Journal of Sustainable Development and Policy
Journal of Sustainable Development and Policy (JSDP) is a peer-reviewed, open-access, interdisciplinary academic journal committed to advancing research, discourse, and practical solutions on issues of sustainability, development, and public policy. The journal serves as a critical forum for scholars, policymakers, practitioners, and civil society actors to explore the dynamic interplay between environmental stewardship, economic resilience, and social equity in an era of accelerating global change. JSDP recognizes that sustainable development cannot be achieved through isolated interventions or sectoral silos. As such, the journal encourages interdisciplinary and cross-sectoral perspectives that bridge the gap between theory and practice, local experiences and global frameworks, and short-term solutions and long-term impacts. Our mission is to publish forward-thinking, empirically grounded, and policy-relevant research that fosters transformative change toward just, inclusive, and sustainable societies.
THE INFLUENCE OF SECURE DATA SYSTEMS ON FRAUD DETECTION IN BUSINESS INTELLIGENCE APPLICATIONS
This study investigated the influence of secure data systems on fraud detection performance within business intelligence (BI) applications through a quantitative analysis. The empirical phase examined five core security dimensions—access control maturity, encryption coverage, monitoring completeness, pipeline security, and data governance strength—across 220 organizations using BI-driven fraud detection. Descriptive analysis showed moderate to high implementation of security features, with mean scores ranging from 4.8 to 5.4 on a seven-point scale. Fraud detection performance indicators demonstrated substantial variability, with detection accuracy averaging 78.4%, false-positive rates averaging 22.7%, and time-to-detection ranging from 1 to 72 hours. Correlation analysis revealed strong positive associations between monitoring completeness and detection accuracy (r = .52), as well as between pipeline security and output consistency (r = .54). Regression analysis indicated that secure data system features explained 39% additional variance in detection accuracy beyond organizational controls. Monitoring completeness (β = .28), pipeline security (β = .24), and governance strength (β = .22) emerged as the strongest predictors. Access control maturity demonstrated a significant negative effect on false-positive rates (β = –.26), while encryption coverage showed meaningful influence on faster time-to-detection (β = .19). Mediation analysis found that security culture partially strengthened relationships between key security features and detection outcomes, whereas BI integration complexity weakened them. Overall, the findings demonstrated that secure data systems play a decisive role in enhancing fraud detection accuracy, stability, and responsiveness within BI environments, highlighting their importance as both protective and performance-enabling components of organizational analytics.
Copyright (c) 2024 Hozyfa Shafa, Mst. Shahrin Sultana (Author)
EMPIRICAL INSIGHTS INTO POWER BI APPLICATIONS FOR DYNAMIC FINANCIAL REPORTING AND PREDICTIVE ANALYSIS
This study addresses a persistent problem in finance analytics, namely the lack of quantitative, finance specific evidence on how technical quality, user proficiency, and interactive design jointly translate self service BI into better forecasts and decisions. The purpose is to estimate the contribution of Power BI enabled dynamic reporting and embedded predictive analysis to decision quality, and to identify the conditions under which these benefits are strongest. Using a quantitative, cross sectional, case based design, we sampled 208 professionals across 29 cloud and enterprise cases that have used governed Power BI environments for at least six months. Key variables include information quality, system quality, dashboard interactivity, user training and proficiency, organizational support, dynamic reporting effectiveness, predictive performance, and decision quality. The analysis plan specified hierarchical OLS models with HC3 robust errors, moderation by organizational support, and bias corrected bootstrap mediation from reporting to decision quality via predictive performance, with assumption checks and sensitivity tests. Headline findings show that dashboard interactivity, information quality, user proficiency, and system quality uniquely predict dynamic reporting effectiveness, with organizational support adding a direct effect and amplifying returns to proficiency and interactivity. Higher reporting effectiveness is associated with meaningfully better forecast accuracy, approximately a 0.9 percentage point reduction in MAPE per one unit increase in reporting effectiveness, and both reporting effectiveness and predictive performance explain decision quality, with a significant indirect effect through predictive performance. Implications for practice emphasize governed self service: invest early in role targeted training, codify semantic and design standards, and maintain visible sponsorship so finance teams consistently realize faster, more accurate, and more auditable decisions.
Copyright (c) 2023 Md Redwanul Islam (Author)
SYSTEMATIC REVIEW OF STRESS AND BURNOUT INTERVENTIONS AMONG U.S. HEALTHCARE PROFESSIONALS USING ADVANCED COMPUTING APPROACHES
This systematic review investigates how advanced computing technologies are transforming the detection and management of stress and burnout among U.S. healthcare professionals, conditions that remain pervasive and systemically embedded within the nation’s healthcare infrastructure. Adhering to PRISMA guidelines, the study systematically analyzed 92 empirical works published between 2013 and 2024 across databases including PubMed, Scopus, Web of Science, IEEE Xplore, ScienceDirect, and PsycINFO, emphasizing interventions utilizing artificial intelligence (AI), machine learning (ML), wearable sensors, predictive analytics, and digital therapeutics. The synthesis revealed that burnout prevalence remains critically high, particularly among nurses and frontline clinicians, driven by workload intensity, emotional fatigue, and administrative burden. Machine learning and AI-based predictive models demonstrated accuracies between 78% and 94% in identifying burnout risk through multimodal data integration, encompassing electronic health records, physiological signals, and communication patterns. Wearable and biosignal monitoring systems, capturing heart rate variability, electrodermal activity, and sleep metrics, achieved approximately 85% accuracy in detecting stress and enabled real-time interventions that reduced physiological arousal. AI-driven behavioral interventions, such as adaptive cognitive-behavioral therapy platforms, chatbots, and virtual reality relaxation modules, reduced self-reported stress levels by nearly 28% and enhanced emotional regulation and resilience. Institutional analytics and decision-support systems using predictive dashboards improved workforce retention by up to 22%, linking data-informed workload management with organizational well-being. Ethical implementation emerged as a critical determinant of technology acceptance, emphasizing compliance with HIPAA standards, transparency in data use, informed consent, and mitigation of algorithmic bias. Collectively, the findings underscore a paradigm shift from reactive, self-reported burnout management to proactive, computationally enabled frameworks that merge predictive intelligence with real-time behavioral support. By embedding these technologies within healthcare infrastructures, institutions can move toward sustainable, ethically governed systems that prioritize psychological well-being alongside operational efficiency, marking a transformative advance in the integration of digital health analytics into occupational wellness and resilience management.
