Journal of Business and Management Studies
The Journal of Business and Management Studies (ISSN: 2709-0876) is a double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. It covers the latest developments in the broad areas of business and management. The journal offers readers free access to all new research issues relevant to business and management. While the journal strives […] Read More.
Al-Kindi Center for Research and Development
Experiences of Registered Nutritionist-Dietitians in Selected Tertiary Government Hospitals in Metro Manila on the Implementation of the Nutrition Care Process (NCP): Basis for a Clinical Nutrition Framework
The Nutrition Care Process (NCP) was developed as a standardized and evidence-based approach to improve the quality and consistency of clinical nutrition care. Despite policy mandates in the Philippines, its implementation in tertiary government hospitals remained inconsistent. This study explored the lived experiences of Registered Nutritionist-Dietitians (RNDs) in implementing the NCP in selected tertiary government hospitals in Metro Manila and served as a basis for developing a Clinical Nutrition Framework. A phenomenological qualitative design was employed, involving purposively selected RNDs. Data were collected through in-depth interviews and analyzed using thematic analysis guided by the Consolidated Framework for Implementation Research (CFIR). The findings revealed that NCP enhanced clinical reasoning and professional accountability but was constrained by limited resources, manpower shortages, and inconsistent organizational support. Leadership engagement, professional commitment, and adaptive strategies were identified as key facilitators. The study concluded that successful NCP implementation depended on the interaction of organizational, individual, and external factors. A Clinical Nutrition Framework was proposed to strengthen implementation through policy support, organizational readiness, capacity building, patient-centered adaptation, and monitoring mechanisms.
Nutrition Care Process, Registered Nutritionist-Dietitians, Clinical Nutrition, CFIR, Government Hospitals, Philippines, Qualitative Study
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Factors Affecting the Utilization of Mental Health Services in Selected Rehabilitation Centers in Lanao del Norte: A Basis for a Quality Improvement Project
The utilization of mental health services remains a critical concern in rehabilitation settings, particularly in resource-constrained areas. This study examined the factors affecting the utilization of mental health services in selected rehabilitation centers in Lanao del Norte. A mixed-methods approach was employed, combining quantitative survey data from 155 respondents with qualitative insights from interviews and focus group discussions. Descriptive statistics and inferential analyses were used to determine relationships between demographic variables, perceived barriers, and service utilization patterns. The findings revealed that most respondents were middle-aged, predominantly male, married, and had at least secondary education. Service utilization was generally moderate, with most respondents accessing services monthly or 3–4 times per month. However, significant barriers persisted, particularly service-related issues, financial constraints, and accessibility challenges. Educational attainment and socioeconomic status were found to have significant relationships with perceived barriers, while age and gender significantly influenced utilization frequency. The absence of younger respondents highlighted a critical gap in youth engagement. The study concluded that mental health service utilization was influenced by a combination of structural, socioeconomic, and psychosocial factors. A multi-component Quality Improvement Project was proposed to enhance accessibility, affordability, and service quality.
Mental Health Services; Service Utilization; Rehabilitation Centers; Barriers to Access; Quality Improvement; Philippines
Financial Management Practices and Risk Attitude of Public High School Teaching Personnel in Parañaque City: A Proposed Personal Finance Management Strategies
This study examined the financial management practices and risk attitudes of public high school teaching personnel in Parañaque City from Parañaque National High School – Main, San Antonio National High School, and Moonwalk National High School. The research gap lies in the limited localized studies focusing on how financial management practices relate to risk attitude among public high school teachers in Parañaque City. Using a quantitative-descriptive design, data were collected through surveys assessing budgeting, saving, investing, debt management, financial planning, and risk attitude. Findings showed moderate financial management practices and a moderately conservative risk attitude among teachers. Based on the results, the study proposed financial literacy, budgeting, investment, and retirement planning strategies to improve teachers’ financial well-being.
Financial management practices; Risk attitude; Personal finance management; Public high school teachers; Financial literacy; Budgeting; Saving; Investing; Debt management; Financial planning.
The Role of Educational Leadership in Influencing Student Retention in Higher Education Institutions
This research proposal will explore the role of educational leadership in influencing student retention within higher education institutions. In every institution across the globe, student retention has been a subject of discussion. Student retention indeed has remained a central challenge across universities and colleges in the United States and globally, with declining persistence rates particularly affecting first-generation, minority, and economically disadvantaged students. Schools have been on the way, and even though significant resources will continue to be devoted to advising, academic support, and institutional reforms, the connection between leadership practices and student persistence will remain underexplored. Within the entire course, this study will employ a mixed-methods design, combining quantitative analyses of institutional retention data with survey-based measures of leadership practices, to determine the extent to which leadership approaches such as transformational, servant, and distributed leadership will influence persistence outcomes. In general, this research proposal will bridge leadership theory with retention research to provide actionable insights for higher education leaders, policymakers, and stakeholders committed to improving both student success and institutional sustainability.
| KEYWORDS Keywords: Public performance - Higher education - Indicators – University
Copyright (c) 2026 Journal of Business and Management Studies https://creativecommons.org/licenses/by-nc/4.0
Reducing Absenteeism in Employees: A Qualitative Study on Clear Bereavement Leave Policies
This study aims to investigate the impact of well-defined bereavement leave policies on employee absenteeism. A qualitative research design is employed to explore employee experiences and managerial perspectives regarding bereavement leave policies. Data is analyzed through an interpretive phenomenological lens to understand the lived experiences of employees. The study contributes to workplace policy development by identifying best practices for reducing absenteeism through structured bereavement leave policies. The findings are expected to provide insights for human resource professionals and policymakers in establishing clear and effective bereavement leave policies that balance organizational efficiency with employee well-being.
