The convergence of digital technologies and sustainability assessments is changing the dynamics of environmental governance and corporate accountability. This chapter analyzes the way in which artificial intelligence, IoT, blockchain, digital twins, cloud analytics, and robotic process automation technologies contribute to sustainability assessment through their use in measuring, monitoring, and reporting on the environmental and social performance of corporations. Relying on the latest academic research, legislation, and business practice in this field, the chapter considers theoretical background, real-world applications, and governance issues related to digital sustainability assessment. A comprehensive analytical structure is provided, comprising data gathering, analysis, verification, and reporting, alongside comparative tables of relevant technologies, methods, legislation, problems, and solutions.
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.
Industry 5.0 emphasises human-centric technologies (HCTs) as essential drivers of sustainable and resilient production. However, their specific contributions to Circular Economy (CE) strategies and the associated skill requirements are not well-defined. This paper investigates how HCTs support Circular Economy practices (CEPs) and which skills and competencies are needed for their effective implementation. A systematic literature review was conducted using Scopus and Web of Science, following established guidelines. The search employed a string that links Industry 5.0, human-centricity, and the 10 R framework of CE. After a multi-stage screening and snowballing process, 41 peer-reviewed contributions published between 2015 and 2025 were selected for analysis through a combination of bibliometric and qualitative content analysis. The review maps the main HCTs, such as AI, digital twin, XR, robotics, blockchain, and IoT, to CEPs and specific 10 R strategies. It identifies seven clusters of skills ranging from analytical and decision-making abilities to human-machine collaboration, CE-specific expertise, and green human resource management practices. A Sankey diagram visualises the primary linkages between technology and strategy. Then, the authors developed a framework (TSC framework) that links skill clusters, CE practices, and enabling technologies and validated it through an illustrative case study. Interpreting the findings through the Resource-Based View, the paper argues that value arises from socio-technical bundles that integrate technologies, circular practices, and human capabilities. The study concludes with implications for policymakers, educators, and practitioners and outlines potential avenues for future research on skills for human-centred circularity.
Muhammad Farooq Shaikh, S. Hamza Hassan, Jawwad Shamsi, Alessia Maccaro · 5 authors
Background and objective The integration of blockchain and digital twin (DT) technologies is increasingly recognised as a promising approach for improving healthcare data integrity, interoperability, privacy, and clinical decision support. While digital twins enable dynamic patient modelling and predictive healthcare applications, blockchain provides secure data governance through decentralised trust, auditability, and access control. However, existing research remains fragmented, with limited synthesis of the architectural integration, regulatory readiness, ethical governance, and interoperability of blockchain-enabled healthcare digital twin systems. This systematic scoping review addresses these gaps by providing a comprehensive architectural and compliance-oriented analysis of the current evidence. Methods A systematic scoping review was conducted following PRISMA 2020 guidelines using Scopus, PubMed, and Web of Science. From 148 identified records, 55 eligible studies published between 2020 and 2025 were included after duplicate removal and eligibility screening. Data were extracted on digital twin functionality, blockchain architecture, healthcare application domains, consensus mechanisms, privacy-preserving strategies, and regulatory and ethical alignment. Structured Python-based visual mapping and comparative analyses were performed to identify architectural, governance, and compliance patterns across the literature. Results The findings demonstrate that blockchain is predominantly employed to provide access control, audit logging, data integrity, consent management, and secure data provenance within healthcare digital twin ecosystems. Patient-level and EHR-centred digital twins represented the most mature application areas, whereas cross-domain and infrastructure-level frameworks dominated early architectural exploration. The review identifies recurring compliance-oriented architectural patterns while revealing substantial gaps in clinically validated deployments, interoperability with established healthcare standards, decentralised governance models, and formal implementation of GDPR- and HIPAA-compliant engineering practices. Comparative heatmap analyses further highlight the uneven maturity of ethical governance and regulatory integration across blockchain functionalities. Conclusion This review provides the first comprehensive compliance-oriented architectural synthesis of blockchain-enabled healthcare digital twin systems by integrating technical architecture, regulatory readiness, ethical governance, and privacy-preserving design patterns within a unified analytical framework. The proposed architectural mapping identifies critical research gaps in interoperability, governance engineering, consensus optimisation, and real-world clinical validation, providing a foundation for the development of trustworthy, GDPR/HIPAA-aligned, FHIR-compatible, and clinically interoperable healthcare digital twin ecosystems.
