Fair East Publishers: E-Journals
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Shaping trust and tension: Strategic leaks and their impact on global cybersecurity norms
No full textThis study investigates the role and impact of strategic leaks in shaping cyber diplomacy between the United States and China. Using data from incident databases, public opinion surveys, cybersecurity agreements, and textual media sources spanning 2010 to the present, a robust quantitative methodology was employed. Techniques included regression analysis, interrupted time series, and Pearson correlation to evaluate diplomatic outcomes, trust dynamics, and ethical considerations. Findings reveal that events like the Microsoft Exchange Hack exerted the most significant impact, with a 0.90 six-month influence score, and catalyzed multilateral cybersecurity collaborations. Ethical concerns such as misattribution showed a negative correlation with public trust (r=-0.75) but positively influenced support for transparency (r=0.70). Recommendations include fostering multilateral coalitions, developing frameworks for managing leaks, and promoting balanced narratives to align escalation strategies with long-term goals. These insights provide actionable pathways for addressing the dual-edged nature of leaks in international cybersecurity governance.
Keywords: Strategic Leaks, Cyber Diplomacy, Us-China Relations, Cybersecurity Norms, Ethical Considerations
The moderating effect of interest rate on exchange rate volatility and foreign direct investment: An insight in some sub-Saharan African countries
No full textThis study investigated the moderating effect of interest rate on exchange rate volatility’s and foreign direct investment inflows in some Sub-Saharan African countries. The study utilized panel data, spanning the period from 1990 to 2022, using a sample of 38 nations, where the Two Stages Least Square estimator was utilized as the main econometric approach in order to address issues of endogeneity, simultaneity, and reverse causality. The findings of the analysis indicated that, although exchange rate volatility negatively impacts Foreign Direct Investment inflows, its effect was not statistically significant across the models. Meanwhile, interest rates exhibited a positive and significant influence on foreign direct investment inflows. Additionally, the analysis reveals that the interaction between exchange rate volatility and interest rate was positive, but not statistically insignificant, thus suggesting that interest rate did not moderate the effect of exchange rate volatility on foreign direct investment inflows in Sub-Saharan African countries. This study concluded by emphasizing the importance of foreign direct investment inflows, underscoring the need for nuanced and context-specific policies in Sub-Saharan African countries. Further recommendations were made based on the findings of the study.
Keywords: Interest Rate, Moderating effect, Two Stage Least Square, Sub-Saharan Africa, Foreign Direct Investment
Harnessing Artificial Intelligence for combating money laundering and fraud in the U.S. financial industry: A comprehensive analysis
No full textThis study explores the transformative role of artificial intelligence (AI), particularly machine learning (ML), in enhancing the detection and prevention of money laundering and fraud within the U.S. financial industry. The study aims to analyze how AI-driven techniques can significantly improve the accuracy, efficiency, and scalability of fraud detection systems. The study focuses on examining various machine learning algorithms, including supervised techniques like logistic regression and decision trees, as well as unsupervised methods such as clustering and anomaly detection. These techniques are utilized to analyze historical data, detect patterns, and identify suspicious transactions or fraudulent behaviors in real-time. The research method includes a comprehensive review of existing case studies and literature on AI applications in fraud detection, highlighting successful implementations of ML models in financial institutions. The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates. Furthermore, the integration of AI with real-time data analysis capabilities enables continuous monitoring and immediate detection of irregularities. The study concludes that financial institutions in the U.S. must leverage AI advancements to enhance risk management systems, improve fraud detection, and mitigate the risks of money laundering. By adopting machine learning algorithms, financial organizations can stay ahead of emerging threats, ensuring the security of their operations and customer assets.
Keywords: Artificial Intelligence, Machine Learning, Money Laundering, Fraud Detection, U.S. Financial Industry
Telehealth as a solution to healthcare disparities in the U.S
No full textThis review paper analyzes telehealth applications to eliminate healthcare disparities within the United States when measured against COVID-19 pandemic requirements. The analysis focuses on how telehealth services achieved improved healthcare accessibility for racial and ethnic minority groups and rural populations as well as individuals with limited socioeconomic means. A broad analysis of telehealth research articles documents its effects on healthcare delivery and outcomes accessibility. Telehealth systems bring substantial gains to patient access because they eliminate geographic and schedule limitations that usually prevent patients from getting timely treatment. The current challenges comprise digital literacy gaps alongside inequalities in technology access levels which potentially will deepen existing inequality gaps. Research evidence shows that telehealth shows promise for healthcare equity, but we need committed attention to digital inequity along with equal utilization strategies for diverse communities. To address digital exclusion the healthcare sector needs to improve broadband capabilities and create digital education programs while implementing new policies that aid telehealth usage by vulnerable communities. This study demonstrates how telehealth functions as an essential healthcare resource for elevating health equity throughout the United States while demanding continuous research to implement policies that maximize its benefits for all patients.
