Journal of Innovation in Governance and Business Practices
The Journal of Innovation in Governance and Business Practices (ISSN 3053-8734) is an industry-focused trade journal, open-access publication dedicated to advancing practice-driven insights at the intersection of governance, innovation, and modern business execution. The journal prioritizes applied research, case-grounded analysis, and implementation-ready frameworks that address real-world challenges in corporate governance, public systems, and enterprise operations within a rapidly evolving global economy. Submissions are strictly limited to professionals from industry and private scientific or R&D laboratories. Manuscripts from authors holding academic institutional affiliations are not considered. Every submission undergoes a rigorous peer-review by three to five independent field experts drawn from diverse industry sectors, ensuring relevance, technical depth, and practical validity. The journal is owned and published by Industrix Dynamics GmbH, headquartered in Germany, and reflects the organization’s commitment to evidence-based industrial knowledge, ethical practice, and innovation-led governance. Ownership and Management: Industrix Dynamics GmbH, Germany Publishing Schedule: The journal releases one consolidated issue each year during the second week of January Article Format: Articles may be submitted in any length in LaTeX or Word format, provided they align with the journal’s scope; final publication is prepared in Adobe InDesign. Who can Submit: As this is a trade journal, Submissions are accepted exclusively from industry professionals, practicing scientists, and independent researchers with active industry engagement.
Analyzing Decision Entropy in Large-Scale Corporate Strategy Models Using Bayesian Networks
The process of strategic decision-making within a corporation is complex resulting in various risk factors. It requires an appropriate analytical framework to assess these risk factors which is fundamentally crucial. This paper proposes a new Bayesian network approach for modelling decision entropy which is a measure of uncertainty in corporate strategy models. Using the available data, we run simulations to show how decision entropy affects different strategic results in various corporate scenarios. We also provide proof using real data that our Bayesian approach using case studies is better than the traditional deterministic way of modelling. The results give the business people and corporate leaders more practical and data-driven ways of getting strategically accurate results. The research solves the gaps in the literature about decision making science and practical gaps in optimizing corporates strategy.
Cross-Domain Transferability of Learned Decision Heuristics in High-Variability Production Networks
Production networks which require quick internal automation and flexibility because of constantly changing workloads or participation of multiple jurisdictions and business areas require a reasoned system of solving decisions problems. Recent progress in machine learning and optimization provide effective domain-specific problem solving heuristics however, domain indifferent industrial environments warrant their use. This paper presents a new approach for assessing and enabling the transfer of learned decision heuristics across domains in heterogeneous production networks. The proposed system combines domain embedding, meta-learning, and adaptive reinforcement tuning to enable the automated application of heuristic decision policies developed for one production environment to different domains without full retraining. Simulations in different industrial scenarios that included both discrete assembly and continuous processes show that the approach can achieve up to 88% of the target performance in the adapted domains and provides over 40% decrease in adaptation effort. Results showed that drift aware re-tuning improves convergence time and stability in the presence of high variability. These results confirm the possibility of cross-domain heuristic transfer and mark an advancement in the development of models for universal production intelligence of Industry 5.0.
Emergent Strategic Behavior in Decentralized Organizations Modeled via Complex Adaptive Systems Theory
Despite growing appreciation for their nimbleness and adaptability, predicting the strategic behavior of decentralized organizations continues to be a challenging task due to the emergent phenomena and fragmentation of decision-making authority. The present research uses Complex Adaptive Systems (CAS) theory to explain how strategic behavior emerges from the local interactions of self-governing actors in decentralized systems. We design a multi-agent simulation with feedback, adaptive learning, and stimuli from the environment to mimic organizational life under market and coordination pressures. In a set of experiments, we explore how coherence and performance are achieved in the absence of central command and how system microstructural configurations condition macro responses. The findings specify the boundaries of self-organization for innovation, strategic drift, and robustness. This work provides new perspectives on emergent strategy and considers how to effectively structure and manage adaptive, inter-organizational systems.
Improving Real Time Decision Support Systems in Management Information Systems Through AI Powered Automation
The current state of Management Information Systems (MIS) has evolved due to the increasing importance of real-time decision support for an organization’s operational efficiency, especially with regard to finance and supply chain management. Most traditional Decision Support Systems (DSS) face challenges relating to the processing of large structured datasets, which tends to lead to delays, inefficiencies, and suboptimal decisions. In what ways does the application of AI powered automation provide a paradigm shift in the processing of structured data to enable efficient, accurate, timely, and data driven decisions? Systematic literature review supported by real-life case studies is what defines the scope of this project. The project analyzes the impact of the AI automation with respect to efficiency of decision making, reduction of uncertainty, and accuracy of prediction. The investigation develops a new generation AI powered DSS system based on deep learning, reinforcement learning, and real time analytics that improves the assessment of financial risks, forecasting of demand, and overall enhancement of supply chain processes. The analysis of traditional DSS versus AI driven DSS systems reveals substantial differences in the effectiveness of decision latency, accuracy, and scalability of the systems, thus affirming AI’s role in mitigating risks and optimizing resource allocation. This study has highlighted some of the advanced essentials of global artificial intelligence and machine learning automation, and at the same time, provided a foundation for some futuristic innovations in finance and supply chain decision-making automation.
Quantifying the Stability of Distributed Enterprise Architectures Through Multi-Agent System Simulations
The digital ecosystem’s steady state is characterized by sophisticated operational dynamics, interrelationships, and disruption scenarios which need to be employed. The complexity and fluidity of an organization’s operations have increased over time leading to the need for developed enterprise systems architectures. This study presents a multi-agent system (MAS) architecture framework for modeling, stress-testing and measuring the stability of distributed enterprise architectures. MAS aids in providing realistic architectural views by allowing mapping of services, data processes and control elements to self-forming software units called agents. The framework captures emerging from local failures, communication delays or breakdowns. It facilitates the realization of multiple architectural styles including microservices, service oriented and event driven architectures or topologies and allows them to be compared under various levels of disruptions. Architectural resilience is quantified by measuring performance indicators like Mean Time To Recover (MTTR), cascading failure probability, service availability, and SLA violations. The analyses reveal important design compromise between modularity, redundancy as well as coordination overhead. The simulations fulfill requirements for modern adaptive systems, where enterprise architects can analyze architectural strength, inform data-driven transformation planning, and proactively design stability-increasing interventions. The results establish a basis for incorporating AI based adaptation and predicting oriented enterprise architecture management.
Tensor Decomposition Techniques for High-Dimensional Decision Analytics in Corporate Risk Assessment
Modern corporate environments are characterized by a high volume of data. Today, risk assessment involves multisourced, high dimensional data containing financial indicators, market trends, ESG data, and time changes. In such datasets, analytical methods that are traditional, matrix-based, often fail to capture the latent structure and multi-modal interdependencies. This paper proposes a new approach that incorporates tensor decomposition techniques such as CP (CANDECOMP/ PARAFAC), Tucker and Tensor-Train models into decision analytics for corporate risk assessment. In particular, we build a pipeline that utilizes tensors to transform heterogeneous corporate data into multi-way arrays which are easier to interpret as opposed to conventional methods, making risk modeling more compact. We validate our model on real life data sets from multinational companies and our model outperforms traditional principal component analysis and machine learning methods in classification accuracy, feature extraction as well as latent factor interpretability. Our results also confirm that tensor methods are beneficial for strategic risk assessment as they retain crucial dependencies among financial, temporal, and environmental dimensions. The results support the claim that tensor decomposition is crucial in high-dimensional financial analysis, and these systems require accurate, and explainable data-driven risk intelligence.