The accelerated progress of Artificial Intelligence (AI) within the accounting field has resulted in a heightened use of this technology in international enterprises, therefore generating noteworthy ethical concerns. This research investigates the ethical implications that arise from the use of AI in accounting practices, focusing on international corporations operating in Jordan. The objective of this research is to provide a comprehensive framework for the ethical and responsible integration of AI within the accounting domain. The research used a survey methods approach while 379 respondents were selected using cluster and proportional sampling. The qualitative component of the research investigates the viewpoints and concerns of persons pertaining to the use of AI. The study results provide significant contributions to the development of a context-specific paradigm for AI ethics that prioritizes concepts such as transparency, fairness, and accountability. The findings of this study have substantial value for multinational corporations engaged in commercial operations in Jordan and similar regions. The results provide organizations with the necessary tools to proficiently address the ethical dilemmas that emerge as a result of using artificial intelligence in accounting procedures.

Explosive material detection considers the identification and classification of explosive materials using techniques from traditional sniffer dogs to cutting-edge sensing technology like thermal imaging, X-ray scanners, and chemical sensors. Explosive detection is applied in various locations, including airports, government buildings, and public areas, to prevent terrorist attacks and criminal actions that attempt to employ explosive devices. The effectiveness of these procedures is dependent on the detection materials, equipment, and environment, so new techniques are continuously explored to increase precision, sensitivity, and detection speed. Because explosive substances present a critical risk to infrastructure, security, and public safety, extensive analysis of existing detection methods is needed. This paper highlights key areas for further research and development in explosive materials detection by addressing identified limitations and challenges. Specifically, advancements in technology, interdisciplinary collaboration, and the integration of AI techniques offer significant opportunities for improving detection accuracy, reducing false positives, and ensuring safer environments for individuals and society.

This study aims to investigate the role of applying artificial intelligence in limiting cybercrime in the 13 Jordanian commercial banks listed in the Amman Financial Market, from the point of view of internal auditors and IT. For this purpose, a questionnaire was designed and distributed to the study sample of (849). The number of respondents to the questionnaire reached (230). The data of the questionnaire were analyzed, and their hypotheses were tested using the statistical program (SPSS) through tests of the arithmetic mean, standard deviation, linear, multiple regression, and the T-test for two independent samples. The study found a statistically significant effect for artificial intelligence with its dimensions (Expert Systems, Artificial Neural Network, Genetic Algorithm, Fuzzy logic) in limiting cybercrime in Jordanian commercial banks. It was also found that there were statistically significant differences in both genetic algorithm and cybercrime attributable to the job variable in favor of IT department employees.

Artificial intelligence (AI) systems have significantly changed the audit process; nevertheless, the AI revolution opponents view this growth as a step-back as many auditors will fail to adapt to this new environment and will drop behind. Our descriptive study examines the perceived ease of use, usefulness, and contribution to the audit quality of different AI’s types. To address local audit firms concerns about their readiness to use AI systems in auditing processes and to advance auditing research, we examine whether perceived ease of use, usefulness, and contribution to audit quality vary by AI systems type (Assisted, Augmented, and Autonomous). An online questionnaire was used to collect data from 124 auditors representing local audit firms in Jordan. Our results indicate that auditors perceive Assisted and Augmented AI systems as ease of use in auditing while perceiving Autonomous AI systems as complicated to use. Besides, Auditors are underestimating Autonomous AI systems’ capabilities and perceived it as not useful for auditing. The results also reveal a significant difference between the perceived contribution to audit quality by the three AI systems types. This study contributes to the existing literature on AI and auditing by developing and testing a measure for AI systems’ perceived contribution to audit quality. This study also provides empirical evidence on how Jordan local firms auditors perceive AI use in auditing.

This study examined the perception of investors toward the factors of Artificial Intelligence (AI) that impact behavioral intention of AI adoption. TAM was used with the addition of three factors of subjective norms, trust in service and service provider, and training. Jordanian stock market investors with basic investment knowledge were selected as the study participants using a convenience sampling method. The analyzed data were obtained from 610 responses. Results demonstrated the significant impact of trust in service and subjective norms on AI. Attitudes, perceived ease of use and perceived usefulness showed statistical significance toward intention to use AI. Results also showed significant impact of training on perceived ease of use.

