@article{65105,
  author       = {{zur Heiden, Philipp and Halimeh, Haya and Hansmeier, Philipp and Vorbohle, Christian and Althaus, Maike and Beverungen, Daniel and Kundisch, Dennis and Müller, Oliver}},
  journal      = {{Communications of the Association for Information Systems}},
  title        = {{{Data Spaces for Heterogeneous Data Ecosystems – Findings from a Design Study in the Cultural Sector}}},
  year         = {{2026}},
}

@article{64863,
  abstract     = {{Traditional dyadic customer-provider interactions are being shifted to polyadic interactions involving diverse participants in digital service ecosystems. Especially, artificial intelligence (AI) is increasingly integrated into these ecosystems, so that they comprise non-human participants (e.g., AI-based chatbots)—fundamentally altering the nature of value (co-)creation. While existing literature examines human-to-human interactions, knowledge of service interactions between human actors and AI-based systems is still underexplored. To address this research gap, we develop a taxonomy, comprising six iterations, that explores the peculiarities of AI as either a resource or a (non-human) agent in digital service ecosystems. We evaluate our taxonomy using a multiple case study and derive the four archetypes of AI in digital service ecosystems: (1) discriminative experience enhancer, (2) protective ecosystem orchestrator, (3) ecosystem innovation companion, and (4) personalized service composer. Our results extend the knowledge on service science by showing how AI-based systems—discriminative or generative, and focusing on the interaction in the ecosystem or the individual service encounter—assume the role of resources and non-human agents. Researchers and practitioners can utilize our results to augment their ecosystems with AI.</jats:p>}},
  author       = {{Hansmeier, Philipp and Schäfer, Jannika Marie and zur Heiden, Philipp}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Artificial intelligence in digital service ecosystems—Towards a taxonomy and archetypes}}},
  doi          = {{10.1007/s12525-026-00879-y}},
  volume       = {{36}},
  year         = {{2026}},
}

@article{60017,
  author       = {{Skolik, Alexander Marcus and zur Heiden, Philipp and Donner, Johannes Aurelius Tamino and Priefer, Jennifer}},
  journal      = {{ECIS 2025 Proceedings}},
  location     = {{Amman, Jordan}},
  title        = {{{Igniting Knowledge Management for Assistance Systems in Maintenance: A Method for Knowledge Gathering}}},
  volume       = {{2}},
  year         = {{2025}},
}

@inproceedings{61378,
  author       = {{Hansmeier, Philipp and zur Heiden, Philipp and Beverungen, Daniel}},
  booktitle    = {{Proceedings of the 20th International Conference on Wirtschaftsinformatik (WI25)}},
  title        = {{{Service Innovation through Data Ecosystems – Designing a Recombinant Method}}},
  year         = {{2025}},
}

@inproceedings{63523,
  abstract     = {{Data spaces have become a strategic pillar of Europe's digital agenda, enabling sovereign, legally compliant data sharing within decentralized ecosystems. As data space initiatives evolve, personalized recommendations are increasingly recognized as key use cases. However, traditional recommendation approaches typically rely on centralized aggregation of user behavior data-directly conflicting with the core ethos of data spaces: sovereignty, privacy, and trust. Federated recommendation systems offer a promising alternative by training models locally and exchanging only intermediate parameters to build a global model. Despite this potential, the integration of federated recommendation techniques and data space architectures remains largely underexplored in research and practice. This paper addresses this gap by designing and evaluating a prototype of a federated recommendation system specifically tailored for data spaces and compliant with their underlying infrastructure. Our findings highlight the viability of developing privacy-preserving, collaborative recommendation systems within data spaces, and contribute to the broader adoption of AI across these emerging ecosystems.}},
  author       = {{Halimeh, Haya and zur Heiden, Philipp}},
  booktitle    = {{2025 27th International Conference on Business Informatics (CBI)}},
  publisher    = {{IEEE}},
  title        = {{{Preserving Sovereignty and Privacy for Personalization: Designing a Federated Recommendation System for Data Spaces}}},
  doi          = {{10.1109/cbi68102.2025.00019}},
  year         = {{2025}},
}

@techreport{63026,
  author       = {{Althaus, Maike and Beverungen, Daniel and Flath, Beate and Halimeh, Haya and Hansmeier, Philipp and zur Heiden, Philipp and Kundisch, Dennis and Müller, Michelle and Müller, Oliver and Oberthür, Simon and Vorbohle, Christian and Momen Pour Tafreshi, Maryam and Mauß, Sebastian and Mücke, Alina and Müller, Jörg and Peter, Malte and Schmitt-Chandon, Ariane and Sellerberg, Kerstin and Steinhäuser, Moritz}},
  title        = {{{Positionspapier Use Case 1: Vernetzte Kulturplattformen}}},
  year         = {{2025}},
}

@article{54446,
  abstract     = {{<jats:title>Zusammenfassung</jats:title><jats:p>Verteilnetzbetreiber in Deutschland stehen vor großen Herausforderungen bei dem Management ihres unternehmensspezifischen Wissens: Mitarbeiterengpässe durch den demographischen Wandel, Wissen ist nur implizit vorhanden und nicht in Wissensmanagementsystemen digitalisiert, teilweise gibt es gar keine Wissensmanagementsysteme oder Konzepte und das Verteilnetz wird immer komplexer. Verbunden mit zunehmender Belastung von zentralen Komponenten im Verteilnetz durch die Energiewende bedarf es neuer Lösungen, besonders für die wissensintensiven Wartungs- und Instandhaltungsprozesse. Generative Artificial Intelligence als aufstrebende Technologie, insb. durch Large Language Models, zeigt hier erste Erfolge für die Anleitung, Entscheidungsunterstützung und den Wissenstransfer. Aufbauend auf dem Design Science Research Forschungsparadigma wird in diesem Beitrag ein ganzheitlicher Ansatz des Wissensmanagements konzipiert, welcher als zentrale Komponente auf einem Assistenzsystem basiert. Ein Large Language Model generiert Hilfestellungen für Netzmonteure während der Wartung und Instandhaltung auf Basis von Anleitungen. Neben der Konzeption zeigt dieser Beitrag auch die erarbeitete Strategie zur Demonstration und zukünftigen Evaluation der Ergebnisse. Der Beitrag liefert ein für Verteilnetzbetreiber neuartiges Konzept Large Language Model basierter Assistenzsysteme zum Wissensmanagement und zeigt zudem nachgelagerte Schritte auf, die vor einer Markteinführung notwendig sind.</jats:p>}},
  author       = {{zur Heiden, Philipp and Kaltenpoth, Sascha Benjamin}},
  issn         = {{1436-3011}},
  journal      = {{HMD Praxis der Wirtschaftsinformatik}},
  publisher    = {{Springer Fachmedien Wiesbaden GmbH}},
  title        = {{{Knowledge Management for Service and Maintenance on the Distribution Grid—Conceptualizing an Assistance System based on a Large Language Model Wissensmanagement für Wartung und Instandhaltung im Verteilnetz – Konzeption eines Assistenzsystems basierend auf einem Large Language Model}}},
  doi          = {{10.1365/s40702-024-01074-3}},
  year         = {{2024}},
}

@article{55500,
  author       = {{Gitzel, Ralf and Hoffmann, Martin and zur Heiden, Philipp and Skolik, Alexander Marcus and Kaltenpoth, Sascha Benjamin and Müller, Oliver and Kanak, Cansu and Kandiah, Kajan and Stroh, Max-Ferdinand and Boos, Wolfgang and Zajadatz, Maurizio and Suriyah, Michael and Leibfried, Thomas and Singhal, Dhruv Suresh and Bürger, Moritz and Hunting, Dennis and Rehmer, Alexander and Boyaci, Aydin}},
  issn         = {{2169-3536}},
  journal      = {{IEEE Access}},
  pages        = {{1--1}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Towards Cognitive Assistance and Prognosis Systems in Power Distribution Grids – Open Issues, Suitable Technologies, and Implementation Concepts}}},
  doi          = {{10.1109/access.2024.3437195}},
  year         = {{2024}},
}

@article{55622,
  abstract     = {{<jats:title>Zusammenfassung</jats:title><jats:p>Die zunehmende Relevanz von Low- und No-Code-Anwendungen in Wissenschaft und Praxis ist darauf zurückzuführen, dass sie Unternehmen die Automatisierung von Prozessen und Aktivitäten trotz begrenzter IT-Kenntnisse ermöglichen. Dies ist von besonderer Bedeutung, da zahlreiche Unternehmen mit Herausforderungen wie dem Fachkräftemangel sowie einer alternden Belegschaft konfrontiert sind. Low- und No-Code-Anwendungen weisen ein beachtliches Potenzial auf, Automatisierungen erfolgreich trotz limitierter Ressourcen umzusetzen. Im Rahmen einer Fallstudie in einem Unternehmen der Energiebranche wurde untersucht, welche Herausforderungen die Implementierung von Low- und No-Code-Anwendungen mit sich bringt und wie diesen begegnet werden kann. Aus den Erkenntnissen wurden vier Erfolgsfaktoren abgeleitet, die für andere Unternehmen als Grundlage dienen können, um die Entwicklung von Low- und No-Code-Automatisierungen erfolgreich umzusetzen. (1) Ein Minimum Viable Product stärkt das Verständnis von LCNC-Plattformen. (2) Die Modularisierung von Entwicklungsaufgaben können zu einer ressourceneffizienteren Entwicklung führen. (3) Nebenprodukte der Entwicklung können fortlaufend Automatisierungsprojekte fördern. (4) Vorzeitige Releases in Livesystemen liefern wertvolle Verbesserungsvorschläge.</jats:p>}},
  author       = {{Skolik, Alexander Marcus and Löhr, Bernd and zur Heiden, Philipp and Bartelheimer, Christian}},
  issn         = {{1436-3011}},
  journal      = {{HMD Praxis der Wirtschaftsinformatik}},
  publisher    = {{Springer Fachmedien Wiesbaden GmbH}},
  title        = {{{Nutzung von Low- und No-Code-Anwendungen zur Automatisierung von Geschäftsprozessen in regulierten Energiemärkten Utilisation of low- and no-code applications to automate business processes in regulated energy markets}}},
  doi          = {{10.1365/s40702-024-01101-3}},
  year         = {{2024}},
}

@article{50649,
  abstract     = {{The energy turnaround and the shift towards sustainable mobility threaten the stability of European energy distribution grids due to substantially increasing load fluctuations and power demand. These challenges can critically impact assets in the distribution grid—e.g., switchgears—intensifying the need to plan, conduct, and manage the maintenance of such assets. Predictive maintenance strategies that analyze assets' current and historical condition data have been discussed as promising approaches toward that end. However, the extant research focuses on designing and improving analytical algorithms or information technology (IT) artifacts while not considering how a maintenance service is cocreated by companies with IT. This research article posits that IT and service must be aligned closely, presenting an ensemble artifact comprising a digital industrial platform and a smart service system for predictive maintenance on the distribution grid. The artifact is evaluated by conducting a willingness-to-pay analysis with asset operators, documenting their demand for condition monitoring and predictive maintenance as an integrated solution, although they still struggle with even getting the condition data of their assets. Building on these results, we formalize the knowledge in the form of design principles and implications for managing the maintenance of critical assets in the distribution grid.}},
  author       = {{zur Heiden, Philipp and Priefer, Jennifer and Beverungen, Daniel}},
  issn         = {{0018-9391}},
  journal      = {{IEEE Transactions on Engineering Management}},
  keywords     = {{Design science research, digital platform, distribution grid, IS design, predictive maintenance, smart services}},
  pages        = {{3641--3655}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Predictive Maintenance on the Energy Distribution Grid—Design and Evaluation of a Digital Industrial Platform in the Context of a Smart Service System}}},
  doi          = {{10.1109/tem.2024.3352819}},
  volume       = {{71}},
  year         = {{2024}},
}

@article{54447,
  abstract     = {{<jats:title>Zusammenfassung</jats:title><jats:p>Verteilnetzbetreiber in Deutschland stehen vor großen Herausforderungen bei dem Management ihres unternehmensspezifischen Wissens: Mitarbeiterengpässe durch den demographischen Wandel, Wissen ist nur implizit vorhanden und nicht in Wissensmanagementsystemen digitalisiert, teilweise gibt es gar keine Wissensmanagementsysteme oder Konzepte und das Verteilnetz wird immer komplexer. Verbunden mit zunehmender Belastung von zentralen Komponenten im Verteilnetz durch die Energiewende bedarf es neuer Lösungen, besonders für die wissensintensiven Wartungs- und Instandhaltungsprozesse. Generative Artificial Intelligence als aufstrebende Technologie, insb. durch Large Language Models, zeigt hier erste Erfolge für die Anleitung, Entscheidungsunterstützung und den Wissenstransfer. Aufbauend auf dem Design Science Research Forschungsparadigma wird in diesem Beitrag ein ganzheitlicher Ansatz des Wissensmanagements konzipiert, welcher als zentrale Komponente auf einem Assistenzsystem basiert. Ein Large Language Model generiert Hilfestellungen für Netzmonteure während der Wartung und Instandhaltung auf Basis von Anleitungen. Neben der Konzeption zeigt dieser Beitrag auch die erarbeitete Strategie zur Demonstration und zukünftigen Evaluation der Ergebnisse. Der Beitrag liefert ein für Verteilnetzbetreiber neuartiges Konzept Large Language Model basierter Assistenzsysteme zum Wissensmanagement und zeigt zudem nachgelagerte Schritte auf, die vor einer Markteinführung notwendig sind.</jats:p>}},
  author       = {{zur Heiden, Philipp and Kaltenpoth, Sascha}},
  issn         = {{1436-3011}},
  journal      = {{HMD Praxis der Wirtschaftsinformatik}},
  publisher    = {{Springer Fachmedien Wiesbaden GmbH}},
  title        = {{{Knowledge Management for Service and Maintenance on the Distribution Grid—Conceptualizing an Assistance System based on a Large Language Model Wissensmanagement für Wartung und Instandhaltung im Verteilnetz – Konzeption eines Assistenzsystems basierend auf einem Large Language Model}}},
  doi          = {{10.1365/s40702-024-01074-3}},
  year         = {{2024}},
}

@article{55941,
  author       = {{Beverungen, Daniel and zur Heiden, Philipp}},
  issn         = {{1867-5905}},
  journal      = {{Wirtschaftsinformatik & Management}},
  publisher    = {{Springer Fachmedien Wiesbaden GmbH}},
  title        = {{{„Digital Responsibility muss verankert, verinnerlicht und umgesetzt werden – vor allem in Bezug auf Daten“}}},
  doi          = {{10.1365/s35764-024-00529-y}},
  year         = {{2024}},
}

@inproceedings{56962,
  author       = {{zur Heiden, Philipp and Gussew, Christian}},
  booktitle    = {{19th International Conference on Business Informatics (WI24)}},
  location     = {{Würzburg}},
  title        = {{{Knowledge Repositories in the Age of AI: Deriving Design Principles from Practice}}},
  year         = {{2024}},
}

@inproceedings{54454,
  author       = {{Hansmeier, Philipp and zur Heiden, Philipp and Beverungen, Daniel}},
  booktitle    = {{Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024)}},
  location     = {{Paphos}},
  title        = {{{MODELING CUSTOMER JOURNEYS IN DIGITAL DATA ECOSYSTEMS: A DOMAIN-SPECIFIC MODELING LANGUAGE }}},
  year         = {{2024}},
}

@inproceedings{54453,
  author       = {{Hansmeier, Philipp and zur Heiden, Philipp}},
  booktitle    = {{Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024)}},
  location     = {{Paphos}},
  title        = {{{CONCEPTUALIZING A HYBRID (ONLINE-OFFLINE) EXPERIENCE FRAMEWORK FOR CULTURAL EVENTS }}},
  year         = {{2024}},
}

@article{46478,
  abstract     = {{High streets across Europe continue to lose consumers to online retail, leading to business closures and the decline of city centres, impairing cities’ overall liveability. To counter this vicious cycle, our study presents smartmarket2, the first instantiation of a digital actor engagement platform designed specifically for high streets. smartmarket2 enables hybrid online-offline customer journeys by connecting consumers to stores and other high street service providers. In an action design research (ADR) project, we design, implement and evaluate smartmarket2, involving 150 high street operators and 2,300 citizens in three cycles of building, intervention and evaluation. We derive four design principles that contribute prescriptive knowledge on the design of digital actor engagement platforms. Our results reveal that such a platform is able to increase engagement, but that it is subject to actors’ engagement dispositions.}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Berendes, Carsten Ingo and Beverungen, Daniel}},
  issn         = {{0960-085X}},
  journal      = {{European Journal of Information Systems}},
  keywords     = {{Digital platform, action design research, design principles, actor engagement, engagement platform, location-based advertising}},
  pages        = {{1--34}},
  publisher    = {{Taylor & Francis}},
  title        = {{{Designing digital actor engagement platforms for local high streets: an action design research study}}},
  doi          = {{10.1080/0960085x.2023.2242847}},
  year         = {{2023}},
}

@inproceedings{35893,
  author       = {{zur Heiden, Philipp and Priefer, Jennifer and Beverungen, Daniel}},
  booktitle    = {{Proceedings of the 56th Conference on System Sciences}},
  title        = {{{Location-Based Service and Location-Contextualizing Service: Conceptualizing the Co-creation of Value with Location Information}}},
  year         = {{2023}},
}

@inbook{42681,
  author       = {{zur Heiden, Philipp}},
  booktitle    = {{DASC-PM v1.1 Fallstudien}},
  editor       = {{Schulz, Michael and Neuhaus, Uwe and Kühnel, Stephan and Rohde, Heiko and Hoseini, Sayed and Theuerkauf, René}},
  pages        = {{29--38}},
  publisher    = {{NORDAKADEMIE gAG Hochschule der Wirtschaft}},
  title        = {{{Projekt FLEMING – Predictive Maintenance von zentralen Komponenten des Mittelspannungsnetzes}}},
  year         = {{2023}},
}

@techreport{47107,
  author       = {{Beverungen, Daniel and zur Heiden, Philipp and Lehrer, Christiane and Trier, Matthias and Bartelheimer, Christian and Bradt, Tobias and Distel, Bettina and Drews, Paul and Ehmke, Jan Fabian and Fill, Hans-Georg and Flath, Christoph M. and Fridgen, Gilbert and Grisold, Thomas and Janiesch, Christian and Janson, Andreas and Krancher, Oliver and Krönung, Julia and Kundisch, Dennis and Márton, Attila and Mirbabaie, Milad and Morana, Stefan and Mueller, Benjamin and Müller, Oliver and Oberländer, Anna Maria and Peters, Christoph and Peukert, Christoph and Reuter-Oppermann, Melanie and Riehle, Dennis M. and Robra-Bissantz, Susanne and Röglinger, Maximilian and Rosenthal, Kristina and Schryen, Guido and Schütte, Reinhard and Strahringer, Susanne and Urbach, Nils and Wessel, Lauri and Zavolokina, Liudmila and Zschech, Patrick}},
  pages        = {{16}},
  publisher    = {{Department of Information Systems, Paderborn University}},
  title        = {{{Implementing Digital Responsibility through Information Systems Research: A Delphi Study of Objectives, Activities, and Challenges in IS Research}}},
  year         = {{2023}},
}

@inproceedings{29146,
  author       = {{zur Heiden, Philipp and Priefer, Jennifer and Beverungen, Daniel}},
  booktitle    = {{Proceedings of the 55th Hawaii International Conference on System Sciences}},
  editor       = {{Bui, Tung X.}},
  isbn         = {{978-0-9981331-5-7}},
  location     = {{Honolulu, HI}},
  title        = {{{Utilizing Geographic Information Systems for Condition-Based Maintenance on the Energy Distribution Grid}}},
  year         = {{2022}},
}