Syllabus
Anmeldung durch das Institut
Day | Date | Time | Room |
---|---|---|---|
Tuesday | 10/03/23 | 02:00 PM - 03:00 PM | TC.2.02 |
Tuesday | 10/03/23 | 03:00 PM - 05:00 PM | TC.4.04 |
Tuesday | 10/10/23 | 01:00 PM - 05:00 PM | TC.3.12 |
Tuesday | 10/17/23 | 01:00 PM - 05:00 PM | D2.0.392 |
Tuesday | 10/24/23 | 01:00 PM - 05:00 PM | D2.0.392 |
Tuesday | 11/07/23 | 01:00 PM - 05:00 PM | TC.3.12 |
Tuesday | 11/14/23 | 01:00 PM - 05:00 PM | D2.0.392 |
Tuesday | 11/21/23 | 01:00 PM - 05:00 PM | D2.0.392 |
Tuesday | 11/28/23 | 01:00 PM - 05:00 PM | TC.3.11 |
Tuesday | 12/05/23 | 01:00 PM - 05:00 PM | D4.0.133 |
Tuesday | 01/09/24 | 01:00 PM - 05:00 PM | D1.1.078 |
Tuesday | 01/16/24 | 01:00 PM - 05:00 PM | D3.0.218 |
Tuesday | 01/23/24 | 01:00 PM - 05:00 PM | D1.1.074 |
This course will delve deeper into questions of digitalisation within big and small enterprises from the perspectives of information systems, knowledge management, and digital ecosystems. In concrete projects and driven by concrete research questions within enterprises, we will touch on one or several of the following umbrella topics (which might be subject to extensions):
· Data and Knowledge Structuring in the Enterprise: While enterprises and holdings acquire and manage more and more data, the data assets often become less manageable through as data grows faster than its accumulation can be documented and structured. Many companies, therefore currently recognize Data Cataloguing and Data Governance Processes as a key requirement, in order to establish a function data ecosystem across departments and divisions.
· Digital products and interfaces in business ecosystems: Many business processes including interactions with clients and partners are carried out predominantly online, or at least leave a rather comprehensive digital trail. This poses new challenges for data management and storage – and new opportunities for innovative digital products and services.
The course will be divided into project teams working on one of these topics. The teams will meet regularly with their supervisors and assigned business partners who provide concrete real world use cases around digital transformation in the above topical areas, as well as periodically with the other groups to synergistically exchange their findings in mutual presentations of the major milestone results.
Upon completion of the course, students are able to
- Critically evaluate a research question in the broad topic of Digital Economy from the view of (micro)economics and information systems
- Plan a research project to answer such a research question
- Perform a structured literature search on a given topic
- Design an experiment or empirical study for a specific research question
- Identify appropriate analysis methods
- Conduct appropriate statistical analyses for said data
- Interpret the results of said analyses and evaluate them critically
- Write a research paper according to current academic standards from the relevant disciplines describing the research project and its outcomes
Students conduct an interdisciplinary research project spanning all stages: from defining a research question, doing a literature research, to stating hypotheses, implementing an experimental or empirical study, analysing the experimental or empirical data and interpreting and critically reflecting the findings. Besides acquiring methodological knowledge, students gain practical experience in planning and carrying out a research project and also take the perspective of a project manager. Students work in groups and are coached regularly by the two lecturers.
1. Research project plan incl. tasks, responsibilities and milestones (15%)
2. Intermediate result report incl. update of research project plan, draft of research paper (15%)
3. Peer review (10 %)
4. Final report (40%) incl. research paper, critical reflection of project plan and project work („lessons learned“), project result poster
5. Final presentation (20%)
Grading scale:
90% to 100% Excellent (1)
80% to <90% Good (2)
70% to <80% Satisfactory (3)
60% to <70% Sufficient (4)
<60% Fail (5)
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