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The primary aim of this study is to investigate the integration of generative artificial intelligence, specifically ChatGPT, into workplace L&D practices, exploring the associated advantages and challenges such integration from an organizational change perspective.

This study uses a qualitative approach, conducting semi-structured interviews with twelve learning and development (L&D) experts.

This study indicates that ChatGPT can positively impact L&D by streamlining processes and potentially enhancing employee performance, engagement and satisfaction. However, to mitigate employee resistance, organizations must clearly communicate the necessity and rationale behind the change, involve employees in the implementation process and address trust issues. Key challenges such as overreliance on ChatGPT, AI skill shortages and technology issues like privacy breaches and misinformation must be managed through strong governance frameworks, including policies, guidelines and regular audits.

The study’s scope is confined to semi-structured interviews with L&D experts, potentially limiting its generalizability. Further research could explore the long-term effects and broader implications of ChatGPT integration in different organizational contexts.

By framing GenAI integration within the context of organizational change, this study offers insights into managing the transition effectively by providing guidance for managers on effectively integrating ChatGPT into L&D practices, emphasizing the importance of mitigating potential negative consequences while maximizing benefits.

Integrating ChatGPT into organizational L&D has the potential to reshape how employees acquire new skills and knowledge, potentially influencing organizational culture and dynamics. However, careful consideration is required to ensure that the integration process aligns with ethical and social norms, minimizing adverse impacts.

This research contributes foundational insights into the integration of ChatGPT in corporate L&D by researching and understanding the opinions of corporate professionals. It serves as a starting point for organizations to identify challenges in adopting GenAI.

The primary objective of this paper is to explore the robust determinants influencing the infection rate and case mortality rate of COVID-19 in both developing and developed economies. The analysis is conducted using a dataset encompassing 148 countries.

To achieve this goal, empirical testing utilizes the Sala-i-Martin version of extreme bounds analysis, a method grounded in the cumulative density function. This approach allows for a comprehensive exploration of potential determinants.

The analysis results reveal that, to a large extent, distinct factors contribute to the infection and mortality rates in developed and developing countries. Notwithstanding these differences, certain common factors emerge, such as the risk environment, the number of tests conducted per million people and the percentage of the population over 65.

Despite acknowledging the potential limitations inherent in official data, this study concludes that the presented results offer valuable insights. The identified determinants, both unique and common, contribute to understanding the dynamics of COVID-19 in diverse economic settings. The information gleaned from this research holds significance for decision-makers involved in combating the ongoing pandemic.

This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

This study aims to examine the effect of corporate governance (CG) mechanisms on financial fragility in non-financial corporations, using Nishi’s operationalization of Minsky’s financial instability hypothesis. Specifically, the study investigates the influence of board size, board independence, CEO duality and audit quality on the financial fragility of non-financial companies (NFCs).

Using a panel logit regression model, the authors analyse annual data from (66) NFCs listed on the Amman Stock Exchange, spanning over the period 2015–2021. This methodology enables us to assess the relationships between the identified CG mechanisms and the categorical proxy of financial fragility.

The findings of this study reveal that a large share of NFCs fall within Minsky’s “Ponzi” classification, indicating elevated levels of financial vulnerability. Remarkably, the analysis demonstrates that larger board sizes and the CEO-Chairman duality exacerbate financial fragility within these firms. Conversely, the study results suggest that board independence and audit quality exhibit limited effects on financial fragility. In addition, profitability, firm size and financial leverage are identified as key predictors of financial fragility.

This study adds to the current literature by using a financial fragility index grounded in Minsky’s financial instability hypothesis. The constructed index is then used to examine specific CG factors in relation to financial fragility, which offers new insights into the dynamics influencing the default exposure of NFCs. Furthermore, the study findings have direct implications for policymakers and stakeholders aiming to enhance CG practices and foster financial stability in the private sector.

This paper aims to investigate the perceptions of financial accountants and both internal and external auditors regarding the impact of corporate governance (CG) and information technology (IT) on the detection and prevention of fraud within organizations.

