The study involved a total of 217 respondents. consisting of key stakeholders in higher education, including undergraduate and postgraduate students, academic staff, and administrative personnel. The respondents were selected using a purposive sampling technique to ensure they had relevant experience and understanding of academic data management systems. Among the participants, the majority were students, representing approximately 65%, followed by academic staff at 20%, and administrative personnel at 15%. In terms of gender distribution, 54% were female and 46% were male. Most respondents were aged between 18 and 30 years, reflecting a digitally active population familiar with emerging technologies. Additionally, a significant proportion of respondents reported prior exposure to digital academic systems, while a smaller percentage demonstrated awareness of blockchain technology applications in education. This distribution ensures that the collected data reflects diverse perspectives within the academic ecosystem and supports the reliability of the analysis conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS).
This article addresses the growing integration of artificial intelligence (AI) and blockchain in enterprise incentive systems, highlighting gaps in the Information Systems literature regarding integrated governance frameworks. By developing a comprehensive AI–Blockchain governance framework, we leverage IT governance theory and sociotechnical systems perspectives, employing a conceptual design methodology alongside scenario-based simulations to assess architectural feasibility and governance efficacy. The proposed framework unifies AI lifecycle governance, programmable smart contract execution, and a metricized oversight model that translates key performance indicators such as security and fairness into measurable metrics. This integrated approach facilitates evaluation, execution, and continuous monitoring, offering managers a structured roadmap for the scalable deployment of tokenized AI incentive systems while advancing IT governance research through operationalized decision-right redistribution via programmable infrastructures.
Alicja Fandrejewska, Monika Eisenbardt, Tomasz Eisenbardt
The chapter provides a comprehensive overview of the functioning of contemporary consumers and services within the digital landscape, emphasizing the transition from traditional marketing and classical market models to customer-oriented approaches in online environments. It examines the impact of rapid technological development, data-driven processes, and the emergence of the information society on the evolution of digital services. Particular attention is given to key technological trends, including e-commerce, e-banking, e-health, e-learning, cloud computing, the Internet of Things, blockchain, and artificial intelligence, as well as to the opportunities and risks associated with these transformations, such as disinformation and increasing uncertainty. The chapter argues that contemporary consumers operate in a complex, dynamic, and highly digitized environment, in which services are increasingly intangible, personalized, and data-driven. It concludes by linking these considerations to the research approach and presenting selected empirical findings related to the issues discussed.
Purpose This study aims to examine how digital cultural values, collaboration, innovation and customer-centricity enable successful technological adoption in the banking sector's digital transformation journey. It explores how emerging technologies such as artificial intelligence (AI), machine learning (ML), blockchain and metaverse-based interfaces are integrated to enhance customer experience and operational efficiency, with emphasis on the role of shared values in shaping strategy, leadership and organizational readiness. Design/methodology/approach A qualitative, case-based exploratory design is adopted. Data were collected through semi-structured interviews with senior managers across strategy, innovation, technology and customer experience functions. These were supplemented with secondary sources, including policy documents, digital strategy reports and industry analyses. Thematic analysis was used to identify cultural patterns and organizational factors influencing digital adoption in a regulated banking context. Findings The findings show that digital cultural values are critical enablers of successful technological adoption. Collaboration enhances cross-functional coordination and accelerates integration of emerging technologies. Innovation fosters experimentation and openness to AI, ML and immersive tools. Customer-centricity ensures that digital investments improve accessibility, transparency and service quality. Collectively, these values strengthen adaptability, operational efficiency and ecosystem integration, highlighting that cultural alignment is as important as technological capability in digital transformation. Originality/value The study positions digital cultural values as central enablers of technology adoption, extending digital transformation literature beyond technological capability perspectives. It contributes to theory by showing how shared values mediate the relationship between emerging technologies and service transformation in regulated banking environments. Practically, it offers guidance for building culturally aligned digital strategies that improve adoption, trust and customer experience.
