Journal of Data Analysis and Critical Management
The Journal of Data Analysis and Critical Management is poised to become a leading platform for researchers, practitioners, and industry professionals seeking innovative insights into data-driven decision-making and management strategies. As data continues to shape global industries, the journal's role in disseminating cutting-edge research will expand in several key areas: Advancements in Data Analytics, Interdisciplinary Research, Real-World Applications, Policy and Ethical Considerations, Integration of Emerging Technologies, Open Access, and Global Collaboration. The Journal of Data Analysis and Critical Management covers a broad spectrum of topics related to data analytics and management science, including but not limited to: Data Analytics and Business Intelligence, Critical Management Studies, AI and Machine Learning in Management, Big Data in Decision-Making –, Financial and Risk Analytics, Human Resource Analytics, Marketing and Consumer Behavior Analytics, Healthcare and Supply Chain Analytics, Ethics, Privacy, and Data Security, Sustainable and Green Data Practices By continuously evolving with technological advancements and business needs, the Journal of Data Analysis and Critical Management aims to be a cornerstone of knowledge in the rapidly growing field of data-driven management. Editor-in-Cheif: Dr. Chum Chadarin Frequency of the journal: Quarterly Year of Start: 2025 Publisher: iLearn, #7A Borey MLVILLA Preakchrey, Phnom Penh, Cambodia, editorial@ilearnkh.com Language: English ISSN: 3080-3322
Multimodal AI Systems for Strategic Business Forecasting and Risk Management
The idea of multimodal artificial intelligence has become a disruptive solution towards improved strategic business forecasting and risk management systems through the combination of various types of data like text, numerical data, images, sensor streams, and market indicators. This paper investigates the role of multimodal AI systems in helping organizations gain a more valuable insight, enhance predictive accuracy, and make business more resilient in dynamic business settings. The multimodal AI enables the integrated study of financial patterns, customer patterns, operational risk, and macroeconomic conditions by using complex fusion architectures e.g. transformer-based cross-modal models together with classical forecasting models. It focuses on the importance of multimodal learning in enhancing uncertainty estimation, better prediction of disruptive events and informed decision-making in strategic planning. Results indicate that these systems are better than unimodal strategies in terms of predictive power, anomaly identification, and simulation of scenarios, which can be of great importance to companies aiming to gain a competitive edge. The paper presents a basis to apply multimodal AI to real-world scenarios such as supply chain optimization, credit risk assessment, market trend forecasting, and crisis management.
Multimodal AI, Business Forecasting, Risk Management, Data Fusion, Strategic Decision-Making, Predictive Analytics, Transformer Models.
Copyright (c) 2025 https://creativecommons.org/licenses/by-sa/4.0
Forecasting the Fiscal Sustainability of U.S. Medicare: Scenario-Based Projections Through 2035
This paper is an analysis on the fiscal sustainability of the U.S. Medicare by considering past spending patterns between the years 2010 and 2023 and in the future to 2035 based on a scenario spanning model by quantifying the patterns of spending. Based on the information provided by the Centers of Medicare and Medicaid Services (CMS), and the Federal Reserve Economic Database, the research incorporates regression analysis, descriptive statistics, and projections of a scenario to determine the dependence of Medicare expenditure, the real GDP, demographics, and medical prices increase. The findings indicate that the actual Medicare expenditure has been steadily increasing over the years, which is mainly caused by an increased number of the aging population, economic growth and increasing the cost of medical services. Regression models show a good overall explanatory power (R 2 = = 0.98), which proves that these macroeconomic variables together explain most of the changes in Medicare spending. The results of forecasts of the outcome in three alternative scenarios- Baseline, High-Cost, and Low-Cost- show that the real Medicare spending will proceed to increase to become between 850 billion and 1 trillion (in 2010 USD) by 2035. The fiscal footprint of the program will grow to an average of 4.2-5.0% of the real GDP as compared to 3.7 in 2023. The results highlight the fact that the fiscal course of Medicare presents a sustainability issue in the long term. Unless the policy interventions are directed at cost containment, efficiency improvements, and payment reforms, the Medicare spending will exceed the rate of economic growth, making federal finances strained. The paper suggests reinforcing value-based care programs, improving the price control, and refining the Medicare Advantage compensation to achieve financial balance and healthcare effectiveness.
Medicare, Fiscal Sustainability, Forecasting, Scenario Analysis, Health Expenditure, Aging Population, Real GDP, Medical Inflation, Cost Containment, U.S. Healthcare Policy.
Inclusion Through Ensemble: Simple Orff Arrangements for Mixed-Ability Groups
This article discusses the use of simplified Orff-based ensemble arrangements as a framework for inclusion in the mixed-ability music classroom. The challenge of creating meaningful opportunities for participation in ensemble music-making for learners with a wide range of cognitive, physical, and emotional needs remains a central focus for classroom music educators. The Orff Schulwerk, with its emphasis on creativity, movement, rhythm, and improvisation, offers a flexible model for inclusive group participation, in which all learners can play a valued and contributing role. In this qualitative, literature-based synthesis, we review the evidence from scholarly and professional literature to identify the pedagogical principles, ensemble strategies, and classroom practices that can help educators create inclusive communities of music-making. We highlight approaches to adaptation and arrangement that feature simplified rhythmic and melodic materials, layered percussion and pentatonic ostinati, and peer-assisted group collaboration. These features provide all learners with entry points for participation while preserving the integrity and expressiveness of ensemble playing. Our findings suggest that inclusive Orff ensembles support social interaction, empathy, and collaborative learning, and can thus enhance both musical and social growth. Students develop a sense of confidence and agency through participation in rhythm circles, movement activities, and collaborative improvisations that are intentionally designed to be accessible to a range of abilities. Teachers also benefit from the flexibility and adaptability of the Orff-based methods, which support differentiation without stigmatizing or singling out individual learners. The study concludes that simple Orff arrangements can help create an inclusive music classroom community in which all differences are seen as a source of richness and creativity. We recommend that teacher education and professional development should include a focus on adaptive arranging skills, differentiated instruction, and ensemble facilitation to support ongoing inclusive practices. Overall, the Orff approach shows great promise for promoting a blend of accessibility, artistry, and social connection in the 21st-century music classroom.
