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
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
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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
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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
Climate Risk, Financial Stability, and Global Capital Allocation: A Predictive Analytics Approach to Assessing Climate-Related Financial Risk in International Investment Markets
Climate change has moved from a peripheral environmental concern to a core driver of cross-border valuation, balance-sheet resilience, and capital allocation in international financial markets. This study develops a predictive analytics framework for assessing climate-related financial risk across international investment markets by integrating physical-risk exposure, transition-risk sensitivity, macro-financial fragility, and sustainable-finance depth in a panel design calibrated with public data. Rather than treating climate risk as a narrow ESG screen, the paper conceptualizes it as a multidimensional source of repricing pressure that can alter sovereign spreads, equity valuations, portfolio flows, funding costs, and the geographic distribution of global capital. It then proposes a transparent empirical architecture that combines gradient-boosted trees, regularized panel models, and scenario-conditioned classification to estimate the probability of climate-related stress episodes and capital reallocation across countries and sectors. Descriptive evidence from official sources shows three reinforcing trends: rapid growth in sustainable investing assets, increased climate-linked debt issuance, and persistent differences in climate resilience and adaptation readiness across jurisdictions. The discussion argues that predictive analytics can improve risk measurement only when embedded in disclosure, interoperable taxonomies, and financial-stability oversight. The paper contributes by linking climate risk pricing to international capital allocation rather than to firm-level ESG outcomes alone, and by outlining a scalable framework for investors, regulators, and multilateral institutions seeking to monitor systemic climate-financial vulnerabilities before they crystallize into disorderly market adjustments.
Climate risk, financial stability, global capital allocation, predictive analytics, sustainable finance, international investment markets, climate-related financial risk
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Enhancing U.S. Financial Compliance and Risk Management through Data-Driven Automation and Anomaly Detection
The growing complexity of the U.S. financial ecosystem has heightened the demands of smart and data-driven compliance tools. These tools must detect emerging risks and identify fraud in various regulatory environments. The study proposes an integrated framework that automates anomaly detection and cross-silo data association. The goal is to improve financial compliance and risk management capacity. The analysis draws on several reproducible and publicly available datasets, such as the U.S. Small Business Administration (SBA), Paycheck Protection Program (PPP), loan information (2020 - 2021) and the Office of Foreign Assets Control (OFAC) Specially Designated Nationals (SDN) list (2021), the U.S. Securities and Exchange Commission (SEC) EDGAR Company Filings data (2021), the Home Credit Loan Default data (2021), and the ULB Credit Card. The suggested framework uses a combination of supervised and unsupervised learning models to assess behavioral deviations, financial irregularities, and entities that are related to sanctions. The study also provides integrity, privacy protection, and reproducibility of data through cryptographic hashing and tokenization. The Cross-silo linkage results indicate that regulatory, transactional and loan datasets improves the accuracy of fraud detection and false- positive alerts. This fusion exhibits hidden relationships between public funds, corporate filings and violation of sanctions that are often overlooked when datasets are analyzed separately. This study presents practical implications to enhance the transparency of audits and financial integrity in the country by mapping findings against the Anti-Money Laundering Act of 2020 (AMLA-2020) and the national financial priorities of FinCEN in 2021.
Financial Compliance Automation, Anomaly Detection, Cross-Silo Data Integration, Regulatory Risk Management, Fraud Detection and Prevention, FinCEN Compliance and U.S. AMLA-2020.
Privacy and Cybersecurity Convergence: GRC Controls for Data Protection
The accelerated pace of digitization of enterprises and their operations has significantly increased the amount of personal and organizational data that is being stored, processed, and transmitted across interconnected systems. This change has not only increased cybersecurity and privacy threats, but data protection has become an important issue for modern-day organizations. Data protection and cybersecurity have traditionally been treated as two separate entities, with cybersecurity focusing on protecting computer systems, networks, and infrastructure from unauthorized access and cyber threats, while privacy protection focuses on the ethical and lawful handling of personal data, adhering to data protection regulations such as GDPR, HIPAA, and other data protection laws and regulations. However, this disjointed approach to data protection and cybersecurity has not only led to ineffective control, reduced visibility of data protection threats, and an increased risk of data breaches and non-adherence to data protection regulations. The Governance, Risk, and Compliance (GRC) platforms are a well-structured approach to integrating the policies of governance, risk management, and compliance monitoring into a single platform. This study aims to develop a conceptual framework for the integration of privacy and cybersecurity controls within a GRC platform to improve the overall capabilities of an enterprise in protecting its data. The study aims to show how the mapping of unified controls, risk management, and governance can help an enterprise become more resilient against cyber threats while still complying with regulations related to privacy. The findings of the study are important in advancing the overall capabilities of an enterprise in protecting its data while providing guidance to organizations seeking to enhance their overall cybersecurity and privacy through the use of GRC platforms.
