Journal of Economics, Finance and Accounting Studies
Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
Al-Kindi Center for Research and Development
Digitalisation and Its Impact on Corporate ESG Performance in Middle East Countries
Digitalisation, ESG (environment, social, and governance) practices have gained importance in recent years. Firms have to mandatorily implement initiatives for ESG. This paper examines digitalisation and corporate ESG performance in Middle East Countries. Digitalisation is about using digital and online methods to frame ESG metrics performance; provide information about ESG to regulatory authorities, and investors, and to track the corporate performance. Since ESG has three dimensions environment, social, and governance. This paper examines the impact of these three dimensions collectively and individually on firm performance, and attitude of investors and firm owners. Three hypothesis were drawn and tested. A panel data of 439 firms from fifteen Middle East countries was drawn and observations were made for 12 variables for the years 2013- 2023. Twelve value relevance models were created, tested, and the data was subjected to regression analysis using SPSS. The findings indicate that except for environment dimension, the other two dimensions give relevant information that digitalisation helps investors to take investment decisions, thereby impacting the firm market value significantly and positively. Digitalisation has a positive and significant relation with ESG performance.
ESG performance, ESG dimensions, digitalisation, value relevance, firm value, Middle East firms
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Dissecting Corporate Defense: A Critique of the Three Lines of Defense (3LOD) and the expanded Five Lines of Defense (5LOD) / Five Lines of Assurance (5LOA) Models
Modern organizational governance demands a relentless dynamic between strict risk control and operational agility. For decades, the Three Lines of Defense (3LOD) framework has served as the standard blueprint for internal audit structures and reporting lines. This literature review evaluates the structural mechanics of both 3LOD and the expanded Five Lines of Defense (5LOD) model, which also is conceptualized by executive leadership as the Five Lines of Assurance (5LOA), and examines their capacity to withstand contemporary corporate pressures. While the 3LOD model provides clear operational boundaries, evidence suggests it can inadvertently isolate executive leadership, treat strategic partners as passive information consumers, and suppress innovation through over-engineered risk avoidance. The 5LOD/5LOA model attempts to resolve these gaps by integrating the governing body and external assurance into a more holistic framework. However, this expansion introduces structural trade-offs, including increased institutional bureaucracy, operational complexity, and the risk of diluted accountability. Fueling this complexity is an accelerating corporate shift from direct reporting lines toward volatile dual-reporting audit frameworks. Ultimately, we demonstrate that choosing an organizational defense structure is a high-stakes strategic decision that requires deep alignment with an organization’s governance maturity, regulatory pressures, and market reality.
Corporate Governance, Risk Strategy, 3LOD vs 5LOD, Five Lines of Assurance (5LOA), Corporate Bureaucracy, Governance Maturity, Innovation Suppressing, Dual-Line Reporting
The Impact of Financial Literacy on Investment Decisions: The Mediating Roles of Investment Strategy and Risk Management
This research paper will examine the effect of financial literacy on Investment decision. Financial literacy is one of the most influential factors in determining the investment decision and developing investment strategy and managing financial risk for the investors. A quantitative cross-sectional study was carried out among individual investors and future investors in Dhaka, Bangladesh. Survey data were collected by structured self-administrated questionnaire in offline and online surveys. For analysis of the proposed conceptual model, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied using SmartPLS 4 software. Reliability, convergent validity, discriminant validity, structural relationships, and mediating effect were tested in accordance with the standard PLS-SEM methods. Financial literacy had a highly significant direct influence on investment decision (β = 0.512, p < 0.001). Financial literacy had a highly significant effect on investment strategy (β = 0.714, p < 0.001) and risk management (β = 0.655, p < 0.001). Investment strategy (β = 0.253, p = 0.012) and risk management (β = 0.574, p < 0.001) positively influenced the investment decision. Mediating effect analysis has found highly significant indirect influence through investment strategy (β = 0.181, p = 0.013) and risk management (β = 0.376, p < 0.001). The aspect of financial literacy plays a vital role in improving investment decisions both directly and indirectly. Better financial education and risk management techniques can help individual investors in making better decisions on investing and adopting sustainable investment behavior.
Financial literacy; Investment decision; Investment strategy; Risk management; PLS-SEM
Future of Banking Risk: How Intelligent Stress Testing Is Transforming Financial Resilience
Stress testing is a critical component of banking risk management and supervisory oversight, providing a structured means of evaluating whether financial institutions can absorb losses and maintain capital resilience under adverse conditions. Yet the increasing speed, complexity, and interconnectedness of financial markets raise questions about the ability of predominantly scenario-based and periodically executed approaches to identify emerging vulnerabilities. This conceptual paper examines how artificial intelligence (AI), machine learning (ML), alternative data, and advanced analytics could augment conventional bank stress testing by enabling more adaptive scenario generation, nonlinear risk estimation, continuous monitoring, and integrated portfolio analysis. The paper uses a structured conceptual synthesis of the stress-testing literature, supervisory principles, and recent research on AI-enabled financial risk modeling to identify limitations in traditional approaches and develop a framework for AI-enabled dynamic stress testing. The proposed framework links data and risk sensing, AI-assisted scenario generation, portfolio and balance-sheet simulation, capital and liquidity impact assessment, management decision-making, and model-risk governance. The analysis argues that AI should complement rather than replace established stress-testing governance and supervisory processes. In particular, model validation, explainability, data quality, human oversight, documentation, and ongoing monitoring are essential for responsible adoption. The paper contributes a research-oriented framework for understanding how banks can move from predominantly periodic stress exercises toward more adaptive and forward-looking risk intelligence while preserving regulatory discipline. It also identifies empirical research opportunities, including comparative testing of AI and traditional models, evaluation of scenario-generation quality, and measurement of governance effectiveness.
