@incollection{vonKeitzEweltKnauer2020,
  author    = {von Keitz, Isabel and Ewelt-Knauer, Corinna},
  title     = {\S 296 HGB},
  series = {Bilanzrecht, Kommentar (Loseblatt) / Hrsg. Baetge/Kirsch/Thiele},
  booktitle = {Bilanzrecht, Kommentar (Loseblatt) / Hrsg. Baetge/Kirsch/Thiele},
  publisher = {Stollfuss},
  address   = {Bonn/Berlin},
  publisher      = {FH M{\"u}nster - University of Applied Sciences},
  pages     = {1 -- 21},
  year      = {2020},
  language  = {de}
}
@incollection{vonKeitz2020,
  author    = {von Keitz, Isabel},
  title     = {\S 294 HGB},
  series = {Bilanzrecht, Kommentar (Loseblatt) / Hrsg. Baetge/Kirsch/Thiele},
  booktitle = {Bilanzrecht, Kommentar (Loseblatt) / Hrsg. Baetge/Kirsch/Thiele},
  publisher = {Stollfuss},
  address   = {Bonn/Berlin},
  publisher      = {FH M{\"u}nster - University of Applied Sciences},
  pages     = {1 -- 13},
  year      = {2020},
  language  = {de}
}
@article{Rupprecht2020,
  author    = {Rupprecht, Manuel},
  title     = {Wirtschaftsprognosen in Corona-Zeiten},
  series = {WISU - Das Wirtschaftsstudium},
  volume    = {49},
  journal   = {WISU - Das Wirtschaftsstudium},
  number    = {7},
  issn      = {0340-3084},
  pages     = {754 -- 755},
  year      = {2020},
  language  = {de}
}
@article{Michaelis2020,
  author    = {Michaelis, Nina V.},
  title     = {Wirtschaften in einer vollen Welt},
  series = {UmweltDialog},
  volume    = {2020},
  journal   = {UmweltDialog},
  number    = {13},
  issn      = {2367-4113},
  pages     = {18 -- 22},
  year      = {2020},
  abstract  = {Unser Wirtschaftssystem st{\"o}ßt an planetarische Grenzen, wie beispielsweise durch den immer schneller voranschreitenden menschgemachten Klimawandel deutlich wird. Es stellt sich die Frage, ob das auch anders geht: Wie kann ein Wirtschaftssystem aussehen, dass mit den Grenzen unseres Erdsystems kompatibel ist? Welche Ans{\"a}tze gibt es und welche werden bereits praktisch umgesetzt? Kann das funktionieren, ohne dass unser Wohlstand abnimmt?},
  language  = {de}
}
@book{OPUS4-12724,
  title     = {Wirtschaft am Scheideweg - Corona, Brexit, Handelskriege und mehr},
  editor    = {Rupprecht, Manuel},
  publisher = {W. Kohlhammer},
  address   = {Stuttgart},
  isbn      = {978-3-17-038866-6},
  publisher      = {FH M{\"u}nster - University of Applied Sciences},
  pages     = {164},
  year      = {2020},
  language  = {de}
}
@inproceedings{Fohrmann2020,
  author    = {Fohrmann, Oliver},
  title     = {Wirtschaft als geldbeglaubigende Erz{\"a}hlung},
  series = {Erz{\"a}hltes Geld: Finanzm{\"a}rkte und Krisen in Literatur, Filme und Medien / Hrsg. Karsten Becker},
  booktitle = {Erz{\"a}hltes Geld: Finanzm{\"a}rkte und Krisen in Literatur, Filme und Medien / Hrsg. Karsten Becker},
  publisher = {K{\"o}nigshausen \& Neumann},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-8260-6930-7},
  year      = {2020},
  language  = {de}
}
@article{BueckerSzepannekGosiewskaetal.2020,
  author    = {B{\"u}cker, Michael and Szepannek, Gero and Gosiewska, Alicja and Biecek, Przemyslaw},
  title     = {Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring},
  series = {arXiv},
  volume    = {2009.13384},
  journal   = {arXiv},
  pages     = {1 -- 30},
  year      = {2020},
  abstract  = {A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, 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. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.},
  language  = {en}
}
@article{FeldmannNiessingGeyer2020,
  author    = {Feldmann, Carsten and Nießing, J{\"o}rg and Geyer, Fred},
  title     = {Three steps to outstanding digital CX},
  series = {Indian Management},
  volume    = {59},
  journal   = {Indian Management},
  number    = {3},
  editor    = {AIMA All Indian Management Association,},
  issn      = {0019-5812},
  pages     = {18 -- 23},
  year      = {2020},
  language  = {en}
}
@article{PerussovanderSijdeLealetal.2020,
  author    = {Perusso, Andre and van der Sijde, Peter and Leal, Rafael and Blankensteijn, Marlous},
  title     = {The effectiveness and impact of action learning on business graduates' professional practice},
  series = {Journal of Management Education},
  journal   = {Journal of Management Education},
  doi       = {10.1177/1052562920940374},
  year      = {2020},
  language  = {de}
}
@article{WolteringSardouxKlasenFeldmann2020,
  author    = {Woltering, Tim and Sardoux Klasen, Andr{\´e} and Feldmann, Carsten},
  title     = {The Economic Value Added of Augmented Reality in the Packing Process},
  series = {Journal of Applied Business and Economics},
  volume    = {22},
  journal   = {Journal of Applied Business and Economics},
  number    = {5},
  doi       = {10.33423/jabe.v22i5.3051},
  pages     = {88 -- 96},
  year      = {2020},
  language  = {en}
}