International Journal of Technology, Management and Humanities
International Journal of Technology, Management and Humanities (IJTMH) The International Journal of Technology, Management and Humanities (IJTMH) is a peer-reviewed, open-access academic journal dedicated to the advancement of interdisciplinary research across the fields of technology, management, and the humanities. The journal, bearing the electronic International Standard Serial Number (e-ISSN) 2454-566X, was established in 2015 with the aim of fostering intellectual dialogue and innovative thinking across diverse disciplines. IJTMH publishes original research articles, case studies, technical notes, and review papers that contribute to the understanding and integration of technological innovations, management practices, and humanistic values. Its scope covers a wide range of topics, including but not limited to information technology, engineering management, business strategies, cultural studies, ethics, and societal impacts of technological change. Over the years, IJTMH has built a strong reputation for promoting research that bridges the gap between technical expertise and human-centered approaches. The journal welcomes contributions from scholars, practitioners, and industry experts worldwide, encouraging a global exchange of ideas and interdisciplinary collaboration. Committed to maintaining high academic standards, IJTMH ensures a rigorous peer-review process and strives for the timely dissemination of knowledge to its diverse readership, including researchers, academicians, and professionals.
Cloud Driven Enterprise Network Architecture for Oracle HR Systems Government Services and Biomedical Artificial Intelligence Platforms
Cloud-driven enterprise network architecture plays a critical role in supporting integrated human resource systems, digital government services, and biomedical artificial intelligence (AI) platforms. Modern organizations increasingly rely on scalable cloud infrastructures to manage workforce operations, public service delivery, and data-intensive biomedical research. Enterprise solutions such as Oracle Corporation Human Resource (HR) systems are widely deployed across government institutions for workforce management, payroll automation, and compliance monitoring. Simultaneously, cloud ecosystems provided by Amazon Web Services and Microsoft Azure enable high-performance computing, secure data exchange, and AI-driven biomedical analytics. This research examines architectural frameworks integrating cloud-native infrastructure, software-defined networking (SDN), zero-trust security, and AI orchestration layers within enterprise networks. It evaluates interoperability among Oracle HR platforms, digital government service portals, and biomedical AI research systems that process large-scale genomic and clinical datasets. The study explores performance optimization, cybersecurity integration, regulatory compliance, and governance models necessary for sustainable digital transformation. By analyzing technical components and operational methodologies, this research proposes a comprehensive architecture model designed to enhance scalability, resilience, security, and innovation across public sector and biomedical AI ecosystems.
Cloud Computing, Enterprise Architecture, Oracle HR Systems, Government Digital Services, Biomedical Artificial Intelligence, Cloud-Native Platforms, Human Capital Management (HCM), Digital Transformation, AI-Driven Analytics, Interoperability Frameworks, Secure Cloud Infrastructure, Data Governance, Regulatory Compliance, Microservices Architecture, Intelligent Automation
Automating Software Builds with Jenkins: Design Patterns and Failure Handling
Automated software build systems now form an essential part of the current software engineering practice, which allows quicker feedback, more stable integration and better software quality. Jenkins has been a popular automation server since it is extensible, flexible in pipeline model, and well-supported ecosystem. This paper looks at the software build automation provided by Jenkins with a special focus on how software design patterns and systematic failure handling mechanisms are applied. It discusses the degree to which established design patterns may be used to organize and construct pipelines to achieve enhanced modularity, reusability, and maintainability as well as dealing with the complexity of large-scale automation environments per se. The paper also examines typical types of build and pipeline failures and fault-tolerant strategies that help increase system robustness, minimize downtime, and minimize the spread of failures. With the implementation of design oriented automation combined with the use of structured failure management, build systems implemented using Jenkins can attain greater reliability, scalability, and efficiency. Whether through architectural discipline applied to building automation or through proactive failure management applied to making the continuous integration processes more resilient, the findings provided serve to underscore the significance of designing to avoid certain failures in the present case.
Jenkins, build automation, continuous integration, software design patterns, failure handling, fault tolerance
An Analysis of B2C and B2B Dynamics in the U.S. Consumer Goods Market
The U.S. consumer goods market is characterized by the coexistence of business-to-consumer (B2C) and business-tobusiness (B2B) exchange systems that differ substantially in structure, behavior, and strategic orientation. Despite their interdependence, these market types are often examined separately, resulting in limited understanding of their comparative dynamics within a single industry context. This study presents a comprehensive analysis of B2C and B2B dynamics in the U.S. consumer goods market by examining buying behavior, value creation mechanisms, distribution channel structures, relationship management practices, and the role of digital transformation. The analysis demonstrates that B2C markets are primarily driven by consumer perceptions of value, brand equity, service quality, and emotional engagement, whereas B2B markets emphasize organizational buying processes, efficiency, risk mitigation, and long-term relational exchanges. Differences in channel governance and supply chain coordination further distinguish the two market structures, influencing performance outcomes and competitive positioning. The study also highlights the increasing importance of e-commerce and integrated supply chain systems in reshaping both B2C and B2B interactions. The findings suggest that firms operating across both market domains must adopt integrated strategic approaches that align marketing, channel management, and relationship development to sustain competitive advantage. By clarifying the structural and strategic distinctions between consumer and organizational markets, this research contributes to marketing theory and provides actionable insights for managers navigating hybrid market environments within the U.S. consumer goods sector.
B2C marketing, B2B marketing, consumer goods market, relationship marketing, distribution channels, supply chain management.