Copyright (c) 2024 Pankaz Roy Sarkar, Sai Praveen Kudapa (Author)
A REVIEW ON SUSTAINABLE BUILDING MATERIALS AND THEIR ROLE IN ENHANCING U.S. GREEN INFRASTRUCTURE GOALS
This study presented a comprehensive systematic review of sustainable building materials and their role in enhancing U.S. green infrastructure goals, focusing on their environmental performance, structural functionality, and policy integration within national sustainability frameworks. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review examined 78 peer-reviewed papers published over the past two decades, encompassing a wide range of material innovations, life-cycle assessments, and infrastructure applications. The objective of this research was to synthesize scientific, technical, and policy-based evidence that explains how sustainable materials contribute to the design, implementation, and resilience of green infrastructure systems in the United States. The reviewed studies collectively demonstrated that low-carbon cementitious binders, bio-based composites, and recycled materials significantly reduced embodied carbon and energy while improving durability, water management, and heat mitigation performance. Quantitative analyses across the selected papers revealed that the substitution of traditional construction materials with sustainable alternatives reduced greenhouse gas emissions by up to 70% and extended the service life of pavements, roofs, and stormwater systems through enhanced resilience to environmental stressors. Moreover, the integration of permeable pavements, green roofs, and recycled aggregates supported improved hydrological balance, pollutant filtration, and thermal regulation across urban landscapes. The study further found that U.S. federal and state-level programs—such as the EPA Green Infrastructure Program and the FHWA Sustainable Pavements initiative—had facilitated innovation through research funding, performance guidelines, and policy incentives, although challenges remained in data standardization, cost variability, and regional material supply chains. The synthesis emphasized the importance of interagency collaboration, interdisciplinary research, and transparent life-cycle reporting to accelerate market transformation and policy coherence. Overall, the review concluded that sustainable building materials play a pivotal role in advancing the ecological, economic, and social dimensions of green infrastructure, serving as critical enablers of climate resilience, resource efficiency, and long-term urban sustainability in the United States.
Copyright (c) 2024 Md. Mominul Haque, Syed Zaki Uddin (Author)
EXPLAINABLE REINFORCEMENT LEARNING FOR HIGH-STAKES DECISION SYSTEMS DEVELOPING INTERPRETABLE RL MODELS FOR AUTONOMOUS VEHICLES, HEALTHCARE, OR FINANCE
The study on Explainable Reinforcement Learning (XRL) for High-Stakes Decision Systems: Developing Interpretable RL Models for Autonomous Vehicles, Healthcare, or Finance had been conducted to investigate how interpretability in reinforcement learning enhances performance, trust, and accountability in critical decision-making environments. This research had reviewed and synthesized findings from 126 peer-reviewed papers spanning the past decade, focusing on the integration of explain ability mechanisms into reinforcement learning models applied to safety-critical and ethically sensitive domains. The study aimed to identify quantitative relationships between key explain ability constructs—fidelity, stability, and comprehensibility—and measurable human or system outcomes such as decision accuracy, response time, trust calibration, and accountability perception. Using a mixed quantitative framework, the research combined simulation-based performance data, human-centered evaluation metrics, and statistical modeling to assess how explainable RL architectures perform compared to non-explainable counterparts. The findings revealed that explainable reinforcement learning models consistently outperformed traditional opaque systems across all three domains. In autonomous vehicles, explanations improved driver response times and reduced intervention rates; in healthcare, they enhanced clinician confidence and treatment decision accuracy; and in finance, they improved risk-adjusted returns and investor trust. Regression and correlation analyses demonstrated that explanation fidelity strongly predicted decision accuracy, while explanation stability and comprehensibility were significant predictors of trust and accountability. Furthermore, repeated-measures ANOVA confirmed statistically significant improvements in user trust and performance under explainable conditions, supported by large effect sizes. The study also identified several persistent challenges, including the trade-off between interpretability and performance, variability in user comprehension, and limitations in real-time explanation delivery. Overall, the review and empirical analysis provided a comprehensive understanding of how explainable reinforcement learning contributes to safer, more transparent, and ethically accountable AI-driven decision systems. The insights derived from the 126 reviewed studies establish a robust foundation for developing future XRL frameworks capable of balancing performance optimization with human interpretability in complex, high-stakes environments such as autonomous vehicles, clinical systems, and financial analytics.
Copyright (c) 2022 Sai Srinivas Matta, Manish Bolli (Author)
THE ROLE OF AI-ENABLED CUSTOMER SEGMENTATION IN DRIVING BRAND PERFORMANCE ON ONLINE RETAIL PLATFORMS
This study examines the role of artificial intelligence (AI)–enabled customer segmentation in enhancing brand performance on online retail platforms, addressing how data-driven audience design translates into measurable marketplace outcomes. Using a quantitative, cross-sectional, case-based design, the research analyzes relationships among AI-enabled segmentation capability, personalization quality, customer engagement, data governance strength, and platform-based brand performance, controlling for firm size, category, advertising spend, tenure, and price tier. Data were gathered through structured five-point Likert-scale surveys from 200 brand-side professionals responsible for e-commerce and performance marketing within a focal marketplace ecosystem. Statistical analyses—including reliability and validity tests, Pearson correlations, hierarchical OLS regressions with robust (HC3) errors, and bootstrapped mediation and moderation models—reveal that AI-enabled segmentation capability has a strong positive effect on brand performance (β = .31, p < .001), explaining an additional 10% of variance beyond structural controls. The relationship is partially mediated by personalization quality and customer engagement, with significant indirect effects (AISC → PQ → BP = .09; AISC → CE → BP = .06, 95% CI excluding zero), indicating that improved relevance and deeper interactions are key pathways through which capability drives performance. Moreover, data governance moderates this relationship (β = .14, p < .01), showing that segmentation under stronger consent, access, and quality controls yields steeper performance gains than under weaker governance. Descriptive findings indicate moderate-to-high maturity across firms (AISC M = 3.78; PQ M = 3.58; BP M = 3.62 on a 1–5 scale), with governance showing the widest dispersion (M = 3.36, SD = 0.82). Overall, the results establish that AI-enabled segmentation enhances brand outcomes when supported by experiential excellence and disciplined data stewardship. The study contributes to marketing analytics and dynamic capability theory by demonstrating that segmentation, personalization, engagement, and governance function as interdependent levers of brand performance, and it recommends that firms institutionalize segmentation as a continuously refreshed, governance-anchored process to maximize platform returns.
Copyright (c) 2024 Abdul Hye, Mohammad Shoeb Abdullah (Author)
AI-BASED LANGUAGE EDUCATION PLATFORMS: A SYSTEMATIC ANALYSIS OF EDTECH TOOLS FOR ENGLISH PROFICIENCY
This study addresses a practical problem in higher education and enterprise deployments: institutions are adopting AI-based language learning platforms without clear, quantified evidence of which features most strongly relate to measurable English proficiency. The purpose is to estimate feature–outcome relationships in authentic settings. Using a quantitative, cross-sectional, case-based design, we analyze multi-institution data from cloud-hosted, enterprise-grade platforms (writing focused, speaking focused, and integrated skills). The sample comprises linked survey, telemetry, and assessment records, with proficiency indicators mapped to CEFR bands or rubric scores. Key variables include engagement intensity, feedback specificity, feedback immediacy, adaptivity breadth, and spacing quality, with controls for baseline proficiency, prior exposure, study time, device access, and demographics. The analysis plan includes descriptive statistics, zero-order correlations, multiple linear regression with case fixed effects and heteroskedasticity-robust errors, moderation by motivation, subgroup contrasts by platform modality, and spline checks for nonlinearity. Headline findings indicate that feedback specificity shows the largest unique association with proficiency (about 0.17 SD per 1 SD increase), followed by engagement (≈0.13), spacing quality (≈0.10), feedback immediacy (≈0.09), and adaptivity breadth (≈0.08). Motivation positively moderates the engagement–proficiency link, speaking cases benefit most from fast feedback and spaced practice, and writing cases benefit most from granular, itemized feedback. Returns to weekly minutes flatten beyond the upper quintile, suggesting calibrated rather than maximal practice. Implications for policy and practice include engineering actionable feedback at scale, setting latency service targets, exposing spacing indices, and aligning dashboards with revision uptake and error convergence to guide instruction and procurement.
