Abdul Razzaq, Ahmed B. Altamimi, Wilayat Khan, Mohammad Alsaffar · 7 authors
CONTEXT: Metaverse is an emerging technology that synchronizes physical and virtual things. It is used to communicate and simulate the virtual world with the physical world through human actions in real-life scenarios. Combining blockchain and metaverse technologies produces an archetype shift in the educational technology domain regarding online certification, largely due to the impact of synchronizing educational technologies. The combined technology elevates the security measure, ensures transparency, enhances accountability, and reduces costs for the online certification process. Proposed Solution: The suggested solution (MetaEduTech) accelerates the certificate verification process by (i) extenuating the risks of misuse by leveraging decentralized storage of the InterPlanetary File System (IPFS), (ii) securing the certificate, and (iii) providing the metaverse environment for certification. We perform experiments and evaluate the MetaEduTech solution by deploying a blockchain-based smart contract model on Ethereum on the Microsoft Windows platform. RESULTS AND IMPLICATIONS: The evaluation results show (i) the efficiency of the query response (5 ms-50 ms), (ii) and the performance of the query execution (CPU utilization between 2%-6%). The findings in this research underscore the effectiveness of the proposed solution with the potential to modernize the certification exam process. The proposed solution and its evaluation can provide insights into how to address the persistent issues surrounding certificate authenticity related to academic verification in a metaverse environment.
Blockchain-based digital assets represent a new stage in the evolution of monetary systems. However, the mechanisms of seigniorage – the revenue derived from issuing these digital assets – remain insufficiently studied, creating a research gap. The author addresses this topic to analyze how seigniorage is transformed in the context of digitalization and what new forms it assumes. The objective of the work is to investigate the evolution and mechanisms of seigniorage in digital assets, including cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs). The tasks include analyzing the historical development of digital assets, comparing seigniorage mechanisms (Proof-of-Work and Proof-of-Stake), and assessing the prospects for implementing the digital ruble in Russia. Research methods: analysis of historical data, comparison of seigniorage mechanisms in blockchain projects (Bitcoin, Ethereum, MakerDAO), and evaluation of the economic aspects of central bank digital currencies. The study utilizes open data, legislative acts, and scientific publications. The results demonstrate that seigniorage in digital assets takes on new forms, such as mining, staking, and algorithmic governance, which contribute to the creation of significant financial value. The implementation of the digital ruble, despite high costs, presents opportunities to enhance the efficiency of the financial system. The scope of application for the results includes developing regulatory approaches to digital assets and optimizing seigniorage mechanisms.
Leonardo Soares dos Santos, Ana Paula Neutzling Gomes, P Rupino
The widespread adoption of distributed energy resources poses challenges to the operation and management of electricity grids. The intrinsic characteristics of such resources, such as variability and dispatchability, require increased flexibility in power systems. Demand-side flexibility is expected to play a significant role in future power systems, necessitating a more active role for consumers and prosumers in the energy system. To effectively operationalize flexibility and accommodate the growth of distributed generation, there is an urgent need for active and automated local management of energy resources alongside local transactions and energy exchanges. Technologies like blockchain and smart contracts offer significant potential for facilitating energy transactions within decentralized systems, mainly due to their capacity to facilitate secure microtransactions over time. Although their potential is recognized and review works exist, a detailed understanding of their characteristics and functionalities is lacking, which is critical for the deployment of those technologies. In this regard, the authors utilize the Prisma protocol to conduct a comprehensive analysis of current developments, identify primary innovative contract functionalities, quantify their utilization, and uncover potential gaps in their application in local energy transactions. They were analyzed 197 smart contracts, where 179 indicated at least one functionality. The findings suggest that most of these functionalities focus on energy transactions without details. They were identified and characterized in terms of the type of blockchain on which these smart contracts were developed. The conclusions show that they primarily work on a private Ethereum, promoting transactions between two peers in real-time and in the day ahead. The study culminates in an inclusive conclusion that spans the range of smart contract functionalities across different aspects of blockchain technology and temporal trade dynamics. This analysis reveals a significant gap in the transaction approach involving multiple sellers and buyers, underscoring the need for further exploration. This gap presents an exciting opportunity for future research and development in energy management, particularly in the context of blockchain's potential to facilitate local energy transactions. • Review of smart contracts for energy trading based on 127 reviewed articles. • Presentation of the smart contract's functionalities for energy trading. • Critical features overview of reviewed energy trading platforms. • Identification of the challenges in applying smart contracts in energy transactions. • Recommendations to consider when implementing smart contracts for energy trading.
