- Office:1055 Budapest, Markó utca 29-31.
- Building:1st Floor, Room 125
- Phone:+36 30 471 2297
- Internal extension:
- Email:kasa.richard@uni-bge.hu
Introduction
I graduated in business management in 2007 and earned my PhD in management and organizational sciences in 2011. Since 2011 I have been a research associate at the Faculty of Finance and Accountancy of Budapest Business School, since 2017 I have been a senior research associate. I am a member of the Public Body of the Hungarian Academy of Sciences, the IX. Department of Human Resources Subcommittee, Hungarian Fuzzy Society.
I spent a semester of 2006/07 at the Ecole de Management de Normandie in France, and two months in 2017 at the Taiwan Institute of Economic Research in Taipei, Taiwan.
I have been a regular guest lecturer at Babes Bolyai University in Cluj-Napoca since 2017 and at the Universidad Pontificia Bolivariana in Colombia, Medellín since 2018.
Office Hours
| Location | PSZK - Menedzsment Tanszék A03 |
|---|---|
| Hours | Szerda 15:00-16:00 |
| Comment | e-mailben történő előzetes egyeztetés alapján |
Subject(s) taught
- Decision Theory and Methodology (Hungarian)
- Quantitative methods (PhD)
- IT Project Management (Hungarian)
- Research Methodology (Hungarian)
- Project Leadership (Hungarian)
- Project Management (Hungarian)
- Organisation and Project Management (Hungarian and English)
Professional career
- 2018Highly Commended Paper Award (Runner up for Outstanding Paper of the Year)
- 2018Hungarian Academy of Sciences Publication Award
- 2018Hungarian Academy of Sciences Publication Award
- 2018BBS Science Award
- From 2016Logistics, Informatics, Management Journal Editor
- 2016BBS Science Award
- 2014BGF Scientific Award
- 2008István Harsányi Scholarship
- 2007Hungarian Patent Office award
- 2007Pro Scientia Gold Medal
- 2007OTDK first place
Research areas
- Methodological problems of business economics research
- Innovation performance of companies, innovation strategies
- Business application of multivariate analysis methods
- Measurement methods, approximation and prediction based on artificial intelligence
- Corruption perception measurement problem