Christian Peukert

Strategy in Digital Markets (Fall 2024)

Master in Management Program at HEC Lausanne, also open to PhD students

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.

Format

This is a Project-Based-Learning course. Each week we have a section on theory, and a section on empirical evidence. Learning is aided by three types of interactions:

1. Theoretical input videos

2. Flipped classroom and discussion of theoretical material

3. Discussion of research papers

The course will be held in a blended format. Class discussions will be in-person, guest speakers join via Zoom.

Learning goals

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

Course structure

Week 1: No class
Week 2: Introduction & Network effects
Week 3: Pricing
Week 4: Platform economics
Week 5: Platform competition
Week 6: Platform launch
Week 7: Platform design
Week 8: No class
Week 9: Make or buy
Week 10: Data Strategy
Week 11: AI Strategy
Week 12: Organizing AI
Week 13: Regulation
Week 14: Q&A

Guest speakers

Jérémie Haese (HEC Lausanne) - Sep 24, 2024
Jérémie Haese is a PhD student interested in the interplay between competition and technology. He was a visiting PhD student at NYU Stern School of Business. Jérémie holds undergraduate degrees in Economics and Management from the Ecole Normale Supérieure de Paris-Saclay and the University Paris 1 Panthéon-Sorbonne, as well as master’s degrees in Network Industries and Digital Economics from the University Paris Dauphine - PSL and in Corporate Finance from the University Paris 1 Panthéon-Sorbonne.

Reinhold Kesler (University of Düsseldorf) - Oct 1, 2024
Reinhold Kesler is an Assistant Professor of Economics at Heinrich Heine University Düsseldorf (DICE). His research centers on digitization, marketing and innovation, and competition policy and regulation. Before joining DICE, Reinhold was a Senior Research Associate at the University of Zurich's Department of Business Administration. He also held research roles at ZEW Mannheim and the University of Mannheim. Reinhold earned his Ph.D. from the University of Zurich, and holds both an M.Sc. and B.Sc. in Economics from the University of Mannheim.

Meng Liu (Washington University St Louis) - Oct 8, 2024
Meng Liu is an Assistant Professor of Marketing at Washington University in St. Louis. Her research explores the economics of AI and algorithms, market design, and quantitative marketing. In addition to her role at Washington University, Meng is a Digital Fellow at Stanford's Digital Economy Lab and a Research Fellow at MIT's Initiative on the Digital Economy. Previously, she was a Visiting Assistant Professor of Marketing at Washington University and a Post-doctoral Associate at MIT. Meng holds a Ph.D. in Economics from Clemson University and a B.S., magna cum laude, in Mathematical Economics from Ball State University.

Andrey Simonov (Columbia University) - Oct 15, 2024
Andrey Simonov is the Gary Winnick and Martin Granoff Associate Professor of Business at Columbia Business School, where he focuses on quantitative marketing. He is a Stigler Center Affiliate Fellow at the University of Chicago Booth School of Business and a Research Affiliate at the Centre for Economic Policy Research. Additionally, Andrey was a National Fellow at Stanford University's Hoover Institution. He holds a Ph.D. in Business (Quantitative Marketing) from the University of Chicago Booth School of Business, along with M.Sc. degrees in Business (Marketing) and Econometrics and Mathematical Economics from Tilburg University, and a B.Sc. in Economics from Lomonosov Moscow State University.

Kai Zhu (Bocconi University) - Oct 22, 2024
Kai Zhu is an Assistant Professor at Bocconi University, specializing in Information Systems. Before joining Bocconi, he served as an Assistant Professor at McGill University. His research covers topics such as content growth, attention contagion in information networks, and the impact of peer feedback on user content generation. He earned his Ph.D. in Information Systems from Boston University, an M.A. in Economics from Indiana University, a B.A. in Economics from Peking University, and a B.S. in Computer Science from Beijing Language and Culture University.

Shrabastee Banerjee (Tilburg University) - Oct 29, 2024
Shrabastee Banerjee is an Assistant Professor of Marketing at Tilburg School of Economics and Management. Her research explores how consumers use cues like reviews, ratings, and prices in e-commerce, employing causal inference, experiments, and machine learning. She also investigates the role of digitization in equity and development. Before joining Tilburg, Shrabastee earned her Ph.D. in Marketing from Boston University, where she was a Rafik Hariri Graduate Fellow. She holds a B.Sc. in Economics from Calcutta University and an M.Sc. from Warwick University as a Commonwealth Scholar.

Michael Impink (HEC Paris) - Nov 12, 2024
Michael Impink is an Assistant Professor of Strategy at HEC Paris. His research focuses on digital entrepreneurship and the impact of digitization on organizational structure. Before joining HEC Paris, Michael was a senior manager at Microsoft based in Seattle and Singapore, a fellow at Harvard University's Weatherhead Center for International Affairs, and at NYU Stern School of Business. Michael has a Ph.D. from NYU Stern.

Martin Quinn (Erasmus University) - Nov 19, 2024
Martin Quinn is an Assistant Professor in Information Systems at the Rotterdam School of Management. His research focuses on the economics of online media, personal information, and intellectual property within the fields of information systems and digital economics. Before joining Rotterdam, Martin was a Postdoctoral Researcher at Católica Lisbon School of Business & Economics and Télécom Paris, where he specialized in the economics of online advertising and digital economics. He holds a Ph.D. in Economics from Télécom Paris.

Daniel Rock (Wharton) - Nov 26, 2024
Daniel Rock is an Assistant Professor at the Wharton School, University of Pennsylvania. His research focuses on the intersection of information technology, intangible capital, and the economics of artificial intelligence. Before joining Wharton, Daniel was a Postdoctoral Associate at MIT’s Initiative on the Digital Economy. He holds a Ph.D. from MIT. Daniel also earned his B.S., summa cum laude, from the Wharton School, with concentrations in Finance, Operations, and Information Management.

Sagit Bar-Gill (Tel Aviv University) - Dec 3, 2024
Sagit Bar-Gill is an Assistant Professor at the Coller School of Management, Tel Aviv University. Her research focuses on the economics of digitization, including online markets, media, and platform economics. Sagit is also a Digital Fellow at MIT’s Initiative on the Digital Economy and Stanford’s Digital Economy Lab. She earned her Ph.D. in Economics from Tel Aviv University and was a Fulbright grantee, spending a year as a visiting Ph.D. student at MIT's Sloan School of Management.

Imke Reimers (Cornell University) - Dec 10, 2024
Imke Reimers is an Associate Professor of Strategy and Business Economics at Cornell University. She is broadly interested in the industrial organization of digital markets, information, and intellectual property. Her research mainly focuses on two specific questions: 1) how does intellectual property policy affect access to information; and 2) how does information technology affect consumer and firm decisions as well as the functioning and efficiency of markets? Imke received a Ph.D. in economics from the University of Minnesota. Before joining Cornell University, Imke spent a year at the NBER in the digitization and copyright initiative, was a faculty member at Northeastern and also became a national champion tennis player in her age group.

Evaluation for Master students

Class participation

You are expected to contribute to the discussion in class. Your active contribution is essential for the quality of class discussion. In my experience class is more interesting for everyone when everyone participates. To be prepared for class, you need to watch and read the material, i.e. videos and research papers for that day before class. I will sometimes cold call and sometimes not call on those who are volunteering to encourage the right climate. If you are not prepared for a particular class, please let me know at the beginning of class. Things happen, and I don’t need to know the reason, but I prefer not to embarrass you by exposing you as unprepared.

To make sure that everybody has the right incentives to prepare for class, there is a rather short weekly assignment and two essays to be completed during the semester. The course closes with an open book final exam.

Weekly assignments (25%)
Following along all semester will be crucial for learning success. However, I do understand that some weeks are busier than others. Therefore you can choose 10 out of 12 papers to read and answer questions about. This will be in the form of online quizzes that you complete before class. The quizzes will be such that it is not necessary to read and digest the paper in full detail (this is what the group assignment is for), but I expect that you carefully read the introduction, discussion and conclusion sections. If you cannot answer a question based on that alone, you can always go back to the paper and read more details.

Essay (50%)
For two research papers, 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 two papers you want to react on, but you must have submitted two reports before the respective papers are presented by the guest speakers.

Final exam (25%)
The final exam will be open book. This is an individual effort over 60 minutes and involves a discussion of a case study using the theoretical concepts we have learned in class. The date will be announced.

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 final exam can be redone in the same format as the initial assessment. 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.

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