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of July, 2025). We integrate multi-dimensional analyses including the automated parsing of Solidity interfaces, graph-based modeling of inheritance structures, contributor profiling, and mining of community discussion data. We distinguish foundational from emerging standards, expose poor cross-version interoperability, and show that growing functional complexity heightens security risks.
This study utilizes version 6 of the regression analysis of time series (RATS) software package to implement the estimation of the bivariate diagonal generalized autoregressive conditional heteroscedasticity (GARCH) model combined with a composite asset selection approach including two hybrid performance measures to solve ‘the trade-off problem between return and risk’ and ‘the inconsistent results from different performance measures’ in the problem of asset allocation within a group of minimum variance portfolios during the pre-COVID-19 and COVID-19 periods. Empirical results show that the optimal portfolios obtained from this approach and the assets added to a portfolio to achieve better performance differ between the pre-COVID-19 and COVID-19 periods. For instance, the optimal portfolios are the Chinese yuan-Ethereum and Bitcoin-Ethereum for the pre-COVID-19 period, but the WTI-Ethereum for the COVID-19 period. To achieve better performance, we added Ethereum to our portfolio during the pre-COVID-19 period, while WTI and Bitcoin were added during the COVID-19 period. Thus, the COVID-19 pandemic had a significant impact on the performance of asset allocation in the three markets. The proposed approaches in this study can be embedded in a computer as an asset allocation algorithm of Robo-advisers.
This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.
This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO2/m3 reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies.
The rapid digitization of the healthcare sector has led to the generation of massive volumes of Electronic Health Records (EHRs), necessitating a robust, secure, and scalable system capable of efficiently managing and accessing this ever-growing data. Ensuring privacy, security, and scalability in managing voluminous and sensitive healthcare data, particularly when stored across various geographical locations, poses critical challenges that require innovative solutions. To address these issues, MeDiStore, a decentralized framework built on the Ethereum blockchain, is proposed. By integrating the InterPlanetary File System (IPFS), MeDiStore ensures scalable and secure storage while mitigating centralization risks and providing improved accessibility for EHRs. The framework leverages Elliptic Curve Cryptography (ECC) to encrypt and secure patient records, ensuring data ownership remains with the patient. To further enhance scalability, security, and reliability, of the blockchain network, the MeDiStore Trust Protocol, introduced a modified Proof of Stake (PoS) consensus mechanism that evaluates validators based on their network stake and reputation score, derived from their historical performance. Additionally, a Data Translation Layer is incorporated to ensure interoperability by converting EHRs into Fast Healthcare Interoperability Resources (FHIR) or Health Level 7 (HL7) systems without compromising security. Performance evaluation across 200 consensus rounds highlights metrics such as smart contract execution time, average IPFS file upload time, and reputation score behavior of validators. A comprehensive security analysis simulates Sybil attack scenarios, demonstrating the system's resilience through reputation-based validator selection. By integrating these factors, MeDiStore offers a scalable, secure, privacy-preserving, and interoperable solution tailored for efficient EHR management in the healthcare domain.
Academic credential fraud threatens educational integrity, especially in developing countries like Bangladesh, where verification methods are primarily manual and inefficient. To address this challenge, we present ShikkhaChain, a blockchain-powered certificate management platform designed to securely issue, verify, and revoke academic credentials in a decentralized and tamper-proof manner. Built on Ethereum smart contracts and utilizing IPFS for off-chain storage, the platform offers a transparent, scalable solution accessible through a React-based DApp with MetaMask integration. ShikkhaChain enables role-based access for governments, regulators, institutions, and public verifiers, allowing QR-based validation and on-chain revocation tracking. Our prototype demonstrates enhanced trust, reduced verification time, and improved international credibility for Bangladeshi degrees, promoting a more reliable academic and employment ecosystem.