Venture Capital Research: An In-Depth-Cluster Analysis
This exploratory bibliometric study reviews and classifies the existing state of venture capital research. By conducting a co-citation analysis with a sample of 1,410 papers, we complement existing qualitative and quantitative studies while visually uncovering the intellectual structure of venture capital research. We identify three broad research areas (venture capital life cycle, interfirm relationships and knowledge exchange as well as economic and policy perspectives) that also reveal the evolution of the research field. These research areas are composed of 13 thematic clusters. In addition, we introduce the idea of connecting and methodology papers: we explore thematic linkages between the respective clusters as these connecting papers bring together research topics from at least two different fields of research. We highlight the most important articles, show how clusters interact with each other and finally identify potential future research directions.
Venture capital, co-citation-analysis, entrepreneurial finance, scientific visualization, start-up financing, bibliometric analysis
Predictive Analytics in Supply Chain Management: Enhancing Demand Forecasting and Operational Resilience under Uncertainty
Supply chains have been pressured, in recent years, by a sequence of disruptive events whose timing and magnitude were difficult to anticipate. From the prolonged shockwaves of the COVID-19 pandemic through the Suez Canal obstruction, the Russia–Ukraine conflict, and the persistent volatility in commodity markets, firms have repeatedly discovered that classical forecasting techniques, calibrated on stationary or near-stationary demand patterns, fail precisely when reliable guidance is most needed. This paper examines how predictive analytics, broadly understood to include statistical learning, machine learning, and deep learning approaches, can be deployed to strengthen demand forecasting and operational resilience under uncertainty. Drawing on a mixed-methods design that combines a structured review of 142 peer-reviewed studies published between 2015 and 2025 with an empirical comparison conducted on a multi-echelon retail dataset, we evaluate seven forecasting models: ARIMA, exponential smoothing state space (ETS), Prophet, XGBoost, Long Short-Term Memory (LSTM) networks, Temporal Fusion Transformers (TFT), and a hybrid ensemble. The empirical results indicate that the TFT and the hybrid ensemble outperform classical benchmarks by 18.6% and 22.4%, respectively, in mean absolute percentage error during volatile demand windows, while also yielding tangible improvements in fill rate, inventory turnover, and bullwhip dampening. Beyond model accuracy, we argue that predictive analytics contribute to resilience only when embedded within an organizational architecture that links forecasts to flexible sourcing, dynamic safety stock policies, and human judgment. We propose an integrative framework, the Predict–Sense–Adapt (PSA) loop, and discuss its managerial, theoretical, and policy implications. The study contributes to the literature on data-driven operations management by clarifying when, why, and under what conditions advanced forecasting techniques translate into resilience gains, and where their limitations remain consequential.
Predictive Analytics; Demand Forecasting; Supply Chain Resilience; Machine Learning; Deep Learning; Uncertainty; Operational Risk; Temporal Fusion Transformer
Factors Contributing to Work-Related Stress: A Qualitative Content Analysis
This article provides a theoretical basis for the causes of work-related stress. Research objective: to identify the factors determining stress experienced at work based on qualitative content analysis. Data were collected by presenting open-ended questions to the study participants (n=303), and the obtained data were processed and interpreted using the content analysis method. The results of the study showed that a heavy workload and unrealistic deadlines, as well as a lack of clearly defined employee responsibilities and functions and a lack of precise instructions for performing tasks, were identified as factors contributing to workplace stress. The study also found that the risk of workplace stress is heightened by limited opportunities for professional development and career advancement, as well as a lack of training on stress management within the organization, as well as a failure to recognize employees’ efforts, a lack of incentives for good performance, and predominantly tense relationships with colleagues and supervisors. The study’s results provided valuable insights into the causes of workplace stress, which, in the context of future research, opens up opportunities to reasonably identify directions for the prevention and management of workplace stress at the organizational level.
Work-related stress, causes of work-related stress, employees
Strategic orientations and innovation performance in Bangladeshi SMEs: Dual role of big data analytics and innovation capabilities
This study investigates how innovation performance (IP) in Bangladeshi manufacturing SMEs is affected by three strategic orientations: entrepreneurial (EO), digital (DO), and environmental (E2O). In addition to examining the moderating influence of big data analytics (BDA) and the mediating function of innovation capabilities (IC) , it further uniquely integrates these perspectives. Through the application of structural equation modeling, data from 369 managers in textile SMEs (weaving, spinning, and dyeing) were evaluated. The data reveal that whilst E2O has no direct influence on IP, whereas EO and DO have a beneficial impact . All three orientation-IP interactions are favorably mediated by IC. BDA has no discernible impact on the E2O-IP relationship, but it does strengthen the EO-IP and DO-IP relationships. Even though EO, DO, IC, and BDA have been shown to improve invention, little is known about how they work together. As the survey suggests on fostering innovation, SMEs are expected to focus on BDA, digital, entrepreneurial, and even environmental strategies. This paper outlines actionable recommendations aimed at policymakers, practitioners, and the managers.
