This research aims to analyze the influence of e-leadership behavior on employee commitment at the immigration office, the influence of the e-Work Environment on employee commitment at the immigration office, and the influence of e-Job satisfaction on employee commitment at the immigration office. The research method used in this research is associative research. Associative research is research that aims to determine the relationship between two or more variables. In this way we can build a theory that functions to predict and control a phenomenon. The population in this study were all immigration office employees. In this research, an analysis model is used, namely Structural Equation Modeling (SEM). The respondents for this research were 678 immigration office employees who were determined using a simple random sampling method. Research data was obtained by distributing online questionnaires via social media. The instrument used in this research uses a Likert scale 7 scale. The data analysis stage of this research is testing the outer model and inner model. The outer model test consists of convergent validity, discriminant validity and composite reliability and the inner model test, namely hypothesis testing or significance testing. The results of data analysis show that e-leadership behavior has a positive and significant relationship to employee commitment at the immigration office, the e-Work Environment has a positive and significant relationship to employee commitment, and e-Job satisfaction has a positive and significant relationship to employee commitment at the immigration office.

This study explores the factors affecting CliQ payment service adoption in Jordan as it represents a major shift in the Jordanian market to Fintech and mobile marketing. This study presented a distinctive model by integrating the “unified theory of acceptance” and the “technology acceptance model” TAM with the “use of technology UTAUT” to measure the behavioral intention of consumers and the satisfaction of the users towards utilizing the CliQ service. The study measured the linkage between four predictors (perceived usefulness, social influence, perceived ease of use, and financial risk) as well as both utilizers’ intention to CliQ service usage and utilizers’ satisfaction with using the service, along with observing the association between these predictors to determine their contribution to the users’ satisfaction mediated by the users’ intention to utilize the service mentioned above. A survey instrument was distributed to 604 respondents, and it was developed and validated by academics, as a pilot test. As well as a pre-test. Hypotheses were tested with multiple linear regression analysis using SmartPLS software to interpret path coefficients, and reliability and validity within the outer model were tested through correlation analysis. Additionally, internal consistency was explored by applying Cronbach’s alpha. The outcomes proved that the predictors significantly affect both utilizers’ behavioral attitudes and satisfaction with using the CliQ service, except for financial risk. The data analysis also indicated a significant indirect influence of all the independent variables on utilizers’ satisfaction through behavioral intention to utilize the CliQ service, except for financial risk, which had an insignificant indirect influence on utilizers’ satisfaction through behavioral intention for service usage. The findings of the current article close the gap found in the literature concerning measuring usability to enhance user satisfaction and adoption of Fintech services. Therefore, bank managers and policy planners can find insights for developing and improving mobile banking apps.

With flexible learning, students are actively engaged in their own education and are held to high standards of performance. Online academic courses make it easier for students to receive personalized education because they provide students with more flexibility to concentrate on what is most important to them and give them greater control over their own education. This study’s objective was to investigate whether there is a correlation between how well students succeed in online classes and the extent to which they make use of the schedule and the geographical and resource flexibility offered by such programmes. This article uses a developing approach for predicting and classifying the flexibility in online learning of students who are at risk of failing due to academic and demographic variables. The K-nearest neighbours (KNN) method, the random forest (RF) method, and the logistic regression method were used to categorise the students participating in flexible online learning. The information for the dataset came from Kaggle, and it was gathered for use in testing machine learning. The dataset had a total of 1,875 instances representing 11 different features. Also, accuracy, precision, sensitivity and f-score metrics were applied to evaluate the system. The results show that the RF algorithm has a high accuracy percentage of 85%. The empirical findings demonstrate that students formed distinct patterns of learning time, location and access to knowledge. This suggests that flexibility was used to a significant degree. Patterns in learning time and the availability of learning materials were shown to have a substantial relationship with the accomplishments of the students. Understanding flexibility use habits may help adapt lessons and boost collaboration among similar students.

This study attempts to study the impact of the moderating role of internal audit between cloud accounting and limiting creative accounting practices in commercial banks. The study utilized a descriptive and analytical approach. The target population of this research consists of all employees of the 13 Jordanian commercial banks listed on the Amman Stock Exchange, while the number of employees in commercial banks is (22,407) employees. This led to the use of a simple random sample of (400) employees. The results found that there is a positive effect of the moderating role of internal audit on the impact of cloud accounting on reducing creative accounting practices in commercial banks. The study suggests that future studies should give recommendations and proposals to improve and develop cloud accounting and internal auditing in commercial banks, increase transparency, monitoring and compliance in accounting and financial operations, and reduce the risks associated with cloud accounting and creative accounting practices.

Social cybernetics, as an interdisciplinary field, has gained increasing interest in the last decade due to the influence of information technologies in society through connectivity, Internet of things, process automation, artificial intelligence among others. This research focuses on exploring the relationship between social cybernetics (communication and control mechanisms) based on information technologies and local development, using structural equation modeling as an analytical tool. The design was non-probabilistic, with a sample of 482 people. The independent variables under study were Use of ICT for communication between local authorities and the population (CAP), Use of ICT for collaboration between public and private institutions (CPPC), Use of ICT for shared decision making (SDM), Use of ICT for local development planning (LDP) and Use of ICT for knowledge management (KM); and the dependent variable was Local Development (LD). It was determined that there is a relationship between all of them except with CAP. The direction and magnitude of the other ratios were: + 0.1390; - 0.3661; + 0.4472 and + 0.8432 respectively. The coefficient of determination (R2) was 93.69% facilitating the prediction of future results. The model has an adequate fit.

The purpose of this study is to explore how various online marketing tools, including Content Marketing, Email Marketing, Social Media Marketing, and SEO, affect customers' decisions to purchase Internet services. The study utilized a survey distributed through simple random sampling, with 410 questionnaires given to Zain Telecom customers in Zarqa city. After excluding incomplete questionnaires, 385 were analyzed using multiple regressions, descriptive analysis, and reliability tests. Results showed that all independent variables had a significant impact on customers' purchasing decisions of Internet services, with Email Marketing having the strongest relationship. The study recommends further investment in Email Marketing strategies to improve customer retention and acquisition.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are promises through which AI-driven marketing will meet business goals and be huge at societies' values.

A Distributed Denial of Service (DDoS) attack occurs when an attacker tries to disrupt a network, service or website by flooding huge numbers of packets on the internet traffic. Detecting DDoS attacks serves the goal of spotting and addressing them promptly to reduce their effects on the network, system or service being targeted. Detecting Distributed Denial of Service (DDoS) attacks is crucial, for people, companies and network managers. The detection of DDoS attacks has ranging uses in industries such as network security safeguarding websites, managing cloud services ensuring the security of online systems and services. Detecting DDoS attacks is essential for safeguarding infrastructure upholding service availability and guaranteeing the security of online systems and services. To achieve this objective, we proposed a framework to detect DDoS attacks including six steps. In step one, we start by gathering information, which includes network activity and system records, for operations as well as instances of DDoS attacks. Step two, we identify characteristics of the data collected such as patterns in network traffic, packet details, IP addresses, types of protocols used and more. Step three, we utilize algorithms for feature selection such as Salp Swarm Algorithm (SSA), Gray Wolf Algorithm (GWA), Particle Swarm Algorithm (PSO) to pinpoint the features that can distinguish between normal activities and DDoS attack patterns. After that in step four, we divide the processed dataset into sections for training and testing purposes to develop and assess the machine learning models such as SVM (support vector machine), and KNN (K-nearest neighbor). Step five we develop a classification model using machine learning techniques like decision trees, forests, support vector machines (SVM) logistic regression models or neural networks. Finally, we assess the effectiveness of models through metrics such as accuracy rates, precision levels, recall rates, and F1 scores. The results show that the proposed models achieve high results (99.9%). In summary detecting DDoS attacks is crucial for protecting networks, systems and online services against disruptions.

This study conducted a thorough analysis of a dataset from a video game to reveal the relationship between game content, interactive features, and classification ratings. The project attempted to convert raw data into meaningful insights by employing Python for data processing and machine learning. The analysis uncovered strong relationships between content descriptors and ESRB ratings, indicating a market that strategically customizes game material to different demographic groupings. Moreover, the inclusion of interactive features such as 'Users Interact' and 'In-Game Purchases' suggests a transition towards gaming experiences that are more immersive and financially interactive. The highlight of this project was the creation of a web-based tool that can accurately forecast game classifications, utilizing advanced models such as XGBoost. The application offers developers and rating organizations a vital tool to achieve accuracy in game classification. The study's conclusions provide a detailed comprehension of console market dynamics, clarifying the present patterns and possible future developments in the gaming industry.

This research focuses on studying the effect of using social media platforms on customer behavior and business objectives in Jordan. The research chose three of the most famous platforms of social media and those platforms are Facebook, Instagram, and Twitter. By using a quantitative model this research collected around 350 research questionnaires using digital surveys designed by using Google drive and distributed online on previous social media platforms. The research analysis process is executed by using AMOS software, and this comprised structural equations (SEM) modeling and regression analyses. The research study output found strong effects and relationships between social media platforms engagement and a variety of consumer expenditure variables, including brand loyalty, product suggestions, and purchase decisions. Important components of each platform, including user interaction, influencer endorsements, and content relevancy, were also identified by the study as having a direct effect on consumer behavior. The research also showed how businesses may achieve their marketing objectives, boost customer engagement, and enhance their reputation by utilizing social media strategies. The significance of social media website platforms in shaping consumer behavior and propelling business success is highlighted by these studies. To reach their goals and have the most influence on customer decisions, businesses should think about using platform-specific features and investing in targeted social media advertising.