Ms. Gunavarthani S, Dr. Princy J, Ms. Samyuktha S K
The textile industry has undergone a dramatic change in recent times because organizations are incorporating digital technology solutions for addressing issues related to sustainability and fast-tracking the journey toward a circular economy. These include Digital Product Passports (DPP), blockchain, Radio Frequency Identification (RFID), the Internet of Things (IoT), Artificial Intelligence (AI), and Industry 4.0 technologies, among others. The current research intends to conduct a systematic review of the literature on the topic of digital transformation and sustainability in the textile industry. A Systematic Literature Review (SLR) was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. In all, 55 peer-reviewed journals from 2020 to 2026 have been reviewed based on a structured selection process and analyzed using the thematic analysis approach. Six themes have been identified in the literature, which are as follows: Digital Product Passport, Digital Traceability Technologies, Industry 4.0 & Artificial Intelligence, Circular Economy Practices and Circular Supply Chains, Sustainability and Environmental, Social & Governance (ESG), and Barriers, Challenges and Future Research Directions. The results show that digital technology greatly improves the traceability of products, efficiency, and resource recycling, facilitating sustainability along the supply chain. Yet, issues such as costly digital technology implementation, inadequate digital infrastructure, the absence of standardization in digital data structures, and organizational readiness hinder digital technologies' broader application. This research fills a gap in the literature in that it identifies a consolidated thematic framework explaining the role of digital technologies in transforming the industry sustainably. The results provide insights useful for academic studies, industry professionals, and policymakers working on sustainable textile ecosystems powered by digital technology.
This study investigates the multidimensional impacts of the Industry 5.0 paradigm on logistics and supply chain management, focusing primarily on human-centric digitalization and sustainability dynamics. Within this scope, aspects of operational efficiency, resilience, and financial performance are analysed as complementary dimensions within the thematic synthesis. Emphasising human-centric digitalization, it examines how the integration of advanced technologies with social responsibility principles reshapes supply chain strategies, fosters organisational transformation, and creates competitive advantages in the context of sustainable development. Following the PRISMA protocol, and using the Web of Science Core Collection as the primary database, a systematic literature review of 47 peer-reviewed studies was conducted, mapping thematic linkages among digitalization, resilience, financial outcomes, and sustainability. The synthesis identifies a conceptual framework that positions human–machine collaboration as a central enabler for sustainable transformation, enhancing decision-making, adaptability, energy efficiency, carbon footprint reduction, green innovation, and financial outcomes. Findings highlight interconnected pathways through which digitalization generates both operational gains and long-term strategic resilience. This study contributes an original analytical lens that unites human-centric digitalization, sustainability, resilience, and financial performance within a single framework, offering actionable insights for aligning technological innovation with sustainable supply chain strategies. In particular, the study points to practical pathways such as the use of digital twins for resource optimisation, blockchain for supply chain transparency, and AI-driven solutions for emission reduction.
Artificial Intelligence (AI) is reshaping contemporary fashion by transforming design processes, production systems, and sustainability strategies in the textile and apparel sector. Amid growing concerns over overconsumption, environmental degradation, carbon emissions, and social inequities, AI has emerged as both a technological enabler and a subject of ethical scrutiny. This study examines the influence of AI on creative practice, circular design implementation, and responsible innovation in fashion. Drawing on a qualitative synthesis of systematic literature, design theory, and industry case analyses, this study proposes a framework that situates AI within sustainable fashion discourse. Findings indicate that generative design tools, virtual prototyping, digital twins, and predictive analytics support waste reduction, virtual sampling, demand-responsive production, and informed material selection. AI-enabled resale systems, automated textile sorting, and blockchain-based traceability strengthen circular economy initiatives by extending product lifecycles and improving transparency. However, algorithmic decision-making challenges authorship, craftsmanship, dataset neutrality, and labor structures. Concerns over bias, intellectual property ambiguity, digital energy consumption, and workforce displacement complicate sustainability narratives. The study argues that sustainable transformation requires a human-centered governance approach in which AI augments rather than replaces creative agency and is supported by ethical regulation and critical design education. By integrating sustainability theory, computational creativity, and AI ethics, this research contributes a holistic framework for responsible AI adoption in fashion systems.
