonstrate the effectiveness of this method in solving fractional moment calculations in engineering. Compared to traditional methods, the proposed method offers significant advantages in stability and accuracy, which can further advance research in the engineering field that employs fractional moments.

(1) Accuracy: Although the proposed method does involve some error, its error level is significantly lower than traditional methods, such as the Taylor expansion method. (2) Stability: The computational error of the proposed method is not only minimal but also remains stable within a narrow range as the fractional order varies. (3) Efficiency: Compared to the widely used Taylor expansion method, the proposed method requires only a minimal number of integer-order moments to achieve the desired results. Additionally, it avoids convergence issues during computation, greatly reducing computational resource requirements. (4) Simplicity: The application steps of the proposed method are very straightforward, offering significant advantages in practical applications.

The article aims to provide an accurate and efficient numerical algorithm for viscous flows in power-law fluids under various thermal boundary and partial slip conditions.

We are conducting a numerical investigation using the Taylor–Galerkin/pressure correction finite element method, which builds upon the work of previous researchers. Here, attention is therefore given to the interplay of various thermal boundary and stick-slip conditions and their impact on non-isothermal inelastic fluid.

The results demonstrate the influence of the Prandtl, Brinkman and Reynolds numbers on the flow’s thermal and hydrodynamic behavior, concentrating on the impact of slip at the wall. Furthermore, we have presented the effects of these dimensionless parameters on the detailed local and average Nusselt numbers, illustrated the high accuracy we obtained for numerical convergence, and compared our results with those of previous papers, observing excellent agreement.

We have successfully tested the code under the presented industrial conditions. Future research directions on this topic aim for efficient and robust solvers for non-Newtonian thermal rheological models; this algorithm can be used for that purpose.

This algorithm has never been used for numerical analysis of such a problem previously.