Omar Al-Jamili, Abdulaziz Fahmi Omar Faqera, Mohd Adan Omar, Shehu M. Sarkintudu · 8 authors
Open Government Data (OGD) has become central to digital transformation and data-driven governance, yet scholarly understanding of how OGD initiatives progress from initial adoption to sustained institutionalization remains fragmented. This study aims to synthesize the existing literature and develop an integrative framework that explains the socio-technical mechanisms underpinning the long-term sustainability and value creation of OGD initiatives. The study integrates bibliometric analysis with a systematic literature review of 481 peer-reviewed articles published between 2010 and 31 December 2024. Quantitative science-mapping techniques are combined with qualitative thematic synthesis to capture the intellectual structure, technological evolution, and theoretical foundations of OGD research. The findings reveal rapid growth and thematic diversification in OGD scholarship, with increasing attention to advanced technologies such as artificial intelligence and blockchain. However, the literature remains theoretically fragmented across behavioral, institutional, and public-value perspectives. Two critical gaps are identified: insufficient theorization of institutional legitimacy as a driver of continuity, and limited exploration of user-centric governance mechanisms shaping sustained data reuse. To address these gaps, the study proposes the Socio-Technical Institutionalization Model (STIM), which conceptualizes OGD sustainability as the dynamic alignment of technological infrastructures, institutional arrangements, and user ecosystems. By combining quantitative science mapping with systematic thematic synthesis and proposing the STIM lifecycle framework, this study offers an integrative synthesis that extends prior OGD reviews. The framework bridges fragmented theoretical perspectives and explains how open data initiatives may evolve from adoption to institutionalized value creation within complex digital governance ecosystems.
Research methodology This case was developed using secondary research methods. Information was collected from publicly available sources including company annual reports, investor presentations, regulatory publications, analyst commentary and reputable media outlets such as Bloomberg and the Financial Times. Industry reports from consulting organizations and international institutions were also used to contextualize developments in the global fintech ecosystem. No primary interviews or confidential company data were used in preparing this case. This case contains no disguised information; all organizations, individuals, financial data and events referenced are real and drawn exclusively from publicly available sources. As this case was developed entirely from publicly available secondary sources and involved no human participants, institutional ethics review board approval was not required. This case offers a distinctive contribution to the published teaching case literature on fintech strategy and platform renewal. While existing cases on fintech strategy tend to focus on a single dimension of disruption, such as Stripe’s developer-led payment infrastructure, Apple’s device ecosystem lock-in or the regulatory challenges facing individual cryptocurrency platforms, this case uniquely combines three simultaneous strategic challenges within a single narrative: the deployment of artificial intelligence (AI)-enabled commerce capabilities, the early-stage integration of stablecoin infrastructure through PYUSD and the organizational complexity created by a decade of acquisition-driven expansion across Venmo, Braintree, Honey and Xoom. No published case in the fintech or platform strategy literature, to the author’s knowledge, addresses this particular combination of AI governance, digital asset experimentation and acquisition integration fragmentation within the context of a large, regulated incumbent facing embedded finance disruption. This case therefore provides a pedagogically distinctive vehicle for exploring strategic renewal in digitally regulated industries. Case overview/synopsis This case places students in the position of Alex Chriss, the newly appointed Chief Executive Officer of PayPal, as he prepares for a critical board strategy review in October 2024. Despite leading one of the world’s largest digital payments platforms, processing over $1.5tn in total payment volume annually and serving more than 430 million active accounts globally, Chriss inherited a company under significant strategic pressure. Revenue growth had slowed, share price performance was deteriorating and analysts were increasingly describing PayPal as a mature incumbent rather than a platform innovator. The organizational inflection point is a dilemma with no comfortable resolution. Chriss must choose a strategic direction to present to the board ahead of PayPal’s quarterly earnings announcement, but every available path carries a different form of risk. Moving aggressively into AI and decentralized finance offers innovation momentum but risks operational disruption and regulatory overexposure across dozens of regulated markets. Consolidating the core platform is operationally safer but risks confirming the narrative that PayPal has lost its competitive edge. Pursuing fintech partnerships accelerates capability building but reduces strategic control. Leading on Environmental, Social, and Governance (ESG) and responsible digital finance builds long-term legitimacy but delivers limited near-term growth. Investors want visible transformation. Merchants need stability. Regulators demand discipline. Employees are already stretched from restructuring. No single option satisfies all of these demands simultaneously, and PayPal’s resource constraints mean Chriss cannot pursue all four directions at once. The case draws on dynamic capabilities theory, the resource-based view, platform ecosystem theory, stakeholder theory and embedded finance literature, and requires no prior knowledge of fintech or payment systems. Complexity academic level This case is appropriate for MBA and Executive MBA courses in strategic management, innovation management and financial technology. It may also be used in final-year undergraduate courses addressing platform strategy, digital transformation or fintech ecosystems. The case is suitable for both in-person and online classroom delivery.
