International Journal of Business and Economics Insights
type: channel
description: 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.
language: en
publication name: International Journal of Business and Economics Insights
items: AI-Powered Predictive and Prescriptive Analytics for Intelligent Healthcare Resource Optimization
items: An Empirical Analysis of the Impact of Robotic Process Automation and Continuous Auditing on the Timeliness And Accuracy Of Financial Reporting Assurance
items: AI-Based Analytics for Financial Risk Assessment and Anomaly Detection in Complex Financial Data Systems
items: High-Performance Computing Simulation of Digital Twin Synchronization in Smart Manufacturing Projects: A Quantitative Analysis of Edge-Cloud Architectures
items: Cloud-Based Accounting Systems and Cybersecurity Frameworks for Financial Data Protection in Emerging Economies: A Meta-Analysis
items: Customer Retention Forecasting in Mobile Wallet Services Using Neural Networks: A Comparative Quantitative Study
items: Digital Compliance and Cybersecurity Frameworks for Strengthening Documentation Integrity Across Financial Institutions
items: Foundational Approaches to Secure Data Collection and Processing in Networked and Distributed Computing Environments
items: AI Diagnostic Frameworks for Accurate, HIPAA-Compliant U.S. Healthcare Analytics Using Federated Learning and Differential Privacy
items: Business Intelligence Enhanced Client Portfolio Profitability Analysis for Corporate Insurance Accounts
items: Automated Financial Reconciliation Systems for Enhancing Efficiency and Transparency in Enterprise Accounting Workflows
items: Systematic Review and Quantitative Evaluation of Advanced Machine Learning Frameworks for Credit Risk Assessment, Fraud Detection, And Dynamic Pricing in U.S. Financial Systems
items: A QUANTITATIVE ANALYSIS OF AI-DRIVEN TRADE-FINANCE RISK ASSESSMENT MODELS FOR STRENGTHENING U.S. IMPORT–EXPORT OPERATIONS
items: AI–IOT CONVERGENCE IN MODERN HEALTHCARE: A FRAMEWORK FOR PREDICTIVE, DATA-DRIVEN, AND PERSONALIZED MEDICAL SYSTEMS
items: A SYSTEMATIC REVIEW OF PREDICTIVE ANALYTICS IN MARKETING DECISION-MAKING EXPLORING AI-DRIVEN CONSUMER SEGMENTATION AND AB TESTING
items: DO BANGLADESHI FOOD AND BEVERAGE ENTREPRENEUR INTEND TO ADOPT THE ADVANCED LEVEL OF IR 4.0 TECHNOLOGY IN MANUFACTURING OPERATIONS?
items: FEDERATED LEARNING MODELS FOR PRIVACY-PRESERVING DATA SHARING AND SECURE ANALYTICS IN HEALTHCARE INDUSTRY
items: QUANTUM-RESISTANT CRYPTOGRAPHIC PROTOCOLS INTEGRATED WITH AI FOR SECURING CLOUD AND IOT ENVIRONMENTS
items: AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION
items: QUANTITATIVE INVESTIGATION OF INFORMATION SECURITY CHALLENGES IN U.S. HEALTHCARE PAYMENT ECOSYSTEMS
items: DATA-DRIVEN APPROACHES TO ENHANCING HUMAN–MACHINE COLLABORATION IN REMOTE WORK ENVIRONMENTS
items: MECHANISMS BY WHICH AI-ENABLED CRM SYSTEMS INFLUENCE CUSTOMER RETENTION AND OVERALL BUSINESS PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL FINDINGS
items: THE ROLE OF DATA SCIENCE IN OPTIMIZING PROJECT EFFICIENCY AND INNOVATION IN U.S. ENTERPRISES
items: THE ROLE OF AI-DRIVEN CYBER RISK ANALYTICS ON CLOUD SECURITY POSTURE MANAGEMENT IN ENTERPRISE SYSTEMS
items: A SYSTEMATIC REVIEW OF DATA-DRIVEN BUSINESS PROCESS REENGINEERING AND ITS IMPACT ON ACCURACY AND EFFICIENCY CORPORATE FINANCIAL REPORTING
items: DATA DRIVEN BUSINESS INTELLIGENCE TOOLS IN AGRIBUSINESS A FRAMEWORK FOR EVIDENCE-BASED MARKETING DECISIONS
items: INTEGRATION OF MACHINE LEARNING AND ADVANCED COMPUTING FOR OPTIMIZING RETAIL CUSTOMER ANALYTICS
items: AI-ENHANCED BUSINESS INTELLIGENCE DASHBOARDS FOR PREDICTIVE MARKET STRATEGY IN U.S. ENTERPRISES
items: OPTIMIZING THE U.S. GREEN HYDROGEN ECONOMY: AN INTEGRATED ANALYSIS OF TECHNOLOGICAL PATHWAYS, POLICY FRAMEWORKS, AND SOCIO-ECONOMIC DIMENSIONS
items: QUANTITATIVE USABILITY STUDY ON IOT-BASED HUMAN-CENTERED SECURITY INTERFACES FOR HOSPITAL CRITICAL INFRASTRUCTURE

