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118 results on '"Zschech, Patrick"'

1. Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability

Author

Kruschel, Sven, Bohlen, Lasse, Rosenberger, Julian, Zschech, Patrick, and Kraus, Mathias

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plots, representing the model's decision-making process. However, the visual properties of these plots, e.g. number of kinks (number of local maxima and minima), can impact their complexity and the cognitive load imposed on the viewer, compromising interpretability. Our study, including 57 participants, investigates the relationship between the visual properties of GAM shape plots and cognitive load they induce. We quantify various visual properties of shape plots and evaluate their alignment with participants' perceived cognitive load, based on 144 plots. Our results indicate that the number of kinks metric is the most effective, explaining 86.4% of the variance in users' ratings. We develop a simple model based on number of kinks that provides a practical tool for predicting cognitive load, enabling the assessment of one aspect of GAM interpretability without direct user involvement., Comment: to be published in proceedings of the 58th Hawaii International Conference on System Sciences (HICSS)

Published
2024

2. Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models

Author

Kruschel, Sven, Hambauer, Nico, Weinzierl, Sven, Zilker, Sandra, Kraus, Mathias, and Zschech, Patrick

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Neural and Evolutionary Computing
Abstract

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, this study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective., Comment: Accepted for publication in Business & Information Systems Engineering (2024)

Published
2024

3. A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis

Author

Zilker, Sandra, Weinzierl, Sven, Kraus, Mathias, Zschech, Patrick, and Matzner, Martin

Subjects
Computer Science - Machine Learning
Abstract

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance - where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees - and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

Published
2024

4. IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight

Author

Stoecker, Theodor, Hambauer, Nico, Zschech, Patrick, and Kraus, Mathias

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature selection are built directly into model learning, providing a fast and attractive option for large amounts of data. Machine learning algorithms, such as penalized regression models (e.g., lasso) are the most common choice when it comes to in-built feature selection. However, they fail to capture non-linear relationships, which ultimately affects their ability to predict outcomes in intricate datasets. In this paper, we propose IGANN Sparse, a novel machine learning model from the family of generalized additive models, which promotes sparsity through a non-linear feature selection process during training. This ensures interpretability through improved model sparsity without sacrificing predictive performance. Moreover, IGANN Sparse serves as an exploratory tool for information systems researchers to unveil important non-linear relationships in domains that are characterized by complex patterns. Our ongoing research is directed at a thorough evaluation of the IGANN Sparse model, including user studies that allow to assess how well users of the model can benefit from the reduced number of features. This will allow for a deeper understanding of the interactions between linear vs. non-linear modeling, number of selected features, and predictive performance., Comment: Preprint conditionally accepted for archival and presentation at the 32nd European Conference on Information Systems (ECIS 2024)

Published
2024

7. Generative AI

Author

Feuerriegel, Stefan, Hartmann, Jochen, Janiesch, Christian, and Zschech, Patrick

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other. In this article, we provide a conceptualization of generative AI as an entity in socio-technical systems and provide examples of models, systems, and applications. Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research. Different from previous works, we focus on generative AI in the context of information systems, and, to this end, we discuss several opportunities and challenges that are unique to the BISE community and make suggestions for impactful directions for BISE research., Comment: Published in Business & Information Systems Engineering (2023)

Published
2023
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10. A Survey of Text Representation Methods and Their Genealogy

Author

Siebers, Philipp, Janiesch, Christian, and Zschech, Patrick

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypothesis. As a consequence, text representation methods have been evolving at such a quick pace that the research community is struggling to retain knowledge of the methods and their interrelations. We contribute threefold to this lack of compilation, composition, and systematization by providing a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art. Our research is a valuable guide and reference for artificial intelligence researchers and practitioners interested in natural language processing applications such as recommender systems, chatbots, and sentiment analysis., Comment: Published online in IEEE Access

Published
2022
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11. GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

Author

Zschech, Patrick, Weinzierl, Sven, Hambauer, Nico, Zilker, Sandra, and Kraus, Mathias

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Human-Computer Interaction
Abstract

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements., Comment: Preprint accepted for archival and presentation at the 30th European Conference on Information Systems (ECIS 2022)

Published
2022

12. Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations

Author

Graf, Johannes, Lancho, Gino, Zschech, Patrick, and Heinrich, Kai

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen's cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts., Comment: Preprint accepted for archival and presentation at the 30th European Conference on Information Systems (ECIS 2022)

Published
2022

14. Survey and Systematization of 3D Object Detection Models and Methods

Author

Drobnitzky, Moritz, Friederich, Jonas, Egger, Bernhard, and Zschech, Patrick

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012-2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces., Comment: accepted at "The Visual Computer"

Published
2022

15. A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

Author

Harl, Maximilian, Herchenbach, Marvin, Kruschel, Sven, Hambauer, Nico, Zschech, Patrick, and Kraus, Mathias

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets., Comment: Preprint accepted for archival and presentation at the 17th International Conference on Wirtschaftsinformatik 2022. 14 pages, 5 figures, 4 tables

Published
2022

16. A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems

Author

Zschech, Patrick, Walk, Jannis, Heinrich, Kai, Vössing, Michael, and Kühl, Niklas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems. We apply a reflective, practice-inspired design science approach and accumulate design knowledge from six comprehensive CV projects. As a result, we identify four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) that inform our derived meta-requirements and design principles. This can serve as a basis for further socio-technical research on CV-based HI systems., Comment: Preprint accepted for archival and presentation at the 29th European Conference on Information Systems (ECIS 2021)

Published
2021

17. A survey of image labelling for computer vision applications

Author

Sager, Christoph, Janiesch, Christian, and Zschech, Patrick

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television., Comment: Published online first in Journal of Business Analytics

Published
2021
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18. Machine learning and deep learning

Author

Janiesch, Christian, Zschech, Patrick, and Heinrich, Kai

Subjects
Computer Science - Artificial Intelligence
Abstract

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization., Comment: Published online first in Electronic Markets

Published
2021
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19. labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds

Author

Sager, Christoph, Zschech, Patrick, and Kühl, Niklas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Within the past decade, the rise of applications based on artificial intelligence (AI) in general and machine learning (ML) in specific has led to many significant contributions within different domains. The applications range from robotics over medical diagnoses up to autonomous driving. However, nearly all applications rely on trained data. In case this data consists of 3D images, it is of utmost importance that the labeling is as accurate as possible to ensure high-quality outcomes of the ML models. Labeling in the 3D space is mostly manual work performed by expert workers, where they draw 3D bounding boxes around target objects the ML model should later automatically identify, e.g., pedestrians for autonomous driving or cancer cells within radiography. While a small range of recent 3D labeling tools exist, they all share three major shortcomings: (i) they are specified for autonomous driving applications, (ii) they lack convenience and comfort functions, and (iii) they have high dependencies and little flexibility in data format. Therefore, we propose a novel labeling tool for 3D object detection in point clouds to address these shortcomings., Comment: Preprint accepted for archival and presentation at CAD 21 Conference

Published
2021

23. Tweeting in IIoT Ecosystems – Empirical Insights from Social Media Analytics About IIoT Platforms

Author

Petrik, Dimitri, Pantow, Katharina, Zschech, Patrick, Herzwurm, Georg, Spagnoletti, Paolo, Series Editor, De Marco, Marco, Series Editor, Pouloudi, Nancy, Series Editor, Te'eni, Dov, Series Editor, vom Brocke, Jan, Series Editor, Winter, Robert, Series Editor, Baskerville, Richard, Series Editor, Ahlemann, Frederik, editor, Schütte, Reinhard, editor, and Stieglitz, Stefan, editor

Published
2021
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24. Deep Learning in der Landwirtschaft – Analyse eines Weinbergs

Author

Zschech, Patrick, Heinrich, Kai, Möller, Björn, Breithaupt, Lukas, Maresch, Johannes, Roth, Andreas, D’Onofrio, Sara, Series Editor, Fröschle, Hans-Peter, Series Editor, Hofmann, Josephine, Series Editor, Knoll, Matthias, Series Editor, Meinhardt, Stefan, Series Editor, Reinheimer, Stefan, Series Editor, Robra-Bissantz, Susanne, Series Editor, Strahringer, Susanne, Series Editor, D'Onofrio, Sara, editor, and Meier, Andreas, editor

Published
2021
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29. Generative AI

Author

Feuerriegel, Stefan, primary, Hartmann, Jochen, additional, Janiesch, Christian, additional, and Zschech, Patrick, additional

Published
2023
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36. Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction

Author

Zilker, Sandra, Weinzierl, Sven, Zschech, Patrick, Kraus, Mathias, and Matzner, Martin

Subjects
ddc:000
Abstract

Proactively analyzing patient pathways can help healthcare providers to anticipate treatment-related risks, detect undesired outcomes, and allocate resources quickly. For this purpose, modern methods from the field of predictive business process monitoring can be applied to create data-driven models that capture patterns from past behavior to provide predictions about running process instances. Recent methods increasingly focus on deep neural networks (DNN) due to their superior prediction performances and their independence from process knowledge. However, DNNs generally have the disadvantage of showing black-box characteristics, which hampers dissemination in critical environments such as healthcare. To this end, we propose the design of HIXPred, a novel artifact combining predictive power with explainable results for patient pathway predictions. We instantiate HIXPred and apply it to a real-life healthcare use case for evaluation and demonstration purposes and conduct interviews with medical experts. Our results confirm high predictive performance while ensuring sufficient interpretability and explainability to provide comprehensible decision support.

Published
2023

40. SURVEY AND SYSTEMATIZATION OF PRESCRIPTIVE ANALYTICS SYSTEMS: TOWARDS ARCHETYPES FROM A HUMAN-MACHINE-COLLABORATION PERSPECTIVE.

