Federated Industrial Platform Ecosystems: Technologies, Business Models, and Data-Driven Artifacts

Permanent URI for this collectionhttps://hdl.handle.net/10125/107514

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Now showing 1 - 7 of 7

Exploring Generative Artificial Intelligence: A Taxonomy and Types
(2024-01-03) Strobel, Gero; Banh, Leonardo; Möller, Frederik; Schoormann, Thorsten
Generative Artificial Intelligence (GAI) is a prevalent topic in recent research and business, seemingly taking the position of a disruptive technology that has the potential to significantly transform industries ranging from productivity (e.g., ChatGPT-4) to creativity (e.g., DALL-E). While the emerging scientific discussion on GAI covers a variety of fields and issues, such as privacy, accuracy, and application scenarios, this paper sheds light on the business side of GAI by investigating the morphologic nature of start-ups and incumbents leveraging GAI. Based on the structured analysis of 100 real-world instances, we report on a taxonomy of GAI applications and services that advances our practical understanding, strengthens the distinguishability, as well as adds clarity to the discourse of GAI potentials. We provide an initial framework and five types of GAI, namely Generator, Reimaginator, Synthesizer, Assistant, and Enabler, that are informed by the core characteristics of the technology paradigm.
Integrating blockchain technology in supply chain management – a process model with evidence from current implementation projects
(2024-01-03) Gürpinar, Tan; Henke, Michael; Ashraf, Riad
In this paper, process models for the integration of information technologies in supply chains are evaluated and utilized for the development of a blockchain-specific model. Case studies are conducted to validate the model based on several implementation projects with the purpose to refine the model’s phases and through focus group interviews and workshops. Even though most of the studied projects demonstrate a clear added value of their blockchain solutions, only few of them make it to the step of running a productive system and integrate the solution in their business processes. The outcome of this paper delivers a practice-oriented process model for integrating blockchain solutions in supply chains. It meets all developed requirements and is validated by interdisciplinary experts that consider a variety of use cases and supply chain application areas.
Design Principles for Quality Scoring—Coping with Information Asymmetry of Data Products
(2024-01-03) Guggenberger, Tobias Moritz; Altendeitering, Marcel; Schlueter Langdon, Chris
Data products are a hot topic within companies since more and more organizations are implementing data meshes and data product management. Product management is closely related to product quality. The concept of data quality is long established, and there are several notions to measure it. However, these approaches are less practical when data is shared between different domains and organizations with undefined contexts. Such as physical products, data products need a clear definition of what is inside and of what quality they are. With this article, we summarize existing approaches to data quality regarding data products, discuss how a lack of information provision restrains efficient data markets, and provide prescriptive knowledge. To do so, we identify meta-requirements from literature and derive design principles for data scoring systems that cope with information asymmetry for data products to enable efficient data markets.
Openness Indicators for the Evaluation of Digital Platforms between the Launch and Maturity Phase
(2024-01-03) Rojahn, Marcel; Gronau, Norbert
In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation.
The adoption of data spaces: Drivers toward federated data sharing
(2024-01-03) Hutterer, Andreas; Krumay, Barbara
Data spaces have gained increasing attention, as they allow federated data sharing among and within participants of interoperable data spaces, for the benefit of all. However, data space initiatives are few in number; moreover, data space adoption among organizations is low. Research thus far has mainly focused on technical factors but lacks a more holistic approach that clarifies what drives data space adoption and federated data sharing as main functions. This exploratory study aims to fill this research gap; it identifies 12 drivers developed by 28 interviewed experts, discussing the coding techniques that are most frequently used in grounded theory. The identified drivers contribute to the current knowledge, while also potentially informing data space projects and organizations’ decisions regarding data space adoption.
The Interplay of Data-Driven Organizations and Data Spaces: Unlocking Capabilities for Transforming Organizations in the Era of Data Spaces
(2024-01-03) Hupperz, Marius; Gieß, Anna
This research paper highlights the relationship between data-driven organizations and data spaces and focuses on unlocking capabilities that can be used to transform organizations and to remain competitive in the era of data spaces. The increasing availability and diversity of data, as well as advances in technology, have led to the emergence of data spaces. However, to fully leverage these opportunities, organizations must be able to effectively access, process and utilize data from these data spaces. Through an in-depth examination of current literature, this paper explores the capabilities required for organizations to participate in data space activities. The TOE framework was used to structure the derived capabilities. The findings of this research provide insights into the capabilities that organizations and data spaces must consider when looking to co-innovate and realize new business cases. We anticipate that our paper will have significant implications for both practitioners and researchers.
Introduction to the Minitrack on Federated Industrial Platform Ecosystems: Technologies, Business Models, and Data-Driven Artifacts
(2024-01-03) Duparc, Estelle; Große, Nick; Van Der Valk, Hendrik; GüRpinar, Tan

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