Advanced (Hot) Topics in Information Systems

Type of Course:

Seminar




Term:

Every term




Target Group:

Business Informatics M.Sc., Management M.Sc., others on request




Language:

Written communication and documents (e.g. slides, assignments) mainly in English. Oral communication mainly in German. Student presentations are either in German or English (largely self-determined by the student group). Details will be announced during the course.




Recommended prior knowledge:

none




Admission requirements:

none




Examination performance:

Intermediate and final presentation, seminar paper

• Each seminar paper is prepared by a group of up to 3 students (max. 5); the size of the group depends on the topic, preferences, applications and available capacities (e.g. time slots)
• Topics and methods are presented at the kick-off event and selected via a prioritization (top 1-3), usually within 48 hours afterwards
• Groups are selected according to the selected prioritization. Priorities for groups (e.g. work with friends) are taken into account
• Registration for a topic means binding participation in the course
• If some of the originally participating students drop out of the course within the given time frame (approx. 48 hours), new participants will be added to the waiting list

Credits:

12






Application:

• The course is limited to 20 students who register on Stud.IP on a first-come, first-served basis. Any further registrations can be placed on a waiting list.
• The Link to the StudIP Course can be found here.
• Only apply if you are available on several dates during the semester, e.g. the kick-off event, the interim presentation and the final presentation. Exceptions (e.g. semester abroad, other appointments, vacation, etc.) are only possible in emergencies.

Assignment:

• Each seminar paper will be prepared by a group of up to 3 students (max. 5); the size of the group depends on the topic, preferences, applications, and available capacities (e.g., time slots).


• Topics and methods will be presented in the kick-off meeting and selected via prioritization (top 1-3) usually within 48 hours afterward.


• Groups will be chosen according to the selected prioritization. Priorities for groups (e.g., working with friends) will be considered.


• The application to a topic will mean the participation in the course is binding.


• Should some of the initially participating students drop out of the course within the given timeframe (about 48 hours), new participants will join through the waiting list.

Procedure:

• There will be a workshop on academic writing. This will refresh and/or expand your knowledge. Participation is optional (but recommended) if you have already participated in such a workshop. More information will be available during the kick-off.


• The submission of the draft version of the paper before the final presentation is mandatory. However, it will not be graded. The paper may be further refined after the draft submission and before the final deadline.

Learning Objectives:

• We are interested in offering you a variety of current topics that suit your personal taste. Possible topics can come from the following areas:
  
  Artificial Intelligence, Human-AI Collaboration, Algorithmic Management, Data-based Business, E-Commerce, Digital Platforms and Transformation, Digital Sustainability and
  Responsibility, Digital Well-Being and Self-Tracking, User Interfaces and Analytics, Personal Use of IT
• We are also interested in offering you a variety of methods. Possible methods depend on the topic and can come from the following toolbox:
  
  Empirical research (quantitative or qualitative), such as conducting experiments or interviews; data analytics (e.g. econometrics, topic modeling), such as analyzing and interpreting given data; design science, such as designing, programming and testing an artifact; literature reviews, such as analyzing and interpreting given data. e.g. the analysis and interpretation of given data; design science, e.g. the design, programming and testing of an artefact; literature reviews, e.g. the review and theorization of existing literature and possible findings (e.g. meta-analyses)