Universität Wien
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040171 SE Artificial Intelligence and the Multinational Company (MA) (2024W)

International Business

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 24 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 17.10. 13:15 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 25.10. 13:15 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 08.11. 13:15 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 21.11. 09:00 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
  • Monday 25.11. 13:15 - 16:30 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 09.12. 13:15 - 16:30 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 17.12. 11:30 - 12:00 Digital

Information

Aims, contents and method of the course

Contents
In this class, students will learn to understand artificial intelligence (AI) and the role it plays for multinational companies (MNCs). We will discuss AI as a tool for business analytics in context-dependent MNCs, we will introduce large language models and how they can be used for business, and we will discuss how MNCs can change to integrate AI and face the challenges resulting from AI integration, with a particular focus on the global dispersion of business activities. We will solve simple isolated exercises, as well as more involved issues in business case studies in an international context. After completing this course, students will be able to run machine learning algorithms, to understand the basics and basic applications of language models, and to understand how these tasks contribute to corporate strategy and competitive advantage in a complex international context. Students taking this class are expected to have a basic understanding of statistics. Although helpful, no prior experience with programming languages is required.

Methods
The class is a workshop-style course, with many interactive elements. Students are expected to give presentations, provide feedback on each other’s work, and discuss their progress with instructors.

Learning outcomes
Students gain insight into machine learning, language models, and their applications in MNCs subject to different institutional contexts.

https://international-business.univie.ac.at/studies/master-courses/

Assessment and permitted materials

Case study report: 30%
Case study presentation: 20%
Homework exercises: 20%
Class participation 15%
Test in session one: 10%
Peer feedback: 5%

Minimum requirements and assessment criteria

Indication of references and tools
Students must indicate any sources, tools, or services they used to create (parts of) submissions (e.g., homework, exams, presentations). This extends, among other things, to all sorts of literature references (e.g., journal articles, books), as well as packages of statistical software (e.g., R, Python) and text-generation programs (e.g., ChatGPT). Failure to do so is considered as violation of good academic practice.

Examination topics

Reading list

Association in the course directory

Last modified: We 08.01.2025 12:05