International Journal of Business and Economics Insights
The International Journal of Business and Economics Insights (IJBEI) is a peer-reviewed, open-access academic journal dedicated to publishing high-quality research that advances understanding in the fields of business, economics, and management sciences. The journal provides a scholarly platform for the dissemination of innovative studies and evidence-based insights that shape the future of global commerce, strategic decision-making, and sustainable economic development. By bringing together academics, practitioners, and policymakers, IJBEI fosters critical dialogue across disciplines and geographies, encouraging integrative approaches to solving contemporary challenges in both theory and practice. At its core, IJBEI seeks to bridge the gap between knowledge creation and application, ensuring that published research not only contributes to scholarly advancement but also provides actionable guidance for industries, governments, and institutions. With a growing demand for timely, reliable, and globally relevant insights, the journal emphasizes inclusivity, methodological rigor, and an international perspective that reflects the complexity of today’s interconnected world.
Customer Retention Forecasting in Mobile Wallet Services Using Neural Networks: A Comparative Quantitative Study
This study addresses the problem that mobile wallet providers often lack reliable, data-driven methods to forecast customer retention early enough to prevent churn, particularly when retention drivers are nonlinear and interact (for example trust and perceived risk). The purpose was to quantify the key determinants of mobile wallet retention and to compare classical regression versus neural networks for retention forecasting in a quantitative, cross-sectional, case-based design using a cloud-delivered, enterprise mobile wallet service context. Survey data from N = 420 active users were analyzed as the bounded “enterprise case,” with constructs measured on 5-point Likert scales: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), habit (HT), price value or incentives (PV), trust or security perception (TR), perceived risk (PR), satisfaction (SAT), and customer retention or continuance intention (CR). Descriptively, retention was moderately high (CR M = 3.84, SD = 0.71), alongside strong PE (M = 4.02) and TR (M = 3.93), while perceived risk was lower (PR M = 2.61, SD = 0.83); internal consistency was acceptable to strong across scales (α = .78 to .91; CR α = .88). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, multiple regression for hypothesis testing, and a matched-input neural network for forecasting under an 80/20 split. Headline findings showed that regression explained substantial variance (R² = .62; F(9,410) = 74.52; p < .001), with SAT as the strongest predictor (β = .31, p < .001), followed by PE (β = .21, p < .001) and TR (β = .19, p < .001), while PR negatively predicted retention (β = −.16, p < .001) and SI was not significant (p = .081). The neural model outperformed regression in prediction (RMSE 0.49 vs 0.57; MAE 0.38 vs 0.44; test R² 0.69 vs 0.62). These results imply that retention programs should prioritize satisfaction engineering, visible security assurance, and risk reduction, while using neural forecasts for more accurate risk scoring and targeted interventions in enterprise mobile wallet operations.
Copyright (c) 2021 Md. Abdur Rauf, Iftekhar Ahmed (Author)
Digital Compliance and Cybersecurity Frameworks for Strengthening Documentation Integrity Across Financial Institutions
This study addresses the growing problem that financial institutions increasingly depend on digital documentation systems while still facing documentation-integrity risks caused by fragmented compliance controls, cybersecurity threats, weak audit trails, poor version control, and inconsistent employee handling of sensitive records. The purpose of the study was to examine how digital compliance and cybersecurity frameworks strengthen documentation integrity across financial institutions. A quantitative, cross-sectional, case-based design was adopted using cloud and enterprise documentation environments within selected financial-service cases, with 210 questionnaires distributed, 191 returned, and 186 valid responses analyzed, yielding an 88.6% valid response rate. The study measured five core variables: digital compliance practices, cybersecurity framework implementation, employee compliance and cybersecurity awareness, cybersecurity risk management practices, and documentation integrity. Data were analyzed using descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. The descriptive results showed high institutional ratings for documentation integrity (M = 4.18, SD = 0.57), digital compliance practices (M = 4.12, SD = 0.61), cybersecurity framework implementation (M = 4.08, SD = 0.58), cybersecurity risk management (M = 4.05, SD = 0.63), and employee compliance and awareness (M = 3.96, SD = 0.66). Correlation analysis revealed significant positive relationships with documentation integrity for digital compliance (r = .71, p < .001), cybersecurity framework implementation (r = .68, p < .001), cybersecurity risk management (r = .64, p < .001), and employee awareness (r = .59, p < .001). Regression findings further showed that the model explained 62.4% of the variance in documentation integrity (R² = .624; F = 42.87, p < .001), with digital compliance practices emerging as the strongest predictor (β = .31, p = .002), followed by cybersecurity framework implementation (β = .28, p = .004), cybersecurity risk management (β = .24, p = .006), and employee awareness (β = .19, p = .018). The study concludes that documentation integrity is strongest when compliance and cybersecurity are jointly aligned, implying that financial institutions should integrate governance, technical controls, staff awareness, and risk oversight to improve auditability, authenticity, and resilience of digital records.
Copyright (c) 2022 Mahfuj Ahmed Ruzel, Rajib Sarkar (Author)
Foundational Approaches to Secure Data Collection and Processing in Networked and Distributed Computing Environments
This study addresses the problem of persistent security vulnerabilities across the distributed data lifecycle in networked and distributed computing environments, where data collection, transmission, processing, storage, and access occur across multiple interconnected nodes and thereby increase exposure to unauthorized access, interception, tampering, and trust failures. The purpose of the study was to examine how foundational security approaches influence secure data collection and processing effectiveness and to identify the most influential controls for strengthening distributed cybersecurity performance. Using a quantitative, cross-sectional, case-based design, the study collected data from 220 respondents drawn from cloud and enterprise related distributed environments, including cloud computing infrastructures (40.0%), hybrid distributed systems (23.6%), edge platforms (15.5%), enterprise distributed networks (13.6%), and IoT-integrated systems (7.3%). The principal variables were secure data collection mechanisms, secure data processing controls, network security architecture, and access control and authentication, with secure data collection and processing effectiveness treated as the dependent variable. Data were gathered through a 5-point Likert-scale questionnaire and analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed high overall agreement across all core constructs, with mean scores of 4.08 for secure data collection mechanisms, 4.14 for secure data processing controls, 4.19 for network security architecture, 4.22 for access control and authentication, and 4.17 for secure data collection and processing effectiveness. Reliability was strong, with Cronbach’s alpha values ranging from 0.81 to 0.89. Correlation analysis revealed significant positive relationships with the dependent variable, ranging from r = 0.68 to r = 0.78, all at p < .001. Regression results confirmed that the overall model was significant, F(4,215) = 64.38, p < .001, explaining 54.6% of the variance in security effectiveness (R² = 0.546), with access control and authentication emerging as the strongest predictor (β = 0.31), followed by network security architecture (β = 0.28), secure data processing controls (β = 0.24), and secure data collection mechanisms (β = 0.19). The study concludes that layered security remains essential in distributed environments and implies that organizations should prioritize identity governance, network defense, secure processing controls, and protected collection mechanisms as an integrated security architecture to improve resilience, trust, and operational reliability.
Copyright (c) 2021 Mohammad Robel Miah, Md. Morshedul Islam (Author)
AI Diagnostic Frameworks for Accurate, HIPAA-Compliant U.S. Healthcare Analytics Using Federated Learning and Differential Privacy
This quantitative study examined AI-driven diagnostic modeling frameworks for enhancing diagnostic accuracy and privacy protection within U.S. healthcare analytics systems. A structured survey instrument measured five key constructs: AI diagnostic accuracy enhancement, privacy protection effectiveness, governance and compliance alignment, data quality readiness, and multi-site deployment feasibility. A total of 210 valid responses were analyzed. Descriptive results indicated strong respondent agreement for governance and compliance alignment (M = 4.11, SD = 0.55), privacy protection effectiveness (M = 4.02, SD = 0.58), and AI diagnostic accuracy enhancement (M = 3.94, SD = 0.62). Data quality readiness showed a moderate mean (M = 3.62, SD = 0.71), while multi-site deployment feasibility produced the lowest mean (M = 3.48, SD = 0.74), reflecting perceived challenges in cross-institution portability. Reliability analysis demonstrated strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.82 to 0.91. Multiple regression analysis showed that governance and compliance alignment was the strongest predictor of AI diagnostic accuracy enhancement (β = 0.39, p < .001), followed by data quality readiness (β = 0.31, p < .001) and multi-site deployment feasibility (β = 0.19, p = .003). The accuracy enhancement model explained 56% of variance (R² = 0.56). A second regression model predicting privacy protection effectiveness explained 63% of variance (R² = 0.63) and showed significant effects for governance and compliance alignment (β = 0.34, p < .001), AI diagnostic accuracy enhancement (β = 0.31, p < .001), and data quality readiness (β = 0.18, p = .001), while multi-site deployment feasibility was not significant (β = 0.10, p = .076). Hypothesis testing supported 6 of 7 proposed relationships. Overall, findings indicated that governance alignment and data readiness were central determinants of perceived diagnostic accuracy and privacy protection in U.S. healthcare analytics systems.
Copyright (c) 2026 Aditya Dhanekula, Sai Praveen Kudapa (Author)
Business Intelligence Enhanced Client Portfolio Profitability Analysis for Corporate Insurance Accounts
Corporate insurance portfolio decisions often depend on profitability reports assembled from fragmented policy, claims, and finance systems, which weakens confidence in results and delays corrective action across pricing, renewals, and loss-control activities. This study examined how business intelligence capability strengthens portfolio profitability analysis effectiveness and whether governance and execution mechanisms further enhance that effect within enterprise cloud-enabled reporting environments. The research adopted a quantitative cross-sectional, case-based design using a five-point Likert survey administered across corporate insurance portfolio operations. Purposive sampling yielded 162 usable responses with at least 95 percent completeness, covering underwriting at 34.6 percent, claims at 23.5 percent, finance at 18.5 percent, portfolio management at 14.2 percent, and BI roles at 9.3 percent, while 71.0 percent of respondents reported weekly or daily dashboard use. Key variables included BI capability comprising data quality, integration and accessibility, reporting and dashboarding, and analytics capability, as well as KPI consistency, portfolio segmentation usefulness, insight-to-action execution, and portfolio profitability analysis effectiveness. The analysis plan applied descriptive statistics to summarize construct levels, Cronbach’s alpha to test reliability, Pearson correlations to assess associations, and multiple regression to estimate the unique contribution of predictors to profitability analysis effectiveness. BI capability was moderate to high with a mean of 3.84 and standard deviation of 0.63, analytics capability was highest with a mean of 4.00 and standard deviation of 0.61, and integration and accessibility was lowest with a mean of 3.69 and standard deviation of 0.74, while profitability analysis effectiveness averaged 3.88 with a standard deviation of 0.65. Reliability was strong with BI capability alpha at .91 and effectiveness alpha at .88. BI capability showed a strong positive correlation with effectiveness at r = .62 and p < .001, and similarly strong relationships were observed for KPI consistency at r = .55 and p < .001 and insight-to-action at r = .60 and p < .001. The regression model explained 54 percent of variance in effectiveness with R² = .54 and F(4,157) = 46.21 and p < .001, with significant effects for BI capability at β = .34 and p < .001, KPI consistency at β = .21 and p = .001, segmentation usefulness at β = .14 and p = .026, and insight-to-action at β = .29 and p < .001. These findings imply that firms should pair BI investments with KPI governance and disciplined action routines, prioritize integration and a shared semantic layer, and embed BI outputs into portfolio decision workflows to convert insights into consistent profitability improvements.
Copyright (c) 2021 Md. Mosheur Rahman, Rebeka Sultana (Author)
Automated Financial Reconciliation Systems for Enhancing Efficiency and Transparency in Enterprise Accounting Workflows
Financial reconciliation in large enterprises is often constrained by manual matching, fragmented cloud and ERP integrations, and inconsistent evidence retention, which prolong the close and elevate audit risk. This study examined whether Automated Financial Reconciliation Systems (AFRS) capabilities improve workflow efficiency and workflow transparency in cloud enabled enterprise accounting cases. Using a quantitative, cross sectional, case study-based design, survey data were collected from N = 186 finance and accounting users involved in reconciliation preparation, review, close support, and control oversight. Independent variables were Automation Capability, Integration Quality, Data Quality Readiness, Audit Trail and Control Integration, and User Competency and Training; dependent variables were Workflow Efficiency and Workflow Transparency. The analysis plan applied descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Audit Trail and Control Integration (M = 4.02) and transparency (M = 4.01) were rated highest, and efficiency was also favorable (M = 3.87). All constructs were reliable (α = 0.81 to 0.91). Correlations were positive and significant, with transparency most strongly associated with Audit Trail and Control Integration (r = 0.71, p < .01) and efficiency most strongly associated with Automation Capability (r = 0.62, p < .01). The efficiency model explained 52% of variance (R² = 0.52) with significant effects from Automation Capability (β = 0.36, p < .001), Integration Quality (β = 0.24, p = .002), Data Quality Readiness (β = 0.19, p = .008), and User Competency and Training (β = 0.14, p = .031). The transparency model explained 63% of variance (R² = 0.63) and was driven primarily by Audit Trail and Control Integration (β = 0.46, p < .001), with additional effects from Data Quality Readiness (β = 0.23, p = .001) and Integration Quality (β = 0.17, p = .009). These findings imply that faster closes require strong automation plus integration and data readiness, while audit readiness depends most on embedded control and audit trail features, reinforced through targeted training where agreement was weakest (Top 2 Box = 52%) in practice.
Automated Financial Reconciliation Systems (AFRS)
Copyright (c) 2024 Faysal Khan, Tahmina Akter Bhuya Mita (Author)
Systematic Review and Quantitative Evaluation of Advanced Machine Learning Frameworks for Credit Risk Assessment, Fraud Detection, And Dynamic Pricing in U.S. Financial Systems
This study examined a key problem in U.S. financial services: advanced machine learning is used for credit risk assessment, fraud detection, and dynamic pricing, but decision value weakens when outputs are not trusted or audit ready across enterprise and cloud deployments. The purpose was to quantify how ML Framework Capability (MLC), the Model Confidence and Trustworthiness Index (MCTI), and Regulatory Readiness and Auditability (RRA) relate to domain effectiveness and overall decision-making effectiveness. Using a quantitative cross-sectional, case-based design, 168 valid responses were collected from cloud and enterprise financial services cases (banks 40.5%, fintech or platform lenders 26.2%, credit unions 14.3%, insurance or Insurtech 19.0%). Key variables were MLC, Credit Risk Effectiveness (CRE), Fraud Detection Effectiveness (FDE), Dynamic Pricing Effectiveness (DPE), Decision-Making Effectiveness (DME), MCTI, and RRA, all measured on 5-point Likert scales with strong reliability (Cronbach’s alpha .85 to .93). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression models predicting each domain outcome from MLC, MCTI, and RRA. Descriptives showed high perceived capability and outcomes (MLC M=4.11, SD=0.56; CRE M=4.02, SD=0.60; FDE M=4.18, SD=0.54; DPE M=3.86, SD=0.66; MCTI M=3.98, SD=0.57; RRA M=3.74, SD=0.63). MLC correlated with CRE (r=.62), FDE (r=.66), DPE (r=.53), and DME (r=.59), all p<.001. Regression results showed added value from trust and readiness: CRE R²=.49 with βMLC=.29 (p<.001), βMCTI=.41 (p<.001), βRRA=.17 (p=.006); FDE R²=.52 with βMLC=.34 (p<.001), βMCTI=.32 (p<.001), βRRA=.19 (p=.003); DPE R²=.40 with βMLC=.25 (p<.001), βMCTI=.35 (p<.001), βRRA=.14 (p=.021). Across domains, fraud detection showed the strongest perceived gains, while pricing was positive but more constrained, consistent with governance sensitivity. MCTI subdimensions indicated strongest audit traceability (M=4.17) and stability (M=4.01) but lower fairness confidence (M=3.83), highlighting a priority gap for deployment. RRA was driven most by audit trail logging (M=4.12) and documentation completeness (M=3.88). Implications are that institutions should operationalize trust-by-design through standardized explanations, fairness confidence checks, monitoring for drift, and auditable model version control to convert technical capability into defensible and consistent decisions.
Copyright (c) 2025 Md Jamil Ahmmed (Author)
A QUANTITATIVE ANALYSIS OF AI-DRIVEN TRADE-FINANCE RISK ASSESSMENT MODELS FOR STRENGTHENING U.S. IMPORT–EXPORT OPERATIONS
This study conducted a quantitative examination of AI-driven trade-finance risk assessment models to evaluate their effectiveness in predicting adverse transaction outcomes within U.S. import–export operations. Using a retrospective dataset of 1,248 trade-finance transactions, the analysis operationalized trade-finance risk through five measurable construct domains: counterparty risk, transaction risk, country and corridor risk, logistics risk, and compliance/documentation risk. Composite indices were developed for each construct and assessed for internal consistency, with Cronbach’s alpha values ranging from 0.79 to 0.88, indicating acceptable to strong reliability. A staged logistic regression framework was applied to estimate incremental explanatory power across predictor blocks while controlling for transaction amount, tenor, firm size, and corridor tier. Model performance improved progressively as constructs were added, with pseudo R² increasing from 0.092 in the controls-only model to 0.267 in the full model, and discrimination performance improving from an AUC of 0.681 to 0.812. Regression results showed that all five constructs were statistically significant predictors of adverse outcomes. Compliance and documentation risk exhibited the strongest effect (odds ratio = 2.64, p < 0.001), followed by counterparty risk (odds ratio = 2.32, p < 0.001) and transaction risk (odds ratio = 1.88, p < 0.001). Country and corridor risk (odds ratio = 1.67, p < 0.001) and logistics risk (odds ratio = 1.34, p = 0.026) also contributed independent explanatory value. Descriptive analysis indicated that between 21.6% and 34.7% of transactions exceeded predefined operational risk thresholds across constructs, with transaction risk showing the highest average score (mean = 0.51, SD = 0.19). Robustness checks across corridor tiers, industries, and firm-size segments confirmed stability of most construct effects, although logistics risk displayed partial sensitivity to segmentation. Overall, the findings demonstrated that AI-driven models integrating financial, transactional, contextual, operational, and compliance signals provided materially stronger predictive performance than baseline approaches, supporting the analytical value of multi-domain risk measurement for trade-finance decision systems.
Copyright (c) 2026 Mostafa Kamal (Author)
AI–IOT CONVERGENCE IN MODERN HEALTHCARE: A FRAMEWORK FOR PREDICTIVE, DATA-DRIVEN, AND PERSONALIZED MEDICAL SYSTEMS
This study addresses an implementation gap in healthcare digitalization: organizations deploy connected sensing and AI analytics yet lack case-based quantitative evidence on which AI-IoT convergence capabilities drive predictive, data-driven, and personalized care benefits. The purpose was to test a convergence framework in a quantitative, cross-sectional, case-study design within a healthcare enterprise case. A five-point Likert survey was administered to 210 stakeholders across clinical, administrative, and health IT roles. Independent variables were AI Analytics Capability (AIC), IoT Data Acquisition Capability (IDC), Interoperability and System Integration (ISI), Data Quality and Governance (DQG), Security and Privacy Assurance (SPA), Infrastructure Readiness (IR), and Clinician/User Acceptance (CUA); outcomes were Predictive Healthcare Effectiveness (PHE), Data-Driven Decision Quality (DDDQ), and Personalized Care Effectiveness (PCE). The analysis plan used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Reliability supported composite scoring (α = .83–.91; PHE α = .90, DDDQ α = .88, PCE α = .91). Baseline ratings exceeded the neutral midpoint (AIC M = 3.86; PHE M = 3.77). Correlations supported positive capability-outcome links (AIC with PHE r = .54; DQG with DDDQ r = .51; p < .01). Regression models showed differentiated drivers: PHE variance explained was 46% (R² = .46), with AIC (β = .31, p < .001), IDC (β = .21, p = .002), DQG (β = .18, p = .006), and IR (β = .20, p = .003) significant; DDDQ variance explained was 44% (R² = .44), led by DQG (β = .34, p < .001) and ISI (β = .22, p = .001), with SPA (β = .16, p = .012) and IR (β = .11, p = .049) also significant; PCE variance explained was 52% (R² = .52), driven by DDDQ (β = .36, p < .001), CUA (β = .29, p < .001), and PHE (β = .19, p = .004). Implications are that healthcare enterprises should prioritize governed data and integration to raise decision quality, pair analytics capability with robust sensing and infrastructure for predictive gains and invest in clinician trust and workflow adoption to convert decision improvements into scalable personalization and inform roadmap priorities for similar enterprise care systems.
Copyright (c) 2025 Abdulla Mamun (Author)
A SYSTEMATIC REVIEW OF PREDICTIVE ANALYTICS IN MARKETING DECISION-MAKING EXPLORING AI-DRIVEN CONSUMER SEGMENTATION AND AB TESTING
This systematic review examines how predictive analytics supports marketing decision-making by synthesizing research on AI-driven consumer segmentation and A/B testing strategies as complementary components of evidence-based marketing systems. Using a PRISMA-guided screening and selection workflow, the review analyzed a total of 57 papers that met predefined eligibility criteria focused on marketing-relevant predictive modeling, segmentation methods used for decision support, and experimentation designs applied to validate marketing interventions and personalization policies. Findings indicate that predictive analytics is most often operationalized as an end-to-end decision pipeline in which diverse data ecosystems—spanning first-party CRM and transaction histories, digital behavioral traces, campaign exposure logs, contextual signals, and content-based attributes are transformed through feature engineering into model-ready representations that drive targeting, retention, personalization, and pricing or promotion decisions. The reviewed studies show that segmentation has expanded beyond traditional strategic planning roles to include tactical campaign governance, analytical feature construction, and high-granularity personalization through clustering, probabilistic membership models, representation learning, and deep latent-space approaches, with interpretability and segment stability repeatedly emphasized as prerequisites for operational use. The experimentation literature positions A/B testing as the central causal validation mechanism for selecting among creative variants, interface designs, targeting rules, and algorithmic policies, while also highlighting the importance of metric governance, attribution windows, statistical power, multiple-comparison control, and operational safeguards such as ramping, logging, and auditability. Across the evidence base, the strongest contributions link prediction, segmentation, and experimentation into integrated decision cycles where model outputs prioritize audiences, segment’s structure heterogeneity for analysis and governance, tests estimate incremental effects, and results update thresholds and rollout rules under real constraints. Overall, this review consolidates a fragmented literature into a coherent synthesis of methods, evaluation standards, and operational patterns that characterize how predictive analytics, AI segmentation, and A/B testing jointly function as a scalable decision architecture in contemporary marketing environments.
Copyright (c) 2023 Rakibul Hasan, Khairul Alam Talukder (Author)
DO BANGLADESHI FOOD AND BEVERAGE ENTREPRENEUR INTEND TO ADOPT THE ADVANCED LEVEL OF IR 4.0 TECHNOLOGY IN MANUFACTURING OPERATIONS?
The performance of SME enterprises will drive the country’s economy forward. Upgrading the technologies currently used in SMEs will enhance the manufacturing process of the food and beverage (F&B) industries. Those firms are still using traditional equipment and machinery in their F&B manufacturing process; however, they are required to adopt the next level of technologies for maximum productivity, which they cannot afford. Thus, the study focuses on the intention to adopt upgraded technologies in F&B SMEs in developing countries. A total of 230 F&B SME owners and top management were surveyed in Bangladesh. The collected data were analysed using PLS-SEM with SmartPLS software. The results showed that disaster preparedness and business continuity were significant in influencing the intention to adopt; similarly, performance expectancy, effort expectancy, and social influence were also significant in influencing the intention to adopt the next level of IR technologies. The moderating role of government support and policy has mixed effects on intention. The study is significant because disaster preparedness and business continuity plans are utilised in technology adoption and intention scenarios. For the investigation of this intention, the study adopted two perspectives: the invasion and role of disasters and hazards on SMEs through disaster preparedness and business continuity plans, and secondly, the unified theory of acceptance and usage of technology (UTAUT) for measuring the intention. The study contributed to these two theories in a new context, utilising specific constructs. The study will also contribute to policymakers in the government in developing constructive policies and effective financial and non-financial support for F&B manufacturing SMEs.
Copyright (c) 2025 Ishraq Jerin, Amer Hamzah Jantan, Abu Bakar Abdul Hamid (Author)
FEDERATED LEARNING MODELS FOR PRIVACY-PRESERVING DATA SHARING AND SECURE ANALYTICS IN HEALTHCARE INDUSTRY
This study investigated the development and evaluation of Federated Learning Models for Privacy-Preserving Data Sharing and Secure Analytics in the Healthcare Industry, presenting a comprehensive quantitative framework to balance privacy, security, and utility in distributed medical data environments. Federated learning enables multiple institutions—such as hospitals, laboratories, and research centers—to collaboratively train shared models without transferring raw patient data, thereby maintaining compliance with global privacy regulations while supporting large-scale analytics. The research compared four configurations: a centralized baseline, federated learning with secure aggregation, federated learning with secure aggregation and differential privacy, and a robustness-enhanced federated model with adaptive aggregation. Empirical analysis across imaging, electronic health record, and wearable sensor datasets demonstrated that federated configurations achieved non-inferior predictive accuracy (AUROC range: 0.917–0.931) compared with the centralized model (AUROC = 0.936) while significantly improving privacy and security indicators. The membership inference advantage declined from 42.5% in centralized settings to below 10% in privacy-enhanced federated models, confirming reduced re-identification risk. Regression and correlation analyses established statistically significant relationships between privacy budgets, encryption depth, and model performance (R² = 0.709; p < 0.001), validating measurable trade-offs between confidentiality and predictive utility. Reliability and validity testing (Cronbach’s α > 0.88) confirmed the internal consistency and reproducibility of quantitative constructs. Findings revealed that privacy-preserving federated learning can achieve secure, efficient, and equitable performance across heterogeneous healthcare institutions with minimal computational overhead (<12%). The study contributes an empirically validated, statistically rigorous model for privacy-preserving healthcare analytics that aligns with international data protection frameworks such as GDPR and HIPAA. By quantifying privacy, security, and performance as measurable system properties, this research establishes federated learning as a practical, ethical, and scalable paradigm for responsible artificial intelligence in healthcare.
Copyright (c) 2024 Tonoy Kanti Chowdhury, Sai Praveen Kudapa (Author)
QUANTUM-RESISTANT CRYPTOGRAPHIC PROTOCOLS INTEGRATED WITH AI FOR SECURING CLOUD AND IOT ENVIRONMENTS
This quantitative study investigated the performance, efficiency, and security resilience of quantum-resistant cryptographic protocols integrated with artificial intelligence (AI) across cloud and Internet of Things (IoT) environments. The research aimed to empirically assess whether AI-enhanced cryptographic systems could outperform conventional post-quantum algorithms in encryption throughput, latency, resource optimization, and security robustness. A factorial experimental design was implemented, encompassing multiple algorithmic classes—lattice-based, hash-based, code-based, and multivariate polynomial systems—under both AI-integrated and non-AI configurations. The analysis incorporated 7,200 experimental runs executed under varying workloads, environments, and simulated attack conditions. Linear mixed-effects models, correlation analysis, and reliability testing were used to validate the statistical integrity of the results. The descriptive analysis indicated that AI-augmented frameworks achieved consistently higher encryption speeds, lower decryption latency, and superior throughput-adjusted security efficiency compared to traditional post-quantum systems. Correlation analysis revealed strong positive relationships between AI detection accuracy, encryption performance, and system stability, confirming that AI optimization significantly improved operational consistency. Reliability and validity tests showed high internal consistency, with Cronbach’s alpha coefficients exceeding 0.90, and factor analysis confirmed that performance indicators loaded strongly on the intended theoretical constructs of cryptographic performance and AI adaptability. Collinearity diagnostics verified the independence of predictors, with all variance inflation factors below 2.0. Regression analysis demonstrated that AI integration was a statistically significant predictor of improved cryptographic outcomes (p < 0.001), increasing throughput efficiency by over 14% on average while reducing latency and energy consumption. The findings confirmed the primary hypothesis that AI-driven cryptographic optimization enhances both computational efficiency and system resilience against classical and quantum attack simulations. Lattice-based and code-based cryptosystems showed the most substantial performance gains when combined with AI learning models. Overall, the results validated that intelligent, adaptive encryption frameworks achieve measurable, statistically significant advantages in performance, scalability, and security across both cloud and IoT domains. These findings provide empirical evidence supporting the integration of AI-based decision systems into post-quantum cryptography for secure and sustainable digital infrastructures.
Copyright (c) 2024 Shaikat Biswas, Md. Wahid Zaman Raj (Author)
AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION
This study investigates the role of Artificial Intelligence driven Business Intelligence (AI-BI) in enhancing predictive decision-making and optimizing strategic resource management across organizations. Using a quantitative research design, data were collected through a structured survey employing five-point Likert-scale measures to examine four key constructs: AI-BI Capability, Decision-Making Effectiveness, Strategic Resource Optimization, and Organizational Performance. The analysis, conducted through Structural Equation Modeling (SEM), revealed that AI-BI Capability significantly influences both Decision-Making Effectiveness (β = 0.71, p < 0.001) and Strategic Resource Optimization (β = 0.67, p < 0.001), while Decision-Making Effectiveness, in turn, has a strong positive impact on Organizational Performance (β = 0.63, p < 0.001). Moreover, Decision-Making Effectiveness was found to partially mediate the relationship between AI-BI Capability and Organizational Performance, suggesting that the cognitive enhancement derived from AI analytics plays a crucial role in translating technological capacity into strategic outcomes. Moderation analysis further revealed that Data Quality and Data Governance significantly strengthen these relationships, highlighting the necessity of reliable, well-managed data environments. The model exhibited high predictive power, with R² values ranging from 0.64 to 0.72 and excellent model fit indices (CFI = 0.951, TLI = 0.945, RMSEA = 0.047). The findings underscore that AI-BI is not merely an analytical tool but a strategic capability that integrates machine learning, automation, and predictive intelligence to drive performance excellence. By linking cognitive decision-making mechanisms with operational intelligence, the study contributes to the theoretical discourse on the Resource-Based View (RBV) and Dynamic Capabilities Theory, positioning AI-BI as a transformative asset that fosters organizational agility, efficiency, and sustained competitive advantage in data-intensive environments.
Copyright (c) 2025 Aditya Dhanekula (Author)
QUANTITATIVE INVESTIGATION OF INFORMATION SECURITY CHALLENGES IN U.S. HEALTHCARE PAYMENT ECOSYSTEMS
This study quantitatively examines information security challenges in U.S. healthcare payment ecosystems and empirically links security control maturity, incident-response capability, and organizational context to measurable payment outcomes—resilience, fraud-loss exposure, and stakeholder trust. Integrating a systematic literature review (2005–2020) with a cross-sectional survey of 124 organizations (providers, payers, clearinghouses, and vendors), the research operationalized constructs such as threat exposure, vulnerability, control maturity, compliance posture, and incident-response capability using five-point Likert scales. Findings indicate that control maturity is the strongest predictor of payment resilience (β = .36, p < .001) and stakeholder trust (β = .28, p < .001), while higher maturity and response capability jointly reduce fraud-loss ratios (β = −.24, p < .001; β = −.17, p = .006). Mediation analysis confirms that incident-response capability partially offsets the detrimental effect of threat exposure on resilience (indirect α×β = −.04, 95% CI [−.08, −.01]). Moderation tests reveal that maturity’s payoff is amplified in larger and cloud-forward organizations and weakened in vendor-dense ecosystems, underscoring the role of automation and interorganizational governance. Complementary anomaly-detection pilots achieve practical precision (0.77) and recall (0.70), demonstrating the viability of analytics-based monitoring in reducing fraud losses. The integrated model explains 29–42% of outcome variance, supporting a socio-technical interpretation in which security maturity and rehearsed response routines form the foundation of resilient, low-loss, and high-trust payment operations. The study contributes validated constructs, a transparent measurement framework, and actionable pathways for CISOs, enterprise architects, and revenue-cycle leaders to strengthen payment-system security and reliability through institutionalized control maturity, incident preparedness, and vendor governance.
Copyright (c) 2022 Mohammad Mushfequr Rahaman, Sai Praveen Kudapa (Author)
DATA-DRIVEN APPROACHES TO ENHANCING HUMAN–MACHINE COLLABORATION IN REMOTE WORK ENVIRONMENTS
This quantitative study investigates how data-driven mechanisms enhance human–machine collaboration in remote work environments by examining the relationships among automation transparency, workload balance, trust calibration, and collaboration efficiency. Using data from 912 participants across 87 distributed teams, the research applied multilevel regression and mediation analyses to quantify the cognitive, behavioral, and systemic factors that influence collaboration outcomes. Descriptive findings revealed that teams operating under transparent automation systems and balanced workload distributions exhibited significantly higher coordination and performance efficiency. Correlation and regression analyses demonstrated that automation transparency was the strongest predictor of collaboration efficiency (β = .38, p < .001), followed by workload balance (β = .29, p < .001) and automation intensity (β = .17, p < .001). Mediation testing confirmed that trust calibration partially mediated the relationship between transparency and efficiency, indicating that user confidence serves as a cognitive mechanism linking system clarity to improved performance. Reliability and validity assessments confirmed robust measurement consistency (α > .80; AVE > .50), and the overall model explained 61% of the variance in collaborative efficiency (R² = .61, p < .001). The findings highlight that human–machine synergy depends on measurable constructs of transparency, workload equity, and calibrated trust, all of which transform remote work into a data-driven ecosystem. The study contributes empirical evidence demonstrating that transparent algorithmic communication, equitable task distribution, and balanced automation intensity enhance coordination, decision accuracy, and trust stability within digital teams. It further establishes that data analytics provides a reliable framework for quantifying collaboration efficiency, thereby offering theoretical and practical insights into optimizing hybrid human–machine systems in globally distributed work environments.
Copyright (c) 2022 Md Harun-Or-Rashid Mollah, Sai Praveen Kudapa (Author)
MECHANISMS BY WHICH AI-ENABLED CRM SYSTEMS INFLUENCE CUSTOMER RETENTION AND OVERALL BUSINESS PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL FINDINGS
This systematic literature review examined the mechanisms through which Artificial Intelligence–enabled Customer Relationship Management (AI-CRM) systems influence customer retention and overall business performance across diverse industrial contexts. A total of 72 empirical studies published between 2018 and 2022 were systematically analyzed to identify the quantitative pathways, mediating constructs, and moderating factors that explain AI’s impact on organizational and relational outcomes. The reviewed literature revealed that AI-CRM systems enhance performance through four primary mechanisms: data-driven personalization, cognitive automation, predictive and prescriptive analytics, and experience-based sentiment intelligence. These mechanisms collectively strengthen customer engagement, satisfaction, and loyalty while improving operational efficiency, decision-making accuracy, and profitability.The findings demonstrated that AI integration within CRM platforms produces significant positive effects on both financial and non-financial outcomes by transforming static data into actionable insights and adaptive customer interactions. Customer retention consistently emerged as the key mediating variable linking AI adoption intensity to profitability, confirming that technological sophistication translates into performance benefits primarily through long-term relational continuity. Moreover, firm size, digital readiness, and data governance maturity were identified as significant moderators that influenced the strength of AI’s impact, indicating that organizational context shapes the effectiveness of AI-CRM strategies.Across sectors such as banking, retail, telecommunications, and hospitality, evidence showed that predictive modeling and automation improved response times, reduced churn, and optimized marketing resource allocation. Personalization mechanisms were particularly influential in customer-facing industries, while cognitive automation drove process efficiency in service-intensive sectors. The synthesis highlights that AI-enabled CRM is not merely a technological enhancement but a strategic transformation capability that integrates analytics, automation, and customer experience intelligence into a unified framework for sustained competitive advantage. This review contributes to the growing empirical foundation of AI-CRM research by offering a comprehensive, evidence-based understanding of how artificial intelligence reshapes customer management and business performance outcomes.
Copyright (c) 2023 Md Musfiqur Rahman, Md.Kamrul Khan (Author)
THE ROLE OF DATA SCIENCE IN OPTIMIZING PROJECT EFFICIENCY AND INNOVATION IN U.S. ENTERPRISES
This study investigates The Role of Data Science in Optimizing Project Efficiency and Innovation in U.S. Enterprises, providing a comprehensive quantitative and literature-based assessment of how enterprise data science capability (EDSC) influences both operational performance and innovation outcomes. Drawing upon insights from 126 peer-reviewed journal articles and empirical studies published between 2010 and 2024, the research synthesizes theoretical perspectives from the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to examine how data-driven infrastructures, governance systems, and human capital integration contribute to measurable organizational advantages. Using multi-sectoral firm-level data encompassing manufacturing, healthcare, finance, and software industries, the study operationalizes EDSC through indicators such as data pipeline automation, analytics workforce density, governance quality, and predictive modeling adoption. Quantitative analysis demonstrates that enterprises with mature data science systems achieve, on average, 22% faster project cycle times, 9% greater cost stability, and 37% fewer quality defects compared to low-EDSC peers. Additionally, innovation outcomes—including patent productivity, citation intensity, and new-product revenue share—show strong elasticities with respect to EDSC, indicating that a 10% increase in data science capability corresponds to approximately 6–7% higher innovation output. The moderating influence of data governance quality and the mediating role of analytics human capital reveal that organizational structure and talent depth are crucial mechanisms that translate technical potential into realized value. Robustness checks, including instrumental-variable regressions and multi-group confirmatory factor analyses, confirm the validity and cross-sector stability of these findings. Collectively, this study provides empirical evidence that data science is not merely a technological tool but a strategic architecture that integrates automation, governance, and human expertise to drive sustainable efficiency and innovation. The results underscore that enterprises capable of aligning data-driven processes with adaptive learning systems will sustain long-term competitiveness in the evolving U.S. digital economy.
Copyright (c) 2025 Md Wahid Zaman Raj (Author)
THE ROLE OF AI-DRIVEN CYBER RISK ANALYTICS ON CLOUD SECURITY POSTURE MANAGEMENT IN ENTERPRISE SYSTEMS
This study examines whether AI-driven cyber risk analytics improve Cloud Security Posture Management (CSPM) in enterprise systems and through which organizational mechanisms. We reviewed 47 prior studies to ground constructs and hypotheses, then executed a quantitative, cross-sectional, multi-case design across 220 cloud or security-team cases drawn from medium-to-large enterprises, with 512 survey responses synchronized to a ninety-day export of objective CSPM metrics. The problem addressed is persistent misconfiguration and alert overload in elastic, multi-tenant clouds that blunt security performance; the purpose is to quantify how analytics capability relates to measurable posture and to test the roles of triage efficiency and governed automation. Key variables include AI analytics capability, alert-triage efficiency, automation level, and CSPM outcomes such as misconfigurations per 100 resources, percent of critical findings remediated within policy windows, compliance score, mean time to detect, and mean time to remediate, with firm size, cloud tenure, provider mix, regulatory intensity, and account topology as controls. The analysis plan comprised reliability and validity checks, descriptive statistics, correlation matrices, hierarchical multiple regression with heteroskedasticity-robust inference, non-parametric bootstrapped mediation, and interaction-term moderation tests, plus robustness diagnostics. Headline findings show analytics capability is positively associated with stronger posture after controls, part of this relationship is mediated by improved alert triage, and the association is amplified at higher automation levels, indicating that explainable analytics plus governed automation yield the largest posture gains. Implications for practice are to invest in coverage-rich, explainable analytics, set explicit triage throughput objectives, and codify safe policy-as-code and auto-remediation so prioritized insights reliably become timely fixes; for scholarship, the work advances a capability to process to outcome model of CSPM conditioned by automation.
Copyright (c) 2025 Anisur Rahman (Author)
A SYSTEMATIC REVIEW OF DATA-DRIVEN BUSINESS PROCESS REENGINEERING AND ITS IMPACT ON ACCURACY AND EFFICIENCY CORPORATE FINANCIAL REPORTING
This systematic review investigates the transformative impact of data-driven business process reengineering (BPR) on the accuracy and efficiency of corporate financial reporting by synthesizing findings from 68 peer-reviewed empirical studies published between 2005 and 2024. The study examines how digital technologies, automation, analytics, and process optimization methodologies reshape financial reporting processes and outcomes across diverse organizational and regulatory contexts. Results from the meta-analysis reveal that data-driven BPR significantly enhances reporting performance, with pooled effect sizes indicating substantial improvements in reporting accuracy (Hedges’ g ≈ 0.58) and efficiency (Hedges’ g ≈ 0.64). Improvements in accuracy manifest in higher accruals quality, reduced restatement frequency, fewer audit adjustments, and stronger internal controls, while efficiency gains are observed through shorter close-to-disclose cycles, increased automation ratios, higher throughput, and reduced rework. The analysis highlights the differential contributions of various interventions, including enterprise resource planning (ERP) systems, robotic process automation (RPA), process mining, and continuous auditing, and demonstrates that the effectiveness of these interventions is strongly influenced by contextual factors such as data governance maturity, regulatory enforcement strength, technological integration depth, and industry-specific characteristics. Organizations operating in highly regulated environments and those with mature governance frameworks achieved the most pronounced performance gains, with financial services and technology sectors exhibiting the largest improvements. The review further identifies automation depth and real-time data utilization as critical drivers of both accuracy and efficiency, underscoring the strategic importance of integrating advanced technologies into financial reporting workflows. By consolidating evidence from a broad range of studies, this research contributes to a more comprehensive understanding of the mechanisms through which data-driven BPR enhances financial reporting performance and offers valuable insights for practitioners, policymakers, and researchers seeking to optimize reporting systems in the era of digital transformation.
Copyright (c) 2022 Md Arman Hossain, Md.Kamrul Khan (Author)
DATA DRIVEN BUSINESS INTELLIGENCE TOOLS IN AGRIBUSINESS A FRAMEWORK FOR EVIDENCE-BASED MARKETING DECISIONS
This study investigates the transformative role of data-driven business intelligence (BI) tools in enhancing evidence-based marketing decision-making within the agribusiness sector. Operating in an environment characterized by climatic variability, volatile demand, fragmented supply chains, and shifting consumer preferences, agribusiness increasingly depends on the ability to collect, integrate, and analyze large volumes of data to remain competitive. Business intelligence systems, encompassing data warehousing, predictive analytics, data mining, visualization dashboards, and machine learning algorithms, enable organizations to convert raw data into actionable insights that guide strategic and operational marketing decisions. This research is based on a comprehensive review of 156 peer-reviewed papers published in journals, conference proceedings, and industry reports, synthesizing theoretical frameworks, empirical findings, and practical applications of BI in agribusiness marketing. The study identifies key determinants of BI adoption, including data quality, system integration, analytics competence, governance structures, and leadership support, and examines how these factors influence BI use intensity and marketing outcomes. It also explores the integration of BI with digital agriculture technologies such as IoT devices and remote sensing systems, highlighting their combined potential to improve forecasting accuracy, enhance market segmentation, optimize pricing, and strengthen supply chain traceability. Quantitative evidence demonstrates that BI adoption significantly improves marketing decision quality, customer acquisition, market share, and overall performance, with decision quality acting as a key mediating factor. Additionally, digital-agriculture integration moderates the BI–performance relationship, amplifying its impact. The study concludes that data-driven BI tools provide a robust framework for transforming agribusiness marketing from intuition-based practices to evidence-based strategies, enabling firms to navigate complexity, improve responsiveness, and achieve sustained competitive advantage. The findings offer valuable insights for practitioners, policymakers, and researchers seeking to harness data analytics to enhance decision-making and performance in the global agribusiness sector.
Copyright (c) 2022 SM. Toufiqur Rahman, Abdul Hye (Author)
INTEGRATION OF MACHINE LEARNING AND ADVANCED COMPUTING FOR OPTIMIZING RETAIL CUSTOMER ANALYTICS
This study examines how the joint deployment of machine learning (ML) and advanced computing (AC) optimizes retail customer analytics across forecasting, personalization, segmentation and targeting, churn/retention and customer lifetime value (CLV), pricing and promotions, and omni-channel operations. Drawing on a structured review of 150 peer-reviewed papers, we synthesize quantitative evidence on model performance (e.g., sequence models, tree ensembles, contextual bandits, survival models, and multimodal recommenders) alongside infrastructure choices (distributed clusters, accelerators, streaming feature stores, and CI/CD practices). We organize findings by prediction and decision tasks, data regimes (transactional, clickstream, catalog/price, and logistics), and compute regimes (batch vs. streaming; CPU vs. GPU/TPU), and we standardize outcomes using commonly reported accuracy, ranking, calibration, and utility measures together with operational key performance indicators such as service level, stock-out exposure, inventory turns, and on-time fulfillment. Across tasks, integrated ML+AC pipelines consistently outperform classical baselines when evaluation respects temporal order, features are governed for point-in-time correctness and freshness, and serving architectures meet tail-latency budgets. Retrieval→ranking recommenders, representation learning augmented with graph signals, and survival-aware churn models translate directly into measurable lift; hierarchical elasticity estimation and credible causal designs support interpretable pricing and promotion effects under operational constraints. Robustness and risk controls—drift surveillance, anomaly detection, adversarial-aware training, and privacy-preserving learning—stabilize performance under demand shocks and data shifts, while standardized cost and carbon accounting clarify the economic and environmental price of incremental accuracy. The synthesis shows that durable value in retail arises less from any single algorithm and more from the end-to-end coupling of sound empirical methods, engineered data pipelines, governable deployment, and transparent trade-off reporting on cost, carbon, and latency. These findings provide an actionable evidence base and a reproducible blueprint for retailers seeking to convert predictive insight into reliable commercial impact.
Copyright (c) 2022 Hozyfa Shafa (Author)
AI-ENHANCED BUSINESS INTELLIGENCE DASHBOARDS FOR PREDICTIVE MARKET STRATEGY IN U.S. ENTERPRISES
This study examined whether AI-enhanced business intelligence (BI) dashboards measurably improve predictive market strategy in U.S. enterprises. Building on a systematic evidence scan of 138 peer-reviewed papers (2012–2025) and industry reports covering AI-assisted analytics, managerial decision support, and dashboard adoption, we designed and executed a multi-site quantitative evaluation combining a cluster randomized controlled trial with complementary observational analyses. The AI dashboard integrated model-generated demand forecasts, prescriptive nudges, and natural-language querying; the control condition used feature-parity descriptive BI without predictive or prescriptive components. Primary outcomes focused on forecast accuracy (MAPE and RMSE), with secondary endpoints including decision cycle time, campaign ROI, and monthly revenue growth. Mediation by user adoption (feature-use rate) and moderation by data maturity, firm size, and market volatility were pre-specified. Across participating business units, AI dashboards produced a statistically significant improvement in forecast accuracy (ATE −2.5 percentage points in MAPE), reduced decision cycle time (−18.2 hours), and increased campaign ROI (+13.6 pp) and revenue growth (+1.8 pp). Approximately one-third of the accuracy gain was mediated by adoption intensity, indicating behavioral uptake as a key pathway from capability to performance. Effects were larger in data-mature environments, while size and volatility showed limited moderating influence after multiplicity control. Rolling-origin back tests and Diebold–Mariano tests confirmed predictive uplift versus baseline models, and calibration diagnostics indicated reliable uncertainty communication. Sensitivity analyses (Did, quantile treatment effects, and per-protocol) supported robustness. Taken together, findings from both the empirical trial and the 138-paper evidence base suggest that AI-enhanced dashboards yield operationally meaningful gains with modest latency/complexity costs, translating into more accurate forecasts, faster decisions, and improved commercial outcomes. The study provides implementation guidance for enterprise rollout, emphasizing standardized onboarding, telemetry-informed adoption support, and governance practices to sustain performance.
Copyright (c) 2025 Md. Jobayer Ibne Saidur (Author)
OPTIMIZING THE U.S. GREEN HYDROGEN ECONOMY: AN INTEGRATED ANALYSIS OF TECHNOLOGICAL PATHWAYS, POLICY FRAMEWORKS, AND SOCIO-ECONOMIC DIMENSIONS
The United States stands at a pivotal juncture in its energy transition, with green hydrogen emerging as a critical enabler for decarbonizing hard-to-abate sectors such as heavy industry, long-haul transportation, and long-duration energy storage. This journal review paper provides a comprehensive analysis of the U.S. perspective on the burgeoning green hydrogen economy, synthesizing current advancements, policy landscapes, and socio-economic implications. The review first examines the rapid technological progress underpinning the hydrogen value chain. It explores innovations in electrolysis, including declining costs for Proton Exchange Membrane (PEM) and Alkaline technologies, and the potential of emerging methods like solar-driven water splitting. Advancements in storage, from high-pressure tanks to geological solutions, and transport, via repurposed pipelines and liquid organic hydrogen carriers (LOHCs), are assessed. The paper also details the expanding applications of green hydrogen, from fertilizer production and refining to fuel cell electric vehicles and power generation. While prior studies have examined hydrogen technology or policy in isolation, this review uniquely integrates technological, regulatory, and socio-economic perspectives to outline a cohesive national roadmap for the U.S. hydrogen economy. It conducts a comparative review of national strategies, highlighting the transformative impact of the U.S. Inflation Reduction Act (IRA), particularly its hydrogen production tax credit (45V), which has positioned the nation as a highly competitive market. This is contrasted with the European Union’s targeted demand-side policies and the ambitious export-oriented strategies of nations in the Middle East and North Africa (MENA) region and the Global South, analyzing the implications for global trade and U.S. leadership. The paper concludes by identifying significant knowledge gaps and future research directions. These include the need for robust methodologies to accurately account for emissions across the hydrogen lifecycle (ensuring additionality, temporal, and geographic matching for grid-powered electrolysis), optimizing integrated regional infrastructure, developing standardized safety protocols, and formulating best practices for community engagement and equitable benefit-sharing.
Copyright (c) 2025 Sharif Md Yousuf Bhuiyan, Md Shahadat Hossain, Saleh Mohammad Mobin, Mohammad Shahadat Hossain Sikdar, Imtiaz Parvez (Author)
QUANTITATIVE USABILITY STUDY ON IOT-BASED HUMAN-CENTERED SECURITY INTERFACES FOR HOSPITAL CRITICAL INFRASTRUCTURE
The increasing digitization of hospital critical infrastructures has intensified the demand for secure yet highly usable interface systems that enable clinicians and administrative personnel to perform tasks efficiently without compromising data protection. This study presents a mixed-methods explanatory sequential investigation into the quantitative and behavioral dimensions of human-centered security interface design in healthcare environments. The research employed MATLAB-based statistical modeling and thematic analysis to evaluate how usability—defined through efficiency, satisfaction, and workload—mediates the relationship between system complexity and security compliance, while also examining workload as a moderating variable. Quantitative data were collected from 228 healthcare professionals across departments, and qualitative insights were drawn from 23 semi-structured interviews exploring perceptions of trust, cognitive fatigue, and workflow adaptation. The quantitative findings revealed that usability significantly predicts compliance (β = .61, p < .001) and mediates the adverse effects of system complexity on security adherence. Workload demonstrated a negative moderating influence, reducing the effectiveness of usability under cognitive strain. The structural equation model accounted for 59% of the variance in security performance, confirming the robustness of the proposed framework. Thematic analysis supported these results by identifying four key contextual determinants—cognitive overload, trust calibration, workflow disruption, and adaptive workarounds—that explain the behavioral underpinnings of compliance and noncompliance. Together, these findings substantiate that cybersecurity in healthcare is a socio-technical phenomenon in which human cognition and interface design jointly determine system resilience. The study contributes to the theoretical and practical advancement of human-centered cybersecurity by empirically validating the usability–security nexus, demonstrating that intuitive, transparent, and cognitively efficient designs foster both operational reliability and institutional security culture. These insights offer a foundation for redesigning hospital security systems that are simultaneously secure, usable, and aligned with the cognitive demands of clinical practice.
THE IMPACT OF DATA-DRIVEN WEB FRAMEWORKS ON PERFORMANCE AND SCALABILITY OF U.S. ENTERPRISE APPLICATIONS
This systematic review examines how data-driven web frameworks shape performance and scalability outcomes in U.S. enterprise applications by integrating evidence across rendering strategies, data-access paradigms, runtime and placement topologies, and delivery protocols. Using a PRISMA-guided methodology and a registered protocol, we searched academic databases and high-rigor practitioner venues for studies published between 2014 and 2024, applied dual screening with adjudication, and retained 115 studies that reported transparent metrics under realistic workloads. Synthesized findings show consistent user-perceived gains when organizations adopt server-first rendering with streaming and disciplined hydration, especially when paired with cache-aware API contracts and explicit delivery priorities. For composite, authenticated views, GraphQL yields tail-latency and payload benefits when guarded by persisted queries, batching, and cost control, while REST remains superior for flat, cacheable reads that amplify edge hit ratios. Proximity emerges as a first-order lever: edge execution and multi-region placement reduce p95 and p99 only when data gravity follows compute, and targeted warm-path strategies are required for serverless estates to avoid cold-start penalties. Across the corpus, the most cost-effective improvements arise from cache-key normalization, deterministic revalidation, and precise prioritization that make early bytes visible to the network scheduler. We advocate a decision map that sequences investments from cache semantics and priorities to server-first plus streaming, to workload-appropriate API design, to selective edge and serverless adoption, all measured with percentile-aware telemetry and distributed tracing. This alignment consistently delivers low double-digit reductions in LCP and high-percentile latencies while protecting error budgets in enterprise conditions.
Copyright (c) 2025 Md Muzahidul Islam (Author)
SMART CORRIDOR SIMULATION FOR PEDESTRIAN SAFETY: : INSIGHTS FROM VISSIM-BASED URBAN TRAFFIC MODELS
Rapid urbanization and the growth of multimodal transport systems have increased the complexity of pedestrian–vehicle interactions at urban corridors, leading to heightened safety risks and congestion. This study develops a quantitative simulation framework using PTV VISSIM to evaluate how smart-corridor interventions—such as adaptive signal control, pedestrian detection systems, and geometric traffic-calming measures—can enhance pedestrian safety without compromising traffic efficiency. A 2.0-km urban corridor featuring multiple signalized and unsignalized crossings was modeled and calibrated with empirical traffic and pedestrian data collected from typical weekday and weekend conditions. Five simulation scenarios were constructed, including a baseline case and four smart-corridor configurations representing various technology integrations. Each scenario was replicated across different demand states using stochastic seeding to ensure statistical robustness. Conflict-based safety indicators such as Time-to-Collision (TTC), Post-Encroachment Time (PET), and Deceleration Rate to Avoid Crash (DRAC) were extracted from trajectory files using the Surrogate Safety Assessment Model (SSAM). These surrogate measures were normalized by pedestrian exposure to compute conflict rates per 1,000 crossings. Results were analyzed using negative binomial generalized linear mixed models (NB-GLMM) to estimate incidence rate ratios (IRRs) for both total and severe conflicts. Complementary linear mixed models (LMM) were applied to evaluate mobility outcomes including pedestrian delay, vehicle delay, and mean speed. Sensitivity analyses tested variations in threshold definitions (TTC, PET) and behavioral parameters to validate the robustness of findings. Preliminary results indicate that smart-corridor configurations integrating adaptive pedestrian detection and traffic-calming geometry achieved up to a 35% reduction in severe conflicts (IRR = 0.65, p < 0.05) while maintaining acceptable vehicle delay increases below 10%. Pedestrian delay improvements were most pronounced under adaptive signal logic, suggesting strong synergies between detection-based actuation and optimized signal timing. The study concludes that smart-corridor designs, when strategically modeled and validated through microsimulation, can serve as a reliable decision-support tool for urban planners seeking to balance pedestrian safety and traffic mobility. The research contributes a reproducible, data-driven framework for evidence-based evaluation of intelligent transportation systems (ITS) in pedestrian-rich urban environments.
Copyright (c) 2021 Mubashir Islam (Author)
IT AUTOMATION AND DIGITAL TRANSFORMATION STRATEGIES FOR STRENGTHENING CRITICAL INFRASTRUCTURE RESILIENCE DURING GLOBAL CRISES
This study addresses a pressing problem for operators of critical infrastructure: how to achieve dependable continuity and rapid recovery during global crises when complex, interdependent systems are under stress. The purpose is to quantify the associations between two strategic capabilities digital transformation strategy intensity and IT automation maturity and organizational resilience outcomes. Using a quantitative, cross-sectional, case-based design, we analyzed survey data from 156 cloud and enterprise cases spanning energy, healthcare, finance, telecommunications, transportation, and water. The work was grounded by a targeted review of 48 scholarly papers to inform construct definitions and instrumentation. Key variables included IT Automation Maturity, Digital Transformation Strategy Intensity, Crisis Severity, and controls for sector, size, legacy technology debt, and baseline cyber posture; the dependent variable was a composite Resilience Outcomes index covering service continuity, recovery speed, incident trends, and availability adherence. The analysis plan combined descriptive profiling, zero-order correlations, and hierarchical ordinary least squares with interaction and moderation terms, followed by robustness checks with sector fixed effects and telemetry-augmented outcomes. Headline findings show that both automation and transformation are positively associated with resilience, their interaction is synergistic, and the benefits of automation strengthen as crisis severity rises. Implications for practice are clear: pair architectural modernization cloud, governed data platforms, API-first and identity-centric controls with codified execution infrastructure-as-code, complete CI/CD pipelines, observability in delivery, progressive releases, and preapproved automated remediation to compress detection and restoration latencies and to localize failures across ecosystems.
MODBUS/DNP3 OVER TCP/IP IMPLEMENTATION ON TMDSCNCD28388D AND ARDUINO WITH SIMULINK HMI FOR IOT-BASED CYBERSECURE ELECTRICAL SYSTEMS
The implementation of Modbus and DNP3 (Distributed Network Protocol) over TCP/IP represents a significant advancement in integrating industrial communication standards with modern IoT-based control and cybersecurity frameworks. This study presents a dual-platform experimental implementation of these protocols using the Texas Instruments TMDSCNCD28388D controlCARD and the Arduino Uno, each interfaced with a Simulink-based Human–Machine Interface (HMI). The system architecture enables seamless data exchange between field devices and supervisory applications over Ethernet, supporting real-time monitoring, remote actuation, and secure data acquisition. The TMDSCNCD28388D, equipped with a dual-core C2000 microcontroller and integrated F28388D processor, provides deterministic control for industrial nodes, while the Arduino Uno serves as a low-cost alternative for small-scale IoT testbeds. Both implementations employ Simulink models for system design, simulation, and code generation, ensuring modularity and platform independence. The study emphasizes the integration of industrial automation and IoT protocols within a cybersecurity-aware framework. A layered encryption model was incorporated into TCP/IP communication to evaluate data confidentiality, integrity, and resilience against common cyber threats such as spoofing and denial-of-service attacks. The Simulink HMI acts as both a visualization and command layer, enabling real-time supervisory control and anomaly detection through embedded MATLAB scripts and dashboard logic. Experimental results demonstrate high communication reliability, with Modbus achieving faster request–response cycles under low-load conditions, while DNP3 exhibited greater robustness against packet loss and network interference. The hybrid approach validates the feasibility of deploying standardized SCADA protocols in distributed IoT environments, supporting industrial cyber-physical systems where interoperability and security are critical. This work contributes to the evolving field of cyber-secure industrial automation by demonstrating an end-to-end methodology for implementing Modbus/DNP3 over TCP/IP using embedded microcontrollers and model-based design tools. The outcomes highlight the importance of integrating communication protocols, cybersecurity measures, and model-based engineering to develop resilient, intelligent, and scalable industrial IoT architectures.
Copyright (c) 2025 Waladur Rahman, Jabed Hasan Tarek (Author)
SECURING ERP SYSTEMS: THE ROLE OF INFORMATION SECURITY ANALYSTS IN U.S. TEXTILE AND MANUFACTURING ENTERPRISES
This study addresses a persistent problem in U.S. textile and manufacturing enterprises: Enterprise Resource Planning environments are integral to operations yet remain vulnerable, and there is limited sector-specific, quantitative evidence on how information security analysts shape ERP security effectiveness. The purpose is to quantify the associations between analyst-centered capabilities and measurable ERP security outcomes. Design is quantitative, cross-sectional, and multi-case. The sample comprises N = 178 firm-level cases spanning cloud, on-premises, and hybrid ERP deployments. The literature review informing the framework synthesized 44 peer-reviewed sources. Key variables include an outcome composite for ERP security effectiveness incident frequency, severity, time to recover, segregation-of-duties posture, and audit signals, and focal predictors for analyst competency, staffing adequacy, and practice maturity, with security culture as a mediator and deployment model as a moderator. The analysis plan specified descriptive statistics, reliability checks, correlations, and multiple regression with heteroskedasticity-robust errors, plus mediation via bootstrap and moderation by cloud, with robustness checks using negative binomial for incident counts, ordinal logit for severity, and log-linear models for recovery time. Headline findings show analyst competency and practice maturity as the strongest unique predictors of effectiveness standardized β ≈ 0.28 and 0.31, adjusted R² ≈ 0.46, staffing adequacy is positive but smaller, security culture partially mediates these relationships, and the competency effect is steeper among cloud adopters; incident counts and recovery time improve materially incidence-rate ratios around 0.79 to 0.75 and 11 to 13 percent faster recovery per one-point increase on five-point scales. Implications are to prioritize ERP-specific IAM and SoD engineering, cadence-driven practice maturity, ERP-aware monitoring, and culture building, and to leverage cloud platform affordances to amplify competency gains.
Copyright (c) 2025 Zamal Haider Shish (Author)