Marc Hübschke, Marius Gros, Benedikt Latos, Elmar Holschbach · 5 authors
Purpose Blockchain technology is widely discussed as an enabler of transparency, efficiency and trust in supply chain management (SCM). However, empirical evidence on which blockchain-related success dimensions translate into value perceptions remains limited. This study aims to examine how the perceived relevance of blockchain success dimensions relates to realized benefits and whether these benefits contribute to overall perceived blockchain value. Design/methodology/approach A quantitative survey of 41 companies with blockchain experience in SCM is conducted. Success dimensions are prioritized using best–worst scaling (MaxDiff). Relationships between perceived relevance, dimension-specific benefits and overall perceived value are analyzed using partial least squares structural equation modeling (PLS-SEM). Exploratory analyses assess company characteristics. Findings Contrary to dominant expectations in academic and practitioner narratives, even highly prioritized blockchain success dimensions fail to translate into measurable firm-level value perceptions. While transparency and traceability are associated with significant dimension-specific benefits, these improvements do not produce statistically significant direct or indirect effects on overall perceived blockchain value. This suggests that localized operational gains alone may be insufficient to generate overall perceived value and indicates that blockchain benefits may depend on broader organizational and technological complements. Originality/value The study moves beyond identifying potential blockchain benefits by empirically differentiating which success dimensions matter and which do not. By combining MaxDiff with PLS-SEM, it offers a structured, mechanism-oriented framework for evaluating blockchain success and highlights boundary conditions for value realization in SCM.
With the standardization of the logistics market and advancements in innovation, trust issues arising from information asymmetry among supply chain participants have become increasingly prominent. This paper examines a blockchain-enabled collaborative regulatory system for logistics service supply chains involving the government, logistics enterprises, and the logistics market. Using evolutionary game theory, a three-party evolutionary game model is constructed and validated through system dynamics simulations to explore the impacts of various factors on the collaborative regulatory system. The results indicate that, during the system’s evolution, logistics enterprises stabilize first, followed by the government, with the logistics market converging the slowest. The added value of logistics services is identified as the core factor driving logistics enterprises to adopt blockchain, exhibiting a significantly stronger impact compared to regulatory benefits or improvements in quality and safety. While robust incentive policies can rapidly increase enterprises’ willingness to adopt blockchain technology, they concurrently weaken the government’s enthusiasm for regulation.
Background Scientific output on digital transformation in healthcare and pharmaceutical supply chains increased substantially after 2020, indicating growing research attention to resilient and digitally integrated logistics systems. However, the literature remains fragmented across technologies such as blockchain, artificial intelligence, Internet of Things, predictive analytics, cold chain monitoring and healthcare logistics optimization. Methods This study conducted a bibliometric analysis of scientific publications related to digital transformation and emerging technologies in healthcare and pharmaceutical supply chains. Data were retrieved from Scopus, PubMed and Web of Science databases following PRISMA 2020 screening principles. After duplicate removal and eligibility assessment, 83 peer-reviewed English-language journal articles published between 2015 and 2026 were included in the final analysis. Bibliometric mapping and thematic analysis were performed using VOSviewer and Bibliometrix/Biblioshiny. Results Within the analyzed corpus, the results showed a substantial increase in scientific publications after 2020, consistent with growing research attention to resilient and digitally integrated healthcare supply chains. Blockchain showed the highest visibility in keyword and citation-based analyses, particularly in relation to traceability, transparency and anti-counterfeit systems. Additional major research areas included artificial intelligence, predictive analytics, IoT-based cold chain monitoring and healthcare logistics optimization. Thematic analysis identified strong literature-based associations between digital technologies, supply chain resilience and pharmaceutical traceability systems. Conclusions Digital technologies are increasingly represented in research on healthcare and pharmaceutical supply chain transformation. The findings suggest that blockchain, AI and IoT technologies may support transparency, traceability and operational resilience. However, implementation barriers related to interoperability, infrastructure costs, data privacy and regulatory complexity remain significant challenges. These findings should be interpreted as bibliometric and thematic patterns within the analyzed English-language journal literature rather than direct evidence of technology implementation effectiveness.
