Course overview

We live in an increasingly digital economy. Information technology (IT) has transformed a large number of markets and advances in Artificial Intelligence will likely further lead to important changes throughout the value chain of individual firms and across a variety of sectors.

The course Strategy in Digital Markets introduces students to some of the fundamental concepts, and strategic challenges and opportunities of digitization. We will explore how digital technologies shape markets and corporate strategy.

We discuss organizational issues, such as whether firms should rely on in-house IT or source these capabilities from the market. A central part of the course relates to pricing strategies and business models. Most prominently, we will discuss platform strategy from the perspective of entry, openness, and competition. We then focus on data as a strategic resource and the organization and market strategy of Machine Learning (ML) applications. Lastly, we discuss the role of governments in regulating digital markets and the strategic responses firms can take.

The overarching aim is to prepare students for careers in industry/consulting and/or dissertations in this field.

Learning goals

1. Knowledge and understanding

Learn, apply and critically evaluate models of technology adoption

Identify the organizational trade-offs of technology adoption

Learn what makes platform markets different

Learn under which circumstances data and ML can be a useful resource

Learn about interactions between government regulation and strategy

2. Subject-specific skills

Practice analyzing markets, firms and strategies

Practice reading, summarizing and critiquing academic papers

Practice how to draw conclusions from data and econometric analysis

3. General skills

Practice to develop hypotheses about general effects from observing examples

Practice presentation and clear communication of complex issues

AI Policy

I strongly encourage the use of AI tools for certain parts of the class. However, you need to be very careful to use them in a productive way. My thoughts behind this policy:

• A chatbot can be like a sage mentor with unlimited time for you: If you ask an LLM the right way, they will be powerful critics of your line of thought. Together you will get to more interesting solutions.

• LLMs are incredibly good at making up text that didn’t exist before but could have. LLMs are very creative and can be incredibly useful for brainstorming ideas. There’s no guarantee that what they say makes sense.

• It’s very easy for an LLM to write something that looks convincing to someone who has some knowledge of the topic (you), but it will raise red flags for someone who has studied the topic for years (me). It’s not a good idea to outsource your entire workflow to AI. The outcome will be low quality and you won’t learn anything.

Evaluation for Master students

Class participation

You’re expected to come prepared and contribute. I may cold call to keep the discussion balanced. If you’re not prepared for a specific class, tell me at the start. No reason needed.

Weekly assignments (30%)
Each week, we work in the same small groups for 10 minutes to discuss the research paper along three dimensions: question, method, implications. Your time is limited, so you need to come prepared: you are expected to have read the paper and closely followed the guest speaker’s presentation; ideally, you have also asked questions during that presentation. At the end, the group submits a memo on Moodle with:

• Claim (≤25 words)

• Key figure/table number from the paper

• Boundary condition where the claim weakens/flips (≤25 words)

• Action for a manager/regulator (≤25 words)

AI policy: The idea of this assessment is to encourage you to think for yourself, listen to others, and engage with their thoughts. You don’t need AI for that.

Essays (70%)
Write four reaction essays (each max 4 pages) on any paper. Start from your group’s memo and further develop your argument. Discuss how you deviate from the group’s memo and why (not). Concrete guidelines and examples will be discussed in class. Each essay is due within 7 days of the corresponding class. Final deadline to have both in is week 14. 

AI policy: You should use AI tools to brainstorm and edit. Include a brief AI trace (tool + the key prompt + 2–4 sentences on what it got wrong or missed). If you cannot explain your essay in a short oral check, the grade may be adjusted.

Re-examination procedure: Students are required to redo failed assessments. The resits will be during the official resit examination period. A student who fails to deliver the required individual assignments can be re-evaluated in a short oral exam; the readings will be the same. Failed group assignments can be redone in the same format as the initial assessments, albeit in a new group or individually. The grade will be calculated on the assessments that are not redone along with the assessments that are redone as per the weighting scheme of the original syllabus.

Evaluation for PhD students

This course is open to PhD students. PhD students are strongly encouraged to actively participate in class, follow along all semester and read the assigned papers and case studies before each class. There are two types of graded assignments.

Active contributions (25%)
PhD students are expected to actively contribute in the discussion of research papers (Friday sessions). Every week, I expect every student to pose at least one question / provide a discussion point. This needs to be done during class and by posting a question / discussion point on the discussion board on Moodle before class. The questions on the Moodle discussion board can be narrowly related to the week’s research paper (e.g. regarding methodology) or broadly related to the overall topic discussed in the week.

Reaction paper (25%)
For one research paper, you will need to write a reaction paper. This means that you will write a detailed discussion of the research paper, relating to the concepts discussed in class and beyond. Detailed instructions and an example reaction paper will be provided. You can choose which paper you want to react on, but you must have submitted your reports before week 14.

Research proposal (50%)
Your second assignment is a research proposal of 15 pages, double-spaced, due four weeks after the last class. This research proposal should be deeply rooted in the literature that we have discussed in class (i.e. beyond the specific papers that we have discussed). Students need to develop hypotheses, describe (anticipated) methods, potentially provide some preliminary findings, and discuss the contribution to the literature and implications for managers/policy.

Re-examination procedure: Students are required to redo failed assessments. The assessment can be redone in the same format as the initial assessment. The resit assignment will be due four weeks after the instructor has informed the student about failing the assessment.