FinTech, Crowdfunding, Digital Finance
Innovations and Analysis in Business and Education
In modern business and trade, digital transformation (DT) has become a key factor in gaining a competitive edge. Rapid developments in blockchain, big data analytics, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) are changing company models, value generation workflows, and organizational strategies. By combining organizational, strategic, and technological viewpoints, this study offers a multifaceted examination of digital transformation. Secondary data from peer-reviewed literature, international industry publications, and corporate disclosures of top companies, such as Amazon, Alibaba Group, Microsoft, and Tesla, Inc., were analyzed using a descriptive and analytical research design. The study creates a conceptual framework that connects performance results, transformation processes, and digital drivers. The results indicate that ecosystem integration, organizational agility, digital capability development, and strategic alignment are necessary for a successful digital transformation. The paper contributes to digital transformation literature by combining findings from several sectors and putting forth an integrated strategic model that can be empirically validated in further studies; the paper adds to the body of knowledge on digital transformation.
This study introduces an integrated conceptual framework for the synergy of artificial intelligence, blockchain, and big data analytics (BDA) as an enabler of sustainable competitive advantage in logistics systems. While the existing literature has extensively explored these technologies separately, the literature is still inconclusive on how these three technologies together support sustainable logistics. To address this gap, the study is conceptual and adopts a systematic and integrative literature review from 2008 to 2025. Guided by a PRISMA–inspired approach, the research identified 92 articles for review and performed a thematic synthesis. The research finds that digital sustainability is a result of the integration of technologies, rather than a mere effect of their individual contribution. Drawing from the lens of the resource-based view, dynamic capabilities theory, and triple bottom line framework, the research conceptualises BDA (as sensing), artificial intelligence (as seizing), and blockchain (as reconfiguring) as complementary elements that collectively contribute to a higher-level construct called digital sustainability capability, thereby creating a digital sustainability competitive advantage between a firm’s digital resources and its economic, environmental, and social performance. This research contributes to the literature by identifying a theoretical gap among fragmented streams of literature and by conceptualising a system-level view of digital transformation for digital sustainability in the supply chain. The research offers managerial implications for how firms can achieve digital sustainability by aligning their digital initiatives and sustainability objectives. Further, the research also suggests areas for future research, including empirical testing of the conceptualised framework, development of measures to assess the level of digital sustainability capability, and contextually specific explorations.
Abstract Internal audit functions in U.S. manufacturing and retail face a growing disconnect between increasingly sophisticated fraud schemes and legacy detection methods that rely on static rules and manual sampling. The Association of Certified Fraud Examiners estimates that organizations lose approximately five percent of annual revenue to fraud, with manufacturing and retail sectors particularly vulnerable due to complex supply chains, high transaction volumes, and decentralized operations. Modern fraud schemes have evolved well beyond simple expense manipulation; they now involve multi-party collusion, cyber-enabled invoice fraud, supply chain manipulation through fictitious vendors, and coordinated point-of-sale skimming networks. Traditional audit approaches typically cover only three to five percent of transactions through periodic sampling, leaving the vast majority of activities unexamined and creating significant windows of exposure. Learningter demonstrates how applied AI (machine learning anomaly detection, natural language processing, and agentic AI) transforms fraud detection from reactive forensics into proactive, continuous assurance. Drawing on five anonymized case studies from active industry engagements, the presentation illustrates measurable outcomes: false-positive rates reduced by up to 70 percent, detection time compressed from months to minutes, and coverage expanded from sample-based testing to full-population analysis. Each case maps legacy controls against AI-augmented alternatives, providing a clear migration pathway. In particular, Agentic AI enables autonomous and continuous monitoring through self-correcting feedback loops that recalibrate detection models in real time without requiring manual intervention, adapting dynamically to emerging fraud patterns and shifting transaction behaviors. The presentation addresses practical adoption challenges (data quality, algorithmic bias, SOX/ICFR compliance, and change management) and offers a structured readiness framework for consulting engagements or dissertation research. Grounded in Boyer's Scholarship of Application, this work connects data science and auditing to real-world problems, demonstrating how cross-disciplinary collaboration produces actionable improvements in governance and risk management. The research is directly relevant to doctoral candidates seeking applied dissertation topics with measurable industry impact and to faculty developing curricula that bridge theoretical foundations with practitioner-oriented pedagogy.