Copyright (c) 2024 Elmoon Akhter (Author)
COMPARATIVE ANALYSIS OF BI SYSTEMS IN THE U.S. AND EUROPE: LESSONS IN DATA GOVERNANCE AND PREDICTIVE ANALYTICS
This study conducted a comprehensive comparative analysis of Business Intelligence (BI) systems in the United States and Europe, emphasizing the interrelationships among data governance maturity, regulatory rigor, organizational data culture, and predictive analytics integration. The purpose of the research was to evaluate how governance frameworks and institutional structures shaped predictive accuracy, model reliability, and overall BI system efficiency across differing regulatory and cultural environments. A total of 132 peer-reviewed academic papers, industry reports, and empirical studies published over the last decade were systematically reviewed to establish the conceptual foundation, measurement framework, and analytical design of this study. Drawing upon this extensive literature base, a cross-sectional quantitative model was developed and applied to 420 organizations operating within multiple sectors, including finance, healthcare, manufacturing, technology, and retail. The analysis employed structural equation modeling (SEM) to test direct, indirect, and moderated effects among governance maturity, regulatory rigor, data culture, and predictive integration. The findings demonstrated that governance maturity significantly and positively influenced predictive accuracy, model reliability, and BI system efficiency, confirming its role as the central determinant of BI performance. Predictive analytics integration was identified as a partial mediator, indicating that governance maturity indirectly enhanced analytical precision through systematic predictive adoption. Regulatory rigor moderated these relationships, with European firms showing stronger governance-performance linkages due to higher compliance obligations, while U.S. firms exhibited greater agility and innovation capacity. Additionally, organizational data culture emerged as a critical enabler that strengthened BI adoption and improved the operationalization of governance principles. The study revealed that effective BI performance stemmed from the alignment of structural, technological, and cultural systems rather than from technological investment alone. The comparative findings underscored that European organizations excelled in data reliability and compliance consistency, whereas U.S. firms demonstrated superior scalability, speed, and analytical flexibility. Together, these outcomes provided valuable lessons for developing hybrid BI governance models that balance regulatory accountability with innovation agility. The research offered both empirical and conceptual contributions by integrating governance theory, analytics capability, and institutional context into a unified framework, advancing understanding of how transatlantic organizations can harmonize governance maturity and predictive intelligence for sustainable data-driven performance.
Copyright (c) 2021 Omar Muhammad Faruk, Mst. Shahrin Sultana (Author)
MACHINE LEARNING-BASED PREDICTIVE MODELING FOR ASSESSING BRIDGE LOAD CAPACITY USING REAL-TIME SENSOR DATA
This study develops and evaluates a real-time, data-driven framework for predicting bridge load capacity by integrating Internet-of-Things (IoT) sensor streams with advanced machine learning. A multi-site dataset of ~200k five-minute records from 48 bridges across temperate, tropical, and continental climates was compiled, cleaned (k-NN imputation; IQR outlier filtering), normalized, and time-synchronized. Models—Random Forest (RF), Gradient Boosting (GBM), Support Vector Regression, and Deep Neural Networks—were trained using blocked time-series cross-validation (80/20 split; five folds) and benchmarked with MAE, RMSE, and R2R^2R2. Ensemble approaches consistently outperformed single learners. RF achieved the best balance of accuracy and stability (MAE ≈ 12.4 kN; RMSE ≈ 18.1 kN; R2R^2R2 ≈ 0.958; fold SD < 2.1%), with an RF–GBM blend reaching R2R^2R2 ≈ 0.962 and 11% lower residual skewness. Incorporating environmental covariates (temperature, humidity) and dynamic features (vibration, deflection rate) improved accuracy by ~10% over structural-only baselines. Sensitivity and correlation analyses identified strain and deflection as dominant predictors (strain rrr ≈ 0.88; deflection rrr ≈ 0.80), with temperature exerting material-dependent moderating effects, particularly in steel bridges. Real-time deployment tests demonstrated operational feasibility with sub-2 s inference latency (RF 1.82 s average), >99% system uptime, and superior accuracy under dynamic loading (RF MAPE ≈ 3.6%). Sliding-window retraining (7-day refresh) mitigated temporal drift and reduced error by ~6–7% relative to static models. Early-warning simulations showed high detection reliability for load-exceedance events (RF true-positive rate 97.2% with low false alarms). Findings establish that harmonized sensing plus ensemble learning yields accurate, robust, and responsive estimates of bridge load capacity, advancing structural health monitoring from periodic inspection toward continuous, anticipatory asset management and providing a reproducible blueprint for physics-aware, data-driven infrastructure decision support.
Copyright (c) 2025 Md Mazharul Islam (Author)
AI-ENHANCED DATA SCIENCE APPROACHES FOR OPTIMIZING USER ENGAGEMENT IN U.S. DIGITAL MARKETING CAMPAIGNS
This quantitative study investigates how artificial intelligence (AI)–enhanced data science methodologies can optimize user engagement within the rapidly evolving landscape of U.S. digital marketing campaigns. Drawing from an extensive review of 312 peer-reviewed journal articles, conference papers, and industry reports published over the past two decades, the research synthesizes theoretical foundations, empirical findings, and methodological advancements to construct a comprehensive framework for engagement optimization. The study explores how predictive analytics, causal inference, reinforcement learning, and creative intelligence can be integrated to transform marketing strategies from static, rule-based approaches into adaptive, data-driven systems that respond dynamically to user behavior. Predictive models are examined for their ability to identify and forecast engagement drivers across behavioral, contextual, and creative dimensions, while causal inference techniques are evaluated for isolating incremental effects and distinguishing true marketing impact from mere correlations. Reinforcement learning is analyzed as a sequential decision-making mechanism capable of optimizing the timing, sequencing, and delivery of marketing interventions to maximize long-term engagement and customer value. Creative intelligence, encompassing natural language processing and computer vision, is investigated for its role in designing emotionally resonant and contextually relevant content that enhances user interaction. Additionally, the study addresses the critical influence of governance, privacy, and fairness considerations on engagement strategies, demonstrating how ethical and regulatory compliance can coexist with performance optimization. The findings reveal that AI-driven approaches significantly outperform traditional methods across key engagement metrics, including click-through rates, dwell time, conversions, and retention, while simultaneously strengthening trust, inclusivity, and user experience. This research contributes to both academic scholarship and marketing practice by offering an integrated, evidence-based framework that advances the theoretical understanding of engagement as a dynamic, multidimensional construct and provides practical strategies for leveraging AI in competitive digital ecosystems.
