Businesses heavily depend on social media to engage with customers, utilizing various platforms for interaction, feedback, and promoting products. The influence of social media on customer trust, loyalty, and satisfaction is a prominent subject. This study seeks to comprehend how businesses leverage social media to attain these objectives, utilizing both qualitative and quantitative methods. The initial exploratory phase collected qualitative data from 24 business enterprises, employing grounded theory techniques such as open, axial, and selective coding to pinpoint the primary factors affecting customer trust, satisfaction, and loyalty. Building on the insights from the exploratory study, the research proposes a model and hypotheses. The subsequent confirmatory study employs a quantitative approach, collecting data from 300 respondents in Jordan and utilizing Structural Equation Modeling (SEM) for analysis. Results underscore the pivotal roles of personalization, user-generated content, communications, word-of-mouth, emotions, promotions, and customer support as social media directions in shaping customer trust, satisfaction, and loyalty. This research provides valuable insights into the dynamics of how social media shapes customer relations, offering guidance to businesses navigating this ever-evolving landscape.

The future of the Internet lies in the utilization of contemporary virtual technology, the metaverse is a virtual ecosystem for communication and interaction. This study aims to determine how the metaverse can enhance customer awareness, interest, engagement and experience. The study population consists of customers of retail clothing stores. A questionnaire was developed to collect study data and distribute it electronically. The number of respondents reached (77) customers. Using path coefficients, the results indicate that metaverse can enhance customer awareness, interest, engagement and experience.

The aim of this study was to evaluate the impact of social media, big data, and data mining on the development of organizational behavior within the telecommunications industry in Jordan. The main objective of this study was to investigate the effects of technological components on the alteration of organizational behavior in the communications sector of Jordan. To accomplish this objective, a thorough empirical investigation was undertaken, encompassing the collecting of data from key stakeholders within the telecommunications sector in Jordan. A sample size of 412 participants, encompassing people from diverse roles within the communications sector, was chosen for the purpose of this study. The participants' replies and perspectives were gathered via the administration of surveys and conducting interviews, resulting in a comprehensive data set suitable for analysis. This study investigated the intricate relationship between the utilization of social media, the application of big data analytics, and the implementation of data mining techniques in influencing the dynamics of organizational behavior. The study's results underscored the substantial impact that social media platforms have on communication patterns and collaboration within telecommunication firms. Furthermore, the utilization of big data analysis has emerged as a significant catalyst for the enhancement of informed decision-making processes, exerting influence on diverse facets of organizational behavior, including strategic planning, employee engagement, and customer interactions. Data mining techniques have been identified as having a crucial function in extracting significant patterns and trends from extensive datasets, hence helping to the improvement of organizational learning and adaptation. The research findings indicated that the incorporation of social media, big data, and data mining technologies had a beneficial effect on the development of organizational behavior within the telecommunications industry in Jordan. The findings underscore the importance for enterprises to proactively utilize these technologies to cultivate a work environment that is characterized by increased agility, responsiveness, and collaboration. This study provides significant contributions to the subject of organizational behavior by examining the impact of social media, big data, and data mining within the specific context of the telecommunication sector in Jordan. The research sheds light on the transformative consequences of these technological advancements. The consequences of these findings have broad relevance for organizational leaders, politicians, and researchers, serving as a basis for further investigations in the dynamic realm of technology-driven organizational behavior.

Technology has significantly transformed knowledge, education, and access to information by introducing online learning platforms, interactive games, and virtual reality simulations in traditional classrooms, creating a dynamic, engaging, and inclusive learning environment. The ChatGBT project (a pre-developed transformer for training) is a remarkable achievement in artificial intelligence technology. It allows students tailored and efficient learning experiences by providing individual feedback and explanations. ChatGPT e-learning platform has been extensively studied for its adoption and acceptance, but there is a significant gap in research on its acceptability and use, highlighting the need for further exploration. The goal of this work is to bridge this disparity by introducing a comprehensive model that includes three basic elements: performance expectation, expected effort, and social impact. A total of 241 graduate students were surveyed and their data were analyzed using structural equation modeling techniques. The results indicate that “expectation of performance and expected effort” have the greatest impact and importance in determining students’ intentions to use learning platforms via ChatGPT, while social influence does not play an important role. This study enhances the current body of knowledge related to artificial intelligence and environmental sustainability, and provides important insights for professionals, policymakers, and producers of artificial intelligence products. These observations may provide guidance for creating and implementing artificial intelligence technologies to match consumers’ needs and preferences more effectively, while also taking into account broader environmental conditions.

The present research endeavours to explore the efficacy of gamified pedagogy in the realm of early childhood education within the context of Jordan, while simultaneously examining its intricate relationship with parental engagement. The examination of data uncovers a discernibly elevated degree of student involvement in the realm of gamified education, thereby suggesting the effectiveness of employing gamified methodologies in captivating the attention and interest of youthful scholars. Moreover, the presence of a moderate degree of parental involvement implies the possibility of proactive parental participation in the scholastic odyssey. The empirical evidence consistently demonstrates a robust and affirmative association between parental involvement and student engagement, underscoring the crucial and influential position that parents occupy in augmenting student motivation and active involvement in gamified educational endeavors. The present study serves as a valuable contribution towards enhancing our comprehension of the intricate relationship between gamified education and parental involvement. Its findings hold significant implications for educators, policymakers, and parents alike, as they strive to maximize the effectiveness of early childhood education.

Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as the multi-objective first time as unsupervised Feature Selection (WDOFS) and the second time as a Clustering algorithm (WDOC). For example, the WDOC outperformed Harmony Search and Particle Swarm in terms of F-measurement by 93.3%; in contrast, text clustering's performance improves 0.9% because of using suggested clustering on the proposed feature selection. With WDOFS more than 50 percent of features have been removed from the other examination of features. The best result got the multi-objectives with F-measurement 98.3%.

This study aimed to determine the impact of liquidity risk on financial performance of Jordanian banks, where liquidity risk was measured by (Liquidity ratio, net working capital, cash and investment ratio to total deposits), and financial performance was also measured through the index (return on assets) and the modifying variable (bank size) measured through the natural logarithm of total assets was also added. To achieve the objectives of the study, the analytical quantitative approach was adopted. The study community consisted of all 13 commercial banks listed on the Amman Stock Exchange. All banks in the study community were selected as a study sample using the comprehensive survey method, and the statistical analysis program (SPSS) was used to test the study hypotheses. Based on the results of the statistical analysis, it was found that there was an impact of liquidity risk on financial performance measured by return on assets in Jordanian commercial banks listed on Amman Stock Exchange, and there was an impact for each of (current liquidity ratio, net working capital, cash and investment ratio to total deposits) on financial performance measured by return on assets in Jordanian commercial banks listed on Amman Stock Exchange. It was also found that the size of the bank contributes to modifying the effect of liquidity risk on financial performance measured by return on assets in Jordanian commercial banks listed on Amman Stock Exchange. The study concluded a set of recommendations, the most important of which are: commercial bank administrations should increase interest in exploiting their liquidity within acceptable risk limits to reach optimal ratios for financial performance by balancing the returns to be achieved with the potential risks of such expenses in a way that ensures the positive impact of liquidity risk on the financial performance of those banks.

As the workforce worldwide goes through a transformative shift towards remote work, this paper discusses the positive effects of this quite flexible work arrangement on team’s innovation performance (TIP) in multinational, non-governmental organizations (NGOs). Adopting cross-sectional, quantitative research design, empirical data were collected through a survey of 268 employees of multinational NGOs operating in Jordan. The collected data were, then, analyzed using structural equation modeling. The results of the analysis showed that remote work has significant, positive effects on TIP in NGOs. Of the various remote work features investigated, spatial flexibility has the highest effect. The study results contribute to the ongoing discourse on the future of the work styles and have implications for leaders, policymakers, and practitioners who seek promoting innovation in multinational NGOs.

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

Ransomware has evolved into a pervasive and extremely disruptive cybersecurity threat, causing substantial operational and financial damage to individuals and businesses. This article explores the critical domain of Ransomware detection and employs Machine Learning (ML) classifiers, particularly Decision Tree (DT), for Ransomware detection. The article also delves into the usefulness of DT in identifying Ransomware attacks, leveraging the innate ability of DT to recognize complex patterns within datasets. Instead of merely introducing DT as a detection method, we adopt a comprehensive approach, emphasizing the importance of fine-tuning DT hyperparameters. The optimization of these parameters is essential for maximizing the DT capability to identify Ransomware threats accurately. The obfuscated-MalMem2022 dataset, which is well-known for its extensive and challenging Ransomware-related data, was utilized to evaluate the effectiveness of DT in detecting Ransomware. The implementation uses the versatile Python programming language, renowned for its efficiency and adaptability in data analysis and ML tasks. Notably, the DT classifier consistently outperforms other classifiers in Ransomware detection, including K-Nearest Neighbors, Gradient Boosting Tree, Naive Bayes, and Linear Support Vector Classifier. For instance, the DT demonstrated exceptional effectiveness in distinguishing between Ransomware and benign data, as evidenced by its remarkable accuracy of 99.97%.