Management Model for Artifitial Intelligence (MMAI)

This study aims to measure the maturity level of data governance in the distribution of social assistance using the COBIT 2019 framework. We chose COBIT 2019 as the methodology due to its comprehensive guidance for information technology governance. Measurements were made by assessing researchers focused on 4 domains APO, BAI, and DSS. The results of the study indicate variations in the level of maturity across processes. Some processes, such as risk management and operational efficiency, have reached the desired level of maturity, while other processes, such as information security and organizational change management, still require significant improvement. The gaps found in this measurement provide valuable insights into areas that require further attention. Recommendations for improvement include improving user training, strengthening security procedures, and improving communication and stakeholder engagement. Implementation of the proposed improvements is expected to improve data governance maturity and, in turn, improve the quality and accuracy of social assistance distribution. This study makes a significant contribution to understanding and improving data governance in the public sector, especially in the context of social assistance distribution. The results and recommendations from this study can be used by related organizations to optimize their data management processes so that they can be more effective and efficient in serving the public.

This study investigates corporate strategies for integrating non‐financial risks and stakeholder engagement into business management frameworks. Our inquiry analyzes the extent to which companies explicitly or implicitly incorporate stakeholder perspectives into their risk management practices. Based on the four fundamental pillars of risk management outlined by the Institute of Risk Management (IRM 2002), this study seeks to elucidate the multidimensional and comprehensive nature of stakeholder engagement in risk management processes. This study is based on a content analysis of 253 sustainability reports and Non‐Financial Statements drawn from different categories of companies representing the Italian market: large companies, small and medium‐sized enterprises (SMEs), cooperatives, and public utilities. The results reveal that companies primarily address risk management and control in their reports, aligning with the literature. Although risk components are equally important in managing adverse events, the results show a high percentage of risk analysis unlike what emerged from the literature, and a smaller percentage of companies focused on risk monitoring and risk communication. Stakeholder engagement levels vary based on company type and the activities performed in the risk governance process. From a theoretical perspective, this study contributes to systemic risk management research by integrating stakeholders into risk governance models. From a managerial perspective, this study offers implications for both risk management and sustainability disclosure practices. Limitations include the sample of Italian companies only and the different publication years of the collected reports.

Purpose: The aim of the study was to assess the impact of data governance on data quality in healthcare institutions. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that institutions with robust data governance protocols experience fewer instances of data inconsistency and errors. This, in turn, improves the reliability of clinical decision-making processes and patient outcomes. Moreover, compliant data governance practices contribute to better data integration across various healthcare systems and departments, facilitating comprehensive patient care and longitudinal health monitoring. By promoting transparency and accountability in data management, healthcare institutions can mitigate risks associated with data breaches and ensure compliance with regulatory standards, thereby fostering trust among patients and stakeholders alike. Implications to Theory, Practice and Policy: Control theory, information theory and agency theory may be used to anchor future studies on assessing the impact of data governance on data quality in healthcare institutions. Healthcare institutions should prioritize the adoption of comprehensive data governance frameworks that integrate policy development, stakeholder engagement, and technological infrastructure. Policymakers should align regulatory frameworks with evolving data governance standards to promote interoperability and data exchange across healthcare networks.

Effective risk management is crucial for the success of oil projects, particularly in volatile markets like Pakistan. This paper examines the key organizational capabilities and external factors that influence risk management practices and project performance in the Pakistani oil industry. Through a comprehensive literature review and analysis of industry reports, the study identifies critical risk factors, effective risk management strategies and the role of organizational competencies in navigating these challenges. The findings suggest that a proactive, data-driven approach to risk management combined with strong project management capabilities and stakeholder engagement can significantly improve project outcomes. The research provides valuable insights for oil companies operating in Pakistan offering a framework to enhance their risk management practices and increase the likelihood of successful project delivery. The oil industry is inherently complex and fraught with various risks ranging from political and economic instability to technological constraints and regulatory hurdles. Specific to the Pakistani context, the sector faces a unique set of challenges that can significantly impact project performance. Analyzing the interplay between organizational capabilities and external factors is crucial for developing effective risk management strategies to navigate these turbulent waters. This research aims to comprehensively examine the elements influencing project performance within the Pakistani oil sector. The study will explore how various risk management practices including risk identification, assessment, mitigation, and monitoring, might influence project results. The research will also examine how organizational competencies such as skilled personnel, risk management resources, and governance frameworks, can contribute to the successful delivery of oil projects in Pakistan. Pakistan's oil industry is subject to a range of context-specific challenges that can profoundly impact project implementation and performance. Effectively managing these risks is vital for oil companies operating in the country as inadequate risk management can compromise project safety, environmental protection and overall profitability.