Copyright (c) 2024 Sai Praveen Kudapa (Author)
DEPLOYMENT OF AI-SUPPORTED STRUCTURAL HEALTH MONITORING SYSTEMS FOR IN-SERVICE BRIDGES USING IOT SENSOR NETWORKS
This study investigates the deployment of artificial intelligence (AI)–supported structural health monitoring (SHM) systems for in-service bridges using Internet of Things (IoT) sensor networks, a rapidly advancing domain that merges cutting-edge sensing, communication, and data analytics to improve infrastructure safety and durability. Traditional bridge inspections and early wired SHM systems have faced persistent limitations, including manual data interpretation, latency in damage detection, and scalability barriers due to cost and complexity. Recent breakthroughs in wireless IoT technologies, multi-modal sensor fusion, and AI-based analytics now offer the potential for continuous, automated, and highly reliable monitoring of structural integrity across diverse bridge typologies and environmental conditions. To critically synthesize the state of knowledge, this systematic review analyzed 146 peer-reviewed papers published between 2000 and 2022 spanning civil engineering, computer science, and information systems. The review explored key dimensions: (a) the historical evolution and objectives of SHM in bridge engineering; (b) IoT sensor modalities and network architectures enabling large-scale monitoring; (c) AI techniques for damage classification, anomaly detection, and signal feature engineering; (d) system reliability and data integrity strategies including calibration, drift compensation, and cybersecurity; (e) deployment challenges and scalability considerations across steel and concrete bridges; (f) comparative field case studies and lessons from global smart infrastructure programs; and (g) emerging research directions such as digital twins, blockchain data provenance, climate-resilient AI, and hybrid human–AI decision systems. The findings indicate that AI-enhanced SHM significantly improves predictive damage detection, reduces false alarms, and supports timely maintenance decisions, especially when combined with multi-sensor IoT networks and robust data governance. However, challenges remain in standardization, model retraining under concept drift, cost–benefit justification for large-scale deployment, and regulatory acceptance of AI-informed safety decisions. The review also highlights knowledge gaps around extreme climate resilience, secure and scalable data management, and human oversight frameworks for trustworthy AI. By consolidating insights across 146 studies, this work provides an integrated roadmap for researchers, engineers, and policymakers aiming to advance the next generation of smart, resilient, and cost-effective bridge monitoring systems.
Copyright (c) 2022 Md Ismail Hossain (Author)
IMPLEMENTATION OF THE DEVELOPED SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE MODEL IN REAL-WORLD APPLICATIONS FOR SUSTAINABLE ENERGY OPTIMIZATION
This study presents an implementation-based investigation into the deployment and real-world validation of a developed solar photovoltaic (PV) system performance model designed to support sustainable energy optimization. Unlike prior works that primarily focused on theoretical formulations or simulation-driven analyses, this research emphasizes the direct application and operational evaluation of the model under diverse environmental and infrastructural conditions. The implementation was carried out across multiple grid-connected PV installations varying in scale, configuration, and climatic exposure to capture a broad spectrum of operational challenges. The framework involved integrating real-time data acquisition systems to continuously monitor solar irradiance, module temperature, ambient conditions, electrical output, and system losses, which were then processed through the developed model for real-time performance estimation. Empirical validation involved comparing model-predicted energy yields with measured field data over a 12-month operational period, incorporating seasonal variability, shading impacts, and maintenance-induced downtimes. The results showed that the implemented model achieved high predictive accuracy, with mean absolute percentage error consistently below 5%, while also identifying site-specific inefficiencies such as temperature-induced derating, mismatch losses, and inverter clipping. The implementation further demonstrated that coupling model predictions with adaptive control strategies enhanced energy harvesting efficiency, reduced curtailment, and improved system reliability. In addition to performance validation, the study ensured compliance with international standards such as IEC 61724 for performance monitoring and IEC 61853 for module characterization, enhancing the replicability and interoperability of the model in heterogeneous energy environments. This practical deployment bridges the gap between conceptual model design and field-based energy management by translating complex algorithms into operational decision-support tools. The findings affirm the model’s readiness for large-scale adoption, offering a scalable and adaptive framework to optimize PV system performance, improve return on investment, and accelerate the transition to sustainable energy systems in real-world contexts.
Copyright (c) 2025 Md Akteruzzaman (Author)
QUANTITATIVE RISK ASSESSMENT OF MEGA REAL ESTATE PROJECTS: A MONTE CARLO SIMULATION APPROACH
This systematic literature review synthesizes how Monte Carlo simulation is designed, implemented, and interpreted for quantitative risk assessment in mega real estate projects. Following a PRISMA-aligned protocol, we searched Scopus, Web of Science, ASCE Library, ScienceDirect, IEEE Xplore, and Emerald Insight, complemented by Taylor & Francis Online and snowballing, screened records in two stages, and extracted standardized methodological variables. The final sample comprises 115 studies, analyzed across four decision-shaping themes: input distributions, dependence modeling, sampling discipline, and integration of cost, schedule, and finance. Most studies adopt triangular or PERT-style inputs, with lognormal alternatives common, while heavy-tailed forms remain underused despite escalation and long-lead exposures. Explicit dependence is unevenly treated, many models assume independence, whereas rank correlation, common drivers, or copulas reveal wider tails. Latin hypercube sampling dominates, quasi Monte Carlo appears in a minority, and only a subset justifies run size against precision targets. Where models link schedule to cost and propagate into cash flow and financing metrics such as debt service coverage ratio, credible bands widen and breach probabilities rise, improving alignment with lender and board thresholds. Sensitivity analysis is frequent yet mixed in rigor, with global and tail-focused measures offering clearer mitigation leverage than tornado charts, and validation by hindcasting or exceedance tests remains rare but decisive for credibility. We conclude that governance-ready practice requires tail-aware input fitting, co-movement, variance-efficient designs with accuracy goals, integrated cost, schedule, and finance modeling, and transparent sensitivity and validation protocols that yield auditable percentile contingencies and covenant risk statements for stage-gate decisions.