inclusion, Orff Schulwerk, ensemble learning, mixed-ability groups, differentiated instruction, peer collaboration, music education.
Points, Levels, and Streaks in Primary EFL: Effects on Vocabulary Growth and Engagement
Gamification is a learning approach with effective outcomes on primary learners' involvement and learning. The purpose of this study is to evaluate the differences between the three types of gamification, namely points, levels, and streaks, in order to assess their potential impacts on the vocabulary learning progress and engagement of the primary English as a Foreign Language (EFL) learners. Ninety EFL learners aged 9-11 were assigned to one of the three gamified conditions and took part in the four-week classroom experiment, during which they practiced their vocabulary by using an EFL vocabulary app based on the retrieve-practice-learn principle and gamified with the three mechanics. Vocabulary achievement tests and engagement and motivation questionnaires, together with observations, were conducted to track the vocabulary performance and learning behavior of the participants. The outcome of the study indicated the benefits of all three gamified conditions, compared to the baseline data collected in the preliminary stage. As for the effect on engagement, students who were in the streak condition exhibited the highest rates of participation over the four-week period. In contrast, the level condition was found to have brought about the most progress in vocabulary performance. On the other hand, while the points condition demonstrated a potential for initiating short-term motivation, it may become redundant in the long run. In general, these three gamified components showed positive effects on the vocabulary progress and engagement of the primary EFL students and could therefore be taken into consideration when gamifying language learning. The findings of the study also suggest that a combination of points, levels, and streaks might serve as the best intervention strategy for sustaining the engagement and learning of primary learners. This study thus concludes that gamification can be effectively incorporated into primary EFL teaching, especially with the aid of technology. Further research might be conducted in the area of long- and short-term vocabulary retention, as well as age and task-specific gamification. In addition, the application of digital data analysis could be useful in this case.
Gamification, Points, Levels, Streaks, Vocabulary Learning, Learner Engagement, Primary EFL, Motivation.
Explainable Machine Learning Pipelines for Customer Risk Scoring in Anti-Money Laundering: A Management and Governance Perspective
The rising application of machine learning (ML) in the context of the Anti-Money Laundering (AML) systems has improved the ability to identify suspicious activities by customers, but the obscurity of most ML models is a cause to question the issue of transparency, accountability, and regulatory adherence. The study suggests a customer risk scoring explainable machine learning pipeline that incorporates explainable artificial intelligence (XAI) methods and effective management and governance structures. Based on socio-technical and responsible AI governance lenses, the analyzing paper forms a conceptual and empirical framework that consists of a model performance and explainability measurements, applicable to the compliance officers and regulators. The proposed pipeline utilizes interpretable modeling techniques like SHAP and LIME to identify high-risk customers using the AML data that can be observed in real life and give a clear and audited explanation of model decisions. Results indicate that explainable pipelines enhance accuracy in detection as well as build stakeholder trust, justification of decisions, and conformity with emerging regulatory issues like the EU AI Act, and the financial model risk management policies. The research is theoretically and practically important in that it provides a governance-based framework of deploying credible, interpretable ML systems in AML settings, which eventually provides a solution to the discrepancy between interpretability of technical models and managerial responsibility.
Explainable AI (XAI), Machine Learning Pipelines, Anti-Money Laundering (AML), Customer Risk Scoring, Governance, Compliance, Responsible AI, Model Transparency.
Copyright (c) 2025 Pristly Turjo Mazumder (Author) https://creativecommons.org/licenses/by-sa/4.0
Digital Forensics and Incident Response (DFIR) Automation: Leveraging AI to Accelerate Breach Investigation, Evidence Collection, and Cyberattack Mitigation
The rapid escalation of cyber threats in both frequency and sophistication has outpaced the capacity of traditional Digital Forensics and Incident Response (DFIR) practices. Conventional manual investigation methods such as log examination, evidence extraction, and threat correlation are often too time-consuming and labor-intensive to meet the demands of real-time incident management. Consequently, organizations are increasingly turning to artificial intelligence (AI) and automation to enhance the speed, accuracy, and scalability of DFIR operations. This paper explores how AI-driven models and automation frameworks can transform digital forensics and incident response, enabling faster detection, investigation, and containment of cyberattacks. It examines the integration of machine learning, natural language processing (NLP), and robotic process automation (RPA) into DFIR workflows to automate evidence collection, pattern recognition, and anomaly detection. Moreover, the study discusses how AI-enabled SOAR (Security Orchestration, Automation, and Response) platforms streamline the decision-making process by automatically correlating multi-source data and executing predefined containment actions. The paper also highlights practical applications across enterprise and national defense contexts, showcasing how predictive forensics and adaptive response mechanisms reduce investigation time and operational fatigue. Despite these advancements, several challenges persist, including AI model bias, data imbalance, interpretability issues, and legal admissibility of AI-generated evidence. To address these concerns, the study emphasizes the need for explainable AI frameworks, standardized forensic data models, and cross-disciplinary training for DFIR professionals. Ultimately, AI and automation do not aim to replace human expertise but to augment it enhancing investigative precision, improving incident readiness, and fostering a new generation of intelligent, resilient cyber defense systems.