Entrepreneurial Orientation, Digital Orientation, Environmental Orientation, Innovation Capabilities, Big Data Analytics, Innovation Performance
Leadership and Organizational Resilience in Multinational FMCG Companies Operating in Geopolitically Volatile Environments
In this original empirical research, the impact of transformational and adaptive leadership on organizational resilience and firm performance is analyzed for multinational fast-moving consumer goods (FMCG) corporations operating within geographically volatile markets. The data for this research were collected through surveys conducted on 328 middle and senior managers employed by Unilever, Nestlé, and Procter & Gamble in Lagos, Nigeria, an emerging economy known for its geographically volatile environment associated with political tensions, monetary crisis, and disruptions in supply chains. It is established through the analysis conducted using partial least squares structural equation modeling (PLS-SEM) that transformational leadership (β = 0.44, p < .001) and adaptive leadership (β = 0.39, p < .001) positively affect organizational resilience, mediating the effects of each type of leadership on profitability (β = 0.53), operational effectiveness (β = 0.49), and sustainability (β = 0.46).
Transformational leadership, adaptive leadership, organizational resilience, multinational FMCG, geopolitical volatility, Nigeria, PLS-SEM
Strategic Financial Architecture of International Stadium-Level Touring: Revenue Diversification, Risk Mitigation, And Scalability in Live Music Events
Stadium-level international touring has emerged as the central profit generator for the global music live entertainment industry. However, the underlying financial architecture of stadium-level international touring is largely understudied. This paper proposes and tests a new theoretical framework for a strategic financial architecture for stadium-level international touring consisting of four interdependent pillars: revenue diversification, cost and logistics discipline, risk management, and leverage scalability. Using an explanatory sequential mixed-method multiple case-study design, this study examines empirical evidence obtained through 28 semi-structured interviews with high-ranking industry executives, financial documents, and a fan expenditure survey (N=1,247). The empirical findings show that the proposed architecture results in a consistent profit margin of 28–35%, net artist take-home earnings of 47% of total gross, and ancillary revenue of 22–34%. Furthermore, residency-hybrid tour strategies have resulted in 39% savings in logistics expenses, while a layered hedging strategy has kept currency fluctuation below 1.8%. Moreover, leveraging digital and metaverse platforms has produced marginal scalability of nearly 100%. This research contributes to the body of knowledge in cultural economics and event management literature by offering a comprehensive financial architecture framework for stadium-level international touring rather than focusing solely on fragmented studies.
Strategic financial architecture, stadium touring economics, revenue diversification, risk mitigation, live music scalability, ancillary revenue, mixed-methods case study, metaverse extensions
AI-Driven Predictive Analytics for Supply Chain Resilience, Financial Risk Management, and Digital Marketing Strategy: A Unified Business Intelligence Framework
The arrival of artificial intelligence and big-data analytics at scale has begun to redraw the strategic map of the modern enterprise. Three areas sit close to the center of that change: how firms manage their supply chains, how they assess financial risk, and how they think about digital marketing. Each has its own substantial literature, yet the three are seldom examined together inside a single, properly governed analytical architecture. This paper sets out to close that gap. We develop and empirically validate a Unified Business Intelligence (UBI) framework that brings machine-learning engines, predictive analytics pipelines, and explainable AI modules into one coherent three-layer design that spans all three domains. The framework is grounded in a structured synthesis of forty-three peer-reviewed studies published between 2023 and 2026 and is supplemented by multi-domain benchmark experiments. The results are consistent and, in places, striking. In supply chain management, the framework reaches 94.1% disruption-prediction accuracy, a 22.9 percentage-point lift over domain-specific baselines and pulls demand-forecast MAPE down from 12.4% to 7.8%. In financial risk, ensemble-transformer hybrids deliver an AUC-ROC of 0.93 on portfolio stress testing and an F1 of 91.4% on credit-risk classification. In marketing, AI-orchestrated cross-domain targeting raises campaign ROI from 14.2% to 45.3%, and churn-prediction recall climbs from 68.0% to 84.7%. Across the seven capability dimensions assessed among them cross-domain integration, real-time processing, and embedded explainability the UBI framework outperforms traditional BI, siloed AI, and integrated MIS benchmarks; no existing paradigm achieves all of these at once. Twelve concrete research gaps are mapped, spanning federated learning, regulatory-grade explainability, privacy-preserving marketing analytics, and questions of geographic generalizability, with a structured agenda proposed for each. The findings are directly relevant to practitioners moving enterprise AI into production, and to policymakers crafting governance for cross-domain algorithmic decision-making.
Artificial Intelligence; Predictive Analytics; Supply Chain Resilience; Financial Risk Management; Digital Marketing; Business Intelligence; Machine Learning; Explainable AI; Big Data; Data Governance.
Paying the Price: Management Implications of Public Market Vendors’ Debts and Its Effect on the Market Price of Goods in San Juan City Agora Market
Microenterprises are significant contributors to economic development and poverty reduction. However, they often struggle with financial capability issues. Market vendors usually resort to informal sources of loans, such as the "5-6" system. The borrowed money comes with extremely high interest rates, which traps market vendors in a cycle of debt dependency and financial strain. Guided primarily by the Cost-Push Inflation Theory, the research focuses on the debt management practices of market vendors and their influence on the pricing of goods at the San Juan City Agora Market. The study utilizes a mixed-methods research design that combines a case study focused on the San Juan City Agora Market with surveys. Structured interviews, observation checklists, and document reviews are used to obtain data, while thematic analysis is employed to interpret it. Results showed a significant relationship between the monthly debt repayment of market vendors for informal loans and their pricing strategies. The active loans that market vendors have, and their willingness to pay high interest rates in order to get money quicker, have a significant influence on the prices they set for the goods they sell, which are often higher than the suggested retail price (SRP). These findings indicate that debt practices, including reliance on informal lenders, such as “5-6,” and agreeing to the usurious interest rates charged by these informal lenders, have a direct impact on how market vendors set prices for the goods they sell, distributing or shifting the burden of paying the usurious interest rates of debts to consumers.