Author

Wissuchek, Christopher and Zschech, Patrick

Subjects
HUMAN-machine systems, ARCHETYPES, BUSINESS analytics, DESIGN research, INFORMATION storage & retrieval systems
Abstract

Prescriptive analytics systems (PAS) represent the most mature form of business analytics (BA), offering advanced decision support. However, current research predominantly focuses on technical facets while neglecting social-technical design aspects. A pluralist research methodology was employed to address this gap, starting with a systematic literature review of over 200 papers. We used these papers to derive a concept matrix of fundamental elements that guided the development of a taxonomy conceptualizing the interplay and collaboration between humans and machines in PAS-based decision-making processes. Based on this taxonomy, we identified four recurring PAS archetypes with salient design characteristics: Informative, executive, adaptive, and autonomous PAS. Our findings have important implications for the BA community, including the need to investigate design options for executive, adaptive, and autonomous PAS; the underrepresentation of the human perspective; the missing links to the broader organizational landscape; and the potential for interpretable machine learning and reinforcement learning in PAS. [ABSTRACT FROM AUTHOR]

Published
2023

46. Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods

Author

Strahringer, Susanne, Buscher, Udo, Technische Universität Dresden, Zschech, Patrick, Strahringer, Susanne, Buscher, Udo, Technische Universität Dresden, and Zschech, Patrick

Abstract

Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descripti, Datengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geei

Published
2020

48. Human Practice. Digital Ecologies. Our Future. : 14. Internationale Tagung Wirtschaftsinformatik (WI 2019) : Tagungsband

Author

Freichel, Chiara, Hofmann, Adrian, Fischer, Marcus, Winkelmann, Axel, Martin, Dominik, Hirt, Robin, Kühl, Niklas, Schermuly, Louisa, Schreieck, Maximilian, Wiesche, Manuel, Krcmar, Helmut, Wanner, Jonas, Wissuchek, Christopher, Janiesch, Christian, Burggräf, Peter, Wagner, Johannes, Koke, Benjamin, Manoharan, Kailashnath, Vonholdt, Stephanie, Stevens, Gunnar, Becker, Darius, Wiedemann, Nina, Kammler, Friedemann, Varwig, Andreas, Thomas, Oliver, Xu, Tingni, Bernardy, Anne, Bertling, Matthias, Stich, Volker, Dannapfel, Matthias, Sonneberg, Marc-Oliver, Werth, Oliver, Leyerer, Max, Wille, Wiebke, Jarlik, Marvin, Breitner, Michael H., Rößler, David, Reisch, Julian, Kliewer, Natalia, Neuß, Daniel, Pfister, Samuel, Ruß, Matthias, Gust, Gunther, Neumann, Dirk, Alter, Steven, Bork, Dominik, Corea, Carl, Deisen, Matthias, Delfmann, Patrick, de Kinderen, Sybren, Kaczmarek-Heß, Monika, Razo-Zapata, Ivan, Ma, Qin, Frick, Norbert, Fürst-Graßl, Andrea, Gebel-Sauer, Berit, Schubert, Petra, Hönigsberg, Sarah, Kollwitz, Christoph, Dinter, Barbara, Schlieter, Hannes, Stark, Jeannette, Burwitz, Martin, Braun, Richard, Schön, Hendrik, Strahringer, Susanne, Furrer, Frank J., Kühn, Thomas, Vogel, Jannis, Bossauer, Paul, Neifer, Thomas, Pakusch, Christina, Staskiewicz, Paul, Staegemann, Daniel, Hintsch, Johannes, Turowski, Klaus, Distel, Bettina, Ogonek, Nadine, Becker, Jörg, Hobert, Sebastian, Meyer von Wolff, Raphael, Löffler, Alexander, Levkovskyi, Borys, Prifti, Loina, Kienegger, Harald, Kampling, Henrik, Schwarze, Anna, Heger, Oliver, Niehaves, Bjoern, Vogelsang, Kristin, Droit, Alena, Liere-Netheler, Kirsten, Laubengaier, Désirée, Wagner, Heinz-Theo, Hahn, Gerd J., Schneider, Tim, Janson, Andreas, Winkler, Rainer, Bittner, Eva, Söllner, Matthias, Bichler, Martin, Merting, Sören, Uzunoglu, Aykut, Griebel, Matthias, Dürr, Alexander, Stein, Nikolai, Heinrich, Kai, Roth, Andreas, Breithaupt, Lukas, Möller, Björn, Maresch, Johannes, Keller, Alexander, Achatz, Hans, Knöpfle, Philipp, Knörr, Johannes, Kupfer, Alexander, Essen, Luciano van, Zimmermann, Steffen, Lommel, Lasse, Riebeling, Meike, Funk, Burkhardt, Junginger, Christian, Nalbach, Oliver, Derouet, Maximilian, Werth, Dirk, Rosenfelder, Markus, Schiller, Alexander, Wimmer, Tristan, Scholz, Michael, Wolbeck, Lena, Amberg, Bastian, Poniatowski, Martin, Neumann, Jürgen, Görzen, Thomas, Kundisch, Dennis, Walter, Matthias, Vasyutynskyy, Volodymyr, Trinh, Duc Anh, Leyh, Christian, Welsch, Giacomo, Hauser, Matthias, Thiesse, Frédéric, Berkemeier, Lisa, Zobel, Benedikt, Werning, Sebastian, Remark, Florian, Ickerott, Ingmar, Betzing, Jan H., Niemann, Marco, Barann, Benjamin, Hoffmeister, Benedikt, Blaschke, Michael, Haki, Kazem, Aier, Stephan, Winter, Robert, Buck, Christoph, Ifland, Sebastian, Renz, Michael, Bärsch, Sören, Bollweg, Lars, Lackes, Richard, Siepermann, Markus, Weber, Peter, Wulfhorst, Valerie, Dremel, Christian, Stoeckli, Emanuel, Wulf, Jochen, Brenner, Walter, Fuchs, Christoph, Barthel, Philipp, Herberg, Ina, Berger, Matthias, Hess, Thomas, Hagen, Simon, Hund, Axel, Kutzner, Kristin, Petzold, Kristina, Knackstedt, Ralf, Oesterle, Severin, Buchwald, Arne, Urbach, Nils, Poeppelbuss, Jens, Lubarski, Alexander, Setzke, David Soto, Rödel, Tom, Böhm, Markus, Packmohr, Sven, Hoppe, Uwe, Wolf, Verena, Krug, Christopher, Agarwal, Nivedita, Schymanietz, Martin, Fritzsche, Albrecht, Bürger, Olga, Drechsler, Katharina, Reibenspiess, Victoria, Eckhardt, Andreas, Espig, André, Klimpel, Nicole, Rödenbeck, Franz, Auth, Gunnar, Karrenbauer, Jens, Klesel, Michael, Weber, Sebastian, Walsdorff, Finja, Prommegger, Barbara, Huck-Fries, Veronika, Segert, Tobias, Holotiuk, Friedrich, Beimborn, Daniel, Albrecht, Simon, Strüker, Jens, Fürstenau, Daniel, Anisimova, Daria, Masak, Dieter, Rothe, Hannes, Schulte-Althoff, Matthias, Adelmeyer, Michael, Meier, Pascal, Teuteberg, Frank, Benedict, Martin, Scheplitz, Tim, Susky, Marcel, Richter, Peggy, Ziemssen, Tjalf, Blankenhagel, Kim