Copyright (c) 2025 John Kuforiji (Author) https://creativecommons.org/licenses/by-sa/4.0
Teaching Leadership Continuity: Integrating Succession Planning into Business and Leadership Education
Purpose This article highlights the urgent need to integrate succession planning into leadership and management education as a core competency rather than a peripheral topic. While leadership programs often emphasize strategy, motivation, and organizational culture, they frequently overlook succession planning a critical factor in ensuring leadership continuity and organizational resilience. The purpose of this study is to demonstrate how embedding succession planning within curricula can strengthen students’ preparedness to navigate transitions and sustain organizational effectiveness. Methodology The article draws upon a qualitative phenomenological study involving 11 leaders from U.S. small-to-medium-sized logistics enterprises (SMEs). Data were collected through semi-structured interviews and analyzed using Braun and Clarke’s (2006) six step thematic analysis framework. This approach was chosen to capture the lived experiences of SME leaders who have directly engaged in succession planning processes and leadership transitions. Theoretical Framework The analysis was informed by two complementary leadership theories: Transformational Leadership Theory, which emphasizes vision, influence, and motivation in guiding organizational change, and Leader Member Exchange (LMX) Theory, which underscores the quality of relationships and trust between leaders and followers as a foundation for effective succession. These frameworks provided conceptual lenses for interpreting the experiences and strategies of the participants. Findings Four major themes emerged from the study: Leadership Experience with Succession: Participants described both proactive and reactive transitions, underscoring the risks of unplanned leadership gaps. Succession Planning Strategies: leaders highlighted formal and informal approaches to preparing successors, especially under resource constraints typical of SMEs. Mentorship and Culture: the role of trust, mentoring, and organizational culture was emphasized as essential in fostering continuity and leadership readiness. Organizational Impact: succession processes directly influenced employee morale, knowledge retention, and long-term sustainability. Practical and Pedagogical Implications The findings suggest that business schools and professional programs can leverage succession planning not only as an organizational tool but also as a pedagogical framework. Integrating succession into curricula through case studies, role-play simulations, reflective exercises, and succession readiness checklists enables students to connect theory with practice. By treating succession planning as a leadership competency, educational institutions can prepare graduates who are equipped to manage leadership continuity, protect organizational culture, and promote resilience in diverse business environments.
Cross-Asset Correlation Shifts in Crisis Periods: A Framework for Portfolio Hedging
Financial crises always demonstrate the weakness of the conventional diversification approach as the correlations between asset classes change in unpredictable and usually disruptive fashion. Equity, bond, commodity, and other alternative assets exhibit quite different correlation patterns during periods of broad-based stress, compared to the patterns when the market is stable. This paper discusses theory and empirical trends of changes in the cross-asset correlation and how these shifts have compromised the traditional risk management activities. Through an overview of essential methodologies such as rolling correlations, copula models, and regime-switching frameworks the study offers an understanding of the mechanisms that lead to breakdowns of correlation, as well as contagion effects. This is then discussed in relation to practical implications on portfolio hedging, where the limitation of the concept of static diversification is emphasized in the event of volatility spikes where safe-haven assets are shown to be conditionally unreliable. A formal framework is suggested to portfolio managers, combining dynamic rebalancing, tactical hedging, and selective utilization of alternative assets to maintain resiliency when there is crisis. The recent history of market turbulence includes examples of cases where successful hedging strategies are based on forecasting and not responding to changes in correlations. Finally, the article suggests a more active, data-based approach to hedging which is more focused on flexibility and on-going monitoring of cross-asset relationships. This in addition to providing a firm with a better chance to withstand any crisis and in the long term, increases risk-adjusted returns in more complex global markets.
cross-asset correlation, crisis periods, portfolio hedging, diversification, volatility, safe-haven assets, risk management.
Tail Risk Hedging Using LEAPS and Structured Options
Tail risk events are extreme events, and low-probability events that particularly have high impact on portfolio stability and long-term investment performance. The paper describes the way these extreme downside risks can be hedged in the case of Long-Term Equity Anticipation Securities (LEAPS) and structured options. Unlike traditional hedging strategies, which are characteristic of short-term option usages with periodic rollover charges and may not provide adequate protection to investors during long-term fluctuations, LEAPS provide long-term investment maturities that are more aligned with long-term investment horizons. Structured options, on the other hand, also provide customized and flexible solutions, such as collars, barrier options and spreads, that enable investors to balance a cost and protection against severe market dislocations. This paper highlights the strengths and weaknesses of each of these tools through comparative analysis and how they can be used in diversified risk management models. Additionally, it addresses some of the key issues that can be encountered in implementing such tools, including the unavailability of liquidity, complicated pricing, and regulatory concerns of which portfolio managers must be aware. These findings suggest that the integration of LEAPS and structured options into portfolio strategies can help find resilience, reduce drawdowns and provide investors with a systematic approach to dealing with uncertainty in more volatile markets. Lastly, the study identifies the importance of proactive hedging in long-term capital and financial stability.
