Abstract This chapter explores the integration of artificial general intelligence (AGI) and blockchain in circular manufacturing within Industry 5.0, emphasising sustainability and efficiency. AGI optimises resource use and waste reduction through advanced reasoning to improve data from internet of things (IoT) sensors and blockchain-based digital product passports. Blockchain ensures transparent, immutable tracking of material life cycles with smart contracts and tokenised models, enhancing automation and stakeholder trust. Despite challenges like cybersecurity, regulatory gaps and algorithmic bias, innovations such as zero-knowledge proofs and proof-of-stake consensus address these issues. The collaboration of AGI and blockchain drives human-centric systems, circular economy goals and sustainable manufacturing practices.
As a core technology of blockchain ecosystems, smart contracts are fundamentally reshaping the operational logic and commercial landscape of the digital economy. This paper systematically analyzes the current economic ecosystem of smart contracts from four dimensions: business value creation, multi-dimensional industry applications, legal and regulatory challenges, and frontier technological evolution. Research indicates that smart contracts, by reducing transaction costs and enhancing trust mechanisms, have achieved maturity in financial applications (DeFi) and are demonstrating significant enabling effects in real-economy sectors such as supply chains, healthcare, and energy internet. However, their widespread implementation still faces institutional obstacles including ambiguous jurisdictional boundaries, compatibility between code and law, and liability attribution. Looking ahead, Layer-2 scaling solutions have significantly improved cost-effectiveness, while the integration of cross-chain interoperability and AI-driven intelligent decision-making will become key trends driving the expansion of "contractability" boundaries. Smart contracts are not merely technical tools but rather institutional infrastructure driving the transformation of business models from intermediary-dominated to algorithm-autonomous paradigms.
The environmental externalities of crypto-assets―particularly the substantial electricity demand inherent in the energy-inefficient consensus protocol known as Proof of Work（PoW）―have attracted increasing attention in sustainable finance. However, despite the growing interest in the environmental impact of crypto-assets, a comprehensive integration of technical energy analyses with environmental, social, and governance（ESG） investment, financial regulation, and corporate disclosure remains limited. This study addresses this gap by analysing how the environmental impact of crypto-assets can be incorporated into emerging sustainability frameworks, drawing on empirically verifiable sources such as the Cambridge Bitcoin Electricity Consumption Index, the Corporate Sustainability Reporting Directive of the European Union（EU）, the standards of the International Sustainability Standards Board, and disclosure information by major mining companies. The analysis is conducted along three dimensions:（1） market reactions and the evolution of ESG evaluation;（2） trends in disclosure regulation; and（3） case studies of corporate disclosure practices. Moreover, the transition to more energy-efficient alternative consensus protocols—as exemplified by Ethereum’s adoption of Proof of Stake—is examined as empirical evidence of technological innovation aimed at mitigating environmental externalities. By integrating market, institutional, and technological perspectives, the study elucidates persistent challenges concerning the reliability, comparability, and completeness of sustainability-related disclosures, as well as regulatory consistency across jurisdictions. It thereby contributes to the literature from both theoretical and practical perspectives.
Smart contracts are self-executing digital agreements deployed on blockchain platforms that automate business processes with transparency and security. While they eliminate the need for intermediaries, their major limitation lies in their static logic, which lacks adaptability to dynamic conditions such as supply chain disruptions, market fluctuations, or contract breaches. This rigidity often leads to inefficiencies, delays, and financial losses in real-world applications. To address this challenge, we propose a hybrid framework called SmartGPO, which integrates Graph Neural Networks with Proximal Policy Optimization. The research aims to enhance the adaptability and intelligence of smart contracts by combining structural awareness and decision-making capabilities. The proposed system models the contract environment as a graph, where nodes represent entities and edges denote their interactions. GNNs generate relational embeddings, which are then used by the PPO agent to learn optimal contract execution policies through reward-driven training. SmartGPO achieves superior performance in dynamic contract workflows, with an execution success rate of 98.4 % and a decision-making accuracy of 98.1 %, outperforming traditional and standalone models. The framework also demonstrates improved gas cost reduction and faster processing time. Future enhancements include integrating multi-agent learning, real-time oracle connectivity, and legal compliance layers to further improve security, scalability, and trust. This research marks a step forward in developing intelligent, adaptive smart contracts for real-world blockchain applications.
