Existieren Wissensmanagement-Schulen? Eine Clusteranalyse von Wissensmanagement-Beiträgen aus den letzten 10 Jahren. - Paul Kruse; Christian Kummer; Patrick Zschech (GeNeMe, 2014)

Das aufstrebende Berufsbild des Data Scientist. - Conny Schumann; Patrick Zschech; Andreas Hilbert (HMD Prax. Wirtsch., 2016)

Are You up for the Challenge? Towards the Development of a Big Data Capability Assessment Model. - Patrick Zschech; Kai Heinrich; Marcus Pfitzner; Andreas Hilbert (ECIS, 2017)

Analyzing Influences on Pivotal ITO Contract Features: A Quantitative Multi-Study Design with Evidence from Western Europe. - Michael Könning; Kai Heinrich; Patrick Zschech; Christian Leyh (AMCIS, 2018)

Constituent Elements for Prescriptive Analytics Systems. - Karolin Stefani; Patrick Zschech (ECIS, 2018)

A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics. - Patrick Zschech (ECIS, 2018)

Predictive Maintenance in der industriellen Praxis. - Raphael Bink; Patrick Zschech (HMD Prax. Wirtsch., 2018)

Data Science Skills and Enabling Enterprise Systems. - Patrick Zschech; Vera Fleißner; Nicole Baumgärtel; Andreas Hilbert (HMD Prax. Wirtsch., 2018)

Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems. - Kai Heinrich; Patrick Zschech; Tarek Skouti; Jakob Griebenow; Sebastian Riechert (AMCIS, 2019)

Towards a Text-based Recommender System for Data Mining Method Selection. - Patrick Zschech; Kai Heinrich; Richard Horn; Daniel Höschele (AMCIS, 2019)

Everything counts: a Taxonomy of Deep Learning Approaches for Object Counting. - Kai Heinrich; Andreas Roth; Patrick Zschech (ECIS, 2019)

Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA's Turbofan Degradation. - Patrick Zschech; Jonas Bernien; Kai Heinrich (ICIS, 2019)

Application of Process Mining Techniques to Support Maintenance-Related Objectives. - Richard Horn; Patrick Zschech (Wirtschaftsinformatik, 2019)

Prognostic Model Development with Missing Labels - A Condition-Based Maintenance Approach Using Machine Learning. - Patrick Zschech; Kai Heinrich; Raphael Bink; Janis S. Neufeld (Bus. Inf. Syst. Eng., 2019)

Objekterkennung im Weinanbau - Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning. - Kai Heinrich; Patrick Zschech; Björn Möller; Lukas Breithaupt; Johannes Maresch (HMD Prax. Wirtsch., 2019)

Fool me Once, shame on You, Fool me Twice, shame on me: a Taxonomy of Attack and de-Fense Patterns for AI Security. - Kai Heinrich; Johannes Graf; Ji Chen; Jakob Laurisch; Patrick Zschech (ECIS, 2020)

White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems. - Jonas Wanner; Lukas-Valentin Herm; Kai Heinrich; Christian Janiesch; Patrick Zschech (ICIS, 2020)

How Much AI Do You Require? Decision Factors for Adopting AI Technology. - Jonas Wanner; Kai Heinrich; Christian Janiesch; Patrick Zschech (ICIS, 2020)

Review and Systematization of Solutions for 3D Object Detection. - Jonas Friederich; Patrick Zschech (Wirtschaftsinformatik (Zentrale Tracks), 2020)

Ein Vergleich aktueller Deep-Learning-Architekturen zur Prognose von Prozessverhalten. - Kai Heinrich; Patrick Zschech; Christian Janiesch; Markus Bonin (Wirtschaftsinformatik (Zentrale Tracks), 2020)

Intelligent User Assistance for Automated Data Mining Method Selection. - Patrick Zschech; Richard Horn; Daniel Höschele; Christian Janiesch; Kai Heinrich (Bus. Inf. Syst. Eng., 2020)

A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. - Patrick Zschech; Jannis Walk; Kai Heinrich; Michael Vössing; Niklas Kühl 001 (ECIS, 2021)

Tweeting in IIoT Ecosystems - Empirical Insights from Social Media Analytics about IIoT Platforms. - Dimitri Petrik; Katharina Pantow; Patrick Zschech; Georg Herzwurm (Software Management, 2021)

labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds. - Christoph Sager; Patrick Zschech; Niklas Kühl 001 (CoRR, 2021)

A survey of image labelling for computer vision applications. - Christoph Sager; Christian Janiesch; Patrick Zschech (CoRR, 2021)

Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. - Kai Heinrich; Patrick Zschech; Christian Janiesch; Markus Bonin (Decis. Support Syst., 2021)

Machine learning and deep learning. - Christian Janiesch; Patrick Zschech; Kai Heinrich (Electron. Mark., 2021)

Mit Computer Vision zur automatisierten Qualitätssicherung in der industriellen Fertigung: Eine Fallstudie zur Klassifizierung von Fehlern in Solarzellen mittels Elektrolumineszenz-Bildern. - Patrick Zschech; Christoph Sager; Philipp Siebers; Maik Pertermann (HMD Prax. Wirtsch., 2021)

