In the changing educational environment brought about by the COVID-19 pandemic, this study investigates the dynamic interactions between digital citizenship, digital empowerment, and academic performance. Data is collected from a survey of students at the College of Business, Al-Ahliyya Amman University. The Partial Least Squares (PLS) method of structural equation Modeling (SEM) was used to analyze the collected data. The findings reveal how digital empowerment is directly impacted by digital education, respect and protection. It was also discovered that digital empowerment directly improves academic performance, highlighting the significance of promoting digital settings that are safe, respectful, and conducive to learning. With the changing educational landscape, this study adds to a better understanding of how digital citizenship influences students' academic performance and learning experiences. It provides educators, organizations, and legislators with useful information to help them better prepare students for success in a fast-evolving educational landscape by enhancing their digital skills and competences.

This study investigates the impact of game-based (gamification) e-Learning techniques on students' engagement, thereby, their satisfaction with e-learning in Jordanian private universities. A conceptual model was developed based on existing empirical evidence from the literature. Data was then collected through a self-administered questionnaire survey from 198 private university students, who were conveniently selected for the study. The data was analyzed using Structural Equation Modeling (SEM) with smart PLS 23. Data analysis revealed a positive effect of gamification on both student engagement and satisfaction, suggesting that incorporating gaming elements into the e-learning process in Jordanian private universities led to higher levels of student engagement, thereby greater student satisfaction with the e-learning experience. A mediating role was also found for student engagement on the effect of gamification on student satisfaction. The findings provide insights to practitioners on how gamification can be utilized as an effective strategy to deliver a more enjoyable and interactive e-learning experience. Research findings were discussed, and conclusions and implications were lastly provided.

The study aims to investigate the impact of Pentagon Fraud Model in five key factors, namely Pressure, Opportunity, Rationalization, Capability, and Action, on the risk of Financial Statement Fraud (FSF). Additionally, it explores the moderating role of Islamic Corporate Governance (ICG) in the relationship between these factors and FSF. The research employed a quantitative approach, utilizing survey data from a sample of 270 respondents. The findings support the hypotheses that all five factors significantly influence FSF, with Pressure exerting the highest impact. Furthermore, ICG is found to moderate the relationships between these factors and FSF, underscoring its role in reducing financial fraud risk. Practically, this study offers essential guidance for organizations in managing FSF risks more effectively. Integrating ethical and governance principles, as moderated by ICG, can help strengthen internal controls, ethics training, and a culture of integrity. Policymakers should consider these findings for enhancing regulatory frameworks, and educational institutions can integrate these results into their curriculum. The study's contribution lies in shedding light on the significance of ICG as a risk-mitigating factor in the context of FSF. The findings provide valuable insights for academics, practitioners, and policymakers. They enhance the understanding of FSF and its mitigating measures and offer a foundation for further research in the field.

This research aims to explore the ways in which the use of WhatsApp for diagonal and lateral communication can improve the achievement of tasks, to what extent it can keep data and information trustworthy and confidential, and in what ways WhatsApp improves the communication of suggestions, instructions, and complaints. The study uses a quantitative research strategy with one independent variable, which is WhatsApp usage in the workplace, and three dependent variables, which are team member communication, employee performance, and confidentiality. To test the proposed research model, the authors conduct an online questionnaire in the United Arab Emirates. Descriptive statistics are used to analyze the quantitative data collected through the questionnaires. The study shows that the use of WhatsApp for communication is positively associated with leader-member exchange (LMX) and team-member exchange (TMX). Both LMX and TMX have a positive correlation with employee performance. WhatsApp is a trusted method to transfer information between team members and between managers and employees. The study also asserts that the use of WhatsApp is an effective tool to improve productivity and performance, and it makes task completion faster. It appears that the study has a limited literature review and lacks previous research on the variables related to data confidentiality and improving team performance. In this case, the study seems to be lacking a thorough examination of prior research related to data confidentiality and its impact on team performance. WhatsApp is a widely used messaging application that offers end-to-end encryption to its users, and this encryption provides a certain level of security and privacy. WhatsApp usage has a positive impact on team performance and productivity. The study presents a concrete understanding of how vertical and horizontal relationships connect the impact of WhatsApp communication on employee performance. The study recommends the use of WhatsApp in the workplace as a safe tool to boost performance and improve productivity and satisfaction.

