This course offers an introduction to the characteristics of digital markets, the strategic competition of firms on these markets and aspects of consumer behavior such as privacy, copyright and social media.

Students learn how market platforms work, how network effects change classical business models and how firms can use particular market strategies (with respect to prices and product differentiation) to remain competitive on digital markets. Aspects such as reputation mechanisms to enhance trust on digital markets and product recommendations will be analyzed and discussed. Copyright and privacy issues as well as the discussion of (dis-)information spread in digital media complement the topics from the consumer perspective.

The course will discuss the basic theory and terminology as well as evidence from the field and applications, and apply basic statistical methods to experimental and empirical data for hypothesis testing.

Upon completion of the course, students are able to:  

• describe the specific characteristics of digital markets and business models.
• explain the existence, functioning and problems of market platforms.
• analyze and critically evaluate market strategies and online competition.
• review how reputation and recommender systems are built and how they reduce asymmetric information on markets.
• reflect upon the importance of digital media for information acquisition, knowledge transport and advertising.
• conduct basic data analyses for hypothesis testing.

· Students must be present in the first unit.

· Regular attendance is compulsory (at least 80% of lectures). Please notify the lecturers about your absence via e-mail before the unit.

The contents of the course build on theoretical models, empirical evidence and practical application, including data analysis. Methods of teaching include interactive lectures, case studies and excercises, group presentations and discussions. Students are expected to actively participate.

• Group presentations:  40%
• Peer review: 20%
• Excercises: 40%

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)

Registration via LPIS.

Acquiring basic knowledge of R and data analysis (see the content covered in the Bridging Course and the recommended prep book) is strongly recommended.

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

We expect students to be familiar with the topics covered in the bridging course Microeconomics.

The course takes places weekly (2 units á 2 SWS). Depending on COVID regulations, the course will take place wholly on campus or partly online. Arrangements will be posted as news on Mylearn at the start of the semester.