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.
Scalable event-driven architectures are now the focus of enterprise supply chain and logistics research as this information is surfaced from transport assets, warehouses, suppliers, platforms and risk environments at a more frequent rate to allow for faster decision making. This review looks at the concepts of peer-reviewed studies of 2015–2025 that have focused on architectures that have the ability to transform distributed events into traceability, resilience, visibility, and automated coordination. The review of the literature shows that there is no single concept but rather scattered concepts in the domain of scalable event-driven logistics within the fields of Internet of Things (IoT) in logistics, Logistics 4.0, big data analytics, blockchain traceability, multi-agent control and digital supply chain twins. These streams have significant challenges around event capture, real time analytics, decentralized provenance, and disruption response. Key gaps remain in latency benchmarking, cross-enterprise semantic interoperability, governance of shared event streams, and validated architecture-level performance evidence. The field is significant due to the increased reliance on enterprise architectures that extend beyond the organizational boundary that are also responsive, auditable and resilient.
AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.
The digital transformation of higher education creates new opportunities to enhance the effectiveness, inclusiveness, and sustainability of dual education systems. However, empirical evidence on the integration of emerging technologies into dual education remains limited in developing and post-Soviet countries. This study investigates stakeholder perceptions of digital transformation in dual higher education in Uzbekistan and explores the potential of Artificial Intelligence (AI), Virtual Reality (VR), and blockchain technologies to support inclusive and sustainable learning environments. A convergent mixed-methods design was used. Quantitative data were collected from 312 students and 80 industry representatives through structured surveys, while qualitative data were obtained from semi-structured interviews with 24 academic staff members involved in dual education programmes. Descriptive statistics, correlation analysis, and thematic analysis were used to examine stakeholder readiness, implementation barriers, and future development priorities. The findings indicate strong support for digital transformation by stakeholders. Most students perceived dual education as more effective than traditional instruction (81%), and 74% expressed interest in AI- and VR-supported learning environments. Employers demonstrated a high readiness to adopt digital assessment tools (85%) and blockchain-based credential verification systems (80%). However, major challenges were identified, including insufficient digital infrastructure, limited funding, inadequate professional development opportunities, and regulatory uncertainty. Only 31% of students considered the existing digital infrastructure sufficient for advanced technology integration.Based on these findings, this study proposes an integrated framework that combines AI-driven personalized learning, VR-based experiential training, and blockchain-enabled credential verification within the principles of Universal Design for Learning (UDL) and Sustainable Development Goal 4 (SDG 4). The framework aims to enhance educational accessibility, strengthen industry–university collaboration, and support equitable participation in dual higher education. This study contributes empirical evidence from a developing country context and offers practical recommendations for policymakers and higher education institutions seeking to implement inclusive and sustainable digital transformation strategies in dual education systems.
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.
This article presents the DigInTraCE Blockchain Module, a secure and scalable framework for managing Digital Product Passports (DPPs) and traceability data across industrial supply chains. Built on Hyperledger Fabric, the solution combines distributed ledger technology, cloud-native infrastructure, smart contracts, and standardized EPCIS 2.0 traceability to enable trusted collaboration among multiple stakeholders. The technical article describes the platform architecture, governance mechanisms, secure API integration, identity management, and blockchain-based validation processes that support transparent, interoperable, and auditable product lifecycle information. The proposed framework provides a robust foundation for future Digital Product Passport implementations and circular industrial value chains.