AI-Powered Predictive and Prescriptive Analytics for Intelligent Healthcare Resource Optimization
type: item
description: This study has examined how artificial-intelligence-powered predictive and prescriptive analytics affects the optimization of healthcare resources and, through that mechanism, the performance of care delivery, and it has developed and tested a framework linking analytic capability to resource outcomes across inpatient beds, operating theatres, critical care, diagnostics, and the clinical workforce. The central problem has been that hospital resource management remains largely reactive and calendar-driven, so that capacity pressure is recognised only when it manifests as a full ward, a boarded emergency patient, a cancelled operation, or an unplanned overtime shift, by which point the available responses are costly, disruptive, and clinically consequential, while the substantial forecasting capability now present in many provider organisations is seldom converted into the allocation, scheduling, and rostering decisions that would relieve the pressure. Guided by five research questions, concerning how predictive and prescriptive analytics affects resource optimization performance, which resource domains are most effectively optimised, how forecast usefulness varies with decision horizon, what risks reactive capacity management carries, and whether resource optimization effectiveness transmits analytic capability into care delivery performance, the study has proposed a framework comprising five contributing constructs: clinical and operational data integration; predictive demand and acuity forecasting; prescriptive capacity and scheduling optimisation; clinical governance, explainability and equity assurance; and operational flexibility and workforce adaptability, with resource optimization effectiveness as the proximal outcome and care delivery performance as the distal outcome. A quantitative, cross-sectional design supplemented by a discrete-event simulation has been used, and data have been collected from hospital operations and capacity managers, clinical leads, n…
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Prescriptive Analytics
  topic: keywords
subject: Healthcare Operations
  topic: keywords
subject: Capacity Management
  topic: keywords
subject: Resource Optimization
  topic: keywords
subject: Patient Flow
  topic: keywords
subject: Clinical Governance
  topic: keywords
subject: Health Equity
  topic: keywords
creator: Md Ashiqur Rahman
rights: Copyright (c) 2026 Md Ashiqur Rahman (Author)
date: 2026-03-06
publication date: 2026-03-06
starting page: 82
ending page: 107
doi: 10.63125/5t121555

An Empirical Analysis of the Impact of Robotic Process Automation and Continuous Auditing on the Timeliness And Accuracy Of Financial Reporting Assurance
type: item
description: This study empirically examined the impact of Robotic Process Automation (RPA) and Continuous Auditing (CA) on the timeliness and accuracy of financial reporting assurance within digitally transformed organizations. A quantitative cross-sectional research design was employed using a structured questionnaire administered to accounting, auditing, finance, compliance, and governance professionals. A total of 450 questionnaires were distributed, 421 were returned (93.56% response rate), and after duplicate screening, missing data assessment, and response consistency evaluation, 392 valid questionnaires (87.11%) were retained for statistical analysis. Data were analyzed using SPSS 29 and SmartPLS 4 through descriptive statistics, reliability and validity assessment, Pearson correlation analysis, multiple regression analysis, and Partial Least Squares Structural Equation Modeling (PLS-SEM). Descriptive statistics indicated high organizational implementation of Robotic Process Automation (M = 4.07, SD = 0.59) and Continuous Auditing (M = 4.12, SD = 0.56), accompanied by favorable Reporting Timeliness (M = 4.10, SD = 0.53) and Reporting Accuracy (M = 4.18, SD = 0.50). The measurement model demonstrated excellent reliability, with Cronbach's alpha values ranging from 0.891 to 0.927, Composite Reliability values between 0.915 and 0.941, and Average Variance Extracted values ranging from 0.641 to 0.761. Pearson correlation analysis revealed significant positive relationships among all constructs, with the strongest correlation observed between Reporting Timeliness and Reporting Accuracy (r = 0.756, p < .001). Structural model analysis demonstrated that Robotic Process Automation significantly influenced Reporting Timeliness (β = 0.361, t = 8.02, p < .001) and Reporting Accuracy (β = 0.334, t = 7.11, p < .001), whereas Continuous Auditing exerted stronger effects on Reporting Timeliness (β = 0.421, t = 9.79, p < .001) and Reporting Accuracy (β = 0.442, t = 10.83, p < .001). The…
subject: Articles
  topic: section
subject: Robotic Process Automation
  topic: keywords
subject: Continuous Auditing
  topic: keywords
subject: Financial Reporting Assurance
  topic: keywords
subject: Reporting Timeliness
  topic: keywords
subject: Reporting Accuracy
  topic: keywords
creator: Mohammad Abir Chowdhury Shovon
creator: Tonay Pal
rights: Copyright (c) 2022 Mohammad Abir Chowdhury Shovon, Tonay Pal (Author)
date: 2022-12-15
publication date: 2022-12-15
starting page: 73
ending page: 126
doi: 10.63125/7p8dcr47

