Blockchain Papers

Hossein Naderi, Alireza Shojaei

No abstract is available for this record.

Annika Steiber, Don Alvarez

Purpose There is a knowledge gap regarding the determinants of open innovation processes and outcomes in a joint value creation context, as well as what role artificial intelligence (AI) and data management play in facilitating open innovation processes. One strategy to better understand joint value creation through open innovation, supported by AI and data management, is to conduct studies on the digital business ecosystem (DBE). The purpose of this paper is to improve our current knowledge of this urgent issue in contemporary management through the lens of an ecosystem-based theory by conducting an empirical study on two DBEs (called ecosystem micro-communities (EMCs)), developed by Haier, as well as multiple literature reviews on the key concepts “Haier EMC” and “digital business ecosystem”. Design/methodology/approach By building on multiple literature reviews and empirical data from a multi-year and ongoing research program driven by Haier, this study examines Haier’s EMC model for AI-driven DBEs. Secondary data were collected through iterative literature reviews on DBEs, the EMC concept and the two selected EMC cases. The empirical data were collected through a qualitative study of two Haier EMCs in China. Findings Haier's ecosystem micro-community concept represents a radical shift towards a more flexible, responsive and innovative cross-industry organizational structure, offering valuable lessons for business leaders and scholars. Haier’s ecosystem micro-community model, part of their RenDanHeYi philosophy and here viewed as a DBE, is a pioneering management concept that not only redefines the management of the firm and the traditional corporate structure, but also the traditional view on innovation management, business strategy, human resource management and marketing (customer centricity). The concept has therefore an important and big impact on traditional management. For scholars, the gap in understanding innovation processes in open business ecosystems is addressed by the concept. However, the concept also opens new areas for academic research, particularly in innovation management, business strategy, human resource management and marketing. The concepts further encourage more interdisciplinary research. Research limitations/implications The DBE is a relatively new research area that will need more research. While the EMC model is promising as an effective version of a DBE, its effectiveness across different industries and organizational cultures needs to be explored further. Future research should investigate its applicability and impact in diverse business environments. To understand the EMC’s long-term impact, longitudinal studies are needed. These should focus on the sustained competitive advantages, potential market disruptions and the evolution of customer value propositions over time. Finally, considering increasing concerns about data privacy and security, future research should also explore how DBEs solve the issue of data protection and IP while promoting open innovation and value sharing. Practical implications For managers and practitioners, the EMC concept could inspire leaders to learn how to foster innovation by creating smaller, autonomous teams that can respond quickly to market changes in the form of a DBE. The concepts exemplify how value creation and capture could be enhanced for any company and even could be a new strategy in the company’s digital transformation and repositioning into a more competitive, high-end player on the market. The concept also emphasizes employee empowerment and ownership, which can lead to higher job satisfaction and retention rates. The concept can further improve companies’ adaptability and resilience by decentralizing decision-making. Finally, the micro-communities allow businesses to be more customer-centric, developing products and services that better meet specific customer needs. Social implications The social implications could be positive, as complex social problems commonly need an ecosystem approach to develop and deliver impactful solutions. In addition, Haier’s ecosystem micro-community model seems inherently scalable and culturally adaptable. Originality/value Haier’s EMC model is well-known in the research literature and is a novel approach to DBEs, which has been proven successful and replicable in different countries and industries. Providing insights from multiple literature reviews and two unique Haier EMC cases will contribute to a better understanding of highly effective data- and AI-driven business ecosystems, as well as of determinants of open innovation processes and outcomes in a joint value creation context, as well as what role AI and data management play in facilitating open innovation processes.

Bambang Leo Handoko, Mohammad Hamsal, Arta Moro Sundjaja, Willy Gunadi

No abstract is available for this record.

Kassim Kalinaki

The Internet of Things (IoT) amalgamation with Blockchain technologies holds immense potential to augment Artificial Intelligence (AI)-driven Business Intelligence (BI) capabilities. As data-driven decision-making becomes paramount, this convergence presents organizations with unprecedented opportunities to enhance their BI systems. This study explores the foundational concepts, technological frameworks, and real-world applications underpinning IoT, Blockchain, and AI fusion within BI ecosystems. Examining the synergies between these cutting-edge technologies elucidates potential benefits, such as fortified data security, heightened transparency, and streamlined operational efficiencies. Concurrently, the study delves into the associated challenges, including interoperability complexities and scalability concerns. This study examines current trends, emerging developments, and future directions in AI-powered BI integrated with IoT and Blockchain. It offers key insights for researchers, practitioners, and decision-makers working in this field.

Viraaji Mothukuri, Reza M. Parizi, James L. Massa, Abbas Yazdinejad

Rampant scams plague decentralized finance (DeFi) projects, creating a DeFi credibility problem that limits the impact of DeFi advances in the availability and variety of financial services. This paper presents a novel solution to the DeFi credibility problem by developing an AI multi-model that generates TrustScore ratings for DeFi projects and clear explanations of the scores. We generate DeFi-project TrustScore by aggregating multiple factors that provide DeFi investors with a holistic view of DeFi project trustworthiness. To rate a DeFi project with a TrustS core, we combine the output of four AI pipelines that analyze smart contract code vulnerabilities, suspicious transactions, anomalous price changes to smart contracts, and social media scam sentiment. Applying four factors exponentially improves the trust-score accuracy over the single-factor approaches done historically. Two of the factors, anomalous price change, and social media sentiment, have not been used before to detect DeFi fraud. Furthermore, we enhanced the most critical factor, smart-contract code vulnerability detection, with the latest Large Language Models (LLMs). Our overall system is a multi-model composed of a TrustS core Explainer LLM that aggregates individual pipeline results, a fine-tuned GPT model to audit smart contract code, the Prophet forecasting tool, FinBERT tailored for financial Natural Language Processing (NLP), and XGBoost for classification. The proposed approach identifies a significant proportion of known fraudulent DeFi projects and generates an accurate and explained TrustScore. Thus, we address the DeFi credibility problem so that investors can make reliable decisions about DeFi projects.

S. Kiruthiga, R. Balamanigandan, R Mahaveerakannan, A. Mary Jenifer

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P<0.05) there is a statistical significance (p=0.007) between these two algorithms. The Novel RF Algorithm achieved an accuracy of 87.7330%, while the KNN Algorithm achieved an accuracy of 72.1250%. When the two algorithms were compared using an independent sample t-test, the difference in accuracy was found to be statistically significant at 0.760.

Markus Rabe, Henrik Körsgen

Ideas about blockchain-based applications are soaring, while the realization of existing enterprise architecture landscapes is more cumbersome. This is also valid for applications based on track &amp; trace blockchains. The main impediment to implementing smart contracts with a subsequent financial flow from one blockchain peer to another is trust. Trust issues are prevalent in many business areas. One of the most effective countermeasures is a demonstration of the power of blockchain information systems. By showing the actors of a supply chain how their nodes in a common blockchain are interacting, suspiciousness is traded for knowledge. For this reason, an example of a realistic blockchain application is discussed in detail. The main components are a widely used SAP Business Technology Platform and Hyperledger Firefly. Since multiple layers are part of such an instance, the focus will be on the Hyperledger side. This study presents a notable advancement in the realm of blockchain-enabled track &amp; trace solutions through the use of a novel system architecture for its end-to-end implementation. Its architecture achieves crucial integrations between web3 technologies and established enterprise systems. It offers new functionalities, such as AI integration, while simultaneously acknowledging the inherent challenges and charting potential trajectories for future research. Received: 9 May 2024 | Revised: 21 June 2024 | Accepted: 29 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in SAP at https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/671fe27096664c2c82ac6d2cabb38dea/74c6b169bfb74953b07a66aa248cd9aa.html?locale=en-US and https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/671fe27096664c2c82ac6d2cabb38dea/02b0ed279b5b430f9f48ad41c7e37f4f.html?locale=en-US. Author Contribution Statement Markus Rabe: Conceptualization, Validation, Writing - original draft, Writing - review &amp; editing, Supervision. Henrik Rainer Körsgen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization, Project administration.

Md. Habibur Rahman, William Yeoh, Shantanu Pal

This paper explores the factors that facilitate and impede blockchain technology adoption (BTA) within business organizations. Analysing 112 scholarly articles via thematic and bibliometric methods reveals a complex adoption landscape, highlighted by the proposition of an innovative conceptual framework that augments the conventional technology-organization-environment (TOE) framework. This augmented framework integrates enabling factors, such as enhanced security mechanisms via smart contracts, and barriers, including scalability challenges and regulatory limitations. The research offers significant insights into cultivating a conducive ecosystem for BTA within organizations and provides practical implications.