The construction sector is always looking for efficient project management techniques. The purpose of this literature review is to build a solid project performance by carefully examining the inherent characteristics of construction projects. This review integrates various project elements, such as project scope, risk management, stakeholder engagement, resource allocation, and technological integration, which highlight the multifaceted nature of construction activities. A factual overview of certain key trends and findings from the research literature that relate to the construction sector encounters difficulties in achieving project success. This abstract also states that an in-depth investigation of the project performance is changing in the construction industry and the characteristics that go along with it. The paper investigates the complex relationship between project success criteria and quality assurance, sustainability, stakeholder engagement, risk management, cost control, scheduling, and industry reports and case studies. This paper provides an in- depth analysis of construction project. Keywords: Construction, Project Performance, Characteristics, Stakeholder Dynamics, Technology, Success Criteria, Sustainability, Risk Management.

The aim of the study is to compare stakeholder involvement and engagement in the work of the ISSB and the GRI. Secondary research data analysis and qualitative content analysis of documents published by the GRI and the ISSB from 2019 to 2023 were used. Previous literature findings on regulatory capture were not confirmed in our analysis for the ISSB, but we did find a significant amount of business and investor participation in the GRI work. The ISSB is more transparent about stakeholder engagement and should focus on investor interests, but still tries to be open to many stakeholders through due process and other channels. In terms of geographical diversification, European Union regulators appear to be one of the most important stakeholder groups for both bodies at present, as they seek to align their standards with forthcoming European legislation. The tension between the ISSB and the GRI (where the double materiality approach reflects the collision point between the investor versus multistakeholder focus) has been partially resolved through the declared cooperation and distribution of competencies, leaving stakeholders with the choice of which of the still competing standard setters to interact with.

This paper reviews recent advances and remaining gaps in urban safety and disaster risk assessment, intelligent transportation and predictive control, warning signals and cross-domain forecasting, and data governance and risk management. Evidence shows that complexity science, machine learning, and multi-source data fusion substantially improve understanding of mobility patterns, forecasting of infrastructure evolution, and accuracy of disaster assessments. Trajectory optimization, multi-level risk assessment, and microclimate simulation advance the safety of autonomous driving and urban air mobility. Probabilistic models, multimodal analysis, and artificial intelligence enhance early risk recognition. Policy analysis, digital twins, and AI-generated content (AIGC) offer new avenues for risk prevention and control. Major challenges remain, including inadequate dynamic adaptability of models, limited cross-domain collaboration, and insufficient integration between data governance and risk management. Future work should strengthen model self-adaptation and updating, optimize heterogeneous data fusion, and build collaborative mechanisms that align data governance with risk management to improve the safety and resilience of complex urban systems.

Sustainable housing projects face unique risks that require specialized management strategies. This research explores strategies for identifying and mitigating risks associated with sustainable housing projects and presents a comprehensive risk management framework tailored to the distinctive challenges of sustainable construction. The risk management framework begins with a detailed process of risk identification, categorizing risks into environmental, economic, social, technological, and regulatory/legal domains. Mitigation strategies are developed for each risk category. For environmental risks, strategies include sustainable sourcing, climate resilience design, and waste reduction programs. Economic risks are mitigated through cost management, thorough market analysis, and diversified funding. Social risks are addressed through community engagement, training programs, and strict health and safety protocols. Technological risks are managed through pilot projects, quality assurance measures, and expert system integration. Regulatory and legal risks are mitigated by regular compliance monitoring, legal advice, and obtaining sustainability certifications. Continuous risk monitoring and review are essential components of the framework. Regular audits, key performance indicators (KPIs), and feedback loops ensure ongoing assessment and adaptation of risk management strategies. Effective communication and reporting, both internally and externally, maintain transparency and stakeholder engagement. The framework also emphasizes the importance of governance and responsibility, with a dedicated risk management team and strong leadership commitment to prioritizing risk management in sustainable construction. By implementing this comprehensive risk management framework, stakeholders in sustainable housing projects can proactively address risks, enhance project success, and contribute to the broader goals of environmental sustainability and social responsibility. Keywords: Mitigation Strategies, Risk Management, Framework, Sustainable construction