Using Predictive Analytics to Enhance Productivity and Innovation in the Advanced U.S. Manufacturing Sectors
The intersection of predictive analytics, Industrial Internet of Things (IIoT), and cloud-based systems is also changing sophisticated manufacturing in the United States. With increased global competition and the emergence of operational resilience as a strategic goal, manufacturers are turning toward data-driven systems, increasing productivity and driving innovation. This paper investigates the operational performance and innovation of advanced sectors of the U.S. manufacturing industry with the help of predictive analytics by using the Smart Manufacturing IoT-Cloud Monitoring Dataset which includes 100,000 real-time sensor observations of 50 industrial machines. The theoretical framework of Industry 4.0 and resource-based perceptions of technological capability make use of the notion of machine learning models that forecast the maintenance demand and approximate the remaining useful life dependent on sensor variables and indicators of anomalies such as temperature, vibration, humidity, pressure, energy consumption, and the risk of downtime. The algorithms of supervised learning, which are Random Forest and Gradient Boosting, are applied to assess the predictive accuracy based on ROC-AUC, F1-score, and RMSE values. The results of empirical studies indicate that predictive analytics can substantially increase the effectiveness of failure detection and minimize unexpected downtime, which improves the effectiveness of devices in general, in addition to enhancing operational reliability. The findings also reveal that predictive maintenance systems are not only efficiency-enhancing systems, but also innovative enablers, as predictive maintenance systems enable real-time decision-making, optimizing processes and resources in an adaptive manner. Using the ideas of turning reactive maintenance systems into proactive, information-driven approaches, the advanced manufacturers will be in a better position to enhance production continuity, cut expenses on the operation, and become more technological. The research is relevant to academic literature because it forms a quantifiable connection between AI-based predictive maintenance and productivity increase and innovation abilities in the advanced manufacturing ecosystems. In terms of policy and management, the results can be used practically to guide the U.S. manufacturers who want to use predictive analytics to improve industrial competitiveness and maintain their long-term growth in the dynamic digital economy.
Predictive Analytics, Advanced U.S. Manufacturing, Industrial Internet of Things (IIoT), Predictive Maintenance, Machine Learning in Manufacturing and Innovation and Productivity
Sustainability claims on social media in relation to brand trust in teenagers
Concerns have arisen about the environmental impact of the fashion industry, which is facing exponential growth. In recent times, as a result, brands are increasingly prompted to utilise social media platforms for sustainable messaging, and findings show that Instagram and TikTok are the primary platforms for exposure. However, it has been noticed that a major portion of respondents encounter environmental messaging only occasionally despite the abundance of social media accessibility worldwide. Therefore, this study examines how this communication influences brand trust and consumer behaviour amongst the adolescent target demographic aged 13-19 in Singapore. Additionally, throughout the research, there is a deeper focus on authenticity and greenwashing awareness, which have been deemed as important factors to consider when discussing how teenagers and the Gen Z audience place their trust in brands today. An online survey was conducted via the process of convenience sampling, and a sample size of 50 adolescents aged 13-19 was requested on a voluntary basis to fill out the survey. The specific structure of the questions asked enabled the collection of both qualitative and quantitative data. Additionally, as a result of the survey results, it was overall noticed that authenticity forms a robust link with transparency, consistency, and third-party validation in place of influencers and celebrity endorsements, which have become increasingly prevalent in recent years, and therefore, this suggests that for brands to engage a teenage consumer base, they must prioritise credibility, consistent communication and integrate environmental mindedness alongside competitive product appeal to bridge the gap between awareness, intention and action.
