The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its relationship with decision-making, artificial intelligence, forecasting, and sentiment analysis is found to be weak, showing significant research gaps in these areas. China and the USA are prominent contributors, indicating geographical concentration. The study highlights the need for broader interdisciplinary exploration in data mining beyond traditional areas, urging global researchers to diversify contributions. The analysis focuses solely on publications indexed in Scopus, potentially excluding relevant studies from other databases or sources. This study provides insights into the evolution of data mining research and identifies areas for further interdisciplinary exploration, contributing to the advancement of the field's boundaries.

Supply chain resilience has rapidly expanded as a research area due to increased vulnerability to disruptions and uncertainties. Integration and re-engineering are essential components of a resilient supply chain that can improve its performance. Nevertheless, no one has yet investigated the effect of Artificial intelligence (AI) on the relationship between integration and re-engineering. Therefore, this study aims at investigating the roles of supply chain integration and re-engineering on supply chain performance. Similarly, it investigated the moderating role of AI in these relationships. This study develops a theoretical framework based on resource-based view and the social construction of technology theory. Based on a quantitative study of 564 responses collected from supply chain and clinical unit managers in the Qatari public healthcare sector, an empirical analysis was made using the partial least squares (PLS) path modelling technique. Results revealed that supply chain integration and re-engineering positively affect supply chain performance. Most significantly, these relationships are found to be positively moderated by AI. This study confirms the impact of supply chain integration and re-engineering on performance, providing empirical evidence for AI's role in strengthening these relationships.

Manufacturers employ AI for monitoring vehicle mileage, inspecting components, and scheduling maintenance. Past studies underscore the need for auto-related plans to prioritize environmental protection, concentrating on AI-driven environmental solutions promoted by AI for Good. AI enhances brand success by improving investment, technology, and promotional capabilities. This study emphasizes consistency in AI application across the automotive value chain for brand sustainability. A web-based poll surveyed 120 AI users in marketing, HR, sustainability, as well as 180 sustainability specialists and regulators. The primary goal is to assess, via structural model evaluation, how extraneous variables affect the development of AI-powered brand sustainability strategies. The study highlights AI's sustainability benefits in the automotive industry improving transportation safety, forecasting maintenance, and creating eco-friendly vehicles. However, challenges involve over-reliance on AI, predicting human behavior, and addressing sustainability threats. AI development should consider regional differences, prioritizing openness, policy harmony, and consumer agency. These findings aid marketing and HR professionals in devising customer-centric long-term plans.

This study aimed to demonstrate the impact of artificial intelligence with its dimensions (genetic algorithms, neural networks, intelligent agents, and machine learning) on tax compliance through the mediating role of electronic auditing. The research method used in this study was the descriptive analytic technique, to describe and analyze the phenomenon of the study, which is the effect of artificial intelligence on tax compliance through the mediation of electronic auditing and its relevance to the Jordanian tax authorities. The targeted study population was 205 auditors comprising the directorate of first commercial, directorate of second commercial, directorate of industrial and directorate of services of the income and sales tax department. These are the persons who represent the parties with special knowledge and experience in tax auditing issues. Because of the restricted number of people in the research, an extensive survey technique was applied to choose the study sample. The sample for this research study became the population amounting to 205 people in the Director of Large Taxpayer and medium Tax Payer Department of Income and Sales Tax. SPSS V. 20 was used to process and analyze all the data employing several statistical techniques. In line with the stated objectives, the study established important conclusions where the first was the presence of a significant effect of artificial intelligence on tax compliance through the influential electronic auditing variable. This means to emphasize the significance and function of technologies and AI in the sphere of electronic auditing and to develop their potential in organizing the tax processes and increasing the level of tax compliance. The first systematic suggestions stress the fact that improvements and modernization of the IT environment within the scope of tax departments is needed in order to ensure sufficient support for electronic auditing and artificial intelligence, and to equip the latter with suitable tools and big data analysis software.

