Call for Papers: Special Issue 2/2021 – Machine Learning – Die Unternehmung/Swiss Journal of Business Research and Practice
 
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.
 
Submission deadline: August 31, 2020
 
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.
 
Guest Editor Contacts:
Dr. Johannes Kriebel (University of Münster) - Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!
Prof. Dr. Andreas Pfingsten (University of Münster) - Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!