Predictive Churn Modeling and Proactive Service Using Customer Interaction Data Objectives: 1. To provide a comprehensive understanding of cloud-native architectures and middleware technologies used for designing scalable, resilient, and high-performance financial trading systems. 2. To explain the core concepts of microservices, containerization, orchestration, distributed messaging, and data management that power modern financial platforms and digital banking ecosystems. 3. To demonstrate the practical implementation of advanced technologies such as Kubernetes, Apache Kafka, Redis, gRPC, and AI-driven solutions for real-time trading and financial service delivery. 4. To equip software engineers, solution architects, researchers, and FinTech professionals with the knowledge required to build secure, fault-tolerant, low-latency, and highly observable trading infrastructures. 5. To explore emerging trends in financial technology, including serverless computing, WebAssembly, Artificial Intelligence, Machine Learning, and Decentralized Finance (DeFi), preparing readers for the next generation of cloud-native financial systems. Table of Contents CHAPTER 1 The Foundation of Customer Retention: Concepts and Definitions CHAPTER 2 The Business Value of Predicting Churn: Impact on ROI CHAPTER 3 Sources of Customer Interaction Data: CRM, Logs, and Beyond CHAPTER 4 The Architecture of a Churn Prediction System CHAPTER 5 Data Acquisition and Quality Assessment CHAPTER 6 Preprocessing High-Dimensional Interaction Data CHAPTER 7 Feature Engineering: Creating Meaningful Indicators from Raw Data CHAPTER 8 Exploratory Data Analysis for Churn Patterns CHAPTER 9 Traditional Statistical Methods in Churn Modeling CHAPTER 10 Machine Learning Approaches: From Random Forests to XGBoost CHAPTER 11 Deep Learning for Temporal Interaction Sequences CHAPTER 12 Natural Language Processing for Sentiment-Based Churn Analysis CHAPTER 13 Handling Class Imbalance in Churn Datasets CHAPTER 14 Evaluating Model Performance: Beyond Accuracy CHAPTER 15 Interpreting Black-Box Models for Stakeholder Trust CHAPTER 16 Real-Time Churn Scoring and Pipeline Automation CHAPTER 17 Designing Proactive Service Interventions CHAPTER 18 Personalized Marketing and Customer Success Strategies CHAPTER 19 Ethical Considerations and Data Privacy in Churn Modeling CHAPTER 20 Case Studies and Future Trends in Predictive Analytics
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
Connects to 16 breakthroughs. AUM Inflow Despite Price Slide - TipRanks From GoogleNews (271,272,274,275,276,277,278,279,285,286,287,288,290,291,293,294). Avg score: 0.24 Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Introduction, Sports science data governance is characterized by persistent tensions between data sharing, stakeholder incentives, and regulatory constraints. These challenges are amplified by fragmented data infrastructures and competing interests among stakeholders, limiting the effective use of data in performance optimization and research. Objective, This study aims to develop and theoretically ground a decentralized autonomous organization (DAO)-based governance framework for sports science data ecosystems, focusing on how decentralized mechanisms can enhance coordination, participation, and compliance. Methodology, A multi-method research design is employed, integrating conceptual case analysis, agent-based modeling (ABM), and survey-based empirical analysis. Structural equation modeling (SEM) is used to examine the relationships between governance perceptions, incentives, and data-sharing intentions. Results, The findings indicate that DAO-based mechanisms can support more distributed and transparent data-sharing processes. Simulation results suggest that participation dynamics follow non-linear patterns, with incentive and reputation mechanisms contributing to system stabilization. Empirical results identify technical usability, perceived regulatory compliance, and incentive structures as significant predictors of stakeholder participation. Discussion, The study contributes to platform governance and institutional theory by conceptualizing a hybrid decentralized governance model for data-intensive environments. The findings highlight the importance of aligning technological design with usability and regulatory requirements. However, limitations related to model assumptions, perception-based data, and interoperability challenges remain. Future research should focus on real-world implementation and the development of standardized governance frameworks.