Leveraging Social Media to Enable Emerging Consumer Brands to Compete with Established Brands
The expansion of social media has fundamentally altered the competitive landscape of consumer markets, enabling emerging brands to challenge the dominance of established firms. Historically, established brands benefited from superior financial resources, strong brand recognition, and control over traditional advertising channels. In contrast, emerging brands faced significant barriers to market entry and visibility. Social media platforms have reduced these barriers by offering accessible, interactive, and cost-efficient channels for brand communication, consumer engagement, and value creation. This study examines how emerging consumer brands leverage social media to enhance competitiveness through strategic branding, consumer engagement, analytics-driven decision making, and innovation support. By synthesizing existing theoretical and empirical research, the study demonstrates that social media enables direct brand–consumer interaction, facilitates electronic word-of-mouth, and supports the development of online brand communities. These mechanisms allow emerging brands to strengthen brand equity, improve customer relationships, and accelerate brand awareness despite limited resources. The findings further suggest that the strategic use of social media analytics and influencer collaboration enhances content effectiveness and performance measurement, while social media participation in product development fosters innovation and organizational learning. Overall, the study positions social media as a competitive equalizer that enables emerging consumer brands to adopt agile, data-informed, and engagement-driven strategies to compete with established brands. The research contributes to marketing literature by offering an integrated perspective on how social media reshapes brand competition and strategic brand management in contemporary consumer markets.
Social media marketing, Emerging consumer brands, Brand competitiveness, Consumer engagement, Digital branding, Online brand communities.
How Blockchain Technology Could Improve Transparency and Reduce Corruption in the Banking System
Corruption and transparency: Corruption and absence of transparency have been thorny issues in the banking systems especially in the developing and fragile economies where the inadequacy of the institutions and the absence of supervision erode the public confidence and sustainability of the financial systems. A new digital technology called blockchain has come up and has the potential to solve such problems by radically transforming the way records of financial transactions are stored, checked, and tracked. This paper will discuss how blockchain technology can enhance transparency and curb corruption in the banking system through its centralized properties of decentralization, immutability and distributed consensus. The paper based on the applicable theoretical frameworks, such as Principal–Agency Theory and institutional governance theories, examines why and how blockchain can help curb discretionary authority, increase audit trail, improve regulatory supervision, and curtail fraud and embezzlement, insider abuse opportunities. There is a special focus on the situation in the developing and conflict-prone banking systems, and it is illustrated by the Democratic Republic of the Congo, where money dependency, poor supervision, and low confidence of the population contribute to the risks of corruption. The paper goes further to describe how blockchains are used in practice, including distributed ledgers, smart contracts, and blockchain-enabled KYC and AML systems, and as well as discusses challenges in implementing blockchains, in terms of infrastructure, regulation, and capacity building. On the whole, the research can add to the scholarly and policy discussion about financial governance by showing how blockchain innovation can yield transparency, accountability, and trust restoration in the banking sector and stating the way to introduce it in a responsible and context-aware way.
Blockchain technology; Banking system; Transparency; Corruption reduction; Financial governance; Distributed ledger technology.
Real Time AI and Machine Learning Systems for Privacy Preserving Digital Advertising in Healthcare
Real-time artificial intelligence and machine learning systems are increasingly shaping the future of digital advertising in healthcare, where personalization, regulatory compliance, and data privacy must be carefully balanced. This paper presents a privacy-preserving architectural framework for healthcare-focused digital advertising platforms that leverage real-time AI and machine learning while safeguarding sensitive patient and consumer data. The proposed system integrates distributed data processing, secure model training, and privacy-enhancing technologies to enable intelligent ad targeting, performance optimization, and contextual relevance without exposing personally identifiable or protected health information. Machine learning models are deployed in real time to analyze behavioral signals, contextual metadata, and anonymized engagement patterns across healthcare digital channels. Privacy-preserving techniques such as data anonymization, tokenization, differential privacy, and federated learning are incorporated to ensure compliance with healthcare data protection regulations while maintaining model accuracy. The framework supports streaming analytics and low-latency inference to enable adaptive advertising strategies based on real-time user interactions, clinical content engagement, and platform performance metrics. The architecture is designed for scalability and resilience, leveraging cloud-native principles, distributed machine learning pipelines, and automated orchestration to handle high-volume advertising workloads across healthcare ecosystems. Security-by-design principles are embedded throughout the system to protect data at rest, in transit, and during model execution. Continuous monitoring and AI-driven anomaly detection enhance trust by identifying misuse, bias, or policy violations in advertising workflows. By unifying real-time AI intelligence with privacy-preserving machine learning and secure data processing, the proposed approach enables ethical, compliant, and effective digital advertising in healthcare environments. This framework supports responsible innovation by aligning personalized advertising outcomes with patient trust, regulatory mandates, and the broader objectives of digital health transformation.
