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.
Fuzzy set qualitative comparative analysis (fsQCA) and related methods have been developed by Charles Ragin and others since the 1980s. In essence, such “configurational comparative methods”, as they have been termed, help to dis- cover combinations of conditions that sufficiently explain a certain outcome. It is not surprising that fsQCA has been embraced enthusiastically by management scholars. In management research, where the effects of various management practices on performance are analysed, it is often a certain combination of vari- ous practices, rather than any single one, that makes a difference. But despite a wave of publications applying set-theoretic methods in management research, results have often been disappointing or hard to interpret. This is because the conventions of the new methods have been developed within the context of cross-country studies with small or medium-sized samples. In management stud- ies, sample sizes typically exceed 100 cases. In his dissertation, Conrad Schulze-Bentrop goes a long way in preparing management research for a better application of fsQCA and related methods. His contribution is threefold. He first guides scholars through the model specifica- tion process in fsQCA, with a focus on the challenges of large datasets. He de- rives a neat formula indicating how many causal conditions should be included in the model, given the sample size and the share of observed combinations of conditions. He secondly illustrates the huge potential of fsQCA and related methods in management. In various fields including strategy, HRM, marketing, and international business, most scholars come up with configurational theories:...
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