Qualitative comparative analysis (QCA) – especially its fuzzy set version – has emerged as a new methodological tool in management studies which is ideally suited to test configurational theories. For the first time, the peculiarities of QCA in large-N designs are comprehensively analysed. Based on a systematic compilation of 145 empirical QCA studies valuable insights for the use of QCA as a quantitative technique are presented. For example, an innovative formula is developed which can substantially improve future model specifications. In a next step, the potential of QCA in management research is outlined by tracing configurational theories in a range of disciplines including strategy, HRM, marketing, and international business. This tour d’horizon through management studies highlights the wide application area of the methodology. Finally, an illustrative study is conducted using the fuzzy set version of QCA.
2. The nature of configurational comparative methods
Since CCMs are a fresh tool in management studies, both the nascent and the established conventions for its application and presentation need to spread in order to ensure comparability of the contributions. However, because CCMs were initially developed for political studies and sociology to study country- level data, the conventions primarily refer to these research fields and their pre- dominant aggregation level. By contrast, management studies by their very na- ture include investigations at the micro- and meso-level and, very frequently, large-N designs. Therefore, the requirements of configurational methods funda- mentally differ from the original demands. Both the technical and the theoretical model specification of a QCA in large-N management studies appear to require considerable attention. The present chapter addresses these two elementary re- search gaps and places special emphasis on the technical aspects. It is worth mentioning that the contribution made to the model specification is of potential interest to quantitative scholars from across disciplines, not only to those in management studies. In theoretical terms, the application of QCA in large-N designs necessitates a solid grounding, which should be reflected in the hypotheses formulation. In technical terms, the ratio of analysed conditions to empirical cases at hand con- stitutes a limiting factor. Given a certain number of cases, a formula developed in this thesis can guide and assist the choice of the proper number of causal con- ditions. At the start of this chapter, the technique and the analytical steps of both the crisp and the fuzzy version of...
You are not authenticated to view the full text of this chapter or article.
This site requires a subscription or purchase to access the full text of books or journals.
Do you have any questions? Contact us.Or login to access all content.