{"id":107143,"date":"2024-12-20T17:00:07","date_gmt":"2024-12-20T16:00:07","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=107143"},"modified":"2024-12-20T17:02:56","modified_gmt":"2024-12-20T16:02:56","slug":"open-access-articles","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/open-access-articles\/","title":{"rendered":"Open Access Articles"},"content":{"rendered":"\n\n
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\n \n \n \n \"Cooperation\n <\/picture>\n <\/div>\n
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Cooperation Routines of Complementors in Digital Ecosystems<\/h4>\n
A microfoundation of integrative dynamic capability<\/div>
Christian Zabel<\/a> \n \"ORCID<\/a>, Tahir Schmidt<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Complementors are central to value creation in digital ecosystems yet have limited leverage and must adapt through dynamic capabilities. Building on the Profiting From Innovation Framework, this study examines how integrative capabilities manifest for complementors through cooperative routines. Based on a systematic literature review of Scopus-indexed studies from 2020 to mid-2025 focusing on the microfoundation \u201corchestrating ecosystem actors\u201d, we identify two routine clusters. Complementors cooperate with other complementors via partner sensing, scouting, coalitions, resource sharing, and risk allocation while protecting critical assets. They cooperate with platform owners via multichannel boundary spanning, quality signaling, governance compliance, boundary resource integration, and co-development, while facing the risk of owner entry. Research gaps concern the formalization of cooperation routines, taxonomy, and B2B contexts. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 22-28 | DOI 10.30844\/I4SE.26.4.3<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Data-Driven\n <\/picture>\n <\/div>\n
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Data-Driven Supplier Selection in Industry 4.0<\/h4>\n
Towards an industrial platform for selection and configuration<\/div>
Purushothaman Ganesh<\/a> \n \"ORCID<\/a>, Baris E. Albayrak<\/a> \n \"ORCID<\/a>, J\u00f6rg Franke<\/a> \n \"ORCID<\/a>, Till Sindel<\/a> \n \"ORCID<\/a>, Jens F\u00fcrst<\/a>, Sebastian Lang<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844\/I4SE.26.4.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Replacing\n <\/picture>\n <\/div>\n
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Replacing Organizational Trust with Technical Guarantees<\/h4>\n
Privacy-enhancing technologies for dynamic industrial value networks<\/div>
Hajeong Jeon<\/a> \n \"ORCID<\/a>, Johannes Lohm\u00f6ller<\/a> \n \"ORCID<\/a>, Klaus Wehrle<\/a> \n \"ORCID<\/a>, Jan Pennekamp<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Long-term relationships are now insufficient to ensure trust in digitalized and dynamic industrial networks where sensitive data sharing is prevalent. While organizational trust mechanisms, such as contracts, remain significant, they are often slow and challenging to scale within evolving digital platforms. This article thus proposes privacy-enhancing technologies as a foundation for embedding technical and verifiable guarantees alongside organizational trust mechanisms. As a result, secure industrial collaboration can be achieved without the persistence of data silos. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 72-80 | DOI 10.30844\/I4SE.26.4.8<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Open Source as Enabler for Smart Data Ecosystems<\/h4>\n
How collaboration shapes sovereignty and interoperability across industrial applications<\/div>
Anna Maria Schleimer<\/a> \n \"ORCID<\/a>, Julia Pampus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n From automotive supply chains to smart factories, data ecosystems promise seamless collaboration across company boundaries. But how can industries build the necessary infrastructure without creating new dependencies? This article explores how open-source software can serve as a foundation and impactful tool for sovereign, interoperable technologies and standards. Yet, open-source software is not a silver bullet; rather, it poses challenges for digital sovereignty in burgeoning data ecosystems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 6-13 | DOI 10.30844\/I4SE.26.4.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Industrial Platforms for the Analysis of Decision Dynamics<\/h4>\n
An agent-based modeling approach<\/div>
Jasmijn Stoffels<\/a>, Norbert Gronau<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial platforms are fundamentally transforming the organization and structure of value creation. Beyond serving as intermediaries that connect machine manufacturers, suppliers, and service providers, they enable data-driven decision-making in production while fostering innovation in product development. In doing so, they create new opportunities for collaboration and value creation through enhanced connectivity and interaction across the platform ecosystem. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 30-40 | DOI 10.30844\/I4SE.26.4.4<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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From Private Law to Private Ordering<\/h4>\n
Rule-making through industrial digital platforms<\/div>
Pia C. Romeike<\/a>, Frederik B\u00e4r<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial digital platforms are considered the driving force behind digital value creation, yet legally they largely operate in a gray area. This article examines what defines industrial digital platforms and what legal framework applies to them. Existing platform regulations apply only to a limited extent to industrial digital platforms, which is why these platforms often establish their own legal framework through their terms and conditions. The article also explores the implications of European regulation, power asymmetries, and the opportunities and limitations of private regulation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 98-105 | DOI 10.30844\/I4SD.26.4.11<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Enabling Digital Trust in Green Hydrogen Markets<\/h4>\n
Trust-Building Information Systems and Mechanisms<\/div>
Johanna Vo\u00df<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Building a global hydrogen economy requires more than technology and investment; it requires trust. As information systems (IS) increasingly mediate collaboration among unfamiliar, distributed actors, the question of how trust can be deliberately built becomes critical. This study systematically maps how IS enable different types of trust and reveals how these mechanisms can support trusting collaboration in emerging hydrogen value chains. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 82-90 | DOI 10.30844\/I4SE.26.4.9<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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The European Dream of a Single Market for Data<\/h4>\n
Common European Data Spaces: The gap between aspiration and reality<\/div>
Nils Wiedemann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With their European strategy for data, the EU aims to improve access to and promote the exchange of data in order to create a European single market. Key instruments in this effort are Common European Data Spaces intended to remove legal and practical barriers to data sharing. This article analyzes selected legal obstacles to data sharing, in particular a lack of oversight, uncertainties regarding data protection law, and regulatory fragmentation. The focus is on the European Health Data Space (EHDS), the first implementation of such a data space. Using the EHDS as a case study, the article examines the extent to which Common European Data Spaces are suitable for enabling data sharing, what limitations exist, and under what conditions a transfer to other sectors is conceivable. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 14-20 | DOI 10.30844\/I4SE.26.4.2<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Platform\n <\/picture>\n <\/div>\n
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Platform Adoption as a Dynamic Capability<\/h4>\n
How SMEs overcome barriers to adoption of B2B collaboration platforms<\/div>
Nikolai Sch\u00e4fer<\/a> \n \"ORCID<\/a>, Marcel H\u00fclsbeck<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digital collaboration and innovation platforms offer SMEs significant potential to compensate for structural resource weaknesses and to participate in innovation ecosystems. Nevertheless, adoption in the B2B context remains low. This paper examines adoption barriers based on a systematic literature review using Teece\u2019s dynamic capabilities approach. The analysis suggests that recurring obstacles can be structured along three dimensions: Sensing\u2014lack of ecosystem awareness, absence of scanning routines; Seizing\u2014IP concerns, governance uncertainty, adoption fatigue; Reconfiguring\u2014closed-innovation culture, lack of absorptive capacity. Building on this, a practice-oriented capability-building framework is developed with recommendations for action. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 42-48 | DOI 10.30844\/I4SE.26.4.5<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Classification\n <\/picture>\n <\/div>\n
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Classification of Digital Supply Chain Management Platforms<\/h4>\n
Review of existing classification approaches from a circular economy perspective<\/div>
Sophia Botsch<\/a> \n \"ORCID<\/a>, Eva Mante<\/a> \n \"ORCID<\/a>, Marcel Papert<\/a> \n \"ORCID<\/a>, Alexander Pflaum<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Assessing the impact of digital industrial platforms on the dynamics and resilience of supply chains requires clear classification of such platforms. This article examines the extent to which a current classification proposal from the field of supply chain management must be further developed in the context of digital platforms for implementing the circular economy (CE). The authors conclude that a fundamental revision is not necessary, as the digital CE platforms under consideration fit well into the existing classification system. However, new research questions arise regarding the distinction between digital service platforms and digital data-oriented platforms, as well as the link between the circular economy and supply chain management\u2014particularly in connection with supply chain control towers, which are becoming increasingly established in supply chain management practice. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 62-70 | DOI 10.30844\/I4SE.26.4.7<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n25<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-107143","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/107143","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=107143"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/107143\/revisions"}],"predecessor-version":[{"id":107144,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/107143\/revisions\/107144"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=107143"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}