real-time AI, machine learning systems, privacy-preserving analytics, digital advertising, healthcare data protection, federated learning, differential privacy, secure data pipelines, intelligent targeting, ethical AI, cloud-native analytics, regulatory compliance
Data-Driven Urban Resilience for Sustainable Cities: Leveraging Smart Infrastructure and Predictive Analytics
Urban areas are increasingly challenged by climate-related hazards, infrastructure stress, and rapid urbanization, making urban resilience a critical component of sustainable city development. In response, cities are adopting data-driven approaches that combine smart infrastructure and predictive analytics to enhance their capacity to anticipate, absorb, and recover from disruptions. This research examines how smart infrastructure systems and data-driven predictive analytics contribute to urban resilience and long-term sustainability. The study adopts a conceptual and analytical approach, synthesizing established theories of urban resilience, smart city development, and urban computing to develop an integrated framework for data-driven resilience planning. Smart infrastructure, supported by Internet of Things technologies and real-time data platforms, enables continuous monitoring of urban systems, improving infrastructure reliability, service efficiency, and adaptive capacity. Predictive analytics further enhances resilience by enabling forecasting, early warning, and proactive decision-making across critical urban sectors such as transportation, environmental management, and infrastructure maintenance. The proposed framework demonstrates how the interaction between digital infrastructure, analytics capabilities, and governance coordination can strengthen urban adaptability and sustainability performance. The findings highlight that data-driven urban resilience is not solely a technological outcome but also depends on effective institutional coordination and strategic integration of analytics into urban planning processes. This study contributes to the growing body of smart city and resilience research by clarifying the mechanisms through which data-driven systems support sustainable and resilient urban transitions.
Urban resilience, Smart infrastructure, Predictive analytics, Sustainable cities, Urban computing, Internet of Things, Smart city governance.
Governance, Institutions, and Urban Resilience: Evaluating Policy Coherence in Sustainable City Transitions
Rapid urbanization, climate variability, and infrastructure pressures are increasing the importance of resilience in contemporary urban planning and governance. Cities are required not only to manage environmental and social risks but also to coordinate institutional responses across multiple policy domains to ensure sustainable development. Governance systems and institutional arrangements therefore play a central role in shaping how urban areas respond to shocks, adapt to change, and transition toward sustainability. Despite growing policy attention to resilience, fragmented institutional structures and inconsistent policy implementation often limit the effectiveness of resilience planning in cities. This study examines the relationship between governance institutions, policy coherence, and urban resilience in the context of sustainable city transitions. Using a qualitative institutional and policy evaluation approach, the research analyzes how coordination among planning institutions, environmental governance systems, and infrastructure policy frameworks influences resilience outcomes. The study emphasizes the importance of integrated governance mechanisms and cross-sector policy alignment in strengthening adaptive capacity and improving sustainability performance in urban systems. The findings suggest that policy coherence and institutional coordination significantly enhance resilience planning and support sustainable urban transitions, while governance fragmentation weakens long-term resilience outcomes. Strengthening institutional collaboration and aligning urban policies across sectors are therefore essential for building resilient and sustainable cities.
urban resilience, governance institutions, policy coherence, sustainable cities, institutional coordination, urban planning
Transforming Database Leadership in the Era of Cloud-Native Automation and Resilient Operations
The rapid expansion of cloud platform adoption and the growing shift toward automated infrastructure created conditions in whichtraditional database administration models were no longer adequate for ensuring reliable and scalable data operations. The present studyexamines how database leadership must evolve within cloud native environments shaped by virtualization, managed database services,configuration automation, resilient architectures, and early DevOps influenced collaboration models. A mixed method approach was used,combining qualitative analysis of operational patterns across organizations adopting cloud hosted data platforms with quantitativeassessment of changes in workload distribution, automation coverage, and reliability indicators following platform oriented transformations. Findings indicate that organizations that reposition database professionals as strategic leaders of automated workflows,resilience planning, and cross functional coordination achieve measurable gains in operational stability and delivery velocity. Evidencealso shows that automation of provisioning, configuration, and schema changes reduces manual interventions and lowers error rates, whileresilience patterns such as replication and controlled failover improve service continuity. The research highlights how databaseresponsibilities expand beyond maintenance toward platform stewardship, architectural decision making, and proactive reliabilitymanagement. These shifts contribute to a more integrated data ecosystem in which database teams collaborate directly with application andoperations groups, improving communication pathways and enabling more predictable release cycles. The study’s contributions lie inidentifying the structural, technical, and organizational adjustments needed to align database leadership with cloud native automationpractices, offering a reference framework for institutions seeking to modernize data operations and strengthen the resilience of theirinformation environments.
Cloud native database platforms, database leadership, infrastructure automation, DevOps collaboration, managed relational services, configuration automation, operational resilience, database provisioning, schema change management, failover strategies, replication patterns, monitoring practices, incident response models, data platform governance, reliability engineering, distributed data environments
The Future of It Program Management: Ai-enhanced Cybersecurity for Next-gen Infrastructure Systems
The rapid evolution of digital technologies and the increasing complexity of information systems have significantly transformed the landscape of IT program management. As organizations migrate toward cloud-based, distributed, and data-intensive infrastructures, cybersecurity risks have grown in scale, sophistication, and frequency. Traditional security mechanisms, which rely heavily on rule-based detection and manual intervention, are increasingly inadequate for protecting next-generation infrastructure systems. In this context, artificial intelligence (AI) has emerged as a critical enabler for strengthening cybersecurity and redefining the future of IT program management. This article explores how AI-enhanced cybersecurity solutions are reshaping IT program management practices in next-generation infrastructure environments. It examines the integration of AI technologies—such as machine learning, deep learning, natural language processing, and predictive analytics—into cybersecurity frameworks to enable proactive threat detection, automated incident response, and continuous risk assessment. By leveraging large volumes of operational and security data, AI-driven systems can identify anomalies, predict potential attack vectors, and respond to threats in real time, thereby improving the resilience and reliability of critical IT infrastructures. The study also highlights the strategic implications of AI-enabled cybersecurity for IT program managers. As cybersecurity becomes deeply embedded within program governance, planning, and execution, program managers must adapt their approaches to risk management, resource allocation, and stakeholder coordination. AI-driven insights support more informed decision-making, enhance situational awareness, and enable alignment between cybersecurity objectives and broader organizational goals. However, the adoption of AI in cybersecurity introduces new challenges, including data privacy concerns, algorithmic transparency, model robustness, and regulatory compliance. By synthesizing existing academic literature and industry perspectives, this article provides a comprehensive view of how AI-enhanced cybersecurity is transforming IT program management in next-generation infrastructure systems. The findings suggest that organizations that strategically integrate AI into their cybersecurity and program management frameworks are better positioned to manage complex risks, ensure operational continuity, and sustain long-term digital transformation. Ultimately, the study underscores the importance of adopting AI-driven cybersecurity not only as a technical solution but as a core component of modern IT program management practice.
