I am an organizational behavior researcher and part of the Coincidence Analysis project at the Department of Philosophy of the University of Bergen. My research focuses on how organizations, through their structures, formal and informal control systems, affect individual work motivation. My methodological interest lies in understanding and further developing variants of Configurational Comparative Methods, i.e. Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA), in order to empower high quality applications especially in Business and Management studies.
- Work Motivation
- Public Service Motivation
- Management Control
- Performance Management
- Configurational Comparative Methods
- Coincidence Analysis (CNA)
- Qualitative Comparative Analysis (QCA)
Swiatczak, M. & Morner, M. (2017). Mit Selbststeuerung komplexe Probleme lösen [Social Control and the Solution of Complex Problems]. Zeitschrift Führung Organisation (zfo), 86(5), 272-277.
Swiatczak, M., Morner, M. & Finkbeiner, N. (2015). How Can Performance Measurement Systems Empower Managers? An Exploratory Study in State-Owned Enterprises. International Journal of Public Sector Management, 28(4/5), 371-403.
Intrinsically Motivating Management Control Systems: A 2-step Qualitative Comparative Analysis in German Public Utilities, PhD-thesis, University of Speyer, 2020
I am a collaborator on the Coincidence Analysis project.
Coincidence Analysis is a member of the family of configurational comparative methods (CCMs) of causal data analysis—also known as set-theoretic or Boolean methods. Since the late 1980ies CCMs have gradually been added to the methodological toolkit in disciplines as diverse as political science, sociology, business administration, management, environmental science, evaluation science, and public health. The most prominent CCM is Qualitative Comparative Analysis (QCA) (Ragin 2008). QCA, however, is unsuited to analyze causal structures with more than one endogenous variable, e.g. structures with common causes or causal chains. To overcome that restriction, Coincidence Analysis (CNA) has been first introduced in Baumgartner (2009a, 2009b). It has meanwhile been generalized in Baumgartner & Ambühl (2018) and is available as software package for the R environment (Ambuehl & Baumgartner 2019).
This project has three objectives. The first is to fill all remaining gaps in the methodological protocol of CNA and to complement the CNA R-package accordingly. In particular, tools for robustness tests of CNA models and strategies for reducing model ambiguities shall be developed. The second objective is to systematically test the inferential potential of CNA by applying it to real-life studies from varying disciplines and, thereby, to explore the applicability of CNA outside of the standard domain of CCMs, for example, in biology, medicine or psychology. The third objective is to analyze the relationship between CNA and methods from other theoretical traditions—in particular Bayes-nets methods (cf. Spirtes et al. 2000; Pearl 2000) and regression-analytical methods (Gelman and Hill 2007). Are there substantive points of contact between these methodological traditions? Are there ways to fruitfully integrate them in multi-method studies? What are the conditions that determine what method is best suited to investigate a given phenomenon or to answer a given research question?
Further collaborators on this project:
- Veli-Pekka Parkkinen
- Mathias Ambühl