|The key knowledge objective is to provide students with the understanding of why some data-driven products, services, and firms perform better than others. More specifically, in this course we aim to:
Methods-wise, the course works towards the following knowledge objectives, teaching students to:
- 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 business performance;
- introduce students into the fairly complex but interesting managerial problems related to data-driven business model configuration (incl. revenue generation mechanisms);
- help students in understanding the role of customer heterogeneity in building new data-driven 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.
The key skills objectives of this course are:
- formulate research questions based on the theoretical perspective. Based on an existing data base, students identify relevant research question(s) using a particular theoretical perspective (e.g., RBV theory, business model theory, ecosystem theory, platform theory)
- be able to select one or a combination of methods to analyze the particular research question (as opposed to applying a pre-defined method on a given dataset);
- understand text mining techniques such as topic modelling and computer-automated content analysis and how they can be used to capture organizations’ strategies and business models.
- team building: you prepare the research assignment as a member of a team; working in teams is an essential part of the course because innovation-related strategy making (like data-driven business strategies) 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 will write a scientific paper that addresses a theoretically interesting research question and, in an academically sound manner, you will investigate research methods fit to address the research question.
- data mining: cleaning and preparing the dataset(s) for analysis as well as conducting a series of analyses.
|The staggering amount and ubiquitous availability of data gives rise to a range of new applications that may be translated into viable business opportunities and generate 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
Lectures; tutorials; workshops; self-study and group work
Type of exams
individual exam, group assignment
Specifics and data sets used
The following topics are covered in the course:
- Resource-based view, order of entry
- Competitive strategy
- Business models
- Revenue models
- Demand-based view, network effects
- Platforms and ecosystems
Each group picks one of the six topics and uses the topic’s theoretical perspective to explore a data set. Herein, students will explore the implications of different types of strategies and business models using data sets such as:
- Panel data set on apps from the US Apple or Google Play App Store.
- Panel data set on Kickstarter projects.
- Panel data set on Video Games.
- Panel data set provided by a company.
- A selection of scientific articles per topic will be made available through Canvas.
- Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons.
- Schmiedel, T., Müller, O., & vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941-968.
The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program.
Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS)