P. zur Heiden, J. Priefer, and D. Beverungen, “Predictive Maintenance on the Energy Distribution Grid—Design and Evaluation of a Digital Industrial Platform in the Context of a Smart Service System,”10.1109/tem.2024.3352819__IEEE Transactions on Engineering Management, vol. 71, pp. 3641–3655, 2024, doi:10.1109/tem.2024.3352819 .
LibreCat: /record/50649
DOI: 10.1109/tem.2024.3352819

P. zur Heiden, J. Priefer, and D. Beverungen, “Location-Based Service and Location-Contextualizing Service: Conceptualizing the Co-creation of Value with Location Information,” 2023.
LibreCat: /record/35893

J. Priefer, “Geographic Information Systems in Information Systems Research - Review and Research Prospects,” in Proceedings of the 56th Hawaii International Conference on System Sciences, 2023.
LibreCat: /record/37497

P. zur Heiden, J. Priefer, and D. Beverungen, “Utilizing Geographic Information Systems for Condition-Based Maintenance on the Energy Distribution Grid,” in Proceedings of the 55th Hawaii International Conference on System Sciences, Honolulu, HI, 2022.
LibreCat: /record/29146

J. Priefer and L. Steiger, “Designing a GIS-AHP-Based Spatial Decision Support System for Discovering and Visualizing Suitable Locations for Electric Vehicle Charging Stations,” in Wirtschaftsinformatik 2022 Proceedings, Nürnberg, 2022, vol. 31.
LibreCat: /record/29805

P. zur Heiden, J. Priefer, and D. Beverungen, “Smart Service für die prädiktive Instandhaltung zentraler Komponenten des Mittelspannungs-Netzes,” in Forum Dienstleistungsmanagement, M. Bruhn and K. Hadwich, Eds. Wiesbaden: Springer Fachmedien Wiesbaden, 2022, pp. 435–457.
LibreCat: /record/32363
DOI: 10.1007/978-3-658-37344-3_14

P. zur Heiden and J. Priefer, “Transitioning to Condition-Based Maintenance on the Distribution Grid: Deriving Design Principles from a Qualitative Study,” in Pre-Conference 16th International Congress on Wirtschaftsinformatik at Universität Duisburg-Essen, Universität Duisburg-Essen, 2021.
LibreCat: /record/24534

J.-P. Kucklick, J. Müller, D. Beverungen, and O. Müller, “Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning Approach,” in European Conference on Information Systems, Virtual, 2021.
LibreCat: /record/22514