To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation.
Supply chain finance (SCF) plays a pivotal role in maintaining liquidity and operational continuity across global value networks. However, systemic supply chain disruptions, macroeconomic volatility, and information asymmetry frequently expose SCF programs to severe friction and default risks. While digital transformation is widely touted as a catalyst for supply chain resilience, empirical evidence regarding the explicit mechanisms through which distinct digital transformation capabilities enhance Supply Chain Finance Resilience (SCFR) remains fragmented. Grounded in the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and Information Processing Theory (IPT), this study develops and tests an integrated framework evaluating the direct and indirect impacts of Artificial Intelligence Capability (AIC), Blockchain Capability (BC), and Data Analytics Capability (DAC) on SCFR, mediated by Digital Trust in SCF Platforms (DT).Using a computational research simulation methodology, a respondent-level dataset (N=500) representing supply chain, finance, operations, and IT decision-makers across international enterprises was algorithmically generated under a defensible latent-variable covariance structure. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap resamples was executed to evaluate the measurement and structural models. The structural analysis reveals that AIC (β=0.241,p<.001), BC (β=0.312,p<.001), and DAC (β=0.284,p<.001) significantly and positively drive Digital Trust in SCF Platforms, explaining 54.2% of its variance (R^2=0.542). Digital Trust, in turn, exerts a substantial direct effect on SCFR (β=0.385,p<.001). Furthermore, direct effects on SCFR were confirmed for DAC (β=0.218,p<.001) and AIC (β=0.152,p=.002), whereas the direct link from BC to SCFR was non-significant (β=0.071,p=.158). Formal mediation testing using percentile bootstrapping confirmed that Digital Trust fully mediates the relationship between Blockchain Capability and SCFR, while partially mediating the relationships for AIC and DAC. The overall structural model accounts for 58.6% of the variance in Supply Chain Finance Resilience (R^2=0.586,Q_"predict" ^2=0.412).This methodological prototype advances theoretical understanding by unpacking the granular capability configurations necessary to foster digital trust and financial resilience in supply networks. For practitioners and policymakers, the findings highlight that investing in blockchain technology yields minimal resilience benefits unless coupled with platform-wide digital trust mechanisms, whereas AI and analytics offer dual-pathway benefits across operational and relational domains.
Digital and intelligent fresh-product supply chains increasingly rely on third-party logistics providers (TPLs) to record and disclose transport-process information. However, the TPL bears data-collection and digital-governance costs while capturing only part of the market value created by credible disclosure. This study develops a supplier-led Stackelberg game for a supplier–TPL–retailer supply chain. Contractual terms are negotiated before operation. Conditional on the negotiated contract, the supplier sets the wholesale price, the TPL selects the disclosure level, and the retailer determines the retail price. We derive decentralized equilibria under blockchain and non-blockchain regimes and compare cost-sharing and joint cost-sharing/revenue-sharing contracts. The results show that cost-sharing increases the TPL’s optimal disclosure level, but disclosure upgrades occur through discrete threshold jumps. Blockchain adoption depends jointly on fixed implementation costs and reliability improvements, and cost-sharing alone may not ensure both adoption and high-level disclosure. Introducing revenue-sharing allows the TPL to internalize part of the demand-side value generated by credible disclosure, leading to a Pareto-improving coordination interval for all supply-chain members. The findings provide a mathematical basis for designing incentive-compatible contracts for blockchain-enabled disclosure in digital fresh product supply chains.