bank stress testing; artificial intelligence; machine learning; dynamic stress testing; model risk; financial resilience; scenario analysis; capital adequacy; enterprise risk management
Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Governance Mechanisms on Earnings Management: Does Audit Quality Moderate? Evidence from Indonesia
This study examines the determinants of earnings management in Indonesia's consumer non-cyclical sector, focusing on the roles of leverage, board gender diversity, managerial ownership, and institutional ownership, with audit quality as a moderating variable. Using 375 firm-year observations from 75 companies listed on the Indonesia Stock Exchange between 2020 and 2024, the study applies Moderated Regression Analysis (MRA). Earnings management is measured using discretionary accruals derived from the Modified Jones Model. The findings reveal that leverage exerts a significant positive effect on earnings management, whereas gender diversity, managerial ownership, and institutional ownership each exert a significant negative effect. Regarding moderation, audit quality significantly weakens the effect of leverage on earnings management while strengthening the suppressive effect of board gender diversity. Audit quality does not moderate the effects of either ownership variable. These results suggest that gender-diverse boards, managerial equity stakes, and institutional ownership represent effective governance mechanisms, whose efficacy is further reinforced when coupled with high-quality external auditing.
Earnings Management, Leverage, Board Gender Diversity, Managerial Ownership, Institutional Ownership, Audit Quality
Financial Statement Analysis and its Impact on Investment Decisions in Ghana
The objective of the study is to investigate the influence of financial statement analysis on investment decisions among investors in the Ghana Stock Exchange Market. Quantitative research design was employed where data was collected through structured questionnaires issued to both individual and institutional investors on the Ghana Stock Exchange Market. In all a total of ninety-seven (97) valid responses were analyzed using descriptive and inferential statistical tools, including correlation and regression analysis. The findings of the study revealed a strong and statistically significant relationship between financial statement analysis and the quality of investment decisions of investors in the Ghana Stock Exchange Market, particularly emphasizing the importance of profitability and liquidity indicators.
Financial Statements, Investment Decisions, Investors, Ghana Stock Exchange, Decision Making
Slow to Adjust: International Diversification and the Speed of Adjustment to Cash and Leverage Targets
Using 53,121 U.S. firm-year observations from 1989 through 2024, I estimate target cash holdings and leverage ratios and measure deviations from these targets as excess cash and excess debt capacity, respectively, for internationally diversified firms. I find that internationally diversified firms hold significantly higher excess cash and excess debt capacity than domestic firms, consistent with greater information asymmetry and weaker external monitoring. More importantly, internationally diversified firms adjust more slowly toward their target cash and leverage levels. This effect is mitigated by product market competition, and the results are robust to an instrumental-variable approach addressing the endogeneity of diversification. Overall, my findings suggest that international diversification impedes firms' ability to adjust toward optimal financial policies, with implications for capital structure and liquidity management in multinational firms.
International diversification; excess cash; excess debt capacity; speed of adjustment; product market competition; information asymmetry.
Coretax in Practice: How Tax Consultants Perceive Digital Tax Reporting Effectiveness
This study aims to analyze the effectiveness of the tax reporting process in the Core Tax Administration System (Coretax) from the perspective of tax consultants in 2025. The study employs a quantitative approach by integrating the DeLone and McLean Information System Success Model and the Technology Acceptance Model (TAM). Data were collected through a questionnaire administered to 250 tax consultants/Coretax users and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The variables examined comprise system quality, information quality, service quality, perceived ease of use as a mediating variable, and tax reporting effectiveness as the dependent variable. The instrument test results indicate that all items are valid and all constructs are reliable, with Cronbach's Alpha values ranging from 0.740 to 0.933, Composite Reliability values from 0.834 to 0.952, and AVE values from 0.504 to 0.799. The structural model shows that system quality, information quality, and service quality positively affect perceived ease of use. System quality and information quality also significantly affect tax reporting effectiveness, whereas service quality does not have a significant direct effect. Perceived ease of use is proven to mediate the effects of system quality and information quality on tax reporting effectiveness. The Importance-Performance Map Analysis (IPMA) indicates that system quality is the most important factor to be maintained, while information quality represents the most realistic improvement area for organizational management. These findings contribute to the literature on tax digitalization and provide practical recommendations for Coretax system managers in improving the quality of tax reporting.
Coretax; system quality; information quality; service quality; perceived ease of use; tax reporting effectiveness
The Evolving Role of Treasury Systems: How Banks Can Outpace Fintechs
Corporate treasury management sits at the intersection of finance, technology, and institutional trust, and it is changing fast. Over the past decade, financial technology companies have reshaped how corporations initiate payments, monitor balances, and manage relationships with multiple banking partners. Their appeal is understandable: fintech platforms are faster to deploy, more intuitive to use, and often far less bureaucratic than the legacy systems offered by traditional banks. But beneath the polished interfaces and compelling value propositions, a structural problem persists. Most fintech treasury solutions are built for one-directional communication, they carry corporate instructions outward to banks but struggle to bring meaningful, real-time feedback back. Transaction confirmations are delayed. Reconciliation is partially manual. Exception handling depends on workarounds. These are not minor inconveniences; for treasury teams managing millions of dollars across multiple currencies and jurisdictions, they represent genuine operational and financial risk. This paper examines that gap and argues that it represents a significant strategic opportunity for traditional banks. Banks are not simply competing with fintechs, they are positioned, structurally and institutionally, to offer something fintechs cannot: a fully integrated, bidirectional treasury ecosystem built on the foundations of regulatory authority, settlement infrastructure, balance sheet depth, and decades of corporate relationship capital. Through a comprehensive review of academic literature and industry practice published between 2016 and 2024, this study develops the Bank Strategic Advantage Framework (BSAF), a five-dimensional model that maps the specific areas where banks hold durable competitive advantages over fintech competitors in the corporate treasury space. The paper further proposes a platform architecture model incorporating Banking-as-a-Service (BaaS) and Channel-as-a-Service (CaaS) frameworks, through which banks can operationalize these advantages into treasury products that rival and surpass current fintech offerings. The findings have practical implications for bank executives, treasury professionals, and policymakers seeking to understand the evolving competitive dynamics of corporate financial services.
treasury management systems, fintech disruption, corporate banking, Banking-as-a-Service, Channel-as-a-Service, bidirectional integration, payment platforms, digital transformation, cash management, SWIFT
Strengthening U.S. Global Leadership in Electric Vehicle Supply Chains Through AI-Driven Fintech Innovation: A Comprehensive Interdisciplinary Analysis
This study examines how artificial intelligence (AI) and fintech innovation can strengthen U.S. leadership in electric vehicle (EV) supply chains. As global competition intensifies among major economies—particularly the United States, China, and the European Union—the United States must enhance supply-chain resilience, battery sourcing security, infrastructure financing, and advanced manufacturing competitiveness. The paper proposes a comprehensive AI-driven fintech framework integrating five core pillars: predictive analytics, blockchain traceability, digital twins, embedded finance, and intelligent risk management. Drawing upon recent interdisciplinary research in AI applications, healthcare analytics, supply-chain optimization, EV adoption behavior, charging infrastructure deployment, and financial transformation, the study argues that AI-enabled financial ecosystems can substantially improve capital allocation, operational efficiency, sustainability performance, and national competitiveness. The findings suggest that coordinated investments in AI, clean energy technologies, fintech infrastructure, and advanced manufacturing can reinforce U.S. economic leadership while simultaneously accelerating the global transition toward sustainable mobility. Policy recommendations, industry implications, and future research directions are discussed.
Artificial intelligence, electric vehicles, fintech, supply chain, blockchain, digital twin, charging infrastructure, U.S. competitiveness
Healthcare System Sustainability for an Aging Population in the United States: Economic Challenges and Policy Pathways
Population aging is one of the major long-term economic challenges facing the U.S. healthcare system. As life expectancy increases and the older-adult population expands, demand is rising for chronic disease management, hospital care, prescription medication, rehabilitation, home care, long-term services and supports, and caregiver assistance. Existing studies have examined aging-related healthcare costs, long-term care financing, medical expenditure burden, workforce shortages, and family caregiving, but these issues are often analyzed separately rather than as one integrated economic sustainability problem. This paper examines the economic sustainability of U.S. healthcare systems serving an aging population by focusing on three major pressures: increasing healthcare demand, long-term care financing strain, and healthcare delivery-capacity constraints. Drawing on peer-reviewed studies, government reports, and policy analyses published before 2025, the paper identifies a gap in the literature: limited integration of Medicare and Medicaid financing, workforce capacity, chronic disease burden, care fragmentation, unpaid caregiving, preventive care, and home- and community-based services into a single sustainability framework. The paper proposes a conceptual framework that links cost control, access, workforce development, technology-enabled care coordination, long-term care reform and caregiver support. The findings suggest that economic sustainability cannot be achieved through increased healthcare spending alone. Instead, the U.S. healthcare system must shift from a reactive, hospital-centered model toward a preventive, coordinated, technology-supported, home and community-based model that improves affordability, access, quality, and aging-care outcomes.
Aging population, Healthcare sustainability, Medicare, Medicaid, Long-term care, Economic burden, United States, Elderly care
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Institutional Learning and the Limits of Fiscal Rules: A Public Choice Perspective
This article investigates the process of constructing fiscal discipline in Brazil from the Camata Law (Lei Complementar No. 82/1995) to the approval of the Fiscal Responsibility Law (FRL) in 2000, and examines its subsequent deterioration from 2014 onwards. It analyses the evolution of personnel expenditure limits, sanction mechanisms and enforcement instruments over the period 1995 to 2000. Using a qualitative approach based on documentary and institutional analysis, the study draws on the relevant legislation and historical fiscal data series. The paper identifies the initial lack of effective sanctions and enforcement tools in the Camata Law, as well as the advances introduced by Constitutional Amendment No. 19/1998 and the New Camata Law (Lei Complementar No. 96/1999). This work shows how, through learning‑by‑doing, these successive reforms culminated in a broader institutional framework with the FRL and it concludes that fiscal discipline depends on societal pressure.
