Journal of Next-Generation Research 5.0
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Emotional Drivers of Financial Decision-Making: Unveiling the Link between Emotions and Stock Market Behavior
Full text linkThe assumption that investors are rational on the stock market is based on assumptions that have been widely criticized by behavioral finance. Behavioral finance has demonstrated the presence of behavioral and cognitive biases, as well as emotions, which may influence the judgment of stock market traders, particularly small investors unfamiliar with the financial markets. The aim of this article is to provide a framework for understanding the emotions felt by individual investors in a bearish stock market. For this purpose, we use experimental finance and methodological elements from qualitative research and, more specifically, the analysis of written documents. This method appears to be rarely used in academic studies, despite the fact that it allows us to get closer to the reality of human emotions and their mutual influence on decision-making. We analyzed the emotional patterns developed by eight students enrolled on a university management course who were asked to trade continuously for three days. At the end of these days, they wrote down in their own words (i.e. without the intervention of the organizers) everything they had felt during the experiment. Using reading guidelines from the literature on emotions, the different passages transcribed were analyzed to determine the corresponding emotion. Our results confirm the strong presence of negative emotions, which may have led to abandonment and withdrawal as time passed and the disappointments experienced. The results obtained are obviously strongly conditioned by the negative stock market context prevailing during the experiment. Our findings - even if they correspond to a particular context and a small sample size - could provide a useful reference for understanding how market sentiments could develop on a large scale
Cybersecurity in Digital Sovereignty: Protecting National Digital Ecosystems against Foreign Cyber Infiltration in the Age of Decentralized Technology
Full text linkDigital sovereignty has emerged as a critical national security imperative as states seek to maintain control over their digital infrastructures amid evolving cyber threats and the increasing decentralization of technology. This study examines how cybersecurity strategies protect national digital ecosystems against foreign infiltration, particularly in the context of decentralized technologies like blockchain and peer-to-peer networks. Through the analysis of systematic reviews and case studies, we identify key threat vectors, including state-sponsored espionage, ransomware attacks, and supply chain compromises, that undermine governmental control over critical systems. Decentralized technologies present paradoxical challenges, simultaneously creating new vulnerabilities through distributed attack surfaces and jurisdictional ambiguities while offering enhanced resilience and trustless security models. The research reveals that adequate digital sovereignty protection requires multi-layered cybersecurity frameworks that integrate legal measures, international cooperation, and emerging technologies, such as artificial intelligence. Case studies from Estonia, the European Union, and Russia illustrate diverse approaches to striking a balance between technological autonomy and international collaboration. Future threats from quantum computing and AI-enabled cyber warfare necessitate adaptive strategies that combine indigenous capabilities with global partnerships to address these emerging challenges
Generic Multi-Agent AI Framework for Weighted Dynamic Corridor Price Optimisation
Full text linkThe objective of this analysis is to address the challenges encountered by pricing systems in managing real-time market dynamics. This study presents a fundamental theoretical framework focused on taxonomy and ontology for a domain-specific multi-agent artificial intelligence (AI), serving as an internal price advisor to optimize pricing strategies for products and services. The system is designed to function in conjunction with other corporate AI systems and an Enterprise Resource Planning System (ERP). The ERP serves as a high-quality data foundation, and several other internal and external sources can provide essential data with varying quality. Methods: The proposed AI model builds upon the Weighted Dynamic Corridor Price Optimization framework, which integrates cost-plus and value-based pricing methodologies within a non-linear price corridor bounded by lower and upper thresholds. In the context of supply chain integration, fully-cooperative pricing models can apply Nash equilibrium to enhance supply chain profitability, whilst semi-cooperative models mitigate information asymmetry through the principal-agent theory. The findings from the theoretical analysis of the generic industry- and product-agnostic multi-agent AI system suggest the system’s potential capacity for dynamically computing optimal prices. A generative AI module could facilitate real-time decision-making, enabling sales teams and similar stakeholders to simulate scenarios and refine pricing strategies. In conclusion, the proposed AI system should be capable of delivering adaptive, context-aware, and data-driven recommendations. Depending on its application, the AI system could become very complex, susceptible to errors, and require significant maintenance. Future research should focus on customizing the proposed AI system for specific industries and product categories and validating its applicability through empirical research
