5668 E-Business
Assoz.Prof PD Dr. Baris Pascal Güntürkün, Univ.Prof. Dr. Nils Wlömert
Contact details
Weekly hours
Language of instruction
02/21/19 to 02/27/19
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 03/12/19 01:00 PM - 04:00 PM D4.0.022
Thursday 03/14/19 01:00 PM - 04:00 PM D3.0.222
Tuesday 03/19/19 01:00 PM - 04:00 PM D4.0.136
Tuesday 03/26/19 01:00 PM - 04:00 PM TC.3.07
Tuesday 04/02/19 01:00 PM - 04:00 PM TC.3.07
Friday 04/05/19 01:00 PM - 04:00 PM TC.5.18
Tuesday 05/07/19 05:30 PM - 08:30 PM D2.0.374
Friday 05/10/19 01:00 PM - 04:00 PM TC.5.18
Tuesday 05/21/19 01:00 PM - 03:00 PM TC.5.03

Developments in information and communication technology are radically changing the ways in which businesses operate and compete in today's global marketplace. This course provides an overview over e-business from a managerial perspective and introduces students to the relevant concepts, models, and strategic issues that are important for businesses that operate in the online environment.

Course contents:
1. The electronic marketplace
1.1. Introduction
1.2. Business models

2. How digitization affects the marketplace
2.1. Big data
2.2. Consumer search costs
2.3. Marginal costs
2.4. Network effects
2.5. Social media
2.6. Long tail
2.7. Recommendation and customization

3. Marketing-mix instruments in the online marketplace
3.1. Product management
3.2. Price management
3.3. Promotion management
3.4. Distribution management

Learning outcomes

The aim of this course is to provide students with an understanding of the impacts that e-business is having on markets, business models, and a firm's marketing activities.

The objectives of the course are:

  • To provide you with an understanding of the core concepts, technologies, and business models in the electronic marketplace
  • To sensitize you to changes in consumer behavior in the online environment
  • To enable you to critically evaluate different ways for firms to adapt their marketing strategies in an increasingly digitized world
  • To deepen your knowledge of empirical research methods and train your ability to analyze consumer behavior empirically
  • To provide you with the foundations to design an online business and motivate you to think “digitally”
  • To improve your communication, presentation and team working skills
Attendance requirements

Class attendance is compulsory. Attendance of at least 80% of classes is necessary to pass the course.

Teaching/learning method(s)

The course is taught using a combination of interactive lectures, class discussions, case analyses, computer exercises, and student presentations. 

The group project in this semester focuses on music streaming services (e.g., Spotify, Deezer). The goal of the project is to determine the relative influence of various potential success drivers on the performance of music tracks on streaming services. You will be provided with an initial set of variables scraped from different websites and your task is to analyze the data and potentially collect additional variables that help to better understand what makes a music track successful on streaming services. This project will introduce you to different techniques of extracting data from online sources (e.g., via web-crawlers and APIs) and analyzing the data to derive relevant insights for managerial decision making.

The course covers practical applications of data analytics for which the software R is required. While prior knowledge in R is not a prerequisite for the course (a short introduction will be provided), these applications require students to interact with the software to conduct analytics by writing code files as part of the group project. R is a powerful tool for data analytics and visualiza-tion that is especially well suited for analyzing large data sets. To be able to work on the project, you will need to download and install the open-source software R and RStudio on your computer and bring the computer to the classroom:
-    Download R: 
-    Download R Studio: 

Please also make use of the abundance of web resources regarding R (e.g.,, For students who would like to further train the materials covered in class, we recommend DataCamp (, an online platform that offers interactive courses in data science at different levels. To facilitate the learning process you will obtain full access to the entire DataCamp course curriculum for the duration of the course. 


Grading is based on the following components:

  • Data analysis challenge (group work; case study involving the application of quantitative methods to a data set using the software R) (30%)
  • Presentation of a scientific journal article from the reading list (group work) (15%)
  • Business model pitch (group work) (5%)
  • Final exam (40%)
  • Class participation (quantity & quality of contributions in class) (10%)

These grading components will be weighted with the respective weights to arrive at the final grade percentage. To successfully pass this course, your weighted final grade needs to exceed 60%.

Please note that to ensure an equal contribution of group members for the group assignment, a peer assessment will be conducted among group members, which enters into the computation of the individual grades for the project. This means that the members of a group are required to assess other students regarding their relative contribution.

Availability of lecturer(s)

I am happy to answer your questions, so feel free to send me a short email or drop by my office if you would like to talk to me in person. I will also try to be available in the classroom after each class or during the breaks of each class.

Last edited: 2019-03-11