The exponential growth in digital healthcare infrastructure has resulted in an overwhelming increase in sensitive medical data generation. However, traditional centralized Electronic Medical Records (EMR) systems continue to face critical security and privacy challenges. These include single points of failure, limited interoperability, data tampering, and unauthorized access. This paper introduces a robust and scalable blockchain-based framework for secure EMR management. Leveraging Ethereum blockchain, IPFS decentralized storage, and smart contracts, the framework ensures tamper-proof data logging and fine-grained access control. The system stores encrypted patient health records on IPFS and logs the corresponding content identifier (CID) on the Ethereum blockchain, eliminating the risk of data exposure. The architecture is designed for future compatibility with Mobile Edge Computing (MEC), allowing for faster data processing closer to the point of care. By offering immutable audit trails, decentralized access governance, and high availability, the proposed framework ensures transparency, security, and data ownership for all healthcare stakeholders.
The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection.
We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.
Hakam Dzakwan Diash, Vannesa Nathania, Mohammad Idhom, Trimono Trimono
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Gossip algorithms are pivotal in the dissemination of information within decentralized systems. Consequently, numerous gossip libraries have been developed and widely utilized especially in blockchain protocols for the propagation of blocks and transactions. A well-established library is libp2p, which provides two gossip algorithms: floodsub and gossipsub. These algorithms enable the delivery of published messages to a set of peers. In this work we aim to enhance the performance and reliability of libp2p by introducing OPTIMUMP2P, a novel gossip algorithm that leverages the capabilities of Random Linear Network Coding (RLNC) to expedite the dissemination of information in a peer-to-peer (P2P) network while ensuring reliable delivery, even in the presence of malicious actors capable of corrupting the transmitted data. Preliminary research from the Ethereum Foundation has demonstrated the use of RLNC in the significant improvement in the block propagation time [14]. Here we present extensive evaluation results both in simulation and real-world environments that demonstrate the performance gains of OPTIMUMP2P over the Gossipsub protocol.
Sure Mamatha, Laxmiprasanna Ambati, P. Vishala, Mamatha Gadde
Blockfund leverages blockchain technology to make philanthropy more accountable and transparent in a world where people’s faith in it is called into question. Trust, integrity, and data security are the three main concerns for this generation of service providers. We see blockchain technology being used to secure gifts and inventions in the future. Prior to the introduction of blockchain, the financial system faced numerous difficulties. There are concerns over their impact because they are sometimes imperceptible and unseen. Security issues have also been brought up because cryptocurrencies alter numerous financial institutions, and data transfer techniques in blockchain deployments are subject to fraud and abuse. For safe financial transactions, it makes use of an interface and a cryptocurrency wallet similar to MetaMask. All transactions become straightforward, safe, and transparent as a result. Through astute communication, transparency is increased by automating the distribution of money according to predetermined standards. Donors will be able to trace their contributions and observe the results of their kindness thanks to BlockFund’s comprehensive donation reporting. The establishment of this Intelligent Alliance is an example of global philanthropy for successful change and societal advancement. Major Findings: BlockFund transforms crowdfunding through blockchain, ensuring transparency and security via Ethereum smart contracts that automate payments and remove intermediaries. By integrating MetaMask and leveraging AI/IoT, it enables global, tamper-proof donations while reducing costs and enhancing donor trust through real-time tracking and decentralized governance.
