The early development of blockchain technology (BCT) has already demonstrated the technology's potential to serve the needs of different industries. BCT has also become established as a popular research topic in different scientific disciplines. This paper aims at introducing how several relevant scientific disciplines—supply chain management; management, economics and finance; computer science; security engineering—see the research and education perspectives for BCT. A field review is conducted to present challenges and opportunities of BCT, as well as suggestions for future research and education on the topic as seen from the selected different perspectives. The paper also presents methods for combining relevant disciplines in a modular online course to address the stated challenges and promote interdisciplinary blockchain education.

Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.

This study investigates the effectiveness of a Retrieval-Augmented Generation (RAG) chatbot to enhan

Many enterprises are currently engaged in developing blockchain-based business models. Enterprise networks offer a variety of potential applications for blockchain solutions as they benefit from transparency and security as well as automation of handling data, material, and financial flows along their supply chains. Despite profound potentials, the indicated business models are still in their early stages and need further investigation. To provide an overview of existing blockchain-based business models in the context of enterprise networks, the underlying paper designs a multidimensional taxonomy and identifies several archetypes of blockchain-based businesses. For the taxonomy development, data from 101 blockchain start-ups serves as a basis for empirical validation. Using hierarchical clustering and the k-means method, seven archetypes that sharpen the understanding of how blockchain solutions affect business models in enterprise networks and enable new business models are derived. The proposed work results are intended to be applied in future research and practice to classify and assess the integration of blockchain solutions into existing business models and to support developing new ones that leverage emerging technological capabilities.

The rise of Web 4.0 marks a shift toward decentralized, autonomous AI-driven ecosystems, where intelligent agents interact, transact, and self-govern across digital and physical environments. This paper presents a layered framework outlining the infrastructural, behavioral, and governance dimensions required for enabling autonomous AI agents in decentralized ecosystems. It also explores how enterprises can strategically adopt Web 4.0 applications while mitigating risks related to decentralization and AI coordination. A conceptual approach is adopted, synthesizing research on blockchain-enabled AI, decentralized governance, and autonomous agent interactions. The paper introduces a six-layer framework visualizing key dimensions for Web 4.0 adoption, alongside a framework focusing on enterprise integration guidelines. The study identifies six essential dimensions – spanning infrastructure, trust, and governance – that collectively enable Web 4.0. AI agents require decentralized coordination, transparent behavioral norms, and scalable governance structures to operate autonomously and ethically. Enterprises adopting Web 4.0 must address challenges in data privacy, AI training, multi-agent interaction, and governance. The findings highlight that successful enterprise adoption will depend on trust mechanisms, regulatory alignment, and scalable AI deployment models that balance autonomy with accountability.

Within the past years, enterprise blockchain solutions were frequently developed within different industry consortia. In most cases, this resulted in isolated solutions competing against each other due to similar approaches and goals. Today, decision makers do not necessarily need to establish entirely new blockchain consortia, as established ones already exist, and participation is a considerable way to avoid unreasonable efforts. In this paper, we apply an iterative literature review to identify different factors relevant for practitioners, who face the challenge of joining an existing enterprise blockchain consortium. In a second step, we discuss these factors utilizing supply chain management as a role model. As a main finding, we propose an evaluation framework for the purpose of enterprise blockchain consortium analysis. Additionally, we provide several questions relevant for practitioners during their evaluation stages. With our evaluation framework we contribute to blockchain research, where - despite its high relevance - the topic of consortium evaluation has so far been neglected. We also contribute to research in the field of technology evaluation by proposing and merging five different evaluation dimensions.