The Impact of AI on Financial Professionals
Full text linkThis paper examines the substantial influence of artificial intelligence (AI) on financial professionals, focusing on how AI technologies change jobs and responsibilities in the financial sector. According to the research, AI acts as both a disruptive force and a catalyst for efficiency, requiring professionals to adapt to technological improvements while providing tools to improve decision-making and productivity. The findings are divided into three sections: first, the scope and key components of AI in finance are defined, with a focus on its historical development; second, the transformation of financial roles through automation, data analytics, and risk management is examined; and finally, case studies from various financial institutions demonstrate the practical application of AI technologies. This analysis demonstrates how AI simultaneously challenges and empowers financial professionals, emphasizing the importance of continual learning and skill improvement to survive in an AI-driven economy
AI in HealthTech: Building HIPAA-Compliant Solutions for Next-Generation Medical Documentation
Full text linkMedical documentation is critical in healthcare, supporting accurate patient records, clinical decision-making, and regulatory compliance. However, traditional documentation methods are plagued by inefficiencies, manual errors, and increased clinician workload, leading to burnout and administrative burdens. Artificial intelligence (AI), utilizing natural language processing, speech recognition, and machine learning, has emerged as a transformative solution for medical documentation by automating transcription and enhancing electronic health record (EHR) integration. This study examines AI-enabled documentation systems, focusing on their impact on clinical efficiency, compliance with the Health Insurance Portability and Accountability Act (HIPAA), and data security challenges. Through qualitative analysis of industry case studies, academic literature, and regulatory frameworks, the research evaluates AI’s ability to reduce errors, save time, and improve interoperability while addressing risks like data breaches and ethical concerns. Findings indicate that AI tools, such as Nuance Dragon Medical and Suki AI, reduce documentation time by up to 50% and achieve transcription accuracy of 95%. However, HIPAA compliance requires secure AI model training, encryption, federated learning, and physician oversight. The study proposes best practices for privacy-preserving AI systems, providing insights for IT developers, healthcare providers, and policymakers to advance compliant, efficient medical documentation
Charting New Horizons: Innovation Meets Sustainability in Business
Full text linkIn the face of escalating environmental challenges and rapid technological advancements, the convergence of innovation and sustainability has become a strategic imperative for businesses worldwide. This synergy offers transformative potential, enabling companies to meet the dual objectives of economic growth and environmental stewardship. Innovation, characterized by the creation and implementation of new ideas, processes, and products, serves as a catalyst for competitive advantage, operational efficiency, and market expansion. Simultaneously, sustainability focuses on ecological, social, and economic health, ensuring practices meet present needs without compromising future generations\u27 ability to thrive. This study explores the intersection of these two concepts, particularly how emerging technologies foster sustainable innovation across diverse sectors such as renewable energy, green technologies, and the circular economy. The research aims to evaluate successful case studies where businesses have integrated sustainability into their innovation processes, yielding benefits such as cost savings, enhanced brand reputation, and increased resilience against market volatility. Furthermore, the study proposes actionable frameworks for embedding sustainability into the innovation lifecycle, overcoming challenges, and addressing barriers to adoption. By prioritizing sustainability within the innovation process, businesses can contribute to a more resilient, equitable future while simultaneously enhancing their competitiveness and long-term success
Reinforcement Learning Guided Engineering Design: from Topology Optimization to Advanced Modelling