Market Turbulence and Financial Contagion in Emerging Economies: A Meta-Analysis
Quantitative syntheses of the financial contagion literature are scarce, with frontier markets at the edge of the emerging-market category particularly under-represented. We meta-analyse 312 effect sizes from 87 studies (2002–2024) spanning 26 economies. A three-level random-effects model with cluster-robust variance estimation and Hartung–Knapp–Sidik–Jonkman adjustment returns a pooled standardised contagion coefficient of 0.187 (95% CI: 0.156–0.218; I-squared = 76.4%; tau-squared = 0.022). Multilevel meta-regression attributes 28% of between-study variance to methodological choice: DCC-GARCH and TVP-VAR connectedness measures exceed the Forbes–Rigobon adjustment by 35–45%, consistent with heteroscedasticity-induced bias. Regional moderators diverge sharply: the BRICS bloc registers 0.241, against 0.124 for Central Asia and Mongolia (k = 14), the lowest sub-sample mean. Selective-reporting diagnostics—Egger regression, trim-and-fill, PEESE, p-curve, p-uniform-star and a Vevea–Hedges selection model—indicate moderate but bounded distortion. Leave-one-out and Cook-distance diagnostics confirm that no single study drives the result. We draw three conclusions: methodological choice has first-order consequences for measured contagion; small open frontier markets exhibit attenuated direct co-movement but pronounced commodity-mediated transmission; and Central Asia and Mongolia warrant a focused research agenda calibrated to commodity- and currency-channel measurement.
Financial contagion; Emerging markets; Meta-analysis; DCC-GARCH; Mongolia; Central Asia
Data-Driven Risk Detection in Digital Financial Systems: Implications for Financial Crime Prevention and Infrastructure Resilience
The increasing digitalization of financial systems has transformed how financial institutions process transactions, deliver services, and manage operational risk. However, the rapid growth of electronic payment systems and online financial platforms has also increased exposure to fraud, identity theft, and cyber-enabled financial crime. This study explores the relationship between the expansion of the digital financial system and financial crime exposure in the United States, using publicly available data from the Federal Trade Commission (FTC) Consumer Sentinel Network and the Federal Reserve Payments Study. Adopting an exploratory and policy-oriented approach, the study uses descriptive trend analysis, comparative visualization, and conceptual financial risk interpretation to examine evolving fraud patterns and digital transaction vulnerabilities. The findings indicate that the growth of digital payment infrastructures has been accompanied by persistently high levels of fraud reports, identity theft incidents, and financial losses, particularly within credit card and electronic transaction environments. In response, the study proposes a conceptual Data-Driven Risk Detection Framework integrating transaction analytics, anomaly detection, compliance monitoring, and adaptive learning mechanisms to strengthen financial crime prevention and infrastructure resilience. The study contributes to the literature by offering a practical, infrastructure-focused perspective on data-driven financial risk governance in increasingly digital financial ecosystems.
Digital Financial Systems; Financial Crime Prevention; Fraud Detection; Financial Risk Analytics; Data-Driven Risk Detection; Financial Infrastructure Resilience
The Impact of New Quality Productive Forces on Corporate Environmental Performance: A Dual Perspective Based on Green Innovation and Environmental Information Disclosure
In the era of coordinated advancement of the “dual carbon” goals and high-quality development, how to transform new quality productive forces into tangible environmental governance outcomes has become a core issue in enterprises’ green transformation. This study takes Chinese A-share listed companies from 2015 to 2024 as the research sample and systematically examines the impact of new quality productive forces on corporate environmental performance, as well as its underlying mechanisms and heterogeneous characteristics. To address potential endogeneity issues, the instrumental variable approach and propensity score matching method are employed. The baseline regression results show that an increase in the level of new quality productive forces significantly improves corporate environmental performance. The conclusion remains robust after a series of robustness checks, including replacing the explanatory variable, changing the regression model, excluding samples from municipalities, and adjusting the clustering level. Mechanism tests reveal that new quality productive forces exert their effects primarily through two pathways: first, by stimulating enterprises’ green technological innovation vitality and promoting the output of green invention patents, thereby providing technological support for environmental improvement; second, by enhancing the quality of environmental information disclosure, which increases the transparency and supervisability of corporate environmental behavior and forms a combined internal and external supervisory force that compels continuous improvement. Heterogeneity analysis indicates that the positive environmental effect of new quality productive forces is more pronounced in enterprises with high profitability, in high-tech industries, and in heavily polluting industries, while the effect is relatively weaker in enterprises with low profitability, in non-high-tech industries, and in lightly polluting industries. This study not only theoretically expands the cognitive boundaries of the environmental effects of new quality productive forces and reveals the dual transmission mechanisms of green innovation and environmental information disclosure, but also provides practical references for enterprises to cultivate new quality productive forces in a differentiated manner according to their own resource endowments, thereby promoting green transformation.
