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This examine the impact of environmental, social and governance (ESG) performance on financial reporting quality (FRQ) before and during COVID-19 in the Egyptian market.

This study uses quarterly data from 2017 to 2021 to draw conclusions, with a sample consisting of 486 firm-year observations for 27 Egyptian companies listed on the Standard and Poor’s/Egyptian Stock Exchange ESG index. This study uses both firms’ ESG scores and the Beneish Model, an earnings detection model, as proxies for FRQ. COVID-19 effects on ESG performance and FRQ were examined by using Pearson’s correlation coefficient and two-stage least squares.

COVID-19 has a significant impact on the link between ESG and FRQ. This implies that corporations with high ESG performance are less likely to manipulate earnings (having a low M-score) and thus provide high FRQ during the COVID-19 pandemic. Moreover, there is a significant positive relationship between firm size, leverage and M-Score, indicating that large firms typically present a high FRQ.

The sample size and data availability are the main research limitations. Additionally, this study only considers the effects of firms’ ESG performance on FRQ during the COVID-19 pandemic. Thus, future research should consider other factors associated with investors’ corporate social responsibility (CSR).

This research has practical implications for market regulators seeking to establish a legislative framework and enhance guidance to mandate managers to provide ESG data and CSR reports appropriate for Egypt and other developing economies in times of crisis.

Promoting the adoption of ESG practices in business, particularly during crises, has the potential to effectively provide high-quality and reliable financial reporting required for investment.

This study aspires to address notable deficiencies in the pertinent literature concerning the relationship between ESG performance and FRQ during COVID-19. To the best of the authors’ knowledge, little is known about how ESG performance changes in response to pandemics in emerging markets. To address this gap, this study examines the effects of COVID-19 on the relationship between ESG performance and FRQ in Egyptian-listed firms from 2017 to 2021.

To investigate the mediating role of work engagement (WE) between job autonomy and cyberloafing and the moderating effect of mindfulness on the linkage between work engagement and cyberloafing.

This study was conducted using an online questionnaire survey. Data were gathered from 266 full-time working professionals in India. Hierarchical regression analysis and, SPSS PROCESS version 4.0 (model 14) were employed to analyze the mediated moderation effect.

Results showed that job autonomy reduced cyberloafing of employees through WE and the mediation effect was stronger when employees were high on mindfulness.

Results indicate that job autonomy and mindfulness have a considerable impact on employee cyberloafing behavior. Organizations seeking to reduce employee cyberloafing behavior could benefit by considering job autonomy as well as supporting employee mindfulness.

This study adds to the understanding of cyberloafing antecedents particularly, the role of job autonomy and WE. Additionally, it examines how mindfulness self-regulates with regard to cyberloafing and contributes to the growing body of mindfulness research and its impact on counterproductive behavior at work.

This study examines the impact of career competencies (CC) (in the form of personal resources) on sustainable employability (SE) under the tenets of the Conservation of Resources theory. Further, we assess the moderating impact of coworker support and supervisor support (work-related social resources) in this relationship.

Data were collected using survey method from 362 doctors employed in private hospitals in Pakistan. The data were analyzed using structural equation modeling (SEM).

The results suggests a significant influence of CC on SE. Further, results also reveal that social support received from coworkers moderates the relationship between CC and SE. However, we find that supervisor support does not moderate the said relationship.

This research has clear novelty as SE is a recently defined construct and is still an area with insufficient empirical research. There is increasing interest in identifying the determinants and underlying mechanism of SE. Thus, this study makes contributions to knowledge by investigating CC and social resources as antecedents of SE. This study also offers implications for theory generally, and within the medical practitioner context more specifically.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.