Haidan Lin, Yuanyuan Lu

Blockchain, a disruptive technology, is built on a shared distributed ledger that is transparent, auditable and resilient. Education 4.0, a new concept that has only been introduced in the last few...

Tze Leung Lai, Haipeng Xing

FinTech or Financial Technology is the term used to describe innovative technologies and software applications that aim to improve and automate the delivery and usage of financial services. FinTech integrates various new technologies into financial services, including asset and wealth management, digital currencies, usage-based insurance, compliance management, and others. It has significantly transformed the traditional way how financial services, transactions, and interactions are conducted. The ABCDs of FinTech is an acronym representing four key trends and concepts within the FinTech industry. It refers to: Artificial Intelligence (AI). It utilizes AI technologies to enhance decision-making processes, automate tasks, and provide personalized financial services. Blockchain . It implements distributed ledger technology for secure and transparent transactions, particularly in areas such as digital currencies and smart contracts. Cloud computing . It leverages cloud-based infrastructure to enable scalability, flexibility, and accessibility of financial services and data. Data (big data and data analytics) . It harnesses large volumes of data and advanced analytics techniques to gain insights, improve risk management, and enhance customer experiences within financial services.

Hemn Barzan Abdalla, Ardalan Husin Awlla, Yulia Kumar, Maryam Cheraghy

This paper presents a comprehensive analysis of the historical progression, current trends, and prospects of Big Data. It explores the technological advancements that have established Big Data as a critical element of contemporary analytics, its extensive impact across various sectors, and the ethical challenges it poses. Beginning with the early recognition of Big Data's potential in the 2000s, the paper traces the development of foundational technologies such as Hadoop and the subsequent diversification of tools and methods. It delves into the integration of advanced analytics and machine learning, the rise of cloud-based Big Data services, and the transformative effects on sectors including healthcare, finance, agriculture, and education. The study also examines ethical considerations such as privacy, bias, transparency, and regulatory compliance, emphasizing the need for robust governance frameworks. It investigates the potential of emerging technologies like AI, IoT, and quantum computing to enhance Big Data capabilities further. It highlights future directions, including decentralized data ecosystems, advanced analytical techniques, and enhanced data privacy measures. By providing a panoramic view of Big Data's development, this paper aims to showcase its potential to revolutionize decision-making processes, improve operational efficiency, and drive innovation across industries; it underscores the importance of balancing technological innovation with ethical responsibility to ensure positive societal advancement and global progress. To add a novelty to the discussion, an AI agent Big D was created to provide a relevant analysis of trends in Big Data. The agent uses a multimodal ChatGPT-4o Large Language Model (LLM) from OpenAI and provides its review based on uploaded files and LLM knowledge.

Ciarán Heavey, Zeki Şimşek, Brian C. Fox

In this chapter, the authors build on the first three chapters to develop additional insights into the implications of Fourth Industrial Revolution (4IR) technologies for strategic leadership research. Contrasting the 4IR with prior industrial revolutions, they examine the transformative consequences and detailed research implications of three core technologies - artificial intelligence and machine learning, big data and analytics, and blockchain and distributed ledger technologies. They then briefly discuss three additional technologies, which have been receiving increasing attention from researchers, including the Internet of Things and connectivity technologies, additive manufacturing, and robotics and automation. The specific research directions provide a glimpse of the promising research avenues regarding each of these key technologies. They conclude with a clarion call for future research efforts that also aim to create shared understanding about the 4IR technologies, in toto.

Yash Gawankar, Srinivas Naik

In this era of rapid technological evolution and ever-changing regulatory landscapes, this chapter explores the transformative journey of data accountability. Beginning with an exploration of data accountability in cloud environments, the narrative sets the stage by defining its scope and surveying the current research landscape. Navigating the intricate terrain of regulatory compliance in the cloud, the discussion unfolds with a focus on prominent regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). A comparative analysis sheds light on the varying compliance requirements and the consequential impact on Cloud Service Providers. This chapter then navigates through diverse accountability models in cloud computing, reevaluating the shared responsibility model and introducing advanced frameworks. It advocates for the integration of accountability directly into the fabric of cloud service level agreements. An in-depth examination of advanced auditing and monitoring mechanisms follows suit, featuring real-time auditing tools, log analysis techniques, and insights into monitoring tool kits offered by major cloud platforms like Google Cloud and AWS. Grounding the discourse in practicality, the narrative showcases case studies that illuminate successful implementations of data accountability. These narratives not only celebrate achievements but also outline encountered challenges and the strategies employed for effective mitigation. A comparative analysis of accountability practices in the realms of Google Cloud and AWS enriches the practical insights. Looking ahead, the narrative concludes with a forward-thinking exploration of future trends in data accountability. It envisions the application of machine learning for anomaly detection, the integration of blockchain and distributed ledger technologies, and anticipates the evolving landscape of regulatory frameworks. As a compass for professionals, researchers, and policymakers, this text serves as an invaluable guide through the dynamic intersection of technology and data governance.

