Search results

This paper aims to explore the extent of corporate social responsibility disclosure (hereafter CSRD) in Jordan and also examine whether ownership structure, board of directors and audit committee characteristics influence CSRD.

The extent of CSRD is measured by constructing a CSRD index for industrial firms listed on the Amman Stock Exchange from 2016 to 2021. Panel regression analysis is used to examine the potential effect of ownership structure, board of directors and audit committee on the level of CSRD.

This study provides empirical evidence that diverse groups of shareholders have different effects on CSR engagement, and board characteristics (board size, board independence and gender diversity) play a vital role in increasing voluntary disclosure, including CSR information. There is no evidence to support that CSRD is influenced by audit committee characteristics.

This study recommends that corporate regulators and policymakers can improve CSRD practices by expanding the scope of existing disclosure requirements related to CSR and developing a structured CSRD index to measure the degree of CSRD practices for comparative purposes. Encourage firms to actively participate in social responsibility programs by granting tax incentives and government facilities to firms with the best CSR reports. Policymakers should introduce initiatives that support female’s representation on board. Finally, firms should restructure their boards by increasing board size and the percentage of independent directors to enhance their effectiveness to support CSRD.

This paper contributes further insights into the literature on CSRD practices and disclosure by analyzing data from developing market contexts.

The fundamental purpose of this study is to analyse the determinants of higher education quality in Afghanistan based on insights from student perceptions. Understanding this part holds paramount importance in enunciating sound policies for the smooth functioning of the higher education sector of Afghanistan.

This study aims to classify students' background and demographic data, distinguishing their perception of higher education quality using a deductive research approach. A sample of 418 students from five top private universities in Afghanistan was chosen to assess their perceptions of higher education dimensions by employing a multinomial regression analysis.

The findings indicate that extracurricular activities, students' scholarship status, parents' education, age, previous academic results and the university they attend significantly impact their perception of the quality of higher education.

This research is essential for education policymakers and university administrators. These findings can be replicated to develop regulations and target specific groups of students to ensure a favourable academic environment and boost the brand image of their universities. This would ensure long-term quality improvement and assurance outcomes, allowing higher education institutions to compete with regional and international institutions.

This study contributes to identifying the determinants of higher education quality based on the perceptions of the students in Afghanistan.

The purpose of this paper is to assess the perceived level of Jordanian nurses’ competencies in offering care to the community during a disaster.

A correlational descriptive design was used to assess nurses’ competencies in offering care for the community during a disaster.

A total of 370 nurses (55 % males) aged 25−55 agreed to participate. The mean score of competencies of nurses who offer care to the community during the disaster was 2.11 (SD = 0.59) points. The results of correlation coefficient tests revealed a significant positive correlation between stated competencies level and nurses’ sex, receiving disaster education and training with rpb (371) = 0.13, p < 0.01; rpb (598) = 0.15, p = 0.004; rpb (598) = 0.21, p < 0.001, respectively. Furthermore, the “care of communities” subscale had a weak positive correlation with the.

Nurses play a critical role in disaster response. However, there was a gap in nurses’ competencies for disaster, which shows there is a crucial need to include disaster management courses in the nursing curriculum and update disaster management courses in hospitals based on nurses’ needs to improve their competencies during disasters.

This study aims to examine the impact of the COVID-19 pandemic on the relationship between auditors’ work stress and client participation with audit quality (AQ).

This study is a descriptive-survey type and the data were collected through a questionnaire distributed online. The statistical population consisted of auditors working in audit firms in Iran and the sample was selected using a random sampling method. Structural equation modeling was used to analyze the data.

The findings of this study suggest that the COVID-19 pandemic exacerbated the negative relationship between auditors’ work stress and AQ. In addition, the results indicate that client participation in the audit process did not significantly impact AQ during the COVID-19 pandemic.

Given the global and widespread impact of the COVID-19 pandemic on individuals’ lives and work settings, this study provides an opportunity to explore the challenges auditors face concerning health protocols and their well-being during the pandemic, specifically within the context of Iran. The unique circumstances of the pandemic have placed additional pressure on auditors to navigate and address the challenges arising from COVID-19 in their workplaces. Although research on the effects of the pandemic on accounting and auditing is ongoing, this study contributes to the literature by expanding our understanding of the specific implications and circumstances faced by auditors during the COVID-19 outbreak.

Because the success of m-payment services depends on the enablers and barriers that affect user satisfaction, the present research explores the effects of perceived value and sacrifices on users' satisfaction with m-payment services. The predicted relationships among perceived value, perceived sacrifices, users' satisfaction, continuance intention, word-of-mouth (WOM), shopping effectiveness, quality of life (QOL) and stickiness were established based on the mobile technology acceptance model (MTAM) and the value-based adoption model (VAM).

A representative data sample of 430 Egyptian banking clients was analyzed to test the hypotheses using partial least squares-structural equation modeling (PLS-SEM).

The findings revealed that all perceived value constructs significantly positively affect users' satisfaction. Moreover, all perceived sacrifice constructs significantly negatively affect users' satisfaction. Users' satisfaction, in turn, has a significant positive effect on continuance intention, WOM, shopping effectiveness, QOL and stickiness with m-payment services.

This is the first study to examine several levels of m-payment outcomes, including m-payment, consumer and bank outcomes, based on the integration of MTAM and VAM models.

The ionization of the air surrounding the phase conductor in high-voltage transmission lines results in a phenomenon known as the Corona effect. To avoid this, Corona rings are used to dampen the electric field imposed on the insulator. The purpose of this study is to present a fast and intelligent surrogate model for determination of the electric field imposed on the surface of a 120 kV composite insulator, in presence of the Corona ring.

Usually, the structural design parameters of the Corona ring are selected through an optimization procedure combined with some numerical simulations such as finite element method (FEM). These methods are slow and computationally expensive and thus, extremely reducing the speed of optimization problems. In this paper, a novel surrogate model was proposed that could calculate the maximum electric field imposed on a ceramic insulator in a 120 kV line. The surrogate model was created based on the different scenarios of height, radius and inner radius of the Corona ring, as the inputs of the model, while the maximum electric field on the body of the insulator was considered as the output.

The proposed model was based on artificial intelligence techniques that have high accuracy and low computational time. Three methods were used here to develop the AI-based surrogate model, namely, Cascade forward neural network (CFNN), support vector regression and K-nearest neighbors regression. The results indicated that the CFNN has the highest accuracy among these methods with 99.81% R-squared and only 0.045468 root mean squared error while the testing time is less than 10 ms.

To the best of the authors’ knowledge, for the first time, a surrogate method is proposed for the prediction of the maximum electric field imposed on the high voltage insulators in the presence Corona ring which is faster than any conventional finite element method.

This research seeks to investigate the impact of individual entrepreneurial orientation (IEO) and entrepreneurial exposure on new-venture gestation activities during COVID-19 pandemic.

This paper adopted negative binomial regression (NBR) modeling with a sample of Saudi female nascent student entrepreneurs (N = 1,032).

The research found that females' IEO is significantly and positively associated with how much progress they make in term of the number of activities they carried out in the new-venture gestation stage. Similarly, females who had high entrepreneurial exposure are more likely to commit to many activities and progress in the new venture. Therefore, those who have entrepreneurial experience perform better different new-venture gestation activities during the COVID-19 pandemic than those who have little or no experience.

Over a transformation of demographic variables and using NBR, this paper is the first paper in Saudi Arabia that develops and tests a theoretically based model linking the perceptual entrepreneurial orientation of nascent student entrepreneurs with objectives variables to the progress of new-venture gestation activities.

This study aims to review the results of relevant studies to shed light on social trust-building in different contexts and the factors that affect it in disaster risk management.

This systematic review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses model. The study keywords were searched for in PubMed, Scopus and Web of Science databases on August 2021. The inclusion criteria were English-written articles published in social trust and disaster relief efforts. Exclusion criteria were lack of access to the full text and article types such as nonoriginal articles.

Out of 1,359 articles found, 17 articles were included in the final analysis using four general categories: six articles on the role of local government in trust-building (local governments), five articles on the role of social media in trust-building (social media), four articles on the role of social capital in trust-building (social capital) and two articles on the importance of community participation in trust-building (community participation).

Understanding the role of social trust and the factors which influence it will help the development of community-based disaster risk management. Therefore, disaster management organizations and other relief agencies should take the findings of this study into account, as they can help guide policymaking and the adoption of strategies to improve public trust and participation in comprehensive disaster risk management. Further studies recommended understanding people’s experiences and perceptions of social trust, relief and disaster preparedness.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.

Normal-weight obesity (NWO), characterized by normal body mass index (BMI) but excess body fat, is a potential contributor to chronic diseases. This study aims to assess the relationship between this phenomenon and some metabolic factors in a population of Iranian employees.

This cross-sectional study was conducted on Iranian employees from the baseline data of Employees Health Cohort Study, Shiraz, Iran. Anthropometric measures, including weight, height, waist circumference and percentage of body fat, were obtained from the cohort database. The participants were divided into three groups: healthy, normal-weight obese and overweight/obese. Metabolic variables including blood pressure, fasting blood sugar, lipid profile, liver function enzymes and metabolic syndrome were assessed in relation to the study groups.

A total of 985 participants aged 25–64 years were included. Males with NWO had significantly higher alanine aminotransferase (ALT) levels compared to the healthy group in the fully adjusted model. Also, high-density lipoprotein (HDL) was significantly lower among females with overweight/obesity than healthy group when adjusted for age and energy intake. Furthermore, after adjusting for age and energy intake, both genders in the overweight/obese group showed significantly elevated systolic and diastolic blood pressure, while this was not observed for the NWO group. Lastly, metabolic syndrome was more prevalent in NWO as well as overweight/obesity.

These findings further encourage identification of excess body fat, even in normal-weight individuals, to prevent chronic metabolic diseases. Special attention should be paid to subgroups with sedentary occupations, as they may be at increased risk for NWO-related health issues.