• define different types of platform businesses and their characteristics;
• correctly use analytical frameworks and concepts to analyze platform and networked businesses based on the required readings;
• have the ability to generate management-relevant insights into how theories from the required readings can help assess business opportunities and risks but also reflect on their limitations;
• discuss the competitive dynamics in industries with strong network effects and platform businesses.

Google, Uber and Amazon have one thing in common: their core business is providing a platform that facilitates interaction between different user groups, such as riders and drivers, buyers and sellers, users and advertisers, developers and end-customers. The success or failure of these business models depends heavily on the scale and engagement of their user bases and on the strength of the “network effects” between them. Understanding platform businesses, their unique management challenges, and their competitive dynamics is crucial for students interested in careers in technology, digital markets, and data-driven industries.

Platform businesses now account for a large and rapidly growing share of the world economy. They are central not only in internet and social media (e.g., Google, Meta, TikTok), but also in sectors such as mobility (Uber, Bolt), commerce (Amazon, Alibaba, Shopify), hospitality (Airbnb), and app ecosystems (Apple, Android). Increasingly, AI-enabled platforms and tools, such as recommendation systems, marketplace matching algorithms, and generative AI services, shape how value is created and captured in these markets.

Platform businesses face distinctive management challenges that differ markedly from those in traditional manufacturing and service industries. Familiar rules for marketing, pricing, and strategy may not apply. For example, platform providers frequently subsidize one side of the market, price below cost, or even give products and services away for free to ignite network effects. Many industries exhibit “winner-take-most” or “winner-take-all” dynamics, with only a few dominant players. At the same time, platform firms must grapple with governance, data and privacy concerns, algorithmic transparency, AI ethics, and evolving regulation.

This course introduces concepts and frameworks to analyze platform business models and to support strategic decision-making within them. Using the case study method, students will examine real-life platform cases, including both established firms and emerging AI-driven platforms, discussing their successes, failures, and strategic options for the future. After completing the course, students will be able to systematically analyze different platform businesses, design and evaluate strategies for growth and monetization, and assess the economic, technological, regulatory, and public-policy context in which such businesses develop.

• Classic and basic theory
• Teacher’s own research

• Development of research questions
• Analysis
• Discussion, critical reflection, modelling

• Harvard Business School case studies (to be downloaded.)

Indicative. Detailed readings will be listed in the syllabus.

• Agrawal, A., Gans, J., & Goldfarb, A. (2022). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press. 
• Brotman, A., & Sack, A. (2025). AI First: The Playbook for a Future-Proof Business and Brand. Harvard Business Review Press. 
• Parker, Geoffrey, Marshall Van Alstyne, and Sangeet Paul Choudary. Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You.  New York: W. W. Norton & Company, 2016.
• Reillier, Laure Claire, and Benoit Reillier. Platform Strategy: How to Unlock the Power of Communities and Networks to Grow Your Business. New York: Routledge, 2017.