Research discipline: Data Entrepreneurship

Data Entrepreneurship unit at JADS

What is the Data Entrepreneurship Unit?

The Data Entrepreneurship (DE) unit focuses on how data can be used to create and capture value for business and society. We are dedicated to fostering a culture of innovation and entrepreneurship in data science. Our mission is to empower entrepreneurial students and local businesses to leverage data as a strategic asset to drive business growth, societal impact, and technological advancement.

Why is Data Entrepreneurship important?

Data entrepreneurship is refining data into information to unveil and explore business opportunities. It is crucial in today’s digital age as it harnesses the power of data to drive innovation, create value, and fuel sustainable growth. By leveraging data analytics, entrepreneurial individuals, organizations, and policymakers can identify opportunities, optimize processes, and make informed decisions. Data entrepreneurship addresses complex societal challenges, ranging from healthcare to sustainability and societal impact. It empowers individuals to transform raw data into actionable insights, unlocking possibilities for driving change in a data-driven world.

Our research

Our research portfolio focuses on structural analysis, statistical modeling, and causal inference to uncover insights into entrepreneurial behavior and ecosystem dynamics. We combine large-scale quantitative analyses with primary data collection methods such as experiments, ensuring a comprehensive understanding of entrepreneurial phenomena. Whether exploring individual-level factors, community dynamics, or macroeconomic implications, our research contributes valuable insights to the field, driving innovation and informing practice. Our work has been published in journals such as the Strategic Management Journal, Journal of Business Venturing, Journal of Business Venturing Insights, Entrepreneurship Theory and Practice, and Journal of Product Innovation Management.

The main research lines are:

  1. Implications of big data and AI: We investigate how and why big data and AI enable established businesses and entrepreneurs to become successful and how their usage affects entrepreneurial value propositions, business models, and strategies.
  2. New data science methods to advance research: We analyze how machine learning techniques, such as computer vision and natural language processing, allow researchers to improve their empirical measurement and theoretical reasoning in strategy, management, and entrepreneurship research.
  3. Data-driven decision making: On the topics of Explainable AI and recommender systems, we augment data science techniques with data-driven decision-making tools on the interface between human cognition and business and innovation.

Research team

Jasmin Kareem

PhD Candidate

Plato Leung

Assistant professor

Werner Liebregts

Assistant professor.

Naomi Moonen

PhD Candidate

Ksenia Podoynitsyna

Associate Professor

Alexander Vossen

Assistant Professor

Martijn Willemsen

Associate professor

Claudia Zucca

Assistant professor

Group 5
Group 6
Group 7