Teaching

Current Courses

Summer Term 25

Causal Data Science for Business Analytics

6 ECTS Module for Master Students

Upcoming Courses

Winter Term 25/26

Deep Learning for Social Analytics

6 ECTS Module for Master Students

Open Thesis Topics

General Info

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.

Open Topics

AI-Assisted Opportunity Identification - Exploring Creativity Workflows and Stakeholder Workshop Scenarios (with Applications to the Circular Economy)

Introduction: Opportunity identification (OI) is a fundamental process in innovation and strategic decision-making across various industries and fields. It enables businesses, policymakers, and other stakeholders to recognize potential areas for development, innovation, and value creation. Traditionally, OI has relied on expert-driven assessments, structured brainstorming sessions, and industry best practices. While these methods have proven valuable, they are often constrained by cognitive biases, siloed thinking, and a lack of real-time data integration, which can lead to missed opportunities and inefficiencies.

Opportunity Identification - Designing an AI-Based Assistant

Introduction: Identifying viable opportunities for innovation requires structured and creative workflows that enable stakeholders to explore new solutions and business models. Traditional methods for opportunity identification (OI) often rely on brainstorming sessions, expert-driven assessments, and industry best practices. While these methods have proven valuable, they can be limited by cognitive biases, siloed thinking, and a lack of real-time data integration. Approaches such as design thinking, lateral thinking, and open innovation have been widely adopted to structure ideation processes, enabling teams to systematically develop and refine new concepts.

Startup Funding - Is It All About The Money? A Comprehensive Overview Of The Current State of Research In Value Added Services

Introduction: In the dynamic ecosystem of startups, funding plays a pivotal role in the success and growth of new ventures. Traditionally, the focus has been on the monetary aspect of funding. However, recent trends indicate that investors are providing more than just financial capital. They offer a range of value-added services (VAS) that can significantly influence the trajectory of startups. These services, which can include mentorship, strategic guidance, networking opportunities, and operational support, are critical in fostering the growth and success of startups.