@incollection{BuchholzMeiners2008,
  author    = {Buchholz, Wolfgang and Meiners, Daniel},
  title     = {Attainment of higher quality for innovative ideas by systematic utilisation of external sources},
  series = {Attainment of higher quality for innovative ideas by systematic utilisation of external sources},
  booktitle = {Attainment of higher quality for innovative ideas by systematic utilisation of external sources},
  address   = {Hamburg},
  year      = {2008},
  language  = {en}
}
@incollection{Buchholz2014,
  author    = {Buchholz, Wolfgang},
  title     = {Borderless business - Bridging the gaps between fairly contrastive management concepts},
  series = {Baaken, T./Teczke, J. [Edit.]: Managing Disruption and Destabilisation},
  booktitle = {Baaken, T./Teczke, J. [Edit.]: Managing Disruption and Destabilisation},
  publisher = {International Management Foundation},
  address   = {Cracow},
  isbn      = {978-3-938137-49-9},
  pages     = {143 -- 156},
  year      = {2014},
  language  = {en}
}
@incollection{EngelkingBuchholzKoehne2020,
  author    = {Engelking, Bastian and Buchholz, Wolfgang and K{\"o}hne, Frank},
  title     = {Design Principles for the Application of Machine Learning in Supply Chain Risk Management: An Action Design Research Approach},
  series = {Supply Management Research / Ed. Christoph Bode},
  booktitle = {Supply Management Research / Ed. Christoph Bode},
  publisher = {Springer},
  address   = {Berlin},
  publisher      = {FH M{\"u}nster - University of Applied Sciences},
  pages     = {1 -- 28},
  year      = {2020},
  abstract  = {The opportunity to anticipate delivery failures, shortages or delays in company's upstream supply chains at an early stage facilitates to take preventive countermeas-ures to mitigate potential damage. However, data-driven predictive technologies such as machine learning (ML) are rarely examined in supply chain risk management (SCRM). The purpose of the following paper is to present a framework of design principles for the application of ML in SCRM. The foundation of this framework is an action design research (ADR) project, which is performed in collaboration with the SCRM department of an automotive company. A predictive ML model is developed and evaluated in collaboration with the company. Based on the findings and observa-tions made during the project, general design principles are derived and grouped by the three interrelated elements of organisation, development and operation, which are to be considered when applying ML in SCRM. Finally, the derived elements and the corresponding design principles are discussed and justified with reference to the literature.},
  language  = {en}
}
@incollection{BuchholzDeBie2021,
  author    = {Buchholz, Wolfgang and De Bie, Holger},
  title     = {Managing the Supply-side of Digital Platforms: Framework, Categorisation and Selection of Complementors for Industrial IoT- and Financial Services Platforms},
  series = {Supply Management Research: Aktuelle Forschungsergebnisse 2021 (Advanced Studies in Supply Management) / Hrsg. Christof Bode},
  booktitle = {Supply Management Research: Aktuelle Forschungsergebnisse 2021 (Advanced Studies in Supply Management) / Hrsg. Christof Bode},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-658-35448-0},
  doi       = {10.1007/978-3-658-35449-7},
  publisher      = {FH M{\"u}nster - University of Applied Sciences},
  pages     = {233 -- 256},
  year      = {2021},
  abstract  = {In the so-called ecosystem economy, new platform-based business models evolve rapidly based on the prospects of digital technology. Thus far, little research has been conducted on the supply side of digital platforms which also explains the lack of empirical evidence. We develop a framework, categorise complementors, and analyse the main factors of influence for the evaluation and selection of complementors. For our analysis, we consider both industrial IoT platforms as well as financial services platforms. In addition, we use an explorative research design and conduct semi-structured interviews to contribute to this research field. Top-level managers of digi-tal platforms in both industries were interviewed as experts. In addition, the study also considered secondary data to increases the overall reliability and validity in terms of triangulation. As a result, our study reveals both a number of similarities and differences with regard to complementor management for industrial IoT- and financial services platforms.},
  language  = {en}
}