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Lukas-Valentin Herm

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2020 – today

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2024
[i3]
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  - journals/corr/abs-2406-01786
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Lars Ackermann, Martin Käppel, Laura Marcus, Linda Moder, Sebastian Dunzer, Markus Hornsteiner, Annina Liessmann, Yorck Zisgen, Philip Empl, Lukas-Valentin Herm, Nicolas Neis, Julian Neuberger, Leo Poss, Myriam Schaschek, Sven Weinzierl, Niklas Wördehoff, Stefan Jablonski, Agnes Koschmider, Wolfgang Kratsch, Martin Matzner, Stefanie Rinderle-Ma, Maximilian Röglinger, Stefan Schönig, Axel Winkelmann:
Recent Advances in Data-Driven Business Process Management. CoRR abs/2406.01786 (2024)
2023
[j7]
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  - journals/ijinfoman/HermHWJ23
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Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, Christian Janiesch:
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. Int. J. Inf. Manag. 69: 102538 (2023)
[j6]
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  - journals/isem/HermJHIHW23
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Lukas-Valentin Herm, Christian Janiesch, Alexander Helm, Florian Imgrund, Adrian Hofmann, Axel Winkelmann:
A framework for implementing robotic process automation projects. Inf. Syst. E Bus. Manag. 21(1): 1-35 (2023)
[c18]
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Lukas-Valentin Herm:
Impact of Explainable AI on Cognitive Load: Insights from an Empirical Study. ECIS 2023
[i2]
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  - journals/corr/abs-2304-08861
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Lukas-Valentin Herm:
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study. CoRR abs/2304.08861 (2023)
2022
[j5]
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  - journals/electronicmarkets/WannerHHJ22
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Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch:
The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electron. Mark. 32(4): 2079-2102 (2022)
[j4]
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Lukas-Valentin Herm, Theresa Steinbach, Jonas Wanner, Christian Janiesch:
A nascent design theory for explainable intelligent systems. Electron. Mark. 32(4): 2185-2205 (2022)
[j3]
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  - journals/hmd/HermJF22
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Lukas-Valentin Herm, Christian Janiesch, Patrick Fuchs:
Der Einfluss von menschlichen Denkmustern auf künstliche Intelligenz - Eine strukturierte Untersuchung von kognitiven Verzerrungen. HMD Prax. Wirtsch. 59(2): 556-571 (2022)
[c17]
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Alexander Mayr, Lukas-Valentin Herm, Jonas Wanner, Christian Janiesch:
Applications and Challenges of Task Mining: A Literature Review. ECIS 2022
[c16]
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Lukas-Valentin Herm, Jonas Wanner, Christian Janiesch:
A Taxonomy of User-centered Explainable AI Studies. PACIS 2022: 9
[c15]
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Jonas Wanner, Lukas-Valentin Herm, Christian Janiesch, Kevin Fuchs, Axel Winkelmann:
Industry 4.0 Maintenance: An Examination of the Readiness of Germany's Industrial Sector. CBI (1) 2022: 216-224
[i1]
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  - journals/corr/abs-2206-10610
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Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, Christian Janiesch:
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. CoRR abs/2206.10610 (2022)
2021
[j2]
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Lukas-Valentin Herm, Christian Janiesch, Alexander Helm, Florian Imgrund, Kevin Fuchs, Adrian Hofmann, Axel Winkelmann:
A Consolidated Framework for Implementing Robotic Process Automation Projects (Extended Abstract). EMISA Forum 41(1): 15-16 (2021)
[c14]
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  - conf/bpm/AxmannHHJ21
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Bernhard Axmann, Harmoko Harmoko, Lukas-Valentin Herm, Christian Janiesch:
A Framework of Cost Drivers for Robotic Process Automation Projects. BPM (Blockchain and RPA Forum) 2021: 7-22
[c13]
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Lukas-Valentin Herm, Christian Janiesch, Hajo A. Reijers, Franz Seubert:
From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots. BPM 2021: 289-305
[c12]
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  - conf/desrist/JanieschWH21
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Christian Janiesch, Jonas Wanner, Lukas-Valentin Herm:
Design Principles for Shared Maintenance Analytics in Fleet Management. DESRIST 2021: 236-247
[c11]
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Lukas-Valentin Herm, Jonas Wanner, Franz Seubert, Christian Janiesch:
I Don't Get IT, but IT seems Valid! The Connection between Explainability and Comprehensibility in (X)AI Research. ECIS 2021
[c10]
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Jonas Wanner, Laurell Popp, Kevin Fuchs, Kai Heinrich, Lukas-Valentin Herm, Christian Janiesch:
Adoption Barriers of AI: a Context-Specific Acceptance Model for Industrial Maintenance. ECIS 2021
[c9]
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Lukas-Valentin Herm, Christian Janiesch:
Towards an Implementation of Blockchain-based Collaboration Platforms in Supply Chain Networks: A Requirements Analysis. HICSS 2021: 1-10
[c8]
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  - conf/hicss/MatejaBOHD21
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Deborah Mateja, Evita Alieke Bartels, Luis Oberste, Lukas-Valentin Herm, Alexa Danelski:
Towards a Reference Architecture for Female-Sensitive Drug Management. HICSS 2021: 1-10
[c7]
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  - conf/i3e/WannerHHJ21
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Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch:
Stop Ordering Machine Learning Algorithms by Their Explainability! An Empirical Investigation of the Tradeoff Between Performance and Explainability. I3E 2021: 245-258
[c6]
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  - conf/pacis/ImgrundHWJFW21
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Florian Imgrund, Lukas-Valentin Herm, Jonas Wanner, Christian Janiesch, Marcus Fischer, Axel Winkelmann:
Critical Success Factors for Process Modeling Projects - Analysis of Empirical Evidence. PACIS 2021: 201
2020
[c5]
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Lukas-Valentin Herm, Christian Janiesch, Alexander Helm, Florian Imgrund, Kevin Fuchs, Adrian Hofmann, Axel Winkelmann:
A Consolidated Framework for Implementing Robotic Process Automation Projects. BPM 2020: 471-488
[c4]
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Lukas-Valentin Herm, Jonas Wanner, Christian Janiesch:
Bridging the Architectural Gap in Smart Homes Between User Control and Digital Automation. DESRIST 2020: 376-381
[c3]
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Jonas Wanner, Lukas-Valentin Herm, Christian Janiesch:
How Much is the Black Box? The Value of Explainability in Machine Learning Models. ECIS 2020
[c2]
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Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch, Patrick Zschech:
White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems. ICIS 2020
[c1]
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  - conf/wirtschaftsinformatik/WannerHLIJ20
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Jonas Wanner, Lukas-Valentin Herm, Marvin Langer, Florian Imgrund, Christian Janiesch:
A Moral Consensus Mechanism for Autonomous Driving: Towards a Law-compliant Basis of Logic Programming. Wirtschaftsinformatik (Zentrale Tracks) 2020: 17-32

2010 – 2019

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2019
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Jonas Wanner, Lukas-Valentin Herm, Dennis Hartel, Christian Janiesch:
Verwendung binärer Datenwerte für eine KI-gestützte Instandhaltung 4.0. HMD Prax. Wirtsch. 56(6): 1268-1281 (2019)

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