The key knowledge objective is to provide students with the basic understanding of why some products, services, and firms perform better than others. More specifically, in this course we aim to:
The key skills objectives of this course are:
- make students familiar with management theories about the basic links between resource configurations and competitive advantage;
- introduce the core concepts related to competitive strategy and differentiation, and their implications for performance;
- introduce students into the fairly complex but interesting managerial problems related to business model configuration (incl. revenue generation mechanisms);
- help students in understanding the role of customer heterogeneity in building new businesses;
- explain the role of ecosystems and network effects in data-driven businesses;
- unpack the dynamics of platforms and their role for big data applications.
Finally, in terms of synthesis and creation objectives, the students will be able to deduce evidence-based optimal strategy to deal with competitors and design an innovative business model for a data-driven business initiative or an organization.
- team building: you are member of a team to prepare the research assignment; Working in teams is an essential part of the course because innovation-related strategy making is a process that is carried out by teams;
- presentations: communicate the results of your work in a clear and effective way, deal with the comments from the audience during the discussion and benefit from feedback;
- writing and handling scientific information; You are expected to draft an academic document; you are going to write a scientific paper that addresses a theoretically interesting research question in an academically sound manner.
The staggering amount and ubiquitous availability of data gives rise to a range of new applications that may be translated into viable opportunities for the generation of durable business value. However, given the ease with which different types of big data become publicly available, or could be reconstructed by competitors, establishing a solid competitive strategy is vital for any firm in the field of data science. To facilitate this, advanced skills are needed that involve selection and application of models to craft competitive strategy and design of how the firm will (often together with multiple stakeholders) create value and monetize its efforts. These knowledge and skills can be implemented in one’s own firm, but can also be used to develop new products and services in incumbent organizations.
Type of instructions
tutorials; self-study and group work
Type of exams
individual exams, group assignment
Specifics and data sets used
The following topics are covering in the course:
Each group picks one of the topics and related theoretical perspectives covered in the course in order to explore the data set from this perspective. Students will thus explore the implications of different types of strategies and business models using either of the following data sets that is supplied for the purpose of this course, or a data set to be assembled by students themselves:
- Resource-based view, order of entry
- Competitive strategy
- Demand-based view, network effects
- Business models
- Revenue models
- Platforms and ecosystems
- Panel data set on apps from the US Apple or Google Play App Store.
- Panel data set provided by a company.
- A selection of scientific articles per topic will be made available through Blackboard.
- Osterwalder and Pigneur, Business Model Generation, Wiley, 2010.