Khaled Naser Magableh, Selvi Kannan, Aladeen Yousef Rashid Hmoud

Blockchain technology (BC) and big data analytics capability (BDAC) are two crucial emerging technologies that have attracted significant attention from businesses and academia. However, their combined effect on business model innovation (BMI), along with the moderating role of environmental uncertainty and the mediating influence of corporate entrepreneurship, remains underexplored. To fill this gap, the present study investigates the combined effects of BDAC and blockchain adoption on BMI and explores the mediating role of corporate entrepreneurship as well as the moderating effect of environmental uncertainty. Drawing on the dynamic capability view (DCV) and the related literature, this study investigates these relationships using a conceptual framework hypothesising that (1) BDAC and blockchain adoption affect BMI through corporate entrepreneurship and (2) environmental uncertainty moderates these relationships. Consistent with the main theoretical arguments, our results, based on a sample of 284 employees working in Australian firms, indicate direct and indirect impacts of both BDAC and blockchain adoption on BMI. Corporate entrepreneurship was found to play a partial mediating role in the relationship between the two technologies, while BMI and environmental uncertainty were found to be significant moderators. These findings have significant theoretical and practical implications for companies striving to innovate their BMI. The results suggest that the synergistic effects of BDAC and blockchain technologies together create entrepreneurial activities and strategies to generate value, thus enabling BMI. Furthermore, the mediating role of corporate entrepreneurship and the moderating effect of environmental uncertainty have important theoretical implications for innovative BMI and management. As such, this study highlights the potential of BDAC and blockchain technologies to drive sustainable business practices, offering insights into how these technologies can contribute to economic, social, and environmental sustainability through innovative business models.

Mohammad Shabaz, Mohammad Zia Ur Rahman, Mahmood Alsaadi, Mohan Raparthi · 9 authors

Wireless biosensor devices have significantly enhanced level of convenience associated with patient treatment. Presently, most uncertified aggregate signature methods struggle to withstand key attacks that employ only specific keys. This paper presents an uncertified parallel key-isolated aggregate signature framework that utilizes blockchain technology in wireless medical sensor networks and biosensor devices for consumer electronics to tackle the challenges. The proximity of the signature verification and aggregation processes to end users is increased by integrating a bio-sensor device for consumer electronics into the cutting-edge framework. This action simultaneously enhances the safeguarding of patient confidentiality and mitigates computational burden on primary cloud server. By integrating the advantages of key-isolated and uncertified technologies, the proposed solution circumvents exposure concerns, difficult certificate administration, and key storage. This study demonstrates that the proposed method is resistant to Type I, Type II, and wholly selected key attacks when implemented in a random oracle model. This scheme shows a reduction in calculation overhead of 82.97%, 74.03%, 84.58%, and 86.79% correspondingly when compared to other schemes. Analysis of performance indicates that in comparison to pertinent uncertified signature systems, this solution can reduce communication overhead by a minimum of 25% and computational cost by a minimum of 74.03%.

Mahd M. Alzoubi

Integrating Blockchain and Artificial Intelligence can change business processes, promising improvements in Security, efficiency, and transparency. This proposed research investigates the synergy between these two technologies, investigating how their convergence can change business operations. AI’s data analysis and decision-making ability, coupled with Blockchain’s Security and transparent data sharing, sets the stage for innovative solutions across diverse sectors. The study aims to explore this integration’s potential benefits and challenges, emphasizing the need for further exploration and development. By conducting a systematic literature review, this research will analyze current findings to provide comprehensive insights into how Blockchain and AI can work together to improve business processes. This investigation uses AI to enhance Blockchain’s capabilities through smart contract automation, data analysis, and decision-making, while Blockchain offers a secure and transparent framework for AI operations. The research seeks to answer critical questions about improving Blockchain networks’ Security and privacy using AI, using Blockchain to enhance AI systems’ transparency and accountability, and using AI to improve Blockchain network performance and scalability. Beyond this research, the Author aims to contribute to the Blockchain and AI integration literature, offering a base for future research.