Using the educational setting of Jordan, this research aims to investigate the complex relationship between teacher-student rapport and student involvement in gamified learning experiences. As it relates to the effective use of gamified learning methodologies, this research digs into the far-reaching ramifications of cultivating strong interpersonal interactions between instructors and students. This research uses a strict quantitative technique to investigate the complex relationships between 400 children and 40 teachers. This study's results shed light on an interesting and statistically significant phenomenon: a significant positive connection (r = 0.742, p 0.001) between teacher-student rapport and the amount of engagement seen in gamified learning sessions. The finding, in line with Jordan's educational reforms, highlights the critical role of positive rapport in generating dynamic and significant participation within modern instructional techniques. The practical repercussions highlight the need for teachers to work to improve their interpersonal skills. It becomes clear that this is a crucial factor in enabling effective teaching and learning, especially within the context of contemporary pedagogical approaches. The current research helps us better understand the complex dynamics at play in teacher-student relationships, illuminating their far-reaching consequences for the pursuit of educational excellence in the Jordann setting.

Education is a fundamental sector in all countries, where in some countries students com-pete to get an educational grant due to its high cost. The incorporation of artificial intelli-gence in education holds great promise for the advancement of educational systems and pro-cesses. Educational data mining involves the analysis of data generated within educational environments to extract valuable insights into student performance and other factors that enhance teaching and learning. This paper aims to analyze the factors influencing students' performance and consequently, assist granting organizations in selecting suitable students in the Arab region (Jordan as a use case). The problem was addressed using a rule-based tech-nique to facilitate the utilization and implementation of a decision support system. To this end, three classical rule induction algorithms, namely PART, JRip, and RIDOR, were em-ployed. The data utilized in this study was collected from undergraduate students at the University of Jordan from 2010 to 2020. The constructed models were evaluated based on metrics such as accuracy, recall, precision, and f1-score. The findings indicate that the JRip algorithm outperformed PART and RIDOR in most of the datasets based on f1-score metric. The interpreted decision rules of the best models reveal that both features; the average study years and high school averages play vital roles in deciding which students should receive scholarships. The paper concludes with several suggested implications to support and en-hance the decision-making process of granting agencies in the realm of higher education.

The advent of technology has dramatically reshaped the ways in which we assimilate knowledge, teach, and access information. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a vibrant, engaging, and inclusive educational landscape. A notable advancement in artificial intelligence technology is ChatGPT (Generative Pre-trained Transformer), which provides personalized and effective learning experiences by delivering customized feedback and explanations to students. Despite the considerable research on the adoption or acceptance of e-learning, there is a paucity of research on the acceptance and utilization of ChatGPT, highlighting the need for further investigation. This study aims to bridge this gap by proposing an integrated model that incorporates three key constructs: perceived learning value, perceived satisfaction, and personal innovativeness. A questionnaire survey was administered to 289 university students in the United Arab Emirates (UAE), and the data collected were analyzed using the partial least squares-structural equation modeling (PLS-SEM) approach. The results revealed that "perceived learning value, perceived satisfaction, and personal innovativeness" are the most influential and critical determinants of students' intentions to use learning platforms through ChatGPT. This research contributes to the existing body of literature on AI and environmental sustainability, providing invaluable insights for practitioners, policymakers, and AI product developers. These insights can guide the development and implementation of AI technologies to better align with users' needs and preferences, while also considering the broader environmental context.

This research examined how virtual reality technology affects the marketing performance of the fashion industry in Jordan. Based on a sample composed of 528 online customers, structural equation modeling (SEM) was utilized as a statistical approach for hypothesis testing. The research findings demonstrated that virtual reality technology had a positive significant impact on marketing performance. This examination provides many implications that managers in the fashion industry in Jordan could recognize, the most notable was encouraging them to focus on increased product demos utilizing virtual reality technology in order to improve customers' comprehension of the product and boost their confidence when making purchase decisions.

Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, the use of green logistics to mitigate these impacts has become increasingly important. To compensate for a gap in the literature, this paper presents a robust bi-objective mixed-integer linear programming (MILP) model for the green capacitated location-routing problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes. The final result of this Robust Multi-Objective Model is to set up the depots and select the routes that offer the highest reliability (Maximizing network service) while imposing the lowest cost and environmental pollution. A Nondominated Sorting Genetic Algorithm (NSGA-II) is used to solve the large-sized instances of the modeled problem. The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model.

The purpose of this study is to examine and analyze the direct and indirect effects of the variable ownership structure, board composition, dividend policy, and financial performance and stock returns in the manufacturing industry on the Indonesia Stock Exchange. The population of this research is manufacturing industrial companies on the IDX since 2015 and was still active until 2019. The sample obtained is 92 issuers who continuously distribute dividends. Testing the research hypothesis, using the structural equation model (SEM) with the Partial Least Square (PLS) software approach. The results show that the ownership structure significantly affected the composition of the board of directors and dividend policy. Ownership structure has no significant effect on stock returns and financial performance. The composition of the board of directors has a significant effect on dividend policy and financial performance but has no significant effect on stock returns. Dividend policy has a significant effect on financial performance but has no significant effect on stock returns. Financial performance has no significant effect on stock returns.