Disaster risk management refers to the application of plans and procedures for reducing disaster risk to prevent new disaster risks, minimize current disaster risk, and manage residual risk. This process helps to reduce disaster losses and increase resilience (Disaster Risk Management | UNDRR, 2007). This study explored the experiences of disaster risk reduction and management personnel through a qualitative research method. The participants of this study were the ten (10) participants chosen from the five (5) DRRM sectors in the municipalities of the Province of Guimaras. All participants were subjected to individual in-depth interviews. The findings of this study are described in emergent themes. Four (4) emergent themes described the positive and negative experiences of the participants in the implementation of disaster risk reduction and management programs. For the positive experiences, two (2) emergent themes were developed, namely: Empowering Community Resilience and Satisfaction of Effective Crisis Response. For the negative experiences, two (2) emergent themes were also developed, namely: Hurdles in Promoting Public Compliance and Inadequate Resources and Manpower Constraints. Meanwhile, two (2) emergent themes were formulated to explain how the participants address the challenges encountered in the implementation of disaster risk reduction and management programs, namely: Collaborative Partnerships for Resource Management and Capacity Building through Continuous Training. Finally, two (2) emergent themes were created to express the Aspirations of the Participants to Improve the Quality of Service, namely: Institutional Support and Resource Sustainability and Fostering Proactive Community Engagement. Based on the emergent themes identified, it is advised that the DRRM personnel be provided with consistent resource management and community engagement training to enhance their ability to address constraints more effectively and promote a proactive stance on disaster risk reduction.

This study explores how the key challenges associated with mega-events revolve around the management of risk, resources, operations planning and stakeholder engagement. Examined throughout this paper are the challenges and achievements of The Kolkata Police Force (KPF) in their role of ensuring safety for the public during the Durga Puja mega-festival. The paper evaluates the various strategies adopted by the KPF to overcome the multifarious challenges of organizing the mega-event. Also offered in this paper is a detailed discussion and suggestions for strategic improvements to multiple-stakeholder collaboration in managing mega-events that are at the scale and scope of the Durga Puja in Kolkata.

Stakeholder evaluations of risk interventions for non-potable recycled water schemes: A case study

Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to an optimum selection of feature subsets. Moreover, Deep Random Vector Functional Link network (DRVFLN) classification technique was applied to properly allot the class labels to the financial data. Furthermore, improved fruit fly optimization algorithm (IFFOA) based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model. For enhancing the better performance of the IFSDL-FRA technique, an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

The increasing globalization of business operations has intensified the demand for Enterprise Resource Planning (ERP) systems capable of integrating diverse functions across geographies, business units, and regulatory environments. However, managing large-scale ERP implementations in global organizations remains a complex challenge involving technological, organizational, and cultural dimensions. This study proposes a Conceptual Framework for Managing Large-Scale ERP Implementations in Global Organizations, designed to address the multifaceted nature of ERP projects and ensure alignment with strategic objectives, operational efficiency, and financial control. The framework integrates principles from project management, change management, and systems theory to establish a holistic model for planning, execution, and post-implementation optimization. The proposed framework emphasizes five core dimensions: strategic alignment, ensuring that ERP initiatives support global business goals; governance and stakeholder engagement, fostering coordination among cross-functional and cross-cultural teams; process standardization and localization balance, managing the tension between global uniformity and regional flexibility; technological integration and data governance, ensuring system interoperability and data accuracy; and change management and capacity building, cultivating organizational readiness and digital competence. The framework further highlights the importance of performance measurement through key indicators such as implementation cost, system adoption rate, data accuracy, and return on investment (ROI). By providing a structured approach to ERP management, this study contributes to the body of knowledge on digital transformation and enterprise systems governance. It offers practical insights for global organizations seeking to mitigate implementation risks, enhance system adoption, and achieve sustainable value creation. Future research should empirically test the framework’s applicability across industries and explore its extension to cloud-based ERP and AI-driven decision-support systems.

