Fair East Publishers: E-Journals
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Revisiting transformational leadership in U.S. higher education: A critical conceptual review
Full text linkThis conceptual review critically examined the theory and practice of transformational leadership (TL) in the context of U.S. higher education. Drawing on foundational theories by Burns (1978) and Bass (1985), the paper explored how TL principles—such as inspirational motivation, individualized consideration, and intellectual stimulation—are interpreted and applied within colleges and universities. While TL is associated with positive outcomes, such as increased innovation, faculty satisfaction, and inclusive leadership, this paper also highlights conceptual weaknesses, including its overreliance on heroic leadership and ambiguity in operationalization. The review evaluated TL through four thematic lenses: shared governance, equity and inclusion, faculty development, and strategic innovation. Each theme illustrates how TL interacts with the cultural, political, and ethical complexities of academic institutions. The paper argued that although TL holds transformative potential for academic leadership, its adoption must be context-sensitive and critically reflective, especially in navigating the democratic traditions of shared governance. This review contributed to ongoing discussions by recommending a revised, participatory model of teacher leadership (TL) that integrates ethical leadership, equity, and collaborative governance as essential to its relevance and effectiveness in higher education.
Keywords: Transformational Leadership, Higher Education, Shared Governance, Faculty Development, Educational Equity, Strategic Innovation
Causal simulation models for targeting and participation in social protection programs
Full text linkSocial protection programs have emerged as critical policy instruments for poverty alleviation and social welfare enhancement in both developed and developing economies. The effectiveness of these programs fundamentally depends on accurate targeting mechanisms and sustained participation rates among eligible beneficiaries. Traditional approaches to program design and implementation have relied heavily on static eligibility criteria and retrospective evaluation methods, which often fail to capture the dynamic nature of poverty and vulnerability. This study introduces a comprehensive framework for implementing causal simulation models to optimize targeting strategies and enhance participation rates in social protection programs. The research examines how advanced computational modeling techniques, including agent-based modeling, discrete event simulation, and machine learning algorithms, can be integrated to create more responsive and effective social protection systems. The causal simulation approach addresses fundamental challenges in program design by incorporating real-time data analytics, predictive modeling, and scenario planning capabilities. Through systematic analysis of beneficiary characteristics, behavioral patterns, and external socioeconomic factors, these models enable policymakers to identify optimal intervention points and resource allocation strategies. The framework emphasizes the importance of understanding causal relationships between program design features and beneficiary outcomes, moving beyond correlation-based analyses to establish robust cause-and-effect relationships. This methodological advancement is particularly crucial given the increasing complexity of modern social protection landscapes and the growing demand for evidence-based policy formulation. The study presents evidence from multiple jurisdictions demonstrating how causal simulation models can significantly improve targeting accuracy while reducing administrative costs and program leakage. Key findings indicate that simulation-based approaches can enhance beneficiary identification by up to 35% compared to traditional methods, while simultaneously reducing exclusion errors by approximately 28%. The models also demonstrate superior performance in predicting program participation rates, with accuracy improvements ranging from 22% to 41% across different program types. Furthermore, the research reveals that dynamic modeling approaches enable more effective resource planning and budget optimization, leading to improved program sustainability and expanded coverage. The implications of this research extend beyond technical improvements to encompass broader considerations of social equity, program accessibility, and institutional capacity building. The study emphasizes that successful implementation of causal simulation models requires comprehensive stakeholder engagement, robust data infrastructure, and continuous model validation processes. The findings suggest that organizations adopting these advanced modeling approaches must invest in both technological capabilities and human capital development to realize the full potential of simulation-based program design.
Keywords: Causal Simulation Models, Social Protection Programs, Targeting Mechanisms, Participation Rates, Agent-Based Modeling, Policy Optimization, Poverty Alleviation, Beneficiary Identification, Predictive Analytics, Program Evaluation
The effect of inflation and exchange rate volatility on consumer buying behavior in Nigeria
Full text linkThis study investigates the impact of inflation and exchange rate volatility on consumer purchasing behavior in Nigeria, employing a descriptive research approach and survey data gathered via Google Forms. The findings indicate that inflation substantially diminishes consumers' purchasing power, forcing households to prioritize essential products above discretionary expenditures and use coping methods such as opting for less expensive alternatives and reducing non-essential costs. Likewise, exchange rate fluctuations increase the expenses of both imported and domestically produced items, further burdening consumer finances and modifying consumption behaviors. The study underscores the resilience of Nigerian consumers, who confront these issues through adaptive strategies, however this may have enduring consequences for financial stability and product quality. This research, based on theoretical frameworks like Purchasing Power Parity and Maslow's Hierarchy of Needs, emphasizes the essential requirement for effective economic policies to control inflation and exchange rates. The study offers significant insights for policymakers and businesses to mitigate the socio-economic effects of economic instability on consumer welfare.
Keywords: Inflation, Exchange Rate, Volatility, Consumer, Nigeria
Verifiable ethics: Integrating Blockchain traceability with environmental and social life cycle assessment for conflict-free mineral supply chains
Full text linkThe hunt for lithium, cobalt, and nickel, which are critical to powering the renewable energy transition, is overshadowed by a growing credibility crisis. While consumers and regulators demand ethical sourcing, traditional mineral supply chain systems fail to provide verifiable proof, allowing greenwashing to thrive in the face of documented environmental devastation, such as the 2.2 million liters of water consumed per ton of lithium mined in arid regions, as well as ongoing social harms, such as child labor in artisanal cobalt mines. This study pioneers a disruptive methodology for closing the accountability gap by seamlessly integrating blockchain technology with Environmental and Social Life Cycle Assessment (ESLCA). We created a Hyperledger Fabric-based traceability system that tracks cobalt from artisanal mines in the Democratic Republic of Congo (DRC) to refining hubs and electric vehicle batteries. It is dynamically linked to an ISO 14040/14044-compliant environmental impact assessment and UNEP/SETAC Guidelines for social impact quantification across five critical categories: water scarcity, human health, child labor, community displacement, and conflict financing. Our 18-month empirical analysis demonstrates that blockchain's cryptographic immutability reduces data tampering risks by 92% compared to traditional audits, while integrated ESLCA identifies hotspots with exceptional precision. DRC cobalt extraction accounts for 78% of overall supply chain socioeconomic impacts. Crucially, technology allows for real-time intervention, such as blocking shipments tied to health concerns. We show that mandatory use of this comprehensive methodology could cut fraudulent ESG claims in mineral sourcing by 40%, transforming opaque supply chains into auditable engines of sustainability. This study equips policymakers and business leaders with the methodological rigor and technology infrastructure required to make verifiable ethics the foundation of energy transformation.
Keywords: Blockchain Traceability, Environmental Life Cycle Assessment (LCA), Social Life Cycle Assessment (SLCA), Conflict Minerals, Ethical Sourcing, Supply Chain Transparency, Critical Minerals, ESG Verification, Cobalt Supply Chain, Sustainable Mining
Emerging Technologies: Transforming industries and societies
Full text linkThe rapid evolution of emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), blockchain, and big data analytics has transformed industries and societies, enabling organizations to optimize operations, enhance decision-making, and create new business models. These advancements have reshaped sectors like healthcare, finance, education, and communication, driving digital innovation and economic growth. However, the implementation of these technologies is not without challenges, including workforce skill gaps, high costs, regulatory hurdles, and ethical concerns. Bridging the gap between technological capabilities and practical applications requires coordinated efforts across academia, industry, and policy to ensure that these technologies can be leveraged for global economic development and societal benefit. This study utilizes a mixed-methods approach, combining qualitative and quantitative analysis, to evaluate the transformative impact of these technologies and to explore strategies for overcoming the barriers to their adoption. The findings highlight the need for investment in education, infrastructure, and ethical guidelines to ensure the sustainable and inclusive implementation of emerging technologies.
Keywords: Emerging Technologies, Artificial Intelligence, Internet of Things, Blockchain, Big Data Analytics, Digital Innovation, Economic Growth, Technology Adoption, Education, Healthcare, Regulatory Challenges, Workforce Skills, Global Digital Divide
AI-driven predictive analytics framework for proactive supply chain disruption management and contingency planning
Full text linkSupply chain disruptions ranging from geopolitical instability and pandemics to natural disasters and cyberattacks have intensified the need for advanced disruption management strategies. Traditional risk management approaches relying on reactive responses are increasingly inadequate in volatile, uncertain, complex, and ambiguous (VUCA) environments. Recent developments in artificial intelligence (AI)-driven predictive analytics present new opportunities for enabling proactive disruption identification, real-time monitoring, and contingency planning in global supply chains. This paper reviews existing literature and proposes a comprehensive conceptual framework integrating machine learning models, big data analytics, digital twins, and scenario-based planning to manage disruptions effectively. By synthesizing over 100 scholarly contributions, we highlight how predictive analytics enables early warning detection, decision-support systems, and automated mitigation strategies. The paper further discusses implementation challenges, including data quality, algorithmic bias, ethical considerations, and interoperability, while offering recommendations for practitioners and researchers.
Keywords: AI Predictive Analytics, Supply Chain Disruption, Contingency Planning Framework, Machine Learning Resilience, Digital Twin Integration, Proactive Risk Management
Predicting chronic kidney disease using supervised machine learning
Full text linkChronic kidney disease (CKD) is a serious global health issue that often progresses without symptoms until irreversible damage has occurred. In this study, we develop and evaluate multiple machine learning models to predict CKD status using routine clinical and laboratory features. Using a publicly available dataset of 400 patients, we implement eight supervised classification algorithms, including Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Stochastic Gradient Boosting, XGBoost, Extra Trees, and K-Nearest Neighbors (KNN). The data were preprocessed through imputation, encoding, and normalization, and models were assessed using accuracy, precision, recall, and F1-score. XGBoost and Extra Trees achieved the highest predictive accuracy (98.3%), followed closely by other ensemble methods. Feature importance analyses consistently identified albumin, hemoglobin, blood urea, and serum creatinine as the most predictive variables. Our findings highlight the utility of ensemble learning techniques for accurate and interpretable CKD prediction, suggesting their potential application in clinical decision support tools.
Keywords: Chronic Kidney Disease, Machine Learning, Classification, XGBoost, Feature Importance, Medical Diagnostics, Ensemble Methods, Clinical Decision Support
Effect of interest rate and inflation on investment in Nigeria, 1981-2023
Full text linkThis research focused on the effect of interest rate and inflation on investment in Nigeria. Empirical evidence suggested that there has been little attention linking interest rate, inflation rate and investment in contemporary economic literature especially using other varieties of interest rates such as prime lending rate and savings rate. This research formulated three objectives which include: to determine the relationship between prime lending rate and investment, ascertain the relationship between inflation rate and total investment; and investigate the extent to which savings rate affect total investment in Nigeria. Data were sourced from central bank of Nigeria statistical bulletin and analyzed using error correction model. The result revealed that there was a significantly negative relationship between prime lending rate and total investment, inflation rate decreased total investment significantly and that savings rate had positive effect on total investment in Nigeria but the positive effect was not statistically significant. The conclusion was that prime lending rate and inflation rate have been detrimental to investment in Nigeria and as a result, it was recommended that savings rate and prime lending rate should be maintained to a stable state in order to encourage investment outlay in Nigeria.
Keywords: Inflation Rate, Interest Rate, Investment, Prime Lending Rate, Savings Rate
The impact of the financial crisis on banking market performance
Full text linkThe aim of this paper is to analyze market indicators of three banks operating in United Arab Emirates during the financial crisis 2007-2008, using four market indicators, market value, earnings per share, market to book value, and share yield. Statistical tools consisting of descriptive analysis, coefficient of variation, correlation analysis and ANOVA analysis were used. Coefficient of variation analysis indicates that the Bank of Sharjah performance indicators reveal a pattern of stability over the years 2006 to 2009 relative to its counterparts. Correlation analysis reveals a strong correlation between the variables of Bank of Sharjah, whereas a weak relationship was observed between share yield and other indicators regarding other two banks. ANOVA analysis for each of market value, earnings per share and share yield indicators shows significant difference between the most of the means. Moreover, the Analysis of Market Value to Book Value indicates an insignificant difference between the means of indicators. The results of this research analyses reveal that the financial crisis affects differently the market performance of these banks.
Keywords: Firm Performance, Banks, Financial Crisis, Market Performance
Exploring the role of Machine Learning and Deep Learning in Anti-Money Laundering (AML) strategies within U.S. Financial Industry: A systematic review of implementation, effectiveness, and challenges
Full text linkAs the U.S. financial sector confronts evolving threats from financial crimes, the integration of Machine Learning (ML) and Deep Learning (DL) into Anti-Money Laundering (AML) strategies has become imperative. This paper explores the role of ML and DL technologies in enhancing AML frameworks to identify, mitigate, and prevent money laundering activities. The paper begins by analyzing prevalent money laundering schemes and the methods used by criminals to bypass traditional AML controls. The study underscores the importance of educating and training financial institution personnel to ensure the effective implementation of AML strategies powered by ML and DL. The findings revealed that a culture of awareness and accountability is vital for managing risks associated with financial crimes. Furthermore, the paper highlights the value of collaboration and information-sharing between financial institutions, regulatory bodies, and technology providers. Industry partnerships, public-private initiatives, and shared threat intelligence are identified as key components in strengthening AML defenses. This research also examines the transformative potential of ML and DL in AML. It shows how these technologies enhance pattern recognition, anomaly detection, and decision-making processes, allowing financial institutions to stay ahead of evolving money laundering tactics. Moreover, the dynamic and self-learning capabilities of ML and DL models enable continuous adaptation to new risks. Through adaptation of a vigilant, collaborative, and technology-driven approach, U.S. financial institutions can leverage ML and DL to enhance AML frameworks, safeguard consumer trust, and protect the integrity of the financial system.
Keywords: Money Laundering, Financial Industry, Deep Learning, US