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| Detailed information |
| Pre-requisites |
SE BC2: Induction: Team development UND SE BC1: Foundations of management UND KS BC3: Foundations of management science
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| Original study plan |
Master's programme Leadership and Innovation in Organizations 2024W |
| Objectives |
This advanced course is designed for students interested in mastering the intersection of AI technologies and evidence-based management practices. This course aims to equip students with advanced research skills focusing on artificial intelligence (AI) applications within evidence-based management. Students will explore methodologies for conducting high-quality research, analyze AI-driven data analytics techniques, and apply these methods to solve complex business problems. The course integrates theoretical knowledge with practical applications, emphasizing critical thinking and ethical considerations in AI research.
Through a combination of lectures, and hands-on projects, and reflection students will learn about the role of AI in identifying research gaps, accelerating evidence synthesis and facilitating decision-making in management contexts.
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| Subject |
- Understand the role of AI in modern research methodologies.
- Develop competencies in designing and implementing evidence-based research projects.
- Critically analyze the implications of AI technologies in management and decision-making.
Learning Outcomes
On successful completion of this course, the students will be able to:
- LO1: Remember and understand the fundamental principles of AI and its application in management research.
- LO2: Apply AI tools and techniques in the collection, analysis, and interpretation of data.
- LO3: Analyze the impact of AI on decision-making processes in business contexts.
- LO4: Evaluate the ethical considerations and challenges associated with AI in research.
- LO5: Create a seminar paper that incorporates AI technologies to address a management problem.
- LO6: Synthesize information from diverse sources to support evidence-based conclusions.
- LO7: Apply AI to evidence-based management research
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| Criteria for evaluation |
The course leverages a comprehensive assessment framework to support diverse learning styles, ensuring a rich educational experience. This strategy encompasses both group and individual assessments:
Group Assessment (50%): Teams will engage in the AI Integration Challenge, formulating strategies to implement AI within management practices to bolster evidence-based decision-making. This component evaluates the strategy's relevance, feasibility, and integration capability, relevant to learning outcomes 4 through 7. An extensive assessment rubric will be utilized for evaluation, complemented by detailed feedback from mentors and a reflective session to consolidate learnings.
Individual Assessment (50%): Students are required to craft a reflective essay, submitted through Moodle, that articulates their major insights from the course, especially focusing on learning outcomes 1 through 3. This reflective piece aims to foster deep personal insights into the fundamental principles of AI, its application in management research, and the ethical considerations entailed. Written feedback will be provided, encouraging further intellectual and professional growth.
In total, students have the possibility to reach 100 points:
- Sehr gut = 88-100 points
- Gut = 75-87 points
- Befriedigend= 62-74 points
- Genügend = 50-61 points
- Nicht genügend = 0-49 points
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| Methods |
The course "Research Skills III: AI & Evidence-based Management Research" utilizes a blend of instructional approaches to meet the learning outcomes (LOs). To ensure students achieve a comprehensive understanding and application of AI in evidence-based management research, the course employs interactive lectures, group projects, and reflective writing.
Interactive lectures will introduce foundational concepts and advanced topics in AI and its relevance to management research, targeting LO1 and LO7. The group project, specifically the AI Integration Challenge, aims to enhance collaborative skills and apply theoretical knowledge to practical problems, addressing LO4 to LO7. Finally, reflective writing assignments will consolidate individual learning, focusing on LO1 to LO3, by encouraging students to introspect on their understanding and application of course material.
To deepen understanding, students will engage in self-directed learning, supported by relevant reading materials (see “study and reading material”).
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| Language |
English |
| Study material |
Core material:
- Slides
- Barends, E., & Rousseau, D. M. (2018). Evidence-based management: How to use evidence to make better organizational decisions (1st). KoganPage.
- Krogh, G. von, Roberson, Q., & Gruber, M. (2023). Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence. Academy of Management Journal, 66(2).
- Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., L Glaser, V., Gehman, J., Petriglieri, G., Lindebaum, D., & D Cameron, L. (2023). The future of research in an artificial intelligence-driven world. Journal of Management Inquiry.
- Lindebaum, D., & Fleming, P. (2023). ChatGPT Undermines Human Reflexivity, Scientific Responsibility and Responsible Management Research. British Journal of Management, Article 1467-8551.12781. Advance online publication. https://doi.org/10.1111/1467-8551.12781
- Weber, E., Wyverkens, A., & Leuridan, B. (2023). Rethinking Evidence-Based Management. Philosophy of Management. Advance online publication. https://doi.org/10.1007/s40926-023-00236-5
(Relevant materials can be retrieved from MOODLE and/or will be announced in class.)
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| Changing subject? |
No |
| Further information |
Themes/Timeline
Will be provided in Moodle in due time.
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| Corresponding lecture |
986CAINCI4K19 KS CI4: Data-driven management
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