Industrial supply chains involve multiple stakeholders, complex logistics operations, and financial transactions that require transparency, traceability, and secure coordination.Traditional supply chain systems suffer from limited transparency, the risk of data manipulation, and insufficient trust among participants.To address these challenges, this paper proposes a decentralized industrial supply chain management system implemented on an Ethereum-compatible blockchain network.The proposed architecture integrates smart contracts to automate workflows, including stakeholder registration and verification, multi-item order processing, shipment tracking, simulated delivery verification (SDV), and escrow-based conditional payment settlement.The system adopts a hybrid on-chain/off-chain storage architecture in which transactional records are maintained on-chain, while raw material and product images are stored off-chain using the InterPlanetary File System (IPFS).This design reduces blockchain storage overhead while preserving data integrity through cryptographic hash references.To improve operational efficiency and reduce overhead from repeated transactions, the proposed system supports multi-item batch transactions during procurement and ordering, while the logistics and settlement stages maintain per-item execution to preserve traceability and accountability.Experimental evaluation was conducted on the Celo Sepolia network to measure gas consumption and transaction fees for both batch-based and functionally equivalent per-item execution workflows under controlled conditions.The evaluation included multiple predefined workload configurations, and statistical analysis using mean and standard deviation was performed to assess execution stability.The results indicate that transaction aggregation reduces gas consumption by approximately 40-43% for raw material order creation and by 40-48% for raw material operations (addToMultipleCart).Product aggregation workflows also demonstrated measurable gas-efficiency improvements.These findings demonstrate the efficiency benefits of multi-item transaction aggregation within the proposed implementation while preserving lifecycle traceability and escrow-enabled settlement correctness.The reported results represent controlled implementation-level efficiency measurements within the proposed blockchain-based supply chain architecture.
Automated Guided Vehicles (AGVs) operating in industrial and critical environments require secure, auditable, and efficient data management. This paper proposes a hybrid architecture that combines ROS/ROS2 robotic middleware, an industrial context platform, and a distributed ledger layer to provide tamper-evident event recording while preserving operational flexibility. A bridging component maps robot states and events into NGSI context entities, which can then be anchored in Hedera Hashgraph or IOTA Shimmer, while a conventional FIWARE/MongoDB deployment is used as an off-chain reference baseline. The proposed architecture is evaluated experimentally using a Raspberry Pi as an AGV emulator under controlled laboratory conditions. Results show that Hedera Hashgraph achieves lower ledger confirmation latency than IOTA Shimmer, with mean DLT-only confirmation times of 4.02 s and 5.01 s, respectively, while the FIWARE/MongoDB baseline provides substantially lower latency (1.52 s) but without immutable auditability. Energy measurements indicate that IOTA exhibits low local device-side energy consumption, though they do not capture the full energy cost of the distributed ledger network. Overall, the study highlights trade-offs among latency, throughput, energy consumption, and auditability in hybrid on-chain/off-chain architectures for industrial AGV systems and provides guidance on ledger selection and deployment design for security-critical industrial applications. Keywords: AGV, DLT, Hashgraph, IOTA, FIWARE, FIROS, IoT, Industrial Robotics, Auditability, Industry, ROS, Industrial Platform
This chapter investigates the role of decentralized finance (DeFi)-enabled digital transformation in advancing sustainable and intelligent practices within the energy and utilities ecosystem, with specific emphasis on Internet of Things (IoT)-driven green steel production. In an ideal industrial landscape, energy-intensive manufacturing systems operate through transparent financing mechanisms, real-time data exchange, and decentralized governance structures that jointly promote efficiency, resilience, and environmental responsibility. Such an ecosystem is expected to harmonize renewable energy integration, adaptive production management, and inclusive investment models. However, contemporary steel manufacturing remains constrained by centralized financial control, limited data monetization, fragmented energy markets, and insufficient incentives for large-scale green transition. Existing studies on Industry 4.0, IoT-enabled 30 manufacturing, and smart energy management highlight the operational benefits of sensor networks, predictive maintenance, and machine learning–based optimization. Parallel research on blockchain and DeFi emphasizes peer-to-peer transactions and decentralized governance in energy markets. Yet, these bodies of work largely evolve in isolation, offering limited insight into their systemic convergence within green industrial production. This study addresses this gap by proposing an integrated conceptual framework that links IoT intelligence, DeFi-based financing, and decentralized energy coordination. Through critical synthesis and analytical evaluation, the paper demonstrates how trustless financial architectures can enhance data-driven decision-making, support renewable energy utilization, and enable scalable green steel ecosystems. By bridging technological and financial decentralization, this research advances a coherent pathway for sustainable industrial transformation.