Tail risk, LEAPS, structured options, hedging strategies, portfolio management, volatility.
Synthetic Data Generation with GenAI for Privacy-Preserving Smart Healthcare IoT Systems
The introduction of the Internet of Things (IoT) in the medical field has enabled the real-time monitoring of patients, the individual diagnosis of patients, and better clinical decisions. But, large scale sharing of sensitive health data raises significant privacy and security concerns. GenAI is an emerging approach to the generation of synthetic information that is both statistically useful and that does not compromise patient privacy. The paper discusses the potential of applying GenAI-based synthetic data generation to the smart healthcare IoT ecosystems, with a particular focus on its integration with federated learning to enhance the privacy protection and system security. We suggest a conceptual framework that combines federated learning and GenAI models- e.g., Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to create high-fidelity synthetic datasets to train diagnostic models, simulate clinical scenarios, and reinforce IoT security. The paper also touches on the ethical and regulatory implications of using synthetic data in healthcare, such as the necessity to comply with privacy laws and regulations across the globe, including HIPAA and GDPR. We find that GenAI-based synthetic data can be deployed to reduce privacy risks and facilitate robust cybersecurity practices in smart healthcare IoT systems, and to facilitate privacy-preserving data-driven innovation.
Synthetic data, Generative AI, Privacy preservation, Smart healthcare, IoT, Federated learning, Cybersecurity.
Lending Behaviour of Indian Commercial Banks in the Post-Liberalization Period
The lending behaviour of Indian commercial banks has undergone significant transformation since the economic liberalization of 1991. This paper examines the patterns and trends in bank lending, focusing on factors such as credit disbursement, sectoral allocation, non-performing assets (NPAs), and regulatory influences. Using secondary data from the Reserve Bank of India (RBI), World Bank, and other financial reports, this study analyses how liberalization policies have influenced banks’ lending strategies. The findings indicate a shift towards increased credit flow to the services and retail sectors, while traditional sectors like agriculture and industry have witnessed fluctuations. The rise in NPAs post-liberalization highlights the challenges in credit risk management. The paper concludes with policy recommendations to improve lending efficiency and financial stability.
Commercial Banks, Lending Behaviour, Economic Liberalization, Credit Growth, NPAs, RBI.
Neuroadaptive DevOps: Real-Time ML-Driven Adaptation of Deployment Pipelines in Edge Environments
The paradigm shift from centralized cloud architectures to decentralized edge computing has catalyzed a growing need for intelligent, adaptive software deployment processes. Traditional DevOps pipelines are predominantly static and centralized, assuming high availability of resources, low network latency, and predictable infrastructure assumptions that often fail in volatile, resource-constrained, and context-sensitive edge environments. In this paper, we propose Neuroadaptive DevOps, a novel framework that integrates real-time machine learning to autonomously reconfigure and optimize DevOps workflows in edge ecosystems. Drawing inspiration from neuroadaptive systems in human cognition, our framework is capable of learning from environmental feedback and telemetry to enable proactive decisions such as dynamic test selection, deployment delay, pipeline rollback, or configuration tuning. We present a modular architecture composed of telemetry sensors, prediction engines, adaptive policy modules, and execution agents. Our evaluation across 250 heterogeneous edge nodes demonstrates significant improvements in latency reduction, deployment success rates, and resource utilization, establishing Neuroadaptive DevOps as a promising approach for intelligent software operations in the edge computing era. This research contributes to the foundation of autonomic systems, offering critical insights for next-generation DevOps workflows that require real-time responsiveness and resilience under uncertainty.
Neuroadaptive DevOps, Edge Computing, Real-Time ML, Deployment Pipelines, Autonomic Systems, Continuous Delivery, Reinforcement Learning, Context-Aware Systems.
Revenue Systems, Governance Structures and Fiscal Health: A Critical Review of U.S. Municipal Finance in Large Cities
This review critically examines the complex interplay between revenue systems, governance structures, and fiscal health in large U.S. cities. It addresses two primary research questions: (1) Does a diversified mixture of revenue streams in large cities enhance fiscal conditions and improve service delivery? The review explores whether cities that rely on a broader array of revenue sources—such as taxes, fees, and transfers—are better equipped to manage economic shocks, maintain budgetary balance, and fund essential public services equitably. It further considers how revenue diversity may affect long-term fiscal stability and reduce dependence on volatile or regressive funding mechanisms. (2) Do cities with a council-manager form of government exhibit better fiscal health compared to other governance structures? This question probes the influence of professionalized administration, separation of powers, and managerial accountability on budgetary outcomes. It evaluates whether such governance arrangements are more likely to implement sound fiscal practices, avoid deficits, and ensure transparency in financial decision-making. By synthesizing existing literature and empirical evidence, the review aims to elucidate the relationships between revenue generation strategies, institutional governance models, and fiscal outcomes. It explores how cities rely on property taxes, sales taxes, user fees, and intergovernmental transfers, and how these are influenced by political, economic, and institutional factors. The analysis further interrogates the impact of governance frameworks—particularly the distinctions between council-manager and mayor-council systems—on fiscal performance. The findings contribute to a nuanced understanding of municipal fiscal health and provide insights to inform policy discussions on sustainable and effective urban financial management.