Primary data were collected from 250 financial accountants, internal auditors and external auditors through questionnaires. The non-probability snowball sampling technique was used for data collection, with the sample t-test, one-way ANOVA and paired sample t-test applied for analysis.

The results indicate that robust CG practices and IT techniques significantly aid in detecting and reducing fraudulent activities by minimizing opportunities, rationalizations, pressures and capabilities of potential employees to commit fraud. Internal controls also play a significant role in reducing instances of fraud. Notably, ethical officers and ethical training were not perceived as significantly effective in preventing and detecting fraud, leading to a perception that fraudulent practices are prevalent and increasing the risk of future fraudulent activities.

This study recommends the adoption of strong CG practices to identify potential fraud within an organization. Moreover, IT techniques should be tailored to specific needs for effective utilization. Furthermore, the government should increase awareness regarding data provision by departments, organizations and other related personnel. Future research could use secondary data from various regions to expand the literature in this field.

This research uniquely combines three significant factors: CG, IT and forensic accounting in fraud detection and prevention. It contributes to the enhancement of literature about fraud and its preventive and detective measures. The results of this study set the seed for future research, government policymaking and enhanced organizational practices.

This study uses instrumental stakeholder theory to explore the relationship between corporate social performance (CSP) and financial performance in various market sectors. It aims to show how CSP, driven by stakeholder demands in different markets, affects financial outcomes.

Using panel data analysis on data from 2007 to 2020, this research examines how stakeholder demand impacts a firm's ability to turn social performance into financial gains. The study ensures reliable results by addressing methodological and endogeneity issues related to CSP.

The results show that a firm's success in converting social performance into financial benefits depends on stakeholder demands in different markets. While better CSP generally leads to improved financial performance, the extent of this benefit varies based on stakeholder expectations. This highlights the importance of managers strategically addressing stakeholder demands to maximize financial returns from social initiatives.

By examining the CSP–financial performance link through the lens of market and stakeholder demands, this research provides new insights into how firms can strategically gain stakeholder support for financial benefits. It shows the long-term value of CSP as an investment that gains stakeholder support over time. This approach broadens the understanding of CSP by considering diverse stakeholder influences across industries, filling a gap in long-term CSP research.

This study aims to explore the factors affecting investment behaviour in cryptocurrencies among Jordanian investors. Specifically, it aims to assess how various motivational and behavioural drivers impact the intention to use cryptocurrencies, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The choice of Jordan as the research context is particularly relevant due to the lack of adequate regulations on cryptocurrency investment.

This study uses a quantitative research approach, using an online survey as the primary method for data collection. The final data set consists of 285 responses collected through a self-administered questionnaire to cryptocurrency users in Jordan. Next, structural equation modelling (SEM) was used to test the developed theoretical framework based on the UTAUT2 model.

The findings reveal that performance expectancy, trust, hedonic motivation and price value significantly enhance the intention to invest in cryptocurrencies, with performance expectancy acting as a mediator. Effort expectancy is not directly related to behavioural intention; however, it positively impacts performance expectancy, validating the mediation hypothesis. Trust affects both the intention to use and the performance expectancy, reinforcing its role as a mediator in cryptocurrency adoption. Hedonic motivation and price value also positively affect the intention to use cryptocurrency. In contrast, social influence and facilitating conditions do not significantly impact behavioural intention, suggesting that cryptocurrency adoption decisions are less influenced by external opinions or the availability of necessary conditions. The findings also show that the demographic profiles of the cryptocurrency users were young, educated males, which suggests a demographic skew in cryptocurrency usage in Jordan.

This study innovatively adapts the UTAUT2 model, focusing on the mediating role of performance expectancy between effort expectancy, trust, and behavioural intention. This study pioneers by examining the mediation effect of performance expectancy, showing how users' ease in using cryptocurrencies positively affects their belief in positive outcomes, subsequently influencing their behavioural intention to use cryptocurrencies. Moreover, this study sheds light on the factors driving cryptocurrency adoption in developing countries like Jordan. It also underscores the demographic trends in cryptocurrency use and proposes targeted recommendations for policymakers and cryptocurrency platforms to foster more inclusive and informed investment environments.

In the dynamic landscape of industrial enterprises, the integration of advanced technologies has emerged as a crucial factor in enhancing both operational efficiency and sustainability. This paper explores the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain technology on accounting sustainability performance. AI's data processing and predictive capabilities, IoT's real-time monitoring, and blockchain's transparency and security are examined for their roles in transforming traditional accounting practices. The discussion highlights how these technologies collectively improve the acceluracy, efficiency, and reliability of financial and sustainability reporting, driving a culture of transparency and continuous improvement. By aligning financial objectives with sustainable practices, industrial enterprises can achieve long-term stability and contribute to global sustainability efforts. This study underscores the importance of technological adoption in navigating the complexities of modern industrial operations and achieving a balance between financial success and sustainable stewardship.

This paper scrutinizes the nexus between firms’ board characteristics; environmental, social and governance (ESG) performance and industry sensitivity, with the aim of examining how the impact of board diversity on ESG performance would vary among sensitive versus non-sensitive industries and identify which board characteristics are more influential on ESG performance in these industries.

A large sample of 31,255 firm-year observations in 5,471 companies listed in the G-7 countries from 2010 to 2022 is examined using a Heckman two-stage least squares (2SLS) approach to address the potential endogeneity concerns within our proposed relationships.

The findings show that the positive influence of diverse boards on a firm’s ESG performance is particularly amplified in sensitive industries and may be attributed to the greater need of these industries to address stakeholder concerns (as posited by the stakeholder and resource-dependence theories) and mitigate agency conflicts (supporting agency theory). Interestingly, the impact of diversity in board gender and education qualifications appears to be particularly influential and remains robust across a series of regression analyses.

This study has important implications for policymakers and legislators as it provides guidelines pertaining to the composition of boards operating in sensitive industries. For practitioners and firms, the results allow for better understanding of firms’ tendency towards sustainability practices, particularly in the context of sensitive industries.

This study has important implications for policymakers and legislators as it provides guidelines pertaining to the composition of boards operating in sensitive industries.

This study contributes to the increasingly growing literature that investigates the nexus between industry sensitivity, board characteristics and ESG performance.

This study examines the impact of female directors on cash holdings in Egyptian listed firms, particularly in light of Decree 123/2019, which mandates female board representation. This study aims to determine if female directors mitigate agency conflicts related to cash holdings and how these dynamics shift post-quota implementation.

Using a panel fixed-effects model, the research analyzes 1,563 firm-year observations from 223 non-financial Egyptian firms listed on the EGX between 2014 and 2022. The robustness of the findings is tested through additional analyses using alternative proxies for cash holdings, different sample periods and a two-stage least squares approach to address endogeneity concerns.

This study finds a significant negative association between female directors and cash holdings, suggesting that female board members may promote more conservative cash management practices. However, this relationship weakens post-quota implementation, becoming statistically insignificant. This implies that while quotas increase female representation, they do not necessarily enhance corporate governance effectiveness regarding cash management. The pre-quota positive link between female directors and excess cash holdings also becomes insignificant post-quota.

The study focuses on female directors’ impact on cash holdings, excluding potential effects on other board subcommittees or functions. It does not capture long-term benefits of increased female representation, which may emerge as the pool of qualified female directors grows. Future research should explore broader implications of gender diversity guidelines and other diversity dimensions across various corporate governance aspects and institutional contexts.

This research provides empirical evidence from an emerging market context on the understudied impact of gender diversity on cash holdings. It critically evaluates the unintended consequences of mandatory gender quotas, highlighting the complexity of regulatory interventions in corporate governance. The study stresses the need for policymakers to address factors limiting the effectiveness of such quotas and to consider potential suboptimal outcomes when increasing female board representation without a corresponding increase in the supply of qualified female directors.