Weitere Publikationen finden Sie auf der vollst?ndigen Publikationsliste von Konstantin Hopf.
Zeitschriftenbeitr?ge (peer-reviewed)
Hopf, K., Nahr, N., Staake, T., Lehner, F. (2025) The group mind of hybrid teams with humans and intelligent agents in knowledge-intense work. Journal of Information Technology 40(1), DOI 10.1177/02683962241296883.
Potthoff, U., Brudermueller, T., Hopf, K., & Wortmann, F. (2025). Optimization of heating curves for heat pumps in operation: Outdoor temperature ranges for energy-efficient heating curve shifts. Applied Energy, 389(125725), 1–21. DOI: 10.1016/j.apenergy.2025.125725
Hopf, K., Müller, O., Thiess, T., Shollo, A. (2023). Organizational implementation of AI: Craft and mechanical work. California Management Review 66(1), DOI: 10.1177/00081256231197445, ausgezeichnet mit dem CMCE Research Award und dem Urwick Prize im Oktober 2024
Shollo, A., Hopf, K., Thiess, T., Müller, O. (2022). Shifting ML Value Creation Mechanisms: A process model of ML value creation. The Journal of Strategic Information Systems, 31(3), 101734. DOI: 10.1016/j.jsis.2022.101734; ausgezeichnet mit dem JSIS 2022 Best paper award im M?rz 2023 und AIS Senior's Scholars Best Journal on IS im Dezember 2024.
Weigert, A., Hopf, K., Günther, S. A., & Staake, T. (2022). Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data. Energy Policy, 169, 113156. DOI: 10.1016/j.enpol.2022.113156
Hopf, K., Weigert, A., Staake, T. (2022). Value creation from analytics with limited data: a case study on the retailing of durable consumer goods. Journal of Decision Systems, Online ver?ffentlicht am 07. April 2022, DOI: 10.1080/12460125.2022.2059172
Hopf, K., Sodenkamp, M., Staake, T. (2018). Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector. Electronic Markets, 28(4) DOI: 10.1007/s12525-018-0290-9; ausgezeichnet mit dem AIS SIGGREEN 2018 Best Journal Paper on Green ISaward im Dezember 2018
Hopf, K. (2018). Mining Volunteered Geographic Information for Predictive Energy Data Analytics. Energy Informatics, 1:4, DOI: 10.1186/s42162-018-0009-3
Beitr?ge in Konferenzb?nden (peer-reviewed)
Rahlmeier, N., Hopf, K. (2024). Bridging Fields of Practice: How Boundary Objects Enable Collaboration in Data Science Initiatives.19. Internationale Tagung Wirtschaftsinformatik, September 17 - 19, Würzburg. WI'24 Best Paper Award.
Hopf, K., Joshi, M., Stelmaszak, M., & Shollo, A. (2024). Crafting Ever-Changing Data Products: Towards a Human-Centered Process Model of Data Work. ECIS 2024 Proceedings. 32. European Conference on Information Systems, Paphos: Zypern.
Haag, F., Stingl, C., Zerfass, K., Hopf, K., Staake, T. (2023). Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support, 44. International Conference on Information Systems 10. - 13. Dezember, Hyderabad: Indien
Haag, F., Günther, S. A., Hopf, K., Handschuh, P. Klose, M., Staake, T. (2023). Addressing Learners' Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments. 18. Internationale Tagung Wirtschaftsinformatik 18. - 21. September, Paderborn; WI'23 Best Paper Award.
Hopf, K., Hartstang, H., Staake, T. (2023). Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting. Vorgestellt auf dem 4. International Workshop on Energy Data and Analytics im Rahmen der 14. ACM e-Energy Konferenz, 20. Juni, Orlando:Florida (USA). DOI: 10.1145/3575813.3597345 [Preprint]
Giacomazzi, E., Haag, F., Hopf, K. (2023). Short-term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources. Vorgestellt auf der 14. ACM e-Energy Konferenz, 20. Juni, Orlando:Florida (USA). DOI: 10.1145/3599733.3600248 [Preprint]
Günther, S. A., Haag, F., Hopf, K., Klose, M., Handschuh, P., Staake, T. (2022). A feedback component that leverages counterfactual explanations for smart learning support: First insights into its empirical evaluation. Tagungsband DiKuLe Symposium 2022 (in Erscheinung)
Haag, F., Hopf, K., Menelau Vasconcelos, P., Staake, T. (2022). Augmented Cross-Selling Through Explainable AI – A Case From Energy Retailing. 30. European Conference on Information Systems (ECIS'22), Timi?oara: Romania [Full-text] [Preprint]
Wastensteiner, J., Weiss, T. M., Haag, F., Hopf, K. (2021). Explainable AI for Tailored Electricity Consumption Feedback – An Experimental Evaluation of Visualizations, 29. European Conference on Information Systems (ECIS'21), Marrakesh: Morocco / Virtual, 14. – 12. Juni, [Full-text] [Preprint]
Weigert, A., Hopf, K., Weinig, N., Staake, T. (2020) Detection of heat pumps from smart meter and open data, 9. DACH+ Conference on Energy Informatics, Sierre, Schweiz, 29. – 30. Oktober, In: Energy Informatics, 3(Suppl 1):21, DOI: 10.1186/s42162-020-00124-6
Hopf, K., Riechel, S., Sodenkamp, M., Staake, T. (2017). Predictive Customer Data Analytics – The Value of Public Statistical Data and the Geographic Model Transferability.38. International Conference on Information Systems (ICIS'17), Seoul: Südkorea, 10. – 13. Dezember
Hopf, K., Dagef?rde, F., Wolter, D. (2015). Identifying the Geographical Scope of Prohibition Signs, 12. International Conference on Spatial Information Theory (COSIT) , 2015 Santa Fe: NM, USA, 12. – 16. Oktober. Proceedings in Lecture Notes in Computer Science, DOI: 10.1007/978-3-319-23374-1_12
Hopf, K., Sodenkamp, M., Kozlovskiy, I., Staake, T. (2014). Feature extraction and filtering for household classification based on smart electricity meter data, 3. D-A-CH+ Energieinformatik Konferenz 2014, 13. -14. November. In: Computer Science - Research and Development 31 (3), pp. 141-148, DOI: 10.1007/s00450-014-0294-4
Software-Bibliotheken
Hopf, K., Weigert, A., Kozlovskiy, I., Staake, T. (2020). SmartMeterAnalytics: Methods for Smart Meter Data Analysis, Bibliothek für die Statistikumgebung GNU R, https://cran.r-project.org/package=SmartMeterAnalytics
Hopf, K., Weigert, A., Weinig, N., Staake, T., (2020). ResidentialEnergyConsumption: Residential Energy Consumption Data, Bibliothek für die Statistikumgebung GNU R, https://cran.r-project.org/package=ResidentialEnergyConsumption
