We can best supervise bachelor or master theses if the topic is related to our research. Therefore, we recommend applicants to first explore our research in order to propose a related topic. We expect a strong interest in working with data in order to theoretically explain innovation and entrepreneurship phenomena. Depending on the applicants' background, topics can also focus on technical aspects in the area of data science, machine learning, natural language processing, network analysis or econometrics. Ideally, applicants find an interesting topic among those suggested below.
We are open to collaborative thesis projects with startups and corporates, preferably under two conditions: (1) Entrepreneurial focus, i.e. projects imply a market-oriented change of company offerings w.r.t. target customers, product features, pricing, marketing or sales. (2) Empirical focus, i.e. the entrepreneurial change can be (a) experimented with in terms of A/B testing, (b) analyzed based on existing data about its potential outcomes, or (c) evaluated on a qualitative, strategic level by thoroughly interviewing stakeholders. Thesis projects involving purely conceptual work without any empirical evaluation or only secondary research about state-of-art, best-practices, competitor benchmarks or market intelligence do rather not qualify.
The first step for applicants is to choose or propose a thesis topic (based on our research or a company collaboration) by submitting an abstract describing the topic, how to approach it and the applicant's backround, via the contact form.