Research projects

Complexity and lacking transparency are constantly increasing in the course of the digital transformation and supply chains are expanding due to increasing interconnectedness, making it more difficult to detect and prevent modern slavery. Goal 8.7 of the United Nations Sustainable Development Goals calls for the abolition of modern slavery, Goal 8.8 aims to protect labour rights and safe working environments, while national legislation also addresses the issue (e.g. the German Supply Chain Duty of Care Act). Innovative technology applications have the potential to contribute to a fairer economic system and the emancipation of marginalised groups. Especially in times of multiple crisis phenomena such as pandemics, economic instability, political crises and wars, the need for a global sustainability transformation becomes clear, which must be designed for the benefit of humanity and especially vulnerable groups such as workers.

The overarching goal of the project is to better identify and classify the complex phenomenon of modern slavery in extended supply chains with the help of modern machine learning methods, to recognize important influencing factors and thus to support civil society actors in particular in taking effective countermeasures according to their abilities and possibilities. The large variety and quantity of data available in the age of digital transformation and the complexity of the phenomenon under consideration offer good prerequisites for the application of artificial intelligence approaches, especially from the field of machine learning. The research question is therefore:

How can a software system based on machine learning be designed to classify risks of modern slavery in extended supply chains and identify important influencing factors?

The project will first create a comprehensive and validated case database based on a definition of modern slavery agreed with experts. Using a user-integrative design science procedure, the project then develops a machine learning approach that builds on existing multidisciplinary theories, methods and data sources on the phenomenon. The approach will be designed and applied as a software prototype, tailored to specific practical requirements, user-integrative and in accordance with ethical principles.

Contact person: Prof. Dr. André Hanelt

The project is funded by the Hans Böckler Foundation.

The objective of the DiBEN project is to develop a unique digital and interactive knowledge platform on the topic of digital business ecosystems. The platform is aimed specifically at managers of Hessian SMEs and intends to support the training and application of management skills in a core area of digital transformation. Moreover, the platform aims to enhance management skill development and application in a key area of digital transformation. The platform will make use of the potential of digital technologies to, on the one hand, disseminate knowledge on the management of digital business ecosystems that has been prepared for the particular application purposes of SMEs, efficiently and flexibly, and, on the other hand, to concretely enable managers for this task by providing a set of data-based digital tools.

The platform's novelty is substantiated by two aspects: First, the project prepares important knowledge—which is currently unavailable to SMEs and is almost entirely found in international journals—to meet the unique demands of SME contexts. Additionally, the platform provides SMEs with flexible and effective access to this knowledge via a digital platform (e.g., videos, podcasts, info articles). Second, DiBEN provides associated data-based tools for each topic area. Interacting with these digital tools is meant to enable SME leaders to independently comprehend and develop their ecosystem.

Contact person: Henrik Pohsner (project leader)