@book{OPUS4-12399,
  title     = {Encyclopedia of Electronic HRM},
  editor    = {Bondarouk, Tanya and Fisher, Sandra},
  publisher = {De Gruyter Oldenbourg},
  isbn      = {978-3-11-062998-9},
  doi       = {10.1515/9783110633702},
  publisher      = {FH M{\"u}nster - University of Applied Sciences},
  pages     = {314},
  year      = {2020},
  language  = {en}
}
@article{AndreBaaken2020,
  author    = {Andre, Perusso and Baaken, Thomas},
  title     = {Assessing the authenticity of cases, internships and problem-based learning as managerial learning experiences: Concepts, methods and lessons for practice},
  series = {The International Journal of Management Education},
  volume    = {18},
  journal   = {The International Journal of Management Education},
  number    = {3},
  doi       = {10.1016/j.ijme.2020.100425},
  pages     = {100425},
  year      = {2020},
  language  = {en}
}
@article{BaakenAlfertKliewe2020,
  author    = {Baaken, Thomas and Alfert, Carina and Kliewe, Thorsten},
  title     = {Baaken Thomas; Alfert, Carina; Kliewe, Thorsten (2020). Corporate Venturing - a new way of creating a company's future},
  series = {Optimum. Studia Ekonomiczne},
  volume    = {99},
  journal   = {Optimum. Studia Ekonomiczne},
  number    = {1},
  issn      = {1506-7637},
  doi       = {10.15290/oes.2020.01.99.01},
  pages     = {3 -- 21},
  year      = {2020},
  language  = {en}
}
@inproceedings{Bettmann2019,
  author    = {Bettmann, Theresa},
  title     = {A Framework for Resilient Data Management for Smart Grids},
  series = {2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)},
  booktitle = {2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)},
  publisher = {IEEE},
  address   = {Berlin, Germany},
  isbn      = {978-1-7281-5138-0},
  doi       = {10.1109/ISSREW.2019.00048},
  pages     = {85 -- 88},
  year      = {2019},
  language  = {en}
}
@techreport{BuchholzdeBie2020,
  type      = {Working Paper},
  author    = {Buchholz, Wolfgang and de Bie, Holger},
  title     = {A Paradigm Shift in Times of Digital Transformation: What are the Determinants of Bank-Fintech Cooperation? Empirical Evidence from the Financial Service Sector},
  pages     = {18},
  year      = {2020},
  abstract  = {The financial services sector is undergoing a digital transformation. But the emerging picture is very different from the innovation-driven revolution that was initially expected. Due to the variety of challenges, banks and mostly young financial technology companies (fintechs) are increasingly cooperating instead of competing. Yet despite the rapidly growing importance of bank-fintech cooperation, there is still a lack of empirical evidence on the determinants. We use an explorative research design and conduct semi-structured interviews to contribute to this research field. Our findings illustrate that banks are primarily concerned with access to innovation, while fintechs mainly focus on balancing their resource constraints.},
  language  = {en}
}
@techreport{BuchholzdeBie2020,
  type      = {Working Paper},
  author    = {Buchholz, Wolfgang and de Bie, Holger},
  title     = {Managing the Supply-Side of Digital Platforms: First Insights from the Financial Services Sector},
  pages     = {19},
  year      = {2020},
  abstract  = {The aim of this paper is to contribute knowledge on the interface and relationship be-tween digital platforms in the financial services sector and the service providers on its supply-side. Based on an explorative research design with literature research and three expert interviews, we examine the categories of service providers that can be distin-guished, the factors of influence that are relevant for selecting service providers, and the main benefits and risks for partnerships with service providers. The results are a starting point for deeper investigation and the creation of research questions for future projects in other industries.},
  language  = {en}
}
@inproceedings{BuchholzKappel2020,
  author    = {Buchholz, Wolfgang and Kappel, Antonia},
  title     = {Purchasing in service networks: The impact of high visibility on purchasing performance},
  series = {IPSERA Conference Proceedings},
  booktitle = {IPSERA Conference Proceedings},
  publisher = {IPSERA},
  doi       = {10.25974/fhms-13742},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:836-opus-137427},
  pages     = {1 -- 25},
  year      = {2020},
  abstract  = {While the service sector is growing rapidly, the purchasing of services has not yet received significant attention in theory or practice. Service purchasers face serious challenges, and existing purchasing practices for services are often non-strategic. We choose an exploratory-qualitative research approach to investigate the purchasing of IT, logistics and Maintenance, Repair, and Overhaul (MRO) services. In particular, we focus on the role of visibility and analyze how service purchasers can benefit from extensive knowledge about their service networks. We determine that visibility indeed adds significant value to service purchasing and can help service purchasers to decrease costs, mitigate risks and maintain competitiveness.},
  language  = {en}
}
@article{BurchardtStuweFeldmann2020,
  author    = {Burchardt, Martin and Stuwe, Stefan and Feldmann, Carsten},
  title     = {Agile Project Management: Not a Silver Bullet},
  series = {Whitepaper},
  journal   = {Whitepaper},
  editor    = {Fusion Consulting,},
  pages     = {1 -- 11},
  year      = {2020},
  language  = {en}
}
@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}
}
@inproceedings{ChakCarminati2020,
  author    = {Chak, Choiwai Maggie and Carminati, Lara},
  title     = {Performing in Community-Academic Health Partnerships: Interplay of Clear, Difficult and Valued Goals},
  series = {Academy of Management Annual Meeting Proceedings},
  booktitle = {Academy of Management Annual Meeting Proceedings},
  doi       = {10.5465/AMBPP.2020.18772abstract},
  year      = {2020},
  language  = {en}
}