Accurately quantifying enterprise digital capability is fundamental to evaluating industrial modernization, yet conventional empirical inquiries predominantly rely on single-dimensional proxy variables or unweighted keyword counts, failing to capture the multidimensional integration of digital assets. Moving beyond causal regression paradigms and reductionist metrics, this inquiry develops a comprehensive, objective Digital Readiness Index (DRI) for physical manufacturing enterprises using an information-theoretic Entropy Weight Method (EWM). Grounded in multi-source text-mining disclosures and corporate balance sheets across 41,756 firm-year observations of Chinese A-share listed manufacturing enterprises spanning 2000 to 2025, the evaluation framework integrates eight discrete operational indicators across three dimensions: Technical Depth (AI, Big Data, Cloud Computing, Blockchain, and Digital Applications), Intangible Capital Endowments, and Governance Oversight. Objective entropy weighting demonstrates that specialized frontier technologies, particularly Blockchain (w=37.81%), Artificial Intelligence (w=15.95%), Big Data Analytics (w=15.93%), and Cloud Computing (w=15.66%)—constitute the primary sources of informational divergence across manufacturing firms. Longitudinal trajectory evaluation reveals a sustained upward trajectory in mean digital readiness, accelerating markedly after the 2015 macroeconomic policy inflection point. Non-parametric Gaussian Kernel Density Estimation uncovers a distinct dynamic polarization pattern, characterized by a shifting rightward distribution and an elongating upper tail. Cross-sectional decomposition establishes substantial structural disparities: high-tech sectors such as Computers and Electronics exhibit the highest mean digital readiness (DRI=16.28), whereas chemical and pharmaceutical sectors display persistent digital inertia (DRI≈4.06). Furthermore, non-state-owned enterprises (Non-SOEs) systematically outperform state-owned enterprises (SOEs) across all asset scale tiers. These findings provide an objective measurement tool and benchmark for corporate technology auditing and industrial policy calibration.
M. Ferreira, Bernardo J. R. Figueiredo, Alexandre Soares dos Santos, João Filipe Matos · 5 authors
Digital twins (DTs) are transforming industries by offering real-time virtual representations of physical assets, enabling smarter decision-making and optimization. However, as these systems become more complex and distributed, maintaining reliable traceability remains a significant challenge. To address this, we propose a multi-context, modular platform that leverages blockchain and distributed ledger technologies (DLTs) to enhance the traceability of digital twins. Our proposal will ensure secure, immutable, and transparent records of data interactions, fostering greater trust, accountability, and interoperability across various domains. The foundation of this work involved identifying the key Architecturally Significant Requirements (ASRs) that must be considered in the platform's design. Based on the first ASRs related to traceability and data flexibility, we developed a conceptual architecture supported by an ontology that addresses the multi-context traceability challenge. The proposed approach was validated through two distinct case studies: livestock management and shop-floor processes. Moving forward, our focus will be on addressing the remaining ASRs that were already identified, to further develop a high-performance, secure, and scalable modular platform capable of supporting diverse contexts and applications.
Order fairness in distributed ledgers refers to properties that relate the order in which transactions are sent or received to the order in which they are eventually finalized, i.e., totally ordered. The study of such properties is relatively new and has been especially stimulated by the rise of Maximal Extractable Value (MEV) attacks in blockchain environments. Indeed, in many classical blockchain protocols, leaders are responsible for selecting the transactions to be included in blocks, which creates a clear vulnerability and opportunity for transaction order manipulation. Unlike blockchains, DAG-based ledgers allow participants in the network to independently propose blocks, which are then arranged as vertices of a directed acyclic graph. Interestingly, leaders in DAG-based ledgers are elected only after the fact, once transactions are already part of the graph, to determine their total order. In other words, transactions are not chosen by single leaders; instead, they are collectively validated by the nodes, and leaders are only elected to establish an ordering. This approach intuitively reduces the risk of transaction manipulation and enhances fairness. In this paper, we aim to quantify the capability of DAG-based ledgers to achieve order fairness. To this end, we define new variants of order fairness adapted to DAG-based ledgers and evaluate the impact of an adversary capable of compromising a limited number of nodes (below the one-third threshold) to reorder transactions. We analyze how often our order fairness properties are violated under different network conditions and parameterizations of the DAG algorithm, depending on the adversary's power. Our study shows that DAG-based ledgers are still vulnerable to reordering attacks, as an adversary can coordinate a minority of Byzantine nodes to manipulate the DAG's structure.