Kundan Mukhia, SR Luwang, Md. Nurujjaman, Tanujit Chakraborty · 6 authors
Scaling laws offer a powerful lens to understand complex transactional behaviors in decentralized systems. This study reveals distinctive statistical signatures in the transactional dynamics of ERC20 tokens on the Ethereum blockchain by examining over 44 million token transfers between July 2017 and March 2018 (9-month period). Transactions are categorized into four types: EOA--EOA, EOA--SC, SC-EOA, and SC-SC based on whether the interacting addresses are Externally Owned Accounts (EOAs) or Smart Contracts (SCs), and analyzed across three equal periods (each of 3 months). To identify universal statistical patterns, we investigate the presence of two canonical scaling laws: power law distributions and temporal Taylor's law (TL). EOA-driven transactions exhibit consistent statistical behavior, including a near-linear relationship between trade volume and unique partners with stable power law exponents ($γ\approx 2.3$), and adherence to TL with scaling coefficients ($β\approx 2.3$). In contrast, interactions involving SCs, especially SC-SC, exhibit sublinear scaling, unstable power-law exponents, and significantly fluctuating Taylor coefficients (variation in $β$ to be $Δβ= 0.51$). Moreover, SC-driven activity displays heavier-tailed distributions ($γ< 2$), indicating bursty and algorithm-driven activity. These findings reveal the characteristic differences between human-controlled and automated transaction behaviors in blockchain ecosystems. By uncovering universal scaling behaviors through the integration of complex systems theory and blockchain data analytics, this work provides a principled framework for understanding the underlying mechanisms of decentralized financial systems.
Blockchain technology, underpinned by distributed ledger systems, has evolved from a novel innovation into a transformative and integral component of enterprise digitization across industries. Since its inception with Bitcoin in 2008, blockchain has expanded beyond cryptocurrencies, with applications in operations management (OM) growing rapidly across industries. Despite its promise, however, the integration of blockchain into OM is not without challenges. Scholars have identified significant barriers to successful implementation, ranging from technological and organizational hurdles to regulatory complexities (Chod et al. 2020; Hanisch et al. 2025; Lin et al. 2022; Lumineau et al. 2021; Sodhi et al. 2022; Zhan et al. 2025). This Special Issue on Operational Perspectives on Blockchain Applications presents cutting-edge research that explores blockchain's opportunities, challenges, and implications for OM. The articles in this issue provide a diverse and empirically grounded examination of blockchain applications across industries and operational contexts. We will discuss each contribution in turn. However, prior to that, it is useful to dig into the operational nuances, opportunities, and challenges presented by the focal context. Our editorial discussion opens accordingly, outlining the technological, organizational, and regulatory challenges while identifying the conditions under which blockchain can deliver value. We also touch on the broader societal implications of blockchain, addressing its political, economic, social, environmental, and legal dimensions before describing how each of the papers in the special issue contributes to understanding, critical to operations management. Finally, our editorial discussion concludes by charting a research agenda, highlighting key questions and interdisciplinary approaches needed to advance both theoretical and practical understanding of blockchain in OM. Working processes need to be discovered, described, and understood before they can be improved, controlled, and prescribed. Quite a bit of work is needed merely to describe some of the important activities, practices, processes, and operating systems utilized in diverse organizations. Only then can we begin to sink our teeth into developing better theories about how best to manage them. For this purpose, Ilk et al. (2021) conceptualize the Bitcoin blockchain (and other mainstream permissionless blockchains) as a two-side dataspace market, where users demand a certain amount of dataspace in a future block to store their transactions, and miners compete to produce such dataspace by creating new blocks. To facilitate this market in a decentralized manner—that is, with no centralized party absorbing demand and controlling supply—users attach a transaction fee (which is higher for users with a higher waiting cost) that becomes one of the miners' sources of revenue. With the increasing popularity1 of Bitcoin and Ethereum, demand frequently exceeds supply, creating contemporaneous system congestions. The congested service pricing literature, which dates back to the management of highway tolls (Naor 1969) and electric power supply (Viswanathan and Edison 1989) and extends in modern days to subscription pricing of cloud services (Cachon and Feldman 2011) and surge pricing of gig economy platforms (Cachon et al. 2017), yields a generalized conclusion. Specifically, “offering multiple service