AI-Based Analytics for Financial Risk Assessment and Anomaly Detection in Complex Financial Data Systems
type: item
description: A modern financial institution watches an unrelenting stream of transactions, positions, prices, and counterparty signals, and somewhere within that stream sit the rare events, a fraud, a limit breach, a building loss, a contagion, that matter most and reveal themselves least. This study is about the machinery organizations build to catch those events: AI-based analytics for financial risk assessment and anomaly detection in complex financial data systems. Rather than asking whether a particular algorithm detects anomalies, it asks a broader, more organizational question, what makes the whole detection-and-response apparatus effective? To answer it, the study models financial risk-management effectiveness as an outcome of six capabilities: risk data aggregation and lineage, AI risk-modeling capability, anomaly detection performance, early-warning and signal timeliness, model risk governance and validation, and an integrating construct of detection–response integration. Evidence came from a structured five-point Likert survey completed by 158 valid respondents out of 176 distributed, an 89.8% valid response rate, drawn from risk analysts and managers, quantitative and model developers, data scientists, compliance and audit staff, and risk-technology specialists, of whom 69.0% worked directly with risk models or anomaly detection. The data were analyzed with descriptive statistics, Cronbach's alpha, Pearson correlation, multiple regression, a system maturity index, and a risk-analytics capability priority matrix. Every capability was rated in the high band, led by financial risk-management effectiveness at a mean of 4.26 and anomaly detection performance at 4.19; reliability was strong, with alpha from 0.83 to 0.94. All correlations were positive and significant, the strongest being r = 0.81 between detection–response integration and effectiveness. The regression model explained 74.1% of the variance in effectiveness, R² = 0.741, adjusted R² = 0.729, F(6,151) = 71.94,…
subject: Articles
  topic: section
subject: Financial risk assessment
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: AI analytics
  topic: keywords
subject: Model risk governance
  topic: keywords
subject: Early-warning systems
  topic: keywords
creator: Sadia Zaman
creator: Binayan Dey
rights: Copyright (c) 2023 Sadia Zaman, Binayan Dey (Author)
date: 2023-12-19
publication date: 2023-12-19
starting page: 105
ending page: 121
doi: 10.63125/224h4a32

