R&D and Intellectual Property (not offered)

NBD404 R&D and Intellectual Property (not offered)

Autumn 2021

  • Topics

    Business innovation and success are massively affected by the characteristics of its institutional environment; subsidized funding possibilities and rewards for innovation such as prizes and intellectual property rights (e.g., patents) are among the prominent ones. In the course we work on understanding these institutions and their consequences for the innovation process. We discuss the benefits and challenges of cumulative innovation, patents- and copyright enforcement, and IP licensing. Furthermore, we analyze the interplay between specific business and market conditions (e.g., competition, network effects) and the incentives to innovate. Thereby, we also focus on the optimal organization of the innovation process (e.g., in joint ventures) and the importance of the market for innovation (specialization on innovation vs. in-house innovation). In a nutshell, the course provides a toolbox to analyze the ingredients for and threats to a successful commercial viability of innovations

  • Learning outcome

    Knowledge - Upon successful completion the student

    • has advanced knowledge of theories on R&D and intellectual property.
    • has thorough knowledge to reflect critically on theories and empirical findings.
    • can demonstrate to be familiar with research in the fields of R&D and intellectual property.

    Skills - Upon completion of the course the student

    • has acquired skills in applying theories, knowledge and tools relevant within innovation and intellectual property.
    • has developed good analytical skills.
    • can undertake a research project on innovation and intellectual property.

    Competences - Upon completion of the course the student

    • can analyze relevant literature and has the insights to connect theory with practice.
    • can reflect critical on theories, methods, tools and data within the field.
    • has developed collaborative and co-creative skills.

  • Teaching

    Lectures, online exercises, group assignment, case discussion, classroom experiments.

    The course can be followed digitally.

  • Credit reduction due to overlap


  • Requirements for course approval

    Group project, 2-4 students in each group - Approved-not approved

  • Assessment

    Portfolio assessment:

    • Individual assignment (30%)
    • Group assignment, 2-5 students (30%)
    • 3 hours home exam (40%)

  • Grading Scale


  • Computer tools

    Knowledge of a statistical software will be helpful for the group assignment.

  • Literature

    To be announced


ECTS Credits
Teaching language

Autumn. Not offered Autumn 2021.

Course responsible

Associate Professor Steffen Juranek, Department of Business and Management Science.