New quality productive forces; corporate environmental performance; level of green innovation; environmental information disclosure quality
Education Outcomes and Structural Constraints in Côte d’Ivoire: A Political Economy Analysis of Human Capital Formation
This study examines the relationship between educational investment, system efficiency, and human capital formation in Côte d’Ivoire within a political economy framework. While human capital theory suggests that increased investment in education promotes productivity and economic growth, evidence from developing economies indicates that educational expansion alone does not necessarily generate improved developmental outcomes. The study aims to analyze how institutional quality, governance structures, labor market alignment, and structural constraints influence the effectiveness of educational investments in Côte d’Ivoire. The research adopts a qualitative analytical approach based on a structured review of academic literature, development reports, and policy documents from institutions including the World Bank, UNESCO, UNICEF, and the African Development Bank. The analysis integrates perspectives from human capital theory, endogenous growth theory, and institutional economics to evaluate the interaction between educational access, learning quality, and socio-economic outcomes. Findings indicate that although Côte d’Ivoire has achieved substantial progress in expanding access to education, major challenges persist in terms of learning outcomes, resource efficiency, labor market mismatch, demographic pressure, and institutional capacity. The study demonstrates that the developmental returns of education depend not only on financial investment but also on governance quality, policy coordination, and the alignment between educational systems and economic structures. The paper contributes to the literature by providing an interdisciplinary interpretation of education outcomes in a developing-country context and by emphasizing the limitations of input-centered education policies. It concludes that sustainable human capital formation requires reforms focused on education quality, institutional effectiveness, accountability, and labor market relevance rather than access expansion alone.
human capital formation; education policy; institutional efficiency; political economy; Côte d’Ivoire; governance; labor market alignment
Electrifying Healthcare Logistics: The Role of Electric Vehicle Transportation in Optimizing Pharmaceutical Value Chains, Digital Marketing, and Financial Performance
The transformation of healthcare logistics through electric vehicle (EV) adoption represents a critical advancement in optimizing pharmaceutical value chains. This study examines the integration of EV transportation systems within healthcare distribution networks and evaluates their impact on operational efficiency, digital marketing capabilities, and financial performance. By synthesizing insights from supply chain analytics, artificial intelligence, and sustainable mobility frameworks, the research identifies key drivers, barriers, and outcomes associated with electrified logistics. The findings suggest that EV-enabled logistics not only reduce environmental impact but also enhance delivery reliability, improve patient-centric service models, and support data-driven marketing strategies. Furthermore, financial analysis indicates long-term cost advantages despite high initial investment, positioning EV logistics as a strategic asset in modern healthcare ecosystems.
Electric Vehicles, Healthcare Logistics, Pharmaceutical Supply Chain, Digital Marketing, Financial Performance, Sustainability, AI Integration
Economic Resilience of Immunization Systems: Development and Validation of the ERIIS Index
The health and economic benefits of vaccination programs are tremendous, yet the financial sustainability will be vulnerable to macroeconomic crises, change of donors and government budget constraints. Current research calculates the health impact and cost-effectiveness and does not determine the economic resilience at the system level. In this paper, Economic Resilience Index of Immunization Systems (ERIIS), a composite and multi-dimensional index, is introduced; the index is designed to validate and forecast the ability of a country to maintain immunization outcomes in the case of fiscal shock. ERIIS is a composite score of five pillars: fiscal autonomy, health economic integration, procurement efficiency, funding diversification and data and forecast capacity into 100 points. According to the model, there are measurable indicators, weighting process, and a validation method that are to be employed in an empirical manner. ERIIS can provide a forecasting tool to policymakers because operationalizing economic resilience can be used to determine vulnerability, improve financial preparedness, and reform sustainable immunszation.
Economic resilience, Immunization systems, Vaccine financing, Health system resilience, Public financial management, Immunization economics, Vaccine procurement, Health policy
Retirement Trends Among PERKESO Contributors: Economic and Social Implications for Malaysia and Policy Insights from Long-term Ageing Governance Experience
Malaysia is increasingly confronting what demographers describe as a silver wave, marked by a rapid rise in the proportion of older persons driven by sustained fertility decline, rising life expectancy, and shifts in population age structure. In 2024, the Department of Statistics Malaysia estimated that individuals aged 60 years and above accounted for 11.6 per cent of the total population, or approximately 3.9 million people. Concurrently, the total fertility rate declined to 1.7 children per woman in 2023, the lowest level in half a century and well below the replacement threshold of 2.1, placing Malaysia on the trajectory toward becoming an aged nation by 2040. This study provides a comprehensive analysis of the economic and social impacts of population ageing in Malaysia, with particular emphasis on retirement trends derived from contribution data from the Social Security Organisation (PERKESO). Using administrative contribution records, the analysis examines patterns of workforce exit, continued labour participation among older adults, and implications for labour supply and social security sustainability. The findings indicate that while population ageing presents significant economic risks including a shrinking workforce, potential constraints on GDP growth, increased pension liabilities, and rising healthcare and long-term care expenditure, which also present opportunities for productivity retention and inclusive growth through extended workforce participation. Although healthcare and elderly care spending currently accounts for a relatively small share of GDP, these expenditures are expected to rise substantially by 2040 in response to age-related demands. In order to situate these empirical findings within the context of a broader policy trajectory, this empirical analysis is complemented with institutional insights drawn from long-term ageing governance experience in Belgium. From a social perspective, a growing older population intensifies dependency ratios, placing pressure on families, communities, and public welfare systems. Challenges such as social isolation and age-unfriendly urban infrastructure further heighten vulnerability among older persons. Nevertheless, the study highlights the role of employment services, retraining initiatives, and community-based support in promoting social inclusion, dignity, and financial security in later life. PERKESO contribution data further reveal a rising trend of delayed retirement, reflecting both financial necessity and a willingness among older adults to remain economically active. Since this trend supports pension sustainability but underscores the need for flexible retirement policies, extended contribution mechanisms, and broader coverage for non-standard and gig employment, Belgium is introduced as an institutional reference point. As this country has already progressed further along the demographic ageing trajectory while operating within a comparable social security and labour market governance framework as Malaysia, its experience proves an analytically relevant case for contextualising Malaysian retirement trends. In Belgium, successive reforms have reconfigured the balance between early exit pathways and active end-of-career participation. This institutional reflection underscores the importance of policy sequencing, targeted incentives, and evidence-based design in avoiding unintended consequences such as deadweight loss or substitution effects. Overall, the study underscores the urgency of repositioning older persons not merely as dependents but as productive economic and social assets. Aligning labour market policies, employment services, healthcare systems, and urban planning with demographic realities will be critical to ensuring Malaysia’s long-term economic resilience and social cohesion in an ageing society.