Transforming legacy SAP systems into smart cloud-based systems is a major shift in today’s digital strategy. This transformation re-engineers old SAP environments, which are often rigid and not easily scalable, by utilizing current technologies, including artificial intelligence and cloud services like AWS. This paper discusses how AI-driven cloud transformation can help organizations transform their SAP ecosystems to be more agile, scalable, and data-driven in their decision-making processes. It addresses enterprise AI, intelligent automation, hybrid cloud environments, and other emerging technologies such as generative AI and distributed ledger systems. The discussion demonstrates how such innovations can be utilized to help create smart enterprises that can make predictions, adapt to changes, and operate more independently. The paper also takes into account the changing role of business analysis and knowledge ecosystems in facilitating this transformation. By integrating these developments and models, this paper provides a comprehensive view of the process of reinventing SAP landscapes to meet the demands of a constantly evolving digital economy.
Supply chain systems increasingly rely on digital technologies to enhance transparency and efficiency, yet this often conflicts with the need to protect sensitive data. Traditional verification mechanisms typically require full data disclosure, raising concerns related to privacy and security. This study proposes the use of zero-knowledge proofs (ZK-proofs) as a privacy-preserving solution within AI-driven supply chains. By enabling verification without revealing underlying data, ZK-proofs help maintain trust while safeguarding confidentiality. Using a conceptual and analytical approach, this research develops an integrated framework combining artificial intelligence, blockchain, and ZK-proofs within a governance structure. The findings suggest that this integration enhances transparency, strengthens security, and supports ethical and regulatory compliance, making it a promising approach for future digital supply chain systems.
Umar Yeni Suyanto, Ratna Rosita Pangestika, Kinanti Puja Prameswari, Heni Setiyaningsih
The integration of Artificial Intelligence (AI) into Small and Medium Enterprises (SMEs) has become a critical lever for achieving resilience, efficiency, and long-term sustainability in the digital era. However, despite AI’s transformative potential, empirical evidence suggests a persistent gap between technological capabilities and actual adoption within the SME sector. This study employs a bibliometric analysis using VOSviewer with the keywords "artificial intelligence" OR "AI" AND "Small and medium enterprises" OR "SMEs" AND "digital", encompassing 150 Scopus indexed articles from 2017 to 2025. The visualizations reveal six prominent thematic clusters, including AI based adaptive strategies, post-pandemic digital transformation, decentralized finance, digital literacy, and emerging concepts such as green cybersecurity. Notably, overlay visualizations indicate that sustainability-oriented digital practices are gaining scholarly momentum, signaling a future research trajectory focused on inclusive, secure, and environmentally conscious AI applications in SMEs. This article proposes a conceptual model SDRAIS (SME Digital Resilience through AI and Sustainability) that integrates three strategic dimensions: Strategic AI Integration, Digital Capabilities, and Sustainability Orientation. The model advances theoretical development by aligning with the Dynamic Capabilities and TOE (Technology Organization Environment) frameworks, while also responding to gaps in Triple Bottom Line (TBL)-driven technology adoption. The findings offer new perspectives for policymakers, SME stakeholders, and researchers by emphasizing the importance of interdisciplinary approaches to foster AI-driven innovation ecosystems that are both competitive and sustainable. This study contributes to the evolving discourse on digital transformation in SMEs and sets a robust foundation for future empirical exploration.
The retail and consumer packaged goods industries are at an inflection point; the autonomous, goal-oriented software agents are substituting the inflexible, analyst-reliant business decision cycles with closed-loop intelligence systems, which can perceive, reason, and act in real-time. The autonomy, proactivity, and constant learning of agentic AI redesign the pricing, trade promotion optimization, and supply chain coordination processes within complicated, multi-account business settings. Based on proven sources of empirical evidence in the literature on machine learning, multi-agent reinforcement learning, and supply chain optimization, the technical architecture of an agentic commercial system is discussed along five related dimensions: autonomous trade performance monitoring through perception-reasoning-action pipelines; cooperative multi-agent system design under the models of centralized training and decentralized execution; scenario simulation engine based on digital twin models; multi-objective trade promotion optimization with Pareto-front metaheuristic algorithms; and practical barriers of data infrastructure, model drift, organizational change management, and algorithmic governance. Bringing these capabilities together into a single agentic decision stack is a paradigm shift in the concept of commercial intelligence in retail and CPG, moving the operational center of gravity off retrospective dashboards and onto adaptive, constantly learning systems that coordinate the decisions on pricing, promotion, and supply.