Recent innovation theories on economics remain largely grounded in assumptions of hierarchical firms and closed organizational boundaries, offering limited insight into how innovation unfolds within decentralized, digitally native organizations. Decentralized Autonomous Organizations (DAOs) represent an emerging form of innovation ecosystem characterized by blockchain-based transparency, open participation, and token-driven governance, in which sustainability can be embedded directly into organizational design. This study compares two standards, ERC-8004 and Google A2A, who address the same agent interoperability question, while the former is governed by DAO and the latter by corporation consortium. They are examined through an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures. The study provides evidence-based insights for scholars, policymakers, and designers seeking to align innovation, technological governance, and sustainability in future organizational forms.
Rouwaida Abdallah, Guillermo Toyos Marfurt, Sara Tucci-Piergiovanni
This work has been accepted for publication in the proceedings of 3SCEA 2026 conference. The deposited manuscript corresponds to the author-accepted version presented at the conference. The final published version will appear in the official conference proceedings. Abstract: Traceability remains a critical challenge in modern supply chains, particularly as industries transition towards sustainability and circular economy models. The Digital Product Passport (DPP) emerges as a vital tool to consolidate and share comprehensive product information across its lifecycle. In this paper, we propose a decentralized, customizable, and self-deployable DPP system, leveraging blockchain technology and an extension of the Fractional Non-Fungible Token (F-NFT) model. This approach enables fine-grained traceability of individual product components and events across the supply chain, ensuring transparency and verifiability. A key strength of our system lies in its flexibility, enabling businesses to deploy tailored solutions without reliance on centralized service providers. The proposed system empowers stakeholders with greater control over product data while supporting selective information sharing. We present a functional implementation of the system and discuss the crucial design decisions that support its real-world applicability.
Dr.S.Janani, Ashwin Prabhu G., Hymlin Rose S. G., M. Kalaimani · 8 authors
Blockchain-enabled digital twin architectures are increasingly recognized as foundational to the development of smart hospital infrastructures that demand secure, interoperable, and intelligent healthcare systems. This chapter per the authors examines the conceptual and technical integration of blockchain technologies with digital twins to support trusted data exchange, real-time clinical modeling, and decentralized coordination across hospital ecosystems. The purpose is to articulate how distributed ledgers enhance data integrity, provenance, and access control, while digital twins enable continuous virtual representations of patients, medical devices, and hospital operations. The chapter synthesizes architectural frameworks, application scenarios, and system-level design principles, highlighting implications for clinical decision support, operational optimization, and regulatory compliance. Emerging challenges, including scalability, interoperability standards, and ethical governance, are also discussed to frame future research and deployment pathways.
S. Tamilselvi, Ravikumar R. N., Duggirala Aravind, Satheesh Kumar A. · 6 authors
The integration of Distributed Ledger Technologies (DLTs) with Digital Twin (DT) systems is transforming smart hospital infrastructures by facilitating secure, transparent, and real-time operational intelligence. As healthcare settings rely more on IoMT devices, AI analytics, and automated processes, it's crucial to ensure that the data shared between real and virtual systems is reliable and accurate. DLT provides decentralized validation, immutable storage, and automated smart-contract governance, ensuring trustworthy Digital Twin updates for predictive maintenance, patient-flow optimization, and resource management. By strengthening interoperability, enhancing cybersecurity resilience, and supporting transparent data-sharing mechanisms, DLT-enabled Digital Twins offer a robust foundation for next-generation intelligent hospital ecosystems. This chapter looks at different design models, real-life examples, performance details, and future research directions that are important for making healthcare changes that are scalable, secure, and ethical.