DATO’ SRI DR. MOHAMMED AZMAN AZIZ MOHAMMED
Adoption of Seventeen SMA Tools in the Saudi Sectors: A Contemporary Analysis
This study investigates the adoption of seventeen strategic management accounting tools (SMAT) across Saudi service sector organizations, integrating technological, organizational, and strategic determinants within a unified TOE–TAM–SMAT–Digital Capability framework. Using a quantitative, cross-sectional design, data were collected from accountants and managers representing diverse service industries, enabling a comprehensive assessment of adoption patterns and the influence of demographic and organizational characteristics. The findings reveal that competitor-oriented tools—particularly benchmarking and competitive position monitoring—are the most widely adopted, reflecting the sector’s emphasis on external market intelligence and strategic positioning. Customer oriented tools exhibit moderate adoption, with strategic pricing and customer profitability analysis leading the category, while advanced customer analytics remain underutilized. Cost management tools show selective but meaningful uptake, with value chain costing and life cycle costing demonstrating the highest usage. Demographic variables such as company size, service type, listing status, and managerial experience exert minimal influence on adoption, indicating that structural characteristics play a limited role. Instead, digital capability, SMAT orientation, and market position emerge as the primary drivers shaping adoption pathways. The moderating effect of market position strengthens the relationship between digital capability and adoption, while SMAT orientation mediates the influence of digital capability on cloud accounting usage. Reliability and validity analyses confirm the robustness of the measurement instrument. Overall, the study provides empirical evidence that SMAT adoption in the Saudi service sector is driven by strategic imperatives and digital readiness rather than demographic factors. The findings highlight the need for enhanced digital and analytical capabilities to support the adoption of advanced SMAT, particularly those requiring long term customer insight and sophisticated cost analytics. The study contributes to the understanding of SMA in digitally transforming service economies and offers practical implications for managers, policymakers, and educators seeking to strengthen strategic decision making and performance measurement practices.
Cloud Accounting; TOE Framework; TAM; SMAT Orientation; Digital Capability; Market Position; Competitive Intelligence; Strategic Agility; Cost Efficiency; Technology Adoption
Electric Vehicles and Healthcare Efficiency: A Critical Perspective on Cost, Access, and Environmental Impact
The intersection of Electric Vehicles (EVs) and healthcare efficiency presents a complex landscape where environmental sustainability, economic viability, and public health outcomes converge. While the transition to EVs is often framed as a purely environmental or automotive shift, its implications for healthcare infrastructure and patient access are profound. This paper critically examines how EV adoption in healthcare—particularly in logistics, emergency response, and patient transportation—affects cost structures, service accessibility, and sustainability outcomes. By integrating insights from digital health technologies, artificial intelligence, and evolving consumer expectations, this study highlights both the opportunities and systemic challenges of electrifying healthcare infrastructure in the United States.
Good Corporate Governance, Financial Performance, Rural Banks.
Client Happiness as a Predictive Economic Variable in Revenue Systems
In the turbulent environment of the contemporary revenue management systems, the conventional client satisfaction indicators, such as Net Promoter Scores, are less predictive as they do not rely on prospective behavioral factors. This study redefines client happiness as an economic variable with measurement, and puts forward a new composite measure, the Emotional Continuity Index (ECI) a new model of emotionally stable, which predicts repeat engagement, quick decision making and retention. ECI is integrated into Customer Happiness Intelligence System (CHIS), a patent-pending framework based on practical executive practice, which gathers operational data through CRM systems, transaction records, and feedback systems and compiles them with the help of machine learning algorithms in real time to deliver a picture of customer satisfaction levels. The research notes the effectiveness of ECI based on a mixed-methodology which involves conceptual modeling, weighted algorithmic formulation, and 12 months of empirical validation using a stratified sample of 749 clients of Sunlocate Properties (commercial real estate sector): Results show 22% increase in revenue predictability, 15% decrease in churn, and 18% faster decision-making. Such results place client happiness as a measurable managerial dial, and this raises the strength of enterprises in complicated settings. Commercial intelligence and customer-centric design implications, which promote structured analytics to develop flexible, scalable business, can be implied. The piece of writing adds a coherent intellectual journey between the executive experience and technological innovation to close the gaps in the theoretical background and the real-life applications to make the best decisions.
Client happiness, revenue predictability, Emotional Continuity Index, commercial intelligence, customer lifecycle management, behavioral analytics
Driving the Future of Healthcare: Electric Vehicles, Data-Driven Marketing, and Financial Innovation in Pharmaceutical Value Chains
The healthcare and pharmaceutical industries are undergoing a transformative shift driven by electric vehicle (EV) adoption, data-driven marketing, and financial innovation. This paper examines how EV integration in pharmaceutical logistics enhances sustainability, reduces delivery costs, and improves supply chain resilience. It further explores how AI-powered marketing systems and blockchain-based financial mechanisms are reshaping customer engagement and operational transparency. The study synthesizes prior research to present a multi-dimensional framework linking EV infrastructure, digital intelligence, and financial optimization in healthcare value chains.
Innovation, Decision-Making, LOTAR Model, Reflective Learning, Cognitive Psychology, Qur’anic Epistemology, Creativity, Educational Management.
Internal Audit Practices, Management Controls and Fraud Prevention among Public Technical and Vocational Education and Training Institutions in Nairobi Metropolitan Area, Kenya
This study examined the effect of internal audit practices and management controls on fraud prevention among public Technical and Vocational Education and Training institutions in the Nairobi Metropolitan Area, where persistent financial mismanagement raises concerns about audit effectiveness using a descriptive research design. The study targeted 141 institutions, with 103 heads of internal audit as respondents. Primary data were collected using structured questionnaires that satisfied validity and reliability requirements. Data were analyzed using correlation and multiple regression analyses. Results from the study, which was anchored on Agency Theory, Fraud Triangle Theory, and Institutional Theory, show strong positive associations between fraud prevention and internal control systems, management controls, and internal audit IT adoption. Regression results indicate that internal audit IT adoption is the most influential predictor of fraud prevention, followed by internal control systems, both of which are statistically significant. The model explains a substantial proportion of variation in fraud prevention. The moderating effect of board oversight is not statistically significant. The study concludes that strengthening internal audit systems, particularly through digital audit adoption and robust internal controls, significantly enhances fraud prevention in public TVET institutions. Management controls further reinforce accountability.
Internal audit practices, internal control systems, fraud prevention, management controls
A Flexible Elasticity Framework for Measuring Firm Internationalization: Theoretical Model and Simulation-Based Evidence
This study develops a flexible elasticity framework for measuring firm internationalization. Unlike standard composite indices based on fixed weights, we model internationalization as a heterogeneous elasticity function in which the contribution of each dimension varies across firms. The model generalizes traditional additive approaches by introducing firm-specific elasticities that depend on observable characteristics of the firms. We provide a theoretical characterization of the model and evaluate its properties using simulations. The results show that fixed-weight indices systematically compress heterogeneity and distort firm rankings, whereas the proposed framework preserves structural variation and provides a more consistent representation of internationalization.
Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model
In contemporary times, decision-making has become one of the most essential managerial, educational, and social competencies. While decisions occur frequently, they become meaningful and transformative only when they embody elements of creativity and innovation. The fundamental question, therefore, is: Where does innovation in human decision-making originate, and what activates this process within the individual? This study, entitled “Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model,” aims to provide a scientific and integrative explanation of innovative decision-making by drawing on cognitive theories and Qur’anic epistemology. Inspired by these principles and integrated with established cognitive frameworks, the proposed LOTAR Model introduces a five-stage dynamic process consisting of Listening, Observation, Thinking, Action, and Reflection. The model argues that innovation does not arise from sudden inspiration, but from the continuous interaction among perception, cognition, experience, and reflective learning. A controlled field experiment was conducted within the Department of Management at Takhar University using a real educational task—improving classroom cleanliness. The results indicated that the LOTAR framework enhances innovation by strengthening Thinking, Action, and Reflection. The findings demonstrate that LOTAR provides a scientifically grounded, culturally contextualized, and Qur’an-informed approach for enhancing reflective thinking and innovative decision-making.
Investigating Effective Factors in Application of the Artificial Intelligence in Iran's Food Industry and its Effects on Employment
The fourth industrial revolution has brought tremendous changes with the introduction of artificial intelligence to various industries. In this study, the effective factors in the use of artificial intelligence in Iran's food industry and its effects on employment have been identified. Due to the growing need to improve the quality of food products, increase productivity, and reduce costs, the use of new technologies, including artificial intelligence, in this industry seems inevitable. This research examines the opportunities and challenges in the use of artificial intelligence and analyzes its effects on employment in Iran's food industry. Investigating these factors in Iran's food industry is of great importance and necessity, and increases productivity, changes in employment, competitiveness, research and development, environmental sustainability, improves food security, and develops appropriate policies. The general goal of investigating these effective factors in the use of artificial intelligence in Iran's food industry is to create a stable and innovative ecosystem that will lead to the improvement of product quality, increase employment, and the realization of sustainable development goals. The type of this research in terms of practical purpose, in terms of descriptive-analytical execution method, and in terms of data collection using studies published until 2023 using PubMed, Cochrane, SCOPUS databases, and Google Scholar search engine with key related words have been searched. This research was selected after a complete review of 30 relevant articles that were consistent with the purpose of the present study. The results show that while artificial intelligence can reduce some traditional jobs, it also creates new job opportunities and increases the need for new skills. In this research, we found that workforce skills and technological infrastructure are of higher importance, government support and organizational culture are of high importance, and costs and benefits are of medium importance, and each of them creates its own effects on employment. Finally, this research provides recommendations to policymakers and the food industry to facilitate the transition process towards artificial intelligence.
Employment, Iran, Artificial Intelligence, Food Industry, Productivity, New Technologies
Redefining Healthcare Consumption: AI-Driven Buyer Persona and Financial Transformation
Artificial Intelligence (AI) is revolutionizing the healthcare industry by reshaping consumer behavior and transforming financial structures. This study examines the evolution of the future healthcare consumer buying persona and evaluates the financial implications of AI integration. The findings suggest that AI-driven personalization, predictive analytics, and digital engagement tools significantly influence consumer decision-making while enhancing operational efficiency and reducing costs for healthcare providers. Additionally, technologies such as blockchain and digital twin systems contribute to transparency and supply chain optimization. The study concludes that AI is fostering a more consumer-centric and financially sustainable healthcare ecosystem.
Artificial Intelligence, Healthcare Consumer Behavior, Financial Impact, Digital Health, Personalization, Predictive Analytics
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Intelligent Pay-by-Bank Architecture: Fraud-Aware Analytics and Risk Modeling for U.S. Account-to-Account Payment Systems
Pay-by-bank is moving from a niche alternative to a strategically important layer in the U.S. payments landscape. Its appeal is clear: direct account-to-account (A2A) transfers can reduce merchant acceptance costs, accelerate funds availability, support recurring and bill-payment use cases, and create a pathway for open-banking-enabled checkout experiences that do not depend on card rails. At the same time, the same structural strengths that make pay-by-bank attractive—irrevocability, speed, richer data exchange, API connectivity, and multi-party orchestration—also create distinctive fraud, operational, and consumer-protection risks. Unlike card networks, many pay-by-bank implementations cannot rely on mature chargeback mechanisms as a primary safety valve, which makes ex ante risk scoring, identity assurance, behavioral analytics, and post-transaction monitoring more important. This paper develops a framework for an intelligent pay-by-bank architecture for the United States, integrating fraud-aware analytics, explainable machine learning, graph-based entity resolution, sanctions and AML screening, and rail-aware control design across ACH, Same Day ACH, RTP, and FedNow-enabled flows. Using official U.S. sources and contemporary scholarly research, the paper synthesizes the structural drivers of A2A growth, maps the major fraud typologies that threaten consumer and merchant adoption, and proposes a layered analytic design that joins customer risk, payment context, device and channel telemetry, open-banking consent events, account validation, and network intelligence into a unified decision stack. The paper also advances a practical governance model centered on explainability, model-risk management, fairness, vendor oversight, and escalation workflows aligned to high-stakes payment decisions. The discussion argues that successful U.S. pay-by-bank adoption depends not only on rail availability but on trusted orchestration: institutions must combine real-time controls, adaptive thresholds, human review for ambiguous cases, and continuous learning from returns, disputes, fraud reports, and customer complaints. The manuscript concludes that intelligent pay-by-bank systems can materially improve the safety and efficiency of U.S. A2A payments when technical architecture, controls, and governance are designed together rather than sequentially.
Pay-by-bank, account-to-account payments, ACH, Same Day ACH, FedNow, fraud analytics, explainable AI, open banking, synthetic identity, U.S. payment systems
Clustering Capital Management Approaches: Evidence from Banking Firms during Economic Disruptions
The COVID-19 pandemic significantly disrupted global markets, supply chains, and business operations, leading to adverse effects on firm performance and economic stability. The banking sector, in particular, was compelled to adapt its capital management strategies to remain resilient amid financial uncertainty. Effective capital management—encompassing efficiency, liquidity, and solvency—became critical, as poor financial decisions during crisis periods can threaten organizational survival. This study aims to examine how banking firms respond to financial uncertainty through their capital management practices and to determine how these responses relate to firm profitability. Specifically, it seeks to identify distinct patterns of capital management and assess their implications for financial performance. A quantitative research design was employed using survey data from bank representatives. K-Means Cluster Analysis, utilizing the Lloyd algorithm, was applied to classify firms based on efficiency, liquidity, and solvency. The findings revealed three distinct clusters: efficient-solvent, high-liquid, and safe-player. Results further indicate a significant relationship between capital management approaches and profitability, highlighting the importance of balancing efficiency, liquidity, and solvency. This study contributes to the literature by offering an integrated clustering perspective on capital management and provides practical insights for banking institutions to develop more resilient financial strategies during periods of economic uncertainty.
Capital management, business uncertainty, profitability, efficiency, liquidity, solvency