A Lifecycle Governance Control Plane for Securing AI Workloads in Multi-Cloud Environments
Modern workloads supporting AI applications increasingly rely on multiple cloud platforms to enable growth, compliance, specialty services and resilience. This introduces a key challenge for governance in terms of dispersed security controls in the identity management system, model registry, deployment infrastructure, runtime endpoints, and monitoring stack. Cloud security techniques are traditionally provider-specific and infrastructure-centric, and thus fail to protect the end-to-end lifecycle of AI in multi-clouds. The present study proposes a multi-cloud AI governance framework, which includes the following three contributions: (i) MAGCP-6, a six-component governance control plane for AI applications in multi-clouds; (ii) LPEM, a lifecycle-based enforcement model for policies in AI in multi-clouds; and (iii) CGAL, a continuous governance assurance loop for AI in multi-clouds. These three components together constitute an integrated lifecycle-centric governance approach, which provides support for validating workload identity, making decisions based on policies, verifying model artifacts, attesting execution environments, monitoring runtime, and sustaining governance assurance
Flavor Of The Past, Taste Of Tomorrow: Business Resilience And Sustainability Of Laguna's Heritage Food Establishments
Laguna’s heritage food establishments are more than businesses; they are the living memory of a province served on a plate. Yet, the individuals who preserve these recipes operate family-owned establishments in an environment that is increasingly Brittle, Anxious, Nonlinear, and Incomprehensible (BANI), where authenticity must compete with disruption on a daily basis. This study examined how five long-standing heritage food establishments—Pancit Maciang and Universal Bakery in San Pedro City; Mang Tony’s Original Pospas and Nila’s Puto in Biñan City; and Aling Taleng’s in Pagsanjan—demonstrate resilience, sustainability, and cultural continuity within this operating context. Anchored in SERVQUAL Theory, Resilience and Sustainability Theory, and the UNESCO 2003 Convention for the Safeguarding of Intangible Cultural Heritage, the research adopted Creswell’s convergent parallel mixed-methods design. Quantitative data were collected from 150 customers using a SERVQUAL-based satisfaction survey, while qualitative data were obtained through in-depth phenomenological interviews with five owner-managers. Findings indicate that customers are highly satisfied across all dimensions, with food quality (M = 3.49) and reliability (M = 3.54) emerging as the strongest indicators, while tangibles and cleanliness (M = 3.32) were identified as the most visible vulnerabilities. The study also found no significant differences in satisfaction across establishments suggesting that legacy, authenticity, and personal accountability function as shared, culture-driven quality standards. Owner narratives confirmed that resilience is present but volatile, sustained primarily by family legacy rather than formalized systems. Drawing from the integrated findings, the study proposes the Heritage Food Resilience and Sustainability Framework (HFRSF), built on four pillars: Authenticity Custodianship and Heritage Mapping, Relational Service Culture, Physical Heritage Stewardship, and Capacity and Succession Systems, with the NCCA, NHCP, DOT, LGU Laguna, and TESDA serving as institutional anchors. The HFRSF offers a practical, culturally grounded pathway for safeguarding Laguna’s culinary heritage while preparing it for future sustainability.
Heritage Food Establishments, Business Resilience, Sustainability, BANI Environment, SERVQUAL, Intangible Cultural Heritage, Mixed-Methods, Laguna, HFRSF
From Preference to Performance: A Mixed-Methods Examination of Leadership Drivers of Generation Z Engagement and Retention
The increasing presence of Generation Z in the workforce has intensified scholarly and practitioner interest in identifying leadership approaches that effectively engage and retain this cohort. While existing research has largely focused on identifying generational preferences, limited empirical work has examined how specific leadership behaviors influence measurable organizational outcomes. This study addresses this gap by examining the relative and combined effects of fairness, structured leadership practices, and empathetic leadership behaviors on employee engagement, satisfaction, and retention intent among Generation Z workers.A mixed-methods design was employed, combining survey data from 250 Generation Z employees across multiple industries with qualitative insights derived from leadership observations and prior interview-based research. The study evaluates both the independent and interactive effects of perceived fairness in organizational decision-making, clarity and structure in leadership practices, and empathetic engagement by supervisors.Findings indicate that perceived fairness is the strongest predictor of retention intent, while structured leadership practices are most strongly associated with employee engagement. Empathy contributes significantly to workplace satisfaction but demonstrates limited effectiveness when not supported by transparent and structured leadership processes. Importantly, the results suggest that the integration of fairness, structure, and empathy produces more robust outcomes than any individual leadership dimension alone. The study contributes to leadership theory by extending generational research beyond preference-based analysis and proposing an integrated framework for leadership effectiveness. Practical implications suggest that organizations seeking to engage and retain Generation Z employees must adopt leadership approaches that combine relational awareness with structured and equitable organizational practices.