The objective of this research is to investigate the impact of Artificial Intelligence (AI) on improving the quality of financial reporting in the Islamic banking industry. The study is conducted within the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the data collected from a sample of 364 professionals working in the field of Islamic banking. The results of our study suggest that Performance Expectancy, Effort Expectancy, and Social Influence are important factors in predicting individuals' Behavioural Intention to use Artificial Intelligence (AI). Additionally, the presence of Facilitating Conditions further enhances the impact of these factors on individuals' actual Use Behaviour. Significantly, it was shown that Use Behaviour played a significant role in determining the perceived quality of financial reporting. Nevertheless, the study did not find empirical evidence to demonstrate the direct influence of Behavioural Intention on Financial Reporting Quality. This implies that the actual implementation of Artificial Intelligence is required to fully realize its advantages. The use of artificial intelligence (AI) into governance frameworks presents a potentially advantageous pathway for Islamic banks to uphold Shariah principles, while concurrently bolstering accountability and fostering ethical banking practices.

The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.

The objective of research is to explore the impact of business intelligence systems, artificial intelligence, and digital leadership sustainability on the quality of learning and business performance at business schools of private universities in Jordan. Upon gathering and scrutinizing the research materials, a grand total of 281 samples were judged suitable for analysis using PLS software. The findings of this study suggest that the implementation of business intelligence systems, artificial intelligence, and digital leadership sustainability positively contribute to improving both the quality of learning and the performance of enterprises. The results demonstrate that the implementation of a business intelligence system, and Competitive Intelligence, directly and positively impacts the enhancement of learning quality and company performance. Furthermore, Artificial intelligence has a positive influence on the quality of learning and the performance of businesses, namely through the utilization of Deep learning, Digital Data, Graphical Processing Unit (GPU), and Data Safety and Security. Furthermore, digital leadership sustainability exerts a direct and favorable impact on the caliber of education and organizational achievement, encompassing Digital Competence, Flexibility and Agility, Interconnected Thinking, and Employee Orientation. Furthermore, the findings indicate that the level of education, encompassing Summative Assessment, Formative Assessment, Quizzes, and Presentations, is positively associated with enhancing firm productivity. Curiously, the present results contradict previous studies suggesting that the variables being examined had an influence on achieving digital leadership sustainability and business performance. Generally, the main research outcomes recommend that corporations should start adopting strategies related to enhancing learning effectiveness and improving business performance. This research focuses on the major aspects of business cleverness systems, artificial intelligence, and digital leadership sustainability to enhance the quality of learning and business performance in the business capabilities of private institutions of higher education in Jordan.

This study provides a comprehensive overview of the field of enhancing the customer-supplier relationship through big data technology and artificial intelligence (AI), reveals existing gaps and offers promising solutions for future research. SMART PLS-4 software was used to analyze the data collected, the results led to the existence of significant relationships between artificial intelligence and enhancing the relationship between the supplier and the customer (customer interaction, customer communication, customer participation, customer learning, customer experience, customer feedback). The study contributes to developing a conceptual model through the application of artificial intelligence in managing customer relationships with suppliers in the banking industry. The study contributes to developing a conceptual model through the application of artificial intelligence in managing customer relationships with suppliers in the banking industry setting. Which supports increasing knowledge in this field and helps managers develop appropriate strategies. This research is the first of its kind to organize and discuss the literature related to using artificial intelligence within the customer-supplier relationship management setting, which provides great importance to the process of using and developing artificial intelligence technology and understanding recent trends in how to develop customer–supplier relationships within the technology era.

This study examines gender differences in attitudes and intentions to adopt artificial intelligence among startup professionals. Utilizing a survey methodology encompassing responses from male and female participants, key constructs including attitude, perceived ease of use, perceived usefulness, and intention to use were analyzed through a comparative lens. The results reveal nuanced disparities between male and female perspectives on AI adoption. While minor differences were observed in the influence of attitude and perceived ease of use on adoption intentions, a significant gender gap emerged in the perception of how ease of use impacts perceived usefulness. These findings underscore the importance of recognizing gender dynamics in shaping attitudes and intentions towards AI adoption, highlighting the need for gender-inclusive strategies in fostering technology adoption among startups. This study contributes to the understanding of gender-specific considerations in AI adoption processes and offers insights for policymakers and industry stakeholders seeking to promote equitable and inclusive technological advancement.