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Purpose – This chapter examines the potential of qualitative comparative analysis (QCA) for strategy research.

Methodology/approach – We introduce the set-theoretic framework of QCA and provide an overview of recent methodological developments.

Findings – We utilize a variety of examples relevant to strategy research to illustrate the action steps and key concepts involved in conducting a QCA study.

Originality/value of paper – We develop examples from core research areas in strategic management to illustrate QCA's potential for examining issues of causality and diversity in strategy research, and in settings involving medium-N samples. We conclude by emphasizing that QCA offers an alternative mode of inquiry to open and redirect important lines of strategy research.

Qualitative Comparative Analysis (QCA), initiated by Charles C. Ragin, is a research strategy with distinctive added value for organization studies. QCA constitutes in essence two configurational approaches, each grounded in set theory. One approach uses crisp-sets (dichotomous variables) to analyze cases. The other approach uses fuzzy-sets. While the use of fuzzy-sets has been increasing over the last few years, the crisp-set (csQCA) approach is still used in a majority of empirical applications. This chapter discusses in-depth the application of csQCA in organization studies. This chapter starts with a stylized presentation of two dominant research strategies, case-based research and variable-based research, and how csQCA relates to them. Subsequently, csQCA is further introduced and the different applications in organization studies are discussed. This section ends with a brief step-wise “how to” presentation. The chapter then turns to a presentation of the main distinctive strengths of the approach. In the final part, the chapter discusses extensively the main criticisms which have been raised with regard to (cs)QCA and draws out some of the main implications of this discussion.

Although QCA was originally developed specifically for small-N settings, recent studies have shown its potential for large-N organization studies. In this chapter, we provide guidance to prospective researchers with the goal of opening up QCA’s potential for widespread use in organization studies involving large-N settings, both as an alternative and as a complement to conventional regression analyses. We compare small-N and large-N QCA with respect to theoretical assumptions and objectives, processes and decisions involved in building the causal model, selecting the sample, as well as analyzing the data and interpreting the results. Finally, we discuss the prospects for large-N configurational analysis in organization studies and related fields going forward.