Artificial Intelligence; IT Program Management; AI-Enhanced Cybersecurity; Next-Generation Infrastructure Systems; Cyber Risk Management; Machine Learning; Predictive Analytics; Threat Intelligence; Network Security; Cloud and Edge Computing Security; Digital Transformation; Cyber Resilience; Automated Incident Response; Governance, Risk, and Compliance (GRC); Infrastructure Stability
Comparative analysis of NLP feature extraction methods for SMS spam classification
Short Message Service (SMS) spam classification is a critical application of natural language processing (NLP) aimed at mitigating unsolicited and malicious communications. This study presents a comparative analysis of widely used NLP feature extraction methods for SMS spam detection, evaluating their effectiveness, efficiency, and robustness. Traditional approaches such as Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) are compared with distributed representations including Word2Vec, GloVe, and contextual embeddings derived from transformer-based models. Using standard benchmark SMS datasets, these feature extraction techniques are assessed in conjunction with common machine learning classifiers. Performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and computational cost. The results highlight the strengths and limitations of each method, showing that while traditional features offer simplicity and efficiency, advanced embeddings provide superior contextual understanding and classification performance. The study offers practical insights to guide the selection of feature extraction methods for effective and scalable SMS spam classification systems.
Enterprise-Scale AI Architecture for Secure Mobile Platforms with Governance-Driven Automation Large-Scale Data Warehousing and Machine Learning
Enterprise-scale adoption of artificial intelligence requires architectures that are secure, scalable, and governed across heterogeneous digital ecosystems. This paper presents an enterprise-scale AI architecture designed for secure mobile platforms that integrates governance-driven automation, large-scale data warehousing, and machine learning capabilities. The proposed architecture enables cross-domain intelligence by unifying data ingestion, storage, processing, and analytics while enforcing security, privacy, and compliance policies throughout the AI lifecycle. Large-scale data warehousing serves as the foundation for managing structured and unstructured data, supporting real-time and batch analytics for machine learning model training and inference. Governance-driven automation ensures transparency, auditability, and ethical AI operations through policy enforcement, access control, and continuous monitoring. The architecture supports scalable deployment across enterprise environments, enhances decision intelligence, and enables secure, data-driven automation for modern mobile platforms. This work demonstrates how integrated AI, data, and governance frameworks can address operational complexity and regulatory requirements in enterprise-scale systems.
Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures
The increasing reliance on data-driven decision-making in modern enterprises has accelerated the adoption of artificial intelligence, cloud-native platforms, and API-first architectures. However, challenges related to trust, security, adaptability, and governance continue to hinder the large-scale operationalization of AI systems. This paper proposes a trustworthy and adaptive AI framework designed for enterprise analytics, cybersecurity, and decision optimization using API-first and cloud-native architectural principles. The framework integrates modular AI services, real-time analytics pipelines, and adaptive learning mechanisms to support scalability, resilience, and rapid innovation. Trustworthiness is ensured through explainable AI, policy-driven governance, continuous monitoring, and secure data exchange via standardized APIs. Cybersecurity capabilities are embedded across the architecture using zero-trust principles, AI-driven threat detection, and automated risk assessment. Experimental and conceptual evaluation demonstrates that the proposed approach improves decision accuracy, system interoperability, and security posture while maintaining low latency and operational flexibility. The findings highlight how enterprises can balance performance, trust, and security in complex digital ecosystems by aligning AI design with cloud-native and API-centric strategies.
Trustworthy Artificial Intelligence, Enterprise Analytics, Adaptive AI Systems, Cybersecurity, Decision Optimization, API-First Architecture, Cloud-Native Systems, Explainable AI, Zero Trust Security, Intelligent Enterprise Platforms.
A Multi-Domain AI Framework for Enterprise Agility Integrating Retail Analytics with SAP Modernization and Secure Financial Intelligence
In the era of digital transformation, enterprises face mounting pressure to enhance agility, optimize operational efficiency, and strengthen cybersecurity resilience. Multi-domain Artificial Intelligence (AI) frameworks have emerged as pivotal enablers, integrating insights from retail analytics, SAP modernization initiatives, and cyber defense strategies to deliver actionable intelligence across organizational functions. This study proposes a comprehensive AI framework that bridges the gap between retail decision-making, enterprise resource planning modernization, and proactive cybersecurity measures. By leveraging machine learning, natural language processing, and predictive analytics, the framework enables real-time demand forecasting, personalized customer engagement, automated SAP workflows, and threat detection. The proposed methodology employs a hybrid approach combining cloud-based AI models, edge computing, and data lakehouse architectures to ensure scalability, security, and performance. Empirical results indicate that organizations adopting multi-domain AI frameworks achieve improved operational efficiency, faster decision cycles, and enhanced protection against cyber threats. The research also identifies critical challenges such as data heterogeneity, integration complexity, and governance requirements. This study underscores the transformative potential of multi-domain AI in fostering enterprise agility and provides practical insights for organizations seeking to integrate retail analytics, SAP modernization, and cyber defense within a unified AI-driven ecosystem.