Copyright (c) 2022 MKA Shahinoor Rahman Jahid (Author)
PROCESS OPTIMIZATION USING SIX SIGMA STRATEGY IN GARMENT MANUFACTURING
Increasing competition among developed countries makes it increasingly pressing for the garment industry in developing economies such as Bangladesh to produce high-quality, defect-free products at competitive prices in order to remain globally viable. As one of the largest contributors to Bangladesh’s economy, the garment sector faces constant pressure to balance quality with efficiency while responding to fluctuating international demand. Within this context, the present study investigates the effectiveness of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology in optimizing the cutting process at Epyllion Group, a leading garment manufacturing company in Bangladesh. The choice of focusing on the cutting process was strategic, as this stage plays a critical role in determining dimensional accuracy, fabric utilization, and overall product quality. Even small errors in this stage can cascade into large-scale defects, increasing rework rates and operational costs.The research systematically identified inefficiencies that contributed to frequent production defects and heightened expenses. Among the most significant issues were fabric misalignment, which caused irregular cuts and waste; bundle mislabeling, which created confusion and delayed workflows; and inconsistent fabric tension, which compromised uniformity in cut panels. These problems were compounded by inadequate operator skills and limited preventive maintenance practices. To address these challenges, the study employed a structured data collection framework supplemented by analytical tools such as Pareto analysis and cause-and-effect diagrams. These techniques allowed the research team to isolate the root causes of the majority of defects and to design targeted solutions. The interventions included operator training programs aimed at enhancing technical precision, machine calibration and maintenance schedules to ensure equipment reliability, the installation of automated tension controllers to standardize material handling, and the introduction of standardized operating procedures to reduce process variation. The impact of these measures was significant and measurable. Results following implementation revealed a 61.5% reduction in defect rates, which directly improved product quality and reduced waste. Machine downtime was cut by 66.7%, reflecting the success of preventive maintenance and calibration measures. Similarly, the product rework rate dropped by 55.6%, which not only lowered costs but also improved throughput and delivery reliability. Additional improvements were observed in measurement accuracy, which increased by 25%, and operator training scores, which rose by 38.5%, indicating that human capital development played a key role in sustaining process improvements. These results demonstrate the combined value of technological upgrades and workforce empowerment, showing that quality improvements in garment manufacturing require both structural and behavioral change.
Copyright (c) 2025 Moin Uddin Mojumder, Hamza Saad (Author)
DIGITAL TWIN TECHNOLOGY IN UTILITY INFRASTRUCTURE MANAGEMENT: OPPORTUNITIES AND CHALLENGES
This meta-analysis presents a comprehensive evaluation of the implementation, performance outcomes, and sectoral maturity of digital twin (DT) technology across two critical utility infrastructure domains: electricity and water. Drawing on 122 peer-reviewed empirical studies published between 2010 and 2024—with a cumulative citation count exceeding 10,000—the study assesses the efficacy of DTs in enhancing operational performance, reducing system downtime, improving cost efficiency, and bolstering infrastructure resilience. The findings indicate that the electricity sector demonstrates the highest degree of DT maturity, characterized by widespread adoption in smart grid optimization, real-time asset monitoring, fault prediction, and renewable energy integration. In contrast, the water sector, while moderately advanced, has achieved significant progress through the deployment of digital twins in hydraulic simulation, leak detection, stormwater forecasting, and wastewater treatment automation. Quantitative evidence reveals average downtime reductions ranging from 15% to 45%, alongside cost savings of up to 30% through predictive maintenance and optimized energy and resource use. Regional benchmarking highlights Europe and Asia as leaders in digital twin innovation, supported by robust regulatory frameworks, significant investment in smart infrastructure, and advanced ICT ecosystems. In contrast, utilities in emerging economies continue to face constraints related to legacy infrastructure, limited digital readiness, and fragmented policy environments. The study also identifies emerging opportunities shaping the next generation of DT deployment, including the integration of artificial intelligence for autonomous operational control, cloud-based Digital Twin-as-a-Service (DTaaS) platforms for scalable adoption, and strategic alignment with sustainability initiatives such as climate resilience, net-zero emissions, and smart city governance. Despite these advancements, persistent challenges remain in achieving data interoperability, ensuring cybersecurity resilience, and facilitating equitable access to DT solutions across regions. This meta-analysis not only consolidates the empirical knowledge base but also provides a forward-looking roadmap for enhancing the role of digital twins as a transformative technology in sustainable utility infrastructure management.
Copyright (c) 2025 Mohammad Hasan Imam (Author)
DATA ANALYTICS FOR STRATEGIC BUSINESS DEVELOPMENT: A SYSTEMATIC REVIEW ANALYZING ITS ROLE IN INFORMING DECISIONS, OPTIMIZING PROCESSES, AND DRIVING GROWTH
This meta-analysis offers a comprehensive synthesis of empirical evidence on the strategic role of data analytics in business development, with particular emphasis on its contributions to informed decision-making, operational process optimization, financial planning, risk mitigation, and customer-centric growth. Drawing from a dataset of 112 peer-reviewed empirical studies published between 2010 and 2024, the study employs a meta-analytic methodology following PRISMA guidelines to ensure methodological rigor and analytical depth. The research systematically categorizes analytics into descriptive, predictive, and prescriptive models, evaluating their implementation across key business functions including marketing, finance, operations, supply chain, human resources, and strategic management. Findings demonstrate that organizations with high analytics maturity achieve significantly better performance outcomes in terms of profitability, process efficiency, decision quality, and return on investment compared to their lower-maturity counterparts. Firms that integrate analytics across departments and align it with enterprise strategy report the greatest benefits, including improved responsiveness to market dynamics, enhanced customer engagement, and more effective capital allocation. The study further reveals that enabling factors such as executive sponsorship, cross-functional data integration, workforce analytics literacy, and robust governance frameworks are critical to unlocking the full potential of analytics. Comparative evaluation of theoretical models—including the Resource-Based View (RBV), Dynamic Capabilities Theory, Information Processing Theory (IPT), and the Technology–Organization–Environment (TOE) framework—provides insight into the mechanisms through which analytics capabilities translate into sustained competitive advantage. The analysis also identifies gaps related to construct standardization, sectoral applicability, and scalability, offering methodological and strategic recommendations for future research and practice. Ultimately, this study affirms that data analytics is not merely a technological function but a transformative, enterprise-wide capability that drives sustainable value creation, operational excellence, and strategic agility in an increasingly data-driven global economy. The findings contribute to a deeper understanding of how organizations can harness analytics to navigate complexity, enhance performance, and foster innovation.
Copyright (c) 2025 Md Musfiqur Rahman (Author)
ADVANCEMENTS IN MACHINE LEARNING FOR CUSTOMER RETENTION: A SYSTEMATIC LITERATURE REVIEW OF PREDICTIVE MODELS AND CHURN ANALYSIS
Customer retention has emerged as a critical strategic objective for organizations seeking to sustain profitability and competitive advantage, particularly in highly saturated and dynamic markets. Predictive modeling, driven by machine learning (ML) techniques, plays an increasingly essential role in enabling firms to identify customers at high risk of churn and to implement proactive retention interventions. This systematic literature review provides a comprehensive synthesis of contemporary advancements in ML-based customer retention analytics, focusing on predictive models and churn analysis across diverse industries, including telecommunications, banking, e-commerce, and subscription-based services. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 112 peer-reviewed studies published between 2015 and 2025 were rigorously selected to ensure methodological rigor and relevance. The review systematically categorizes ML techniques into supervised, unsupervised, and hybrid approaches, with a particular emphasis on widely adopted algorithms such as logistic regression, decision trees, support vector machines, random forests, gradient boosting machines, and deep neural networks, including convolutional and recurrent architectures. In addition, it critically evaluates feature engineering methods, data preprocessing practices, dataset properties, and model evaluation metrics, including accuracy, precision, recall, F1-score, AUC-ROC, and cost-sensitive measures. Special attention is given to emerging research domains, including explainable artificial intelligence (XAI), real-time predictive analytics, transfer learning, and federated learning, which enhance model transparency, adaptability, and privacy compliance. Findings from the review reveal that ensemble methods and deep learning models consistently outperform traditional classifiers in detecting intricate churn patterns, particularly when behavioral, transactional, and sentiment-based features are integrated. The review also underscores the growing importance of interpretable models, post-hoc explanation techniques such as SHAP and LIME, and privacy-preserving methodologies to address challenges related to algorithmic opacity, ethical compliance, and deployment scalability. By mapping the evolution of ML-driven churn analytics, identifying persistent research gaps, and proposing actionable directions for future inquiry, this study contributes to both the academic literature and practical applications in customer retention strategy development.
