MCI Professorship Awarded to Lisa Perkhofer

The Entrepreneurial School® confers Professorship on Lisa Perkhofer

In recognition of her outstanding achievements in research and teaching, as well as her significant contributions to the continued development of the Entrepreneurial School®, MCI has awarded the title of Professor to Lisa Perkhofer of the Department of Business & Management. In the presence of family and colleagues, she received the honor from MCI Rector Andreas Altmann and Franz Pegger, Head of the MCI Academic Council, before offering engaging insights into her research during her inaugural lecture.

Lisa Perkhofer completed both her bachelor’s and master’s degrees in Controlling, Accounting, and Financial Management at the University of Applied Sciences Upper Austria. She graduated with distinction from both her master’s program and her subsequent doctoral studies in Social and Economic Sciences at the Vienna University of Economics and Business.

Her academic career is marked by extensive teaching and research experience. At the University of Applied Sciences Upper Austria, she served for many years as a lecturer, project manager, and assistant professor, playing a key role in developing a research focus within the “Controlling, Accounting and Financial Management” program. She also taught research methods at the Salzburg University of Applied Sciences.

She initiated numerous third-party funded and industry research projects and was recognized as a Young Researcher of the Year at the University of Applied Sciences Upper Austria in 2022. In addition, she serves as regional head (Tyrol/South Tyrol/Vorarlberg) within the International Association of Controllers (ICV).

Since August 2023, she has been teaching and conducting research at MCI in the fields of Management Accounting, Business Intelligence, Data Science, and IT Systems. In her inaugural lecture titled “Predicting Financial Distress in Austrian SMEs: Data Engineering as the First Step Toward Robust Machine Learning Models”, she presented insights into a current research project focused on the early detection of financial distress in Austrian small and medium-sized enterprises.

The lecture centered on the question of how reliable predictive models can be developed despite incomplete and limited corporate data. Drawing on recent research findings, she demonstrated that not only the choice of model is decisive, but that the quality, structure, and preparation of data play a crucial role in determining the effectiveness of machine learning approaches.