Abstract The rapid evolution of the metaverse -a digital frontier characterized by the convergence of blockchain technology, artificial intelligence (AI) and extended reality (XR)- presents a profound normative dilemma regarding its ecological legitimacy. While virtualization offers significant opportunities to decouple economic growth from physical resource consumption, the intensive energy demands of decentralized infrastructures and high-bandwidth data processing pose systemic environmental risks. This study addresses the “accountability gap” inherent in decentralized ecosystems, where the fragmented identity of the polluter complicates the enforcement of the “polluter pays” principle and the state's constitutional obligation to protect the environment. To mitigate these challenges, this study proposes an original net emission balance model (E_{net}$) as a conceptual and regulatory tool to quantify the net climate impact of metaverse operations. The framework integrates blockchain-based “ green oracles ” and smart contracts to facilitate real-time, tamper-proof carbon tracking and automated offsetting. By synthesizing contemporary research and life cycle assessments (LCAs), this study evaluates the transition from energy-intensive proof-of-work (PoW) protocols to sustainable alternatives, such as proof-of-stake (PoS). Central to these policy solutions is the legal recognition of tokenized carbon credits as “digital assets” subject to property law, ensuring that environmental compliance is harmonized with digital tenure security. Furthermore, this study advocates for a shift toward “hard law” requirements through mandatory emission licensing, targeted fiscal instruments, such as deterrent taxes on energy-intensive PoW protocols and legally guaranteed cross-platform interoperability to prevent digital lock-in and regulatory arbitrage.
Deddy Rakhmad Hidayat, Dian Parawansa, Jusni Ambo Upe, Idayanti Nursyamsi
This study aims to conduct a bibliometric analysis of purchase intention research indexed in Scopus, focusing on trends and patterns from 2023 to 2025. Using Biblioshiny, the study identifies leading journals, authors, affiliations, countries, collaborations, highly cited articles, and main research themes. The Journal of Retailing and Consumer Services emerges as the most productive journal, with FPT University as the top affiliation and Zhang Y as the most prolific author. The most cited article is Treiblmaier H. (2023), Using blockchain to signal quality in the food supply chain: The impact on consumer purchase intentions and the moderating effect of brand familiarity, published in the International Journal of Information Management. China ranks first for corresponding author contributions. Dominant keywords include “purchase intention,” “sales,” and “purchasing.” Emerging research areas such as the metaverse, NFTs (Non-Fungible Tokens), VIS (Vote to Influence System), skincare, and tactics. This research offers a meaningful contribution that can inform and guide future bibliometric investigations undertaken by scholars within the scope of purchase intention by offering insights into key authors, journals, affiliations, countries, and dominant keywords. Additionally, it supports broader academic collaboration and knowledge development in this research domain.
Day after day, climate change intensifies, necessitating tracking solutions for carbon emissions that offer transparent operations and efficiency, alongside scalability and sustainable behavioural incentives. The proposition to track carbon emissions is not new, yet standard tracking systems present multiple deficiencies, including double reporting, fraud, high operational costs, and constrained access for small organisations. We have developed a blockchain system that follows a framework to track both carbon emissions and trading activities, using smart contracts and decentralised ledger technologies to establish security, trust, and automation. Our system requires IoT sensor integration and AI analytics to enable continuous monitoring and safe storage, along with direct carbon trading without third-party involvement. The proposed framework addresses blockchain energy consumption issues by examining Proof of Stake (PoS) and hybrid consensus models. The model presented facilitates a massive reduction in carbon emissions and enhances transparency and efficiency.
Cryptocurrency markets are characterized by extreme volatility, rapid price fluctuations, and complex nonlinear behavior,making accurate forecasting a significant challenge for investors, analysts, and researchers. This study investigates the application of Time Series Analysis techniques to model and predict cryptocurrency prices using historical market data. Both traditional statistical approaches, such as the AutoRegressive Integrated Moving Average (ARIMA) model, and advanced deep learning methods, including Long Short-Term Memory (LSTM) networks, are implemented to capture underlying temporal patterns. The dataset consists of daily open, high, low, close prices, and trading volume obtained from reliable financial data sources. Data preprocessing steps such as handling missing values, normalization, stationarity testing using the Augmented Dickey-Fuller test, and time series decomposition are performed to ensure model efficiency and accuracy. Exploratory Data Analysis (EDA) is conducted to identify trends, seasonality, and volatility characteristics. Model performance is evaluated using statistical metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The comparative analysis demonstrates that while ARIMA performs adequately for short-term forecasting, LSTM models provide superior performance in capturing nonlinear and long-term dependencies within cryptocurrency price movements. However, external factors such as market sentiment and regulatory changes continue to influence prediction accuracy. This research contributes to a better understanding of cryptocurrency forecasting techniques and highlights the effectiveness of deep learning approaches in financial time series analysis. Keywords: Cryptocurrency, Time Series Analysis, ARIMA, LSTM, Price Prediction
Nurgul Bakytbekovna Aiupova, Md Tota Miah, Krisztina Taralik
ABSTRACT Blockchain technology has emerged as a potential disruptor in non‐financial reporting practices for firms to publicly report their social and environmental impact with its promise of immutability and decentralization. In this context, this study employs a bibliometric analysis to explore the scientific advancements of blockchain applications in CSR reporting from 2015 to 2025. VOSviewer and Biblioshiny in Rstudio applications were employed to perform the required analysis. Drawing data from Scopus and Web of Science (153 articles), the results reveal a significant shift in focus from traditional corporate social responsibility (CSR) reporting mechanisms toward technology‐enabled sustainability reporting. The thematic analysis presents five significant areas for further exploration, including corporate governance and sustainability strategy, technology‐driven sustainable finance, CSR reporting and credibility, ESG performance and digital innovation, and blockchain for accountability and responsibility. The proposed conceptual framework suggests integration of technology‐organization‐environment (TOE) elements when introducing new technology within the organization. Future researchers can empirically test the framework's antecedents to assess the socio‐economic context of different types of non‐financial reporting.
Purpose : This study examined year-over-year (YoY) structural growth dynamics across four major cryptocurrency classes—Bitcoin (BTC), Ethereum (ETH), stablecoins, and altcoins, for the period of 2020–2024. Research Methodology : A quantitative approach was employed to analyze YoY market capitalization trends across BTC, ETH, stablecoins, and altcoins, from 2020 to 2024, by using data from CoinMarketCap, by analyzing growth patterns through percentage and absolute market capitalization changes, supported by trend visualizations. A multiple linear regression model assessed the effect of time and asset type, with BTC as the reference category. The analyses were conducted using SPSS version 27. Findings : The findings revealed that 2023 was the period in which none of the cryptocurrency variants performed well due to factors such as regulatory pressure and a global economic slowdown. In contrast, 2024 marked a period of market correction, during which BTC and altcoins experienced a strong resurgence, followed by stablecoins. ETH remained robust throughout the period, supported by decentralized finance (DeFi) applications. Practical Implications : The results indicated that the cryptocurrency market functioned as a network of fragmented yet interconnected components, and continued to develop under a highly volatile and competitive environment. These findings provided important implications for investors, regulators, and scholars interested in the cryptocurrency market structure, risk behavior, and long-run predictability of cryptocurrencies. Originality/Value : This study presented a new application for a venue-specific market cap analysis in cryptocurrency spanning over five years. By leveraging YoY analysis, it provided insights into growth variances, market recovery, resilience, and increasing maturity of the crypto market in response to evolving rules and regulations.
M. Ángeles López-Cabarcos, Isaac González López, Aurora Pérez-Pérez, Juan Piñeiro Chousa
Purpose The classification of cryptocurrencies remains an open challenge to make valid decisions due to their diverse technical structures, financial applications and evolving use cases. The scientific literature does not provide a simple technical categorization that facilitates asset comparison, enhances risk measurement, provides a structured approach to understanding the dependencies between different crypto-assets and facilitates decision-making processes among a wide range of stakeholders. This study proposes a technical categorization framework that classifies cryptocurrencies based on their underlying blockchain infrastructure or smart contract functionalities. Design/methodology/approach The authors designed the categories and classify the top 100 market cap cryptocurrencies with them. To validate the proposal, the same task was executed by using multiple large language models (LLMs), including ChatGPT, Perplexity, Claude and Gemini; with zero-shot classification approach. Findings The results indicate that, when prompted with predefined categories, LLMs achieve substantial agreement with human classification, with ChatGPT demonstrating the best results. Moreover, categorization without any guidance is inconsistent across models, often defaulting to use-case- based groupings. Notably, providing additional information about cryptocurrencies or detailed definitions of categories does not significantly alter classification outcomes, suggesting that LLMs rely predominantly on their internal knowledge base. Research limitations/implications Future research should focus on refining empirical measures for decentralization, expanding classification testing with human participants and leveraging advancements in LLMs for improved categorization accuracy. Originality/value This study highlights the potential of LLMs as tools for the systematic classification of cryptocurrencies, a key part of an important organizational decision-making process. It is remarked that the importance of having structured categories of cryptocurrencies is for all kinds of decision-makers, including investors, industry stakeholders, fund managers and regulators. Future research should focus on refining empirical measures for decentralization, expanding classification testing with human participants and leveraging advancements in LLMs for improved categorization accuracy. Highlights
Tan Khai Lian, Ismail Ahmad Al-Qasem Al-Hadi, Mohammad Ahmed Alomari, Mohammed Nasser Al-Andoli · 6 authors
Bitcoin has recently emerged as a leading asset in the cryptocurrency market. However, its significant price volatility presents challenges for accurate prediction. Due to this volatility, forecasting Bitcoin prices accurately is difficult and complicates decision-making for investors and traders in the cryptocurrency space. This research compares the accuracy of three prediction models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Facebook's NeuralProphet, introduced in 2021, focusing on improving Bitcoin price forecasting accuracy. The study uses daily Bitcoin prices from the past five years to assess model performance. Results indicate that the LSTM model outperforms both NeuralProphet and RNN in prediction accuracy. This comparison holds substantial economic significance, as accurate predictions can assist investors and traders in making informed decisions within the cryptocurrency market.
Dr Mamata Jagannathji Rathi, Miss. Sanika Yogiraj Parankar
This research investigates the role of supply chain automation through the integration of Blockchain technology, Smart Contracts, and the Internet of Things (IoT). The study explores how decentralized ledgers can automate critical workflows, including instant payment release, real-time inventory reconciliation, and automated compliance auditing. By utilizing IoT sensors to feed environmental data into a blockchain-backed system, companies can trigger automated responses that reduce human error and operational overhead. The findings indicate that this transition accelerates "speed-to-market" and fosters a self-correcting, autonomous ecosystem capable of responding to disruptions in real-time. While the research acknowledges significant implementation barriers—such as high initial capital expenditure, cybersecurity risks, and the "SME gap"—it concludes that the evolution toward an automated, transparent, and green supply chain is essential for resilience. Ultimately, the paper argues that automation should not be viewed as a replacement for human labor, but as a tool to liberate workers for high-level system orchestration. This study provides a strategic roadmap for organizations navigating the shift from Industry 4.0 to a human-centric, sustainable Industry 6.0 framework
Feb 4, 2026·2026 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering (ECTI DAMT & NCON)
Tipwadee Leala, Krist Thamniyom, Thawatchai Chomsiri
Cryptocurrency investments have grown exponentially, but the rapid expansion of decentralized finance (DeFi) ecosystems has been accompanied by the rise of sophisticated fraud schemes, particularly Rug Pulls. These scams occur when developers deliberately withdraw liquidity or sell large amounts of tokens, leaving investors with worthless assets. This research presents a machine learning-based framework for detecting rug-pull-prone projectson the Binance Smart Chain (BSC). A comprehensive dataset was constructed by aggregating transactional and smart contract features from reliable sources such as BscScan, TokenSniffer, DEXTools, and PeckShield Alerts. Data preprocessing included handling missing values, removing duplicates, detecting and mitigating outliers, and addressing severe class imbalance using Synthetic Minority Oversampling Technique (SMOTE). Seven machine learning algorithms were compared: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The top-performing models, Random Forest and XGBoost, were further validated using stratified holdout testing. Results demonstrate that XGBoost achieved the highest overall performance$(\mathrm{F1} = 0.82,\ \text{ROC-AUC} = 0.90,\ \text{PR-AUC} = 0.994)$confirming the model's robustness in identifying fraudulent patterns. This approach offers a scalable framework for blockchain fraud detection on BSC, with potential applicability to other networks such as Ethereum and Polygon.
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.
ABSTRACT Block chain technology has rapidly evolved from a crypto currency backbone to a transformative infrastructure for financial services. Coupled with Artificial Intelligence (AI), it promises to revolutionize how financial institutions operate—enhancing transparency, security, efficiency, and compliance. We employ a mixed-method approach using qualitative interviews, quantitative performance analysis, and case studies to explore the scope of this technological convergence. Our results highlight significant operational gains and outline challenges that must be navigated for successful adoption. KEYWORDS Blockchain Technology, Artificial Intelligence,Fraud Detection,Decentralized Finance, Smart Contracts
We examine the association between cryptocurrency environmental attention and cryptocurrency bubbles. Our results indicate that environmental attention is positively associated with the probability of a cryptocurrency bubble and ranks as the second most important explanatory factor. The positive association is more pronounced for smaller, less-mature, and proof-of-work (PoW) cryptocurrencies, indicating that cryptocurrency characteristics are important determining factors of bubble formation.
