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Research projects

Current projects

The "Cranfield Network on International Strategic Human Resource Management" (CRANET) has been collecting international data for 30 years now with the aim of identifying trends in human resource management.

According to the slogan "Hello Future. Hallo HR", 47 universities worldwide cooperate to create a valid data basis for researching relevant practices and trends in human resource management.

The current focus of the CRANET Study 2015/16 is on researching topics such as:

Participatory decision logics
Democratic leadership principles
Flexible work and organizational structures
Design of creative learning rooms/innovation labs
Use of agile methods like Design Thinking/Scrum
Automated HR process with mobile devices

The data are collected in the form of standardized questionnaires, which are sent to national organizations by the respective network partners. The special feature of the surveys is that the questionnaires are identical, with the exception of language and, if necessary, country-specific modifications. Thus, they offer a unique and comparable database for science and practice, which is used as a basis for relevant research. 

The studies of 2015/16, for example, provide interesting initial results:

The increasing importance of digitization in recruiting, talent management and learning
The use of flexible and agile work forms, such as interactive learning spaces, flexible organizational structures and work models, etc.
The uncertainty of HR departments to deal with these issues and to be prepared for digital restructuring
Contact person at the HHU: 

Jun. professor Dr. Marius Wehner
Universitätsstr. 1
Building: 24.31
Floor/room: 02.01
40225 Düsseldorf
Phone: +49 211 81-10248

mail: Marius.wehner@hhu.de

The project "Manchot AI" deals with the question how the promotion of talent in a company by using algorithms and artificial intelligence (AI) can lead to discrimination and compliance violations. 

The increasing use of AI and digitally available data in the HR sector is generating more and more new information for talent management:

Activity-based data: e.g. absenteeism, performance metrics, organizational metrics,
Personal data: e.g. demographic characteristics, relationship status, children, religion,
Subjective data: e.g. executive ratings, data from social networks.

In addition to these new possibilities, the use of AI to identify talent also creates new problems and legal issues. In the past, cases of implicit discrimination of applicants were already identified during online job searches and personnel selection using AI. This kind of discrimination is also conceivable in the context of personnel measures for career advancement.

In the "Manchot AI" project, companies that already use algorithmic procedures for talent promotion in their operational practice are first interviewed in order to gain expert experience with regard to the use of AI for talent promotion in practice. 

Subsequently, realistic scenarios for the use of AI in the context of talent management will be developed, which represent the use of algorithmic procedures to varying degrees.

Further information about this project can be found at www.heicad.hhu.de.

If you as a company are interested in this research project and would like to support us with your experience, please contact Alina Köchling (alina.köchling@hhu.de). 

Contact person at the HHU: 

Alina Köchling 

Research Assistant

Heinrich Heine University Düsseldorf 
Building 24.31 Room 00.33
Universitätsstraße 1 
40225 Düsseldorf

Phone +49 (0) 211 81-10193

mail: Alina.koechling@hhu.de

Joint project between the HTW Berlin and the Heinrich-Heine-University Düsseldorf

The project "LADi - Learning Analytics and Discrimination" is to deal with how discrimination according to gender, age, origin or learning type can be promoted or prevented through the use of algorithmic evaluations in digital learning systems and processes. 

The use of digital learning environments generates more and more data about learners and learning processes. These data concern: 

the learning process: teaching materials considered, exercises performed, time required, time of day of the learning process, number of repetitions 
the learning success: achieved points, correct solutions, passed examinations 
the learner: demographic characteristics, personal characteristics (learning type, interaction type) 
Interactions between students or between teachers and students. 
The data generated in the learning and teaching process can be evaluated algorithmically to measure or predict learning success and to adapt the teaching material (Learning Analytics).
Discrimination can occur, for example, if: 

learners with a lower social status or a certain gender are predicted to have a lower learning success 
the assessment is not only based on the result but also on the documented learning process. 
teaching material is adjusted based on predicted learning success, so that learners with a poor prognosis have no chance of achieving a high level of learning 

In the project "LADi", existing real data will be analyzed using typical algorithms, and the extent to which the algorithms are susceptible to discrimination will be investigated. In addition, we will experimentally investigate how the abundance of data about learners affects teachers and to what extent this results in additional potential for discrimination. Furthermore, the perception of students will be investigated.

For this purpose, we conduct experiments with students and teachers. If you as a school are interested in our research project, please contact Alina Köchling (alina.koechling@hhu.de).

Contact person at the HHU: 

Alina Köchling 

Research Assistant

Heinrich Heine University Düsseldorf 
Building 24.31 Room 00.33
Universitätsstraße 1 
40225 Düsseldorf

Phone +49 (0) 211 81-10193

mail: Alina.koechling@hhu.de

Project duration

1.11.2018 – 31.10.2021

Project Management

Prof. Dr. Katharina Simbeck
Jun. prof. Dr. Marius Wehner

Project team members

Shirin Riazy
Alina Köchling


Federal Ministry of Education and Research

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