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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.

Purpose: This chapter identifies performance factors that have the strongest impact on companies’ sustainable outcomes and compares the obtained results across different sectors.

Methodology: About 3,384 observations were gathered from 2015 to 2022 from companies in communication services, energy, financials, real estate, and utilities sectors that comprise the ‘STOXX Global ESG Leaders Select 50’ index. The multiple regression model is constructed with companies’ ESG scores as dependent variables and independent variables representing operational, financial, and market performance.

Findings: Companies that tend to have higher operational and financial performance in the financial sector are more likely to have higher ESG performance. The financial performance results of companies showed the strongest statistically significant relationship with environmental and the weakest with governance scores.

Implications: Results benefit private and institutional investors aiming to create more sustainable portfolios. The obtained results indicate that these investors should focus on companies operating in the financial and energy sectors with higher performance results. Better ROE, ROA, and Tobin’s Q may have a negative impact on sustainable outcomes for companies operating in the real estate and utility sectors.

Limitations: Firstly, not all ESG index providers disclose information about their index constituents. Secondly, within the chosen ‘STOXX Global ESG Leaders Select 50’ index, not all constituents had complete ESG data available on the Bloomberg platform. When selecting the analysis period, it was observed that the accessible ESG data on Bloomberg covers a relatively short time span, only from 2015 onwards.

Future research: A larger number of companies by choosing a more comprehensive available ESG index.

Purpose: This study focuses on the business case of Environmental, Social and Governance (ESG), namely its economic benefits and long-term value creation by attracting environmental-friendly and socially responsible investors.

Methodology: The central result of the von Neumann–Morgenstern (VNM) expected utility theory is that the optimal strategy under uncertainty is given by maximising the expected utility. The study introduces a second utility function to represent externalities. Total utility can be derived by a sum of the two functions where h is a scalar value which indicates to what degree the actor is interested in maximising the utility of externalities. The payouts could be set by ESG scores for the given companies, then the whole equation can be solved for simple cases such as the normal case.

Findings: By extending the traditional risk/return MPT framework to account for the additional utility of contributing towards externalities (in this case specifically ESG goals) the utility maximisation algorithm can be applied to the ESG dimension in a holistic manner and not as a separate filter on the investment universe nor a synthetic boost to expected returns.

Implications: Portfolio and asset managers can more efficiently optimise for consumer risk, return and sustainability preferences, allowing access to the widest possible investment universe while at the same time delivering an optimal bespoke solution for the specific sustainability preferences of the investors.

Future research: How to measure investment’s sustainability impact and what is the best way to estimate that. How to determine monetary impact of damages and externalities. Estimation of Hamilton’s coefficient.

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.

This study aims to investigate the effects of three dimensions of ChatGPT strategic value – organization support (OS), managerial productivity (IM) and decision aids (DA) – on the business sustainability (BS) of Malaysian public universities.

A quantitative methodology was adopted for this study to examine the relationships between ChatGPT strategic value and the BS of Malaysian public universities.

The study found that two dimensions of ChatGPT strategic value, namely, OS and IM, influence BS, whereas DA do not.

To the best of the author’s knowledge, this study is the first to address the relationship between ChatGPT strategic value and BS in a specific context – Malaysian public universities – providing new contributions to theory by extending the literature on the topic.

The findings are expected to guide universities in Malaysia in leveraging ChatGPT strategic value for enhancing BS.

To the best of the author’s knowledge, this empirical study is the first in the literature to examine the relationships between ChatGPT strategic value and BS in the education sector. Supported by an original conceptual model, the insights provided should extend the literature dedicated to ChatGPT strategic value and BS in the context of a South Asian economy.

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.

More and more literature points out that compared to fragmented strategic HRM, interactive or internally consistent HR systems can generate synergies and more effectively predict employee outcomes. Different HR subsystems (such as performance-oriented and maintenance-oriented HRM practices), respectively, play a critical role for organizations and employees. However, the impact of the synergy effect of different practices within the HRM system is less concerning to researchers. Based on self-regulation theory, this paper explores the congruence effects within the dual-oriented HR system on employee ethical behaviors (prosocial/unethical behavior).

Data were collected in a two-wave survey from 252 employees working in high-tech and service companies in China. Polynomial regression and response surface analyses were used to examine the hypotheses.

The results indicate that the internal congruence of performance-oriented and maintenance-oriented HRM practices is positively related to employees’ prosocial behavior but negatively related to employees’ unethical behavior. Employees have more prosocial behavior and less unethical behavior when they perceive the high-performance-oriented and high-maintenance-oriented HRM practices than the low-performance-oriented and low-maintenance-oriented HRM practices. Employees also have more prosocial behavior and less unethical behavior when they perceive the low performance-oriented and high maintenance-oriented HRM practices than the high performance-oriented and low maintenance-oriented HRM practices.

Drawing on self-regulation theory and the “Yin-Yang balancing” perspective, this paper extends the limited understanding of the influence of dual-oriented HR system internal congruence between performance-oriented and maintenance-oriented HRM practices on employee behaviors. This paper is of great significance for a better understanding of the complexity and potential of HR systems.