High-Performance Computing Simulation of Digital Twin Synchronization in Smart Manufacturing Projects: A Quantitative Analysis of Edge-Cloud Architectures
type: item
description: This study examined the problem of weak digital twin synchronization in smart manufacturing projects, where delayed data exchange, fragmented edge-cloud architecture, limited simulation capacity, and poor virtual-physical alignment reduce production visibility, decision quality, predictive maintenance, and project performance. The purpose was to quantitatively assess how high-performance computing simulation and edge-cloud architecture improve digital twin synchronization effectiveness and smart manufacturing project performance. A quantitative, cross sectional, case-based design was used with structured five-point Likert survey data from 180 valid respondents representing engineering operations, IT and automation, data systems, project management, production supervision, and cloud or enterprise smart manufacturing cases. The key variables were high-performance computing simulation capability, edge computing capability, cloud computing capability, edge-cloud integration, digital twin synchronization effectiveness, and smart manufacturing project performance. The analysis plan included descriptive statistics, Cronbach’s alpha reliability testing, normality screening, Pearson correlation, regression modeling, hypothesis testing, Edge-Cloud Synchronization Performance Index analysis, and HPC Simulation Readiness and Architecture Maturity Mapping. Findings showed high agreement across all constructs, with digital twin synchronization effectiveness recording the highest mean score, M = 4.15, SD = 0.58, followed by high-performance computing simulation capability, M = 4.12, SD = 0.61, smart manufacturing project performance, M = 4.10, SD = 0.62, edge computing capability, M = 4.08, SD = 0.64, cloud computing capability, M = 4.01, SD = 0.67, and edge-cloud integration, M = 3.96, SD = 0.69. Reliability was strong, with Cronbach’s alpha values ranging from 0.801 to 0.913. Correlation results showed that synchronization effectiveness was strongly related to project performanc…
subject: Articles
  topic: section
subject: High-Performance Computing Simulation
  topic: keywords
subject: Digital Twin Synchronization
  topic: keywords
subject: Edge-Cloud Architecture
  topic: keywords
subject: Smart Manufacturing Project Performance
  topic: keywords
subject: Synchronization Effectiveness
  topic: keywords
creator: Chapal Barua
creator: Binayan Dey
rights: Copyright (c) 2023 Chapal Barua, Binayan Dey (Author)
date: 2023-12-19
publication date: 2023-12-19
starting page: 65
ending page: 104
doi: 10.63125/58khxn65

Cloud-Based Accounting Systems and Cybersecurity Frameworks for Financial Data Protection in Emerging Economies: A Meta-Analysis
type: item
description: This study examines the problem of protecting sensitive financial data in cloud-based accounting systems within emerging economies, where organizations increasingly adopt digital accounting platforms but often face uneven cybersecurity maturity, weak employee training, vendor-governance gaps, and regulatory compliance challenges. The purpose of the study was to assess how cloud accounting adoption, cybersecurity framework implementation, data privacy controls, employee cybersecurity awareness, and regulatory compliance influence financial data protection. A quantitative, cross-sectional, case-based research design was used, drawing evidence from cloud and enterprise accounting cases involving accountants, auditors, finance officers, IT/cybersecurity officers, compliance officers, and managers. Out of 270 distributed questionnaires, 256 were returned, and 250 valid responses were analyzed, producing a usable response rate of 92.59%. The study used a five-point Likert-scale questionnaire and analyzed the data through descriptive statistics, reliability testing, Pearson correlation, multiple regression, a Cloud Accounting Cybersecurity Readiness Index, and vulnerability pattern analysis. The descriptive findings showed high levels of cloud accounting adoption (M = 3.91, SD = 0.64), cybersecurity framework implementation (M = 3.78, SD = 0.69), data privacy controls (M = 3.84, SD = 0.66), employee cybersecurity awareness (M = 3.62, SD = 0.74), regulatory compliance (M = 3.71, SD = 0.70), and financial data protection (M = 3.88, SD = 0.63). Reliability was acceptable to high, with Cronbach’s Alpha values ranging from 0.78 to 0.89. Correlation results confirmed significant positive relationships between financial data protection and cloud accounting adoption (r = 0.61), cybersecurity framework implementation (r = 0.68), data privacy controls (r = 0.66), employee awareness (r = 0.54), and regulatory compliance (r = 0.59), all at p < 0.01. Regression results showed that the …
subject: Articles
  topic: section
subject: Cloud-based accounting systems
  topic: keywords
subject: Cybersecurity frameworks
  topic: keywords
subject: Financial data protection
  topic: keywords
subject: Data privacy controls
  topic: keywords
subject: Regulatory compliance
  topic: keywords
creator: Risha Alam
creator: Mst Shurovi Akter
rights: Copyright (c) 2023 Risha Alam, Mst Shurovi Akter (Author)
date: 2023-12-19
publication date: 2023-12-19
starting page: 26
ending page: 64
doi: 10.63125/zb0h3n85

Customer Retention Forecasting in Mobile Wallet Services Using Neural Networks: A Comparative Quantitative Study
type: item
description: 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 as…
subject: Articles
  topic: section
subject: Mobile Wallet Retention
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: UTAUT2
  topic: keywords
subject: Trust and Perceived Risk
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Md. Abdur Rauf
creator: Iftekhar Ahmed
rights: Copyright (c) 2021 Md. Abdur Rauf, Iftekhar Ahmed (Author)
date: 2021-12-15
publication date: 2021-12-15
starting page: 70
ending page: 102
doi: 10.63125/dyrpc387