Generation Z, leadership, employee engagement, retention, workplace environment, fairness, structured leadership, empathy, organizational behavior, EELM
Building Economic Empowerment through Cooperative Trust: A Model Based Analysis in Siquijor
Cooperatives play a critical role in promoting economic empowerment among micro, small, and medium enterprises (MSMEs) in underserved and geographically isolated areas by improving access to finance, enhancing financial literacy, and supporting income generation through trust-based and contractual mechanisms. Anchored on Agency Theory and Social Capital Theory, this study examines how cooperative surety bonds can reduce lending risks, address structural and financial constraints, and strengthen the credibility and growth potential of MSMEs in island economies such as Siquijor. The study employed descriptive and inferential statistical methods, including weighted mean and Pearson Product–Moment Correlation, with significance testing using p-values. Results were further presented through a correlation-based path diagram to analyze relationships among variables without implying causality. Data were collected from 187 respondents across registered cooperatives in Siquijor through voluntary participation, ensuring informed consent, anonymity, confidentiality, and the right to withdraw at any stage. Findings reveal that cooperatives significantly contribute to economic empowerment across multiple dimensions, with access to finance emerging as the most prominent factor, alongside positive outcomes in income generation, financial literacy, ownership, market participation, control over resources, and risk mitigation. Furthermore, cooperative trust influences these outcomes through both risk reduction and collective trust pathways, with incentive-based mechanisms demonstrating stronger relationships compared to governance and risk-related factors. The study concludes that economic empowerment is more effectively driven by aligned incentives and shared economic benefits. It recommends that cooperative federations institutionalize incentive alignment systems, expand financial inclusion programs, standardize capacity-building initiatives, strengthen governance through monitoring and incentives, and develop collective risk management mechanisms to support sustainable and inclusive development.
Economic Empowerment, Cooperative, Trust, Social Development, Collective Risk
Artificial Intelligence Applications in Supply Chain Management: A Systematic Review of Empirical Evidence and Future Research Directions
The rapid advancement of Artificial Intelligence (AI) has significantly influenced various business domains, including Supply Chain Management (SCM). While AI is widely expected to transform supply chain processes and operational paradigms, previous waves of technological enthusiasm have not always fulfilled their anticipated potential. This study aims to provide a systematic and evidence-based assessment of AI applications in SCM by conducting a Systematic Literature Review (SLR) of empirical research published over the past decade. Using a structured review protocol, this study synthesizes empirical findings to identify dominant technological approaches, key application areas, and critical factors influencing AI adoption in supply chains. The analysis categorizes the existing literature into four major research themes: (1) data and system requirements, (2) technology implementation and deployment processes, (3) inter- and intra-organizational integration, and (4) performance outcomes and implications. The findings reveal that while AI demonstrates substantial potential to enhance efficiency, decision-making, and supply chain responsiveness, its successful implementation is highly contingent on data quality, organizational readiness, and integration capabilities. Additionally, contextual factors—such as industry characteristics, technological infrastructure, and organizational capabilities—play a crucial role in shaping AI effectiveness. By focusing exclusively on empirical studies, this review reduces the influence of conceptual bias and technological hype, offering a more grounded understanding of AI’s actual contributions to SCM. The study provides a comprehensive foundation for future research and offers practical insights for managers seeking to implement AI-driven solutions in supply chain operations.
Artificial Intelligence-Driven Supply Chain Capabilities: Enhancing Agility, Resilience, and Competitive Advantage in MSMEs
Micro, small, and medium enterprises (MSMEs) face increasing disruptions arising from global health crises, geopolitical instability, and climate-related challenges, highlighting the urgent need for agile and resilient supply chain (SC) systems. Due to resource constraints and limited technological readiness, MSMEs are particularly vulnerable to such uncertainties. Drawing on the Resource-Based View (RBV), this study examines how artificial intelligence (AI) capabilities enhance competitive advantage (CA) by strengthening supply chain agility (SCA) and supply chain resilience (SCR), while considering the moderating role of competitive pressure (CP). A conceptual model is developed and empirically tested using data collected from 350 MSME owners and managers, analysed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI capabilities significantly improve both supply chain agility and supply chain resilience, and directly enhance competitive advantage. Furthermore, both agility and resilience contribute positively to competitive advantage, while competitive pressure strengthens the relationship between AI capability and competitive advantage. This study contributes to the literature by extending AI-enabled supply chain frameworks to the MSME context and highlighting the strategic role of AI as a driver of supply chain capabilities and firm competitiveness. It also offers practical insights for MSMEs to leverage AI technologies in enhancing agility, resilience, and sustainable competitive advantage in dynamic and highly competitive environments.
Financial Technology Adoption and Change Management of Rural Banks in compliance with BSP NSFI 2022-2028: Basis for Business Transformation Roadmap
This study investigated the relationship and impact between the perceived level of financial technology adoption and the extent of change management process among Philippine Rural Banks in compliance and strategic context of the Central Bank of the Philippines’ (BSP) National Strategy for Financial Inclusion (NSFI) 2022–2028 under the core pillar one of inclusive digital financial service. Utilizing a Quantitative-Correlational Research Design with Impact Analysis, data were collected from thirty-nine (39) purposively selected decision makers of BSP-regulated and RBAP-member rural banks in Laguna, Philippines. The findings revealed that the overall perceived level of FinTech adoption among the rural banks was "Adopted," with a composite mean of 3.03. Consumer Protection and Data Security ranked highest (3.27, Highly Adopted), while Digital Infrastructure was identified as the most significant technical hurdle (2.61, Adopted). The extent of the change management process was assessed as "Implemented" with an overall mean of 2.92. The banks demonstrated proficiency in Implementation and Training Execution (2.97) but faced their greatest challenge in the Institutionalization phase (2.87). Also, there is a significant positive relationship between FinTech adoption and the change management process, yielding a Spearman rho coefficient of 0.804 (p < 0.001) and rejecting the first null hypothesis. While the impact analysis for change management yielded a p-value of 0.346 leading to a failure to reject the second null hypothesis at the aggregate level, specific dimensions had profound impacts. Most notably, Alignment with BSP Digitalization Policies emerged as a highly significant impact of organizational Readiness and Urgency (p=0.001) and Communication and Buy-in (p=0.002). Strategic alignment with national regulatory directives acts as a more powerful catalyst for organizational readiness and communication than technical infrastructure alone. Based on the study, a Business Transformation Roadmap is proposed to help rural banking leaders bridge the gap between compliance-driven implementation and the permanent institutionalization of inclusive digital financial services.
