Call for Papers: Die Unternehmung: Machine Learning Methods as Components of Existing Business Models


Guest Editors of the Special Issue 2/2021:

Dr. Johannes Kriebel
Prof. Dr. Andreas Pfingsten

Machine Learning Methods
as Components of Existing Business Models

Machine learning and artificial intelligence have lately been hotly debated topics. This is reflected in substantial public attention, increased consideration in both business and economics research, and also in the aspirations of career starters.

The underlying methods were often developed decades ago and further refined over time. However, the pace of innovation in this area has greatly increased in recent years as a result of significant resources being dedicated to the issue, within and beyond academia, vast data sources, and equally vast computing capacities. Spectacular successes have been presented to the general public, such as IBM's Watson, Google's AlphaGo, or self-driving cars. Strategic plans for artificial intelligence formulated by major governments and industry leaders further emphasize that this is a trend that is here to stay.

It is foreseeable that these new technologies will fundamentally change existing business models. This includes fully automated customer communication for tasks such as address changes, enquiries, and changes to contractual conditions. In the near future, insurance companies could settle claims automatically, based entirely on descriptions and images. Only a few years from now, audit firms could have a wide range of manually performed tasks carried out by computer programs. There are already many applications in production processes and supply chain management.

This development has the potential to fundamentally reorganize market competition and the position of stakeholders in companies. However, the full extent of this transition and the widespread dissemination of machine learning applications into business models are still developing. Digital business models are still being designed and many organizations are currently developing the necessary know-how. In some cases, however, regulatory hurdles stand in the way. The interpretability of machine learning methods continues to represent an obstacle to trust in automated decision-making processes.

This special issue is therefore dedicated to the question of which business applications of machine learning methods are already being implemented successfully, and which applications can be expected in the near future. The issue is thus aimed at researchers who wish to provide practical insights into their current work in this field. This includes both empirical and conceptual/theoretical contributions. Furthermore, the special issue welcomes contributions on the effects of machine learning technologies on organizations and their stakeholders.

The issue is open to contributions from all areas of business administration, as well as from technical and social science research fields, in particular computer science. Suitable contributions from practitioners are also most welcome.

Manuscripts can be submitted either in English or German. Please submit your paper by email (doc or PDF- file) to the guest editors of the special issue. For further information and questions, please contact the guest editors. Prior to submission please visit the author guidelines on and follow the instructions provided.

Important Dates

Submission deadline: August 31, 2020
First round notification: October 31, 2020
Revision due date: January 10, 2021
Final manuscript due date: March 14, 2021
Publication date: May 2021

Guest Editor Contacts

Dr. Johannes Kriebel 
Chair of Banking
University of Muenster
Universitaetsstr. 14-16
D-48143 Muenster
Phone: +49 251 83-22692

Prof. Dr. Andreas Pfingsten
Chair of Banking
University of Muenster
Universitaetsstr. 14-16
D-48143 Muenster


Prof. Dr. Frauke von Bieberstein, University of Bern
Prof. Dr. Dr. h.c. mult. Manfred Bruhn, University of Basel Prof. Dr. Pascal Gantenbein, University of Basel
Prof. Dr. Markus Gmür, University of Fribourg
Prof. Dr. Klaus Möller, University of St.Gallen
Prof. Dr. Günter Müller-Stewens, University of St.Gallen Prof. Dr. Margit Osterloh, University of Zürich
Prof. Dr. Dieter Pfaff, University of Zürich
Prof. Dr. Martin Wallmeier, University of Fribourg

Chief Editor
Prof. Dr. Klaus Möller
University of St.Gallen
Professor for Controlling / Performance Management
Tigerbergstrasse 9
CH-9000 St. Gallen
Tel. +41 71 224 7406

„Die Unternehmung“ pursues the goal of spreading new insights from business management research, drawing attention to important challenges in business practices, introducing scientifically based practical solution approaches as well as promoting the exchange between science and practice.

„Die Unternehmung“ addresses scientists, university students and professors as well as decision makers in business. With its concept of combining theoretical standards and practical relevance in high-quality contributions, it ranks among the leading management journals in German.

All submitted contributions are subject to a Double-Blind-Review.

Relevant authors information and guidelines can be found on: