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.

In this study, the impact of COVID-19 lockdown on Jordanian high school students’ reading habits and attitudes was examined. COVID-19 has indeed affected education systems all over the world; education institutions all over the world were compelled to implement innovative technological approaches so that education could still be delivered to students, fulfilling the academic expectations, while the Sustainable Learning and Education (SLE) ideals are consistently embraced. One of these approaches has been the use of mobile learning applications (MLA). These applications (MLAs) employ some prominent features of mobile apps, to allow students to collaborate and participate in purposeful online learning. Still, the success of any technology is dictated by the acceptance of the user, in this context, the acceptance of students. In other words, student acceptance of MLA will determine the success of MLA. Accordingly, the effect of COVID-19 lockdown on the information behavior of high school students was examined in this study, with MLA being used by these students. Data were gathered from 394 high school students in Jordan. These students were chosen randomly, and they were all mobile phone users. The data covered the 2022–2023 fall term and were analyzed using Structural Equation Modeling (SEM). Based on the analyses results: Self-Efficacy and Perceived Compatibility had significant impact on Perceived Performance Expectancy and Perceived Effort Expectancy; Perceived Convenience and Perceived Effort Expectancy had significant impact on Perceived Performance Expectancy; Perceived Enjoyment had significant impact on the Behavioral Intention to use MLA; COVID-19 had significant impact on the Behavioral Intention to use MLA; Perceived Compatibility showed no significant impact on Perceived Enjoyment; and Perceived Effort Expectancy, Perceived Performance Expectancy and Perceived Compatibility showed no significant impact on the Behavioral Intention to use MLA. The outcomes of this study demonstrate a practical indication in support of digital information behavior among high school students in this era.

The proliferation of online violence has given rise to a spate of malignant incidents, necessitating a renewed focus on the identification of cyberbullying comments. Text classification lies at the heart of efforts to tackle this pernicious problem. The identification of cyberbullying comments presents unique challenges that call for innovative solutions. In contrast to traditional text classification tasks, cyberbullying comments are often accompanied by subtle and arbitrary expressions that can confound even the most sophisticated classification networks, resulting in low recognition accuracy and effectiveness. To address this challenge, a novel approach is proposed that leverages the BERT pre-training model for word embedding to retain the hidden semantic information in the text. Building on this foundation, the BiSRU++ model which combines attentional mechanisms is used to further extract contextual features of comments. A multi-task learning framework is employed for joint training of sentiment analysis and cyberbullying detection to improve the model's classification accuracy and generalization ability. The proposed model is no longer entirely reliant on a sensitive word dictionary, and experimental results demonstrate its ability to better understand semantic information compared to traditional models, facilitating the identification of potential online cyberbullying comments.

Industries all throughout the world are preparing to understand the ramifications of the emerging metaverse, which is a merger of the virtual and physical worlds. Notably, the banking industry stands on the cusp of a monumental shift, with the metaverse offering unprecedented operational enhancements. While the potential transformations brought about by the metaverse are discussed in various sectors, there is a discernible gap in understanding its specific applications in banking, especially with respect to advanced technologies such as NFTs, blockchain, and smart contracts. The study adopts a comprehensive approach to bridge this knowledge gap, employing convenience non-probability sampling to engage 48 subject matter experts specializing in Metaverse-Enabled Innovation in Banking. Data was collected using both mailed and electronic questionnaires. The empirical analysis offers strong evidence supporting the pivotal role of technologies like Digital Twins, Artificial Intelligence, and Blockchain-Based Assets in the metaverse's preliminary stages. We discover a plethora of business potential for banks within the metaverse, including client communication, cross-border transactions, mortgages, digital assets, green loans, and data security.‎‎

The firms and the industries still using the ways and means of promotion and publicizing the products have still not achieved the level of success as the modern means of social media is providing in creating a huge demand for their products. This study involves the modern means of communication agility promises success, development, and significance of the product in an efficient manner. Social media plays a vital role in creating a demand for the products by explaining the need of the product and a very effective source of creating a link among industries, firms, management, employees and especially the individual ones who come to know the skills and expertise. The impact of social media in strengthening the relationship with the customer has been very keenly observed and the findings show that such research has detailed that the use of social media has influenced the internal and the external capabilities. The data gathered in this study shows that the firms and the industries using the approach of social media have a revolutionary impact on building and strengthening the relationship between the customer and the management, employees. This study shows a strong bond between a firm and the customer using social media.

The present research endeavor delves into the profound effects of gamified assistive applications on the levels of user engagement, motivation, and academic attainment within the population of students grappling with learning disabilities in the esteemed nation of Jordan. The study involved individuals who actively interacted with a gamified assistive application that was specifically developed to offer tailored educational opportunities and enhance their scholarly advancement. The analysis of descriptive statistics unveiled a noteworthy degree of app engagement, signifying the app's proficiency in captivating and maintaining students' focus. The utilization of paired-samples t-tests revealed noteworthy enhancements in both intrinsic and extrinsic motivation subsequent to the utilization of the application, thereby underscoring the favorable impact of gamified components on student motivation. Furthermore, a notable enhancement in scholastic attainment was noted, underscoring the application's influence on augmenting students' educational results. The findings of the correlational analysis unveiled a noteworthy association between the utilization of mobile applications, the presence of intrinsic motivation, and the attainment of academic success. This implies that heightened levels of engagement and motivation are linked to enhanced academic performance. The results of this study highlight the considerable promise of gamified assistive applications in fostering motivation and providing support to students who face challenges associated with learning disabilities. The incorporation of gamification into educational technologies presents educators with a promising strategy to cultivate active participation and elevate scholarly accomplishments within this demographic, ultimately advancing inclusivity and fostering educational triumph.