Business Performance Optimization through Literacy Finance and Technology Finance with Role Government as Moderation in Small and Medium Enterprises Regency South Nias
Study this aim for test and analyze influence positive and significant literacy finance to performance SME business, influence positive and significant technology finance to performance SME business, role government moderate influence positive and significant literacy finance with performance SME business and its role government moderate influence positive and significant technology finance with performance SME business. Population in study as many as 523 perpetrators SME business while sample as many as 242 perpetrators SME business. Method deep data analysis study This use method Structural Equation Model (SEM) with approach Partial Least Square (PLS). Research results show that literacy finance influential positive strong and significant in increase performance business SMEs, technology finance influential negative and no significant to performance SME business, role government moderate negative and significant between influence literacy finance to performance SME business and its role government moderate negative and no significant between influence technology finance with performance SME business. It is hoped that to SME actors to increase ability literacy finance, utilizing service formal finance and integrating technology finance in activity business. Share government area expanding inclusion programs finance, providing means literacy finance and compiling integrated policies​ between inclusion finance, literacy finance, and utilization technology finance. Share institution banking increase innovation product finance, strengthening role consultative and collaborative with technology finance.
Performance business; literacy finance; technology finance; role government.
Assessing the Fusion of ChatGPT into Employee Retention: Effectiveness and Limitations in the Omani Public Sector
This study aimed to investigate the potential effectiveness of fusing ChatGPT as a recommendation instrument for employee retention purposes in the Omani public sector. It also identified potential limitations associated with its implementation. The study employed a descriptive approach and utilized a survey instrument to gather data from 376 employees and administrators working in the Omani public sector for participation in the current study. The researcher designed a questionnaire to investigate the potential effectiveness and limitations of using ChatGPT as a recommendation tool for employee retention purposes in the Omani public sector. The questionnaire, in its final format, consisted of three main axes; the first axis was potential effectiveness, the second one was possible limitations of using ChatGPT, while the third shed light on affective factors. The results revealed that there were varied potential effectiveness of using ChatGPT as a recommendation tool for employee retention purposes in the Omani public sector. All potential effectiveness dimensions obtained moderate degrees. ChatGPT can develop personalized career paths, identify turnover risk factors, design effective reward mechanisms, and provide accessibility & availability. Possible limitations included a lack of contextual understanding, privacy and security concerns, and a lack of emotional intelligence. All these limitations obtained a high response degree that could be attributed to the possibility of using ChatGPT to generate harmful content. Affective factors, when analyzed, did not result in a statistically significant influence on participant responses. From a practical standpoint, the research suggests the development of comprehensive educational resources to augment user awareness of ChatGPT’s functionalities and stress the need for the aggregation of training data from diverse origins, including authentic human interactions across diverse cultures and communities.
ChatGPT; Public Sector; Employee Retention; potential effectiveness; possible limitations; affective factors
Optimization of Real-Time Carbon Emission Monitoring on the Power Demand Side Based on System Dynamics
Against the backdrop of China’s “dual carbon” strategic goals, the precise monitoring of carbon emissions on the grid’s demand side is crucial for advancing the transformation of the energy structure and achieving low-carbon development. Current mainstream hourly-level monitoring methods have insufficient temporal resolution, coarse information granularity, and weak localization data support. These issues compromise the effectiveness of carbon reduction efforts and may even lead to secondary risks and economic losses. As a major province with high energy-consuming industries, Henan Province experiences frequent short-term high-energy consumption events in its heavy industry sector, where carbon emission characteristics are complex, making traditional monitoring methods inadequate. To address these challenges, this study focuses on thedemand side of the power grid in Henan’s heavy industry. It specifically analyzes the carbon emission characteristics of high-energy-consuming equipment and industrial electricity usage patterns during short-term high-energy events. Innovatively, a system dynamics model integrating STL, LightGBM, and a stream computing subsystem is constructed. This model not only achieves a technical upgrade from hourly to minute-level carbon emission monitoring but also fully considers the internal structures and coupling relationships among subsystems. Empirical research demonstrates that the model possesses the capability for minute-level data collection and dynamic updates of carbon emission factors, significantly improving the temporal resolution and response efficiency of the monitoring system. It enables high-frequency dynamic tracking of instantaneous load during industrial electricity consumption. These results provide robust data support for grid dispatch optimization and carbon market trading. The model enhances the accuracy of carbon emission predictions and offers reliable foundations and new optimization directions for exploring technological pathways toward energy conservation and emission reduction. Its application holds significant theoretical value and practical importance for promoting the modernization of regional energy governance, optimizing industrial structure, and formulating scientifically sound policies.