Decentralized Finance (DeFi) is a recent advancement of the cryptocurrency ecosystem, giving plenty of opportunities for financial inclusion, innovation, and growth domains by providing services such as lending, borrowing, and trading without traditional intermediaries. However, inadequate regulatory oversight and technological vulnerabilities raise pressing concerns around market manipulation, fraud, and regulatory compliance, exposing a clear research gap in effective DeFi risk management. This paper addresses this gap by proposing a utility-based framework to evaluate six leading DeFi tracking platforms—Chainalysis, Elliptic, Nansen, Dune Analytics, DeBank, and Etherscan—focusing on two critical metrics: transaction accuracy and real-time responsiveness. Applying a mixed methods approach that combines a quantitative survey (n = 138) with qualitative interviews (n = 12), we identified critical platform features and found significant differences across these platforms with respect to compliance features, advanced analytics, and user experience. We used a utility-based model that links accuracy and responsiveness metrics, allowing us to adjust differing priorities and risk management needs for users. The results show the need for balanced, user-centric solutions that accommodate regulatory, technological efficiency and affordability requirements. Our study contributes to the growing knowledge base by providing a structured evaluation model and empirical insights, offering clear directions for practitioners, platform developers, and policymakers aiming to strengthen the DeFi ecosystem.
Purpose:This study provides a user-prioritized, data-driven framework for evaluating DeFi risk management platforms and offers actionable insights for developers, investors, and regulators seeking to enhance the transparency, security, and sustainability of the DeFi ecosystem.Design/Methodology/Approach: This study investigates the effectiveness of six leading DeFi tracking platforms-Chainalysis, Elliptic, Nansen, Dune Analytics, DeBank, and Etherscan-in mitigating these risks.Employing a mixed-methods approach, the research integrates survey data (n = 138), expert interviews, and platform metrics, analyzed through advanced statistical techniques such as T-Test, MANOVA, Logistic Regression, Kruskal-Wallis H Test, Cohen's D Effect Size, Survival Analysis, Cluster Analysis, and a Utility-Based Scoring Model.Findings: Results reveal significant differences in platform performance, with Chainalysis and Etherscan emerging as top performers in compliance and usability, respectively.Practical Implications: The rapid expansion of Decentralized Finance (DeFi) has revolutionized financial services by eliminating intermediaries and enabling peer-to-peer interactions through blockchain-based smart contracts.However, this innovation introduces significant risk management challenges, including smart contract vulnerabilities, transaction opacity, and compliance limitations.Originality value: The utility model highlights the importance of real-time alerts, trust, and compliance tools in platform adoption.
The article addresses the scientific problem of forming an efficient approach to assessing the market value of business projects in the rapidly evolving area of decentralized finance (DeFi) within the digital economy. The article analyzes the limitations of applying traditional financial evaluation methods, specifically discounted cash flow (DCF) models and economic value added (EVA), which lose their relevance in the DeFi context due to the instability of cash flows, absence of centralized reporting, and the specific profitability structure of tokenized assets. In order to overcome the mentioned limitations in the study, a new multifactor model has been proposed, which combines classical financial indicators with key tokenomic metrics: total value locked (TVL), the utility function of the token in governance, liquidity mining incentives, as well as the distribution of tokens over time (vesting schedules). An empirical validation of the model’s efficiency was conducted through the construction of a multiple linear regression based on data from 20 leading DeFi projects for the years 2023–2024. The results obtained demonstrated a high statistical significance of the included tokenomic variables (p < 0.01) and a high explanatory power of the model (R? = 0.92), confirming its efficiency for predicting the market capitalization of digital assets. It is demonstrated that tokenomic characteristics have a decisive impact on the value of DeFi projects, while traditional indicators (DCF, EVA) are secondary or insignificant due to the changing nature of value in the Web3 economy. The proposed model enables the development of a sound methodology for the strategic analysis of investment attractiveness of decentralized platforms, particularly from the perspective of DAO organizations, venture funds, and analytical agencies.
In recent years, the integration of blockchain technology into Corporate Social Responsibility (CSR) practices has gained significant attention due to its potential to address transparency, accountability, and sustainability challenges in business operations.Blockchain, with its decentralized nature, offers a secure and immutable platform that allows organizations to track, verify, and share data related to their CSR initiatives in real time.This level of transparency enhances trust among stakeholders and provides a reliable way to demonstrate a commitment to ethical and sustainable business practices.Blockchain technology is emerging as a transformative tool for enhancing Corporate Social Responsibility (CSR) practices, offering innovative solutions to foster transparency, accountability, and sustainability.By providing a decentralized, immutable ledger, blockchain ensures that CSR-related activities are traceable, verifiable, and free from manipulation, thereby building trust among stakeholders and mitigating issues such as fraud and resource misallocation.This study explores the potential of blockchain technology to transform Corporate Social Responsibility (CSR) practices, focusing on its role in promoting transparency, accountability, and sustainability.Blockchain's decentralized and immutable ledger enables organizations to track and verify transactions, enhancing trust among stakeholders and ensuring ethical business practices.The study identifies key blockchain innovations such as smart contracts, decentralized finance (DeFi), and energy-efficient consensus mechanisms that contribute to achieving the United Nations Sustainable Development Goals (SDGs).However, the adoption of blockchain in CSR initiatives faces challenges including regulatory uncertainty, integration complexities, scalability issues, and privacy concerns.To overcome these barriers, the research offers strategic recommendations, such as the adoption of energy-efficient blockchain solutions, the integration of smart contracts, and enhanced stakeholder education.This paper contributes to understanding how blockchain can be leveraged to create a sustainable and efficient CSR ecosystem, aligning business operations with global sustainability objectives.
The paper presents a novel framework for implementing decentralized algorithms based on non-fungible tokens (NFTs) for digital twin management in aviation, with a focus on component lifecycle tracking. The proposed approach uses NFTs to create unique, immutable digital representations of physical aviation components capturing real-time records of a component’s entire lifecycle, from manufacture to retirement. This paper outlines detailed workflows for key processes, including part tracking, maintenance records, certification and compliance, supply chain management, flight logs, ownership and leasing, technical documentation, and quality assurance. This paper introduces a class of algorithms designed to manage the complex relationships between physical components, their digital twins, and associated NFTs. A unified model is presented to demonstrate how NFTs are created and updated across various stages of a component’s lifecycle, ensuring data integrity, regulatory compliance, and operational efficiency. This paper also discusses the architecture of the proposed system, exploring the relationships between data sources, digital twins, blockchain, NFTs, and other critical components. It further examines the main challenges of the NFT-based approach and outlines future research directions.
Although academic and practical interest in non-fungible tokens (NFTs) has continuously increased over the last few years, there is still a need to better understand their social acceptability. The aim of the study was to explore the double edge of NFT legitimacy for NFTs by unveiling the role of sustainability and by adopting technology legitimacy and the field of sustainability transition studies as a theoretical lens. Specifically, this research investigates the role of sustainability in securing and maintaining technology legitimacy within NFT projects. We interviewed 12 experts through exploratory qualitative research. The findings highlight three main ways in which sustainability participates in the legitimation of NFT projects. While sustainability can be inherent in the NFT project itself, this legitimation can also be derived from the perceived sustainability of the NFT technology or be part of innovative business models. Theoretical contributions and managerial implications are then discussed. JEL CODES: O33, O35, O50
This article shows a brief history of Techno-Economic Assessment (TEA) in Communications, a proposed redefinition of TEA as well as the new challenges derived from a dynamic context with cloud-native virtualized networks, the Helium Network & alike blockchain-based decentralized networks, the new network as a platform (NaaP) paradigm, carbon pricing, network sharing, and web3, metaverse and blockchain technologies. The authors formulate the research question and show the need to improve TEA models to integrate and manage all this increasing complexity. This paper also proposes the characteristics TEA models should have and their current degree of compliance for several use cases: 5G and beyond, software-defined wide area network (SD-WAN), secure access service edge (SASE), secure service edge (SSE), and cloud cybersecurity risk assessment. The authors also present TEA extensibility to request for proposals (RFP) processes and other industries, to conclude that there is an urgent need for agile and effective TEA in Comms that allows industrialization of agile decision-making for all market stakeholders to choose the optimal solution for any technology, scenario and use case.
Don Husereau, Terrence Sullivan, Harriet Feilotter, Marcio M. Gomes · 9 authors
AIMS: The Canadian province of Ontario provides full coverage for its residents (pop.14.8 M) for hospital-based diagnostic testing. Historical governance of the healthcare system and a legacy scheme of health technology assessment (HTA) and financing has led to a suboptimal approach of adopting advanced diagnostic technology (i.e. protein expression, cytogenetic, and molecular/genetic) for guiding therapeutic decisions. The aim of this research is to explore systemic barriers and provide guidance to improve patient and care provider experiences by reducing delays and inequity of access to testing, while benefitting laboratory innovators and maximizing system efficiency. MATERIALS AND METHODS: = 2). The forum considered evidence of good practices in adoption, implementation, and financing laboratory services and identified barriers as well as feasible options for improving advanced diagnostic testing in Ontario. RESULTS: Overarching challenges identified included: barriers to define what is needed; need for a clear approach to adoption; and the need for more oversight and coordination. Recommendations to address these included a shift to an anticipatory system of test adoption, creating a fit-for-purpose system of health technology management that consolidates existing evaluation processes, and modernizing the governance and financing of testing so that it is managed at a care-delivery level. CONCLUSIONS: The proposals for change in Ontario highlight the role that HTA, governance, and financing of health technology play along the continuum of a health technology life cycle within a healthcare system where decision-making is highly decentralized. Resource availability and capacity were not a concern - instead, solutions require higher levels of coordination and system integration along with innovative approaches to HTA.
Open access
Health Systems, Economic Evaluations, Quality of Life
Today’s world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people’s adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.