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
Healthcare supply chains face increasing challenges related to counterfeit products, fragmented information flows, limited traceability, and insufficient coordination among distributed stakeholders.Existing centralized and partially decentralized approaches still encounter difficulties in maintaining immutable records, real-time verification, and trusted operational transparency across the pharmaceutical distribution process.This study investigates a distributed medical supply chain framework that improves traceability, compliance control, and operational reliability in healthcare logistics.A blockchain-enabled architecture was developed by integrating dynamic quick response (QR)-based identification, customizable smart contracts, and a hybrid consensus mechanism combining Proof-of-Work (PoW) and Proof-of-Stake (PoS).The framework assigned a unique cryptographic identity to each medicine unit and supported end-to-end verification through blockchain-linked QR validation.Smart contracts were designed to automate ownership transfer, compliance checking, and counterfeit detection throughout the supply chain workflow.The framework was implemented and evaluated in a simulated distributed environment using pharmaceutical transaction scenarios.The experimental results showed that the proposed approach achieved average validation accuracy of approximately 98.1%, maintained transaction throughput between 150 and 320 transactions per second (TPS), and reduced consensus delay through adaptive PoW-PoS coordination.The system also demonstrated strong resistance to forgery attempts and stable operational performance across repeated validation experiments.The results indicate that integrating blockchain governance mechanisms with QR-enabled authentication can improve transparency, trust, and traceability in distributed healthcare supply chains.The proposed framework provides a scalable systems engineering solution for pharmaceutical logistics management and offers a practical foundation for compliance-oriented digital transformation in healthcare supply networks.
Abstract While blockchain technology is expected to reduce transaction costs, network congestion execution costs remain understudied in operations management. Analyzing 62,142 Ethereum transactions from seven firms (January–March 2026), we study "on-chain peak shaving"—scheduling transactions toward low-congestion windows to reduce gas fee exposure. Gas fees peak at 10 AM ET, driven by speculative-arbitrage rather than operational activity. Firm scheduling responses vary: only three firms transact off-peak, while four transact during peak windows due to deadlines or governance cycles. This heterogeneity is driven by transaction deferrability and gas intensity. We formalize these into an On-Chain Scheduling Matrix mapping firms to four regimes that predict fee savings and residual cost floors. Theoretically, we extend Transaction Cost Economics to account for time-varying execution costs from congestion externalities, classifying gas fees as execution costs in timing but maladaptation costs in origin. Ultimately, managing gas fees requires systematic operational planning akin to energy procurement.
Ethereum's EIP-1559 fee mechanism was designed under the assumption of homogeneous, myopic agents responding to a single congestion signal. We examine how this assumption interacts with the heterogeneous demand structure of real-world Ethereum users. Analyzing 62,142 confirmed transactions from seven operational firms across seven industries (January--March 2026), we document significant intraday gas-fee variation: fees peak at hour~12 UTC (7\,AM ET, $\hatβ_{12}=\$0.054$ above the U.S.\ evening baseline, $p<0.001$) and are associated with periods of elevated speculative-arbitrage activity. Operational firms exhibit heterogeneous scheduling responses moderated by transaction deferrability and gas intensity. Residual cost floors, i.e. the gap between observed expenditure and the counterfactual under perfect off-peak scheduling, range from 40.7\% to 92.5\% of actual expenditure, and persist even during the lowest-cost hours ($h\in\{20,21,22,23\}$ UTC, 3--6\,PM ET). We introduce an On-Chain Scheduling Matrix that maps firms to four scheduling regimes as a practical framework for managing gas-fee exposure under the current mechanism.
The retail and consumer packaged goods industries are at an inflection point; the autonomous, goal-oriented software agents are substituting the inflexible, analyst-reliant business decision cycles with closed-loop intelligence systems, which can perceive, reason, and act in real-time. The autonomy, proactivity, and constant learning of agentic AI redesign the pricing, trade promotion optimization, and supply chain coordination processes within complicated, multi-account business settings. Based on proven sources of empirical evidence in the literature on machine learning, multi-agent reinforcement learning, and supply chain optimization, the technical architecture of an agentic commercial system is discussed along five related dimensions: autonomous trade performance monitoring through perception-reasoning-action pipelines; cooperative multi-agent system design under the models of centralized training and decentralized execution; scenario simulation engine based on digital twin models; multi-objective trade promotion optimization with Pareto-front metaheuristic algorithms; and practical barriers of data infrastructure, model drift, organizational change management, and algorithmic governance. Bringing these capabilities together into a single agentic decision stack is a paradigm shift in the concept of commercial intelligence in retail and CPG, moving the operational center of gravity off retrospective dashboards and onto adaptive, constantly learning systems that coordinate the decisions on pricing, promotion, and supply.
The integration of blockchain technology into supply chain management represents a fundamental shift in how goods are tracked, verified, and transferred across global networks. This comprehensive research examines the implementation, impact, and challenges of distributed ledger technology across diverse supply chain ecosystems, with particular focus on transparency enhancement, counterfeit prevention, process efficiency, and stakeholder collaboration. Through a mixed-methods approach analyzing deployment data from 127 organizations across 18 industries over a four-year period, this study demonstrates that blockchain-enabled supply chains achieve an average improvement of 41.3% in traceability accuracy, reduce documentation processing times by 67.8%, and decrease disputes among supply chain partners by 52.4%. The research further reveals that smart contract implementations automate approximately 38.6% of routine supply chain transactions, reducing administrative costs by an average of 31.7% while minimizing human error in compliance verification. Counterfeit detection capabilities improve by 89.2% in pharmaceutical and luxury goods sectors through immutable product provenance tracking. However, the study identifies significant implementation barriers including interoperability challenges with legacy systems, scalability limitations during peak transaction periods, regulatory uncertainty across jurisdictions, and substantial upfront investment requirements averaging $2.3 million per enterprise implementation. The carbon footprint of certain consensus mechanisms, particularly proof-of-work, presents environmental concerns that necessitate alternative approaches for sustainable adoption. This paper proposes a phased implementation framework emphasizing pilot testing, stakeholder education, hybrid architecture models, and regulatory engagement to balance innovation with operational stability. The findings indicate that while blockchain technology offers transformative potential for supply chain transparency and efficiency, successful adoption requires strategic alignment with business objectives, collaborative ecosystem development, and measured progression from discrete applications to integrated systems. The research contributes to both academic understanding and practical implementation guidelines for distributed ledger technology in complex supply chain environments.
This study identifies a key distinction between digital and traditional supply chain finance (SCF): technology-empowered financial service providers (FSPs) are no longer completely uninformed parties in financing markets. By examining an underexplored digital SCF scenario involving upstream focal firms and financially constrained downstream dealers, we reveal the effective signals that enable downstream small and micro enterprises (SMEs) to access SCF, and explore how and when FSPs use different screens to refine financing decisions. Using a dataset from MYBank, a leading Chinese big tech lender with nationwide coverage and a dominant market share in digital SCF, we find that a dealer's procurement amount from focal firms is a strong signal, particularly when credit limits are higher. Meanwhile, FSPs utilize stakeholder cues and digital footprints within their ecosystems to refine financing decisions. Three key stakeholder groups—owners, focal firms, and peer dealers—and regional digital financial inclusion influence the effectiveness of procurement signals. The impact of distinct signaling-screening mechanisms varies across firm size, platform registration duration, and regional marketization. Robustness tests, including IV-2SLS regressions, the Heckman two-step method, stringent fixed effects and subsample analysis, validate our findings. The study enriches the SCF literature by revealing the role of supply chain data in credit creation and identifying various signaling-screening mechanisms. It also extends screening theory by illustrating the contingent nature of screens. The findings respond to China's recent policy initiatives on decentralized supply chain loans and provide guidance for SCF practitioners.