Revenue diversification, municipal finance, council-manager governance, fiscal health, urban economics, U.S. cities, local governments, service delivery, governance structures.
Quantum-Safe Networking for Critical AI/ML Infrastructure
The integration of artificial intelligence (AI) and machine learning (ML) into critical infrastructure has raised urgent concerns about data and model security, particularly in light of emerging quantum computing capabilities. Quantum algorithms threaten to render classical cryptographic methods obsolete, exposing AI/ML systems to potential breaches in confidentiality, integrity, and availability. This paper investigates the implications of quantum computing for securing AI/ML data both in transit and at rest and explores the development of quantum-safe networking protocols and cryptographic techniques. I examine post-quantum cryptographic (PQC) solutions including lattice-based, code-based, and hash-based algorithms, alongside the role of quantum key distribution (QKD) and AI-enhanced security orchestration. The study further addresses secure edge intelligence, federated AI systems, and emerging standards for 6G and beyond. My findings highlight both the necessity and complexity of transitioning to quantum-resilient infrastructure. Key challenges include computational overhead, legacy interoperability, and ethical concerns around AI-powered surveillance in quantum-secured environments. The paper concludes by emphasizing the need for proactive policy, investment in quantum-safe R&D, and cross-sector collaboration to safeguard AI/ML infrastructure in the post-quantum era.
Quantum-safe networking, post-quantum cryptography, artificial intelligence, machine learning, data security, quantum computing, edge intelligence, federated AI, 6G, cybersecurity.
Copyright (c) 2025 Oluwatosin Oladayo ARAMIDE (Author) https://creativecommons.org/licenses/by-sa/4.0
Federated Learning for Distributed Network Security and Threat Intelligence: A Privacy-Preserving Paradigm for Scalable Cyber Defense
Considering the amount, level of sophistication, and variety of cyber threats, network security is required to be intelligent, real-time, and privacy-preserving. Although successful, traditional centralized machine learning models have a number of drawbacks such as the risk of privacy, data bottleneck, and single points of failure phenomena. Our proposal is the federated learning (FL) framework of distributed network security and threat intelligence with a plan to provide a solution that takes full advantage of the diversity of data distributed on heterogeneous nodes without imposing serious privacy risks to users. The framework allows distributed edge devices to jointly train deep learning models in a locally-distributed fashion and only exchanging model updates with an aggregator. We compare the performance of the system within the benchmark intrusion detection datasets in the presence of IID and non-IID data sets. The presented results show that the suggested FL-based framework maintains a reasonable level of detection accuracy, enables enormous failures in communication overhead, and creates increased privacy assurances as opposed to the conventional centralized methods. Moreover, the system possesses resistiveness to frequent adversarial attacks, e.g., data poisoning and model inversion. The work provides a scalable and flexible architecture of next-generation cybersecurity infrastructures, especially IoT, edge, and smart cities.
Ethical Branding and Consumer Trust: Marketing with a Moral Purpose
In an era marked by increasing consumer awareness, digital transparency and global socio-environmental challenges, ethical branding has emerged as a crucial paradigm in restoring trust between businesses and the public. This article explores the philosophical, strategic and policy dimensions of ethical branding, arguing that ethics is not merely a branding trend but the foundational currency of sustainable trust. Drawing from classical moral theories—particularly Kantian ethics—and analyzing contemporary market behavior, the study examines how brands rooted in authenticity, transparency and purpose-driven values create deeper emotional and cognitive connections with consumers. Through illustrative case studies of Indian and global brands such as TATA, Amul, Patagonia and The Body Shop, the article identifies effective strategies and warns against the pitfalls of greenwashing and ethics-washing. The paper also proposes a multi-layered strategic and institutional framework to integrate ethics into business culture, regulatory policy and consumer education. Finally, it envisions a future where marketing in 21st-century India becomes a morally conscious activity—aligned with Sustainable Development Goals, especially SDG 12 on Responsible Consumption. Ethical branding, it contends, is not just a moral imperative but a transformative force that can redefine capitalism itself.
Ethical Branding, Consumer Trust, Sustainable Marketing, Moral Capitalism, Greenwashing, CSR, Responsible Consumption, Indian Brands, Brand Authenticity, Business Ethics, Purpose-driven Marketing, Marketing Philosophy.
Artificial Intelligence for Dynamic User Experience Personalization in SaaS
In the competitive landscape of Software as a Service (SaaS) platforms, delivering a personalized user experience has become a critical factor in increasing user satisfaction, engagement, and retention. With the rapid evolution of artificial intelligence (AI), SaaS platforms now have the opportunity to leverage AI-powered solutions to optimize user experience (UX) by tailoring interfaces, content, and functionality to the unique needs of individual users. This paper explores the integration of AI technologies in SaaS platforms to create personalized user experiences, focusing on the key drivers, methodologies, benefits, and challenges involved. The use of AI in SaaS platforms is transforming how businesses engage with their users. By analyzing vast amounts of user data, including browsing behavior, usage patterns, preferences, and demographic information, AI systems can build detailed user profiles that inform personalization strategies. Machine learning (ML) algorithms, such as recommendation systems and predictive analytics, play a central role in this process, enabling SaaS platforms to deliver dynamic and contextaware experiences. For instance, a SaaS platform can use AI to suggest relevant features, provide tailored content, and even adapt the user interface based on real-time user interactions. These personalization features help users achieve their goals faster, enhancing their overall experience and satisfaction
AI, SaaS platforms, user experience, personalization, machine learning, recommendation systems, data privacy, deep learning.
