TY - CHAP A1 - Kurzhals, Kerstin A1 - Chak, Choiwai Maggie T1 - „Innovation für Society“ T2 - Poster presentation held at the Forum Citizen Science 2019, "Die Zukunft der Bürgerwissenschaften", 26.-27.09.2019, Münster Germany Y1 - 2019 UR - https://www.buergerschaffenwissen.de/sites/default/files/grid/2019/10/18/a4Poster_InnovationSociety_Kurzhals.pdf ER - TY - CHAP A1 - Wasserman, Michael A1 - Fisher, Sandra ED - Bissola, Rita ED - Imperatori, Barbara T1 - “Mind the Gap”: A Human Resource Management Perspective on Virtual Reality Training T2 - Proceedings of the 7th International E-HRM Conference: HRM 4.0 for Human-Centered Organizations N2 - Virtual reality (VR) is starting to realize some of its promise as a tool to improve training effectiveness. However, research on VR for training and development is limited. Existing theories and models relating to organizational training and learning are infrequently used in the VR literature. A greater understanding of why VR works in the training context would help training designers create effective programs that leverage this continuously developing technology. This paper provides a typology of VR technologies specifically relevant to HR and integrates HR training frameworks and theory into findings on VR training from these other literatures. We specifically focus on immersive VR technology and seek to better understand reasons for the effectiveness of VR technologies for both training and assessment. We review findings, integrate related streams of research, and offer guideposts for those contemplating VR implementation in four important areas: training reactions in a VR context, VR-specific learning outcomes, opportunities for assessment using VR, and the effect of VR on training transfer. We conclude the paper by identifying a VR-training agenda for HR researchers. Y1 - 2018 SP - 227 EP - 247 PB - Università Cattolica del Sacro Cuore CY - Milan, Italy ER - TY - CHAP A1 - Fohrmann, Oliver T1 - Wirtschaft als geldbeglaubigende Erzählung T2 - Erzähltes Geld: Finanzmärkte und Krisen in Literatur, Filme und Medien / Hrsg. Karsten Becker KW - Geld KW - Ökonomik Y1 - 2020 SN - 978-3-8260-6930-7 PB - Königshausen & Neumann CY - Würzburg ER - TY - CHAP A1 - Li, Zhan Wen A1 - Wasserman, Michael A1 - Beekman, Amy A1 - Compeau, Marc A1 - Draper, Erin T1 - What You See is Not Always What You Get: Customer Perceptions and Behaviors in the Fuzzy 'For-Benefit' Space T2 - United States Association for Small Business and Entrepreneurship Conference Proceedings Y1 - 2016 SP - IK1 EP - IK7 PB - United States Association for Small Business and Entrepreneurship ER - TY - CHAP A1 - Matthies, Benjamin T1 - What to Do With All These Project Documentations? – Research Issues in Reusing Codified Project Knowledge T2 - Proceedings of the 19th Pacific Asia Conference on Information Systems (PACIS) Y1 - 2015 ER - TY - CHAP A1 - Kurzhals, Kerstin A1 - Uude, Katrin T1 - What impedes a successful Third Mission? Identifying and avoiding the main barriers in transdisciplinary cooperation T2 - Presentation at UIIN Conference 2021, 14.-16.06.2021, Amsterdam, Netherlands (digital) Y1 - 2021 CY - Amsterdam, Netherlands (digital) ER - TY - CHAP A1 - Kurzhals, Kerstin A1 - Junker, Christian T1 - Von KI bis Flugtaxi: Ist die Welt ein digitales Dorf? T2 - Panel discussion held at the Pushcon 2019, "Zukunft. Macher. Treffen", 19.-20-09.2019, Ahaus, Germany Y1 - 2019 ER - TY - CHAP A1 - Wesbuer, Annika A1 - Kurzhals, Kerstin A1 - Uude, Katrin T1 - USC Ecosystem: A Comprehensive Framework for university-society co-creation T2 - University-Industry Interaction Conference 2022 Y1 - 2022 CY - Amsterdam, Netherlands ER - TY - CHAP A1 - Schneid, Konrad A1 - Kuchen, Herbert A1 - Thöne, Sebastian A1 - Di Bernardo, Sascha T1 - Uncovering Data-Flow Anomalies in BPMN-Based Process-Driven Applications T2 - Proceedings of the 36th Annual ACM Symposium on Applied Computing N2 - Process-Driven Applications flourish through the interaction between an executable BPMN process model, human tasks, and external software services. All these components operate on shared process data, so it is even more important to check the correct data flow. However, data flow is in most cases not explicitly defined but hidden in model elements, form declarations, and program code. This paper elaborates on data-flow anomalies acting as indicators for potential errors and how such anomalies can be uncovered despite implicit and hidden data-flow definitions. By considering an integrated view, it goes beyond other approaches which are restricted to separate data-flow analysis of either process model or source code. The main idea is to merge call graphs representing programmed services into a control-flow representation of the process model, to label the resulting graph with associated data operations, and to detect anomalies in that labeled graph using a dedicated data-flow analysis. The applicability of the solution is demonstrated by a prototype designed for the Camunda BPM platform. KW - BPMN KW - Data-Flow Anomalies KW - Process-Driven Application KW - Control-Flow Graph Analysis Y1 - 2021 SN - 9781450381048 U6 - http://dx.doi.org/10.1145/3412841.3442025 SP - 1504 EP - 1512 PB - Association for Computing Machinery CY - New York, NY, USA ER - TY - CHAP A1 - Bücker, Michael A1 - Szepannek, Gero A1 - Biecek, Przemyslaw A1 - Gosiewska, Alicja A1 - Staniak, Mateusz ED - Crook, Jonathan T1 - Transparency of Machine Learning Models in Credit Scoring T2 - CRC Conference XVI Papers N2 - A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make “black box” machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power. Y1 - 2019 UR - https://crc.business-school.ed.ac.uk/wp-content/uploads/sites/55/2019/07/C13-Transparency-of-Machine-Learning-Models-in-Credit-Scoring-B%C3%BCcker.pdf SP - 1 EP - 1 PB - Credit Research Center, University of Edinburgh CY - Edinburgh ER -