Multi-domain AI, enterprise agility, retail analytics, SAP modernization, cyber defense, predictive analytics, AI frameworks, digital transformation, cloud AI, data integration.
Digital Strategy Alignment and Business Performance in SMEs
This study examines the relationship between digital strategy alignment and business performance in small and medium-sized enterprises (SMEs). The purpose of the study is to assess how aligning digital initiatives with overall business strategies influences organizational performance. A quantitative research approach was adopted, using survey data collected from selected SMEs. The data were analyzed using descriptive and inferential statistical techniques to evaluate the impact of digital strategy alignment on financial, operational, and market performance. The findings reveal that SMEs with a high level of digital strategy alignment experience improved efficiency, enhanced customer engagement, and stronger overall business performance compared to those with weak alignment. The study concludes that digital strategy alignment plays a significant role in driving sustainable performance in SMEs and recommends that SME owners and managers integrate digital planning into their core business strategies to maximize competitive advantage.
Change Management Challenges in SME Digital Transformation Initiatives
This study examines the unique change management challenges faced by small and medium-sized enterprises (SMEs) during digital transformation initiatives. While digital transformation is often approached from a technological perspective, this research emphasizes the human and organizational factors that determine success or failure in resource-constrained SME environments. The primary purpose is to identify, categorize, and analyze the most critical change management barriers specific to SMEs undergoing digital transformation. The study aims to provide actionable insights for SME leaders, consultants, and policymakers to better navigate the people-centric challenges of technological adoption. A qualitative multi-case study approach was adopted, involving semi-structured interviews with owners, managers, and employees across 12 SMEs in various sectors currently undergoing or recently completing digital transformation projects. Supplementary data was collected through document analysis and participant observation. Thematic analysis was used to identify recurring patterns and challenges. Successful digital transformation in SMEs is less dependent on technological sophistication and more on effective, empathetic change leadership. SME-specific advantages—such as organizational agility and closer interpersonal relationships—can be leveraged to overcome challenges. Practical mitigation requires visible leadership, continuous communication framed around employee benefits ("WIIFM"), incremental implementation, and investment in ongoing peer-supported learning. Future research should explore sector-specific frameworks and the role of digital coaching in SME contexts.
Ethical and Regulatory Challenges in Managing AI-Centric Cybersecurity Programs for Critical Infrastructure
Artificial intelligence (AI) has become a prevalent feature of modern cybersecurity programs for critical infrastructure systems such as those used in energy, transportation, healthcare, and industrial control systems. AI-based security solutions can provide more accurate detection, adaptive response, and scalability than traditional rule-based systems. However, the use of these systems in safety-critical and highly regulated sectors of the economy raises significant ethical and regulatory issues which present additional challenges to their effective management. These include risks of adversarial machine learning, explainability limitations of complex models, accountability gaps for autonomous decisions, and the challenge of meeting regulatory requirements for cybersecurity practices and data privacy in AI-driven operations. This paper presents a thorough, literature-based exploration of the ethical and regulatory challenges of program management for AI-focused cybersecurity programs for critical infrastructure. Building on existing research in areas such as adversarial machine learning, ethical AI, and cybersecurity for critical infrastructure, this work takes a program-level perspective to understand how technical, ethical, and regulatory risks manifest and intersect at various stages of the AI lifecycle. By reviewing existing frameworks, standards, and principles in these areas, this study identifies the structural limitations of traditional cybersecurity program governance frameworks when applied to AI-based solutions. By conceptually organizing these issues along several dimensions of responsible AI - namely robustness, transparency, accountability, and regulatory compliance - this study aims to provide an analytically coherent foundation to understand responsible AI in the context of critical infrastructure cybersecurity. The insights presented highlight opportunities for holistic governance approaches that balance innovation, ethics, and legal considerations, while supporting sustainable, long-term trust in AI-enabled cybersecurity solutions.
AI-centric cybersecurity, critical infrastructure protection, ethical AI governance, regulatory compliance, adversarial machine learning, cybersecurity program management.
AI-Enabled Secure Cloud and Network Transformation for SAP Migration and Regulated Healthcare Data
The rapid adoption of cloud technologies in healthcare enterprises has intensified the need for secure, intelligent, and compliant digital transformation strategies, particularly for SAP system migration and regulated data exchange. Healthcare organizations must balance scalability and innovation with stringent security, privacy, and interoperability requirements. This paper proposes an AI-enabled secure cloud and network transformation framework that supports SAP migration while ensuring compliant healthcare data exchange across distributed environments. The framework integrates cloud-native architectures, AI-driven security analytics, and network governance mechanisms to enable proactive threat detection, automated risk assessment, and policy-based access control. By leveraging intelligent monitoring and predictive analytics, the proposed approach enhances migration reliability, optimizes network performance, and ensures adherence to healthcare regulations. The framework also facilitates seamless interoperability between SAP platforms and external healthcare systems through secure APIs and standardized data exchange models. Experimental analysis and architectural evaluation demonstrate improvements in security posture, operational efficiency, and regulatory compliance, making the proposed solution suitable for large-scale healthcare digital transformation initiatives.
