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
  href="https://studip.uni-goettingen.de/dispatch.php/course/details?sem_id=184559c3a62dbecb16263bc429bb64db&again=yes"
  target="_blank"
  rel="noopener"
  >
  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)