American Journal of Advanced Technology and Engineering Solutions
type: channel
description: The American Journal of Advanced Technology and Engineering Solutions (AJATES) Online ISSN (3067-0470) is a leading international, peer-reviewed, open-access journal dedicated to publishing high-quality research and advancements in technology and engineering. AJATES serves as a platform for researchers, academicians, industry professionals, and innovators to present cutting-edge solutions, novel methodologies, and interdisciplinary studies that address contemporary challenges in engineering and technology. Published monthly, AJATES focuses on fostering innovation and collaboration across diverse disciplines, including mechanical engineering, electrical engineering, computer science, materials science, robotics, and sustainable technologies. By bridging the gap between theory and practice, the journal contributes to the global discourse on technological advancements and their practical applications.
publisher: American Scholarly Publishing Group Inc
language: en
publication name: American Journal of Advanced Technology and Engineering Solutions
issn: 3067-0470
items: THE IMPACT OF BIM AND DIGITAL TWIN TECHNOLOGIES ON RISK REDUCTION IN CIVIL INFRASTRUCTURE PROJECTS
items: IRRADIATION-ENHANCED CREEP–FATIGUE INTERACTION IN HIGH-TEMPERATURE AUSTENITIC STEEL: CURRENT UNDERSTANDING AND CHALLENGES
items: DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
items: INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW
items: ROLE OF DIGITAL TWINS AND BIM IN U.S. HIGHWAY INFRASTRUCTURE ENHANCING ECONOMIC EFFICIENCY AND SAFETY OUTCOMES THROUGH INTELLIGENT ASSET MANAGEMENT
items: DATA-DRIVEN DECISION-MAKING THROUGH CUSTOMER RELATIONSHIP MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW IN MODERN ENTERPRISES
items: COMPARATIVE ANALYSIS OF PETROLEUM INFRASTRUCTURE PROJECTS IN SOUTH ASIA AND THE US USING ADVANCED GAS TURBINE ENGINE TECHNOLOGIES FOR CROSS INTEGRATION
items: PROSPECT OF USING AI- INTEGRATED SMART MEDICAL TEXTILES FOR REAL-TIME VITAL SIGNS MONITORING IN HOSPITAL MANAGEMENT & HEALTHCARE INDUSTRY
items: EVALUATING AGILE BUSINESS ANALYSIS IN POST-COVID RECOVERY A COMPARATIVE STUDY ON FINANCIAL RESILIENCE
items: IMPACT OF PREDICTIVE DATA MODELING ON BUSINESS DECISION-MAKING: A REVIEW OF STUDIES ACROSS RETAIL, FINANCE, AND LOGISTICS
items: GRAPH NEURAL NETWORK MODELS FOR DETECTING FRAUDULENT INSURANCE CLAIMS IN HEALTHCARE SYSTEMS
items: QUANTITATIVE RISK ASSESSMENT OF RAIL INFRASTRUCTURE PROJECTS USING MONTE CARLO SIMULATION AND FUZZY LOGIC
items: QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELING OF BIOACTIVE COMPOUNDS FROM MANGIFERA INDICA FOR ANTI-DIABETIC DRUG DEVELOPMENT
items: EVALUATING THE EFFECTIVENESS OF AI-GENERATED, CONTEXT-SPECIFIC CASE STUDIES IN IMPROVING CRITICAL THINKING SKILLS IN ODL
items: COST-BENEFIT ANALYSIS IN PRE-CONSTRUCTION PLANNING: THE ASSESSMENT OF ECONOMIC IMPACT IN GOVERNMENT INFRASTRUCTURE PROJECTS
items: ANALYSIS OF AI CONTRIBUTION TOWARDS REDUCING FUTURE PANDEMIC LOSS IN SME SECTOR: ACCESS TO ONLINE MARKETING AND YOUTH INVOLVEMENT
items: MACHINE LEARNING-ENHANCED STATISTICAL INFERENCE FOR CYBERATTACK DETECTION ON NETWORK SYSTEMS
items: REAL-TIME CYBERSECURITY INTEGRATION IN PLCS AND IOT GATEWAYS: A PERFORMANCE-CONSCIOUS APPROACH TO ENCRYPTION, AUTHENTICATION, AND INTRUSION DETECTION
items: CYBERCRIME, LEGAL ACCOUNTABILITY, AND CONTRACTUAL RISK: A SYSTEMATIC REVIEW OF JURISPRUDENCE AND PROTECTIVE FRAMEWORKS
items: CUSTOMER RELATIONSHIP MANAGEMENT AND DATA-DRIVEN DECISION-MAKING IN MODERN ENTERPRISES: A SYSTEMATIC LITERATURE REVIEW
items: IN SILICO DRUG REPURPOSING FOR INFLAMMATORY DISEASES: A SYSTEMATIC REVIEW OF MOLECULAR DOCKING AND VIRTUAL SCREENING STUDIES
items: THE ROLE OF PERCEIVED ENVIRONMENTAL RESPONSIBILITY IN ARTIFICIAL INTELLIGENCE-ENABLED RISK MANAGEMENT AND SUSTAINABLE DECISION-MAKING
items: BEHAVIORAL FACTORS IN LOAN DEFAULT PREDICTION A LITERATURE REVIEW ON PSYCHOLOGICAL AND SOCIOECONOMIC RISK INDICATORS
items: A REVIEW OF AI-POWERED DATA VISUALIZATION IN ENTERPRISE REPORTING: DASHBOARD DESIGN AND INTERACTIVE ANALYTICS
items: IMPLEMENTING ADVANCED TECHNOLOGIES FOR ENHANCED CONSTRUCTION SITE SAFETY
items: DETERMINANTS OF WORKFORCE EFFICIENCY: AN ANALYSIS OF EMPLOYEE PERFORMANCE DRIVERS IN JIANGSU'S AUTOMOTIVE SECTOR
items: A STUDY OF BUSINESS PROCESS AUTOMATION WITH DEVOPS: A DATA-DRIVEN APPROACH TO AGILE TECHNICAL SUPPORT
items: A SYSTEMATIC REVIEW OF PUBLIC BUDGETING STRATEGIES IN DEVELOPING ECONOMIES: TOOLS FOR TRANSPARENT FISCAL GOVERNANCE
items: AI INTEGRATION IN E-COMMERCE BUSINESS MODELS: CASE STUDIES ON AMAZON FBA, AIRBNB, AND TURO OPERATIONS
items: NAVIGATING ETHICS AND RISK IN ARTIFICIAL INTELLIGENCE APPLICATIONS WITHIN INFORMATION TECHNOLOGY: A SYSTEMATIC REVIEW

THE IMPACT OF BIM AND DIGITAL TWIN TECHNOLOGIES ON RISK REDUCTION IN CIVIL INFRASTRUCTURE PROJECTS
type: item
description: This study investigated how Building Information Modeling (BIM) and Digital Twin (DT) technologies influenced risk reduction in civil infrastructure projects using a cross-sectional quantitative design. The dataset comprised 168 civil infrastructure projects across highways (36.9%), bridges (24.4%), rail corridors (19.6%), tunnels (10.7%), and water infrastructure (8.3%), delivered through design–bid–build (39.9%), design–build (32.1%), and PPP/alliance procurement (28.0). Digital implementation showed mid-to-high BIM practice (BIM adoption intensity M = 3.74, SD = 0.71; BIM maturity M = 3.58, SD = 0.68) and more dispersed DT practice (DT utilization M = 3.12, SD = 0.89; DT predictive capacity M = 3.26, SD = 0.84), while integration maturity remained moderate (M = 3.41, SD = 0.79). Risk reduction outcomes demonstrated substantial variability (cost risk reduction M = 64.8, SD = 12.5; schedule M = 61.3, SD = 13.9; safety M = 55.7, SD = 15.6; quality M = 59.9, SD = 14.2; operational M = 67.2, SD = 11.8; composite index M = 61.8, SD = 10.7). Correlations were positive across domains, with integration maturity showing the strongest association with the composite risk index (r = 0.61). Reliability and validity were adequate for all constructs (Cronbach’s alpha 0.81–0.89, composite reliability 0.86–0.92, AVE 0.53–0.64), and collinearity remained acceptable (VIF 1.20–2.27). Hierarchical regression indicated that BIM predictors explained the largest incremental variance in cost and schedule risk reduction (ΔR² = 0.18–0.20), DT predictors contributed most to safety and operational risk reduction (ΔR² = 0.15–0.22), and integration maturity added broad multi-risk explanatory power, especially for the composite index (ΔR² = 0.14, β = 0.41, p < .001). Moderation tests showed complexity weakened BIM scheduling effects, whereas data reliability and team digital capability strengthened DT and integration impacts. Overall, BIM stabilized delivery risks, DTs strengthened safety and op…
subject: Articles
  topic: section
subject: BIM
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Risk Reduction
  topic: keywords
subject: Civil Infrastructure
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
creator: Md Milon Mia
creator: Md. Mominul Haque
rights: Copyright (c) 2023 Md Milon Mia, Md. Mominul Haque (Author)
date: 2023-12-08
publication date: 2023-12-08
starting page: 01
ending page: 41
doi: 10.63125/xgyzqk40