AI-Enabled Security, Cloud Transformation, SAP Migration, Healthcare Data Exchange, Network Governance, Regulatory Compliance, Predictive Analytics.
Regulatory Fragmentation in Healthcare AI: A Policy Review of FDA Oversight, HIPAA, and Implementation Barriers
Background: The rapid integration of artificial intelligence (AI) into U.S. healthcare has exposed gaps in existing governance frameworks. Although the FDA has cleared more than 150 AI-enabled devices since 2019, implementation in clinical practice remains slow, uneven, and burdened by regulatory fragmentation. Methods: This study conducted a mixed-methods secondary analysis combining (1) FDA regulatory pathway review of AI/ML medical devices (2019–2023), (2) case study analysis of 72 publicly reported AI implementations in U.S. healthcare systems, and (3) structured review of HIPAA regulations and policy documents. Descriptive statistics summarized regulatory pathways, approval timelines, and post-market surveillance requirements. Thematic synthesis identified recurring compliance challenges and governance gaps. Results: The FDA database analysis revealed that most AI tools (67%) were cleared via the 510(k) pathway, with an average review time of 6.1 months. However, only 34% of devices had explicit post-market surveillance obligations. Case study review showed that 82% of implementations encountered HIPAA-related barriers, particularly around data de-identification and consent for secondary use. Smaller hospitals reported longer implementation delays and higher relative compliance costs, with regulatory uncertainty cited as the primary barrier. Conclusion: Current U.S. healthcare AI governance is fragmented across HIPAA and FDA frameworks, creating operational, legal, and economic burdens for health systems. Harmonized, risk-based oversight that integrates privacy and safety requirements could reduce inefficiencies and improve equitable AI adoption. These findings provide an evidence-based foundation for policy reforms that balance innovation with patient protection.
Artificial Intelligence, Healthcare Regulation, HIPAA, FDA, Policy Analysis, Implementation Barriers
Copyright (c) 2026 International Journal of Technology, Management and Humanities
Predictive AI for Household Hazard Prevention
The increasing frequency and complexity of household hazards, including fire outbreaks, environmental exposure, and infrastructure-related accidents, have intensified the need for proactive and intelligent prevention mechanisms. Predictive artificial intelligence (AI) offers a transformative approach by enabling early identification of risk patterns through the integration of heterogeneous data sources such as sensor streams, environmental indicators, and behavioral signals. This paper examines the application of predictive AI models for household hazard prevention, drawing on established risk prediction frameworks from disaster management, healthcare, transportation safety, and smart infrastructure systems. By synthesizing insights from machine learning–based early warning systems and validated predictive models, the study highlights how AI-driven risk scoring and forecasting can enhance household preparedness and reduce vulnerability. The paper further discusses governance, ethical, and policy considerations, including data privacy, model transparency, and equitable access to predictive safety technologies. The findings underscore the potential of predictive AI to shift household safety strategies from reactive response to anticipatory risk management, contributing to broader resilience and sustainable risk reduction objectives.
Predictive AI, Household Hazard Prevention, Risk Assessment, Machine Learning, Early Warning Systems, Smart Safety, Disaster Risk Reduction, Ethical AI
Emerging Technologies for Sustainable Treatment of Industrial Wastewater
Industrial wastewater management remains a critical environmental and regulatory challenge in the United States due to increasing industrial activity, stringent discharge standards, and growing concerns over water scarcity and ecosystem degradation. Conventional treatment methods, while effective for basic pollutant removal, often fall short in addressing emerging contaminants, energy efficiency, and long-term sustainability goals. This study examines emerging technologies for the sustainable treatment of industrial wastewater, with a specific focus on their applicability within the U.S. industrial and regulatory context. Key technological advancements reviewed include advanced membrane systems, electrochemical and advanced oxidation processes, biotechnology-driven treatments, and nature-based hybrid solutions. In addition, the integration of digital innovations such as artificial intelligence, Internet of Things enabled monitoring, and smart process control is explored as a means to enhance operational efficiency and compliance. Emphasis is placed on sustainability outcomes, including water reuse, resource recovery, reduced carbon footprint, and alignment with circular economy principles. The analysis also considers policy frameworks, economic feasibility, and scalability challenges influencing technology adoption in the United States. Overall, the study highlights the transformative potential of emerging wastewater treatment technologies to support industrial sustainability, regulatory compliance, and long-term water security in the U.S.
Industrial wastewater; Sustainable treatment technologies; Water reuse; Resource recovery; Smart water systems; United States environmental policy.