Janine, Witte, Anne-Katrin, Zarnekow, Rüdiger, Davcheva, Elena, Adam, Martin, Benlian, Alexander, Trübenbach, Bianca, Rieder, Annamina, Lehrer, Christiane, Jung, Reinhard, Rockmann, Robert, Maier, Christian, Schmidt-Kraepelin, Manuel, Thiebes, Scott, Stepanovic, Stefan, Mettler, Tobias, Sunyaev, Ali, Mueller, Marius, Kordyaka, Bastian, Kaufhold, Marc-André, Grinko, Margarita, Reuter, Christian, Schorch, Marén, Langer, Amanda, Skudelny, Sascha, Hollick, Matthias, Hartwig, Katrin, Kirchner, Jan, Schlegel, Noah, Weidinger, Julian, Schlauderer, Sebastian, Overhage, Sven, Diederich, Stephan, Brendel, Alfred Benedikt, Kolbe, Lutz M., Feine, Jasper, Morana, Stefan, Gnewuch, Ulrich, Gimpel, Henner, Nüske, Niclas, Rückel, Timon, Entreß-Fürsteneck, Matthias von, Gravemeier, Laura Sophie, Lenz, Annika, Pflügner, Katharina, Mattke, Jens, Schuhbeck, Veronika, Siegfried, Nils, Dorner, Verena, Schryen, Guido, Wittek, Dorothee, Goffart, Klaus, Wulfert, Tobias, Werder, Karl, Müller, Marius, Müller, Michelle, Gutt, Dominik, Harborth, David, Heidt, Margareta, Olt, Christian Michael, Buxmann, Peter, Labadie, Clément, Legner, Christine, Maass, Max, Walter, Nicolas, Herrmann, Dominik, Wessels, Nora, Wagner, Amina, Sarswat, Jayesh Prakash, Wirth, Jakob, Laumer, Sven, Steudner, Tobias, Widjaja, Thomas, Schumann, Jan H., Helbig, Raphaela, Marx Gómez, Jorge, Hemke, Felix, Lütje, Anna, Arndt, Hans-Knud, Wohlgemuth, Volker, Herrenkind, Bernd, Lichtenberg, Sascha, Keller, Robert, Röhrich, Felix, Schmidt, Lukas, Fridgen, Gilbert, Lübbecke, Patrick, Mehdiyev, Nijat, Fettke, Peter, Nolte, Mario, Fritsch, Andreas, Betz, Stefanie, Paukstadt, Ute, Schulz, Thomas, Gewald, Heiko, Töppel, Jannick, Kleih, Karoline, Boden, Alexander, Warnecke, Danielle, Redepenning, Felix, Heim, David, Friedrich-Baasner, Gregor, Fuchs, Anna, Kunisch, Christian, Fromm, Jennifer, Mirbabaie, Milad, Stieglitz, Stefan, Maier, Sophia Bettina, Jussupow, Ekaterina, Heinzl, Armin, Meske, Christian, Kissmer, Tobias, Marx, Julian, Nitschke, Clara S., Williams, Susan P., Przybilla, Leonard, Rahn, Maximilian, Rietz, Tim, Benke, Ivo, Maedche, Alexander, Uebernickel, Falk, Weierich, Andrea, Hess, Sarah, Sommerauer, Peter, Müller, Oliver, Maxim, Leonard, Østman, Niels, Weissenfeld, Katinka, Abramova, Olga, Krasnova, Hanna, Durward, David, Simmert, Benedikt, Blohm, Ivo, Peters, Christoph, Bachmann, Nina, Drasch, Benedict, Miksch, Michael, Schweizer, André, Enders, Tobias, Schüritz, Ronny, Frey, Wiebke, Förster, Matthias, Bansemir, Bastian, Roth, Angela, Hermes, Sebastian, Hodapp, Daniel, Remane, Gerrit, Hanelt, Andre, Hille, Matthias, Lederer, Matthias, Forster, Dominik, Toutaoui, Jonas, Badmaeva, Tsagana, Hüllmann, Joschka Andreas, Endress, Clarissa, Fischer, Thomas, Pehböck, Alexander, Riedl, René, Furmanek, Lukas, Daurer, Stephan, Horn, Richard, Zschech, Patrick, Kraus, Daniel, Meironke, Anja, Seyffarth, Tobias, Damarowsky, Johannes, Rübel, Sarah, Rebmann, Adrian, Emrich, Andreas, Klein, Sabine, Loos, Peter, Salikutluk, Vildan, Schneider, Dominik, Plate, Franziska, Stadtländer, Maren, Schoormann, Thorsten, Werner, Philipp, Petrik, Dimitri, Berendes, C. Ingo, Carros, Felix, Wieching, Rainer, Lüssem, Jens, Müller, Lena, Wulf, Volker, Hoffmann, Sven, Fabiano Pinatti de Carvalho, Aparecido, Jasche, Florian, Kirchhübel, Jasmin, Ludwig, Thomas, Kotthaus, Christoph, Pipek, Volkmar, Satzger, Gerhard, Meurer, Johanna, Pyrtek, Mirco, Xu, Sascha, Maass, Wolfgang, Sekulla, André, Schmitz, Christopher, Pape, Sebastian, Struzek, David, Müller, Claudia, Ternes, Benjamin, Strecker, Stefan, Rosenthal, Kristina, Barth, Hagen, Unbehaun, David, Aal, Konstantin, Gulden, Jens, Bock, Alexander, España, Sergio, Houy, Constantin, Rehse, Jana-Rebecca, Scheid, Martin, Koch, Stefan, Mühlburger, Manuel, Lorig, Fabian, Timm, Ingo J., Mertens, Peter, Neuhaus, Uwe, Schröder, Hinrich, Schulz, Michael, Winterberg, Lars, and Bala, Christian

Subjects
Digitalisierung, Wirtschaftsinformatik, 330 Wirtschaft, Tagung
Abstract

Aus dem Inhalt: Track 1: Produktion & Cyber-Physische Systeme Requirements and a Meta Model for Exchanging Additive Manufacturing Capacities Service Systems, Smart Service Systems and Cyber- Physical Systems—What’s the difference? Towards a Unified Terminology Developing an Industrial IoT Platform – Trade-off between Horizontal and Vertical Approaches Machine Learning und Complex Event Processing: Effiziente Echtzeitauswertung am Beispiel Smart Factory Sensor retrofit for a coffee machine as condition monitoring and predictive maintenance use case Stakeholder-Analyse zum Einsatz IIoT-basierter Frischeinformationen in der Lebensmittelindustrie Towards a Framework for Predictive Maintenance Strategies in Mechanical Engineering - A Method-Oriented Literature Analysis Development of a matching platform for the requirement-oriented selection of cyber physical systems for SMEs Track 2: Logistic Analytics An Empirical Study of Customers’ Behavioral Intention to Use Ridepooling Services – An Extension of the Technology Acceptance Model Modeling Delay Propagation and Transmission in Railway Networks What is the impact of company specific adjustments on the acceptance and diffusion of logistic standards? Robust Route Planning in Intermodal Urban Traffic Track 3: Unternehmensmodellierung & Informationssystemgestaltung (Enterprise Modelling & Information Systems Design) Work System Modeling Method with Different Levels of Specificity and Rigor for Different Stakeholder Purposes Resolving Inconsistencies in Declarative Process Models based on Culpability Measurement Strategic Analysis in the Realm of Enterprise Modeling – On the Example of Blockchain-Based Initiatives for the Electricity Sector Zwischenbetriebliche Integration in der Möbelbranche: Konfigurationen und Einflussfaktoren Novices’ Quality Perceptions and the Acceptance of Process Modeling Grammars Entwicklung einer Definition für Social Business Objects (SBO) zur Modellierung von Unternehmensinformationen Designing a Reference Model for Digital Product Configurators Terminology for Evolving Design Artifacts Business Role-Object Specification: A Language for Behavior-aware Structural Modeling of Business Objects Generating Smart Glasses-based Information Systems with BPMN4SGA: A BPMN Extension for Smart Glasses Applications Using Blockchain in Peer-to-Peer Carsharing to Build Trust in the Sharing Economy Testing in Big Data: An Architecture Pattern for a Development Environment for Innovative, Integrated and Robust Applications Track 4: Lern- und Wissensmanagement (e-Learning and Knowledge Management) eGovernment Competences revisited – A Literature Review on necessary Competences in a Digitalized Public Sector Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents Teaching the Digital Transformation of Business Processes: Design of a Simulation Game for Information Systems Education Conceptualizing Immersion for Individual Learning in Virtual Reality Designing a Flipped Classroom Course – a Process Model The Influence of Risk-Taking on Knowledge Exchange and Combination Gamified Feedback durch Avatare im Mobile Learning Alexa, Can You Help Me Solve That Problem? - Understanding the Value of Smart Personal Assistants as Tutors for Complex Problem Tasks Track 5: Data Science & Business Analytics Matching with Bundle Preferences: Tradeoff between Fairness and Truthfulness Applied image recognition: guidelines for using deep learning models in practice Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting Reading Between the Lines of Qualitative Data – How to Detect Hidden Structure Based on Codes Online Auctions with Dual-Threshold Algorithms: An Experimental Study and Practical Evaluation Design Features of Non-Financial Reward Programs for Online Reviews: Evaluation based on Google Maps Data Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics Leveraging Unstructured Image Data for Product Quality Improvement Decision Support for Real Estate Investors: Improving Real Estate Valuation with 3D City Models and Points of Interest Knowledge Discovery from CVs: A Topic Modeling Procedure Online Product Descriptions – Boost for your Sales? Entscheidungsunterstützung durch historienbasierte Dienstreihenfolgeplanung mit Pattern A Semi-Automated Approach for Generating Online Review Templates Machine Learning goes Measure Management: Leveraging Anomaly Detection and Parts Search to Improve Product-Cost Optimization Bedeutung von Predictive Analytics für den theoretischen Erkenntnisgewinn in der IS-Forschung Track 6: Digitale Transformation und Dienstleistungen Heuristic Theorizing in Software Development: Deriving Design Principles for Smart Glasses-based Systems Mirroring E-service for Brick and Mortar Retail: An Assessment and Survey Taxonomy of Digital Platforms: A Platform Architecture Perspective Value of Star Players in the Digital Age Local Shopping Platforms – Harnessing Locational Advantages for the Digital Transformation of Local Retail Outlets: A Content Analysis A Socio-Technical Approach to Manage Analytics-as-a-Service – Results of an Action Design Research Project Characterizing Approaches to Digital Transformation: Development of a Taxonomy of Digital Units Expectations vs. Reality – Benefits of Smart Services in the Field of Tension between Industry and Science Innovation Networks and Digital Innovation: How Organizations Use Innovation Networks in a Digitized Environment Characterising Social Reading Platforms— A Taxonomy-Based Approach to Structure the Field Less Complex than Expected – What Really Drives IT Consulting Value Modularity Canvas – A Framework for Visualizing Potentials of Service Modularity Towards a Conceptualization of Capabilities for Innovating Business Models in the Industrial Internet of Things A Taxonomy of Barriers to Digital Transformation Ambidexterity in Service Innovation Research: A Systematic Literature Review Design and success factors of an online solution for cross-pillar pension information Track 7: IT-Management und -Strategie A Frugal Support Structure for New Software Implementations in SMEs How to Structure a Company-wide Adoption of Big Data Analytics The Changing Roles of Innovation Actors and Organizational Antecedents in the Digital Age Bewertung des Kundennutzens von Chatbots für den Einsatz im Servicedesk Understanding the Benefits of Agile Software Development in Regulated Environments Are Employees Following the Rules? On the Effectiveness of IT Consumerization Policies Agile and Attached: The Impact of Agile Practices on Agile Team Members’ Affective Organisational Commitment The Complexity Trap – Limits of IT Flexibility for Supporting Organizational Agility in Decentralized Organizations Platform Openness: A Systematic Literature Review and Avenues for Future Research Competence, Fashion and the Case of Blockchain The Digital Platform Otto.de: A Case Study of Growth, Complexity, and Generativity Track 8: eHealth & alternde Gesellschaft Security and Privacy of Personal Health Records in Cloud Computing Environments – An Experimental Exploration of the Impact of Storage Solutions and Data Breaches Patientenintegration durch Pfadsysteme Digitalisierung in der Stressprävention – eine qualitative Interviewstudie zu Nutzenpotenzialen User Dynamics in Mental Health Forums – A Sentiment Analysis Perspective Intent and the Use of Wearables in the Workplace – A Model Development Understanding Patient Pathways in the Context of Integrated Health Care Services - Implications from a Scoping Review Understanding the Habitual Use of Wearable Activity Trackers On the Fit in Fitness Apps: Studying the Interaction of Motivational Affordances and Users’ Goal Orientations in Affecting the Benefits Gained Gamification in Health Behavior Change Support Systems - A Synthesis of Unintended Side Effects Investigating the Influence of Information Incongruity on Trust-Relations within Trilateral Healthcare Settings Track 9: Krisen- und Kontinuitätsmanagement Potentiale von IKT beim Ausfall kritischer Infrastrukturen: Erwartungen, Informationsgewinnung und Mediennutzung der Zivilbevölkerung in Deutschland Fake News Perception in Germany: A Representative Study of People’s Attitudes and Approaches to Counteract Disinformation Analyzing the Potential of Graphical Building Information for Fire Emergency Responses: Findings from a Controlled Experiment Track 10: Human-Computer Interaction Towards a Taxonomy of Platforms for Conversational Agent Design Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis Self-Tracking and Gamification: Analyzing the Interplay of Motivations, Usage and Motivation Fulfillment Erfolgsfaktoren von Augmented-Reality-Applikationen: Analyse von Nutzerrezensionen mit dem Review-Mining-Verfahren Designing Dynamic Decision Support for Electronic Requirements Negotiations Who is Stressed by Using ICTs? A Qualitative Comparison Analysis with the Big Five Personality Traits to Understand Technostress Walking the Middle Path: How Medium Trade-Off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents Theory-Based Affordances of Utilitarian, Hedonic and Dual-Purposed Technologies: A Literature Review Eliciting Customer Preferences for Shopping Companion Apps: A Service Quality Approach The Role of Early User Participation in Discovering Software – A Case Study from the Context of Smart Glasses The Fluidity of the Self-Concept as a Framework to Explain the Motivation to Play Video Games Heart over Heels? An Empirical Analysis of the Relationship between Emotions and Review Helpfulness for Experience and Credence Goods Track 11: Information Security and Information Privacy Unfolding Concerns about Augmented Reality Technologies: A Qualitative Analysis of User Perceptions To (Psychologically) Own Data is to Protect Data: How Psychological Ownership Determines Protective Behavior in a Work and Private Context Understanding Data Protection Regulations from a Data Management Perspective: A Capability-Based Approach to EU-GDPR On the Difficulties of Incentivizing Online Privacy through Transparency: A Qualitative Survey of the German Health Insurance Market What is Your Selfie Worth? A Field Study on Individuals’ Valuation of Personal Data Justification of Mass Surveillance: A Quantitative Study An Exploratory Study of Risk Perception for Data Disclosure to a Network of Firms Track 12: Umweltinformatik und nachhaltiges Wirtschaften Kommunikationsfäden im Nadelöhr – Fachliche Prozessmodellierung der Nachhaltigkeitskommunikation am Kapitalmarkt Potentiale und Herausforderungen der Materialflusskostenrechnung Computing Incentives for User-Based Relocation in Carsharing Sustainability’s Coming Home: Preliminary Design Principles for the Sustainable Smart District Substitution of hazardous chemical substances using Deep Learning and t-SNE A Hierarchy of DSMLs in Support of Product Life-Cycle Assessment A Survey of Smart Energy Services for Private Households Door-to-Door Mobility Integrators as Keystone Organizations of Smart Ecosystems: Resources and Value Co-Creation – A Literature Review Ein Entscheidungsunterstützungssystem zur ökonomischen Bewertung von Mieterstrom auf Basis der Clusteranalyse Discovering Blockchain for Sustainable Product-Service Systems to enhance the Circular Economy Digitale Rückverfolgbarkeit von Lebensmitteln: Eine verbraucherinformatische Studie Umweltbewusstsein durch audiovisuelles Content Marketing? Eine experimentelle Untersuchung zur Konsumentenbewertung nachhaltiger Smartphones Towards Predictive Energy Management in Information Systems: A Research Proposal A Web Browser-Based Application for Processing and Analyzing Material Flow Models using the MFCA Methodology Track 13: Digital Work - Social, mobile, smart On Conversational Agents in Information Systems Research: Analyzing the Past to Guide Future Work The Potential of Augmented Reality for Improving Occupational First Aid Prevent a Vicious Circle! The Role of Organizational IT-Capability in Attracting IT-affine Applicants Good, Bad, or Both? Conceptualization and Measurement of Ambivalent User Attitudes Towards AI A Case Study on Cross-Hierarchical Communication in Digital Work Environments ‘Show Me Your People Skills’ - Employing CEO Branding for Corporate Reputation Management in Social Media A Multiorganisational Study of the Drivers and Barriers of Enterprise Collaboration Systems-Enabled Change The More the Merrier? The Effect of Size of Core Team Subgroups on Success of Open Source Projects The Impact of Anthropomorphic and Functional Chatbot Design Features in Enterprise Collaboration Systems on User Acceptance Digital Feedback for Digital Work? Affordances and Constraints of a Feedback App at InsurCorp The Effect of Marker-less Augmented Reality on Task and Learning Performance Antecedents for Cyberloafing – A Literature Review Internal Crowd Work as a Source of Empowerment - An Empirical Analysis of the Perception of Employees in a Crowdtesting Project Track 14: Geschäftsmodelle und digitales Unternehmertum Dividing the ICO Jungle: Extracting and Evaluating Design Archetypes Capturing Value from Data: Exploring Factors Influencing Revenue Model Design for Data-Driven Services Understanding the Role of Data for Innovating Business Models: A System Dynamics Perspective Business Model Innovation and Stakeholder: Exploring Mechanisms and Outcomes of Value Creation and Destruction Business Models for Internet of Things Platforms: Empirical Development of a Taxonomy and Archetypes Revitalizing established Industrial Companies: State of the Art and Success Principles of Digital Corporate Incubators When 1+1 is Greater than 2: Concurrence of Additional Digital and Established Business Models within Companies Special Track 1: Student Track Investigating Personalized Price Discrimination of Textile-, Electronics- and General Stores in German Online Retail From Facets to a Universal Definition – An Analysis of IoT Usage in Retail Is the Technostress Creators Inventory Still an Up-To-Date Measurement Instrument? Results of a Large-Scale Interview Study Application of Media Synchronicity Theory to Creative Tasks in Virtual Teams Using the Example of Design Thinking TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter Application of Process Mining Techniques to Support Maintenance-Related Objectives How Voice Can Change Customer Satisfaction: A Comparative Analysis between E-Commerce and Voice Commerce Business Process Compliance and Blockchain: How Does the Ethereum Blockchain Address Challenges of Business Process Compliance? Improving Business Model Configuration through a Question-based Approach The Influence of Situational Factors and Gamification on Intrinsic Motivation and Learning Evaluation von ITSM-Tools für Integration und Management von Cloud-Diensten am Beispiel von ServiceNow How Software Promotes the Integration of Sustainability in Business Process Management Criteria Catalog for Industrial IoT Platforms from the Perspective of the Machine Tool Industry Special Track 3: Demos & Prototyping Privacy-friendly User Location Tracking with Smart Devices: The BeaT Prototype Application-oriented robotics in nursing homes Augmented Reality for Set-up Processe Mixed Reality for supporting Remote-Meetings Gamification zur Motivationssteigerung von Werkern bei der Betriebsdatenerfassung Automatically Extracting and Analyzing Customer Needs from Twitter: A “Needmining” Prototype GaNEsHA: Opportunities for Sustainable Transportation in Smart Cities TUCANA: A platform for using local processing power of edge devices for building data-driven services Demonstrator zur Beschreibung und Visualisierung einer kritischen Infrastruktur Entwicklung einer alltagsnahen persuasiven App zur Bewegungsmotivation für ältere Nutzerinnen und Nutzer A browser-based modeling tool for studying the learning of conceptual modeling based on a multi-modal data collection approach Exergames & Dementia: An interactive System for People with Dementia and their Care-Network Workshops Workshop Ethics and Morality in Business Informatics (Workshop Ethik und Moral in der Wirtschaftsinformatik – EMoWI’19) Model-Based Compliance in Information Systems - Foundations, Case Description and Data Set of the MobIS-Challenge for Students and Doctoral Candidates Report of the Workshop on Concepts and Methods of Identifying Digital Potentials in Information Management Control of Systemic Risks in Global Networks - A Grand Challenge to Information Systems Research Die Mitarbeiter von morgen - Kompetenzen künftiger Mitarbeiter im Bereich Business Analytics Digitaler Konsum: Herausforderungen und Chancen der Verbraucherinformatik