grades that each render a different delay distribution at a different price” improves both perceived customer satisfaction and service provider profit (Van Mieghem 2000, 1249). Permissionless blockchains, as congested service systems, are no exception to this rule. Although no centralized party (i.e., firm or platform) sets the priority price menu, users bid transaction fees to differentiate the service grades (i.e., transaction confirmation speeds) they desire. More details on the process view of permissionless blockchain transactions can be found in Shang et al. (2023, 106–108). Although early Ethereum-based smart contract applications were rarely associated with OM or any other real-world assets, their ingenuity inspired a whole class of permissioned blockchains (also referred to as private or consortium chains), in which only an authorized group of users can participate, setting the stage for enterprise applications (Fan et al. 2024; Pun et al. 2021). While blockchain offers considerable potential, its successful implementation is hindered by technological, organizational, and regulatory barriers. Below, we highlight seven of the most critical challenges to blockchain implementation discussed in the press and in the literature. Low throughput and high transaction fees. The primary reason that mainstream cryptocurrency systems cannot be used for day-to-day payment is their throughput limits: 3 per second for Bitcoin and 13 per second for Ethereum.2 This limitation is in sharp contrast with the processing capacity of established financial systems like Visa, which is capable of handling approximately 5000 transactions per second (Malik et al. 2022). Such scalability limits are largely inevitable for permissionless blockchains that aim to ensure decentralization and security, widely known as the “blockchain trilemma” in the industry.3 The throughput limit results in a frequently congested service system with transaction fee spikes (Ilk et al. 2021; Shang et al. 2023), which has been 2.87 USD per transaction for Bitcoin in 2020. This hinders the economic viability of small value transactions even in situations where network latency is less of a concern (e.g., users with high waiting tolerance). Meanwhile, permissioned blockchains typically do not face throughput limits, as dataspace suppliers are usually the blockchain owners and hence do not have to be incentivized via instruments such as transaction fees. However, due to the lack of public visibility and the corporate ownership of these blockchains, this solution is unlikely to be suitable for all applications. Algorithm fairness. Advocates of permissionless blockchains often highlight their morally significant goal of improving access to money transfer services for unbanked and underbanked populations (Andreasson 2022). Importantly, much of the wealth on permissionless blockchains is created through mining/staking revenue—that is, through participation on the supply side—and small users typically cannot meet the entrance threshold for this revenue stream. Further, while large senders can develop sophisticated algorithms to estimate the desired transaction fee more accurately, small senders typically rely on the free-to-use fee recommendation tools crypto wallets provide. Encouragingly, this disparity is somewhat alleviated by new transaction fee mechanism designs (Zhao, Wu, et al. 2025). Decentralization–efficiency tradeoff. The management of a cryptocurrency system is typically maintained by a decentralized autonomous organization (DAO). A DAO's daily operational tasks include the development of, voting on, and execution of crowdsourced proposals (Zhao et al. 2022). Yet, not all project decisions are strategic enough to warrant crowdsourcing of ideas from stakeholders, and the inefficiency of doing so affects operational agility and the quality of service provided by the DAO. Further, while decentralization can improve service levels for users and providers, it reduces profits for founders, reflecting a broader tension between decentralization and efficiency (Gan et al. 2023). Governance frictions are compounded by token-weighted voting, where those holding more tokens have greater influence, creating a mismatch between token ownership and subject expertise (Benhaim et al. 2023, 2025; Tsoukalas and Falk 2020). Cross-chain interoperability. A successful blockchain application often requires coordination of activities across multiple chains. This is especially true for enterprise applications, where material flow needs to be traced on a permissioned blockchain (for obvious business confidentiality reasons) and payment of goods should preferably happen on a permissionless blockchain. In general, the lack of universal standards creates a fragmented landscape in which disparate blockchain platforms are developed in isolation. This technical challenge of interoperability is further complicated by the need to integrate blockchain with legacy systems, which typically lack the flexibility to accommodate cryptographic protocols and distributed data synchronization (Babich and Hilary 2019). Standardization of input data. Many of the cargo tracking and supply chain traceability blockchain applications assume the existence of a data on-ramp that is accessible to and standardized across participants. This is far from reality. As Fan et al. (2024, 3) put it, “a small supplier, say, in India or China, is unlikely to have the resources or expertise to set up an arrangement to access blockchain.” Even if such access is set up by a large participant of the permissioned blockchain, such as a superstore retailer, the input data from thousands of small suppliers across the world might not be properly digitized and standardized. Both the invasive and non-invasive approaches to bridging the physical–digital interface in blockchain applications have merits and drawbacks (Klöckner et al. 2023). Buy-in from partner organizations. Lin et al. (2022) highlight buy-in from partners along with information complexity as two important drivers that determine the success of blockchain pilots in real life. They stress the importance of reducing information complexity as well as increasing buy-in among supply chain partners. Critically, the cost and hassle of implementation are borne by all organizations that the cargo passes through, including port authorities, customs agencies, shipment forwarders, trucking companies, and so on. Some of these organizations lack the basic incentive to even digitize their paperwork, let alone upload information onto a blockchain owned by another company. Regulatory uncertainty. Regulatory challenges present another significant barrier to blockchain adoption in OM. The regulatory framework for blockchain is still in a nascent stage, with many jurisdictions lacking clear guidelines regarding its use, especially in non-financial contexts such as OM (Wagner et al. 2025). The cross-border nature of many supply chains makes it even more challenging to reconcile diverse regulatory environments; thereby complicating large-scale implementations (Wamba and Queiroz 2020). In summary, blockchain presents a range of unique characteristics, implementation challenges, and potential transformative impacts. Figure 1 captures many of these, as well as presenting new opportunities to apply common theoretical lenses used by researchers to understand this new technology, including Transaction Cost Economics (TCE), Principal Agent Theory (PAT), and Resource-Based View (RBV). These features are pushing the OM community to consider additional theoretical arguments regarding blockchain-related operational dynamics so as to more comprehensively understand, anticipate, and ultimately contribute to practice and scholarship in this domain. More specifically, traditional theoretical frameworks commonly applied in OM, such as transaction cost economics, principal–agent theory, and the resource-based view, have proven effective for analyzing centralized systems where information is controlled and trust is built through well-established interorganizational relationships. However, blockchain disrupts these conventional relationships by enabling peer-to-peer interactions governed not by a central authority but by cryptographic mechanisms and consensus protocols. For instance, the immutability of recorded transactions and the inherent decentralization of blockchain networks modify the traditional calculus of trust and coordination costs. These features create “trustless” environments where the need for intermediaries is significantly reduced. This shift calls into question the applicability of many preexisting theoretical models that assume reliance on centralized control and interpersonal trust (Lumineau et al. 2023). Given these fundamental differences, one promising direction for future research is to expand network theory and social capital theory in OM by integrating the notion of distributed trust. Whereas social capital theory has been used to explain performance improvements arising from strengthened interorganizational relationships (Saberi et al. 2019), blockchain technology challenges these premises by redistributing trust across the network without necessarily relying on strong personal or organizational ties (Lumineau et al. 2023). Similarly, although transaction cost theory provides insight into how blockchain can lower the costs of verification and contracting by obviating the need for costly intermediaries, the theory does not fully account for the dynamic interplays that arise when trust is engineered digitally and contractual obligations are embedded in smart contracts (Halaburda et al. 2024). As Babich and Hilary (2019) note, new theoretical models need to capture not only the cost-saving benefits of disintermediation but also the potential trade-offs in terms of data insecurity and operational inflexibility. There is also a growing recognition that hybrid frameworks, which merge elements of institutional theory and network governance with emerging blockchain paradigms, may be necessary to understand new organizational forms like DAOs (Zhao et al. 2022). The need for novel theoretical frameworks is particularly critical when considering the impact of blockchain on various stakeholders within the OM ecosystem. Traditional models generally emphasize dyadic relationships between buyers and suppliers, but blockchain enables