Keywords: Telehealth, Health Disparities, Access To Care, Racial Minorities, Rural Health
Strengthening teacher training and professional development in low-resource settings: A systems reform approach
No full textMany low- and middle-income countries face an acute teacher-training crisis, with severe shortages of qualified teachers and under-resourced professional development (PD) systems. These deficits undermine instructional quality and student learning, perpetuating a global learning crisis. This paper draws on the author’s experience supporting teacher development through Rotary International and USAID in 19 African and Asian countries, highlighting program design elements (contextualized training, coaching, and adaptive curricula), implementation challenges (resource gaps, logistics, teacher turnover), and documented successes (improved classroom practices, student engagement, and retention). We then review scalable, systemic strategies proven effective in resource-poor settings, such as sustained mentoring, school- or community-based professional learning communities, and practice-based coaching. These approaches—especially when integrated with technology and teacher agency—can enhance instructional quality at scale.Finally, we discuss implications for underserved U.S. districts, which share barriers like chronic underfunding and staffing shortages. Adapting global equity-driven models (e.g. teacher residencies, peer networks, ongoing coaching) could help these districts invest in teachers as the most critical lever for improving student outcomes.
Keywords: Low-Resource Education Systems, Education Policy Reform, Equity in Teacher Training, Teacher Professional Development
Rethinking learner-centered teaching in early education: Lessons from a global practice perspective
No full textThis paper examines learner-centered pedagogy in early education from a global practice perspective, with particular focus on Uganda and other low- and middle-income contexts. Learner-centered teaching – grounded in constructivist theories of Piaget and Vygotsky – emphasizes active learning, collaboration, and student autonomy. Its relevance for early childhood lies in fostering problem-solving, creativity, and social-emotional skills from a young age.
We synthesize international literature and field reports to map the current landscape of learner-centered approaches in early grades, noting examples from Africa, Asia, and Europe. In Uganda, the 2007 “thematic” curriculum reform explicitly aimed to deliver learner-centered instruction in local language but faced implementation barriers and yielded no clear learning gains. Common challenges across low-resource settings include large class sizes, inadequate materials, and weak teacher preparation. Innovative strategies – such as community-based preschool models in Kenya and policy supports for play-based learning – show promise in addressing these barriers.
We conclude with policy implications for the United States, highlighting the need for robust teacher training, culturally responsive pedagogy, and equity-oriented early learning initiatives, supported by evidence from international research.
Keywords: Learning centered Pedagogy, Early Childhood Education, Education Policy Reform, Global Comparative Education
Technological innovation and social infrastructure on employee’s productivity: A study of selected universities in Delta State
No full textThis study examines the effect of technological and social innovation on employees’ productivity in selected universities in Delta State. In answering questions on the issues of training introduced to upskill employee productivity towards adapting to the use of the technological innovations and the level of employee acceptance of the technology, the study anchored its argument on the fact that effectiveness in organizational operations is directly proportionate to employee level of productivity and organizational growth in the university system when technological innovations are accepted and adapted to work. The study Adopted the Technology Acceptance Model (TAM), this study conducted an ex-post-facto research on how perceived usefulness and the ease to use the technological innovations affects the actual system use to enhance employee’s productivity in selected universities in Delta State. 400 participants were randomly selected from 3 out of the 11 universities. Results were analyzed using multiple inferential statistical techniques. The study revealed that there was a strong and positive relationship between staff training and their level of accepting and adapting to the technology and technological innovation in the university system that employees do not have adequate knowledge on and not acceptable cannot be adapted to by employees to enhance productivity. The study concluded that since social infrastructure such as motivational factors and job security mediate between technological innovations and employee’s productivity in the university system, university’s management should employ these social infrastructures in making sure that employees do not only upskill in the technological innovations but accept and adapt it towards productivity
Toward sustainable human resource systems: Evidence-based strategies for workforce retention and well-being in U.S. social impact organizations
No full textU.S. social impact organizations, including nonprofit and mission-driven healthcare institutions, are experiencing critical workforce challenges characterized by high turnover, burnout, and declining staff well-being. This structured review explores evidence-based human resource (HR) strategies that can promote sustainable workforce retention and engagement in these sectors. Drawing from interdisciplinary literature, the paper synthesizes theoretical frameworks such as Sustainable Human Resource Management (HRM), the Job Demands–Resources (JD-R) model, and Social Exchange Theory to establish a foundation for sustainable HR practices. Key strategies identified include comprehensive burnout prevention programs, data-driven HR decision-making using AI and analytics, fair and transparent compensation systems, and leadership models that prioritize empathy and inclusion. Each intervention is contextualized with practical applications and empirical evidence, highlighting their impact on reducing attrition and enhancing organizational resilience. For example, studies on AI implementation in healthcare demonstrate improvements in workflow efficiency and reduced clinician stress, suggesting promising parallels for HR analytics in nonprofit management. The manuscript offers actionable recommendations for HR professionals, such as phased implementation, continuous evaluation, and engaging staff in co-creating solutions. Emphasizing that workforce sustainability requires systemic change, it encourages HR leaders to treat employee well-being and retention as core strategic objectives. Ultimately, the review presents a roadmap for social impact organizations to strengthen their human capital infrastructure, ensuring mission continuity and organizational longevity in an increasingly volatile labor landscape.