Copyright (c) 2025 Md Nur Hasan Mamun (Author)
A META-ANALYSIS OF CYBERSECURITY FRAMEWORK INTEGRATION IN GRC PLATFORMS: EVIDENCE FROM U.S. ENTERPRISE AUDITS
This meta-analysis critically examines the integration of cybersecurity frameworks into Governance, Risk, and Compliance (GRC) platforms and its impact on audit performance, compliance outcomes, and enterprise risk management across U.S.-based organizations. Leveraging quantitative data from 78 peer-reviewed studies and industry reports published between 2010 and 2024, the research aggregates and evaluates the effectiveness of implementing widely recognized cybersecurity frameworks—including the NIST Cybersecurity Framework (CSF), ISO/IEC 27001, COBIT, and CIS Controls—within digital GRC environments. Using a random-effects model to account for sectoral and methodological heterogeneity, the study calculates standardized effect sizes and analyzes how such integrations influence key organizational metrics such as audit exception rates, control failure frequency, policy adherence levels, risk visibility, and regulatory response capabilities. The findings demonstrate statistically significant improvements in audit readiness, reduction in compliance violations, enhanced policy enforcement, and faster detection and containment of security incidents when cybersecurity frameworks are embedded within GRC systems. Sector-specific insights reveal that financial services, healthcare, and federal agencies benefit the most from integration, attributed to higher regulatory scrutiny and more mature risk governance infrastructures. In contrast, small and medium-sized enterprises (SMEs), along with sectors reliant on legacy systems, face implementation challenges related to system interoperability, workforce skill gaps, and resource constraints. The analysis also identifies key enablers of successful integration, including leadership engagement, cross-functional governance teams, standardized control taxonomies, and continuous training programs. Additionally, behavioral factors such as user acceptance, organizational culture, and change management practices significantly influence the long-term sustainability of integration efforts. This study contributes a comprehensive, data-driven understanding of how cybersecurity-GRC convergence enhances operational efficiency, regulatory alignment, and strategic resilience. The results offer practical implications for CISOs, compliance officers, IT auditors, and executive leadership seeking to modernize governance processes, manage cyber risks more proactively, and meet evolving regulatory expectations in an increasingly complex digital landscape.
Copyright (c) 2025 Md Omar Faruq (Author)
THE ROLE OF BUILDING INFORMATION MODELING (BIM) IN RISK MANAGEMENT FOR SUSTAINABLE BRIDGE PROJECTS: A SYSTEMATIC REVIEW AND META-ANALYSIS
This systematic review and meta-analysis investigates the evolving role of Building Information Modeling (BIM) in managing multidimensional risks associated with sustainable bridge infrastructure projects. Drawing upon the PRISMA methodology, the study rigorously screened and analyzed a total of 87 peer-reviewed journal articles and conference papers published between 2010 and 2024. The selected literature was sourced from five leading academic databases—Scopus, Web of Science, IEEE Xplore, ScienceDirect, and the ASCE Library—ensuring comprehensive coverage of global research on BIM-based risk management practices. The review focuses on how BIM contributes to the identification, analysis, mitigation, and continuous monitoring of technical, financial, environmental, and operational risks throughout the lifecycle of bridge projects. Findings reveal that BIM’s capabilities extend well beyond 3D visualization, offering robust tools for 4D scheduling, 5D cost estimation, and real-time data integration that collectively enhance proactive decision-making. BIM facilitates early risk detection through clash detection, geospatial analysis, and scenario simulations during the planning and design stages, reducing design inconsistencies and constructability issues. Moreover, the study synthesizes evidence demonstrating how BIM supports cost and schedule risk control by enabling dynamic simulation of construction sequences and integration with live pricing data, resulting in reduced cost overruns and schedule delays. The review also highlights the increasing convergence of BIM with Internet of Things (IoT) technologies, digital twins, and Geographic Information Systems (GIS), enabling real-time monitoring of structural health, predictive maintenance, and environmental risk modeling in operational phases. Lifecycle-oriented risk governance through BIM emerged as a key theme, particularly in studies focused on sustainability assessments, material optimization, and long-term asset performance. Institutional adoption patterns, regulatory influences, and policy frameworks were also examined, revealing that countries with mandated BIM protocols exhibit more advanced integration of BIM into public infrastructure risk management. Despite persistent challenges such as fragmented data standards, skill gaps, and limited interoperability, the majority of the reviewed literature—accumulating over 3,400 citations—converges on the conclusion that BIM is an essential enabler of risk-informed, sustainable bridge engineering.
Copyright (c) 2025 Abdul Rehman (Author)
STRATEGIC USE OF ENGAGEMENT MARKETING IN DIGITAL PLATFORMS: A FOCUSED ANALYSIS OF ROI AND CONSUMER PSYCHOLOGY
This systematic review investigates the strategic application of engagement marketing in digital platforms, focusing on its impact on return on investment (ROI) and consumer psychology. Drawing from a comprehensive analysis of 87 peer-reviewed articles published between 2005 and 2024, this study synthesizes existing literature to uncover the mechanisms, measurement models, and ethical dimensions of digital engagement. The review reveals that engagement is a multidimensional construct encompassing emotional, cognitive, and behavioral dimensions, with emotional resonance and psychological alignment playing a central role in fostering meaningful consumer-brand interactions. Advanced ROI measurement frameworks such as customer lifetime value (CLV), multi-touch attribution models, and sentiment analytics have emerged as superior alternatives to traditional metrics like impressions or click-through rates. Moreover, platform-specific strategies tailored to the structural and algorithmic features of social media channels like TikTok, Instagram, Facebook, and LinkedIn are shown to be more effective in generating sustained user engagement. Cultural variability, symbolic representation, and ethical considerations especially regarding data privacy, manipulation, and algorithmic bias further shape engagement practices in global contexts. The study also highlights the rise of prosumerism, wherein consumers act as co-creators of content and community narratives, contributing significantly to brand visibility and authenticity. Regulatory frameworks such as GDPR and CCPA have begun to reshape engagement strategies, emphasizing transparency, user consent, and ethical responsibility. Overall, this review positions engagement marketing not merely as a tactical function but as a strategic, culturally adaptive, and ethically grounded approach that is central to digital brand success. The findings contribute to a more holistic understanding of engagement as both a measurable business outcome and a human-centered experience, offering insights for marketers, researchers, and platform developers seeking to optimize digital interaction in an increasingly complex media landscape.
