Blockchain Papers

Haotian Xie, Yung Po Tsang

No abstract is available for this record.

Naiema Shirafkan, Hamed Rajabzadeh, Marcus Wiens

The increasing integration of blockchain technology in supply chains has brought about significant challenges due to the volatility of cryptocurrencies, as it has become an essential aspect of customers’ risk considerations. This study addresses the problem of managing supply chain operations amid such volatility, focusing specifically on pricing, advertising, manufacturer subsidy, and cybersecurity strategies within a manufacturer-retailer framework involving two cryptocurrency-based retailers that have higher market capitalisation compared to others: Ethereum and Bitcoin. The proposed solution employs game theory – a simultaneous game and two Stackelberg games with either retailer as the leader – to identify optimal strategies based on the corresponding parameter values. Accordingly, the study uniquely delivers blockchain-related risks by applying game theory to analyze the decision variables, providing insights into competitive pricing adjustments and leadership strategies for the cryptocurrency-based retailers under varying volatility levels. Results demonstrate that retailer pricing strategies must adapt to changes in wholesale prices and to the difference in cryptocurrency volatility. It also identifies crucial subsidy levels for manufacturers and optimal strategies for retailers under different volatility conditions to sustain profitability and demand.

Raji Ramakrishnan Nair, Punam Rattan, Mukesh Kumar, Vivek Bhardwaj

The pandemic outbreak has revealed significant flaws in the complex and highly fragmented Healthcare Supply Chain’s (HSC’s). However, two major issues persist in the HSCs, leading to inefficiencies: transparency in vaccine distribution and accuracy in demand forecasting. The recent pandemic has highlighted and intensified existing vulnerabilities in HSC’s, leading to the effective utilization of digital technologies to manage them. This research proposes a novel framework that merges Blockchain (BC) and Machine Learning (ML) to bolster the HSCs amidst pandemics, by developing a framework named the Predictive BlockVax Distribution Network (PBDN) model. The proposed PBDN model utilizes BC for securing transactions and Long Short-Term Memory (LSTM), for precise demand prediction. Leveraging Hyperledger Besu, which represents an Ethereum client that is accessible for public use, the PBDN framework ensures BC’s privacy, scalability, and efficient network operations, while LSTM’s advanced forecasting outperforms traditional models and Deep Learning (DL) techniques. This integration showcases a significant leap in managing vaccine distribution and enhancing system resilience, fairness, and transparency. The proposed PBDN model illustrates the potential of BC and ML together to tackle pandemic-induced Supply Chains (SC’s) disruptions, providing a decentralized solution that supports autonomous, informed decision-making without third-party dependency. This approach not only addresses immediate challenges but also sets a precedent for future crisis response, emphasizing the need for robust, Transparent Supply Chain’s (TSC’s).

Mohammad Akbarzadeh Sarabi, Ata Allah Taleizadeh, Arijit Bhattacharya

With the increasing emphasis on environmental sustainability, both governments and consumers are more concerned than ever about the greenness of products. In this complex landscape, Supply Chains (SCs) face challenges in building trust and avoiding greenwashing accusations. Blockchain technology offers a promising solution by ensuring transparency and circularity within SCs, particularly in identifying customers for product recycling. This study pioneers the exploration of consumers' distrust in pricing and product greenness, alongside the impact of carbon policies (taxes and subsidies) within a closed-loop supply chain (CLSC). Using classical Stackelberg game theory, we develop two models that identify equilibrium decisions for SC members, focusing on pricing, green production investment, circularity, and blockchain adoption. Additionally, we propose an evolutionary game theory model to find the optimal government policies and identify the long-term behaviour of the CLSC and government in two heterogeneous populations. Our findings reveal that if the retailer's share of blockchain costs falls below a certain threshold, blockchain adoption becomes less profitable than exclusive investment in green production. A higher (lower) subsidy rate benefits (harms) the retailer but disadvantages (benefits) the collector. Blockchain adoption is generally more profitable for manufacturers and retailers, though less so for collectors, and it also drives greater investment in green production. While subsidies encourage blockchain adoption, they are not a sustainable long-term strategy for governments. Ultimately, the evolutionarily stable strategy for SCs involves a balanced investment in both green production and blockchain or green production alone, depending on market characteristics and cost-sharing structures.

Punav Anirudh Potluri, Nischal V Pattedar, Jayarama Krishna A., Nalini Sampath

Non-Fungible Token (NFT) is a new technology primarily engaged in blockchain, which is utilized to produce and exchange digital assets in fields like art, gaming, and real estate. The problem encountered in NFTs is the management and maintenance of ownership records. The suggested method addresses these problems with a dynamic allocation into shards. They are tiny subdivisions of blockchain transactions that hold NFTs according to the volume of transactions, metadata of tokens, and storage needs to meet the demand. The implementation is aimed at comparing how NFT transactions perform under static and dynamic sharding to conclude the most appropriate kind of technique for certain requirements. The findings of our experiments over different numbers of transactions indicate that static sharding performs better in minting or aggregating NFTs. However, dynamic sharding is superior in NFT operations such as Querying data and retrieving transaction information owing to quick access time and uniformly distributed transaction load.

Shuai Liu, Benedict Jun, Weijian Zhang, T.C.E. Cheng · 6 authors

No abstract is available for this record.

Yajing Wang, Jian Li, Shichao Zhu, Shouyang Wang · 5 authors

No abstract is available for this record.

Ashkan Emami, Mehdi Seifbarghy, Antragama Ewa Abbas, Wichai Chattinnawat · 5 authors

Supply chain operations have tended to become more complex, thus placing significant pressure on one of the most critical processes: supplier selection and order allocation (SSOA). This process involves a focal company selecting suppliers and allocating orders to obtain required materials. Achieving effective SSOA processes is challenged by (1) reliance on centralized governance and (2) ensuring effective contract management. While so called “smart contracts” could address these challenges, design knowledge about such technology — particularly in the SSOA context — is underexplored in the literature. In this paper we design a smart contract for SSOA in supply chains. We conducted a design science research study and developed three core artifacts: (1) a mathematical description of SSOA; (2) a system model of actor interactions; and (3) SSOA-relevant algorithms. Utilizing the Ethereum blockchain, we demonstrated and tested our smart contracts through scenario analysis. We found that our design is feasible and highly likely to address centralization and effectiveness challenges in SSOA. This paper contributes to the literature by demonstrating how smart contract design focusing on SSOA can further enhance blockchain-driven business models. In addition, we offer prescriptive knowledge on developing smart contracts for SSOA in supply chains.

Abhinay Mishra, Tanmoy Kundu, Rohit Kapoor, Mark Goh

No abstract is available for this record.

Dnyaneshwar Jivanrao Ghode, Vinod Yadav, Rakesh Jain, Gunjan Soni

Industries aims to have a paradigm shift in supply chains (SC) to provide transparency in the shared information for the economic and social benefits of the stakeholders in an SC. The revolution of Blockchain Technology (BT) allows all the parties in the network to share secured data among themselves. This paper aims to develop a framework to integrate an SC with BT for the exchange of physical products and secured information among the stakeholders. The framework has been implemented by developing a generic SC with BT using Python 3.8.1. The framework comprises a blockchain-based distributed ledger that shares transaction information among manufacturers, distributors, retailers, and customers. For each transaction, a hash code was generated using the SHA-256 algorithm, and the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm was used to verify the transactions. The quantity and rate of products have been checked through a smart contract. The influencing factors are inter-organizational trust, regulatory governance, data transparency, data immutability, interoperability, product type, social influence, and behavioural intention. This framework provides transparency in transactions between SC stakeholders and the provenance of products throughout the SC.

Fujiang Yuan, Bo Liang, Jie Gao

No abstract is available for this record.