Where was COVID-19 first discovered? Designing a question-answering system for pandemic situations. - Johannes Graf; Gino Lancho; Patrick Zschech; Kai Heinrich (ECIS, 2022)

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. - Patrick Zschech; Sven Weinzierl; Nico Hambauer; Sandra Zilker; Mathias Kraus (ECIS, 2022)

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. - Maximilian V. Harl; Marvin Herchenbach; Sven Kruschel; Nico Hambauer; Patrick Zschech; Mathias Kraus (Wirtschaftsinformatik, 2022)

A Survey of Text Representation Methods and Their Genealogy. - Philipp Siebers; Christian Janiesch; Patrick Zschech (IEEE Access, 2022)

Artificial Intelligence for Sustainability - A Systematic Review of Information Systems Literature. - Thorsten Schoormann; Gero Strobel; Frederik Möller; Dimitri Petrik; Patrick Zschech (Commun. Assoc. Inf. Syst., 2022)

Survey and Systematization of Prescriptive Analytics Systems: Towards Archetypes from a Human-Machine-Collaboration Perspective. - Christopher Wissuchek; Patrick Zschech (ECIS, 2023)

Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction. - Sandra Zilker; Sven Weinzierl; Patrick Zschech; Mathias Kraus; Martin Matzner (ECIS, 2023)

Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models. - Justus Zipfel; Felix Verworner; Marco Fischer; Uwe Wieland; Mathias Kraus; Patrick Zschech (Comput. Ind. Eng., 2023)

Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines. - Patrick Zschech (Inf. Syst. E Bus. Manag., 2023)

IGANN Sparse: Bridging Sparsity and Interpretability with Non-Linear Insight. - Theodor Felix Stoecker; Nico Hambauer; Patrick Zschech; Mathias Kraus (ECIS, 2024)

How Risky is my AI System? A Method for Transparent Classification of AI System Descriptions by Regulated AI Risk Categories. - Sven Weinzierl; Sandra Zilker; Patrick Zschech; Mathias Kraus; Tobias Leibelt; Martin Matzner (ICIS, 2024)

Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict. - Simon Hofmann; Christoph Sommermann; Mathias Kraus; Patrick Zschech; Julian Rosenberger (Wirtschaftsinformatik, 2024)

The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment. - Julian Rosenberger; Sophie Kuhlemann; Verena Tiefenbeck; Mathias Kraus; Patrick Zschech (Wirtschaftsinformatik, 2024)

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. - Sandra Zilker; Sven Weinzierl; Mathias Kraus; Patrick Zschech; Martin Matzner (CoRR, 2024)

Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. - Sven Kruschel; Nico Hambauer; Sven Weinzierl; Sandra Zilker; Mathias Kraus; Patrick Zschech (CoRR, 2024)

Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability. - Sven Kruschel; Lasse Bohlen; Julian Rosenberger; Patrick Zschech; Mathias Kraus (CoRR, 2024)

Interpretable generalized additive neural networks. - Mathias Kraus; Daniel Tschernutter; Sven Weinzierl; Patrick Zschech (Eur. J. Oper. Res., 2024)

Generative AI. - Stefan Feuerriegel; Jochen Hartmann 001; Christian Janiesch; Patrick Zschech (Bus. Inf. Syst. Eng., 2024)

Survey and systematization of 3D object detection models and methods. - Moritz Drobnitzky; Jonas Friederich; Bernhard Egger 001; Patrick Zschech (Vis. Comput., 2024)

Challenges in Managing the Relationship Between Agentic AI Systems and Humans in Organizations. - Christopher Wissuchek; Patrick Zschech (BIS, 2025)

Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework. - Christopher Wissuchek; Patrick Zschech (PACIS, 2025)

CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations. - Julian Rosenberger; Lukas Wolfrum; Sven Weinzierl; Mathias Kraus; Patrick Zschech (Expert Syst. Appl., 2025)

Leveraging interpretable machine learning in intensive care. - Lasse Bohlen; Julian Rosenberger; Patrick Zschech; Mathias Kraus (Ann. Oper. Res., 2025)

Beware of "Explanations" of AI. - David Martens; Galit Shmueli; Theodoros Evgeniou; Kevin Bauer; Christian Janiesch; Stefan Feuerriegel; Sebastian Gabel; Sofie Goethals; Travis Greene; Nadja Klein; Mathias Kraus; Niklas Kühl 001; et al. (CoRR, 2025)

Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users. - Julian Rosenberger; Philipp Schröppel; Sven Kruschel; Mathias Kraus; Patrick Zschech; Maximilian Förster (CoRR, 2025)

Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions. - Paul Gümmer; Julian Rosenberger; Mathias Kraus; Patrick Zschech; Nico Hambauer (CoRR, 2025)

Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior? - Lasse Bohlen; Sven Kruschel; Julian Rosenberger; Patrick Zschech; Mathias Kraus (CoRR, 2025)

Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification. - Lukas Pätz; Moritz Beyer; Jannik Späth; Lasse Bohlen; Patrick Zschech; Mathias Kraus; Julian Rosenberger (CoRR, 2025)