Demand Side; Carbon emission monitoring; System dynamics; Minute-level monitoring
Risk-Based Governance for Autonomous Decision Systems
Artificial intelligence-based autonomous decision systems are being implemented in the fields of finance, healthcare, transportation, and governance of the people. They are used to improve the accuracy, speed, and scalability of decisions through processing high volumes of data and autonomously generating responses without requiring human intervention. Nevertheless, the adoption of autonomous decision systems is rapid, and it presents new governance issues in transparency, accountability, algorithms, as well as operational risks. The old models of governance still depend on manual supervision and fixed policy enforcement that cannot be used to deal with the dynamic AI-driven environments. The following paper proposes a risk-based governing framework that is aimed at providing responsible and trustworthy functioning of autonomous decision systems. The suggested framework incorporates risk identification, risk assessment, governance policy enforcement, and on-going monitoring mechanisms to handle operational and ethical risks. The framework enables organizations to implement the right governance controls by categorizing autonomous systems according to the level of risk and still providing flexibility in operations. Moreover, the architecture has elements of explainability and compliance monitoring to reinforce transparency and accountability. The research emphasizes the role of risk-based governance in allowing organizations to reduce the risks associated with decisions, enhance the adherence to regulations, and increase trust in AI-based decision systems. The suggested framework offers an organized method of governance that facilitates innovation and sustainable use of autonomous decision technologies.
Leading Autonomous AI: Review of Governance Frameworks, and the Scholar-Practitioner Gap in Financial Services for C-Suite Executives
The emergence of Agentic Artificial Intelligence (Agentic AI)—autonomous systems capable of independent reasoning, planning, and executing actions across enterprise environments—presents a defining strategic paradox for Managing Directors and C-Suite executives in financial services. These leaders face an unprecedented challenge: aggressively deploying autonomous AI systems to drive operational efficiency and competitive advantage while maintaining unyielding regulatory compliance, operational stability, and risk management. This paper examines this paradox through a dual lens of practitioner job descriptions and scholarly literature. Practitioner sources reveal that executives are expected to simultaneously architect cloud-native AI platforms, operationalize Model Risk Management (MRM) frameworks for autonomous agents, define portfolio prioritization rubrics for agentic systems, establish human-in-the-loop and human-on-the-loop control mechanisms, and deliver quantifiable ROI—often without established playbooks or precedents. These roles demand deep fluency across a complex technical stack, including multi-agent orchestration frameworks, tool-use architectures, MLOps for agent monitoring, and governance systems for autonomous decision-making. Scholarly literature offers foundational insights through transdisciplinary research models, temporal perspectives on the academic-practitioner gap, and critical pragmatism as a bridging philosophy. However, a critical gap exists at the intersection of agentic AI implementation and executive strategic leadership: there is no empirical understanding of how senior leaders actually navigate the organizational, regulatory, and technical complexities of scaling autonomous AI systems in highly regulated environments. Adopting a Scholar-Practitioner approach, this proposed research will investigate how Managing Directors, CIOs, CTOs, and CDOs in banking and insurance navigate these challenges. The study will employ a qualitative multiple-case study design, integrating adaptive leadership theory with AI governance constructs to explore how executives balance innovation imperatives with control requirements in the age of autonomous AI. Findings will contribute actionable frameworks for executive decision-making, organizational design, and risk governance, bridging the gap between academic theory and practitioner need.
Agentic Artificial Intelligence, Executive Leadership, AI Governance, Financial Services, Scholar‑Practitioner Research, Autonomous Systems, Model Risk Management, Adaptive Leadership, Responsible AI, Regulatory Compliance
Impact of Implicit Disagreement in MPC meetings on long run Government Bond Yields: A Panel Analysis
This study focuses on the impact of implicit disagreement among MPC members of UK, Australia, Japan and South Korea on long-term government bond yields. The implicit disagreement measures are constructed in the context of inflation using NLP techniques specifically trained on FOMC meetings’ minutes. We see that disagreement(inflation) has a positive and significant impact on long-term government bond yields. Unlike postulates in the existing literature, the findings of this study establish the importance of disagreement in guiding movements in bond yields and thereby advocate incorporating it into important policy discussions that influence the economic health of nations.
Safeguarding Federal Payment Infrastructure: Trustworthy AI for Improper-Payment Prevention, Synthetic Identity Detection, and Public-Benefit Disbursement Integrity in the United States
Federal payment integrity has become a high-stakes governance problem in the United States because public-benefit disbursement systems must now operate at digital scale while defending against increasingly adaptive fraud, identity manipulation, and data-quality failures. Recent federal estimates show that improper payments remain material across major programmes, while agencies and oversight bodies have increasingly explored data matching, risk analytics, and automated screening to strengthen prevention and recovery. Yet the policy challenge is not simply whether artificial intelligence can detect more anomalies. In public disbursement settings, analytical performance must be reconciled with due process, transparency, data governance, and the need to avoid delaying legitimate payments to eligible recipients. This paper develops a literature-based conceptual framework for trustworthy AI in federal payment infrastructure, with particular attention to improper-payment prevention, synthetic identity detection, and public-benefit disbursement integrity. Using an integrative review of peer-reviewed scholarship and authoritative U.S. government sources published, the paper synthesises four literatures that are often treated separately: payment integrity, digital identity assurance, AI-enabled fraud analytics, and public-sector AI governance. The paper proposes a multilayer framework that combines identity-proofing controls, multimodal risk scoring, graph-based network analysis, human-centred adjudication, and continuous governance monitoring. Its central argument is that effective federal payment integrity requires not only predictive accuracy but institutional trustworthiness: auditable models, calibrated thresholds, fairness safeguards, contestability, and shared cross-programme infrastructure. The paper contributes a structured analytical model, implementation logic, policy implications, and an evaluation template for future empirical testing.
Trustworthy AI; federal payments; improper payments; synthetic identity fraud; payment integrity; public-benefit disbursement; digital identity; fraud analytics; public-sector AI; FinTech
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Quantitative and Data-Driven Evaluation of Blockchain-Based Financial Systems: Transaction Efficiency, Transparency, Cost Optimization, and Performance Metrics in Global Markets
Blockchain-based financial systems are increasingly evaluated not only as speculative infrastructures but as operational payment, settlement, and record-keeping networks that can be benchmarked against incumbent financial rails. This paper develops a quantitative, data-driven framework for assessing blockchain-based financial systems across four dimensions that matter in global markets: transaction efficiency, transparency, cost optimization, and overall performance resilience. Drawing on evidence from public blockchain networks, payment and remittance statistics, policy experiments, and institutional distributed-ledger pilots, the study synthesizes academic literature with world data from the World Bank, the Federal Reserve Bank of New York, the Bank for International Settlements, Visa, Bitcoin, Ethereum, and Solana documentation and analytics. The paper proposes a metrics architecture that combines latency, throughput, fee burden, settlement certainty, auditability, availability, governance quality, and interoperability into a unified comparative scorecard. It then applies the framework to three categories of blockchain-based finance: public permissionless chains, permissioned institutional distributed ledgers, and hybrid tokenized payment systems. The evidence suggests that blockchain systems create measurable gains in traceability, programmability, and atomic settlement, especially in cross-border and multi-party workflows where reconciliation frictions are costly. However, these gains are uneven. Public chains often face volatility in fees, congestion risk, and governance externalities, while permissioned systems improve control and compliance at the expense of openness and composability. The paper argues that the relevant policy and managerial question is therefore not whether blockchain is universally superior, but under which transaction environments it dominates legacy systems on speed, transparency, cost, and operational risk. The study concludes with a research agenda for standardized blockchain performance metrics, institution-grade benchmarking, and explainable analytics for digital financial infrastructure.
Blockchain finance, distributed ledger technology, cross-border payments, transaction efficiency, transparency, cost optimization, tokenization, digital financial infrastructure
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The Algorithmic Auditor: Seven Strategic Applications of Artificial Intelligence in Financial Reporting and Analysis
The digital transformation of the finance function has accelerated as Artificial Intelligence (AI) matures from a theoretical construct into a core operational tool. This paper presents a conceptual framework examining seven pivotal applications of AI that are redefining financial reporting and analysis. By transitioning from retrospective manual processes to real-time, predictive modeling, firms may achieve meaningful improvements in accuracy and strategic foresight. The study examines the technical underpinnings of these tools—including machine learning, natural language processing, and robotic process automation—while also addressing the ethical and implementation challenges that organizations must navigate to realize the potential of an AI-augmented finance function. Future empirical research is needed to validate the performance claims associated with each application area.
Artificial Intelligence, Financial Reporting, Predictive Analytics, Financial Accounting, Machine Learning, Corporate Governance
Capability Bundles, Policy Frictions, and Sales–Liquidity Divergence in Crisis: Evidence from the World Bank Business Pulse Surveys, 2020–21
This study develops and tests a “continuity capability stack” that distinguishes sales recovery from financial continuity (liquidity survival). We examine which capability bundles—digital adaptation and operational reconfiguration—are associated with better outcomes, and whether public support complements these capabilities or is blunted by access frictions. We analyzed repeated cross-sectional indicator panels from the World Bank Business Pulse Surveys (2020–2021) across 45 economies, using economy–time–stratum observations by firm size and sector. Outcome coverage was 34 economies for arrears and 43 for sales. We constructed two composite indices: an Adaptation Capability Index (ACI), capturing digital platforms/online sales, remote work, operational protocols, alternative payments, and reconfiguration measures; and a Continuity Stress Index (CSI), summarizing arrears risk and sales/labor distress. Two-way fixed-effects models with lagged exposures related ACI, support access, and their interaction to arrears risk and monthly sales change. We derived resilience archetypes using unsupervised clustering on standardized capability, stress, and outcome profiles. Capability bundles were more predictive of sales recovery than of liquidity survival. Lagged ACI was positively associated with sales change (about +3.5 percentage points; 95% CI +0.8 to +6.3), whereas its association with arrears risk was not statistically distinguishable from zero. Public support access and the ACI × support interaction were not robustly associated with either endpoint in core specifications, consistent with a friction mechanism in which delivery and access constraints weaken translation of support into continuity gains. Micro and small firms experienced larger sales contractions and higher arrears risk than large firms, with sectoral heterogeneity. Clustering distinguished digitally adaptive profiles from liquidity-constrained and high-stress profiles, showing that adaptation can coexist with financial vulnerability. Digital and operational capabilities are central levers for sustaining continuity in sales but are insufficient to protect against liquidity distress. Policy design should prioritize delivery architecture, administrative simplicity, eligibility design, and disbursement speed rather than nominal support availability alone. The study introduces a continuity capability stack and a replicable capability–stress typology that separate the drivers of sales recovery from determinants of liquidity vulnerability, offering actionable insight for crisis preparedness and response design.
Business continuity; resilience; crisis management; digital transformation; liquidity stress; public support; access frictions; fixed effects; World Bank Business Pulse Surveys