Optimizing Business Insights through SAP Analytics Cloud
In the era of digital transformation, businesses across various sectors are leveraging advanced analytics platforms to derive actionable insights from data. SAP Analytics Cloud (SAC) has emerged as a powerful tool for organizations seeking to streamline their data-driven decision-making processes. This research paper explores the potential of SAP Analytics Cloud in enhancing the efficiency of data analysis and improving the decision-making landscape for enterprises. By integrating various data sources and providing real-time analytics capabilities, SAC enables organizations to derive more accurate and actionable insights, contributing to smarter business strategies and improved operational efficiencies. The study begins with an overview of SAC’s capabilities, including its integration with both SAP and third-party data sources. The platform supports a wide range of data connections, allowing for seamless data modeling, visualization, and reporting. One of SAC’s most significant advantages is its ability to combine historical data with real-time information, empowering organizations to make decisions based on up-to-date information. This paper also delves into SAC’s cloudbased architecture, which provides businesses with the flexibility to scale their analytics needs without the constraints of on-premise infrastructure. A key focus of this research is the role of machine learning and artificial intelligence in SAP Analytics Cloud. The platform integrates AI-driven features such as predictive analytics, trend analysis, and anomaly detection, which help businesses identify emerging patterns and anticipate future outcomes. By incorporating AI into its analytics capabilities, SAC offers predictive insights that can inform strategic planning, resource allocation, and risk management. Additionally, SAC’s natural language processing (NLP) features enable users to interact with data through conversational queries, making it accessible to non-technical stakeholders and fostering a data-driven culture within organizations.
SAP Analytics Cloud, data-driven insights, machine learning, real-time analytics, business intelligence, AI integration, cloud-based analytics, data visualization.
Innovative VLSI Architectures for Modern Telecommunication Systems
The fast-moving telecommunications industry is driving the demand for Very Large Scale Integration (VLSI) design approaches that can achieve higher performance at lower power consumption and higher reliability. This paper presents enhanced VLSI design methodologies tailored for telecommunications systems, with an emphasis on key performance metrics optimization, such as speed, power efficiency, and scalability. The new strategies presented incorporate novel design paradigms including power-aware architectures, adaptive clocking mechanisms, and high-density integration techniques. Additionally, deep sub-micron technology has brought with it a whole new set of challenges, such as increased leakage currents and signal integrity problems, which are mitigated through novel circuit-level techniques and layout optimizations. One of the major focuses in the research is hardware integration, especially the accelerators needed for specialized functions in telecommunications: signal processing and encryption. Next, design methodologies supported by innovative CAD tools and simulation environments have been discussed; this helps increase design efficiency as well as helps reduce time-to-market. To that end, low-power techniques such as multi-threshold voltage and power gating have been focused on in relation to the design of energy-efficient hardware for telecommunications. This paper demonstrates, through simulation results and case studies, that the proposed VLSI strategies can improve system performance significantly while maintaining power efficiency and low cost. The findings contribute to the ongoing research and development in VLSI technology and offer a roadmap for designing next-generation telecommunications systems capable of supporting emerging applications such as 5G, IoT, and beyond. These strategies not only enhance the robustness of the design but also open the way toward more scalable and adaptive telecommunications infrastructures
VLSI design, telecommunications systems, power efficiency, signal processing, adaptive clocking, hardware accelerators, low-power techniques, deep sub-micron technology, CAD tools, scalable architectures.
A Swot-Based Review of E-Marketing Strategies in India: Sectoral Trends, Challenges, and Strategic Implications
This paper presents a comprehensive SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of e-marketing strategies in India. With the increasing penetration of internet-enabled devices and government-backed initiatives such as Digital India, businesses across various sectors—especially MSMEs—are transitioning towards digital platforms. This review synthesizes findings from 2010 to 2023, highlighting the potential, structural limitations, sector-specific applications, and threats facing India’s e-marketing landscape. Using a systematic narrative approach, this paper identifies key strategic insights and proposes actionable recommendations for businesses, policymakers, and researchers. The findings underscore the urgent need for digital literacy, structured strategy documentation, cybersecurity enhancement, and inclusive digital infrastructure to realize India’s e-marketing potential.
E-marketing, SWOT analysis, Digital marketing, MSMEs, India, Digital transformation, Strategy
Secure and Privacy-Preserving Data Migration Techniques in Cloud Ecosystems
With the rapid rise in multi-cloud and hybrid cloud environments, data migration has become one of the most requested and sought after efficient and secure ways of data transfer. Nonetheless, data migration between different types of cloud environments is fraught with serious security and privacy issues, such as unauthorized access or data breaches and non-compliance with various regulations. The paper will discuss the state-of-the-art methods of safe and privacypreserving data migration in the cloud ecosystem. We provide an in-depth review of the existing approaches, including encryption-based models, privacy-preserving cryptographic protocols, and secure transfer frameworks. Besides that, we also develop a layered migration framework that incorporates homomorphic encryption, access control policies as well as data anonymization to protect sensitive data both in transit and at rest. The framework is compared to the main security criteria, such as confidentiality, integrity, and privacy preservation. It demonstrates the minimization of attack surfaces and a higher adherence rate to data protection regulations. Since cloud migration processes raise the problem of data sovereignty, trust, and regulatory compliance, our results indicate the need to adopt proactive security architectures to support these processes. The research is relevant to the creation of secure cloud migration patterns according to the dynamic cybersecurity environment.