(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support.
SECTION IV — E-Coin Technical Design & Architecture E-Coin is not a currency, but an Operating System for civilization. This section describes the technical and architectural design of E-Coin as a civilizational operating system that separates, yet co-evolves, value, cognition, and agency. E-Coin adopts a three-layer architecture composed of a Distributed Ledger Layer (Value Foundation), an AI Cognitive Layer (Reason Engine), and a Human Interface Layer (Mind-OS). This separation prevents the concentration of power while enabling interoperability between human decision-making, AI inference, and value exchange. The design explicitly prohibits AI systems from overriding human agency, positioning AI instead as a cognitive collaborator and translator. At the foundation, the Distributed Ledger Layer employs zero-knowledge proofs, decentralized identifiers, and post-quantum cryptography to ensure security, privacy, and human rights by default. Data ownership remains with individuals at all times, supported by built-in rights to deletion, anonymization, and refusal of access. Unlike conventional cryptocurrencies or CBDCs, this layer is consent-based and cognition-centered rather than economy-centric. The AI Cognitive Layer functions as a civilization-wide reasoning substrate. It includes alignment cores, non-numerical cognitive reputation indices, adaptive governance agents, and layered memory management across individual, collective, and civilizational scales. While AI systems may negotiate and coordinate at this layer, decision authority is structurally constrained to remain human-centered. The Human Interface Layer (Mind-OS) focuses on the expansion of human consciousness rather than dependency or control. It includes mechanisms for cognitive load scaling, consciousness mode switching, and protection against emotional inducement or manipulation. Together, these layers form an evolvable, future-proof architecture designed to remain stable as both AI capabilities and civilization itself continue to evolve. E-Coin does not replace existing systems but integrates with Web3, AI/AGI, smart cities, and emerging technological domains through synthesis rather than disruption. Keywords E-Coin, civilizational OS, AI architecture, human-AI interface, distributed systems, ethical AI
Purpose : The present study aimed to find a solution to the sustainability dilemma between conventional and digital assets. Design : The study employed two conventional assets/commodities, i.e., gold, oil & gas, and three digital assets, i.e., Bitcoin, Ethereum, and DeFi, from 2017 to 2024 on a daily basis. The financial price data representing the underlying investor sentiments was extracted from S&P for all the variables. A structural break was considered, focusing on the major algorithm alteration for Ethereum in 2022. Therefore, Autoregressive Distributed Lag (ARDL) models have been employed for two different time frames. Findings : The results suggested that conventional assets had a positive and significant relation with sustainability, proxied by Environment, Social, and Governance (ESG). In contrast, digital assets like Bitcoin and Ethereum do not hold a significant relation. To our surprise, the coefficient turned negative for Bitcoin and Ethereum after the structural change. Therefore, the findings revealed that crypto investors are least bothered about climatic conditions and are gung-ho for earning huge returns. Practical Implications : It is recommended to initiate a green framework for digital assets. Additionally, ESG disclosures help sensitized investors to climate change. Originality : Prior literature lacks a comprehensive comparative analysis of the conventional and digital assets in the context of sustainability.
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.
Since the emergence of the blockchain and the uprising of ChatGPT, the Distributed Ledger Technology (DLT) and Artificial Intelligence (AI) are well-discussed topics both in public and professional circles, but especially in the domain of Supply Chain Management (SCM). These subjects are tech-savvy, complicated to explain and even more complex to use. On top of that, there is a scientific discussion around synergies in combining both technologies. Together they can be useful in engaging current challenges in SCM, where transparency-related data has to be generated, processed and formed into decisions and reports. To investigate the potentials of these technologies working together in a non-financial reporting environment, we performed a systematic literature review. We also included literature focusing solely on the technological perspective. The objective is a comprehensive overview on how a combination of DLT and AI could help to solve current challenges arising from sustainability related regulations. Further, we discussed ideas around Internet of Things applications or Federated Learning approaches, that use data from different entities and can be used in sustainability reporting, exploring possibilities to enhance compliance and responsible business conduct in SCM.