Designing sustainable and scalable educational systems is a challenge. Artificial Intelligence (AI) offers promising solutions to enhance the effectiveness and sustainability of blended learning systems. This research paper focuses on the integration of the Chat Generative Pre-trained Transformer (ChatGPT), with a blended learning system. The objectives of this study are to investigate the potential of AI techniques in enhancing the sustainability of educational systems, explore the use of ChatGPT to personalize the learning experience and improve engagement, and propose a model for sustainable learning that incorporates AI. The study aims to contribute to the body of knowledge on AI applications for sustainable education, identify best practices for integrating AI in education, and provide insights for policymakers and educators on the benefits of AI in education delivery. The study emphasizes the significance of AI in sustainable education by addressing personalized learning and educational accessibility. By automating administrative tasks and optimizing content delivery, AI can enhance educational accessibility and promote inclusive and equitable education. The study’s findings highlight the potential benefits of integrating AI chatbots like ChatGPT into education. Such benefits include promoting student engagement, motivation, and self-directed learning through immediate feedback and assistance. The research provides valuable guidance for educators, policymakers, and instructional designers who seek to effectively leverage AI technology in education. In conclusion, the study recommends directions for future research in order to maximize the benefits of integrating ChatGPT into learning systems. Positive results have been observed, including improved learning outcomes, enhanced student engagement, and personalized learning experiences. Through advancing the utilization of AI tools like ChatGPT, blended learning systems can be made more sustainable, efficient, and accessible for learners worldwide.

The purpose of this study is to investigate the impact of using artificial intelligence applications on the performance of accountants and audit firms. The final sample for this study comprises 38 audit firms. This study uses a survey-based methodology in the context of Saudi Arabia. The results of the multiple regression revealed that the audit firms using artificial intelligence applications perceive them as useful instruments that increase the performance of accountants and audit firms. They can reduce the cost, effort, and time of the audit process, achieve a competitive advantage for the audit firms, help auditors better determine materiality, achieve a competitive advantage, improve the performance of the audit team, carry out the continuous audit process better than the traditional audit, enable auditors to select audit samples with high efficiency, improve the quality of control procedures on electronic transactions and files used by the client, contribute to the management of operations and tasks with more sophisticated and intelligent mechanisms, increase the efficiency and effectiveness of the audit process and the efficiency and effectiveness of planning and supervising the audit process, reduce uncertainty and audit risk. The results reported by this study can be valuable for the accounting and auditing professions, audit firms, and standards and auditing regulators to deeply understand the extent to which artificial intelligence applications influence the performance of accountants and audit firms.

This study has examined factors, such as technology and employee influence on artificial intelligence (AI) adoption of e-innovative projects in the United Arab Emirates. The present study revealed the success or failure of e-innovation adoption in the public sector of the UAE and hinted at potential e-innovative projects to consider essential factors before adopting it. The study's sample covered the government sector, and the data collection method was a survey questionnaire with a sample size of 1037 responses made up of government employees. This paper was mainly built upon the diffusion of innovation and technology acceptance theories. The analysis findings showed that technology (an external factor) significantly and positively contributed to adopting AI e-innovation technology. Further analysis revealed that employee (internal factor) proxies directly influenced the adoption of AI e-innovation technology. Overall, internal and external factors contributed to adopting e-innovation technology in the United Arab Emirates. For future directions, additional factors related to the market should be considered to explore their contribution.

The purpose of the study is to present a digital marketing data analytics model to analyze campaign efficacy and inform strategy based on website performance, social media metrics, email marketing performance, customer data for targeting and personalization, and customer journey analysis. This model defines campaign success criteria for strategy. A statistical analysis approach was used to analyze the data for the research. Data was gathered through a survey. This study analyzes demographic parameters descriptively using the structural equation model (SEM). From comprehensive surveys, 125 digital media and 115 online shop subjects responded. Sampled were 240 people. According to the findings, social media data, customer journey research, successful advertising, and informed approaches are highly correlated. Compared to the previous study, website performance evaluation does not match the marketing plan's success. The model's results can be used by any company that communicates with clients online.

The spread of the Covid-19 pandemic and the interruption of personal communication between the teacher and students in higher education led to the need for finding solutions that enable the continuation of the educational process and ensure access to accurate information that improves the level of human capital in dealing with dynamic environments. Therefore, this research sought to analyse the impact of the application of artificial intelligence in entrepreneurship education in Qatari higher education institutions after the Corona pandemic. The measurement of artificial intelligence was based on dimensions (machine learning, natural language processing, expert systems, and machine vision), while entrepreneurship education was measured by dimensions of (entrepreneurial cognition, entrepreneurial competence, and innovation spirit). The research followed an experimental quantitative approach based on collecting data from Qatari university students using a questionnaire developed for the research purpose. Hence, the convenience sample used in the research was composed of 402 students from various Qatari universities, which represents a response rate of 67% from the distributed questionnaires. The statistical analysis of the research data was based on the covariance-based structural equation modeling technique (CB-SEM). The results of the research indicated that all dimensions of artificial intelligence had a positive impact on entrepreneurial education, with the highest impact being machine vision and the lowest impact being natural language processing. Accordingly, the results of the research revealed the need to invest in technological capabilities for supporting the educational system aimed at generating innovative human resources capable of coping with the uncertainty of the work environment.