Publications of Dr. Konstantin Hopf

Hopf, K., Nahr, N., Staake, T., & Lehner, F. (2024). The group mind of hybrid teams with humans and intelligent agents in knowledge-intense work. Journal of Information Technology, Online First, 1–26. https://doi.org/10.1177/02683962241296883

Hopf, K., Müller, O., Shollo, A., & Thiess, T. (2023). Organizational Implementation of AI: Craft and Mechanical Work. California Management Review, 66(1), 23–47. https://doi.org/10.1177/00081256231197445

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, Published online: 07 Apr 2022, 1–37. https://doi.org/10.1080/12460125.2022.2059172

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). https://doi.org/10.1016/j.jsis.2022.101734

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(October), 1–15. https://doi.org/10.1016/j.enpol.2022.113156

Hopf, K. (2018). Mining volunteered geographic information for predictive energy data analytics. Energy Informatics, 1(4). https://doi.org/10.1186/s42162-018-0009-3

Stingl, C., Hopf, K., & Staake, T. (2018). Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland. Energy Informatics, 1(Suppl. 1), 50. https://doi.org/10.1186/s42162-018-0028-0

Hopf, K., Sodenkamp, M., & Staake, T. (2017). Enhancing energy efficiency in the residential sector with smart meter data analytics. Electronic Markets, (2018), First Online: 17 March 2018. https://doi.org/10.1007/s12525-018-0290-9

Hopf, K., Sodenkamp, M., Kozlovskiy, I., & Staake, T. (2016). Feature extraction and filtering for household classification based on smart electricity meter data. Computer Science - Research and Development, 31, 141–148. https://doi.org/10.1007/s00450-014-0294-4

Hopf, K. (2024). KI-Wertsch?pfung in Unternehmen: Evolution statt Revolution. Red Stack Magazin, 4, 18–21.

Hopf, K., & Linnik, B. (2024). Ph?nix aus der Asche: KI braucht langfristige Perspektiven. Heise online, KI Navigator #4.

Bayer, D.R., Haag, F., Pruckner, M. and Hopf, K. (2024), “Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study”, Proceedings of the 9th International Conference on Smart and Sustainable Technologies 2024 (SpliTech), Vol. 10, presented at the 9th International Conference on Smart and Sustainable Technologies, IEEE, pp. 1–6, doi: 10.23919/splitech61897.2024.10612563.

Hopf, K., Joshi, M., Stelmaszak, M. and Shollo, A. (2024), “Crafting Ever-Changing Data Products: Towards a Human-Centered Process Model of Data Work”, ECIS 2024 Proceedings, presented at the 32. European Conference on Information Systems (ECIS), AISeL, New York, pp. 1–17.

Rahlmeier, N. and Hopf, K. (2024), “Bridging Fields of Practice: How Boundary Objects Enable Collaboration in Data Science Initiatives”, Wirtschaftsinformatik 2024 Proceedings, presented at the 19. Internationale Tagung Wirtschaftsinformatik (WI 2024), AIS, New York.

Giacomazzi, E., Haag, F. and Hopf, K. (2023), “Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources”, Proceedings of the 14th ACM International Conference on Future Energy Systems, presented at the 14th ACM International Conference on Future Energy Systems (e-Energy 2023), ACM, New York, pp. 353–360, doi: 10.1145/3575813.3597345.

Haag, F., Günther, S., Hopf, K., Handschuh, P., Klose, M. and Staake, T.R. (2023), “Addressing Learners’ Heterogeneity in Higher Education: an Explainable AI-based Feedback Artifact for Digital Learning Environments”, Proceedings of the 18th Internationale Tagung Wirtschaftsinformatik, presented at the 18. Internationale Tagung Wirtschaftsinformatik, AISeL, AIS electronic library.

Haag, F., Stingl, C., Zerfass, K., Hopf, K. and Staake, T. (2023), “Overcoming Anchoring Bias: the Potential of AI and XAI-based Decision Support”, Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies: ICIS 2023, Proceedings, presented at the Forty-Fourth International Conference on Information Systems, ICIS 202, AISeL, New York, pp. 1–17.

Hopf, K., Hartstang, H. and Staake, T.R. (2023), “Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting”, Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy), presented at the e-Energy Workshop 2023: International Workshop on Energy Data and Analytics, ACM digital library, pp. 32–39, doi: 10.1145/3599733.3600248.

Haag, F., Hopf, K., Menelau Vasconcelos, P. and Staake, T. (2022), “Augmented Cross-Selling Through Explainable AI: a Case From Energy Retailing”, ECIS 2022 Proceedings, presented at the 30. European Conference on Information Systems (ECIS), AISeL, pp. 1–19.

Fteimi, N. and Hopf, K. (2021), “Knowledge Management in the Era of Artificial Intelligence: Developing an Integrative Framework”, ECIS 2021 Proceedings, presented at the 29. European Conference on Information Systems (ECIS), Association for Information Systems (AIS).

Wastensteiner, J., Weiss, T.M., Haag, F. and Hopf, K. (2021), “Explainable AI for Tailored Electricity Consumption Feedback: An Experimental Evaluation of Visualizations”, ECIS 2021 Proceedings, presented at the 29. European Conference on Information Systems (ECIS), Association for Information Systems (AIS), pp. 1–19.

Weigert, A., Hopf, K., Weinig, N. and Staake, T. (2020), “Detection of heat pumps from smart meter and open data”, Energy Informatics, Vol. 3, Springer Science and Business Media {LLC}, p. 14, doi: 10.1186/s42162-020-00124-6.

Weigert, A., Hopf, K. and Staake, T. (2019), “A Cognitive Computing Solution to Foster Retailing of Renewable Energy Systems”, presented at the HCI/MIS Workshop 2019: The 18th Annual Pre-ICIS Workshop on HCI Research in MIS Sponsored by AIS SIGHCI, 2019, doi: 10.20378/IRB-47040.

Hopf, K., Kormann, M., Sodenkamp, M. and Staake, T. (2017), “A Decision Support System for Photovoltaic Potential Estimation”, IML ’17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning, presented at the IML 2017: International Conference on Internet of Things and Machine Learning, October 17 - 18, 2017, ACM Digital Library, New York, pp. 1–10, doi: 10.1145/3109761.3109764.

Hopf, K., Riechel, S.J., Sodenkamp, M. and Staake, T. (2017), “Predictive Customer Data Analytics: The Value of Public Statistical Data and the Geographic Model Transferability”, ICIS 2017 Proceedings, presented at the 38. International Conference on Information Systems (ICIS 2017), December 10th- 13th 2017, AIS Electronic Library, pp. 1–20.

Sodenkamp, M., Kozlovskiy, I., Hopf, K. and Staake, T. (2017), “Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector”, Proceedings Der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), AIS Electronic Library (AISeL), pp. 1235–1249.

Hopf, K., Sodenkamp, M. and Kozlovskiy, I. (2016), “Energy Data Analytics for Improved Residential Service Quality and Energy Efficiency”, Proceedings of the 24. European Conference on Information Systems (ECIS), AIS Electronic Library (AISeL).

Hopf, K., Sodenkamp, M., Kozlovskiy, I. and Staake, T. (2016), “Household classification using annual electricity consumption data”, presented at the 4th D-A-CH Conference Energieinformatik; 12-13 November 2015, opus, Bamberg.

Kozlovskiy, I., Sodenkamp, M., Hopf, K. and Staake, T. (2016), “Energy Informatics for Environmental, Economic and Societal Sustainability: A Case of the Large-Scale Detection of Households with Old Heating Systems”, Proceedings of the 24. European Conference on Information Systems (ECIS), AIS Electronic Library (AISeL).

Hopf, K., Dagef?rde, F. and Wolter, D. (2015), “Identifying the Geographical Scope of Prohibition Signs”, Spatial Information Theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, Proceedings, presented at the 12th International Conference, COSIT 2015, October 12–16, 2015, Springer International Publishing, Cham, pp. 247–267, doi: 10.1007/978-3-319-23374-1_12.

Hopf, K. (2019), Predictive Analytics for Energy Efficiency and Energy Retailing, University of Bamberg Press, Bamberg, doi: 10.20378/irbo-54833.

Sodenkamp, M., Hopf, K. and Staake, T. (2015), “Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing”, in Tavana, M. and Puranam, K. (Eds.), Handbook of Research on Organizational Transformations through Big Data Analytics, IGI Global, Hershey, Pennsylvania (USA), pp. 320–333, doi: 10.4018/978-1-4666-7272-7.

Weigert, A., Hopf, K. and Staake, T. (2020), Analyseverfahren zur Erkennung von Effizienz- und Selbstversorgungspotenzialen individueller Haushalte: Schlussbericht zum Teilprojekt 0350010 im Verbundvorhaben “SmartLoad”; Projektlaufzeit: 01.06.2017-31.03.2020, Technische Informationbibliothek (TIB), Hannover, doi: 10.2314/KXP:1754715761.

Weigert, A., Hopf, K., Staake, T., Rast, A. and Marckhoff, J. (2020), SmartLoad: Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector, Bundesamt für Energie, Sektion Energieforschung und Cleantech, Bern.

Hopf, K. and Staake, T. (2019), Methoden der Energiedatenanalyse: Schlussbericht; Vorhaben im Rahmen des Eurostars Projekts E! 9859 BENginell “Energy Data Analytics: Increasing Service Quality and Energy Efficiency in the Residential Sector”, TIB, Hannover, doi: 10.2314/KXP:1687331642.

Sodenkamp, M., Hopf, K., Kozlovskiy, I. and Staake, T. (2016), Smart-Meter-Datenanalyse für automatisierte Energieberatungen (“Smart Grid Data Analytics”) - Schlussbericht, opus, Bamberg.

Hopf, K. and Reifenrath, S. (2021), “Filter Methods for Feature Selection in Supervised Machine Learning Applications: Review and Benchmark”, arXiv, doi: 10.48550/arxiv.2111.12140.