This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

This study explores the critical role of the retail sector in the global economy and the importance of working capital management within retail businesses. Recognizing retail’s influence beyond just income generation, the research examines its impact on economic stability, job creation, and national GDP, and how it links industries such as manufacturing and logistics. Employing a blended-methods approach, the study integrates quantitative analysis using AMOS software with qualitative insights from interviews with financial managers and retail experts. Key focus areas include cash flow management, market demand, and supplier relationship management in the context of working capital management. Findings highlight the necessity of effective working capital management in maintaining financial stability, optimizing shareholder wealth, and ensuring long-term business viability in the retail sector. Strategies for enhancing profitability, such as improving supplier relationships and adapting to market demands, are identified. This research contributes to understanding the economic impact of the retail sector and the intricacies of working capital management. It offers insights for policymakers, retail managers, and academics, emphasizing the need for supportive retail industry measures and effective financial management practices. The study fills a gap in literature and sets a foundation for future research in this critical area of economic studies and retail management.

The objective of this study is to examine the extent of awareness, intention, and behavior among university students in relation to green marketing. It is recognized that the present cohort of students, as well as future generations, will have a substantial impact on shaping the course of the world. The respondents for this study consisted of university students, and the collected data was subsequently analyzed using SPSS (Statistical Package for Social Sciences) 25 in order to test the stated hypothesis. University students exhibit a comprehensive understanding of green marketing and a conscious inclination toward embracing favorable intentions and behaviors in relation to this domain. The results of this study suggest that there exists a statistically significant and positive correlation between individuals’ level of green awareness and their intention to participate in environmentally friendly consumer practices. Furthermore, it has been observed that the intention of consumers to engage in green practices has a noteworthy influence on their subsequent behavior in terms of adopting environmentally friendly behaviors. The findings obtained from studies on green marketing are of utmost importance in offering valuable guidance and orientation toward a future characterized by heightened environmental awareness and sustainability. The novelty of this study is to provide a lucid comprehension of students’ perceptions about green marketing. Several factors can potentially impact the intention and behavior of environmentally conscious consumers, including personal values, social norms, and economic factors. Additional research is necessary in order to obtain a more thorough comprehension of the complexity of these variables, and how they interact to impact consumer behavior.

The introduction of artificial intelligence (AI) marks the beginning of a revolutionary period for the global economic environments, particularly in the developing economies of Africa. This concept paper explores the various ways in which AI can stimulate economic growth and innovation in developing markets, despite the challenges they face. By examining examples like VetAfrica, we investigate how AI-powered applications are transforming conventional business models and improving access to financial resources. This highlights the potential of AI in overcoming obstacles such as inefficient procedures and restricted availability of capital. Although AI shows potential, its implementation in these areas faces obstacles such as insufficient digital infrastructure, limited data availability, and a lack of necessary skills. There is a strong focus on the need for a balanced integration of AI, which involves aligning technological progress with ethical considerations and economic inclusivity. This paper focuses on clarifying the capabilities of AI in addressing economic disparities, improving productivity, and promoting sustainable development. It also aims to address the challenges associated with digital infrastructure, regulatory frameworks, and workforce transformation. The methodology involves a comprehensive review of relevant theories, literature, and policy documents, complemented by comparative analysis across South Africa, Nigeria, and Mauritius to illustrate transformative strategies in AI adoption. We propose strategic recommendations to effectively and ethically utilize the potential of AI, by advocating for substantial investments in digital infrastructure, education, and legal frameworks. This will enable Africa to fully benefit from the transformative impact of AI on its economic landscape. This discourse seeks to offer valuable insights for policymakers, entrepreneurs, and investors, emphasizing innovative AI applications for business growth and financing, thereby promoting economic empowerment in developing economies.

Interdependence between the United States (U.S.), European Union (EU) and Asia in the semiconductor industry, driven by specialization, can serve as a preventive measure against disruptions in the global semiconductor supply chain. Moreover, with rising geopolitical tensions, the cost-intensive nature of the semiconductor industry and a slowdown in demand, interdependence and partnership provide countries with opportunities and benefits. Specifically, by analyzing global trade patterns, developing the Interdependence Index within the semiconductor market, and applying the Grubel-Lloyd Index to the U.S., the EU, and Asian countries from 2011 to 2022, our findings reveal that interdependence enhances regional semiconductor supply chains, such as the establishment of semiconductor foundries in the U.S., Japan, and the EU; reduces dependence on a single supplier, such as the U.S. distancing from China; and increases market share in different semiconductor segments, as demonstrated by Taiwan in automobile chips. The evidence indicates that China heavily depends on foreign sources to meet its semiconductor demand, while Taiwan and South Korea specialize as foundry service providers with lower Interdependence Index values. The U.S., with a robust presence in semiconductor manufacturing and design, has a moderate dependence on semiconductor imports, whereas the EU demonstrates a higher level of interdependence because it lacks semiconductor foundries. The stage-specific analyses indicate that the U.S. and the EU rely on Asia for semiconductor devices, while China and Taiwan have a higher dependence on American intermediate inputs and European lithography machines.