multi-stakeholder environments in which data transparency, provenance, and auditability permeate complex, global supply networks. For example, Chod et al. (2020) show how blockchain can improve financing in agricultural supply chains by enabling farmers to use harvest inventory as loan collateral. Using multi-signature setups tied to an immutable blockchain, transactions require confirmation from both humans (e.g., lenders or warehouse operators) and automated systems (e.g., IoT sensors). This approach allows for real-time verification of collateral, reduces information asymmetry, and unlocks capital, particularly in settings prone to fraud. Together, the articles in this Special Issue make a multifaceted contribution to OM, demonstrating the impact of blockchain technology in various operational forms on strategic decision-making, worker participation, competitive and network dynamics, and intellectual property protection across different sectors. These studies use robust empirical methods and diverse theoretical frameworks to offer novel insights into the role of blockchain in OM. Some of the studies use qualitative methods for developing theory concerning conditions for successful and failed blockchain adoption. Zhan et al. (2025) develop theory through an inductive, multi-case research design revealing the influence of founder power on blockchain adoption. Meanwhile, Hanisch et al. (2025) use an in-depth, longitudinal case study to explore the centralization–decentralization paradox of a group of studies or designs at the of or a and et al. 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Evgeniya Ishchukova, Sergei Petrenko, A. Petrenko, Konstantin Gnidko · 5 authors
Today, blockchain technologies are a separate, rapidly developing area. With rapid development, they open up a number of scientific problems. One of these problems is the problem of reliability, which is primarily associated with the use of cryptographic primitives. The threat of the emergence of quantum computers is now widely discussed, in connection with which the direction of post-quantum cryptography is actively developing. Nevertheless, the most popular blockchain platforms (such as Bitcoin and Ethereum) use asymmetric cryptography based on elliptic curves. Here, cryptographic primitives for blockchain systems are divided into four groups according to their functionality: keyless, single-key, dual-key, and hybrid. The main attention in the work is paid to the most significant cryptographic primitives for blockchain systems: keyless and single-key. This manuscript discusses possible scenarios in which, during practical implementation, the mathematical foundations embedded in the algorithms for generating a digital signature and encrypting data using algorithms based on elliptic curves are violated. In this case, vulnerabilities arise that can lead to the compromise of a private key or a substitution of a digital signature. We consider cases of vulnerabilities in a blockchain system due to incorrect use of a cryptographic primitive, describe the problem, formulate the problem statement, and assess its complexity for each case. For each case, strict calculations of the maximum computational costs are given when the conditions of the case under consideration are met. Among other things, we present a new version of the encryption algorithm for data stored in blockchain systems or transmitted between blockchain systems using elliptic curves. This algorithm is not the main blockchain algorithm and is not included in the core of modern blockchain systems. This algorithm allows the use of the same keys that system users have in order to store sensitive user data in an open blockchain database in encrypted form. At the same time, possible vulnerabilities that may arise from incorrect implementation of this algorithm are considered. The scenarios formulated in the article can be used to test the reliability of both newly created blockchain platforms and to study long-existing ones.
Efficient smart contract implementation affects gas fees required for deployment and invocation, contributing to the usability and sustainability of blockchain applications that rely on smart contracts. Optimizing smart contract codes is crucial to curb the continued rise of costs related to deploying and invocating smart contracts. This work proposes an extended version of static smart contract optimizer to reduce unnecessary gas fees caused by inefficient smart contract code implementation. Thirty open-licensed Ethereum smart contract codes in the Solidity programming language are included for optimization using the proposed static optimizer. The results show a decrease of 11,447 gas for deployment and 25 for invocation. Additional optimization using the Solidity compiler optimizer reveals a further gas reduction of 9,331 for deployment. Although there was a slight gas increase of 23 during invocation. These findings demonstrate the contribution of the proposed static optimizer in optimizing code implementation for Solidity smart contracts in terms of deployment and invocation. In addition to the gas reductions, the functionalities of the optimized smart contracts remain the same.