M. S. Rahman, Md Sazzad Hossain, Md Khalilor Rahman, Md Rasibul Islam · 7 authors

Blockchain technology is increasingly redefining supply chain management paradigms with unprecedented levels of transparency, traceability, and trust in the USA. With increasingly complex supply networks worldwide, the integrity and real-time visibility of transactional information become vital for operational reliability and adherence. This study presents a data-driven examination of the ways distributed ledger technology (DLT), specifically blockchain, facilitates increased supply chain transparency across stakeholders through immutable record-keeping and verifiable sharing of data. The main goal of the current research was to create a synthesis of the secure, immutable nature of blockchain and the predictive and diagnostic power of machine learning (ML) to boost supply chain transparency. The dataset used in this work is formatted blockchain logs, extracted from a permissioned, distributed ledger system simulating a U.S.-based supply chain network. Every log entry stores transactional metadata, high-value data such as accurate timestamps of transactions, cryptographic verdicts, digital handovers between supply chain entities (suppliers, logistics providers, distributors), and route signatures, derived from geolocation-based smart contract activators. In the selection of suitable machine learning models, three classifiers that considered the multi-dimensionality of blockchain supply chain data were used. The training and validation approaches were tailored to maintain the models' robustness and generalizability. The dataset was divided into a 70/30 train-test split using stratified sampling to preserve the proportion of fraudulent versus non-fraudulent instances, guaranteeing that both subsets contained a balanced representation of the classes. By looking at the comparative bar plots of the performance of our models on our blockchain-based supply chain dataset, we observed that the Random Forest Classifier had a slightly greater accuracy and F1-score than the Logistic Regression and the XG-Boost Classifier. In the Food and Agriculture industry, supply chain analytics with blockchain technology can greatly improve traceability, specifically under United States Department of Agriculture (USDA) standards. At U.S. Customs and Border Protection (CBP) checkpoints and international borders, blockchain solutions bring significant advancements in verification speed and counterfeit prevention. By applying analytical tools against the recorded events and metadata, organizations in the USA not only track assets and events but also proactively discover potential risks, streamline processes, and gain a greater insight into their supply chain dynamics. Towards the future, some promising avenues of research open up with the combination of blockchain and machine learning. One such exciting area is the blending of smart contracts with automated responses. Lastly, federated learning among decentralized blockchain nodes is a pioneering line of research that might resolve the issues of sparsity and generalizability of the data and avoid the compromise of the decentralized nature of blockchain.

Hui Li, Devika Kannan, Qi Xu

No abstract is available for this record.

Haidi Zhou, Qiang Wang, Xiande Zhao

No abstract is available for this record.

Özgür Karaduman, Gülsena Gülhas

As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.

Xiongfei Zhao, Hou-Wan Long, Z Li, Jiangchuan Liu · 5 authors

The rapid growth of blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including sandwich attacks, liquidations, transaction replay etc. have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with sandwich attacks being particularly impactful. Additionally, the growing adoption of blockchain in traditional finance highlights the challenge of TID, wherein high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS) that integrates forking mechanisms with an Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of a Normalized Allocation Disparity Metric (NADM) that ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrated that the DTSS effectively mitigated BEV risks, enhanced transaction fairness, and significantly improved the security and transparency of DeFi ecosystems. • Distributed Transaction Sequencing Strategy (DTSS) was proposed address TOD, BEV, and TID issues. • DTSS adapts block size based on transaction attributes. • An optimization framework was introduced to determine optimal parameters for DTSS. • Experimental results show the superiority of DTSS in mitigating risks associated with BEV. • Results also show that DTSS can ensure a fair and transparent transaction ordering.

Roghayyeh Alizadeh, Mohammad Reza Akbari Jokar

No abstract is available for this record.

Gideon Adjorlolo, Zhiwei Tang, Gladys Wauk, Philip Adu Sarfo · 7 authors

Corruption in public procurement remains a challenge to good governance, especially in developing nations. Blockchain technology has been espoused as a new paradigm for achieving sustainable public procurement practices for effective service delivery and, by extension, promoting sustainable development. Given the potential of blockchain technology, its implementation has been slow in developing countries. Additionally, there is an inadequate decision support framework to prioritize corruption-prone stages of the public procurement cycle for strategic blockchain integration at the most critical corruption-prone stages of the public procurement cycle given the scarce resources available in developing countries. Therefore, we employed a matured theory that is the principal-agent theory to identify key agency problems related to public procurement in developing countries. An interview with 25 experts and a thorough review of Ghana’s Auditor General produced seven public procurement cycle stages. Further, a survey was designed for experts and stakeholders to prioritize the identified procurement stages under the agency problems through the Analytic Hierarchy Process (AHP). Our results revealed that tender evaluation was the most critical stage susceptible to corruption, followed by contract management and procurement planning in the public procurement stages. Additionally, for the relative importance of the criteria, information asymmetry was ranked first, followed by moral hazard, and then adverse selection. This study offers a targeted framework for blockchain deployment in public procurement from an African country perspective. The outcome of this study provides insights for policymakers and procurement practitioners to know the most critical stages of public procurement stages and leverage blockchain technology given the scarcity of resources in developing countries to aid sustainable public procurement. The proposed blockchain framework can enhance service delivery, citizens’ trust, and international donor confidence in partnership and funding for public procurement projects in developing countries.

Xiaoping Xu, Jiahao Chen, Shuai Liu, Yugang Yu · 5 authors

We consider a supply chain comprised of two competing manufacturers, where one is a blockchain-enabled disclosed quality information manufacturer (BP manufacturer), and the other is a manufacturer without blockchain support (OP manufacturer). The duopoly game, OP manufacturer-led Stackelberg game, and BP manufacturer-led Stackelberg game are considered. In addition, we also consider two types of consumers, namely expert consumers who exactly know blockchain and rookie consumers who have a limited knowledge of blockchain. The results demonstrate that (a) the BP manufacturer should serve both expert and rookie consumers if the percentage of expert consumers is sufficiently low; (b) the optimal blockchain-enabled disclosed quality information level increases with the blockchain ability and percentage of expert consumers; (c) blockchain adoption leads to higher profits for the OP manufacturer when the blockchain ability is high and this finding is robust under various power structures; and (d) the BP (OP) manufacturer-led Stackelberg game benefits the OP (BP) manufacturer best, and as the BP manufacturer’s decision-making grows, she needs to use blockchain to provide more detailed product quality information. Extending our work to several scenarios, we find that some of the results are robust while the others change.

Juan‐Juan Qin, FU Hui-ping, Ziping Wang, Xiaochen Lyu

This paper explores a low-carbon supply chain comprising a capital-constrained manufacturer and a retailer under cap-and-trade regulation. The manufacturer can obtain financing support for both production and carbon emission reduction through either Bank Financing (BF) mode or Mixed Financing (MF) modes. The incorporation of blockchain technology is posited to enhance the transparency of uncertain emission reduction data within the supply chain, allowing banks to adjust interest rates accordingly via smart contracts. Four modes are analyzed: BF without blockchain technology, BF with blockchain technology, MF without blockchain technology, and MF with blockchain technology. Under MF, the retailer provides financing support for production cost and the bank provides financing support for carbon emission reduction. The findings indicate that the utilization of blockchain technology improves supply chain profits when its cost is moderate. Without blockchain, BF mode will be chosen when faced with intermediate bank interest rate. Conversely, when the manufacturer employs blockchain technology, the strategic choices of both the manufacturer and retailer regarding the BF and MF modes are independent of the associated cost. Additionally, BF mode becomes more attractive when the trigger point for emission reduction output is moderate and the cost of adopting blockchain technology is minimal.

SeyyedHossein Barati

This study investigates the impact of blockchain technology on demand forecasting and the associated costs in supply chain management using system dynamics modeling. With the increasing complexity and challenges of demand prediction in modern supply chains, the potential of blockchain to enhance the accuracy of demand forecasting and reduce related costs has become a critical area of interest. The research employs system dynamics to model the interrelationships between key factors such as blockchain adoption, data accuracy, transaction transparency, and supply chain performance. The findings highlight that blockchain integration significantly improves demand forecasting accuracy by ensuring real-time data sharing, reducing information asymmetry, and enhancing decision-making processes. Moreover, the simulation results show that blockchain adoption can reduce forecasting errors, thereby lowering operational costs. This research contributes to the existing literature by demonstrating the practical benefits of blockchain in supply chain operations, offering valuable insights for practitioners and researchers. It also provides a foundation for future studies to explore the scalability of blockchain in different sectors and its broader applications in optimizing supply chain functions.

Elmira Mohammadhosseini Fadafan, Rudolf Vetschera

Abstract Contractual relationships between buyers and sellers can be disrupted by unanticipated shocks to attributes of the exchanged good or service; in manufacturing, such relationships often involve one buyer of components or intermediate goods and many potential sellers. We study the buyer’s selection of a seller given the option to initially agree on a smart contract which, in the advent of such unanticipated shocks, automatically adjusts the exchange price. Our benchmark analysis focuses on the case where a positive potential shock raises attribute values for both contracting parties, implying that the seller benefits more than the buyer from executing the original contract at the agreed exchange price. Taking the perspective of the buyer, we vary the shock and utility parameters to arrive at conclusions regarding the determinants of smart contract dominance in random buyer-seller matches. One of the key issues analyzed in this paper is the possibility that after the potential shock, another seller might be better and a buyer who anticipates this might be led to select a different seller. For the case of the Nash bargaining-solution, we further investigate the impact of increasing the number of utility-generating attributes on these switch rates.

Mingli Yuan, Ruozhen Qiu, Minghe Sun, Songshi Shao · 6 authors

The increasing concerns over product safety and adulteration risks have heightened the need for traceability and transparency in supply chains. Blockchain technology provides a potential solution, but its adoption involves costs and strategic decisions about information disclosure. This study investigates a dual-channel supply chain consisting of a supplier and a retailer under four blockchain technology adoption scenarios and two market power structures, where the supplier uses a price-matching policy in the online channel. Stackelberg game models are formulated, and backward induction is used to derive equilibrium decisions on retail price, wholesale price, and amount of blockchain-linked information. The supplier and retailer equilibrium decisions and profits are analyzed and compared across different blockchain technology adoption scenarios and market power structures. Numerical analyses are used to verify the main theoretical results and examine the influences of the parameter values on the equilibrium results. The findings reveal the supplier’s strong incentive to adopt blockchain technology and the retailer’s decision complexity influenced by factors such as consumer shopping convenience, consumer preferences, and retailer competitive position. Additionally, the findings underscore the supplier profitability potential through the traditional retail channel and the value of the price-matching policy to optimise profits for both the supply chain members.

Bruna Alves Lima, Gilberto Miller Devós Ganga, Moacir Godinho Filho, Luis Antonio de Santa-Eulália · 7 authors

Purpose Using the resource-based view (RBV), our study aims to provide theoretical and empirical insights into blockchain capabilities’ (BCs) compounded and sequential effects on supply chain competitive advantages (CA). Design/methodology/approach We combined a systematic literature review and an expert interview. Interpretive Structural Modelling and a Matrix of Cross-Impact Multiplications Applied to Classification were used to determine the relationship between the capabilities. Simple Additive Weighting assessed each capability’s relative importance and impact. Findings We reveal a sequential development path for BCs. Foundational capabilities, such as cybersecurity, provide immediate performance benefits, establishing a unique, valuable and inimitable resource. As firms progress to advanced capabilities, the compounded value of these capabilities generates a stronger, dynamic resource for sustained CA. Moreover, the study underscores the strategic importance of timing in adopting and developing BCs, as early adoption can secure a competitive edge difficult for later entrants to replicate. Practical implications Our proposed framework guides managers in incorporating blockchain technology into supply chain management (SCM) processes once it demonstrates that firms can enhance their CA by prioritizing the technical basics BC, leveraging the informational capabilities in level two and enabling effective problem-solving through level three. Our framework also shows that a learning process occurs as BCs are used and their results are explored. Originality/value Our study extends the RBV by demonstrating BCs’ cumulative and interdependent nature in SCM. It emphasizes the synergistic interactions between these capabilities, which collectively enhance CA.