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2021/2022  KAN-CDSCO2003U  Data Economics

English Title
Data Economics

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory (also offered as elective)
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Ioanna Constantiou - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Strategy
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 14-06-2021

Relevant links
Learning objectives
During the course, the students will develop analytical skills and abilities to assess market developments, and thereby, improve their capabilities to engage in individual and group decision making under uncertainty and solve specific business problems. Upon completion of the course the students will be able to develop and present concrete solutions to market problems and advise firms about how to deal with datafication challenges and opportunities.
  • Explain the role of information and data in economic theories and strategy tools
  • Explain how datafication influences markets, and firms through examples based on business cases or sectoral analysis.
  • Use a set of theoretical tools to analyse the impact of datafication in the markets
  • Identify and reflect upon the strategic implications of digital transformation for the different market players
Examination
Data Economics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
re-exam is also home written assignment, individual exam
Description of the exam procedure

The individual student chooses a business case and analyse and reflect upon a specific problem/challenge, i.e., how it can be addressed by using a combination of theoretical concepts, framework and models from the course. The student can start working on the assignment after the last course session where feedback is provided.

 
Course content, structure and pedagogical approach

The major theories of economics of information, network economics and business economics will be the building blocks of this course. The course will start by presenting that are prominent for managing information and data, efficiently, and for analysing business strategies of digitalization. Then, selected issues with wide impact on firm’s strategy and market analysis will be presented. Indicative examples include pricing of information goods, auction design, economic effects of datafication and digitalization on business processes, products, and services and their economic impacts in terms of business models, competitive advantages, and market valuation. It will also cover topics such as customization, personalization, micro-targeting, network effects, switching costs.
Description of the teaching methods
The course will be conducted in sessions of three time-slots (3x45). Each session is strongly based on students’ participation. Following the introduction, each session includes lectures presenting specific theoretical issues, practical examples to further the understanding of the theory as well as students’ participation in the discussions about specific business cases. CBS Learn is used for sharing documents, slides, exercises etc. as well as for interactive lessons if applicable.
Feedback during the teaching period
During the first session the students will be prompt to decide on the business idea for the project. In the following sessions the students will be provided feedback on how the topics of each session could relate to their project through a set of questions presented in the slides that would be discussed in the corresponsing session.
Student workload
Lectures 30 hours
Prepare to class 100 hours
Class presentations of business cases 30 hours
Exam and preparation 46 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before buying any books.

 

Textbook:

Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: strategy and leadership when algorithms and networks run the world. Harvard Business Press.

 

Notes, articles, chapters and webpages will be handed out/made available during the course

 
Last updated on 14-06-2021