Copyright (c) 2025 Anika Jahan Mou, Md Soyeb Rabbi, Tahmina Akter Rainy (Author)
DEVELOPMENT OF A FOG COMPUTING-BASED REAL-TIME FLOOD PREDICTION AND EARLY WARNING SYSTEM USING MACHINE LEARNING AND REMOTE SENSING DATA
This study introduces a novel, fog computing-based real-time flood prediction and early warning system that integrates advanced machine learning algorithms with remote sensing technologies to address the limitations of traditional flood monitoring infrastructures. Existing systems often struggle with latency, over-reliance on centralized cloud computing, and fragmented data integration, which collectively hinder their effectiveness during critical flood events. To overcome these challenges, this research employed a mixed methods research design, combining quantitative experimentation with qualitative inquiry to comprehensively evaluate the system’s predictive performance, operational resilience, and stakeholder usability. The quantitative component involved the deployment of an optimized wireless sensor network, designed using Low Energy Adaptive Clustering Hierarchy (LEACH) and Particle Swarm Optimization (PSO), to collect real-time hydrological data including rainfall, river levels, and soil moisture. These data streams were fused with high-resolution remote sensing imagery from Sentinel-1 SAR and Sentinel-2 MSI, and processed using advanced machine learning models such as Long Short-Term Memory (LSTM) networks and Random Forest classifiers. The LSTM model achieved high predictive accuracy with an RMSE of 0.38 and NSE of 0.84, while fog computing nodes reduced latency by over 60%, enabling localized, real-time alert dissemination even during internet outages. The qualitative component involved 23 semi-structured interviews with disaster management authorities, field technicians, and community representatives to assess the system’s usability, trustworthiness, and practical challenges. The triangulation of findings confirmed the technical validity, operational reliability, and end-user relevance of the system. By integrating decentralized processing, intelligent forecasting, and human-centered design, this research contributes a scalable, adaptive, and field-tested framework for intelligent flood early warning systems. It holds significant potential for deployment in climate-vulnerable, infrastructure-constrained regions globally, advancing both technological innovation and disaster resilience in the face of increasing hydrometeorological extremes.
Copyright (c) 2025 Md Atiqur Rahman Khan, Md Abdur Rouf, Niger Sultana, Mst Shamima Akter (Author)
PUBLIC FINANCE AND POLICY EFFECTIVENESS A REVIEW OF PARTICIPATORY BUDGETING IN LOCAL GOVERNANCE SYSTEMS
This systematic review explores the role of participatory budgeting (PB) in enhancing public finance systems and improving policy effectiveness within local governance contexts. Drawing upon the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework, this study synthesizes findings from 92 peer-reviewed articles and high-quality institutional reports published between 2000 and 2024. The review investigates how PB contributes to fiscal decentralization, budget transparency, equitable public expenditure, service delivery, social inclusion, and civic engagement. Through a rigorous analysis of empirical and theoretical contributions, the review highlights the multidimensional value of PB in realigning public spending with local needs and promoting democratic accountability. Key findings indicate that PB strengthens the alignment between public resource allocation and community-defined priorities, reduces corruption through participatory oversight mechanisms, and fosters institutional trust by enhancing transparency and inclusiveness. The evidence shows that PB not only empowers marginalized populations—such as women, youth, and ethnic minorities—but also encourages sustained civic learning, social cohesion, and citizen-state collaboration. However, successful implementation of PB is contingent upon several enabling conditions, including strong political commitment, sufficient administrative capacity, legal frameworks that institutionalize participatory practices, and the presence of an active and organized civil society. Comparative analysis across Latin America, Europe, Africa, Asia, and North America reveals that while PB principles are globally adaptable, their effectiveness varies depending on local political culture, institutional maturity, and infrastructural readiness. The review also underscores the potential of digital PB platforms in expanding access and participation, though challenges remain regarding inclusivity and the digital divide. Despite promising impacts, the review identifies notable research gaps, such as the absence of longitudinal impact assessments, limited integration of intersectional frameworks, and the underrepresentation of PB practices in fragile, post-conflict, or authoritarian contexts. Furthermore, most evaluations focus on output measures (e.g., number of projects funded) rather than long-term governance or developmental outcomes. By consolidating diverse strands of literature, this review underscores PB’s transformative potential as both a governance mechanism and a fiscal tool. It calls for more context-sensitive, interdisciplinary research to fully understand PB’s long-term contributions to inclusive development, democratic renewal, and sustainable public finance reform.
Copyright (c) 2025 Sazzad Islam (Author)
WORKFORCE ANALYTICS IN MANUFACTURING: A REVIEW OF MIS TOOLS FOR LABOR PLANNING, ABSENTEEISM MONITORING, AND PRODUCTIVITY OPTIMIZATION
This meta-analysis explores the transformative impact of Management Information Systems (MIS) on workforce analytics within the manufacturing sector, focusing specifically on labor planning, absenteeism monitoring, and productivity optimization. By systematically reviewing and synthesizing data from 53 empirical studies published between 2010 and 2024, the research provides a comprehensive evaluation of how MIS tools enhance workforce-related decision-making and operational outcomes. The findings demonstrate that MIS-based workforce analytics significantly improve labor efficiency, with firms reporting an average 22% increase in labor planning accuracy through the integration of scheduling modules and predictive simulation tools. Real-time absenteeism monitoring systems, including biometric and RFID-enabled attendance tracking, resulted in an 18% reduction in unplanned absences, while predictive absence analytics achieved a forecasting accuracy rate of 82%, enabling proactive labor interventions. Furthermore, the use of performance monitoring dashboards led to a 15% rise in output per labor hour, enhancing both individual accountability and team productivity. The study also reveals that MIS platforms facilitate skill-based task allocation, cross-functional coordination, and seamless integration of HR, production, and quality data, contributing to improved workforce agility and long-term cost efficiency. These outcomes are theoretically grounded in Human Capital Theory, Socio-Technical Systems Theory, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Strategic Alignment Model, all of which support the role of MIS as a strategic enabler of labor optimization and enterprise-wide coherence. The results underscore the importance of embedding data-driven workforce analytics into manufacturing operations to support continuous improvement, reduce operational disruptions, and build a high-performance, future-ready labor management infrastructure. This study provides critical insights for industry leaders, system designers, and policymakers aiming to leverage MIS for sustainable workforce transformation in complex, competitive manufacturing environments.
