• Understand the debate on the impact of innovations on organizations and society
• Identify and apply appropriate theories to analyze the determinants and effects of innovations
• Argue and critically assess how empirical analyses relate to theories about the determinants and effects of innovations
• Identify and apply appropriate econometric methods to analyze the determinants and effects of innovations
• Interpret estimation results and critically assess the validity of the applied empirical methodology
• Set empirical results in the context of the broader debate on the impact of innovations on organizations and society

What are the effects of innovation on firms and society?

Who becomes an inventor?

What drives innovation in firms? 

What are innovation policies, and are they effective?

How can innovation be measured?

What are patents, and why are they relevant?

Answering these and similar questions is important since innovations are highly relevant in today's rapidly changing global economy. This impacts various industries, including large corporations like Lego and Novo Nordisk, tech giants such as Amazon, and creative sectors like fashion and music. Small and medium-sized enterprises (SMEs) and non-profit organizations, such as universities, are also key players. At the same time, analysis tools to assess the use and impact of innovation from an empirical perspective, using large amounts of data (e.g., big data), have emerged. These methods to determine correlations or causal relationships range from simple linear regression models to more advanced methods such as machine learning.

This course introduces students to central topics related to the economics and management of innovation that will lead to a quantitative analysis of an innovation-centered problem. Topics to analyze the determinants of innovation and their impact on organizations and society include, but are not limited to:

• Innovation incentives (e.g., market structure and competition, firm characteristics such as size),
• Innovation policy (e.g., public procurement, subsidies, taxation)
• Intellectual property (IP) rights (e.g., patents and other types, IP design, and IP management),
• Licensing (e.g., commercialization of IP, collective rights management, compulsory licensing),
• Private-public partnerships (e.g., university patenting, government innovation, open science)
• Financing (e.g., financing of innovation, IP as a financial asset)
• Labor market impacts (e.g., automation, human capital)
• Measurement of innovation and the value of R&D and patents 

In this course, it is planned to proceed in three steps. First, the theoretical foundation from an economic, management, and legal perspective will be discussed. Second, building on this foundation, practical causes and consequences will be discussed using real-world examples, case studies, legal cases, and results of empirical studies. Third and finally, empirical examples using statistical software (Stata or R) and real-world data will be conducted, presented, and discussed. While the third step also serves as an introduction to statistical software, blended learning instances will help the students deepen their understanding of the statistical software, empirical methods, and empirical applications.  

• Classic and basic theory
• New theory
• Teacher’s own research
• Methodology
• Models

• Development of research questions
• Data collection
• Analysis
• Discussion, critical reflection, modelling
• Activities that contribute to new or existing research projects
• Students conduct independent research-like activities under supervision

Core reading for the innovation lecture part includes selected chapters from:

• Bryan, K.A. and Williams, H.L. (2021). Innovation: market failures and public policies. In Handbook of Industrial Organization (Vol. 5, No. 1, pp. 281-388). Elsevier.
• Hall, B.H. and Rosenberg, N. (2010). Handbook of the Economics of Innovation. North-Holland.
• Scotchmer, S. (2004). Innovation and incentives. Cambridge, Mass.: MIT Press.

• Schilling, M. A. (2022). Strategic management of technological innovation (Seventh edition). McGraw Hill LLC.

Core reading for the empirical part includes selected chapters from:

• Békés, G., & Kézdi, G. (2021). Data analysis for business, economics, and policy. Cambridge University Press.
• Cunningham, S. (2021). Causal inference: The mixtape. Yale university press.

More literature will be announced in the syllabus.