An AI-Driven Decision Automation Framework for Cargo Consolidation in High-Volume Trade Hubs
The growing complexity of global supply chains and the expansion of high-volume trade hubs have increased operational demands on cargo consolidation companies. These organizations are required to manage large volumes of shipments while ensuring accurate documentation, efficient space utilization, and timely decision-making. Despite ongoing digital transformation efforts in logistics, many cargo consolidation processes remain heavily reliant on manual intervention or loosely integrated information systems. This dependence often results in operational inefficiencies, delays, and elevated error rates, particularly in environments characterized by high variability and regulatory complexity. This study presents the design and implementation of an artificial intelligence driven automation framework aimed at enhancing decision support in cargo consolidation operations. The proposed framework integrates advanced data processing, intelligent decision logic, and automated execution mechanisms to support critical consolidation activities, including documentation validation, cargo space optimization, and real-time operational prioritization. A mixed-methods research approach is employed to evaluate the framework, combining qualitative insights from logistics professionals and artificial intelligence specialists with quantitative analysis of key operational performance indicators such as processing time, documentation accuracy, and resource utilization efficiency. The results demonstrate that the adoption of AI-driven automation leads to significant improvements in decision accuracy, operational speed, and overall efficiency within cargo consolidation workflows. In addition to performance gains, the framework enhances operational resilience and scalability, supporting modern logistics transformation initiatives. By addressing an underexplored area of supply chain automation, this research contributes a practical and transferable framework that advances both academic understanding and industry practice in intelligent logistics systems operating within high-volume trade hubs.
Artificial intelligence, Cargo consolidation, Supply chain automation, Decision support systems, Logistics 4.0, Digital transformation.
Defining Success in Probabilistic Products: Key Performance Indicators and Lifecycle Management for Generative AI Applications in Enterprise
Generative Artificial Intelligence introduces probabilistic behavior into enterprise software systems, fundamentally challenging the deterministic assumptions underlying traditional product management and Software-as-a-Service success metrics. Conventional indicators such as Daily Active Users, churn rate, and uptime fail to capture the economic, operational, and risk dimensions inherent in stochastic model outputs. This paper proposes a novel product management framework for probabilistic AI systems, grounded in enterprise deployments of generative applications launched from zero to production scale. A new class of AI-native Key Performance Indicators is introduced, including Response Accuracy, Hallucination Rate, Token Efficiency, and Human Intervention Rate, alongside a Probabilistic Product Lifecycle model integrating continuous evaluation and human-in-the-loop governance. Through comparative analysis and applied case evidence, the study establishes a new standard for measuring success in enterprise generative AI products, reframing uncertainty from a liability into a measurable and manageable product dimension.
Generative AI, Probabilistic Systems, Product Management, KPIs, LLMOps, Enterprise AI, Lifecycle Management.
AI Driven DevSecOps Security Pipelines for Fraud Detection in SAP Based Business and Marketing Processes
The increasing adoption of SAP-based cloud platforms for enterprise business and marketing processes has amplified the scale, complexity, and sophistication of fraud and cybersecurity threats. Traditional perimeter-based and rule-driven security mechanisms struggle to provide real-time protection across distributed SAP landscapes, particularly in environments governed by continuous integration and continuous deployment (CI/CD) practices. This paper proposes an AI-driven DevSecOps security pipeline that integrates machine learning–based fraud detection, multivariate analytics, and automated threat intelligence into SAP-centric business and marketing workflows. By analyzing heterogeneous data sources—including SAP transaction logs, marketing campaign data, user behavior metrics, and system telemetry—the framework enables early detection of anomalous activities, financial fraud, and process-level abuse. The proposed approach embeds security controls throughout the DevSecOps lifecycle, ensuring continuous risk assessment, policy enforcement, and rapid incident response. Experimental evaluation demonstrates improved fraud detection accuracy, reduced false positives, and enhanced resilience of SAP-enabled enterprise systems operating in cloud-native environments.
Artificial Intelligence, DevSecOps, SAP Security, Fraud Detection, Business Processes, Marketing Analytics, Machine Learning, Cloud Security, Multivariate Analysis, Threat Intelligence
Transforming Public and Enterprise Business Processes with Cloud AI Data Governance SAP and DevOps for Intelligent Secure and Inclusive Digital Ecosystems
Public institutions and enterprises are undergoing rapid digital transformation to improve efficiency, transparency, security, and inclusivity. This paper examines how the integration of Cloud computing, Artificial Intelligence (AI), Data Governance frameworks, SAP enterprise platforms, and DevOps practices can transform public and enterprise business processes to enable intelligent, secure, and inclusive digital ecosystems. Cloud technologies provide scalable and resilient infrastructure, AI enhances decision-making and service automation, and data governance ensures compliance, trust, and ethical data usage. SAP systems enable standardized and interoperable business processes, while DevOps accelerates innovation through continuous delivery and collaboration. By aligning technological capabilities with governance and process reengineering, organizations can deliver citizen-centric and customer-centric services, enhance operational resilience, and support sustainable digital growth across public and private sectors.
Public Governance, Enterprise Digital Transformation, Cloud Computing, Artificial Intelligence, Data Governance, SAP, DevOps, Business Process Transformation, Security, Inclusivity, Digital Ecosystems.
Advancing Data Consistency and Control Across Global Financial Institutions by Enterprise Master Data Platforms
This research examines how enterprise master data platforms enable data consistency and operational control across global financial institutions operating in complex, highly regulated environments. As financial institutions expand across jurisdictions, products, and legal entities, fragmented client and reference data has emerged as a persistent source of operational risk, reporting inconsistency, and governance failure. The purpose of this research is to examine enterprise master data management not as a technical integration initiative, but as a strategic control capability that underpins trusted business operations. The study adopts a qualitative, evidence-based approach, combining architectural analysis with structured evidence mapping derived from large-scale implementations within global financial institutions. The analysis identifies recurring design patterns in master and reference data platforms, including governance structures, stewardship models, control workflows, and lineage mechanisms, that collectively enable consistent enterprise-wide data representation. Key findings suggest that institutions achieving high levels of data consistency experience reduced reconciliation effort, improved auditability, and stronger alignment between operational processes and regulatory expectations. The study further demonstrates that operational control is strengthened when data governance is embedded into platform workflows and business decision points rather than managed as an isolated compliance function. From an academic perspective, this research extends existing data governance literature by explicitly linking enterprise data consistency to operational control outcomes. From a strategic industry perspective, it provides a practical framework for designing, governing, and sustaining enterprise master data platforms that support risk management, regulatory confidence, and decision integrity. The study concludes that enterprise master data platforms constitute a foundational capability for resilient, transparent, and controllable operations in global financial institutions.