Cognitive dissonance, Greenwashing, Sustainability, social media, Brand perception, Adolescents, Consumer behaviour
Impact Evaluation in TD&E: Limits of ROI Against Investment Alternatives
This theoretical essay examines impact evaluation in Training, Development and Education (TD&E) as support for organisational decisions. Based on established evaluation models and Mincer’s human capital approach, it proposes indicators such as time to proficiency, application rate and productivity variation, which allow results to be measured over time and compared across programmes. Return on investment (ROI) is treated as a consequence of measurement quality rather than as a design in itself. The accounting treatment of training under CPC 04 (R1)/IAS 38 is examined, highlighting the asymmetry between immediate recognition as expense and benefits that extend beyond the reporting period. As a conditional proposition, selected TD&E outlays may be recognised as an intangible asset only when identifiability, control, probable future economic benefits and reliable measurement are all met; otherwise expenditure should be recognised in profit or loss. The accounting angle remains ancillary to the decision problem in work and organisational psychology, since accounting reflects evidence generated by evaluation. It concludes that the dissemination of evaluation methodologies strengthens competitiveness and legitimises investment in TD&E.
In-service Training; Program Evaluation; Return on Investment; Human Capital; Professional Development
Determinants of Online Shopping Behavior: An Analysis of Perceived Benefits, Reputation, and Consumer Experience
Online shopping in the Philippines is growing significantly because of technological advancements and increased internet usage. The purpose of this study is to determine what factors influence online shopping behavior among Filipinos by examining the areas of perceived benefits, store reputation, customer experience, and lifestyle. A multi-method approach was used, where surveys were distributed to 338 online shoppers in Santiago City, allowing for the subsequent analysis of the data collected using descriptive and correlational statistical analyses. While consumers generally have positive ratings of both their perceived experience (M = 3.02) and store reputation (M = 3.00), overall ratings for perceived benefits are significantly lower than either of these two variables (M = 2.85). The study also found strong positive correlations between store reputation and customer experience (r = 0.681, p < .001) and between lifestyle and perceived benefits (r = 0.608, p < .001). In addition, a discussion highlighted a "perception gap" resulting from consumers' risk-averse nature when assessing the security of online shopping websites and the quality of products purchased online, which has prevented these companies from reaching the number of online sales that was anticipated. To reduce this gap, it is recommended that retailers provide additional transparency by ensuring product quality, using secure methods for payment processing, and utilizing targeted marketing techniques.
online shopping behavior, perceived benefit, store reputation, consumer experience, lifestyle factor
AI- Driven On-Chain Behavioral Pattern Discovery for Whale Sentiment in US Crypto Markets
This study investigates the use of artificial intelligence to uncover behavioral patterns of whale participants in US cryptocurrency markets and to infer their impact on market sentiment. By leveraging on-chain transaction data and exchange activity, the research constructs behavioral features capturing transaction frequency, transfer volume, and wallet clustering. Machine learning models, including tree-based learners, recurrent neural networks, attention-enhanced architectures, and ensemble frameworks, are employed to identify distinct whale archetypes and to translate their activity into predictive sentiment signals. The findings reveal differentiated behavioral strategies among whales, with high-frequency accumulators, occasional large movers, and directional distributors exerting unique influences on market dynamics. Furthermore, attention mechanisms and feature importance analysis enhance the interpretability of model predictions, enabling insights into the timing and nature of whale-driven market movements. Overall, the study demonstrates the potential of AI-driven on-chain analytics to provide actionable intelligence for traders, exchanges, and regulators, bridging the gap between raw blockchain data and meaningful behavioral insights.
Copyright (c) 2025 Journal of Business and Management Studies https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Consumer Behavior Insights and Human-Centered Marketing Strategies for Enhancing Engagement and Innovation
Artificial intelligence (AI) in marketing has in the past few years been spreading very rapidly across the world. The world AI in marketing market grew by about US 17.48 billion in 2022, to US 22.64 billion in 2023, implying that AI in marketing market has a compound annual growth rate (CAGR) of about 29.5%. This academic article explains why the insights of the AI-driven consumer behavior collected through the support of big data analytics, machine learning, recommendation algorithms, and sentiment analysis could be used along with the human-centered-based marketing strategy to improve the customer engagement, innovation, and brand loyalty in the long-term. Empirical research indicates that AI-based personalization and data-based targeting could significantly drive up the conversion rates, utilize the marketing funds more efficiently, and enable more personalized and relevant user experiences. According to the recent surveys, companies, which exploit AI to learn more about their clients, report that the processes of product innovation are 45% more rapid, and the production of the campaign returns is increased 33%, as compared to companies, which apply traditional tools of analytics. These outcomes contribute to the idea that AI is not another personalization tool but a cause of business growth and creative thinking. Finally, the study finds some recommendations to be feasible that should be implemented by marketers: invest in good data governance, concentrate on customer privacy and transparency, merge feedback loops, and automate but not neglect human empathy. Those findings indicate that the exploitation of AI as the source of additional knowledge of consumers, and not its destruction, is a viable opportunity available to every business wishing to achieve success in the competitive online markets.
