Monika Kackovic
Lab Director & Associate professor of Entrepreneurship and Innovation
Monika Kackovic is an Associate Professor of Entrepreneurship and Innovation. She combines academic research with industry experience to investigate performance dynamics in the arts and entertainment industries. Her research integrates theoretical perspectives in business science with computational social science methodologies to understand patterns of creativity, innovation, and entrepreneurial success in creative and cultural sectors. She has published in leading academic journals and the popular media, contributing insights into understanding the success of entrepreneurs and organizations that thrive in rapidly evolving markets. Beyond academia, she serves on Supervisory Boards, like the Dutch Council for Culture (Raad voor Culture), actively contributing to strategic oversight in the cultural sector. She holds an MSc (cum laude) and Ph.D. from the University of Amsterdam, graduated from the Rietveld Academie, a visual arts school, and maintains an artistic practice alongside her academic work. This combination of research with industry experience and creative practice enables her to bridge theoretical frameworks with real-world applications, offering unique perspectives on innovation and success in the creative and cultural industries.
Research by Monika Kackovic
Being Ranked in a Material World: The visual originality of an artwork and its effects on the artist’s canonization
Artists finding galleries: Entrepreneurs gaining legitimacy in the art market
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
Influence Beyond Similarity: A Contrastive Learning Approach to Object Influence Retrieval
ProtoHG: Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
Third-party signals and sales to expert-agent buyers: Quality indicators in the contemporary visual arts market
The promise of potential: A study on the effectiveness of jury selection to a prestigious visual arts program
Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings
