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.

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 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.

This research investigates how social media content marketing impacts customer engagement and it gives more weight to consumer cognition as an intermediary means. According to the study, digital communication has transformed significantly in our modern era, from simple communicative and content-sharing platforms to social media networks evolved into major marketing platforms. Organizations now fundamentally bring about transformations in their interaction with consumers depending on how they choose to use these public forums. As well, this paper begins to provide a comprehensive review of the social media content marketing literature, with topics such as augmented reality, credibility of content, user-generated content, and customer perceptions. The paper has a survey sample of 350 managers from relevant organizations, selected to provide a broad representative range across fields and industries. This study, under the Technology Acceptance Model, aims to understand better, how consumers accept and employ social media content marketing. Research questions to be addressed in this forthcoming paper include an investigation into how consumer belief serves to mediate the relation between social media content marketing and customer engagement Additionally, it aims to investigate the extent to which consumer cognition intervenes in this link, and whether consumers' beliefs moderate results of an interaction with social media platform contents source (such as reading a blog or watching a video). With this theoretical framework and the related literature, the project aims to provide significant insights and make a more successful marketing practice.

In the ever-evolving landscape of supply chain management, the quest for efficiency has become paramount. This abstract explores a groundbreaking solution that combines the power of permissioned blockchain technology with innovative data reduction strategies to redefine how supply chains operate. Traditional supply chain systems often grapple with data overload, causing delays, inaccuracies, and operational inefficiencies. However, this abstract presents a promising approach that unleashes efficiency by harnessing the capabilities of a permissioned blockchain. Through data reduction techniques tailored to the needs of supply chain management, this approach streamlines the flow of information while maintaining security and trust among participants. This paper seeks into the technical foundations of permissioned blockchains, highlighting their suitability for supply applications where confidentiality and controlled access are imperative. Furthermore, it examines various data reduction methodologies, emphasizing their role in minimizing redundant data, optimizing communication, and enabling real-time decision-making. The impact of this innovative approach on supply chain stakeholders is profound. It reduces data related bottlenecks, enhances transparencies, and fosters collaboration among participants. Additionally, it provides a scalable framework adaptable to diverse supply chain ecosystems. As supply chain efficiency becomes increasingly important in our interconnected world, this permissioned blockchain-driven data reduction strategy offers a compelling vision for the future. It promises to unlock a new era of streamlined operations, cost savings, and improved customer satisfaction, ultimately shaping the next generation of supply chain management.

This study provides an analytical view of the correlation between several factors that influence the credibility of information available through various sources on the Internet. The most critical factors include information quality, source credibility, argument strength, message credibility, and average credibility. Additionally, the study explores the impact of gender and years of experience as demographic variables on the nature and size of these relationships. The study relied on a critical review of previous related literature. In addition, it adopted an analytical approach using a sample of 300 Internet users through a questionnaire designed based on the study hypotheses. Most of these relationships were found to be at an average level, except for the relationship between source credibility and information credibility. No statistical indicators were observed. Consequently, the researchers acknowledge the need for caution when generalizing these results to society. The study also found that the gender of the recipient and the number of years of experience did not necessarily play a mediating role in the relationship between the tested factors and the credibility of information received via the Internet.

The current study aims to determine the role of artificial intelligence (AI) in developing the accounting system (AS) in Jordanian Islamic banks. Currently, Islamic banks in Jordan are included in the research population. Using a quantitative research approach, 128 workers of Islamic banks in Jordan were chosen as a sample for this study. The study used a survey questionnaire instrument that was created based on past relevant literature and studies to collect the required data. The results indicated that there is an influence of AI (big data, intelligent agents, expert systems and automation processes) on the development of the AS in Jordanian Islamic banks. Accordingly, the study recommends that to improve AS tasks and reduce associated costs, accountants and accounting companies should always increase their understanding of artificial intelligence.

Breakthroughs and advances in translation technology by virtue of AI-powered MT tools and techniques contributed significantly to providing near-perfect translation. This study aims to evaluate the accuracy of three translation technologies (Google Translate, Gemini, and ChatGPT) in translating multidisciplinary Arabic research titles in the Humanities and Social Sciences into English. A corpus of 163 titles of Arabic research articles from various disciplines, including media studies, literature, linguistics, education, and political science, was extracted from a Scopus-indexed journal, namely Dirasat: Human and Social Sciences Series. The research methodology in the present study lends itself largely to Koponen’s (2010) translation error strategy framework. Based on the data analysis, the findings showed that the renditions provided by these programs were categorically marked with either sense or syntax errors, which often rendered the translations inaccurate. Many polysemous terms with multiple related senses were mistranslated. The results showed that the Gemini translations contained the least errors. In contrast, the human translations contained the least mistranslation and diction errors. Google Translate and ChatGPT, on the other hand, contained the highest number of equivalence-based errors. Unexpectedly, the human translations contained the highest number of syntactic errors, reflecting a lack of target language proficiency. The study's conclusions and findings would be beneficial to translators, students, and scholars who may consider translating their Arabic study research titles and abstracts through the most commonly used AI tools.