Operations and supply chains have witnessed spectacular transformations through Industry 4.0 and Industry 5.0. Is the next industrial revolution – Industry 6.0 – unfolding in the context of artificial intelligence (AI) and human-AI collaboration? And, possibly, even Industry 7.0 and superintelligence (SI) are just around the corner? In this paper, we conceptualize the transition toward Industry 6.0 as the Ecosystem Age that builds upon technologies developed in Industry 4.0 and viability-centric socio-ecological principles proposed in Industry 5.0, emerging into a cyber-socio-technical-ecological industrial revolution. We create a taxonomy of industrial revolutions based on the types of work that have been replaced/transformed by machines over time, and utilize it to delineate Industry 6.0 and forecast Industry 7.0 framed in technology (e.g., generative AI, agentic AI, edge AI, and humanoid robots), organization (i.e., decentralized, autonomous, agentic-driven planning and control), and modelling (OR-AI symbiosis) dimensions. Second, we discuss potential impacts of the transition toward Industry 6.0 on Operations Research (OR) with associated challenges and chances, outlining a 7-layer architecture of OR-AI symbiosis in digital twins. We elaborate on the technology and viability principles that frame Industry 6.0 and discuss scenarios for further transitioning toward next industrial revolutions and cyber-virtual, AI-driven networks that learn, adapt, self-organize, and regenerate. We conclude by outlining research opportunities for OR in the new era of supply chain and operations management in the AI and superintelligence age.
This article aims to examine how the metaverse is reshaping business and management by providing a review of existing literature, identifying critical research gaps, and proposing a novel conceptual framework—the Metaverse Ecosystem Model—that integrates technological, human, and sustainability dimensions with strategic business outcomes in the Web3 era. The article will embrace a conceptual knowledge and literature review that articulates conceptual underpinnings, marketing and consumer behaviour, sectoral uses, and sustainability/workforce/boundaryless futures. This was synthesised directly into the creation of the Metaverse Ecosystem Model that connects three pillars (technological infrastructure, workforce skills, and energy and sustainability) to the business opportunities, challenges, and quantifiable results. The review shows that, although the metaverse can be used to conduct immersive marketing, operational efficiency via digital twins, sustainable industrial use, and inclusive development in emerging economies, the studies are disjointed and siloed. Among the critical areas of gaps, there are the lack of integrated frameworks between the foundational enablers and outcomes and the scarcity of empirical focus on long-term sustainability and workforce readiness. The suggested Metaverse Ecosystem Model fills these gaps by showing causal relationships between the three pillars via opportunities and constraints to innovation, new business models, and high customer engagement. It represents the first comprehensive framework of the ecosystem, specific to business and management, which provides managers and policymakers with a useful roadmap to responsible adoption.
Manuel Lagos Rodríguez, Hilda Romero Velo, Álvaro Leitao Rodríguez, Javier Pereira Loureiro · 5 authors
Ethereum use as a decentralized platform for executing smart contracts has driven the adoption of standards that optimize interoperability in industrial environments. Ethereum Requests for Comments (ERC) establish uniform patterns for smart contracts, facilitating their integration and operation in network nodes, which are essential for industrial applications such as supply chain management or process automation. However, this standardization can propagate vulnerabilities or inefficiencies in critical systems if the contracts are not optimized, affecting the reliability of industrial processes. Since operations in Ethereum consume computational resources (measured in gas) with associated economic costs, poor design can lead to significant losses in industrial settings. This paper evaluates the efficiency and security of ERC standards by examining their functional diversity and technical complexity. Thus, it analyzes existing implementations to identify common errors and proposes improvements to enhance the robustness and optimization of three of the most popular standards: ERC-20, ERC-1400 and ERC-3643. The ultimate goal is to support the effective adoption of ERCs in industrial applications. Considering the Ethereum network incentives on lower complexity logic and the obtained results, it is advisable to use simple standards, which also reduce error risks and ease maintainability.