Adinda Amalia Putri Abidin, Andry Alamsyah, Herry Irawan

Pharmaceuticals play an important role in healthcare. It ensures safe and quality patient care, focusing on tracking medication origins and expiration dates. However, limitations in the complex and non-transparent pharmaceutical supply chain ultimately allow the circulation of counterfeit or expired medicines. The circulation of counterfeit medicines has the potential to cause health problems for consumers and even death. Therefore, we harnessed blockchain technology's capability of detecting fraud and errors to improve safety and transparency in the pharmaceutical supply chain. Additionally, it revolutionizes pharmaceutical supply chain management by combining security and transparency, enabling a decentralized network across manufacturers, distributors, pharmacists, doctors, and patients, ensuring patient data security and collaboration. The author conducted this research by reviewing the existing literature and observations, then proposed a blockchain-integrated model that allows stakeholders to track pharmaceutical information securely.

Andrea Pinna, Maria Ilaria Lunesu, Roberto Tonelli, Simone Sansoni

This article focuses on the concept of blockchain soulbound tokens, their potential applications, and their implementation in Ethereum-based blockchains. Soulbound tokens add an important piece to blockchain technology, as they could be the key to building Web3 and a trustworthy decentralized society. Issued and strictly linked to an account, representing the soul of a user, the soulbound token makes it possible to represent a property that only the user can have and that cannot be transferred, but only removed, which enhances security. To evaluate their impact on blockchain development, we first examine the concept of soulbound tokens, their potential applications, and their effective adoption. The application sectors include the creation of digital identity certificates, ownership certificates, reputational certificates, governance, and the healthcare sector. We then report and describe relevant blockchain token standards, including soulbound token standards, provided in the form of Ethereum Improvement Proposals. Finally, to study the efficacy of the implementation of soulbound tokens, we propose a case study that includes the design and development of a decentralized vaccine certification prototype based on soulbound tokens. In our system, the vaccination data produced by the health authority is fully decentralized and implemented as the issuance of soulbound tokens for the benefit of a citizen’s soul account. As a result, the citizen is the only owner of the vaccination data.

Gigi Yong, Sherene Tyng Xin Saw, Jhen Nee Tang, Teng Li · 6 authors

In the domain of Industrial Internet of Things (IIoT), the demand for robust and secure methods for goods tracking and management has become growingly critical. Conventional methods face significant challenges, including authentication, computational overhead, cyber security, and data integrity. To address these issues, this paper proposes a block-chain based system for goods management and tracking with enhanced authentication mechanism by leveraging the decentralized nature of block-chain technology and integrating Elliptic Curve Digital Signature Algorithm (ECDSA) with Elliptic Curve Cryptography-Zero Knowledge Proof (ECC-ZKP). The proposed solution aims to ensure the authenticity and the integrity for all the transaction while providing high level privacy-preserving verification without revealing information. The research in this paper demonstrates that the proposed block-chain-based system significantly enhances security performance, key management and operational efficiency, addressing the existing challenges in IIoT goods tracking and management, providing a resilient framework for more secure industrial operations in managing goods.

Rakibul Hasan Chowdhury

Supply chain management (SCM) plays an important role in organizations by creating efficiency and cost advantages in operating supply chain activities across industries. It will also be important to know that traditional SCM systems have various inherent problems, including not having enough information about the system, complicated tracking of products, and the fact that fraudulent activities easily compromise most systems. The modern solution for SCM seems to be based on blockchain technology that provides an operational walls-built ledger, significantly increasing transparency and traceability rates within the supply chain processes. Consequently, this paper considers the role of SCM in incorporating blockchain technology to address potential issues such as scalability and conformity to legal frameworks. Examples from Walmart's food supply chain management and Maersk and IBM's TradeLens give a concrete realization of blockchain's benefits in supply chain clarity and functioning. Future trends of blockchain for SCM, particularly multichain and integration with other emerging technologies, reveal that the field is set to expand toward offering increased resolution and reliability to the supply chain.

Gaurav Tiwari, Pradeepa Channakkalavara, Gurinderdeep Singh, Prakash Nathaniel Kumar Sarella

Blockchain technology has emerged as a promising solution to address persistent challenges like lack of transparency, inefficient tracking, and counterfeiting issues in pharmaceutical supply chains. However, there are still limitations in integrating blockchain with existing legacy systems, lack of common standards, scalability concerns due to its decentralized architecture, and lack of regulatory clarity that need to be addressed before successful implementation in the pharmaceutical industry is feasible. This review explores the potential benefits and current use cases of blockchain in enabling drug traceability, establishing authenticity and integrity of data, facilitating smart contracts for seamless financial transactions, and optimizing overall supply chain operations. The current blockchain-based solutions employed by various stakeholders like drug manufacturers, wholesalers, distributors, retailers, hospitals, and regulators across the different stages of the pharmaceutical value chain are reviewed in this article. Recent research efforts to mitigate the key challenges are discussed, including technical improvements in blockchain architecture for increased security and privacy, the use of permissioned blockchains, innovative consensus protocols to improve scalability, and growing regulatory guidance. The future outlook highlights the tremendous opportunities for blockchain to enable end-to-end visibility, accountability, automation, real-time monitoring and combating counterfeiting in pharmaceutical supply chains through collaborative efforts among stakeholders.

Igor Calzada

In our contemporary digital age, our relationship with technology and data has become unsustainable. This chapter addresses the multifaceted challenges surrounding data sustainability. It highlights the pervasive influence of Big Tech corporations and the urgent need for addressing data extractivism, privacy, ethics, ownership, and digital rights concerns. As digital citizens, technology profoundly shapes our lives. Utopian resistance movements have emerged to challenge the dominance of data giants, seeking to redefine data ownership and ethical rules. Emerging digital citizenship regimes play a central role in reshaping the data landscape, with evolving regulatory frameworks and initiatives promoting responsible and sustainable data practices. The chapter explores emancipatory datafication strategies, including (i) blockchain-based decentralized data architectures, (ii) decentralized autonomous organizations (DAOs), and (iii) data co-operatives, as means to empower individuals and communities in reclaiming control over their data. In conclusion, the chapter advocates for a paradigm shift in data governance, emphasizing the importance of transdisciplinary collaboration. It calls for collective efforts to build more equitable and sustainable data spaces and ecosystems, with a focus on prioritizing data sovereignty and achieving digital emancipation. Navigating utopian resistance while tracing emancipatory datafication strategies is crucial in light of data’s pivotal role in global digital economies.

Patrick Herbke, Sid Lamichhane, Kaustabh Barman, Sanjeet Raj Pandey · 7 authors

Supply chain data management faces challenges in traceability, transparency, and trust. These issues stem from data silos and communication barriers. This research introduces DID-Chain, a framework leveraging blockchain technology, Decentralized Identifiers, and the InterPlanetary File System. DIDChain improves supply chain data management. To address privacy concerns, DIDChain employs a hybrid blockchain architecture that combines public blockchain transparency with the control of private systems. Our hybrid approach preserves the authenticity and reliability of supply chain events. It also respects the data privacy requirements of the participants in the supply chain. Central to DIDChain is the cheqd infrastructure. The cheqd infrastructure enables digital tracing of asset events, such as an asset moving from the milk-producing dairy farm to the cheese manufacturer. In this research, assets are raw materials and products. The cheqd infrastructure ensures the traceability and reliability of assets in the management of supply chain data. Our contribution to blockchain-enabled supply chain systems demonstrates the robustness of DIDChain. Integrating blockchain technology through DIDChain offers a solution to data silos and communication barriers. With DIDChain, we propose a framework to transform the supply chain infrastructure across industries.

L. Friedrich

Data offers companies the potential for a future competitive advantage as a strategic resource. Companies own a wide variety of data, from customer data, transaction data to supply chain data. To date, most companies exlusively rely on their own data. However, through efficient data sharing, companies can extend their databases and improve the quality of the data. In doing so, they jointly enhance the quality of data-driven decision-making, derive better business ideas, and improve their internal processes. To share their data, companies can decide between two options: Sharing data on established centralized databases on digital platforms or exploring decentralized distributed ledger technologies like blockchain. These two differ not only in their architecture (centralized/decentralized), but also in data privacy, access control, data security, and governance. This dissertation contains seven scientific articles that emphasize the strategic importance of data sharing. In particular, scientific and practical decision-makers can learn about the benefits and risks of data sharing and can base their willingness to share data on a more comprehensive knowledge foundation.