The transformation of digital payment systems through blockchain technology has brought new challenges and opportunities in the development of secure, efficient, and decentralized crypto tokens. One emerging approach is the use of smart contract-based tokens, such as ERC-20 tokens running on the Ethereum network. This research proposes the design and implementation of the SDSPay (SDS) token as a prototype Ethereum-based ERC-20 token, with a modern smart contract approach to meet the needs of a more efficient and secure digital payment system. The SDS token adopts the basic ERC-20 standard but is equipped with advanced features that enhance functionality and security, such as role-based access control (RBAC) to regulate access and control over transactions, pauseable transactions to pause transactions if necessary, and compatibility with EIP-2612 permits that enable more efficient transaction authorization in terms of gas. These features are designed to improve the efficiency and security of transactions on blockchain networks, thus enabling the use of tokens in a more reliable digital payment system. The SDS token prototype was tested on the Sepolia Testnet using Remix IDE and MetaMask to develop and manage smart contracts. Additionally, a static security audit was conducted using Slither Analyzer to detect potential vulnerabilities. The test results showed that the SDS token was successfully deployed and performed well, with an average transaction time of 10–12 seconds and stable gas fees. The Slither audit also found no significant vulnerabilities, indicating that the smart contract structure adheres to security best practices. This study confirms that the development of standardized smart contract-based tokens can be carried out using an efficient, reliable, and replicable methodology for other applications in future blockchain-based payment systems. This implementation of the SDSPay (SDS) token can serve as a foundation for designing secure and efficient digital payment systems, paving the way for the broader development of blockchain technology.
Nicknames for Group Signatures (NGS) is a new signature scheme that extends Group Signatures (GS) with Signatures with Flexible Public Keys (SFPK). Via GS, each member of a group can sign messages on behalf of the group without revealing his identity, except to a designated auditor. Via SFPK, anyone can create new identities for a particular user, enabling anonymous transfers with only the intended recipient able to trace these new identities. To prevent the potential abuses that this anonymity brings, NGS integrates flexible public keys into the GS framework to support auditable transfers. In addition to introducing NGS, we describe its security model and provide a mathematical construction proved secure in the Random Oracle Model. As a practical NGS use case, we build NickHat, a blockchain-based token-exchange prototype system on top of Ethereum.
This study constructs a machine learning-driven multi-factor model for Ethereum quantitative trading, combining traditional technical indicators (RSI, MACD), on-chain metrics (gas usage, active addresses), and X platform social sentiment to predict short-term returns. Backtesting from Q4 2021 to Q3 2024, using online learning and genetic algorithms for dynamic factor updates, yields a 97% annualized return, a Sharpe ratio of 2.5, and an information ratio of 1.2, outperforming Ethereum's raw returns. Simulated trading in Q4 2024 (bull market) achieves a 33% quarterly return with an 18% maximum drawdown, while Q1 2025 (bear market) records a -10% quarterly return with a 12% drawdown, confirming robustness. Technical and sentiment factors drive performance, though a 22% maximum drawdown in backtesting highlights volatility risks. An optimal Z-score threshold (±1.0) and 4-hour trading frequency balance profitability and costs. Future enhancements include high-frequency mainnet data integration and advanced risk management to strengthen model resilience in Ethereum's volatile market.
The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms.
Annes Maria Pangidoan, Putu Wira Buana, Fajar Purnama
Data, including digital and physical documents, is a valuable asset often vulnerable to forgery, theft, and reliance on centralized servers, which are costly and prone to failure. This study develops a prototype of a decentralized document storage application by combining blockchain and the InterPlanetary File System (IPFS). The system is designed as a web-based decentralized application (DApp), integrating Ethereum smart contracts to immutably record document metadata and access history, while the actual files are stored in IPFS and identified using unique Content Identifiers (CIDs). User interactions are facilitated through MetaMask for authentication and transaction approval. The system is developed using the Waterfall methodology. Functional testing is conducted through unit tests using Ganache as a local Ethereum blockchain, and the smart contract is also deployed to the Sepolia Ethereum testnet. The results show that the system successfully stores documents via IPFS and records metadata and access activities transparently on the blockchain. Access and download tracking features enhance document accountability. This solution provides a secure, efficient, and transparent alternative to centralized document storage and contributes to the advancement of distributed digital archiving systems.