Opportunity Hunters Pro - Thinking Like Innovative Entrepreneurs

At the heart of this module are founders who share their career path and founding story and serve as entrepreneurial role models. Through their stories, students gain a concrete understanding of how startups are created and built. Alongside this, the module provides methodological inputs on opportunity recognition and problem space discovery, enabling students to think like entrepreneurs and read problems as opportunities in their own project work.

The module is structured around weekly speaker sessions and independent fieldwork:

• Kick-off & introduction: Introduction to opportunity recognition and problem space discovery; overview of the module
• Advanced Methods masterclass: methodological inputs on problem space discovery and opportunity recognition, plus an introduction to qualitative research methods (interviews, field observations, Erlebnistage), data analysis and synthesis to explore and validate problem spaces.
• Approx. 8 speaker sessions: in the main part, founders and industry leaders share their career path and founding story — how an idea became a startup, the hurdles they overcame, and what shaped them. They are role models, not textbook cases. In a short additional part, they describe unsolved problems they currently see in their field; students can choose these problem spaces for their project work.
• AI-assisted problem space exploration: students use AI tools to map their chosen problem space for the first time — sub-problems, stakeholders, existing solutions, and trends are generated by AI. The goal is a complete AI analysis as a starting point, before any primary fieldwork begins.
• Independent fieldwork phase (without AI): students explore the same problem space through primary research — interviews, observations, Erlebnistage. They validate and quantify identified sub-problems and assess which hold the greatest entrepreneurial potential: how many people are affected, where the problem occurs, what costs it creates for whom, and how frequently it arises. They also investigate whether problems are systemic and how they are connected. Trend analyses help filter relevant signals.
• Peer review workshop and showcase: selected students present their problem analyses and opportunity assessments; all students receive an introduction to the peer review process.

Core content areas: entrepreneurial career path & founding stories (role-modelling); opportunity recognition; problem space discovery and analysis; sub-problem decomposition; validation and quantification; systems analysis and problem interdependencies; trend analysis and data filtering; interview and observation techniques; 4U Framework; AI-assisted research and critical AI evaluation.

At the end of the module, students are able to:

• understand and reflect on how startups are founded and built — based on the career paths, founding stories, and role-model function of the speakers;
• identify and describe complex, real-world problem spaces from industry and society;
• decompose a broad problem space into concrete, addressable sub-problems;
• validate and quantify sub-problems: how many people or organisations are affected, where the problem occurs, what costs it creates for whom, and how frequently it arises;
• investigate whether identified problems are systemic in nature or stand alone, and how they are connected to and conditioned by other problems;
• conduct trend analyses and filter relevant signals from large datasets to understand the development trajectory of a problem space;
• apply qualitative research methods (interviews, field observations, Erlebnistage) to explore a real-world problem space;
• use AI tools strategically for an initial exploration of a problem space, developing effective prompting strategies in the process;
• critically evaluate AI-generated analyses by systematically comparing them with primary research findings, identifying discrepancies, and articulating the weaknesses of the AI analysis with evidence;
• reflect critically on their own assumptions and articulate how their perspective on a problem evolved through direct fieldwork;
• evaluate the relevance and urgency of a problem using structured frameworks (e.g. the 4U Matrix);
• communicate their findings clearly and in a structured manner in a written document;
• embed academic and practitioner literature from the reading list purposefully into their own problem analysis and establish theoretical connections;
• independently plan, conduct, and systematically analyse an extensive primary research process comprising at least 8 interviews.

The module assessment consists of a project work (Projektarbeit) with four mandatory components:

1. AI Analysis of the Problem Space (mandatory AI component; complete AI output incl. prompts: approx. 4–5 pages): Students first explore a problem space using AI — before conducting any primary research. The analysis covers a self-chosen real-world problem space selected from a range of topic areas introduced through the guest lectures in the course with the help of AI. The complete AI output (sub-problems, stakeholders, systemic connections, trends, and prompts used) is submitted as a mandatory appendix. Students then write a dedicated critique section in which they systematically analyse the AI output. Weighting: 5%

2. Problem Space Document (main deliverable, approx. 4–5 pages): A structured written analysis of a self-chosen real-world problem space, selected from a range of topic areas introduced through the guest lectures in the course. The document follows a prescribed template and covers:

• description of the problem space and decomposition into sub-problems;
• validation and quantification of sub-problems: number of people affected, geographic distribution, costs created for whom, frequency and severity;
• analysis of systemic connections: are the problems systemic in nature? How are they connected to and conditioned by other problems?
• trend analysis: how is the problem space developing? What relevant signals can be filtered from larger datasets?
• findings from at least 8 primary interviews and/or on-site observations (Erlebnistag);
• reflection on how the student's perspective on the problem evolved throughout the process;
• references to texts from the distributed reading list, used to theoretically ground the analysis.

Weighting: 50%

3. Reflection: Students reflect on the differences between the AI analysis and their own findings:

• What did the AI identify correctly?
• What did their interviews and observations reveal that the AI missed entirely?
• Where does the AI analysis diverge from real-world data — and why? (with concrete evidence)
• What weaknesses does the AI show in analysing systemic connections, quantifications, and trend analyses?

This component assesses both strategic AI use and critical judgment of AI outputs. Weighting: 25%

4. Peer Review (Übungsleistung): Each student reviews the Problem Space Documents of five peers using a structured rubric and provides written constructive feedback. Weighting: 20%

The project work is completed individually. Repeat examination is possible in the following semester.

No prior knowledge in entrepreneurship or innovation is required. An open mindset and curiosity about real-world problems are recommended. Fluency in English is required, as all course sessions and assessments are conducted and submitted in English. Regular attendance at the weekly speaker sessions is strongly recommended, as the problem spaces presented form the basis for the independent project work; students are responsible for ensuring adequate preparation. The project work is completed individually. Students are responsible for independently planning and conducting their fieldwork (interviews, observations) within the given timeframe.

• Osterwalder, A. et al. (2014). Value Proposition Design. Wiley. Brown, T. (2009). Change by Design. HarperBusiness. Blank, S. & Dorf, B. (2012). The Startup Owner's Manual. K&S Ranch. Dweck, C. (2006). Mindset: The New Psychology of Success. Random House. Skok, M. (2013). 4 Steps to Building a Compelling Value Proposition. Forbes. Design Sprint Academy: The 4U Framework for Prioritizing Problems. designsprint.academy. Additional texts are distributed to students each semester as a curated reading package. Master's students are required to reference at least 3 texts from this package in their project work.

Weekly lectures / speaker sessions: industry practitioners present unsolved problems, providing authentic and motivating learning inputs. This format is chosen because real-world problem owners offer a more credible and inspiring problem framing than textbook cases. Input session on problem spaces and problem validation, including Q&A. Methods masterclass (advanced): in addition to the introduction to qualitative methods, master's students receive training in advanced qualitative field research — including theoretical sampling, coding procedures, and reflexivity. Independent project work (Projektarbeit): students pursue their own problem space investigation over 4–6 weeks, developing autonomy, initiative, and research skills. This format ensures deep engagement rather than surface-level exposure. AI-supported learning materials on Moodle (video tutorials on interviewing, observation techniques, and data analysis): asynchronous access allows students to revisit content at their own pace. Reading list: master's students receive a curated reading package with academic and practitioner texts, which they actively incorporate into their project work. Peer review: fosters critical reflection and communication skills through structured feedback exchange.