IRRADIATION-ENHANCED CREEP–FATIGUE INTERACTION IN HIGH-TEMPERATURE AUSTENITIC STEEL: CURRENT UNDERSTANDING AND CHALLENGES
type: item
description: This study presents a comprehensive quantitative assessment of irradiation-enhanced creep–fatigue interaction in high-temperature austenitic stainless steels (304/304L, 316/316L/316H/316LN, and 347H), materials that anchor nuclear, petrochemical, and advanced power infrastructures. Creep–fatigue interaction the concurrent action of time-dependent creep and cyclic plasticity becomes critically accelerated under neutron irradiation, which alters microstructural pathways through hardening, dislocation channeling, and radiation-induced segregation. To quantify these coupled effects, a cross-sectional, case-based dataset of 168 tests across 12 campaigns was curated, harmonizing variables for dose (dpa), test temperature, strain range, tensile-hold duration, environment, and metallurgy. Multiple linear and mixed-effects regressions were pre-specified to estimate adjusted associations between these predictors and log life (ln Nf), explicitly testing irradiation–temperature (dose×T) and irradiation–dwell (dose×hold) interactions. Energy-augmented models incorporating cycle-resolved hysteresis and creep work were used to link statistical effects to physical damage mechanisms, while an expert Likert survey (n = 18) independently captured practitioner consensus on variable salience and data quality constraints. Results demonstrate that dose, temperature, strain range, and tensile hold each impose statistically significant, independent life penalties. Two interactions dominate: higher temperature steepens the life reduction per unit dose (negative dose×T), and irradiation amplifies dwell sensitivity (negative dose×hold). Metallurgical stabilization offers a modest but significant attenuation of irradiation-related life debit. Energy-augmented extensions confirm that increased creep power during tensile holds correlates with reduced life, aligning regression coefficients with mechanistic expectations. Cross-validation, penalized stability checks, and environment-stratified analy…
subject: Articles
  topic: section
subject: Irradiation-Enhanced Creep–Fatigue
  topic: keywords
subject: Austenitic Stainless Steel
  topic: keywords
subject: Displacements Per Atom (Dpa)
  topic: keywords
subject: Tensile Dwell (Hold Time)
  topic: keywords
subject: Mixed-Effects Regression
  topic: keywords
creator: Md Ariful Islam
rights: Copyright (c) 2022 Md Ariful Islam (Author)
date: 2022-12-20
publication date: 2022-12-20
starting page: 148
ending page: 181
doi: 10.63125/e46gja61

DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
type: item
description: This study investigates how data-driven capabilities and sustainable resource-allocation policies jointly influence supply chain resilience (SCRes) under disruption and uncertainty. Adopting a quantitative, cross-sectional, multiple case-study design, the research integrates survey-based measurement, archival operational data, and stochastic simulation–optimization to link organizational capabilities—visibility, collaboration, flexibility, supplier diversification, redundancy, risk orientation, and allocation efficiency—to key resilience outcomes, including service recovery, time-to-recovery (TTR), backorder intensity, cost variance, and emissions. Data were collected from 190 professionals across four international firms in discrete manufacturing, FMCG, healthcare logistics, and electronics sectors, each providing both perceptual and objective data spanning 12–24 months of operations. Hierarchical multiple regression models, supported by mediation and moderation analyses, revealed that collaboration, digital visibility, and allocation efficiency were the strongest predictors of resilience performance, while flexibility and diversification contributed moderate incremental effects. Allocation efficiency partially mediated the impact of visibility and collaboration on outcomes, demonstrating that information and coordination enhance resilience primarily through improved resource allocation. Moreover, capability effects intensified under uncertainty—visibility’s and collaboration’s benefits were significantly amplified by demand volatility and lead-time variability. A composite Resilience Performance Index (RPI) was developed to align statistical and operational metrics, linking survey constructs to observed KPIs. Monte Carlo simulation experiments using empirically calibrated disruption parameters validated these statistical insights: sustainability-aware optimized policies improved mean service levels by 3–6 percentage points, reduced TTR by 15–27%, lowered backorder…
subject: Articles
  topic: section
subject: Supply Chain Resilience
  topic: keywords
subject: Sustainable Resource Allocation
  topic: keywords
subject: Stochastic Simulation
  topic: keywords
subject: Optimization
  topic: keywords
subject: Visibility
  topic: keywords
creator: Sudipto Roy
creator: Md. Hasan Imam
rights: Copyright (c) 2024 Sudipto Roy, Md. Hasan Imam (Author)
date: 2024-06-29
publication date: 2024-06-29
starting page: 01
ending page: 32
doi: 10.63125/p0ptag78

INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW
type: item
description: This systematic review investigates the performance, optimization, and governance practices of business intelligence (BI) systems with a focus on SQL-driven dashboards, streaming pipelines, cloud scalability, and organizational integration. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 142 studies published between 2005 and 2024 were systematically identified, screened, and synthesized. The review highlights several key findings. First, SQL continues to serve as the foundational language in BI, maintaining its dominance through adaptability across distributed and hybrid environments. Second, dashboards have evolved beyond their original descriptive function, now acting as governance instruments that embed compliance, accountability, and transparency into organizational workflows. Third, sectoral and regional variations demonstrate that BI adoption and optimization are shaped by contextual factors, with financial and healthcare sectors prioritizing compliance and risk management, while retail and e-commerce emphasize customer analytics and speed. Fourth, performance optimization remains a critical concern, with techniques such as query pushdown, incremental refresh, caching, and streaming pipelines shown to reduce latency and improve scalability in real-time analytics. Fifth, monitoring, testing, and maintainability practices are increasingly emphasized, aligning BI with DevOps principles and continuous integration frameworks. Additionally, cloud and hybrid scalability emerge as both technical and economic challenges, requiring elastic infrastructures balanced against cost and governance demands. Finally, the review underscores the need for conceptual integration of technical, organizational, and governance dimensions, as BI success depends on harmonizing these three axes. Collectively, the findings confirm earlier scholarship while extending it with new empirical and conceptual insights, illustrating that BI ha…
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: SQL
  topic: keywords
subject: Dashboards
  topic: keywords
subject: Governance
  topic: keywords
subject: Scalability
  topic: keywords
creator: Momena Akter
creator: Md Nur Hasan Mamun
rights: Copyright (c) 2023 Momena Akter, Md Nur Hasan Mamun (Author)
date: 2023-04-30
publication date: 2023-04-30
starting page: 01
ending page: 30
doi: 10.63125/4aa43m68

ROLE OF DIGITAL TWINS AND BIM IN U.S. HIGHWAY INFRASTRUCTURE ENHANCING ECONOMIC EFFICIENCY AND SAFETY OUTCOMES THROUGH INTELLIGENT ASSET MANAGEMENT
type: item
description: To synthesize evidence on how Building Information Modeling (BIM) and digital twins enhance economic efficiency and safety in U.S. highway infrastructure through intelligent asset management. Guided by PRISMA 2020, searches across engineering, transportation, and standards repositories (2000–August 2023) yielded 3,146 records. After removing 1,012 duplicates, 2,134 titles/abstracts were screened; 378 full texts were assessed; 92 studies were included (empirical, mixed-methods, and rigorously reported gray literature). Data were extracted on model uses, governance, and outcomes; heterogeneity precluded meta-analysis, so a structured narrative synthesis was undertaken. Evidence clustered across four domains. (1) Economic efficiency: sixty-three studies reported fewer design clashes, reduced rework and RFIs, and more stable schedules and quantities under 3D/4D/5D coordination. (2) Predictive maintenance and lifecycle value: forty-nine studies linked digital-twin condition monitoring and deterioration forecasting to earlier, better-targeted treatments and fewer emergency interventions. (3) Safety and risk reduction: fifty-two studies associated time-aware staging, traffic management visualizations, and real-time hazard indices with lower exposure in work zones and shorter detection-to-response intervals. (4) Intelligent asset management: fifty-eight studies documented benefits when digital as-builts, IFC/COBie handovers, and sensor feeds populated governed asset registers, improving auditability and budget formulation. Cross-jurisdictional comparisons (forty-one studies) showed stronger, more consistent outcomes where open standards and owner information requirements were embedded in procurement and delivery. Across ninety-two studies, BIM provides the authoritative, object-based backbone for asset information, while digital twins synchronize that information with operational state. Under interoperable standards and clear governance, this pairing consistently supports c…
subject: Articles
  topic: section
subject: BIM/Digital-Twins
  topic: keywords
subject: Highways
  topic: keywords
subject: Asset-Management
  topic: keywords
subject: Safety
  topic: keywords
subject: Efficiency
  topic: keywords
creator: Mubashir Islam
creator: MKA Shahinoor Rahman Jahid
rights: Copyright (c) 2023 Mubashir Islam, MKA Shahinoor Rahman Jahid (Author)
date: 2023-08-30
publication date: 2023-08-30
starting page: 54
ending page: 81
doi: 10.63125/hftt1g82

DATA-DRIVEN DECISION-MAKING THROUGH CUSTOMER RELATIONSHIP MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW IN MODERN ENTERPRISES
type: item
description: This study presents a systematic review of the integration between Customer Relationship Management (CRM) and Data-Driven Decision-Making (DDDM) in modern enterprises, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure rigor and transparency. A total of 1,264 records were initially identified, of which 156 studies met the inclusion criteria after duplicate removal, screening, and full-text assessment. The reviewed literature spans conceptual models, technological enablers, sector-specific applications, international perspectives, and organizational challenges. Findings indicate that CRM is conceptualized not merely as a technological system but also as a strategic orientation grounded in relationship marketing, while DDDM is positioned as both an analytical process and a managerial philosophy. Together, these frameworks enable enterprises to enhance customer insights, optimize decision-making, and strengthen long-term competitiveness. Technological enablers such as cloud computing, Software-as-a-Service platforms, artificial intelligence, machine learning, and social media analytics were identified as pivotal in transforming CRM systems into predictive and real-time decision-making tools. Sector-specific applications demonstrated consistent value in retail, e-commerce, banking, financial services, and healthcare, highlighting the adaptability of CRM-DDDM integration across industries. International perspectives revealed that while CRM-DDDM principles are globally relevant, adoption and effectiveness vary according to cultural, institutional, and digital maturity factors, underscoring the importance of contextual adaptation. Challenges were also noted, particularly organizational resistance, poor data quality, system complexity, ethical concerns, and difficulties in measuring return on investment, all of which continue to limit effectiveness. The review further identified critical debates regarding technolog…
subject: Articles
  topic: section
subject: Customer Relationship Management
  topic: keywords
subject: Data-Driven Decision-Making
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Customer Equity
  topic: keywords
subject: Systematic Review
  topic: keywords
creator: Md. Musfiqur Rahman
rights: Copyright (c) 2024 Md. Musfiqur Rahman (Author)
date: 2024-10-30
publication date: 2024-10-30
starting page: 30
ending page: 59
doi: 10.63125/vgr9th76

COMPARATIVE ANALYSIS OF PETROLEUM INFRASTRUCTURE PROJECTS IN SOUTH ASIA AND THE US USING ADVANCED GAS TURBINE ENGINE TECHNOLOGIES FOR CROSS INTEGRATION
type: item
description: This study presents a comparative analysis of petroleum infrastructure projects in South Asia and the United States, with a focus on the role of advanced gas turbine engine technologies in enabling effective cross integration between fuel supply systems and power generation assets. The research followed a systematic methodology to ensure transparency and rigor, drawing upon a comprehensive body of 92 peer-reviewed articles and technical reports, collectively cited over 1,850 times, to evaluate infrastructure maturity, technological deployment, governance structures, operational resilience, and environmental performance across both regions. The findings highlight stark contrasts: the United States demonstrates a highly developed and interconnected petroleum infrastructure anchored by extensive pipelines, underground storage, LNG export facilities, and a diverse mix of heavy-duty and aeroderivative turbines optimized for flexible grid participation. South Asia, in contrast, exhibits a more fragmented infrastructure profile characterized by concentrated LNG terminals, limited cross-border pipelines, and heavy reliance on baseload combined-cycle units that often face challenges linked to fuel quality variability, maintenance dependence, and climatic vulnerabilities. Environmental and safety standards also diverge significantly, with the U.S. operating under stringent regulatory regimes and continuous monitoring systems, while South Asia faces weaker enforcement capacity and greater exposure to natural hazards. Governance and finance emerge as pivotal factors, as U.S. projects benefit from mature markets and diversified capital flows, whereas South Asian projects remain heavily reliant on donor financing and sovereign guarantees. The study concludes that cross integration is not solely a technical challenge but one shaped by infrastructure maturity, institutional strength, and environmental resilience. By synthesizing insights from a broad literature base, this research …
subject: Articles
  topic: section
subject: Petroleum Infrastructure
  topic: keywords
subject: Gas Turbines
  topic: keywords
subject: Cross Integration
  topic: keywords
subject: South Asia
  topic: keywords
subject: United States
  topic: keywords
creator: Sabuj Kumar Shil
creator: Zobayer Eusufzai
rights: Copyright (c) 2022 Sabuj Kumar Shil, Zobayer Eusufzai (Author)
date: 2022-12-25
publication date: 2022-12-25
starting page: 123
ending page: 147
doi: 10.63125/wr93s247

PROSPECT OF USING AI- INTEGRATED SMART MEDICAL TEXTILES FOR REAL-TIME VITAL SIGNS MONITORING IN HOSPITAL MANAGEMENT & HEALTHCARE INDUSTRY
type: item
description: This study explores the prospect of using AI-integrated smart medical textiles for real-time vital signs monitoring in hospital management and the broader healthcare industry. Smart textiles, which embed biosensors and conductive materials within wearable fabrics, have evolved from experimental prototypes into clinically relevant tools capable of continuously tracking physiological parameters such as cardiac activity, respiratory function, body temperature, oxygen saturation, blood flow, and muscle activity. The integration of artificial intelligence enables advanced signal processing, artifact suppression, predictive modeling, and real-time classification of health events, thereby transforming raw data into actionable clinical insights. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 152 peer-reviewed articles were systematically reviewed, collectively cited more than fifteen thousand times, ensuring a comprehensive synthesis of technological, clinical, operational, and regulatory perspectives. The findings revealed that AI-enhanced smart textiles deliver accuracy comparable to conventional monitoring devices while offering advantages in comfort, mobility, and continuous data capture. Hospitals implementing these systems reported operational benefits such as reduced nurse workload, improved alarm management, and expanded monitoring coverage, alongside clinical gains including earlier detection of patient deterioration, reductions in adverse events, shortened lengths of stay, and decreased readmissions. International case studies highlighted adoption across Europe, North America, and Asia-Pacific, while regulatory discussions underscored the importance of compliance with FDA, EMA, ISO, and IEC standards for successful deployment. Beyond inpatient care, the review identified applications in rehabilitation, chronic disease management, maternal and neonatal monitoring, occupational health, and telehealth …
subject: Articles
  topic: section
subject: Smart Medical Textiles
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Vital Signs Monitoring
  topic: keywords
subject: Hospital Management
  topic: keywords
subject: Healthcare Innovation
  topic: keywords
creator: Md Rezaul Karim
creator: Md Takbir Hossen Sarker
rights: Copyright (c) 2024 Md Rezaul Karim, Md Takbir Hossen Sarker (Author)
date: 2024-10-30
publication date: 2024-10-30
starting page: 01
ending page: 29
doi: 10.63125/d0zkrx67

EVALUATING AGILE BUSINESS ANALYSIS IN POST-COVID RECOVERY A COMPARATIVE STUDY ON FINANCIAL RESILIENCE
type: item
description: This study examines the role of agile business analysis in strengthening financial resilience during the post-COVID recovery period, with a focus on organizational adaptability across different industries, firm sizes, and regional contexts. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a total of 86 scholarly articles were systematically reviewed, covering both empirical and conceptual contributions. The analysis reveals that agile business analysis enhances resilience through mechanisms such as liquidity preservation, cost efficiency, improved revenue durability, and the integration of financial guardrails into iterative decision-making processes. The findings demonstrate that while small and medium-sized enterprises primarily rely on agile practices to ensure short-term survival and cash preservation, large multinationals employ scaled frameworks to balance global strategies with localized adaptation. Sectoral variations highlight the flexibility of agile analysis, as healthcare, retail, technology, and manufacturing organizations adapted its practices to their specific challenges. Regional comparisons further show that institutional, cultural, and regulatory factors shape the way agile analysis contributes to financial resilience, with significant differences observed between North America, Europe, and Asia-Pacific. The study also underscores the importance of embedding governance, compliance, and risk management within agile practices to balance adaptability with accountability. Collectively, the evidence positions agile business analysis as a strategic capability that extends beyond project delivery to function as a financial resilience framework. By synthesizing insights from 86 reviewed studies, this research contributes to the understanding of how agile practices enable organizations to recover effectively, safeguard financial stability, and sustain performance in environments characterized by volatility and…
subject: Articles
  topic: section
subject: Agile Business Analysis Strengthens Resilience
  topic: keywords
subject: Financial Adaptability Supports Organizational Survival
  topic: keywords
subject: Post-COVID Recovery Reshapes Business Practices
  topic: keywords
creator: Md Hasan Zamil
creator: Moin Uddin Mojumder
rights: Copyright (c) 2022 Md Hasan Zamil, Moin Uddin Mojumder (Author)
date: 2022-09-30
publication date: 2022-09-30
starting page: 01
ending page: 28
doi: 10.63125/6nee1m28