This study identifies major approaches in token design for founders in the cryptocurrency/web3/blockchain space. The high failure rate of blockchain companies means that successful long-term performance will depend greatly on well-designed tokens. This study will integrate all prior research to highlight the most important aspects of structured tokenomics, including token utility, governance, and security. The study also contributes to the literature by introducing the Business Model Canvas as a conceptual framework that enables the integration of best practices for token design, drawing on both academic and industry literature. The results indicate significant gaps in the literature. This study offers new and practical insights for founders to enhance stakeholders’ engagement, improve regulatory compliance, and ensure project viability in the volatile cryptocurrency market. Furthermore, this research generates new knowledge that bridges the gap between the theory and practice of tokenomics, laying the groundwork for future research to develop and refine token design strategies.
The increasing deployment of agentic artificial intelligence (AI) systems and decentralized digital infrastructures has challenged traditional assumptions about organizational administration, control, and governance. While AI has advanced task-level optimization and decision support, administrative functions such as coordination, compliance, and accountability remain largely centralized and dependent on humans. This paper introduces Autonomous Administrative Intelligence (AAI), a governance-aware AI capability that enables autonomous agents to execute and adapt administrative decisions within strategically defined constraints and decentralized governance mechanisms. Building on the Strategic Decentralized Resilience–AI (SDRT-AI) framework, the study develops a layered architecture and operational flow integrating agentic decision-making, governance-aware learning, and protocol-based validation. The proposed framework explains how strategic intent, organizational capabilities, and decentralized trust jointly enable scalable administrative autonomy while preserving accountability and control. By reframing administration as an AI-mediated governance process, this paper extends research on agentic AI and contributes to administrative science by providing a conceptual foundation for the design and governance of autonomous administrative systems in decentralized organizations.
The Fourth Industrial Revolution, commonly referred to as Industry 4.0, represents a fundamental transformation of manufacturing systems through the integration of advanced digital technologies such as the Industrial Internet of Things (IIoT), artificial intelligence, big data analytics, cloud computing, and autonomous robotics. This paradigm shift enables the development of cyber-physical systems and smart factories characterized by real-time connectivity, decentralized decision-making, and data-driven optimization. The present study examines the conceptual foundations, technological pillars, and operational impacts of Industry 4.0, with particular emphasis on automation, productivity enhancement, and sustainability outcomes. Using a synthesis of recent empirical studies, global market data, and evidence from World Economic Forum “Lighthouse” factories, the paper evaluates how digital transformation influences manufacturing efficiency, energy use, emissions reduction, and workforce dynamics. The findings indicate that Industry 4.0 adoption significantly improves labor productivity, operational flexibility, and resource efficiency, while also presenting challenges related to cybersecurity, legacy system integration, and skills gaps. The study concludes that Industry 4.0 is not merely a technological upgrade but a strategic and organizational transformation essential for achieving competitive advantage and sustainable industrial development in an increasingly volatile global economy.
Mostafa Shabani, Sina Tavakoli, Hossein Ghanbari, Ronald Ravinesh Kumar · 5 authors
The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance.
The use of blockchain technology in smart contracts is changing the way businesses are run in the modern world, with the ability to perform automated, transparent and additional contractual terms without being tampered with. This paper examines how smart contracts can be utilised with real-time business performance analytics to improve the efficiency of operations, accountability and strategic decision-making. Smart contracts can be utilized to validate transactions in a decentralized system by including a set of rules that ensure that the transactions are automatically validated immediately, minimize the reliance on intermediaries and minimize human error. Through the integration of the real-time analytics tools, the organizations will be able to track the key performance indicators (KPI), financial operations, supply chain operations and compliance indicators more precisely and promptly. The integration of these technologies promotes integrity of data, reinforcement of audit trails and predictive insights due to the continuous flow of data stream. Moreover, the study points to its use in finance, supply chain management, healthcare and digital services, noting that it has better transparency, cost reduction in operations and the trust of stakeholders. In spite of the benefits, there are still issues like scalability, interoperability, regulatory unpredictability and data privacy. The paper comes to a conclusion that a combination of smart contracts and real-time analytics offers a strategic platform to data-driven companies, which make agility decisions and sustainable competitive edges in dynamic digital markets.