Keywords: Human Resources, Employee Well-Being, Nonprofit Workforce, Workforce Sustainability, Performance Management, HR Interventions
Exchange rate volatility and its effect on foreign direct investment inflows in sub-Saharan African region
No full textThe perturbation and volatility of exchange rate has been a challenging factor in attracting foreign direct investment (FDI) to African countries particularly those that lie within the Sub-Sahara African (SSA) region. This has necessitated the study which aimed to empirically examine how the fluctuations in exchange rate have impacted on the inflow of FDI to SSA. Data from 14 selected SSA countries covering the period between 1996 and 2022 were used. The panel regression analysis that involve the pooled regression, fixed effect and random effect models and panel cointegration were employed as the basic econometrics approach. The findings from the pooled regression, fixed effect model and the parsimonious Error Corrections Model (ECM) indicated that real exchange rate volatility (REXRV) had a negative and significant relationship with the FDI, implying that the volatility of the exchange rate had detrimental effect on FDI inflow. While real exchange rate (REXR) had a positive and significant impact on the level of FDI inflow, thus suggesting an improved FDI inflow due to depreciation of currency. Similarly, Gross Domestic Product (GDP) and trade openness (TOP) have a positive and significant impact on FDI. On the contrary, the interest rate had a negative and significant impact on the flow of FDI. The rate of inflation (ROI) also has a negative but insignificant impact on FDI. The statistical significance of the ECM indicates a satisfactory speed of adjustment correcting the significant errors in each period. Therefore, we recommend improved productivity of export goods and guided foreign exchange rate deregulation in order to stabilize the exchange rate and consequently enhance.
Keywords: Exchange Rate Volatility, Foreign Direct Investment Inflow, Panel Data Regression, Sub-Saharan Africa, Growth
The dynamics of optimal tax revenue generation for socio-economic development: Examining the influence of key macroeconomic indicators on United States’ fiscal sustainability
No full textThis study examines the relationship between key macroeconomic indicators and optimal tax revenue generation in the United States, with a focus on their implications for fiscal sustainability. The research explores the effects of interest rates, consumer price index (CPI), Real Gross Domestic Product (GDP), and unemployment rates on total tax revenue hence providing an empirical assessment of how these variables influence the stability and efficiency of tax collection. The research therefore aims to assess how fluctuations in these economic variables influence the federal government's ability to sustain fiscal balance and generate adequate tax revenue for socio-economic development. Notably, the research would explore the responsiveness of tax revenue generation to changes in monetary and fiscal policy measures. This study leverages historical data and econometric modeling by identifying trends in tax revenue responsiveness to economic changes hence offering insights into policy measures that enhance fiscal resilience. The findings shall contribute to the ongoing discourse on tax policy optimization by highlighting strategies that balance revenue sufficiency with economic stability. The study also aims to provide recommendations for tax policy reforms that enhance revenue stability while fostering an environment conducive to economic expansion. The research would emphasize the necessity of a balanced approach in designing tax policies that respond effectively to macroeconomic shifts hence ensuring long-term fiscal sustainability. This research would hence serve as a valuable resource for policymakers, economists, and tax authorities seeking data-driven approaches to strengthening the U.S. tax system's role in long-term fiscal sustainability.
Keywords: Revenue Optimization, Fiscal Sustainability, Macroeconomic Indicators, Economic Stability, Consumer Price Index, GDP, Interest rate, Unemployment