Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

The aviation industry is experiencing over and over again a technological revolution, nowadays with airports at the forefront of embracing smart technologies to enhance operational efficiency, security and passenger experience. This article comprehensively analyzes the benefits, challenges, and legal implications of adopting smart technologies in airport facilitation and security control. It examines the regulatory framework established by the International Civil Aviation Organization (ICAO) on an international level and by sovereign states on a national level. It explores using smart solutions such as automated systems, data and biometric verification, artificial intelligence (AI), and the Internet of Things (IoT) devices in airport operations. The authors’ purpose is to highlight the improvements in airport facilities and security measures brought about by these technologies, while addressing concerns over privacy, cost, technological limitations and human factors. By emphasizing the importance of a balanced approach and considering innovation alongside legal and operational imperatives, the article underscores the transformative potential of smart and integrated technologies in shaping the future of air travel.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.

The initiation of tapering, sparked by heightened inflation in the United States, reverberates across global markets, with notable implications for Indonesia. This study delved into the nuanced impact of tapering on Sharia-compliant stocks in both Indonesia and Malaysia. The rationale behind selecting Sharia stocks for analysis lies in their composition, featuring companies boasting low debt-to-asset and equity ratios, thereby positing robust resilience in the face of the Federal Reserve’s implementation of tapering. Employing a time series dataset with a weekly sampling period spanning from January to September 2022, the analysis adopted the Error Correction Model (ECM) within a multiple regression framework to circumvent potential spurious regression pitfalls. The results of this study indicate that the impact of tapering off policy in Indonesia has a positive impact in the short term and long term, while in Malaysia it tends to be insignificant in the short term and has a positive impact from the US 10-year bond yield variable and a negative impact from US 1-Year Treasury Bills. This result is interesting because it differs from the general theory. The causal factors include the agility of the Indonesian central bank in maintaining the benchmark interest rate spread with the Fed, the economic stability of both countries, and the increasing trend of coal, with Indonesia being one of the largest producers of the commodity. Investors, in navigating these intricate dynamics, may find strategic insights derived from this research invaluable for shaping their investment decisions. while government policymakers may use them as a reference for shaping policies related to Sharia stock investments, including the incorporation of artificial intelligence.