Digital Compliance and Cybersecurity Frameworks for Strengthening Documentation Integrity Across Financial Institutions
type: item
description: 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), followe…
subject: Articles
  topic: section
subject: Digital Compliance
  topic: keywords
subject: Cybersecurity Frameworks
  topic: keywords
subject: Documentation Integrity
  topic: keywords
subject: Financial Institutions
  topic: keywords
subject: Risk Management
  topic: keywords
creator: Mahfuj Ahmed Ruzel
creator: Rajib Sarkar
rights: Copyright (c) 2022 Mahfuj Ahmed Ruzel, Rajib Sarkar (Author)
date: 2022-09-28
publication date: 2022-09-28
starting page: 84
ending page: 122
doi: 10.63125/pxzmq202

Foundational Approaches to Secure Data Collection and Processing in Networked and Distributed Computing Environments
type: item
description: 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 ove…
subject: Articles
  topic: section
subject: Distributed Computing Security
  topic: keywords
subject: Secure Data Collection
  topic: keywords
subject: Secure Data Processing
  topic: keywords
subject: Network Security Architecture,
  topic: keywords
subject: Access Control And Authentication
  topic: keywords
creator: Mohammad Robel Miah
creator: Md. Morshedul Islam
rights: Copyright (c) 2021 Mohammad Robel Miah, Md. Morshedul Islam (Author)
date: 2021-12-16
publication date: 2021-12-16
starting page: 32
ending page: 69
doi: 10.63125/thrtkw71

AI Diagnostic Frameworks for Accurate, HIPAA-Compliant U.S. Healthcare Analytics Using Federated Learning and Differential Privacy
type: item
description: 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 protect…
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Diagnostic Modeling
  topic: keywords
subject: Privacy
  topic: keywords
subject: Governance
  topic: keywords
subject: Healthcare
  topic: keywords
creator: Aditya Dhanekula
creator: Sai Praveen Kudapa
rights: Copyright (c) 2026 Aditya Dhanekula, Sai Praveen Kudapa (Author)
date: 2026-02-05
publication date: 2026-02-05
starting page: 35
ending page: 81
doi: 10.63125/x4wdbp36

Business Intelligence Enhanced Client Portfolio Profitability Analysis for Corporate Insurance Accounts
type: item
description: 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 …
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: Portfolio Profitability Analysis
  topic: keywords
subject: KPI Consistency
  topic: keywords
subject: Insight-to-Action
  topic: keywords
subject: Corporate Insurance
  topic: keywords
creator: Md. Mosheur Rahman
creator: Rebeka Sultana
rights: Copyright (c) 2021 Md. Mosheur Rahman, Rebeka Sultana (Author)
date: 2021-09-18
publication date: 2021-09-18
starting page: 01
ending page: 36
doi: 10.63125/qcs8d475

Automated Financial Reconciliation Systems for Enhancing Efficiency and Transparency in Enterprise Accounting Workflows
type: item
description: 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 auto…
subject: Articles
  topic: section
subject: Automated Financial Reconciliation Systems (AFRS)
  topic: keywords
subject: Workflow Efficiency
  topic: keywords
subject: Workflow Transparency
  topic: keywords
subject: Audit Trail and Control Integration
  topic: keywords
subject: Cloud ERP Integration
  topic: keywords
creator: Faysal Khan
creator: Tahmina Akter Bhuya Mita
rights: Copyright (c) 2024 Faysal Khan, Tahmina Akter Bhuya Mita (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 134
ending page: 172
doi: 10.63125/0mf6qw97

Systematic Review and Quantitative Evaluation of Advanced Machine Learning Frameworks for Credit Risk Assessment, Fraud Detection, And Dynamic Pricing in U.S. Financial Systems
type: item
description: 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.…
subject: Articles
  topic: section
subject: Advanced machine learning
  topic: keywords
subject: Credit risk assessment
  topic: keywords
subject: Fraud detection
  topic: keywords
subject: Dynamic pricing
  topic: keywords
subject: Model trustworthiness
  topic: keywords
creator: Md Jamil Ahmmed
rights: Copyright (c) 2025 Md Jamil Ahmmed (Author)
date: 2025-12-27
publication date: 2025-12-27
starting page: 1329
ending page: 1369
doi: 10.63125/9cyn5m39