Artificial Intelligence; Consumer Behavior Insights; Human-Centered Marketing; Customer Engagement; Marketing Outcomes; Marketing Innovation
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Resilient Supply Chain Architectures: Machine Learning Frameworks for Risk Anticipation, Disruption Mitigation, and Adaptive Decision-Making
Global supply chains face various disruptions related to demand volatility, geopolitical tensions, weather-related disruptions, and operational failures, making conventional rule-based and reactive resilience approaches inadequate for addressing these disruptions. Recent developments in artificial intelligence (AI) and machine learning (ML) offer promising opportunities for transforming supply chain resilience from conventional reactive approaches to proactive, adaptive, and learning-enabled decision-making systems. However, the prevailing literature primarily focuses on standalone AI-related activities without discussing the architectural aspects of integrating AI for end-to-end supply chain resilience under uncertainty. This study proposes a unified resilient supply chain architecture with the incorporation of machine learning technologies in the processes of anticipation, mitigation, and adaptive decision-making. The research methodology used in the study is the design science research approach, which resulted in the development of a multi-layered framework consisting of the data intelligence layer, the risk anticipation layer, the prescriptive decision and mitigation layer, and the adaptive learning feedback layer. The framework ensures the integration of various machine learning algorithms with supervised learning, unsupervised learning, and reinforcement learning techniques with various categories of supply chain risks. In order to test the theoretical robustness of the proposed architecture, a scenario-based evaluation approach is followed, and various disruption scenarios, which cover failures from the suppliers, disruptions in the logistics network, and demand disruptions, are analyzed. New evaluation criteria, which are classified under the term "resilience-oriented evaluation," are also proposed in the paper, which move beyond the traditional evaluation criteria of costs and services. This paper contributes to the literature in the fields of supply chain and operations management, as an integrative AI architecture for building resilience is proposed, and the conceptualization of adaptive supply chains is advanced, while also providing guidance for managers and policymakers who are interested in leveraging AI as a strategic tool for building resilience in an ever-increasingly volatile environment.
Supply Chain Resilience, Machine Learning, Disruption Mitigation Frameworks, Intelligent Supply Chain Architectures
Decoding Customer Satisfaction and Loyalty in the Era of Smartphone Shopping Applications in India– Moderating Effect of Experience
The widespread adoption of smartphones has revolutionized individual activities, enabling users to engage in diverse tasks at any time and from any location. By recognizing this shift, marketers are increasingly tailoring their strategies through a mobile-first approach to enhance the user experience. This trend is particularly prominent in emerging markets like India, where mobile shopping applications are experiencing rapid growth. In the face of intensified market competition and evolving customer expectations, understanding the determinants of customer satisfaction and loyalty is paramount for marketers in mobile shopping applications. This study delves into the post-adoption behavior of customers, presenting a research model integrating the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model (ECM). Drawing data from 535 current users of mobile shopping applications, the empirical validation of the research framework reveals that perceived trust, perceived usefulness, confirmation, and attitude emerge as pivotal factors influencing satisfaction. Additionally, loyalty is shaped by satisfaction, perceived usefulness, attitude, and perceived trust. The study further elucidates the indirect effects of mobile shoppers' beliefs on satisfaction and loyalty. Implications and avenues for future research are discussed, shedding light on the intricate dynamics of customer interaction in the mobile shopping landscape.
Mobile shopping application, loyalty, satisfaction, price saving orientation, multi-group analysis
Knowledge and Quality Management: How knowledge sharing influences the application of quality tools
Organizations are becoming increasingly competitive and are constantly seeking new ways to raise productivity and cut costs. In this context, quality management, using the seven quality tools, can help better align products and services with customer expectations and support faster, more reliable decision-making. Knowledge Management also offers strategies that add value to organizational processes and serve as a key element in strengthening competitive advantage. Accordingly, the main objective of this study is to examine whether knowledge-sharing practices can promote the dissemination and use of Quality Management Tools in an agribusiness company. A descriptive and exploratory study was carried out using a structured questionnaire. The research can help the organization apply quality management tools through knowledge-sharing practices, enabling smarter and more efficient work. It also contributes to academic literature, given the shortage of studies that address the intersection between Knowledge Management and quality management.