Cloud Security, Data Migration, Privacy Preservation, Encryption, Homomorphic Encryption, Multi-Cloud, Data Anonymization, Secure Cloud Computing, Cybersecurity, Cloud Compliance
Detecting the Abuse of Generative AI in Cybersecurity Contexts: Challenges, Frameworks, and Solutions
The sudden momentum of generative artificial intelligence (AI) models like large language models (LLMs), generative adversarial networks (GANs), and AI-based code generators has brought new abilities never seen before in many fields. However, the same models are being used with increasingly malicious intent, such as generating highly personalized phishing emails, deepfake content, social engineering scripts, and AI-generated malware. Legacy cybersecurity solutions are poorly equipped to identify or prevent the security threats posed by generative AI tools, presenting an urgent and significant security infrastructure gap. This paper suggests a conceptual model for detecting generative AI abuse in cybersecurity scenarios. The framework consists of behavioral analysis, AI-based content fingerprinting, and adversarial prompt detection to learn misuse patterns. We also investigate the issue of detecting differences between human and AI-generated malicious artifacts, and we assess what this means in terms of detection effectiveness and ethical monitoring. Our results highlight the importance of adaptive and AI-aware cybersecurity defenses to remain ahead of evolving threats in the era of generative AI.
Generative AI, Cybersecurity, Adversarial AI, Phishing Detection, Deepfake, AI Abuse, Large Language Models, Threat Detection, AI Security, Content Fingerprinting.
Performance of Corporate Social Responsibility in India – An Econometric Exploration
Corporate Social Responsibility (CSR) has emerged as a critical tool for businesses in India to contribute to sustainable development and social welfare. This paper examines the social impact of CSR initiatives in India through an econometric analysis, supported by case studies and multiple-regression modelling. The study aims to quantify the relationship between CSR expenditures and social outcomes, such as poverty reduction, education, healthcare, and environmental sustainability. The paper also explores the challenges and opportunities associated with CSR implementation in India, providing actionable insights for policymakers, corporations, and stakeholders.
Green Finance and State Initiatives for Environmental Sustainability – With Special Reference to Uttar Pradesh
Green finance, an emerging paradigm, promotes sustainable development by directing financial resources toward projects with environmental benefits, including renewable energy, pollution control, and climate resilience. As global concerns about climate change and environmental degradation grow, governments are increasingly adopting policies to support green finance initiatives, aligning public and private sector efforts to meet sustainability goals. These state-led initiatives aim to create frameworks and incentives to encourage investments in environmentally friendly projects, driving the transition to a low-carbon economy. Many countries have introduced green bonds, environmental tax reforms, and subsidies for green technologies, fostering an ecosystem where financial institutions can support sustainable practices. Regulatory bodies are also enhancing transparency in green financing, mandating environmental risk disclosures, and encouraging green credit through preferential lending rates. Such initiatives mobilize private sector participation, helping bridge the funding gap for green projects while reducing carbon footprints. However, significant challenges remain, including the need for standardized definitions, reliable green finance metrics, and international collaboration to create a globally cohesive framework. The role of green finance in mitigating climate risks and enhancing resilience against environmental crises positions it as a critical tool in global sustainability efforts. Consequently, state initiatives that support green finance act as catalysts, not only encouraging green investments but also aligning financial markets with the United Nations Sustainable Development Goals (SDGs) in the field of Green Finance. This paper explores how these initiatives foster environmental sustainability, emphasizing the need for continued governmental and private sector collaboration in achieving long-term ecological resilience and financial stability.
Green Finance, Low Carbon Economy, Green Credit.
SHGs as a Catalyst for Women’s Empowerment: A Study of Gaighata Block in North 24 Parganas
Women empowerment as a concept and philosophical basis of public policy is of recent origin. It means transitions from a position of enforced powerlessness to one of power. It promotes women’s inherent strength and positive self-image. Employment literally means making someone powerful, facilitating the weak to attain strength, to increase one’s self esteem, to help someone to be assertive and self-confident, to enable someone to confirm in justice and to support someone to fight for her rights. In the view of Amartya Sen the Nobel laureate improvement is an essential component of development according to this perspective development has to provide women with greater and choice beach employees increasing probabilities to make choice Women’s roles in India have evolved from being limited to the home to participating in business and economic growth. India is expected to become the second-largest startup ecosystem globally, with women making up 48% of the population. Top states for women-owned businesses include Tamil Nadu, West Bengal, Andhra Pradesh, and Maharashtra. Social entrepreneurship, including Self-Help Groups (SHGs), addresses social problems like poverty and marginalization. SHGs support women, especially in rural areas, to achieve a stable life and reduce poverty. This study examines factors encouraging women to join SHGs in North 24 Parganas, West Bengal, including employment generation, gender inequality, and social value creation, to empower women entrepreneurs and improve their status. This study explores the impact of self-help groups (SHGs) on rural women’s socioeconomic status in Gaighata Block, North 24 Parganas district. SHGs have been instrumental in promoting women’s empowerment, financial inclusion, and social development. Through a mixed-methods approach, this research examines the effectiveness of SHGs in enhancing rural women’s socioeconomic status, focusing on income generation, education, healthcare, and social participation.