Title Strategic Evaluation of Advanced Engineering Systems and Pedagogical Innovation: A Professional Audit of Dr. Tshingombe Fiston Tshitadi Overview Dr. Tshitadi’s career integrates electrical engineering , security systems , and education policy , with a focus on 4IR readiness, curriculum innovation, and traceable compliance systems that support socio‑economic development. Scope Engineering Systems: AC/DC theory, transformers, motors, PLCs, SCADA. Security Engineering: Access control, forensic policing logic, infrastructure integrity. Education Policy: SAQA/DHET frameworks, NATED pathways, curriculum compliance. Socio‑Economic Development: Parliamentary submissions, rural energy research, youth mentorship. Abstract A concise audit of a trajectory from TVET foundations to doctoral systems research. Emphasis on automation, nanotechnology, AI integration, and low‑code solutions (VBA UserForms) that enable signal‑driven, audit‑ready technical education and resilient industrial practice. Description Academic Path: NATED N1–N3 (St Peace College) → Master’s & Doctorate (Atlantic International University). Technical Competence: Power systems, motor control, PLC/SCADA, diagnostic engines, DLT for secure records. Research Contributions: Modular learning signals; autodidactic “Copilote” methodology; innovation & discovery record keeping. Policy Engagement: Submissions to South African parliamentary portfolio committees on higher education, energy, and rural development. Keywords Engineering Systems • Security Integrity • Curriculum Policy • 4IR • PLC • SCADA • VBA • Logigram • Nanotechnology • AI • DLT • Compliance • Audit • Socio‑Economic Development Statement — Delivery Management System A decentralized ecosystem using VBA UserForms and logigram logic to manage: student registration and transcripts; engineering diagnostics and trade readiness simulations; inventory and financial compliance; automated audit sheets and bilingual credential exports. Information & Communication Skills Technical translation: Convert engineering detail into policy and compliance language. Academic writing: Thesis, research papers, portfolios of evidence. Stakeholder engagement: Parliamentary submissions, institutional correspondence, moderation templates. Administration & Investigation Administration: Credential mapping, version control, inventory tracking. Investigation: Forensic policing logic, CCMA/Labour Court processes, complaint tracking. Governance: Tender documentation, bid compliance, socio‑economic advocacy. Advantages & Disadvantages Advantages: modular, audit‑ready frameworks; scalable low‑code tools; integration of AI and nanotech for resilience. Disadvantages: cross‑domain complexity; interoperability and standards alignment challenges; need for stronger data governance. Data Analysis & Visual Basic Logigram Data models: Complaint → Ruling → Rehabilitation; Credential → Export → Audit. Logigram logic: Star‑Delta motor startup flows, ATM simulation, PLC diagnostics, curriculum signal mapping. Automation: VBA diagnostic engines, compliance audits, curriculum dashboards and export routines. Audit Conclusion The portfolio demonstrates a coherent systems approach linking engineering practice to policy impact. To scale impact, prioritize standardized schemas , formal data governance , and interoperable interfaces across SAQA/DHET/QCTO and industry partners. Bibliography (Selected Works) Application Trade Discovery: Job Education Research Methodology, Operational Autodidactic Copilote Distance (F1000Research, 2025). Career Drawing Total Programming: Analysis, Design, Investigation (F1000Research, 2025). Parliamentary submissions to South African Portfolio Committees (Higher Education, Mineral Resources, Agriculture, 2023). Thesis: Rural Energy, Integrity Science, and Educational Frameworks (Atlantic International University). Technical portfolios: VBA UserForms for diagnostics, compliance, and curriculum management. I can convert this profile into a polished infographic‑style HTML report with interactive charts and a timeline using the thematic percentages and competency matrix already outlined; reply Yes to proceed or specify any data points you want included.

This article presents a comprehensive exploration of data quality management through automation, examining the evolution from manual processes to sophisticated, technology-driven approaches. Drawing on empirical research across multiple organizations, the article investigates how automated data quality frameworks and machine learning-based anomaly detection can address the complexities of ensuring data accuracy, completeness, consistency, and other critical dimensions in modern data ecosystems. The article introduces a structured implementation framework for organizations seeking to enhance their data quality capabilities while navigating integration challenges with existing architectures. The article reveals significant improvements in defect detection, incident reduction, and operational efficiency when automated solutions replace traditional manual methods, with most organizations achieving a return on investment within 8-14 months. Beyond technical benefits, the article highlights how quality automation catalyzes broader organizational changes in data governance, stakeholder engagement, and quality culture. As data volumes grow and business dependencies on high-quality data increase, the emerging technologies and trends identified in this research—including quality-ascode practices, federated management approaches, and self-healing systems—will likely shape the future landscape of data quality management, transforming it from a technical function to a strategic business capability.