Enabled by blockchain progress, ICOs emerged as an alternative to traditional equity financing, with global investor reach, decentralized governance, and reduced transaction costs. However, the performance of ICOs under competitive product market conditions remains underexplored. This paper develops a game theoretic Cournot competition model to compare firms' financing and operational strategies under two benchmark scenarios, depending on whether product value is insensitive or sensitive to managerial effort. The analysis examines how cost structure, market volatility, product substitutability, and managerial risk attitude jointly affect financing choices and production decisions. To ensure research rigor, we further analyze a generalized model and conduct robustness checks by varying key parameters, confirming the stability of the equilibrium outcomes beyond the benchmark settings. The results show that optimal financing choices depend critically on market structure. Greater product substitutability intensifies competition and widens the utility gap between financing modes, strengthening the dominance of the more suitable strategy. Equity financing is more favorable for risk averse firms or those operating in low innovation, high substitutability industries such as manufacturing and utilities, due to its risk sharing and operational flexibility. In contrast, ICOs are better suited for innovation driven ventures such as DeFi and NFTs, which benefit from incentive alignment and reduced equity dilution. This study provides managerial insights into the strategic selection of financing mechanisms under competition and contributes to a deeper understanding of token financing in modern capital markets.
Pavel Ciaian, d’Artis Kancs, Miroslava Rajcaniova
Abstract Around three-quarters of Bitcoin transactions occur off-chain. While most empirical studies focus exclusively on on-chain transactions, only few papers analyse off-chain transactions. The empirical evidence of Bitcoin market considering both types of trading strategies remains limited. This paper is one of the first to present an empirical analysis of both on- and off-chain demand and supply-side factors and their short- and long-run relationship with the Bitcoin price. Employing the ARDL approach with daily data from 2019 to 2024, we demonstrate a differentiated contribution of on-chain and off-chain drivers to the Bitcoin price. In the long-run, off-chain demand pressures have a significant relationship with the Bitcoin price. In the short-run, both off-chain demand and supply factors are statistically significantly related to the Bitcoin price. The relationship between blockchain transactions and the Bitcoin price is also present, albeit likely operating through a different channel than off-chain trades. These findings confirm the dual nature of the Bitcoin market, in which price movements are related to both market fundamentals and speculative considerations captured by on- and off-chain trades, respectively.
Christian Finke, Tamino Marahrens, Matthias Schümann
As supply chains (SCs) face increasing pressure from ecological demands, ethical expectations, and global disruptions, Distributed Ledger Technology (DLT) is gaining attention as a potential enabler of transparent, secure, and automated processes, helping to meet the expectations of customers and regulatory authorities. Nevertheless, the lack of generally valid design recommendations hinders its implementation. Therefore, we adopted grounded theory principles within a design science research approach to address this gap. Subsequently, we derived 11 overarching expert insights for developing a DLT operating model in SCs and 15 for its implementation by conducting 16 expert interviews. These insights were finally used to extract 19 generally valid design recommendations for applying DLT in SC processes that contribute to practical implementations and the framing of realistic adoption expectations by guiding researchers and practitioners.
The complexity of modern supply chain networks requires sophisticated approaches to inventory management that can effectively handle demand uncertainty and coordinate decisions across multiple organizational levels. This paper proposes a novel hierarchical multi-agent reinforcement learning framework for dynamic inventory allocation in multi-echelon supply chains facing stochastic demand patterns. The hierarchical architecture decomposes the inventory control problem into strategic and operational decision layers, where high-level agents coordinate allocation policies across distribution networks while low-level agents optimize local replenishment decisions. The framework integrates Centralized Training with Decentralized Execution paradigm, enabling autonomous agents to learn coordinated policies through shared experience while maintaining operational independence during deployment. Experimental results demonstrate that the proposed approach achieves significant reductions in total system costs compared to traditional base-stock policies and single-agent reinforcement learning methods, while effectively mitigating the bullwhip effect in supply chains with high demand variability.