| Supervisor |
Prof. Dr. Alexander Benlian Dr. Armin Alizadeh Furkan Özdemir M.Sc. Maria Dörr M.Sc. |
| Course format | Lecture + exercise |
| Target group | Students of business informatics and business engineering |
| Cycle | The course takes place in the summer semester. |
| Documents | The documents are freely available on Moodle. |
Course Content
This lecture examines recent developments in Algorithmic Management (AM), which refers to the delegation of managerial tasks from humans to advanced algorithmic systems. AM is transforming organizations across industries, from platforms such as Uber, where algorithms have largely replaced human management, to more traditional organizations that increasingly integrate algorithmic tools into managerial decision-making.
The lecture explores both the technological foundations driving these developments and the implications of AM for different stakeholders. It covers how intelligent systems and related technologies reconfigure managerial roles, shape worker experiences, and transform organizational structures and broader economic dynamics. Particular attention is given to the opportunities, risks, and ethical considerations that AM creates for various types of organizations.
Students will engage with theory, empirical cases, and practical tools to critically analyze and design algorithmic management systems that enable responsible and effective management practices. The lecture will also feature guest talks from diverse stakeholders involved in AM, including platform operators, software developers, managers, and labor representatives, who will share their unique perspectives and experiences.
Learning Outcomes
By the end of the course, students will be able to:
- Define and explain the central concepts and principles of algorithmic management (AM).
- Describe and evaluate the technological foundations of AM, including algorithms, data infrastructures, and challenges such as data quality, bias, and governance.
- Analyze algorithmic management from multiple perspectives, including organizational, managerial, worker, technological, and ethical viewpoints.
- Critically assess the opportunities, risks, and implications of AM in different organizational and institutional contexts.
- Develop strategies for the responsible and effective implementation and configuration of AM systems.
- Apply theoretical and conceptual frameworks to real-world cases in order to identify and address challenges of algorithmic management.