Workshop and ORG Seminar Kick-Off by Professor Andreas Schwab
18 Oct 2024
We hosted two insightful presentations by Prof. Andreas Schwab at ISTO! He kicked off the ORG Seminar, sharing his research on star performance on digital platforms. Besides, he gave a workshop on Bayesian analysis, sparking engaging discussions.
We recently had the privilege of hosting two insightful presentations by Prof. Dr. Andreas Schwab. He shared his latest research on star performance on digital platforms and led a workshop on conceptual Bayesian analysis, sparking engaging discussions with junior researchers and faculty members from the LMU Munich School of Management.
Andreas Schwab is a Professor at Iowa State University, the Director of Undergraduate Entrepreneurship Programs, and the Dean's Fellow at the Ivy College of Business. His research focuses on organizational theory, entrepreneurship, and strategic management, with a particular interest in performance variability and innovation in digital platforms. Prof. Schwab is widely recognized for his work on Bayesian analysis and its applications in management studies.
On October 15, he presented his research project entitled “Star Entrepreneurs on Digital Platforms”, being the first invited speaker of this winter term's ORG Seminar. The paper explores the extreme variability in entrepreneurial performance, particularly on digital platforms. It highlights how platform-specific features, such as network effects and low marginal costs, lead to heavy-tailed performance distributions, producing standout "star" entrepreneurs. Using data from an online learning platform, the study identifies proportional differentiation as a key mechanism and proposes a new framework for analyzing performance outliers, emphasizing the importance of understanding non-normal performance distributions in digital entrepreneurship.
On October 16, Prof. Schwab also led a workshop introducing Bayesian analysis as a powerful tool for management research. He provided participants with a conceptual understanding of Bayesian methods, emphasizing their advantages over traditional statistical significance testing. Topics covered included the basics of Bayesian parameter estimation, the use of prior and posterior distributions, and the practical applications of Bayesian analysis for hypothesis testing and theory building.
These presentations were jointly organized by the Institute for Strategy, Technology, and Organization at LMU Munich and the LMU Center for Advanced Management Studies.