Published
2019
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49. FOOL ME ONCE, SHAME ON YOU, FOOL ME TWICE, SHAME ON ME: A TAXONOMY OF ATTACK AND DEFENSE PATTERNS FOR AI SECURITY.

Author

Heinrich, Kai, Graf, Johannes, Ji Chen, Laurisch, Jakob, and Zschech, Patrick

Subjects
ARTIFICIAL neural networks, ARTIFICIAL intelligence, HUMAN behavior, INTERNET security, MACHINE learning
Abstract

Advances in the area of AI systems lead to the application of complex deep neural networks (DNN) that outperform other algorithms in critical applications like predictive maintenance, healthcare or autonomous driving. Unfortunately, the properties that render them so successful also lead to vulnerabilities that can make them the subject of adversarial attacks. While these systems try to mimic human behavior when transforming large amounts of data into decision recommendations, they remain black-box models so that humans often fail to detect adversarial behavior patterns in the model training process. Therefore, we derive a taxonomy from an extensive literature review to structure the knowledge of possible attack and defense patterns to create a basis for the analysis and implementation of AI security for scientists and practitioners alike. Furthermore, we use the taxonomy to expose the most common attack pattern and, in addition, we demonstrate the application of the taxonomy by projecting two real-world cases onto the taxonomy space and discuss applicable attack and defense patterns. [ABSTRACT FROM AUTHOR]

Published
2020

50. How Much AI Do You Require? Decision Factors for Adopting AI Technology.

Author

Wanner, Jonas, Janiesch, Christian, Heinrich, Kai, and Zschech, Patrick

Subjects
ARTIFICIAL intelligence, MACHINE learning, ANALYTIC hierarchy process, INNOVATION adoption, INFORMATION technology, DECISION support systems
Abstract

Artificial intelligence (AI) based on machine learning technology disrupts how knowledge is gained. Nevertheless, ML’s improved accuracy of prediction comes at the cost of low traceability due to its black-box nature. The field of explainable AI tries to counter this. However, for practical use in IT projects, these two research streams offer only partial advice for AI adoption as the trade-off between accuracy and explainability has not been adequately discussed yet. Thus, we simulate a decision process by implementing three best practice AI-based decision support systems for a high-stake maintenance decision scenario and evaluate the decision and attitude factors using the Analytical Hierarchy Process (AHP) through an expert survey. The combined results indicate that system performance is still the most important factor and that implementation effort and explainability are relatively even factors. Further, we found that systems using similarity-based matching or direct modeling for remaining useful life estimation performed best. [ABSTRACT FROM AUTHOR]

Published
2020

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