Inside the School Ecosystem: Examining the Relationship Between School Culture and Teachers’ Job Satisfaction Through Stoll and Fink’s Framework
This research investigated how school culture influences teachers’ job satisfaction across 19 secondary schools within the DepEd Schools Division of Siquijor. Anchored in Stoll and Fink’s School Culture Framework and Herzberg’s Two-Factor Theory, the study explored the impact of various school culture types namely Moving, Cruising, Strolling, Struggling, and Sinking on teachers’ levels of job satisfaction. The findings underscore the critical role of school culture in shaping key organizational outcomes within the educational setting. The primary goal of this study was to assess the extent to which different cultural environments affect teachers’ overall job satisfaction. A descriptive correlational design was utilized, drawing data from teachers through a school culture assessment tool and a modified job satisfaction survey. Descriptive and inferential statistics were used to analyze the data. Results show that Moving Culture stood out with a strong positive correlation to teachers’ job satisfaction. In contrast, Cruising Culture, Strolling Culture, and Struggling Culture all showed non-significant negative correlations. Finally, Sinking Culture shows the strongest negative correlation with teachers’ job satisfaction. These findings highlight the importance of school culture in cultivating supportive and motivating work environments for teachers. The study recommends targeted interventions concerning culture enhancement strategies for schools exhibiting less desirable cultural characteristics to elevate teacher morale and institutional performance.
school culture, teachers’ job satisfaction, DepEd
Perceived Academic Self-Efficacy of Department Heads at Majmaah University and Its Relationship to Crisis Management Performance
This study aimed to examine the degree to which department heads at Majmaah University practice perceived academic self-efficacy and to explore its relationship with the development of their performance in crisis management. The study adopted a descriptive survey design and was conducted on a sample of 30 department heads. To achieve the study objectives, the Perceived Academic Self-Efficacy Scale was administered after establishing its psychometric properties in terms of validity and reliability. The results indicated that the level of perceived academic self-efficacy among department heads was high, as was their level of performance in crisis management. The findings also revealed no statistically significant differences in perceived academic self-efficacy attributable to gender, while statistically significant differences were found according to years of experience. Furthermore, the results demonstrated a strong, positive, and statistically significant correlation between perceived academic self-efficacy and crisis management performance. In addition, perceived academic self-efficacy was found to be predictable based on crisis management performance, with an explanatory power of 45%. In light of these findings, the study recommends greater attention to professional development programs aimed at enhancing the perceived academic self-efficacy of department heads, with particular emphasis on integrating crisis management skills into such programs. Moreover, perceived academic self-efficacy may serve as a valuable indicator for predicting the capacity of academic leaders to effectively confront and manage crises.
Perceived academic self-efficacy, crisis management, department heads, academic leadership, higher education, professional development
What Do We Know So Far About the Dark Triad at the Workplace? A Socio-Analytic Adaptationist Perspective
In this research, we reviewed how people with “dark” personalities adapt to their workplace. Although often viewed as socially undesirable for violating norms of cooperation and social exchange, behaviors associated with the “dark side” of personality may reflect a “predator” adaptation, which enables individuals with “dark” personalities to thrive in competitive situations. To date, little is known about how these individuals adapt to their workplace. Nevertheless, we strongly believe that 1) the predatory approach they use in their relationships during their time off is also manifested in their workplace relationships and 2) understanding how these individuals operate is important because it may help practitioners know how to approach them. Considering the existing literature, it seems that individuals with “dark” traits seek influential professional positions, such as leaders, managers, politicians, and entrepreneurs, which they try to occupy through opportunistic and exploitative behaviors often linked with detrimental organizational outcomes, such as low job performance and counterproductive work behaviors. This orientation, especially in the case of those high in psychopathy and Machiavellianism, may reflect their fast life history strategy manifested in getting ahead by getting against, in contrast to getting along. On the other hand, narcissists are initially perceived as good employees and also as good leaders due to their charisma. However, as soon as they spend enough time in the workplace, they are rated as questionable colleagues and managers by their co-workers and followers respectively. Variability in organizational outcomes between the Dark Triad traits, along with key issues and controversies are addressed.
Dark Triad, organizational behavior, job performance, leadership, life history theory