IMPACT OF PREDICTIVE DATA MODELING ON BUSINESS DECISION-MAKING: A REVIEW OF STUDIES ACROSS RETAIL, FINANCE, AND LOGISTICS
type: item
description: This study investigates how predictive data modeling influences business decision-making across retail, finance, and logistics, emphasizing the practices that convert predictive accuracy into measurable organizational impact. Evidence from 100 peer-reviewed empirical studies linking predictive outputs to operational actions such as inventory replenishment, dynamic pricing, credit approvals, fraud triage, routing optimization, and service-level promise windows was synthesized. Studies employing temporal validation, probability calibration, explicit operating thresholds, and structured translation into operational policies reported business improvements in 93 percent of cases, achieving median gains of approximately 9–12 percent on the primary KPI. In contrast, minimally aligned designs succeeded in only 48 percent of cases, with modest gains of about 3–5 percent. Sector-specific results revealed consistent yet domain-sensitive patterns. In retail, hierarchical forecasting methods and decision-aware pricing systems yielded a median 2.8 percentage-point reduction in stockouts and a 2.2 percent revenue lift when forecast distributions were directly integrated into service curves and inventory or pricing rules. In finance, calibrated scorecards, cost-sensitive thresholds, and temporally validated probability estimates reduced expected credit loss by nearly 8 percent at constant approval rates or raised approvals by approximately 3.5 percentage points at constant risk levels. Fraud detection and anti–money laundering systems achieved a median 22 percent reduction in false positives while improving cost per true positive when precision–recall evaluation, network features, and workload-aware thresholds informed operational decision-making. In logistics, uncertainty-aware demand and travel-time prediction models enhanced on-time delivery by about 3.9 percentage points and reduced routing costs by nearly 6 percent when embedded into promise windows, lateness-penalty formulati…
subject: Articles
  topic: section
subject: Predictive Data Modeling
  topic: keywords
subject: Business Decision Making
  topic: keywords
subject: PRISMA
  topic: keywords
subject: Retail
  topic: keywords
subject: Finance
  topic: keywords
subject: Logistics
  topic: keywords
subject: Calibration
  topic: keywords
subject: Operating Threshold
  topic: keywords
creator: Md Redwanul Islam
creator: Md. Zafor Ikbal
rights: Copyright (c) 2022 Md Redwanul Islam, Md. Zafor Ikbal (Author)
date: 2022-06-30
publication date: 2022-06-30
starting page: 33
ending page: 62
doi: 10.63125/8hfbkt70

GRAPH NEURAL NETWORK MODELS FOR DETECTING FRAUDULENT INSURANCE CLAIMS IN HEALTHCARE SYSTEMS
type: item
description: Fraudulent insurance claims in healthcare systems represent a persistent and costly challenge, undermining the efficiency, equity, and sustainability of healthcare delivery worldwide. Traditional approaches to fraud detection, including rule-based systems and statistical models, have provided valuable early insights but often fail to capture the complex, relational, and evolving nature of fraudulent behavior. This study addresses these limitations by investigating the application of Graph Neural Networks (GNNs) as an advanced analytical framework for detecting fraudulent claims. By representing patients, providers, and claims as interconnected nodes within graph structures, GNNs leverage relational dependencies and structural patterns that are frequently overlooked by conventional models. The research employed a mixed-methods design, combining quantitative experimentation with multiple GNN architectures—such as Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE—with qualitative insights gathered from healthcare fraud investigators to evaluate interpretability and usability. The quantitative findings revealed that GNN models consistently achieved higher accuracy, precision, and recall compared to traditional classifiers, while the qualitative analysis highlighted the importance of interpretability and visualization in building stakeholder trust and improving investigative efficiency. Importantly, the literature review synthesized evidence from 112 peer-reviewed studies, providing a comprehensive overview of existing fraud detection methods and situating GNNs within the broader progression of healthcare fraud research. The results underscore that GNNs not only advance the technical accuracy of fraud detection but also offer practical tools for detecting collusive fraud networks and managing large-scale, heterogeneous claims data. This study contributes to both academic discourse and practical applications by demonstrating that GNNs are uniquely posi…
subject: Articles
  topic: section
subject: Graph Neural Networks (GNNs)
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Healthcare Insurance
  topic: keywords
creator: Md. Tarek Hasan
rights: Copyright (c) 2022 Md. Tarek Hasan (Author)
date: 2022-04-30
publication date: 2022-04-30
starting page: 88
ending page: 109
doi: 10.63125/r5vsmv21

QUANTITATIVE RISK ASSESSMENT OF RAIL INFRASTRUCTURE PROJECTS USING MONTE CARLO SIMULATION AND FUZZY LOGIC
type: item
description: Rail infrastructure programs frequently face complex, intertwined risks spanning cost overruns, schedule delays, safety concerns, and interface uncertainties. This systematic review critically examines how quantitative risk assessment (QRA) methods—Monte Carlo simulation (MCS), fuzzy logic (FL), and hybrid approaches—have been employed across the rail project lifecycle to manage these multidimensional challenges. Following PRISMA guidelines, we conducted a comprehensive search across Scopus, Web of Science, IEEE Xplore, ASCE Library, ScienceDirect, and TRB databases, applied pre-registered eligibility criteria, implemented double-screening for study inclusion, and rigorously appraised methodological practices encompassing data provenance, dependence modeling, validation, and sensitivity analysis. From an initial pool of studies, 95 peer-reviewed publications met all inclusion standards. Findings indicate MCS dominates (47%) owing to its strength in producing distributional forecasts and governance-ready percentiles; FL supports imprecise or linguistic inputs (33%) often encountered during early-stage planning and safety screening; while hybrid models (20%) bridge probabilistic propagation and evidential uncertainty, particularly in interface-intensive phases. Applications cluster within construction (68%), followed by design (46%), feasibility analysis (39%), testing/commissioning (24%), and operations & maintenance (21%). Methodologically, MCS studies primarily use triangular and PERT/beta distributions, with approximately 42% employing Latin hypercube sampling. However, dependence modeling remains limited—38% of studies assume independence, 23% employ rank or copula methods, and only 31% jointly simulate cost–schedule interactions. FL studies typically apply triangular/trapezoidal membership functions with centroid defuzzification; while two-thirds disclose reproducible rule bases, one-third lack transparency. Hybrid models frequently convert fuzzy assessments int…
subject: Articles
  topic: section
subject: QRA
  topic: keywords
subject: rail infrastructure
  topic: keywords
subject: Monte Carlo
  topic: keywords
subject: fuzzy logic
  topic: keywords
subject: hybrid models
  topic: keywords
subject: cost–schedule risk
  topic: keywords
subject: dependence modeling
  topic: keywords
creator: Md. Sakib Hasan Hriday
rights: Copyright (c) 2022 Md. Sakib Hasan Hriday (Author)
date: 2022-04-30
publication date: 2022-04-30
starting page: 55
ending page: 87
doi: 10.63125/h24n6z92

QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELING OF BIOACTIVE COMPOUNDS FROM MANGIFERA INDICA FOR ANTI-DIABETIC DRUG DEVELOPMENT
type: item
description: This study evaluates how quantitative structure–activity relationship (QSAR) modeling can accelerate anti-diabetic drug discovery from Mangifera indica (mango) phytochemicals. Diabetes mellitus remains a global health burden, and natural products represent an abundant but under-optimized resource for therapeutic leads. To address this gap, we conducted a comprehensive screening of major scientific databases and ultimately analyzed 113 peer-reviewed studies that reported computable molecular structures, harmonizable bioactivity endpoints, and reproducible modeling workflows. The review focused on clinically relevant antidiabetic targets including α-glucosidase, α-amylase, dipeptidyl peptidase-4 (DPP-4), and protein tyrosine phosphatase 1B (PTP1B). Across the included studies, curated datasets were normalized to pIC₅₀ and pKᵢ scales to enable meaningful comparisons, while feature engineering spanned physicochemical descriptors, topological indices, and molecular fingerprints such as ECFP and MACCS. Machine learning approaches ranged from penalized regression models to advanced ensemble algorithms (e.g., boosting, bagging, and kernel-based methods), with rigorous validation achieved through scaffold-aware data splits, external test sets, Y-randomization, and explicit applicability domain (AD) assessment. Convergent lines of evidence—including QSAR predictivity within AD, mechanistically plausible docking, and ADME/toxicity filtering—consistently highlighted polyphenolic chemotypes, particularly xanthones such as mangiferin and its aglycone norathyriol, as promising inhibitors of carbohydrate-metabolizing enzymes. In contrast, scaffolds targeting signaling enzymes (DPP-4 and PTP1B) demanded early consideration of selectivity, pharmacokinetics, and off-target liabilities to improve translational viability. This synthesis also identifies recurring optimism traps, such as reliance on internal-only validation, assay heterogeneity across studies, and insufficient reporting o…
subject: Articles
  topic: section
subject: QSAR
  topic: keywords
subject: Mangifera Indica
  topic: keywords
subject: Mangiferin
  topic: keywords
subject: Polyphenols
  topic: keywords
subject: Α-Glucosidase
  topic: keywords
subject: Α-Amylase
  topic: keywords
subject: DPP-4
  topic: keywords
subject: PTP1B
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Applicability Domain
  topic: keywords
subject: Molecular Docking
  topic: keywords
creator: Sadia Tasnim
rights: Copyright (c) 2022 Sadia Tasnim (Author)
date: 2022-06-30
publication date: 2022-06-30
starting page: 01
ending page: 32
doi: 10.63125/ffkez356

EVALUATING THE EFFECTIVENESS OF AI-GENERATED, CONTEXT-SPECIFIC CASE STUDIES IN IMPROVING CRITICAL THINKING SKILLS IN ODL
type: item
description: The advancement of artificial intelligence (AI) in education has created new opportunities to enhance learner-centered pedagogies, particularly in open and distance learning (ODL) systems where geographic, cultural, and infrastructural challenges often limit instructional effectiveness. Critical thinking, defined as the ability to analyze, evaluate, and synthesize information for evidence-based judgment and problem-solving, remains a central educational outcome and a global competency emphasized by higher education frameworks. Case-based learning has long been recognized as an effective approach for fostering critical thinking; however, static case materials often lack contextual relevance and adaptability for diverse learner populations in ODL contexts. This study investigated the effectiveness of AI-generated, context-specific case studies in improving critical thinking skills among Bangladeshi ODL learners. A quasi-experimental design was employed with pre-test and post-test measures administered to an experimental group (n = 100) and a control group (n = 100). The experimental group interacted with AI-generated cases designed to provide personalization, authenticity, adaptive feedback, and active engagement, while the control group studied traditional instructor-developed case materials. Findings revealed significant improvements in the experimental group’s critical thinking performance, with large effect sizes on post-test scores compared to the control group. Regression analysis confirmed that personalization, engagement, authenticity, and adaptive feedback each had a strong positive effect on critical thinking outcomes, with path coefficients ranging between β = .27 and β = .46. Structural equation modeling further demonstrated that these four variables collectively explained 68% of the variance in critical thinking development, confirming the overall model hypothesis. The results underscore the effectiveness of AI-generated case studies in addressing key lim…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Case-Based Learning
  topic: keywords
subject: Critical Thinking Skills
  topic: keywords
subject: Open and Distance Learning (ODL)
  topic: keywords
subject: Context-Specific Pedagogy
  topic: keywords
creator: Md Arif Uz Zaman
rights: Copyright (c) 2024 Md Arif Uz Zaman (Author)
date: 2024-12-29
publication date: 2024-12-29
starting page: 83
ending page: 106
doi: 10.63125/apftx366

COST-BENEFIT ANALYSIS IN PRE-CONSTRUCTION PLANNING: THE ASSESSMENT OF ECONOMIC IMPACT IN GOVERNMENT INFRASTRUCTURE PROJECTS
type: item
description: This study presents a systematic literature review of 100 peer-reviewed, DOI-indexed articles examining the application of cost–benefit analysis (CBA) in the pre-construction stage of government infrastructure projects. Guided by the PRISMA framework, the review synthesizes evidence from transport, energy, water, and social infrastructure sectors to evaluate how welfare-consistent economic impact assessment is operationalized before major public investment decisions are finalized. Inclusion criteria required an explicit counterfactual (do-nothing or do-minimum baseline), monetized benefits and costs derived from willingness-to-pay or shadow-pricing methods, and clearly stated decision indicators including net present value (NPV), benefit–cost ratio (BCR), and internal rate of return (IRR) with a transparent price base and discount rate. The analysis explores eight thematic domains: welfare-consistent definitions and indicators, appraisal regimes and parameter libraries, baseline demand and exposure modeling, valuation of user benefits, safety, and environmental externalities, discounting and time horizons, uncertainty architecture, distributional analysis, and treatment of wider economic impacts. Findings reveal that transport dominates the literature at 52 percent, followed by energy at 20 percent, water at 18 percent, and social infrastructure at 10 percent. Most studies employ do-minimum baselines (84 percent) and constant real discount rates (72 percent) with growing adoption of declining schedules for long-lived assets. While deterministic sensitivity testing is universal, fewer studies employ probabilistic analysis (37 percent) or reference-class adjustments (21 percent). The review identifies strengths such as increasing parameter transparency, improved handling of induced demand, and avoidance of double counting as well as gaps in distributional weighting, probabilistic robustness, and integration of wider impacts. The synthesis offers a standardized reporti…
subject: Articles
  topic: section
subject: Cost–Benefit Analysis
  topic: keywords
subject: Pre-Construction Planning
  topic: keywords
subject: Government Infrastructure
  topic: keywords
subject: Welfare Economics
  topic: keywords
subject: Economic Impact Assessment
  topic: keywords
subject: Net Present Value (NPV)
  topic: keywords
creator: Mubashir Islam
creator: Abdul Rehman
rights: Copyright (c) 2022 Mubashir Islam, Abdul Rehman (Author)
date: 2022-12-30
publication date: 2022-12-30
starting page: 91
ending page: 122
doi: 10.63125/kjwd5e33

ANALYSIS OF AI CONTRIBUTION TOWARDS REDUCING FUTURE PANDEMIC LOSS IN SME SECTOR: ACCESS TO ONLINE MARKETING AND YOUTH INVOLVEMENT
type: item
description: The COVID-19 pandemic imposed unprecedented operational disruptions on small and medium-sized enterprises (SMEs), with youth-led businesses emerging as one of the most vulnerable segments due to limited capital reserves, constrained digital infrastructure, and dependence on in-person transactions. Lockdowns, mobility restrictions, and shifts in consumer demand patterns accelerated the imperative for digital transformation, positioning artificial intelligence (AI) as a strategic enabler of business continuity, market adaptation, and resilience. This study investigates the role of AI-enabled tools—specifically customer interaction systems, predictive analytics, social media marketing automation, eCommerce enablement, and personalization—in supporting the survival and competitive positioning of youth-led SMEs during the pandemic. Findings reveal that AI-driven customer interaction tools maintained service continuity and reduced operational costs by up to 40%, with youth-led SMEs exhibiting greater adaptability in deploying low-code AI due to higher digital fluency. Cross-regional comparisons underscore disparities in adoption depth, shaped by infrastructural readiness, data quality, AI literacy, and regulatory frameworks. While developed economies leveraged advanced digital ecosystems for comprehensive AI integration, SMEs in developing regions often adopted modular, low-code solutions to circumvent skill and infrastructure constraints. The study also identifies persistent ethical and governance challenges—such as algorithmic bias, privacy risks, and uneven access—that were deprioritized in favor of immediate operational survival. These findings contribute to the growing body of literature on AI-enabled SME resilience by highlighting the unique absorptive capacities of youth-led enterprises, the synergistic effects of integrating multiple AI applications, and the contextual factors mediating adoption outcomes. The paper concludes by proposing a conceptual framework for…
subject: Articles
  topic: section
subject: AI Use
  topic: keywords
subject: Pandemic
  topic: keywords
subject: Online Marketing
  topic: keywords
subject: SME
  topic: keywords
subject: Technological Progress
  topic: keywords
subject: Youth Involvement
  topic: keywords
creator: Danish Mahmud
rights: Copyright (c) 2023 Danish Mahmud (Author)
date: 2023-08-18
publication date: 2023-08-18
starting page: 32
ending page: 53
doi: 10.63125/y4cb4337

MACHINE LEARNING-ENHANCED STATISTICAL INFERENCE FOR CYBERATTACK DETECTION ON NETWORK SYSTEMS
type: item
description: This study presents a comprehensive systematic review of 126 peer-reviewed publications on the integration of machine learning and statistical inference for cyberattack detection in network systems. The primary objective is to critically evaluate how adaptive computational models, when combined with probabilistic reasoning frameworks, enhance detection accuracy, interpretability, and operational efficiency in dynamic and evolving cyber threat landscapes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research process ensured transparency, methodological rigor, and replicability during literature identification, screening, and synthesis. The reviewed studies encompass a broad spectrum of machine learning paradigms—supervised, unsupervised, hybrid, and deep learning architectures—integrated with statistical inference methods such as Bayesian updating, likelihood estimation, hypothesis testing, probabilistic calibration, and statistical drift detection. Evidence consistently demonstrates that these integrated frameworks achieve superior true positive rates, reduced false positives, and greater resilience against zero-day and polymorphic attacks compared to traditional rule-based or signature-based systems. Notably, the studies highlight the pivotal role of dataset quality, diversity, and timeliness, with optimal results achieved when recent, representative data are combined with statistical preprocessing, dimensionality reduction, and adaptive feature selection techniques. Operational challenges in real-time deployment—such as minimizing latency, optimizing computational resources, and sustaining adaptability—are effectively addressed through innovations like lightweight statistical screening layers, adaptive thresholding, and distributed processing. Comparative experimental results further validate that integrated approaches deliver measurable improvements not only in technical detection metrics but also in sc…
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: statistical inference
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: network systems
  topic: keywords
creator: Md.Kamrul Khan
creator: Md Omar Faruq
rights: Copyright (c) 2022 Md.Kamrul Khan, Md Omar Faruq (Author)
date: 2022-12-30
publication date: 2022-12-30
starting page: 65
ending page: 90
doi: 10.63125/sw7jzx60

REAL-TIME CYBERSECURITY INTEGRATION IN PLCS AND IOT GATEWAYS: A PERFORMANCE-CONSCIOUS APPROACH TO ENCRYPTION, AUTHENTICATION, AND INTRUSION DETECTION
type: item
description: The integration of real-time cybersecurity mechanisms into embedded systems has become a critical priority in the context of Industry 4.0, where programmable logic controllers (PLCs) and Internet of Things (IoT) gateways serve as foundational components of industrial automation. These devices are increasingly exposed to cyber-physical threats as they connect to broader networked infrastructures, yet their computational constraints and real-time operational requirements often preclude the use of conventional security measures. This study proposes and evaluates a performance-conscious cybersecurity framework designed to embed lightweight encryption, mutual authentication, and decentralized intrusion detection directly within industrial control devices. The research methodology involved the development of a modular security architecture tested on commercial-grade PLCs and IoT gateways, utilizing communication protocols such as Modbus/TCP, OPC UA, MQTT, and Profinet. Lightweight encryption algorithms, including SPECK, LEA, and ChaCha20, were implemented alongside elliptic curve-based authentication schemes to assess their latency, throughput, and system resource impact. Additionally, hybrid intrusion detection models—combining statistical baselining and anomaly detection—were deployed locally on embedded nodes to monitor network behavior in real time. The findings demonstrate that full-spectrum security can be achieved in embedded systems without disrupting deterministic control cycles. Encryption tasks remained within sub-millisecond boundaries, mutual authentication completed with minimal handshake delay, and intrusion detection maintained over 94% accuracy with low false positives. Resource utilization across CPU, memory, and power remained within device tolerances, and the architecture sustained operational stability during prolonged testing. The study confirms that embedded cybersecurity, when implemented with algorithmic efficiency and system-aware scheduling, can…
subject: Articles
  topic: section
subject: Real-Time Cybersecurity
  topic: keywords
subject: Programmable Logic Controllers (PLCs)
  topic: keywords
subject: IoT Gateways
  topic: keywords
subject: Intrusion Detection Systems (IDS)
  topic: keywords
subject: Lightweight Encryption
  topic: keywords
creator: H M Shamsuzzaman
creator: MD Mosleuzzaman
creator: Mohammad Shah Paran
rights: Copyright (c) 2025 H M Shamsuzzaman, MD Mosleuzzaman, Mohammad Shah Paran (Author)
date: 2025-07-29
publication date: 2025-07-29
starting page: 32
ending page: 57
doi: 10.63125/g937f918

CYBERCRIME, LEGAL ACCOUNTABILITY, AND CONTRACTUAL RISK: A SYSTEMATIC REVIEW OF JURISPRUDENCE AND PROTECTIVE FRAMEWORKS
type: item
description: This systematic review investigates the evolving legal intersection between cybercrime and contractual liability, with a focus on how courts, regulators, and contracting parties address cybersecurity risks through enforceable legal frameworks. Drawing upon the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, a total of 87 peer-reviewed articles, case law commentaries, and legal-technical policy studies representing over 1,300 cumulative citations were rigorously analyzed to identify prevailing legal doctrines, risk mitigation practices, and enforcement trends. The review explores key thematic areas including the rise of cybersecurity-specific clauses in digital service agreements, the misalignment between cyber insurance policies and commercial contracts, the legal treatment of third-party vendor breaches, and the contractual implications of data protection regulations such as the GDPR, CCPA, and HIPAA. Findings reveal a clear doctrinal shift: courts are increasingly recognizing cybersecurity failures as breaches of contract, especially when they violate performance warranties or industry standards. Furthermore, vague or boilerplate clauses have proven ineffective during litigation, underscoring the importance of specificity and alignment with technical benchmarks such as NIST and ISO/IEC standards. The review also identifies a growing reliance on Data Processing Agreements (DPAs), Standard Contractual Clauses (SCCs), and enforceable indemnity and audit rights to manage legal risk in complex digital ecosystems. High-profile cases such as Merck v. ACE Insurance, Schrems II, and the British Airways GDPR enforcement illustrate how regulatory action and private litigation are catalyzing more rigorous contract drafting and cyber risk governance. Overall, the study concludes that in the face of rising transnational cyber threats, contractual instruments must evolve beyond static legal templates to become dynamic tools of compliance,…
subject: Articles
  topic: section
subject: Cybercrime
  topic: keywords
subject: Contractual Liability
  topic: keywords
subject: Cybersecurity Law
  topic: keywords
subject: Data Protection Regulations
  topic: keywords
subject: Cyber Insurance
  topic: keywords
creator: Md Nazrul Islam Khan
creator: Md Soyeb Rabbi
rights: Copyright (c) 2024 Md Nazrul Islam Khan, Md Soyeb Rabbi (Author)
date: 2024-04-21
publication date: 2024-04-21
starting page: 71
ending page: 100
doi: 10.63125/228bwz17

CUSTOMER RELATIONSHIP MANAGEMENT AND DATA-DRIVEN DECISION-MAKING IN MODERN ENTERPRISES: A SYSTEMATIC LITERATURE REVIEW
type: item
description: In an era where digital transformation and customer-centricity drive competitive differentiation, the integration of Customer Relationship Management (CRM) systems with Data-Driven Decision-Making (DDD) practices has emerged as a critical enterprise capability. This study presents a systematic literature review and meta-analysis of empirical research conducted between 2010 and 2024, synthesizing findings from 78 peer-reviewed articles across disciplines including marketing, information systems, and organizational science. The primary objective was to evaluate the effectiveness of CRM-DDD integration on organizational performance and to identify functional, sectoral, and contextual factors influencing these outcomes. A rigorous search strategy, guided by PRISMA standards, was used to extract eligible studies from five major academic databases. Meta-analytic procedures were performed using a random-effects model to account for heterogeneity across industries, geographies, and CRM configurations. The analysis revealed a statistically significant and moderately strong overall effect size (r = 0.46), affirming that CRM systems embedded with analytics capabilities lead to superior customer satisfaction, retention, marketing ROI, and strategic responsiveness. Among CRM functionalities, analytical CRM demonstrated the highest impact (r = 0.52), followed by collaborative (r = 0.44) and operational CRM (r = 0.37), indicating that insight generation and cross-functional alignment are central to maximizing CRM value. Sectoral analyses showed that CRM-DDD integration yields the greatest benefits in retail, finance, and healthcare, while also delivering measurable gains in B2B and industrial environments. Moderator analyses further revealed that effect sizes were stronger in developed economies, large enterprises, and organizations with advanced digital maturity. Robust statistical diagnostics confirmed the stability and replicability of findings, while theoretical triangulation …
subject: Articles
  topic: section
subject: Customer Relationship Management (CRM)
  topic: keywords
subject: Data-Driven Decision-Making
  topic: keywords
subject: Business Intelligence (BI)
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Customer Analytics
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Tahmina Akter Rainy
creator: Md Arifur Rahman
creator: Anika Jahan Mou
rights: Copyright (c) 2024 Tahmina Akter Rainy, Md Arifur Rahman, Anika Jahan Mou (Author)
date: 2024-04-29
publication date: 2024-04-29
starting page: 57
ending page: 82
doi: 10.63125/jetvam38

IN SILICO DRUG REPURPOSING FOR INFLAMMATORY DISEASES: A SYSTEMATIC REVIEW OF MOLECULAR DOCKING AND VIRTUAL SCREENING STUDIES
type: item
description: The escalating global burden of inflammatory diseases—marked by persistent immune dysregulation, multisystem involvement, and complex molecular etiologies—has intensified the need for innovative therapeutic strategies that minimize cost, reduce development timelines, and increase success rates. Drug repurposing, the practice of identifying new therapeutic uses for existing drugs, has emerged as a strategic alternative to de novo drug discovery, particularly through in silico methodologies such as molecular docking, virtual screening, and cheminformatics-guided candidate selection. This systematic review synthesizes and evaluates recent advancements in computational repurposing approaches aimed at inflammatory disorders, including rheumatoid arthritis, inflammatory bowel disease, psoriasis, and systemic lupus erythematosus. Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted across multiple scientific databases—including PubMed, Scopus, Web of Science, Embase, and IEEE Xplore—to identify peer-reviewed studies published between January 2010 and April 2022. A total of 65 articles met the inclusion criteria, encompassing diverse in silico workflows that examined drug-target interactions using molecular docking platforms such as AutoDock, AutoDock Vina, Schrödinger’s Glide, MOE, and GOLD, often combined with ADMET profiling tools (e.g., SwissADME, pkCSM) and molecular dynamics simulations to validate binding stability. Target proteins of interest commonly included pro-inflammatory mediators such as TNF-α, IL-6, IL-1β, JAK1/2, and NF-κB, with FDA-approved kinase inhibitors and anti-cancer drugs frequently emerging as high-affinity binders suitable for cross-disease application. In addition, the review documents methodological convergence in scoring thresholds, ligand library design, and reproducibility standards across computational studies. Several case studies demonstrate successful downstream validation of in silico predictions via in …
subject: Articles
  topic: section
subject: Drug Repurposing
  topic: keywords
subject: Molecular Docking
  topic: keywords
subject: Virtual Screening
  topic: keywords
subject: Inflammatory Diseases
  topic: keywords
subject: In Silico Pharmacology
  topic: keywords
creator: Mansura Akter Enni
creator: Md Abdul Ahad Maraj
rights: Copyright (c) 2022 Mansura Akter Enni, Md Abdul Ahad Maraj (Author)
date: 2022-12-29
publication date: 2022-12-29
starting page: 35
ending page: 64
doi: 10.63125/j1hbts51

THE ROLE OF PERCEIVED ENVIRONMENTAL RESPONSIBILITY IN ARTIFICIAL INTELLIGENCE-ENABLED RISK MANAGEMENT AND SUSTAINABLE DECISION-MAKING
type: item
description: As artificial intelligence (AI) becomes increasingly embedded in sustainability governance, understanding the social and ethical contexts that influence its deployment is critical. This study investigates the role of Perceived Environmental Responsibility (PER) as a central construct in moderating stakeholder trust, enhancing AI adoption, and improving the performance of AI-enabled risk management systems in environmentally sensitive decision-making. While AI offers capabilities such as predictive analytics, real-time monitoring, and environmental impact simulations, its effectiveness is often shaped by how stakeholders interpret the intentions and values of the organizations that deploy these technologies. This research explores the hypothesis that PER not only influences stakeholder acceptance of AI but also determines organizational readiness, transparency outcomes, and alignment with sustainability objectives. Using a systematic meta-analysis approach guided by PRISMA methodology, the study synthesizes findings from 122 peer-reviewed articles published between 2010 and 2024. The findings reveal that PER functions as both a moderating and mediating variable: it amplifies trust in AI-based decisions, enhances stakeholder interpretability of complex models, and improves strategic integration of AI into environmental, social, and governance (ESG) frameworks. Organizations with high PER demonstrated stronger performance in AI-based compliance reporting, emissions management, and environmental decision-making, while those with low PER experienced reputational risk, stakeholder skepticism, and underutilization of AI tools. The study contributes to the literature on stakeholder theory, responsible innovation, and sustainable technology adoption by positioning PER as a foundational variable in ethical AI integration. It also aligns PER with the principles of the Triple Bottom Line (TBL) and ESG reporting, emphasizing its operational and reputational importance in digital…
subject: Articles
  topic: section
subject: Perceived Environmental Responsibility
  topic: keywords
subject: AI-Enabled Risk Management
  topic: keywords
subject: Sustainable Decision-Making
  topic: keywords
subject: ESG Integration
  topic: keywords
subject: Stakeholder Trust
  topic: keywords
creator: Subrato Sarker
creator: Md. Nuruzzaman
rights: Copyright (c) 2024 Subrato Sarker, Md. Nuruzzaman (Author)
date: 2024-04-29
publication date: 2024-04-29
starting page: 33
ending page: 56
doi: 10.63125/7tjw3767

BEHAVIORAL FACTORS IN LOAN DEFAULT PREDICTION A LITERATURE REVIEW ON PSYCHOLOGICAL AND SOCIOECONOMIC RISK INDICATORS
type: item
description: This systematic review investigates the psychological and socioeconomic risk indicators that influence loan default behavior, aiming to bridge the gap between traditional credit assessment models and emerging behavioral insights. As financial institutions increasingly face challenges in accurately predicting borrower defaults, it becomes crucial to explore non-traditional variables such as cognitive biases, personality traits, financial literacy, income volatility, and employment stability. Drawing on a comprehensive synthesis of 67 peer-reviewed studies published between 2010 and 2024, this review analyzes a wide range of empirical evidence from diverse geographical, cultural, and lending contexts. The findings indicate that behavioral factors particularly impulsivity, time-inconsistency, and overconfidence play a critical role in undermining repayment discipline, especially when compounded by limited financial literacy and socioeconomic instability. Moreover, the review highlights the growing use of behavioral interventions, such as personalized nudges, commitment devices, and financial education tools, which have shown measurable effectiveness in reducing default rates. The integration of behavioral analytics into credit risk assessment, as seen in emerging hybrid models, represents a shift toward more holistic, personalized, and accurate prediction frameworks. Additionally, the review underscores the importance of tailoring financial products and risk models to cultural and contextual realities, particularly in underserved markets. By synthesizing interdisciplinary research across economics, psychology, and finance, this study provides a comprehensive framework for understanding the multidimensional drivers of loan default and offers strategic insights for lenders, policymakers, and fintech innovators aiming to enhance creditworthiness assessment and borrower support systems.
subject: Articles
  topic: section
subject: Loan default prediction
  topic: keywords
subject: Behavioral finance
  topic: keywords
subject: Psychological risk indicators
  topic: keywords
subject: Socioeconomic factors
  topic: keywords
subject: Financial literacy
  topic: keywords
creator: Abdullah Al Maruf
creator: Md Masud Kowsar
creator: Mohammad Mohiuddin
creator: Hosne Ara Mohna
rights: Copyright (c) 2024 Abdullah Al Maruf, Md Masud Kowsar, Mohammad Mohiuddin, Hosne Ara Mohna (Author)
date: 2024-05-02
publication date: 2024-05-02
starting page: 43
ending page: 70
doi: 10.63125/0jwtbn29

A REVIEW OF AI-POWERED DATA VISUALIZATION IN ENTERPRISE REPORTING: DASHBOARD DESIGN AND INTERACTIVE ANALYTICS
type: item
description: The increasing complexity and volume of data within modern enterprises have significantly elevated the importance of advanced, intuitive, and adaptive visualization tools for reporting and analytics. This study systematically reviews and conducts a meta-analysis of existing research on artificial intelligence (AI)-powered data visualization dashboards, with a specific emphasis on their design, interactivity, and analytical effectiveness within enterprise contexts. It critically evaluates the role of AI technologies—including predictive analytics, anomaly detection, and natural language processing—in enhancing dashboard usability, interpretability, scalability, and responsiveness. By integrating findings from diverse industry sectors, including healthcare, finance, public administration, and humanitarian organizations, the analysis demonstrates consistent positive impacts of AI-enhanced dashboards on user satisfaction, decision-making efficiency, and cognitive load reduction. The synthesis highlights best practices in adaptive interface design, emphasizing personalized visualization strategies that dynamically respond to user roles and organizational functions. Furthermore, systematic benchmarking and rigorous comparative analysis illustrate dashboards' substantial value in maintaining competitive advantage and facilitating organizational agility. Critical challenges identified include ensuring algorithmic transparency, maintaining high data quality, and addressing infrastructure constraints for scalability. Overall, the findings offer robust evidence that strategic, user-centered implementation of AI-driven dashboards significantly enhances organizational decision-making capabilities, operational efficiency, and user engagement. This extended review contributes valuable insights for practitioners and researchers aiming to leverage AI technologies effectively in enterprise analytics, emphasizing continuous improvement through benchmarking, transparent algorithmic pra…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Data Visualization
  topic: keywords
subject: Interactive Dashboards
  topic: keywords
subject: Enterprise Analytics
  topic: keywords
subject: Business Intelligence
  topic: keywords
creator: Sadia Zaman
creator: Mohammad Ariful Islam
creator: Sumyta Haque
creator: Hosne Ara Mohna
rights: Copyright (c) 2022 Sadia Zaman, Mohammad Ariful Islam, Sumyta Haque, Hosne Ara Mohna (Author)
date: 2022-05-02
publication date: 2022-05-02
starting page: 32
ending page: 54
doi: 10.63125/gabst658

IMPLEMENTING ADVANCED TECHNOLOGIES FOR ENHANCED CONSTRUCTION SITE SAFETY
type: item
description: The construction industry remains one of the most hazardous sectors worldwide, consistently reporting high rates of workplace accidents, injuries, and fatalities. Traditional safety management approaches, while essential, are often reactive and limited in their ability to predict and prevent incidents. As the industry evolves, there is an urgent need for a paradigm shift toward more proactive and technology-driven safety strategies. This study investigates the implementation of advanced technologies—including the Internet of Things (IoT), Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), unmanned aerial vehicles (drones), and wearable safety devices—to significantly enhance safety outcomes on construction sites. IoT-enabled sensors can continuously monitor environmental conditions such as temperature, noise, air quality, and structural integrity, offering real-time data for early detection of potential hazards. AI algorithms further analyze this data to identify patterns, predict risks, and automate responses before accidents occur. VR and AR provide immersive training experiences and on-site hazard visualization, helping workers to recognize and respond to dangerous situations with higher accuracy and preparedness. Drones offer aerial surveillance capabilities, allowing for safe inspection of hard-to-reach or hazardous areas. Meanwhile, wearable technologies such as smart helmets, vests, and biometric monitors track workers’ vital signs, movements, and proximity to danger zones, enabling real-time alerts and safety interventions. Collectively, these technologies represent a transformative approach to construction site safety by shifting from manual and fragmented safety processes to integrated, data-driven, and responsive systems. The integration of these tools not only reduces the probability of human error but also enhances compliance with safety regulations and improves overall situational awareness. The anticipated outcome of this proj…
subject: Articles
  topic: section
subject: Construction Safety
  topic: keywords
subject: Advanced Technologies
  topic: keywords
subject: Wearable Devices
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Md Ismail Hossain
creator: Md Mahfuj Hosen
creator: Mohammad Aman Ullah Sunny
creator: Shofiul Azam Tarapder
rights: Copyright (c) 2025 Md Ismail Hossain, Md Mahfuj Hosen, Mohammad Aman Ullah Sunny, Shofiul Azam Tarapder (Author)
date: 2025-05-02
publication date: 2025-05-02
starting page: 01
ending page: 31
doi: 10.63125/3v8rpr04

DETERMINANTS OF WORKFORCE EFFICIENCY: AN ANALYSIS OF EMPLOYEE PERFORMANCE DRIVERS IN JIANGSU'S AUTOMOTIVE SECTOR
type: item
description: The rapid expansion of China’s automotive sector, particularly in Jiangsu Province, underscores the need to identify key drivers of workforce efficiency to sustain productivity and competitive advantage. This study examines the impact of leadership style, supervision, work discipline, and motivation on employee performance within Jiangsu’s automotive industry. Drawing on existing literature, we posit that these factors critically influence workforce productivity and operational outcomes. Adopting a quantitative approach, primary data were collected through structured surveys administered to 410 employees, with rigorous adherence to ethical guidelines. Our analysis reveals that leadership style, supervision, work discipline, and motivation exhibit statistically significant effects on employee performance (p < 0.05). While external factors remain influential, the findings demonstrate that targeted management practices can enhance workforce efficiency. The study contributes actionable insights for industry practitioners, suggesting that optimizing these determinants can improve performance metrics. For future research, we recommend investigating the mediating roles of organizational culture, technological integration, and macroeconomic conditions to develop adaptive strategies for sustained productivity growth. Results are presented through descriptive and inferential statistics, with empirical support for all hypothesized relationships.
subject: Articles
  topic: section
subject: Workforce Efficiency
  topic: keywords
subject: Employee Performance
  topic: keywords
subject: Leadership
  topic: keywords
subject: Motivation
  topic: keywords
subject: Automotive Industry
  topic: keywords
subject: China
  topic: keywords
subject: Emotional exhaustion
  topic: keywords
creator: Chen Xinjian
creator: Mohamad Idrakisyah
rights: Copyright (c) 2025 Chen Xinjian, Mohamad Idrakisyah (Author)
date: 2025-04-30
publication date: 2025-04-30
starting page: 636
ending page: 650
doi: 10.63125/przyp417

A STUDY OF BUSINESS PROCESS AUTOMATION WITH DEVOPS: A DATA-DRIVEN APPROACH TO AGILE TECHNICAL SUPPORT
type: item
description: The convergence of Business Process Automation (BPA) and DevOps represents a transformative shift in enterprise operations, aiming to bridge the gap between process efficiency and continuous software delivery. This systematic literature review investigates the role of BPA-DevOps integration in enhancing agile technical support systems, operational agility, and organizational scalability. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a total of 147 peer-reviewed scholarly articles published between 2014 and 2024 were meticulously selected from six prominent academic databases. The selected studies span a range of domains, including IT services, SaaS, healthcare, finance, and public sector applications, offering a comprehensive cross-sectoral perspective. The review identifies that the synergistic adoption of BPA and DevOps contributes to streamlined deployment pipelines, automated incident resolution, enhanced customer support responsiveness, and measurable performance improvements such as reduced change failure rates and faster lead times. Notably, mature automation environments were found to yield greater consistency, resilience, and scalability across organizational functions. However, the review also surfaces critical research gaps—particularly the lack of unified implementation frameworks, inconsistent performance measurement indicators, limited empirical assessments of long-term automation impacts, and insufficient evaluation of ethical and socio-cultural considerations. Furthermore, the study emphasizes the pivotal role of organizational culture, leadership engagement, role redefinition, and continuous skills development in ensuring successful BPA-DevOps adoption. Through thematic synthesis, the review advances the academic discourse by integrating technical, operational, and managerial dimensions, while also advocating for future research that prioritizes inclusivity, longitudinal evaluation, and do…
subject: Articles
  topic: section
subject: Business Process Automation (BPA)
  topic: keywords
subject: DevOps Integration
  topic: keywords
subject: Agile Technical Support
  topic: keywords
subject: Data-Driven Decision Making
  topic: keywords
subject: CI/CD Automation
  topic: keywords
creator: Zahir Babar
rights: Copyright (c) 2024 Zahir Babar (Author)
date: 2024-12-05
publication date: 2024-12-05
starting page: 01
ending page: 32
doi: 10.63125/3w5cjn27

A SYSTEMATIC REVIEW OF PUBLIC BUDGETING STRATEGIES IN DEVELOPING ECONOMIES: TOOLS FOR TRANSPARENT FISCAL GOVERNANCE
type: item
description: This systematic review investigates the evolution, implementation, and effectiveness of public budgeting strategies in developing economies, with a particular focus on transparency, equity, and institutional accountability. Drawing on a total of 94 peer-reviewed articles and high-quality institutional studies published between 2015 and 2025, the study examines the adoption and outcomes of key budgeting reforms, including participatory budgeting, performance-based budgeting, gender-responsive budgeting, and donor-supported fiscal initiatives. Employing the PRISMA 2020 guidelines, the review followed a rigorous multi-stage process involving identification, screening, eligibility assessment, and synthesis, resulting in a thematically structured evaluation of reform models across diverse regional contexts. The findings demonstrate that while budgeting innovations such as participatory budgeting in Sub-Saharan Africa and Latin America, performance-based budgeting in Southeast Asia, and gender-responsive budgeting in South Asia have produced measurable improvements in service delivery and transparency, their effectiveness remains uneven and highly context-dependent. Key barriers to sustained reform include limited administrative capacity, fragmented inter-ministerial coordination, weak political will, and the pervasive influence of informal institutions such as clientelism and patronage networks. Furthermore, donor-driven reforms, while often effective in catalyzing change, frequently suffer from limited local ownership and post-project sustainability challenges. The review also identifies critical gaps in the existing literature, including a dearth of longitudinal impact assessments, insufficient multi-country comparative analyses, and limited empirical work on the integration of digital platforms for budget transparency and citizen engagement. The study contributes to the growing body of public financial management scholarship by offering a comparative, evidence-based s…
subject: Articles
  topic: section
subject: Public Budgeting
  topic: keywords
subject: Developing Economies
  topic: keywords
subject: Fiscal Transparency
  topic: keywords
subject: Participatory Budgeting
  topic: keywords
subject: Performance-Based Budgeting
  topic: keywords
creator: Sazzad Islam
rights: Copyright (c) 2025 Sazzad Islam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 602
ending page: 635
doi: 10.63125/wm547117

AI INTEGRATION IN E-COMMERCE BUSINESS MODELS: CASE STUDIES ON AMAZON FBA, AIRBNB, AND TURO OPERATIONS
type: item
description: This systematic review explores the integration of Artificial Intelligence (AI) within contemporary e-commerce business models by analyzing a total of 126 peer-reviewed scholarly articles published between 2013 and 2023. The review aims to provide a comprehensive understanding of how AI technologies such as machine learning, deep learning, natural language processing, and predictive analytics are being utilized to enhance operational performance, customer engagement, and strategic decision-making across major e-commerce platforms. Drawing on case studies from industry leaders such as Amazon, Airbnb, and Turo, the study investigates core applications of AI in areas including personalized recommendation systems, dynamic pricing algorithms, automated logistics and supply chain management, fraud detection, customer service automation, and trust-building mechanisms in peer-to-peer marketplaces. The findings reveal that AI significantly contributes to improving service accuracy, responsiveness, and scalability, while also reinforcing trust and reducing operational risks through autonomous systems. Furthermore, the study discusses the divergent implementation of AI across centralized and decentralized e-commerce models, illustrating the contextual variability in system design and use. The review also addresses the growing influence of international regulatory frameworks, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other jurisdictional mandates, which are increasingly shaping AI governance practices. Key gaps in the literature are identified, including the underrepresentation of small-to-medium enterprises (SMEs), the lack of cross-industry comparative analyses, and limited longitudinal studies on the socio-economic and ethical implications of AI integration. By synthesizing current evidence and highlighting both opportunities and limitations, this review contributes to a deeper academic and practical understanding of A…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: E-Commerce Platforms
  topic: keywords
subject: Business Model Innovation
  topic: keywords
subject: Case Study Analysis
  topic: keywords
subject: Digital Platform Operations
  topic: keywords
creator: Rajesh Paul
rights: Copyright (c) 2023 Rajesh Paul (Author)
date: 2023-08-18
publication date: 2023-08-18
starting page: 01
ending page: 31
doi: 10.63125/1ekaxx73

NAVIGATING ETHICS AND RISK IN ARTIFICIAL INTELLIGENCE APPLICATIONS WITHIN INFORMATION TECHNOLOGY: A SYSTEMATIC REVIEW
type: item
description: Advancements in artificial intelligence (AI) have profoundly transformed a wide array of sectors, with Information Technology (IT) standing at the forefront of this revolution. AI technologies have reshaped IT operations by introducing new levels of automation, predictive capabilities, decision-making precision, and efficiency, resulting in sweeping changes across organizational infrastructures and service delivery models. However, alongside these technological breakthroughs, the integration of AI has surfaced numerous ethical concerns and associated risks that warrant critical and comprehensive exploration. Issues such as algorithmic bias, privacy breaches, lack of transparency, accountability dilemmas, and cybersecurity vulnerabilities remain pervasive, posing significant challenges for organizations seeking to deploy AI responsibly. Recognizing these complexities, this study focuses on examining the ethical challenges, risk factors, and essential considerations surrounding the deployment of AI within IT environments. This research addresses the urgent and growing necessity for organizations to adopt robust ethical frameworks and effective risk management strategies to ensure that AI integration promotes fairness, transparency, and accountability. A comprehensive synthesis of existing literature further enriches this foundation by offering diverse perspectives on the ethical and risk-related challenges posed by AI, highlighting both the transformative potential and the vulnerabilities associated with its integration into IT systems. Through this review, key gaps in the current body of knowledge are identified, particularly regarding the practical implementation of ethical standards and risk mitigation strategies across varied organizational contexts. Adopting a qualitative research methodology, the study employs a case study approach to explore the intricate, multifaceted issues involved in AI integration within IT operations. This methodological choice allows for…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Information Technology (IT)
  topic: keywords
subject: AI Ethics
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Responsible AI Integration
  topic: keywords
creator: Ishtiaque Ahmed
rights: Copyright (c) 2025 Ishtiaque Ahmed (Author)
date: 2025-04-27
publication date: 2025-04-27
starting page: 579
ending page: 601
doi: 10.63125/590d7098