Social entrepreneurship, Marginalization, Environmental degradation, Employment generation, Gender Inequality.
Optimizing Real-Time Web Applications in 2025: A Performance and Scalability Study of Node.js Backend with Angular Frontend Architectures
As real-time web applications become increasingly prevalent across domains such as finance, e-commerce, and collaborative platforms, the need for performant and scalable full-stack architectures has never been more critical. This study explores the integration of Node.js as a non-blocking, event-driven backend with Angular as a modular, component-based frontend framework in the context of real-time application development in 2025. By benchmarking performance across diverse use cases—such as live chat, real-time dashboards, and collaborative editing tools—this research evaluates metrics including response latency, throughput under load, and client-side rendering efficiency. The paper further investigates enhancements in Node.js (v20+) and Angular (v17+) that impact asynchronous processing, state management, and HTTP streaming. A comparative analysis with traditional RESTful stacks and newer alternatives like WebSockets and GraphQL is also presented. Findings indicate that the Node.js–Angular stack, when optimized using server-side caching, Ahead-of-Time (AOT) compilation, and efficient data binding strategies, can significantly outperform legacy approaches. This research offers actionable insights and architectural best practices for developers and system architects aiming to deliver high-performance, scalable, and responsive web solutions in a real-time digital landscape.
Real-time web applications, Node.js, Angular, scalability, performance optimization, non-blocking I/O, AOT compilation, web architecture, WebSockets, HTTP streaming.
FINANCIAL INCLUSION AND DIGITAL PAYMENTS
Financial inclusion is essentially defined as the planning of financial services to everybody. Digital payments take the form of electronic transactions to either receive or transfer funds. It does not include paper cash in the process of the transaction. A digital payment actually involves an electronic-transfer of money through various means, such as online banking and mobile wallet, to digital platforms. The provision of a cashless and efficient transaction method by which transactions are conducted securely is digital payment. Financial inclusion and digital payments together could help people easily adopt financial services and ultimately enhance their lives, thus contributing to economic development. This paper aims to investigate the influence of digital financial inclusion on different areas of north Uttar Pradesh. A comparative analysis of digital payment and financial inclusion between banked and unbanked people of Uttar Pradesh. Data collection was carried out using a survey questionnaire and by the study of previous research papers
Digital Payment, Online Payment, UPI, cashless transaction, electronic payments
Copyright (c) 2025 Journal of Data Analysis and Critical Management
Decentralizing Education Functions in Cambodia: Lessons from Battambang 2019-2022
This article presents a detailed examination of the decentralization of education functions in Cambodia, focusing on the pilot initiative implemented in Battambang province between 2019 and 2022. It begins by outlining the legal and institutional frameworks—most notably Sub-Decrees No. 182 and 184—that enabled the transfer of selected education responsibilities from central authorities to District and Municipal Administrations (DMAs). The analysis also explores the roles of key provincial and national institutions in supporting the implementation process, as well as the dual modalities of assignment and delegation. Drawing on qualitative field research, including semi-structured interviews with education officials and local stakeholders, policy document reviews, and participant observations, the article highlights both the achievements and persistent challenges in operationalizing decentralization. It identifies issues such as capacity gaps, inconsistent regulatory interpretations, unclear mandates, and communication bottlenecks across administrative levels. The findings underscore the need for clearer procedural guidelines, sustained capacity-building efforts, and the establishment of structured provincial support mechanisms. By capturing the nuanced experiences from Battambang, this article provides critical insights and practical recommendations for strengthening the decentralization process. It argues that, if effectively addressed, the lessons from this pilot can inform a more coherent, inclusive, and scalable strategy for transferring education functions across Cambodia’s provinces—ultimately contributing to more responsive, accountable, and community-centered education service delivery
Decentralization, Cambodia, Piloting, Transferring functions.
Rethinking Global Governance: Cooperation in a World of Power
Using AI for Real-Time Threat Detection and Anomaly Identification
Cybersecurity threats have grown in complexity, requiring organizations to adopt advanced technologies for timely detection and mitigation. Artificial Intelligence (AI) has emerged as a powerful solution for real-time threat detection and anomaly identification, leveraging its ability to analyze vast amounts of data and recognize patterns indicative of malicious activity. This paper explores the implementation of AI algorithms, including machine learning and deep learning techniques, to enhance cybersecurity systems. Key methods such as supervised learning for classification, unsupervised learning for anomaly detection, and reinforcement learning for adaptive defense are examined. Additionally, the paper presents a comparative analysis of AI models based on accuracy, speed, and false positive rates. Emphasis is placed on the advantages of real-time detection systems, particularly in identifying zero-day attacks and sophisticated threats. Despite its potential, challenges such as data privacy, model bias, and adversarial attacks remain. The study concludes by proposing future directions for improving AI-powered cybersecurity systems through explainable AI (XAI), federated learning, and continuous model training. Ultimately, the integration of AI in cybersecurity represents a transformative step toward more resilient and proactive threat management.
AI, cybersecurity, real-time threat detection, anomaly detection, machine learning, Artificial Intelligence, Cybersecurity, Real-Time Threat Detection, Anomaly Identification, Machine Learning, Deep Learning.