A QUANTITATIVE ANALYSIS OF AI-DRIVEN TRADE-FINANCE RISK ASSESSMENT MODELS FOR STRENGTHENING U.S. IMPORT–EXPORT OPERATIONS
type: item
description: 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…
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Trade Finance
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Import–Export
  topic: keywords
subject: Analytics
  topic: keywords
creator: Mostafa Kamal
rights: Copyright (c) 2026 Mostafa Kamal (Author)
date: 2026-01-10
publication date: 2026-01-10
starting page: 01
ending page: 34
doi: 10.63125/agnvxs76

AI–IOT CONVERGENCE IN MODERN HEALTHCARE: A FRAMEWORK FOR PREDICTIVE, DATA-DRIVEN, AND PERSONALIZED MEDICAL SYSTEMS
type: item
description: 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 a…
subject: Articles
  topic: section
subject: AI-IOT Convergence
  topic: keywords
subject: Predictive Healthcare Effectiveness
  topic: keywords
subject: Data-Driven Decision Quality
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Clinician Acceptance
  topic: keywords
creator: Abdulla Mamun
rights: Copyright (c) 2025 Abdulla Mamun (Author)
date: 2025-12-26
publication date: 2025-12-26
starting page: 1288
ending page: 1328
doi: 10.63125/402hjt35

A SYSTEMATIC REVIEW OF PREDICTIVE ANALYTICS IN MARKETING DECISION-MAKING EXPLORING AI-DRIVEN CONSUMER SEGMENTATION AND AB TESTING
type: item
description: 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 cycl…
subject: Articles
  topic: section
subject: Predictive analytics
  topic: keywords
subject: AI segmentation
  topic: keywords
subject: A/B testing
  topic: keywords
subject: Marketing decisions
  topic: keywords
subject: Experimentation
  topic: keywords
creator: Rakibul Hasan
creator: Khairul Alam Talukder
rights: Copyright (c) 2023 Rakibul Hasan, Khairul Alam Talukder (Author)
date: 2023-03-30
publication date: 2023-03-30
starting page: 68
ending page: 96
doi: 10.63125/2hvfh110

DO BANGLADESHI FOOD AND BEVERAGE ENTREPRENEUR INTEND TO ADOPT THE ADVANCED LEVEL OF IR 4.0 TECHNOLOGY IN MANUFACTURING OPERATIONS?
type: item
description: 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.
subject: Articles
  topic: section
subject: Adoption Intention
  topic: keywords
subject: Bangladesh
  topic: keywords
subject: Business Continuity
  topic: keywords
subject: Disaster Preparedness
  topic: keywords
subject: F&B Manufacturing SMEs
  topic: keywords
subject: Government Support and Policy
  topic: keywords
subject: IT technology
  topic: keywords
subject: UTAUT Model
  topic: keywords
creator: Ishraq Jerin
creator: Amer Hamzah Jantan
creator: Abu Bakar Abdul Hamid
rights: Copyright (c) 2025 Ishraq Jerin, Amer Hamzah Jantan, Abu Bakar Abdul Hamid (Author)
date: 2025-12-16
publication date: 2025-12-16
starting page: 1271
ending page: 1287
doi: 10.63125/26wcwe41

FEDERATED LEARNING MODELS FOR PRIVACY-PRESERVING DATA SHARING AND SECURE ANALYTICS IN HEALTHCARE INDUSTRY
type: item
description: 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…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Secure Analytics
  topic: keywords
subject: Healthcare Data
  topic: keywords
subject: Data Sharing
  topic: keywords
creator: Tonoy Kanti Chowdhury
creator: Sai Praveen Kudapa
rights: Copyright (c) 2024 Tonoy Kanti Chowdhury, Sai Praveen Kudapa (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 91
ending page: 133
doi: 10.63125/c2dzn006

QUANTUM-RESISTANT CRYPTOGRAPHIC PROTOCOLS INTEGRATED WITH AI FOR SECURING CLOUD AND IOT ENVIRONMENTS
type: item
description: 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 reduc…
subject: Articles
  topic: section
subject: Quantum Cryptography
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: IoT
  topic: keywords
subject: Encryption
  topic: keywords
creator: Shaikat Biswas
creator: Md. Wahid Zaman Raj
rights: Copyright (c) 2024 Shaikat Biswas, Md. Wahid Zaman Raj (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 60
ending page: 90
doi: 10.63125/dryw3b96

AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION
type: item
description: 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…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Resource Optimization
  topic: keywords
creator: Aditya Dhanekula
rights: Copyright (c) 2025 Aditya Dhanekula (Author)
date: 2025-11-01
publication date: 2025-11-01
starting page: 1238
ending page: 1270
doi: 10.63125/dkt2w457

QUANTITATIVE INVESTIGATION OF INFORMATION SECURITY CHALLENGES IN U.S. HEALTHCARE PAYMENT ECOSYSTEMS
type: item
description: 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 prepar…
subject: Articles
  topic: section
subject: Healthcare Payments
  topic: keywords
subject: Information Security
  topic: keywords
subject: Control Maturity
  topic: keywords
subject: Incident Response
  topic: keywords
subject: HIPAA
  topic: keywords
subject: PCI DSS
  topic: keywords
creator: Mohammad Mushfequr Rahaman
creator: Sai Praveen Kudapa
rights: Copyright (c) 2022 Mohammad Mushfequr Rahaman, Sai Praveen Kudapa (Author)
date: 2022-12-15
publication date: 2022-12-15
starting page: 42
ending page: 73
doi: 10.63125/gcg0fs06

DATA-DRIVEN APPROACHES TO ENHANCING HUMAN–MACHINE COLLABORATION IN REMOTE WORK ENVIRONMENTS
type: item
description: 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 …
subject: Articles
  topic: section
subject: Human–Machine Collaboration
  topic: keywords
subject: Automation Transparency
  topic: keywords
subject: Trust Calibration
  topic: keywords
subject: Remote Work Systems
  topic: keywords
subject: Data-Driven Decision-Making
  topic: keywords
creator: Md Harun-Or-Rashid Mollah
creator: Sai Praveen Kudapa
rights: Copyright (c) 2022 Md Harun-Or-Rashid Mollah, Sai Praveen Kudapa (Author)
date: 2022-09-28
publication date: 2022-09-28
starting page: 47
ending page: 83
doi: 10.63125/wt9t6w68

MECHANISMS BY WHICH AI-ENABLED CRM SYSTEMS INFLUENCE CUSTOMER RETENTION AND OVERALL BUSINESS PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL FINDINGS
type: item
description: 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 i…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: CRM Systems
  topic: keywords
subject: Customer Retention
  topic: keywords
subject: Business Performance
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Md Musfiqur Rahman
creator: Md.Kamrul Khan
rights: Copyright (c) 2023 Md Musfiqur Rahman, Md.Kamrul Khan (Author)
date: 2023-03-30
publication date: 2023-03-30
starting page: 31
ending page: 67
doi: 10.63125/qqe2bm11

THE ROLE OF DATA SCIENCE IN OPTIMIZING PROJECT EFFICIENCY AND INNOVATION IN U.S. ENTERPRISES
type: item
description: 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 strateg…
subject: Articles
  topic: section
subject: Data Science
  topic: keywords
subject: Project Efficiency
  topic: keywords
subject: Innovation
  topic: keywords
subject: Governance
  topic: keywords
subject: U.S. Enterprises
  topic: keywords
creator: Md Wahid Zaman Raj
rights: Copyright (c) 2025 Md Wahid Zaman Raj (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 586
ending page: 600
doi: 10.63125/jzjkqm27

THE ROLE OF AI-DRIVEN CYBER RISK ANALYTICS ON CLOUD SECURITY POSTURE MANAGEMENT IN ENTERPRISE SYSTEMS
type: item
description: 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 sch…
subject: Articles
  topic: section
subject: AI-Driven Cyber Risk Analytics
  topic: keywords
subject: Cloud Security Posture Management
  topic: keywords
subject: Automation
  topic: keywords
subject: Alert Triage Efficiency
  topic: keywords
subject: Quantitative Cross-Sectional
  topic: keywords
creator: Anisur Rahman
rights: Copyright (c) 2025 Anisur Rahman (Author)
date: 2025-09-15
publication date: 2025-09-15
starting page: 649
ending page: 683
doi: 10.63125/fcgjv566

A SYSTEMATIC REVIEW OF DATA-DRIVEN BUSINESS PROCESS REENGINEERING AND ITS IMPACT ON ACCURACY AND EFFICIENCY CORPORATE FINANCIAL REPORTING
type: item
description: 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 ra…
subject: Articles
  topic: section
subject: Business Process Reengineering
  topic: keywords
subject: Data-Driven
  topic: keywords
subject: Financial Reporting
  topic: keywords
subject: Accuracy
  topic: keywords
subject: Efficiency
  topic: keywords
creator: Md Arman Hossain
creator: Md.Kamrul Khan
rights: Copyright (c) 2022 Md Arman Hossain, Md.Kamrul Khan (Author)
date: 2022-12-28
publication date: 2022-12-28
starting page: 01
ending page: 41
doi: 10.63125/btx52a36

DATA DRIVEN BUSINESS INTELLIGENCE TOOLS IN AGRIBUSINESS A FRAMEWORK FOR EVIDENCE-BASED MARKETING DECISIONS
type: item
description: 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 p…
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: Agribusiness
  topic: keywords
subject: Evidence-Based Marketing
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Decision-Making
  topic: keywords
creator: SM. Toufiqur Rahman
creator: Abdul Hye
rights: Copyright (c) 2022 SM. Toufiqur Rahman, Abdul Hye (Author)
date: 2022-03-30
publication date: 2022-03-30
starting page: 35
ending page: 72
doi: 10.63125/p59krm34

INTEGRATION OF MACHINE LEARNING AND ADVANCED COMPUTING FOR OPTIMIZING RETAIL CUSTOMER ANALYTICS
type: item
description: 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 s…
subject: Articles
  topic: section
subject: Retail
  topic: keywords
subject: Analytics
  topic: keywords
subject: Machine-learning
  topic: keywords
subject: Computing
  topic: keywords
subject: Personalization
  topic: keywords
creator: Hozyfa Shafa
rights: Copyright (c) 2022 Hozyfa Shafa (Author)
date: 2022-12-25
publication date: 2022-12-25
starting page: 01
ending page: 46
doi: 10.63125/p87sv224

AI-ENHANCED BUSINESS INTELLIGENCE DASHBOARDS FOR PREDICTIVE MARKET STRATEGY IN U.S. ENTERPRISES
type: item
description: 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 …
subject: Articles
  topic: section
subject: AI dashboards
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Market strategy
  topic: keywords
subject: Business intelligence
  topic: keywords
subject: Randomized field experiment
  topic: keywords
creator: Md. Jobayer Ibne Saidur
rights: Copyright (c) 2025 Md. Jobayer Ibne Saidur (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 603
ending page: 648
doi: 10.63125/8cvgn369

OPTIMIZING THE U.S. GREEN HYDROGEN ECONOMY: AN INTEGRATED ANALYSIS OF TECHNOLOGICAL PATHWAYS, POLICY FRAMEWORKS, AND SOCIO-ECONOMIC DIMENSIONS
type: item
description: 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 methodolo…
subject: Articles
  topic: section
subject: Hydrogen
  topic: keywords
subject: Hydrogen Economy
  topic: keywords
subject: US Hydrogen Pathway
  topic: keywords
subject: Green Hydrogen
  topic: keywords
creator: Sharif Md Yousuf Bhuiyan
creator: Md Shahadat Hossain
creator: Saleh Mohammad Mobin
creator: Mohammad Shahadat Hossain Sikdar
creator: Imtiaz Parvez
rights: Copyright (c) 2025 Sharif Md Yousuf Bhuiyan, Md Shahadat Hossain, Saleh Mohammad Mobin, Mohammad Shahadat Hossain Sikdar, Imtiaz Parvez (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 586
ending page: 602
doi: 10.63125/xp8exe64

QUANTITATIVE USABILITY STUDY ON IOT-BASED HUMAN-CENTERED SECURITY INTERFACES FOR HOSPITAL CRITICAL INFRASTRUCTURE
type: item
description: 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–securi…
subject: Articles
  topic: section
subject: Human-centered design
  topic: keywords
subject: Usability engineering
  topic: keywords
subject: Hospital critical infrastructure
  topic: keywords
subject: Security interface design
  topic: keywords
subject: Healthcare cybersecurity
  topic: keywords
creator: Siful Islam
rights: Copyright (c) 2025 Siful Islam (Author)
date: 2025-10-09
publication date: 2025-10-09
starting page: 559
ending page: 585
doi: 10.63125/4k914c72