Enterprise master data management, enterprise data governance, data consistency and control, global financial institutions, reference data standardization, client data integration, enterprise data platforms, operational risk management, regulatory reporting integrity, data lineage and traceability, data stewardship models, risk data aggregation.
Enterprise-Scale Data Stewardship Enablement Using Workflow-Driven Governance Mechanisms in Financial Services
Large financial enterprises face persistent challenges in governing reference data assets due to organizational fragmentation, regulatory pressure, and increasing dependency on shared data domains across business lines. This study examines how workflow-driven governance mechanisms can be operationalized to enable scalable and sustainable data stewardship models within complex financial services environments. Drawing on evidence from the BNY Mellon Reference Data Hub and QBE Insurance implementations using enterprise business glossary and metadata platforms, the paper analyzes how structured workflows, role-based accountability, and policy-driven controls contribute to improved data quality, lineage transparency, and governance enforcement. The study adopts a qualitative, design-oriented approach, synthesizing implementation artifacts, governance operating models, and stewardship processes observed across these large enterprises. Findings indicate that workflow-centric governance architectures significantly enhance stewardship effectiveness by formalizing decision rights, reducing manual intervention, and aligning business and technology stakeholders around shared data standards. The study contributes to a generalized enterprise stewardship framework that integrates workflow orchestration, reference data lifecycle management, and governance escalation models. This framework offers practical guidance for financial institutions seeking to mature their data governance capabilities while meeting regulatory expectations and operational scalability demands. The paper positions workflow-enabled stewardship not as a tooling initiative but as an institutional capability critical to enterprise data reliability and risk management.
Data stewardship, workflow governance, reference data management, financial services data governance, enterprise data hubs, metadata management, business glossary, policy enforcement, stewardship operating model, data quality control, regulatory compliance, master data governance
The Economic Impact of Intermittent Fasting on Workforce Productivity in the United States
Intermittent fasting (IF) has already received considerable scientific interest as a potentially health-promoting diet, but there has been little discussion about the economic aspects of its feasibility to workforce productivity. In this paper, the researcher will analyze the correlation between IF and labor efficiency in the United States with reference to the effects of fasting protocols on cognitive performance, energy levels, absenteeism and the overall output of employees. Based on the findings of nutritional science, behavioral health, and labor economics, the study compares the possible economic benefits and expenses in terms of mass implementation of IF among working adults. The findings are expected to guide organizational wellness interventions, as well as, the policy on the role of fasting in the overall health of the population to inform the opportunities and constraints to implementing the fasting practice in the corporate health program. The research results add up to an emerging evidence concerning the connection of food consumption with economic productivity in modern labour markets.
Intermittent fasting, workforce productivity, labor efficiency, employee health, economic impact, United States
Ethical and Regulatory Challenges of AI in Finance
The recent accelerated incorporation of artificial intelligence (AI) in the financial industry has revolutionized the fundamental operations in determining creditworthiness, fraud detection, algorithmic trading and customer interaction. Although such applications can result in considerable efficiency, accuracy, and scalability, they also present difficult ethical and regulatory issues that should be criticized closely. This paper focuses on the ethical issues that are entailed in AI-based financial systems, such as algorithmic discrimination, absence of explainability and transparency, threats to the privacy of personal data, and whether automated decisions are accountable. Concurrently, it evaluates regulatory problems, which are posed due to the constraints of the current financial and data protection standards, cross-jurisdictional discrepancies, and the impossibility of regulating unclear and evolving AI models. The abstract displays the conflict between the promotion of innovation and consumer protection, financial stability, and trust on the part of the population. By summarizing the existing controversies and regulation strategies, the paper highlights the importance of powerful codes of ethics, dynamic regulation frameworks, and proper oversight systems to make AI implementation in finance fair, transparent, and socially accountable.
Artificial intelligence; financial services; ethics; regulation; algorithmic bias; transparency; data privacy; AI governance
Artificial Intelligence Product Management - Identifying Opportunities and Developing AI Product Solutions
Artificial Intelligence (AI) is becoming a new reality of product management, as it will make possible the development of innovative solutions to solve difficult business and customer issues. Management of AI products should entail the identification of high-impact opportunities, conception of AI-based solutions, and the development and implementation of the solutions. It involves a thorough knowledge of market requirements, technical practicality and ethical aspects along with cross-functional teams that involve data scientists, engineers and stakeholders. Through a systematic integration of AI capabilities and business goals, companies are able to design products that can provide quantifiable value, improve user experiences and remain competitive. The current paper discusses the identification of opportunities, designing AI solutions, and overcoming the challenges in AI product management, illustrating the best practices and frameworks that enable to achieve a successful AI product lifecycle.
Artificial Intelligence, Product Management, AI Product Development, Opportunity Identification, AI Solutions, Ethical AI, Data-Driven Innovation.