Full text linkApplying reinforcement learning for topology optimization is a novel approach to engineering design. In this work, I produced 6 by 6 and 5 by 5 grid topologies by the PPO algorithm and 4 4 by the HRL algorithm in adequate compute wall-clock time. I have also addressed increasing the calculation speed for artificial intelligence-driven topology optimization by combining genetic algorithms and reinforcement learning in a straightforward sequential (one method after another). First, I apply genetic algorithms to get an outline of the topology, and then I \u27fine-tune\u27 or refine the obtained topology by reinforcement learning approach. This way, I can obtain more optimal topologies and reduce wall-clock time. In particular, I optimized topology for a 10 by 10 grid, which can be seen as an improvement over a 6 by 6 topology obtained by reinforcement learning alone. The genetic algorithm alone could not produce such an optimal topology as a combination of reinforcement learning and genetic algorithms in comparable wall-clock times
Empowering Innovation: A Novel Startup in Educational Advisory
Full text linkThis paper explores the strategic development and innovation approach of InnoBIZ Edvisory™ (IBE), a startup dedicated to enhancing organisational adaptability through educational advisory services. Rooted in frameworks by Tidd and Bessant (2020), Christensen (1997), and Schumpeter (1934), IBE aligns corporate education with innovation-driven growth strategies. By blending design thinking, agile methodologies, and evidence-based decision-making, IBE supports organisations in addressing the strategic dilemmas of "play to win," "play not to lose," and "choose not to play." The study contextualises the firm within a volatile market landscape shaped by geopolitical uncertainty, technological disruptions, and regulatory shifts. The paper identifies sector-specific gaps in innovation capabilities using a structured innovation framework and survey analysis of 34 global executives. It highlights discrepancies in design thinking readiness between management and employees across industries, revealing the need for targeted educational interventions. IBE\u27s services - including digital transformation strategies, executive workshops, and open innovation partnerships - are positioned to bridge these gaps through tailored offerings. Findings from the Innovation Value Chain and Design Ambition Matrix confirm that IT and Technology sectors lead in innovation integration, while Industry and Manufacturing face the steepest barriers. IBE\u27s agile focus strategy and evidence-based prototyping foster iterative learning and rapid market adaptation. The proposed innovation opportunity - an AI-enabled corporate training platform - reflects IBE\u27s commitment to scalable, data-informed solutions. The paper concludes with an action plan prioritising strategic partnerships, incremental service innovations, and continuous client feedback mechanisms to sustain competitive advantage. Recommendations urge IBE to institutionalise cross-functional collaboration, establish KPIs, and invest in innovation labs to remain adaptive and responsive. Future research should examine emerging technologies like fog computing and evaluate sectoral nuances to refine advisory models. This work contributes to the growing discourse on innovation in corporate education and offers a replicable framework for strategic entrepreneurship in the educational advisory sector.
DropletCoin DropletCoin: Pioneering Sustainable AI and Emerging Technologies through Blockchain Innovation
No full textDropletCoin represents an innovative fusion of blockchain technology and renewable energy solutions, targeting the substantial energy demands of AI and emerging technologies. This paper presents the UMD v3.0 IoT device, designed for logging solar energy production, and its seamless integration with the Dandelion Blockchain for efficient data capture and processing. By utilizing tokenized energy credits and IoT-based monitoring, DropletCoin enables decentralized, carbon-neutral AI computing networks. Findings reveal a 30% reduction in energy costs and a 40% decrease in carbon emissions in smart city applications
Optimizing 5G Resource Allocation in PSO with Machine Learning Approach to Open RAN Architectures
Full text linkThis paper proposes a novel machine learning-based approach to solve the resource allocation problem in 5G Open Radio Access Networks (O-RAN). While traditional methods rely on meta-heuristic optimization techniques such as Whale Optimization Algorithm (WOA), we present an ensemble learning framework that combines multiple advanced algorithms to achieve efficient and practical resource allocation. Our approach decomposes the complex mixed-integer non-linear programming (MINLP) problem into two complementary tasks: Remote Radio Head (RRH) assignment through classification and Physical Resource Block (PRB) allocation through regression. Through extensive experimentation, we demonstrate that our ensemble method achieves 75-78\% accuracy in RRH assignment with mean squared error of 0.3922 in PRB allocation, while providing near-instantaneous decision-making capabilities after training. The proposed solution offers significant advantages in computational efficiency and scalability compared to traditional optimization approaches, particularly in scenarios requiring real-time resource allocation decisions. Furthermore, we present a comprehensive comparative analysis between our machine learning approach and existing optimization-based methods, highlighting the trade-offs and complementary strengths of each approach. Our findings suggest that machine learning-based resource allocation can serve as a viable alternative or complement to traditional optimization methods in 5G networks