Copyright (c) 2025 Golam Qibria Limon (Author)
A SYSTEMATIC REVIEW OF HUMAN-AI COLLABORATION IN IT SUPPORT SERVICES: ENHANCING USER EXPERIENCE AND WORKFLOW AUTOMATION
The rapid evolution of Artificial Intelligence (AI) has profoundly impacted IT support services, leading to a paradigm shift from traditional, hierarchical support structures to integrated, human-AI collaborative ecosystems. In these emerging models, AI systems complement human expertise by automating routine tasks, enabling predictive service responses, and enhancing decision accuracy, while human agents contribute contextual judgment, ethical oversight, and emotional intelligence. This systematic review investigates how such collaboration is transforming IT support operations, focusing specifically on its influence on user experience (UX), workflow automation, human cognitive adaptation, and institutional readiness. Adhering to the PRISMA 2020 guidelines, a total of 98 peer-reviewed articles published between 2013 and 2024 were rigorously identified, screened, and synthesized from leading academic databases including Scopus, Web of Science, IEEE Xplore, and ACM Digital Library. The reviewed literature reveals that AI technologies—such as chatbots, machine learning-driven ticketing systems, robotic process automation (RPA), and natural language processing (NLP)—have become central to the modern support landscape, improving first-contact resolution rates, reducing operational costs, and enabling 24/7 support scalability. The review further maps adoption patterns using established theoretical frameworks such as the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and various digital transformation models, all of which highlight key enablers like performance expectancy, ease of use, social influence, and strategic alignment. Despite promising outcomes, persistent challenges remain—including data fragmentation, algorithmic bias, interface limitations, and workforce resistance to automation—which must be addressed through thoughtful design and ethical governance. By synthesizing insights across technical, human, and organizational dimensions, this review contributes a comprehensive and interdisciplinary understanding of how human-AI collaboration is redefining IT support services, offering practical implications for researchers, system designers, and IT leaders seeking to deploy intelligent, sustainable, and user-centric support systems.
Copyright (c) 2025 Zahir Babar, Rajesh Paul, Md Arifur Rahman, Tonmoy Barua (Author)
A SYSTEMATIC REVIEW OF DATA ANALYTICS IN BUSINESS STRATEGY: MODELS, TOOLS, AND COMPETITIVE ADVANTAGE
In the age of digital transformation and data abundance, data analytics has become a fundamental driver of strategic decision-making in modern enterprises. This systematic review critically examines the role of data analytics in shaping business strategy, with a focus on analytical typologies, technological tools, and the mechanisms through which analytics contributes to competitive advantage. Guided by the PRISMA methodology, a total of 162 peer-reviewed journal articles published between 2015 and 2025 were systematically identified, screened, and analyzed from major academic databases including Scopus, Web of Science, and IEEE Xplore. The review synthesizes findings across multiple domains, revealing that descriptive, diagnostic, predictive, and prescriptive analytics each contribute uniquely to strategic planning and performance optimization. Business Intelligence tools such as Tableau and Power BI, along with AI-driven forecasting models and cloud-based analytics infrastructure, emerged as critical enablers of data-informed strategic decisions. Moreover, the study highlights the organizational conditions—such as leadership support, data governance, and analytics maturity—that determine the effectiveness of analytics implementation. Sectoral and geographic analyses further reveal disparities in adoption and outcomes, emphasizing the contextual nature of analytics strategy. Theoretical foundations including the Resource-Based View (RBV), Dynamic Capabilities View (DCV), and Technology-Organization-Environment (TOE) framework provide a conceptual lens for interpreting the findings. By consolidating evidence from 162 scholarly sources with over 40,000 combined citations, this review offers a comprehensive perspective on how analytics capabilities are developed, deployed, and leveraged to create strategic value. The study also identifies persistent gaps in longitudinal ROI assessments and calls for more diverse empirical research across industries and global contexts. This review provides actionable insights for researchers, practitioners, and policymakers aiming to harness analytics for sustained organizational advantage.
Copyright (c) 2025 Hosne Ara Mohna (Author)
IOT-ENABLED CONDITION MONITORING IN POWER DISTRIBUTION SYSTEMS: A REVIEW OF SCADA-BASED AUTOMATION, REAL-TIME DATA ANALYTICS, AND CYBER-PHYSICAL SECURITY CHALLENGES
The evolution of modern power distribution systems has been profoundly influenced by the rapid integration of Internet of Things (IoT) technologies, which enable advanced condition monitoring, enhance operational visibility, and facilitate early fault detection across critical infrastructure. This systematic review investigates the multifaceted role of IoT in transforming conventional Supervisory Control and Data Acquisition (SCADA) systems into intelligent, interoperable platforms that support automation, real-time analytics, and adaptive control within power distribution networks. Emphasis is placed on understanding how IoT-enabled frameworks leverage embedded sensor networks, communication protocols, and distributed computing to improve the resilience, efficiency, and responsiveness of electrical grids. By incorporating diverse sensor modalities—monitoring parameters such as voltage, temperature, current, and vibration—utilities can shift from periodic inspections to continuous, data-driven monitoring paradigms that offer real-time insights into equipment health and network performance. Recent advancements in machine learning and artificial intelligence have enabled utilities to deploy predictive models that analyze historical and real-time data to forecast failures, optimize maintenance schedules, and reduce operational costs. These data-driven models are increasingly embedded into SCADA dashboards and human-machine interfaces (HMIs), empowering operators with enhanced decision support tools and dynamic visualizations. The review further evaluates how real-time analytics platforms—such as Apache Spark and Flink—are integrated into the energy sector to support fast data processing, anomaly detection, and system optimization in both centralized and decentralized grid contexts. In addition to technological benefits, the review also addresses the growing concerns related to cyber-physical security in IoT-intensive power distribution systems. This study synthesizes key findings from literature on cryptographic techniques, intrusion detection systems, secure communication protocols, and regulatory compliance frameworks such as IEC 62443 and NERC CIP. The analysis underscores the imperative of embedding cybersecurity as a core design principle in smart grid development. Ultimately, this review offers a comprehensive synthesis of scholarly advancements, identifies unresolved challenges, and proposes future research directions to guide utility engineers, system designers, and policymakers in developing secure, scalable, and intelligent power distribution infrastructures capable of supporting the transition to sustainable energy systems.
Copyright (c) 2025 Md. Nuruzzaman, Sohel Rana (Author)
CYBERCRIME AND CONTRACTUAL LIABILITY: A SYSTEMATIC REVIEW OF LEGAL PRECEDENTS AND RISK MITIGATION FRAMEWORKS
This systematic review examines the evolving intersection of cybercrime and contractual liability, with a focus on legal precedents, evidentiary challenges, risk allocation mechanisms, and regulatory influences in the digital era. As cyberattacks increasingly disrupt contractual relationships across sectors, the need to understand how courts interpret such incidents within the framework of private law has become more urgent. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, this study systematically reviewed and synthesized findings from 84 peer-reviewed articles published between 2000 and 2024. The review explores how judicial systems in common law and civil law jurisdictions assess liability in cyber-induced contractual breaches, interpret clauses related to data protection, and resolve disputes involving complex digital supply chains. It identifies key legal trends, including the growing enforceability of cybersecurity clauses, the nuanced treatment of force majeure and indemnity provisions, and the increasing reliance on digital forensic evidence and expert testimony in litigation. The review also highlights the role of regulatory frameworks such as the GDPR, CCPA, and HIPAA in shaping the content and enforcement of cybercontractual obligations. Further, the findings underscore critical challenges related to attribution, privity, and jurisdiction in multi-party and cross-border environments. Overall, this study contributes a comprehensive, legally grounded analysis of how contractual liability is being redefined in response to the complexities of cyber risk, offering insights for legal practitioners, policymakers, and organizations navigating digital contracts in an era of rising cyber threats.
Copyright (c) 2025 Md Nazrul Islam Khan, Debashish Goswami (Author)